Alarm condition data processing method and device, computer equipment and storage medium

Abstract

The invention relates to the field of data processing, and discloses an alarm data processing method and device, computer equipment and a storage medium, and the alarm data processing method comprises the steps: receiving to-be-processed alarm voice data, and carrying out the voice recognition processing of the alarm voice data, so as to generate alarm text data corresponding to the alarm voice data; the method comprises the steps of acquiring alarm condition text data, analyzing the alarm condition text data, extracting target alarm condition data related to the alarm condition, generating alarm condition decision support information related to the target alarm condition data based on the target alarm condition data, and generating structured alarm condition abstract data according to the alarm condition decision support information. The generated alarm summary information is clearer and more visual, cooperation of multiple departments and rapid transmission of data are facilitated, the cooperation efficiency and decision-making level of alarm handling are remarkably improved, and comprehensive and reliable technical support is provided for modernized alarm management.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to a method, apparatus, computer device, and storage medium for processing police situation data. Background Art

[0002] With the continuous improvement of public safety requirements, the police situation data processing system plays an important role in modern policing work. Police situation data processing usually includes links such as voice data reception, parsing, conversion into structured data, and generation of decision support information. Existing technologies generally adopt a code-writing-based approach to implement the parsing and processing of police situation voice data through a series of hard-coded logics. These technologies have met the needs of traditional police situation processing to a certain extent, but in the face of complex and changeable police situation scenarios and high-concurrency application environments, the following significant problems have gradually emerged:

[0003] Lack of flexibility: Most existing solutions update the police situation data model by code adjustment. For complex and changeable police situation voice data, frequent adjustments require stopping the service for update, seriously affecting the real-time nature of data processing and service continuity.

[0004] Obvious performance bottleneck: Most existing technologies run based on a single thread, concentrating data reception, processing, and output on a single server. With the growth of large-scale police situation data and the increase in high-concurrency access, the system resource consumption is too high, easily leading to slow or even interrupted process handling.

[0005] High maintenance cost: The code-writing-based processing mode requires frequent manual modification and update of logics, significantly increasing the development and maintenance costs. In addition, the high code coupling degree and poor reusability result in limited system scalability and difficulty in meeting the dynamic change requirements of police situation services.

[0006] Insufficient reliability and scalability: Most existing solutions are centralized architectures, lacking multi-threading and cluster support, and it is difficult to horizontally expand to improve the system throughput capacity and processing efficiency, unable to meet the requirements of high reliability and high performance in high-concurrency scenarios.

[0007] Therefore, how to enhance flexibility, improve performance, reduce maintenance costs, and achieve real-time and efficient police situation data processing, while meeting the scalability and reliability requirements in a high-concurrency environment, has become an important problem to be solved urgently. Summary of the Invention

[0008] In view of this, the embodiments of this application provide a method, apparatus, computer device, and storage medium for processing police situation data, which can effectively solve the problems of lack of flexibility, performance bottleneck, high maintenance cost, and insufficient reliability and scalability in the existing technology.

[0009] In a first aspect, the embodiments of this application provide a method for processing police situation data, including:

[0010] Receive police situation voice data;

[0011] Perform speech recognition processing on the police situation voice data to generate police situation text data corresponding to the police situation voice data;

[0012] Perform parsing processing on the police situation text data to extract target police situation data related to the police situation;

[0013] Based on the target police situation data, generate police situation decision support information associated with the target police situation data;

[0014] Generate structured police situation summary data according to the police situation decision support information.

[0015] In some embodiments, the receiving of the police situation voice data includes:

[0016] Real-time dynamically receive the police situation voice data through the NIFI system deployed on the cluster nodes, wherein the NIFI system listens for the input of the police situation voice data in real time through the configured message queue;

[0017] Based on the load balancing policy, distribute the received police situation voice data to different NIFI cluster nodes, and temporarily store the police situation voice data in the disk of the server where the NIFI cluster node is located.

[0018] In some embodiments, the performing of speech recognition processing on the police situation voice data to generate police situation text data corresponding to the police situation voice data includes:

[0019] Send the police situation voice data to the speech recognition interface by calling a preset speech recognition interface;

[0020] Use the speech recognition interface to process the police situation voice data based on the speech recognition algorithm to generate corresponding police situation text data.

[0021] In some embodiments, the performing of parsing processing on the police situation text data to extract target police situation data related to the police situation includes:

[0022] Perform text cutting on the police situation text data to extract police situation keywords;

[0023] Based on the police situation keywords, perform matching in a preset police situation classification rule library to extract target police situation data related to the police situation;

[0024] Wherein, the target police situation data includes event type, involved tool information, event location information, and event time information.

[0025] In some embodiments, generating the police situation decision support information associated with the target police situation data includes:

[0026] Matching the event type, the involved tool information, the event location information, and the event time information with the rules in a preset police situation handling rule library to generate the police situation decision support information including the nature of the police situation, police type suggestions, and public opinion control strategies.

[0027] In some embodiments, generating the police situation decision support information including the nature of the police situation, police type suggestions for handling, and public opinion control strategies includes:

[0028] Based on the event type and the involved tool information, matching a preset police situation classification rule library to determine the nature of the police situation;

[0029] Generating police type keywords corresponding to the police situation according to the nature of the police situation;

[0030] Based on the nature of the police situation and the police type keywords, generating decision keywords for determining whether combined operations are needed;

[0031] When it is determined that combined operations need to be initiated, generating keywords for public opinion control based on preset public opinion handling rules and determining the corresponding public opinion control level;

[0032] Based on the event location information, retrieving and generating detailed address information of the event through an address library interface, and generating alarm time information based on the event time information.

[0033] In some embodiments, generating structured police situation summary data according to the police situation decision support information includes:

[0034] Integrating the nature of the police situation, the police type keywords, the decision keywords, the public opinion control keywords and the corresponding public opinion control level, the detailed address information, and the alarm time information;

[0035] Structuring the integrated information according to a preset data structure to generate the police situation summary data.

[0036] In a second aspect, an embodiment of the present application provides a police situation data processing device, including:

[0037] A data receiving module for receiving police situation voice data;

[0038] A police situation text generation module for performing speech recognition processing on the police situation voice data to generate police situation text data corresponding to the police situation voice data;

[0039] A text data parsing module for parsing and processing the police situation text data to extract target police situation data related to the police situation;

[0040] A decision information generation module for generating police situation decision support information associated with the target police situation data based on the target police situation data;

[0041] A police situation summary generation module for generating structured police situation summary data according to the police situation decision support information.

[0042] Thirdly, an embodiment of the present application provides a computer device, which includes a processor and a memory. The memory stores a computer program, and the processor is configured to execute the computer program to implement the police situation data processing method in the first aspect above.

[0043] Fourthly, an embodiment of the present application provides a computer-readable storage medium, when the computer program is executed on a processor, implementing the police situation data processing method in the first aspect above.

[0044] The embodiments of the present application have the following beneficial effects:

[0045] The police situation data processing method of the present application realizes an efficient processing link from voice data reception to structured police situation summary generation through a systematic design of the entire process of police situation data processing. First, the speech recognition technology is used to quickly convert the unstructured police situation voice data into text form, providing basic support for subsequent processing. Secondly, by parsing the police situation text data, target police situation data such as event type, involved tools, location, and time are accurately extracted to ensure the integrity and accuracy of key information. Based on the target police situation data, police situation decision support information including police situation nature, recommended disposal police types, and public opinion control strategies is generated by rule matching and intelligent decision-making, providing guidance for rapid and accurate police situation response. In addition, the police situation summary data generated through structured processing displays the core information of the police situation in an intuitive and clear form, significantly improving the efficiency and collaboration ability of police situation management and providing a reliable guarantee for intelligent and automated police situation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 FIG. shows a schematic application scenario diagram of a police situation data processing method according to an embodiment of the present application;

[0048] Figure 2 shows a flowchart in a method for processing police situation data according to an embodiment of the present application;

[0049] Figure 3 shows a schematic diagram of a workflow in a method for processing police situation data according to an embodiment of the present application;

[0050] Figure 4 shows a schematic structural diagram of a device for processing police situation data according to an embodiment of the present application. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0052] Generally, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0053] Hereinafter, the terms "including", "having" and their cognates that can be used in various embodiments of the present application are only intended to represent specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be construed as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or increasing the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0054] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the various embodiments of the present application belong. The terms (such as those defined in a general-use dictionary) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present application.

[0055] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0056] In view of the problems in the prior art, such as insufficient flexibility, performance bottlenecks, high maintenance costs, and insufficient reliability and scalability, a method for processing police situation data is proposed. By performing speech recognition processing on police situation voice data, police situation text data corresponding to the police situation voice data is generated; the police situation text data is parsed to extract target police situation data related to the police situation; based on the target police situation data, police situation decision support information associated therewith is generated; finally, structured police situation summary data is generated according to the police situation decision support information. The processing of police situation data in this application can significantly improve the real-time performance and accuracy of police situation handling, providing efficient and reliable technical support for police situation management.

[0057] A method for processing police situation data provided by an embodiment of this application can be applied in an application environment such as Figure 1 In the application environment, the method for processing police situation data in this application is applied in a computer system, which includes a client and a server as shown in Figure 1 The client is responsible for collecting and transmitting police situation data. For example, police situation voice data is obtained through voice input or file upload and transmitted to the server. In addition, the client can also implement simple preprocessing functions, such as formatting and noise reduction processing on the voice data to improve the efficiency of subsequent processing. As the core processing unit, the server receives the police situation voice data from the client and sequentially performs the following operations: converting the voice data into corresponding police situation text data through speech recognition technology; parsing the police situation text data to extract target police situation data; based on the target police situation data, matching with the police situation processing rule library to generate police situation decision support information; finally, generating structured police situation summary data according to the decision support information and sending the generated summary data back to the client or relevant departments for subsequent police situation handling and instruction execution. The client and the server achieve data interaction through a network connection. The entire system can support high-concurrency police situation data processing requests, and at the same time has high real-time performance and reliability, and is suitable for complex and changeable police situation management scenarios.

[0058] Figure 2 A flowchart showing the method for processing police situation data according to an embodiment of this application is shown. Exemplarily, the method includes the following steps:

[0059] Step S100, receive police situation voice data.

[0060] Exemplarily, the police situation data includes alarm information in voice form. For example, the voice data submitted by the alarm person through a phone call, voice input, or other terminal recordings. This police situation voice data contains basic information of the event, such as the time and location of the event, the type of the event, and possible information about the involved tools, etc.

[0061] In an alternative embodiment, in step S100, receiving alarm voice data includes:

[0062] To achieve efficient and reliable reception of alarm voice data, in this embodiment, the Apache NIFI system is deployed in multiple cluster nodes, as Figure 3 shown, for realizing the dynamic reception of alarm voice data. As a distributed data flow management tool, the NIFI system can adapt to the input of alarm data from multiple sources and multiple protocols through its flexible component configuration and scalability. The collaboration between cluster nodes enables the system to have high-concurrency data processing capabilities, ensuring the real-time and stability of the received data.

[0063] The NIFI system configures message queues (such as Kafka, RabbitMQ, etc.), as Figure 3 shown in ①, to monitor the input stream of alarm voice data in real time. When the alarm terminal or other devices push voice data to the message queue, the NIFI cluster nodes can immediately capture this data and start processing. The dynamic configuration ability of the message queue enables the system to adjust parameters such as monitoring topics and queue priorities according to actual needs without interrupting the service.

[0064] To ensure the efficient processing of alarm data, this embodiment also adopts a load balancing strategy. The NIFI cluster nodes will distribute the received alarm voice data to different cluster nodes according to preset load balancing rules. This distribution strategy can not only improve data processing efficiency but also prevent a single node from affecting the overall system performance due to overload. Each NIFI cluster node temporarily stores the received alarm voice data in the disk of its corresponding server, as Figure 3 shown in ②. The temporary storage mechanism ensures that data will not be lost in case of network fluctuations or cluster node failures, and at the same time provides a reliable data source for subsequent parsing and processing.

[0065] The alarm data processing method of the embodiment of this application realizes the efficient reception, distribution, and storage of alarm voice data through a clustered deployment mode and a load balancing strategy, effectively avoiding the problems of slow data processing or interruption caused by excessive consumption of server computing resources during the process of excessive alarm voice data volume and complex data parsing. Every step from the reception to the subsequent processing of alarm voice data runs on multiple server clusters for horizontal expansion. When the performance is insufficient, only deploying or adding service clusters can improve the processing efficiency of alarm voice data. For example, when the volume of alarm voice data continues to increase and the processing flow of the existing servers becomes slower, it can be solved by adding server nodes and joining the NIFI cluster, and only simple node configuration in NIFI is required to achieve cluster synchronous data processing.

[0066] Meanwhile, the police situation data processing method according to the embodiments of the present application supports high throughput, high concurrency, and multi-threaded processing modes, and can quickly respond to the real-time processing requirements of large-scale police situation voice data. Users do not need to care about the size of the data and the complexity of the business. Each step in the data processing flow is optimized through the horizontal expansion of multiple server clusters, significantly improving the processing efficiency of the system, so that complex police situation data can also quickly obtain reliable processing results.

[0067] In addition, this flexible configuration ability is applicable to various police situation data sources and can dynamically adjust the data flow management strategy according to requirements. In addition, the distributed architecture of NIFI ensures the high reliability and high availability of the system, can meet the requirements of high efficiency and stability for police situation processing in practical applications, and is particularly suitable for high-concurrency processing of multi-source police situation data and police situation management in complex scenarios.

[0068] Step S200: Perform speech recognition processing on the police situation voice data to generate police situation text data corresponding to the police situation voice data.

[0069] Exemplarily, after the NIFI cluster node temporarily stores the received police situation voice data on the server disk, it processes the data through speech recognition technology, converts the unstructured voice form into structured text data, and provides basic support for subsequent police situation analysis and processing, as Figure 3 shown in ③. The core goal of speech recognition processing is to extract the semantic information in the speech signal and represent it in text form. This process requires significant real-time performance and high accuracy to ensure that the voice-to-text conversion can be quickly and accurately completed in the emergency state when a police situation occurs, so as to provide guarantee for the timely start of subsequent processing links.

[0070] In an optional embodiment, in step S200, performing speech recognition processing on the police situation voice data to generate police situation text data corresponding to the police situation voice data includes:

[0071] Specifically, the implementation of speech recognition is completed by calling a preset speech recognition interface. The speech recognition interface is a standardized module with speech processing functions, and this interface can be called through system configuration and the police situation voice data can be input to it for processing.

[0072] For example, the received alarm voice data is sent to a preset voice recognition interface in a standardized format (e.g., WAV or MP3). This interface communicates with the voice recognition service through an HTTP protocol docking, and can quickly translate voice into text content. The voice recognition service can be a cloud-based solution (e.g., Google Speech-to-Text, Azure Cognitive Services) or a locally deployed voice recognition engine (e.g., Kaldi or other customized models). In this embodiment, the invoke HTTP processor of Apache NIFI is used. Utilizing its flexible componentization characteristics, the alarm voice data is sent to the voice recognition service by docking the HTTP interface.

[0073] During the data sending process, the voice file and related parameters (e.g., language type, model type) are transmitted to the API endpoint of the voice recognition service through an HTTP POST request. The invoke HTTP processor of NIFI is responsible for managing the sending of HTTP requests and the receiving of responses, ensuring that the voice data can be efficiently and securely transmitted to the voice recognition service. At the same time, to improve the recognition accuracy, the system will perform necessary preprocessing operations on the voice data, such as noise reduction, slicing, or format conversion.

[0074] After receiving the voice data, the voice recognition service parses and processes the input audio data based on its internal voice recognition algorithms. These algorithms usually adopt deep learning techniques, such as recurrent neural networks (RNN), transformers, or end-to-end voice recognition models. Through these algorithms, the semantic information in the voice signal is accurately extracted and converted into text content. After processing, the invoke HTTP processor of NIFI will receive the HTTP response returned by the voice recognition service, which contains the generated alarm text data.

[0075] These alarm text data contain key information provided by the alarm caller, such as: "I saw someone stabbing people with a knife at a certain intersection"; "I'm Xiaoming. At a certain intersection on a certain street in a certain city, I saw someone stabbing several passersby with a knife. Some fainted and some were bleeding profusely. Please send police as soon as possible." Through the efficient transmission of the HTTP interface and the processing ability of NIFI, the real-time and integrity of the information are ensured. Its highly structured characteristics enable it to be directly used as the input for subsequent alarm parsing and processing, providing a solid foundation for alarm decision support.

[0076] The method for processing police situation data in the embodiments of the present application efficiently converts unstructured police situation voice data into structured text data by calling a preset voice recognition interface and combining advanced voice recognition algorithms, significantly improving the processing efficiency and accuracy of police situation information, and providing an efficient and reliable basis for subsequent police situation analysis, decision support, and response.

[0077] Step S300: Parse and process the police situation text data to extract target police situation data related to the police situation.

[0078] The police situation text data is one of the core inputs of the police situation processing system and contains the key information of the police situation described by the alarm caller through voice or text. These information may include the event type, involved tools, event location, time, and personnel status, etc. However, the original text data is usually unstructured and not conducive to direct use in subsequent processing. Therefore, in this embodiment, through the method of parsing and processing, the police situation text data is converted into structured target police situation data, as Figure 3 shown in ④ below.

[0079] The core goal of parsing and processing is to extract the key information related to police situation processing and map the semantic content in the text to standardized target fields, such as the event type field, involved tool field, location field, and time field. This process can significantly improve the usability of police situation data and subsequent processing efficiency.

[0080] In an optional embodiment, in step S300, parsing and processing the police situation text data to extract target police situation data related to the police situation includes:

[0081] First, perform word segmentation on the police situation text data, and use natural language processing (NLP) technology to cut the complete text into several police situation keywords. For example, in a police situation text "Someone was stabbed and injured at a certain intersection, and many people were injured at the scene", police situation keywords such as "intersection", "stabbed with a knife", "injured someone", and "many people injured" can be cut out. Word segmentation can accurately identify valuable information for police situation processing in combination with the context.

[0082] Then, match the extracted police situation keywords with a preset police situation classification rule library. The police situation classification rule library contains standard rules and mapping relationships related to police situation processing. For example, "stabbed with a knife" is mapped to "involved tool: knife category", and "many people injured" is mapped to "event type: injury event". Through rule matching, the police situation keywords are associated with the target fields, thereby extracting target police situation data related to the police situation, including the following content:

[0083] Event type: Identifies the classification to which the police situation belongs, such as "injury event" or "traffic accident".

[0084] Information of the involved tool: Describe the tools mentioned in the police report, such as "knife", "dagger", etc.

[0085] Information of the incident location: Based on keywords such as "intersection", further obtain specific location information through the address library interface.

[0086] Information of the incident time: Extract the time expressions in the police report text, such as "this morning" or "8:30", and convert them into the standard time format.

[0087] The method for processing police report data in the embodiments of the present application can efficiently extract target police report data such as the incident type, involved tool, incident location, and time from unstructured text by parsing and processing the police report text data and combining text cutting and police report classification rule matching techniques, significantly improving the structuring degree and usability of police report data, and providing accurate data support for subsequent police report analysis and rapid response.

[0088] Step S400: Generate police report decision support information associated with the target police report data based on the target police report data.

[0089] Exemplarily, the target police report data includes the incident type, involved tool information, incident location information, and incident time information extracted from the parsing of the police report text. The purpose of generating the police report decision support information is to provide a basis and guidance for the response strategy of police report handling. By analyzing different dimensions of the target police report data, such as the severity of the incident, the danger of the tool, the urgency of the location, and the timeliness of the time, comprehensive decision support information can be generated, so as to provide customized solutions for different police reports.

[0090] The generation of the police report decision support information is based on the decision rule logic preset in the system, and these logics cover many aspects such as police report classification, police force allocation, and public opinion management. The generated decision support information includes but is not limited to the nature of the police report, the recommended police type for handling, the response level, and the public opinion control strategy, etc., and can provide accurate data support for all aspects of police report handling, as Figure 3 shown in ⑤.

[0091] In an alternative embodiment, in step S400, generating the police report decision support information associated with the target police report data based on the target police report data includes:

[0092] Exemplarily, by parsing the target police report data, including the incident type, involved tool information, incident location information, and incident time information, the system matches the rules in the preset police report handling rule library to generate the police report decision support information including the nature of the police report, the recommended police type, and the public opinion control strategy.

[0093] The police situation handling rule library consists of a series of predefined rules, which are classified according to different types of police situations and their characteristics, including the judgment of the severity of the incident, suggestions for the appropriate police types for handling, and control measures for the possible impact of public opinion. Through the matching of the rule library, decision support information highly relevant to the actual police situation can be quickly generated, providing strong data support for subsequent police situation handling.

[0094] Specifically, based on the incident type and the information about the involved tools in the target police situation data, the system matches with the preset police situation classification rule library to determine the nature of the police situation. For example, an "assault with a knife" incident may be classified as a "first-level injury incident" or a "serious violent incident".

[0095] According to the determined nature of the police situation, police type keywords corresponding to it are generated to suggest specific handling departments. For example, for an "assault with a knife" incident, police type keywords such as "SWAT", "patrol police", and "criminal police" may be generated to clarify the division of handling responsibilities.

[0096] Based on the nature of the police situation and the police type keywords, decision keywords for judging whether to initiate joint operations are further generated. For example, if the incident involves multiple locations, multiple tools, or personnel, the system can generate a "joint operations" keyword to indicate the necessity of coordinated handling by multiple departments.

[0097] When it is judged that joint operations need to be initiated, public opinion control keywords are generated based on the preset public opinion handling rules, such as "delete", "seal off", "picture", etc. At the same time, according to the sensitivity or social impact of the incident, the level of public opinion control is determined, for example, "level-one control" or "level-two control".

[0098] Based on the incident location information in the target police situation data, the specific detailed address of the incident is retrieved and generated through the address library interface. For example, "a certain intersection, a certain street, a certain district, a certain city". In addition, the system generates a standardized alarm time format based on the time field in the target police situation data (such as "9 am" or "this morning") to ensure the accuracy of the time information.

[0099] The police situation data processing method of the embodiment of the present application generates key decision-making information such as the nature of the police situation, police type suggestions, and public opinion control strategies through the matching of the target police situation data with the preset police situation handling rule library, and provides accurate detailed address and alarm time information in combination with the incident location and time information, realizing the intelligent analysis and decision support of police situation data. It greatly improves the efficiency and accuracy of police situation handling, providing strong technical support for rapid response and comprehensive handling.

[0100] It should be noted that in an alternative embodiment, Figure 3③-⑥ shown in the figure can be embodied in the form of a data model, that is, a data model is constructed based on the NIFI system. The reception of police situation voice data and the process of the data model both run on the processes of multiple nodes in the NIFI cluster. Then, the data model is adjusted through a flexible drag-and-drop operation method to obtain structured police situation summary data in subsequent steps.

[0101] Step S500: Generate structured police situation summary data according to the police situation decision support information.

[0102] Exemplarily, by processing the police situation decision support information, structured police situation summary data is generated. The police situation decision support information includes content in multiple dimensions such as the nature of the police situation, police type keywords, decision keywords, public opinion control strategies and their levels, detailed address information, and alarm time information. These contents exist in an unstructured or semi-structured form and need to be transformed into a standardized data format through structured processing for subsequent police situation analysis, response scheduling, and data storage.

[0103] The goal of generating structured police situation summary data is to refine complex police situation information into a refined and intuitive data form, providing the police situation management system with the ability to quickly access and query.

[0104] In an alternative embodiment, in step S500, generating structured police situation summary data according to the police situation decision support information includes:

[0105] As Figure 3 shown in ⑥, integrate the data in each dimension in the police situation decision support information, including: Nature of the police situation: Describe the category and severity of the police situation (for example, "assault with a knife", "Level 1 police situation"). Police type keywords: Indicate the police types for handling, such as "patrol police", "emergency unit", etc. Decision keywords: Relevant decision support information on whether combined operations are required. Public opinion control keywords and levels: Involve public opinion control measures (for example, "delete", "seal off") and their emergency levels (for example, "Level 1 control"). Detailed address information: Specific event location information generated through the address library interface. Alarm time information: Standardized event occurrence time to ensure the accuracy of time expression.

[0106] The integrated information is processed according to a preset data structure to generate standardized police situation summary data, as Figure 3 shown in ⑦. For example, the data structure can be designed to include the following fields: "Time", "Location", "Event type", "Involved tool", "Casualty situation", etc. Through this processing method, the police situation information is transformed into a unified format, facilitating storage, display, and distribution.

[0107] The finally generated police situation summary data is output in natural language. For example: "On a certain date, a first-level police situation of stabbing and wounding occurred at a certain address, causing casualties to several people. Please send police to a certain village group and a certain emergency unit in the vicinity immediately to check and handle it, and apply for the cooperation of cyber police to control public opinion and delete relevant source images."

[0108] The police situation data processing method of the embodiment of the present application generates clear and concise police situation summary data through the integration and structured processing of police situation decision support information. It significantly improves the readability and transmission efficiency of police situation information, enabling each disposal department to quickly obtain key information and respond in a timely manner.

[0109] Figure 4 A schematic structural diagram of a police situation data processing device according to an embodiment of the present application is shown. Exemplarily, the device 400 includes:

[0110] A data receiving module 410, configured to receive police situation voice data;

[0111] A police situation text generation module 420, configured to perform speech recognition processing on the police situation voice data to generate police situation text data corresponding to the police situation voice data;

[0112] A text data parsing module 430, configured to perform parsing processing on the police situation text data to extract target police situation data related to the police situation;

[0113] A decision information generation module 440, configured to generate police situation decision support information associated with the target police situation data based on the target police situation data;

[0114] A police situation summary generation module 450, configured to generate structured police situation summary data according to the police situation decision support information.

[0115] It can be understood that the device in this embodiment corresponds to the method in the above embodiment, and the optional items in the above embodiment also apply to this embodiment, so they will not be repeated here.

[0116] The present application also provides a computer device. Exemplarily, the computer device includes a processor and a memory. Among them, the memory stores a computer program, and the processor runs the computer program to enable the computer device to execute the above method or the functions of each module in the above device.

[0117] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, etc., and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.

[0118] The memory can be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. Among them, the memory is used to store a computer program, and after receiving an execution instruction, the processor can execute the computer program accordingly.

[0119] The present application also provides a computer-readable storage medium for storing the computer program used in the above computer device. For example, the computer-readable storage medium can include, but is not limited to: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program code.

[0120] In several embodiments provided by this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the part of the module, program segment, or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the structure diagram and / or flowchart, as well as the combination of blocks in the structure diagram and / or flowchart, can be implemented by a dedicated hardware-based system that executes the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] In addition, each functional module or unit in various embodiments of this application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0122] If the above functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application.

[0123] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application.

Claims

1. A method for processing police information data, characterized in that: The method comprises: Receive alarm voice data; Performing voice recognition processing on the alarm voice data to generate alarm text data corresponding to the alarm voice data; Parsing the police situation text data to extract target police situation data related to the police situation; Based on the target alarm situation data, generating alarm situation decision support information associated with the target alarm situation data; Based on the police situation decision support information, structured police situation summary data is generated.

2. The method for processing police information data according to claim 1, characterized in that: The receiving of the alarm voice data comprises: The alarm voice data is received dynamically and in real time by a NIFI system deployed on a cluster node, wherein the NIFI system monitors the input of the alarm voice data in real time by configuring a message queue; Based on the load balancing strategy, the received alarm voice data is distributed to different cluster nodes, and the alarm voice data is temporarily stored in the disk of the server where the cluster node is located.

3. The method for processing police information data according to claim 1, characterized in that: The performing speech recognition processing on the alarm voice data to generate alarm text data corresponding to the alarm voice data includes: By calling a preset voice recognition interface, the alarm voice data is sent to the voice recognition interface; The voice recognition interface is used to process the alarm voice data based on a voice recognition algorithm to generate corresponding alarm text data.

4. The method for processing police information data according to claim 1, characterized in that: The step of parsing the police situation text data and extracting target police situation data related to the police situation includes: Performing text segmentation on the police situation text data to extract police situation keywords; Based on the police situation keywords, matching is performed in a preset police situation classification rule library to extract target police situation data related to the police situation; The target alarm data includes event type, information about tools involved, event location, and event time.

5. The method for processing police information data according to claim 4, characterized in that: The generating, based on the target alarm situation data, alarm situation decision support information associated with the target alarm situation data comprises: According to the event type, the information of the tools involved, the event location information and the event time information, the rules in the preset police situation processing rule library are matched to generate the police situation decision support information including the nature of the police situation, police type recommendations and public opinion control strategies.

6. The method for processing police information data according to claim 5, characterized in that: The generation of police situation decision support information including the nature of the police situation, suggestions for handling the police situation, and public opinion control strategies includes: Based on the event type and the tool information involved, a preset alarm classification rule library is matched to determine the nature of the alarm; Generate a police type keyword corresponding to the police situation according to the nature of the police situation; Based on the nature of the police situation and the police type keywords, generating decision keywords for determining whether a synthetic operation is required; When it is determined that a combined operation needs to be initiated, keywords for public opinion control are generated based on the preset public opinion processing rules, and the corresponding public opinion control level is determined; Based on the event location information, the detailed address information of the generated event is retrieved through the address library interface, and the alarm time information is generated based on the event time information.

7. The method for processing police information data according to claim 6, characterized in that: Generating structured police situation summary data according to the police situation decision support information includes: Integrate the nature of the police situation, the police type keywords, the decision-making keywords, the public opinion control keywords and the corresponding public opinion control level, the detailed address information and the alarm time information; The integrated information is structured according to a preset data structure to generate the police situation summary data.

8. A warning data processing device, characterized in that: The device comprises: A data receiving module, used for receiving alarm voice data; A warning text generation module, used for performing voice recognition processing on the warning voice data to generate warning text data corresponding to the warning voice data; A text data parsing module is used to parse the police situation text data and extract target police situation data related to the police situation; A decision information generation module, used to generate, based on the target alarm data, alarm decision support information associated with the target alarm data; The police situation summary generation module is used to generate structured police situation summary data according to the police situation decision support information.

9. A computer device, characterized in that: The computer device includes a processor and a memory, the memory stores a computer program, and the processor is used to execute the computer program to implement the alarm data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed on a processor, implements the alarm data processing method according to any one of claims 1-7.

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