A real-time data collection and intelligent distribution system for digital relay calls for emergency dispatch

Abstract

The present invention relates to the field of digital relay technology, and discloses a real-time collection and intelligent distribution system for digital relay calls for emergency dispatch. The system performs the following steps through five modules: the time domain buffer management module receives and regularizes UDP data; the cross-protocol association engine completes the spatiotemporal matching of calls and media streams; unsuccessful data triggers a reverse tracing service for remediation; the intelligent dispatch controller processes and dynamically allocates resources based on the degree of urgency; and finally, the data is intelligently distributed to the message queue cluster through the elastic load balancing module. The present invention achieves accurate routing of emergency instructions and zero loss of key data by constructing a multi-level heterogeneous data processing framework, as well as dynamic redundant transmission, intelligent spatiotemporal alignment, and elastic resource scheduling mechanisms. At the same time, it enhances the system's adaptive fault tolerance for abnormal working conditions such as device clock deviation and burst traffic, providing high-availability technical support for the digital upgrade of emergency dispatch.

Description

Technical Field

[0001] The present invention relates to the field of digital relay, and more particularly to a digital relay voice real-time collection and intelligent distribution system oriented to emergency dispatch. Background Art

[0002] With the rapid development of smart cities and emergency response systems, emergency dispatch scenarios have placed higher demands on the real-time, reliability, and intelligent processing of digital relay traffic.

[0003] Traditional systems in emergency dispatch networks with mixed multi-vendor equipment and clock deviations face technical bottlenecks such as UDP broadcast packet loss, high-concurrency media stream distribution, multi-channel load balancing, and maintaining traffic-media data correlation. Therefore, a digital relay traffic real-time collection and intelligent distribution system is proposed to solve the above problems. Summary of the Invention

[0004] The present invention provides a real-time collection and intelligent distribution system for digital relay traffic for emergency dispatch, which solves the technical problems of UDP broadcast packet loss, high-concurrency media stream distribution, multi-channel load balancing and traffic-media data correlation maintenance in emergency dispatch networks in related technologies.

[0005] The present invention provides a real-time collection and intelligent distribution system for digital relay calls oriented to emergency dispatch, comprising:

[0006] Time Domain Buffer Management Module: This module receives and verifies the time deviation of UDP packets, stores advanced data in the future buffer, processes real-time data directly, and performs historical compensation alignment on lagging data. It also regularly clears expired caches and merges and outputs standardized data streams to downstream processing modules.

[0007] Cross-protocol correlation engine module: This module parses and standardizes traffic and media data packets, builds a spatiotemporal joint index, calculates field similarity, and establishes candidate matching pairs. It then dynamically verifies time window deviations and outputs correlation results or triggers a tracing process.

[0008] Reverse tracing service module: This module receives uncorrelated media packets and generates tracing query keys, performs multi-level searches in the real-time buffer and historical storage, calculates similarity and dynamically expands the time window, and returns the best matching result to the correlation module, or records unmatched data features.

[0009] Intelligent Scheduling Controller Module: This module integrates successful correlation and retroactive remediation data streams, calculates a multi-dimensional emergency index, and divides the processing queues into three levels. It then dynamically allocates resources, handles queue overflow risks, and ultimately outputs scheduling results to the elastic load balancing module and generates a system status report.

[0010] Elastic load balancing module: By collecting cluster node status and quantifying health in real time, it performs quantized resource allocation, detects abnormal nodes, triggers circuit breakers, dynamically migrates traffic, and distributes data to MQ cluster nodes. It also generates resource reports and provides load status feedback.

[0011] Furthermore, the steps performed in the time domain buffer management module are as follows:

[0012] UDP data packet reception and timestamp extraction: Receive UDP broadcast messages sent by the voice collector, parse the timestamp field in the message header, and obtain the gateway system time;

[0013] Time window classification decision: classify data packets according to clock deviation and assign them to corresponding processing channels;

[0014] Future buffer storage management: advanced packets are organized and stored by time slots, and a ring buffer pointer is maintained;

[0015] History compensator time domain alignment: aligns the time scale of the delayed packet with the historical data, and uses linear interpolation to compensate for clock deviation;

[0016] Regular cleaning service: Eliminate expired data during each cleaning cycle to maintain the effectiveness of the buffer space;

[0017] Data flow control: Merges and processes data streams and outputs them to the cross-protocol correlation engine module.

[0018] Furthermore, the steps performed in the cross-protocol correlation engine module are as follows:

[0019] Dual-protocol data input and standardization: Receives regularized data streams from the time-domain buffer management module, parses traffic packets and media packets, and extracts standardized fields.

[0020] Spatiotemporal index construction: Build a joint index for the two types of data and generate spatiotemporal hash keys;

[0021] Correlation matrix generation: Calculate cross-protocol field similarity and establish a set of candidate matching pairs;

[0022] Timing tolerance verification: Checks whether the timestamp deviation is within the permitted range and dynamically adjusts the time window;

[0023] Association decision and output: Perform final matching based on similarity scores, output association results, or trigger tracing.

[0024] Furthermore, the steps performed in the reverse tracing service module are as follows:

[0025] Trigger conditions and input preprocessing: Receive uncorrelated media packets output by the cross-protocol correlation engine module and extract key traceability fields;

[0026] Retrospective query key generation: Build a composite index key and define the traceable time range;

[0027] Multi-level buffer search: prioritizes querying the real-time buffer of the time domain buffer management module and expands the search history storage area;

[0028] Cross-protocol similarity calculation: compares the field similarity of candidate traffic packets and calculates the comprehensive matching score;

[0029] Dynamic window expansion decision: determines whether the tracing limit has been reached and automatically expands the spatiotemporal search range;

[0030] Result feedback and recording: Return the best matching result to the cross-protocol correlation engine module and record the unmatched data features.

[0031] Furthermore, the steps performed in the intelligent scheduling controller module are as follows:

[0032] Multi-source data integration input: Receives the correlation success data stream and the retrospective remediation data stream, and merges them into the input queue;

[0033] Multi-dimensional urgency assessment: Calculates the composite urgency index of a data packet and dynamically adjusts the weight based on business rules;

[0034] Dynamic three-level queue division: divide the processing queue according to the emergency index and set the queue capacity warning line;

[0035] Flexible resource allocation: monitor the load status of each queue and dynamically adjust the computing resource allocation ratio;

[0036] Exception handling and degradation: Detect queue overflow risks and trigger resource borrowing mechanisms;

[0037] Output control and feedback: Push scheduling results to the elastic load balancing module to generate a system health status report.

[0038] Furthermore, the steps performed in the elastic load balancing module are as follows:

[0039] Multi-dimensional load status collection: real-time collection of message queue cluster node status indicators, and receiving queue status feedback from the intelligent scheduling controller module;

[0040] Node health quantification: calculate the node's comprehensive load index and classify the node's health level;

[0041] Quantum resource allocation: abstract node resources into quantum units and allocate processing quantum according to health;

[0042] Abnormal node circuit breaker detection: Detects potential overloaded nodes and triggers circuit breaker protection mechanisms;

[0043] Dynamic traffic redistribution: Calculates the amount of traffic migration between nodes and implements a smooth migration strategy;

[0044] Data distribution and monitoring feedback: Distribute data to MQ cluster nodes according to quantum allocation, generate resource allocation reports and feedback status.

[0045] Furthermore, in the step of generating the correlation matrix, for any traffic packet i and media packet j, the calculation formula of the elements in the correlation matrix is as follows:

[0046] ;

[0047] The channel similarity is:

[0048] ;

[0049] in, is the call identification weight, is the channel number weight, is the exact matching function, is the channel similarity calculation function, is the call identifier of the i-th traffic packet, is the call identifier of the j-th media packet, The element in the i-th row and j-th column of the association matrix represents the similarity score between the i-th traffic packet and the j-th media packet. Indicates the timestamp of the i-th traffic packet, Indicates the timestamp of the j-th media packet, Indicates the channel number of the i-th traffic packet, Indicates the channel number of the jth traffic packet, Half the width of the immediate window.

[0050] Furthermore, in associating decision and output, the decision rule formula is as follows:

[0051] ;

[0052] The output format is as follows:

[0053] ;

[0054] in, represents the element in row i and column j of the incidence matrix, is the similarity threshold, Standardize the field set for traffic packets, Standardize the set of fields for media packets, Output of the association result, The empty collection marker.

[0055] Furthermore, in the retrospective query key generation, the key generation formula is as follows:

[0056] ;

[0057] ;

[0058] in, is the generated traceback query key, is the number of time window extensions, the initial value is 0, is the maximum time deviation threshold, is a hash function, is the string concatenation operator, is the media packet call identifier, The corrected timestamp of the media packet.

[0059] Furthermore, in the intelligent scheduling controller module, the scheduling results include priority tags, target node allocations, resource quotas, timeliness constraints, and routing paths.

[0060] The beneficial effects of the present invention are:

[0061] This technical solution significantly improves the real-time response capability and system reliability of digital relay traffic in emergency dispatch scenarios by constructing a multi-level heterogeneous data processing framework. It effectively solves core problems of traditional systems in complex network environments, such as UDP data loss, high-concurrency media stream distribution congestion, and cross-protocol timing misalignment. Through dynamic redundant transmission, intelligent time and space alignment, and elastic resource scheduling mechanisms, it achieves accurate routing of emergency commands and zero loss of key data. At the same time, it enhances the system's adaptive fault tolerance capabilities for abnormal working conditions such as equipment clock deviation and burst traffic, providing high-availability technical support for the digital upgrade of emergency dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a structural block diagram of a digital relay call real-time collection and intelligent distribution system for emergency dispatch proposed by the present invention;

[0063] Figure 2 The present invention Figure 1 Execution flow chart of module 1;

[0064] Figure 3 The present invention Figure 1 Execution flow chart of module 2;

[0065] Figure 4 The present invention Figure 1 Execution flow chart of module 3;

[0066] Figure 5 The present invention Figure 1 Execution flow chart of module 4;

[0067] Figure 6 The present invention Figure 1 Execution flow chart of module 5. DETAILED DESCRIPTION

[0068] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0069] like Figures 1-6 As shown, a real-time collection and intelligent distribution system for digital relay calls for emergency dispatch includes:

[0070] Module 1, Time Domain Buffer Management Module: This module receives and verifies the time deviation of UDP packets, stores advanced data in the future buffer, processes real-time data directly, and performs historical compensation alignment on lagging data. It also regularly clears expired caches and merges and outputs standardized data streams to downstream processing modules.

[0071] like Figure 2 As shown, the following steps are performed through this module:

[0072] 1.1, UDP data packet reception and timestamp extraction: Receive the UDP broadcast message sent by the voice collector, parse the timestamp field in the message header ( ), get the gateway system time ( );

[0073] The formula is:

[0074] ;

[0075] in, is the device timestamp, is the gateway timestamp, is the timestamp difference, ;

[0076] 1.2, Time Window Classification Decision: Classify data packets according to clock deviation and assign them to corresponding processing channels;

[0077] Classification rules:

[0078] ;

[0079] in, is the advance judgment threshold, , is half the width of the immediate window, , is the hysteresis compensation threshold, ;

[0080] 1.3, Future Buffer Storage Management: The advanced packets are organized and stored by time slots, and the ring buffer pointer is maintained;

[0081] Storage Policy:

[0082] ;

[0083] Override rules:

[0084] ;

[0085] in, is the storage slot index, is the time slot granularity, , is the maximum capacity of the ring buffer, , is the current pointer position, is the previous pointer position, is the advance judgment threshold;

[0086] 1.4, History Compensator Time Domain Alignment: The time scale of the delayed packet is aligned with the historical data, and linear interpolation is used to compensate for the clock deviation;

[0087] Compensation formula:

[0088] ;

[0089] Among them, α is the compensation coefficient, , is the corrected timestamp, is the device timestamp, is the system timestamp;

[0090] 1.5, Regular cleaning service: Eliminate expired data in each cleaning cycle to maintain the validity of the buffer space;

[0091] Cleanup rules:

[0092] ;

[0093] in, Indicates the validity period of the data. is a set of buffers, is the system timestamp, The device timestamp for cached data. To cache data;

[0094] 1.6, Data flow control: Merge the processed data stream and output it to the next processing module;

[0095] Output rules:

[0096] ;

[0097] in, is the final output data set, For each type of buffer, is the buffer type index, , =future, =real, =history, represents the future buffer, represents the real-time processing channel, represents the history compensator;

[0098] Module 2, the cross-protocol correlation engine module, parses and standardizes traffic and media data packets, builds a spatiotemporal joint index, calculates field similarity and establishes candidate matching pairs, dynamically verifies time window deviations, and outputs correlation results or triggers a tracing process.

[0099] like Figure 3 As shown, the following steps are performed through this module:

[0100] 2.1, Dual-protocol data input and standardization: Receive the regularized data stream from the time domain buffer management module and parse the traffic packet ( ) and Media Pack ( ), extract standardized fields;

[0101] Traffic package:

[0102] ;

[0103] Media Kit:

[0104] ;

[0105] in, is the corrected timestamp, is the immediate window threshold, For traffic protocol data packets, For media protocol data packets, is the call identifier of the traffic packet, is the traffic packet channel number, The timestamp of the traffic packet after correction. is the media packet call identifier, is the media packet channel number, The corrected timestamp for the media packet. is the original data of the media package, Standardize the set of fields for media packets, Standardize the set of fields for traffic packets;

[0106] 2.2, Spatiotemporal index construction: Build a joint index for the two types of data and generate spatiotemporal hash keys;

[0107] Index generation formula:

[0108] ;

[0109] in, is a hash function, is a string concatenation operator, is the immediate window threshold, is the call identifier, is the corrected timestamp, The generated hash key value;

[0110] 2.3, Correlation Matrix Generation: Calculate cross-protocol field similarity and establish a set of candidate matching pairs;

[0111] For any traffic packet i and media packet j, the element calculation formula in the correlation matrix is:

[0112] ;

[0113] The channel similarity is:

[0114] ;

[0115] in, is the call identification weight, is the channel number weight, is the exact matching function, is the channel similarity calculation function, is the call identifier of the i-th traffic packet, is the call identifier of the j-th media packet, The element in the i-th row and j-th column of the association matrix represents the similarity score between the i-th traffic packet and the j-th media packet. Indicates the timestamp of the i-th traffic packet, Indicates the timestamp of the j-th media packet, Indicates the channel number of the i-th traffic packet, Indicates the channel number of the jth traffic packet;

[0116] 2.4, Timing tolerance verification: Check whether the timestamp deviation is within the permitted range and dynamically adjust the time window;

[0117] Verification condition formula:

[0118] ;

[0119] Window adjustment rules:

[0120] ;

[0121] in, is the maximum allowable time deviation, is the current processing queue depth, is the safety queue threshold, is the immediate window threshold, The timestamp of the traffic packet after correction. Corrected timestamp for media packets;

[0122] 2.5, Association decision and output: Perform final matching based on similarity scores, output association results or trigger tracing;

[0123] Decision rule formula:

[0124] ;

[0125] Output format:

[0126] ;

[0127] in, The element in the i-th row and j-th column of the association matrix represents the similarity score between the i-th traffic packet and the j-th media packet. is the similarity threshold, , Standardize the field set for traffic packets, Standardize the set of fields for media packets, Output of the association result, Marks an empty collection;

[0128] Module 3, reverse tracing service module: This module receives uncorrelated media packets and generates tracing query keys, performs multi-level searches in the real-time buffer and historical storage, calculates similarity and dynamically expands the time window, and returns the best matching result to the correlation module, or records unmatched data features.

[0129] The following steps are performed through this module:

[0130] 3.1, Trigger conditions and input preprocessing: Receive the uncorrelated media packets output by the cross-protocol correlation engine module and extract key traceability fields;

[0131] Receiving unassociated media packets:

[0132] ;

[0133] Extract key traceability fields:

[0134] ;

[0135] in, Records the media packages that were not successfully associated. Standardize the set of fields for media packets, For the empty collection marker, The set of key fields required for tracing, is the media packet call identifier, is the original data of the media package, Corrected timestamp for media packets;

[0136] 3.2, Traceability query key generation: Build a composite index key and define the traceability time range;

[0137] Key generation formula:

[0138] ;

[0139] ;

[0140] in, is the generated traceback query key, is the number of time window extensions, the initial value is 0, is the maximum time deviation threshold, is a hash function, is half the width of the immediate window, is the string concatenation operator, is the media packet call identifier, Corrected timestamp for media packets;

[0141] 3.3, Multi-level buffer search: Prioritize the real-time buffer of the time domain buffer management module and expand the search history storage area;

[0142] Search scope definition:

[0143] ;

[0144] Priority rules:

[0145] ;

[0146] in, For the search time range, The corrected timestamp for the media packet. is the number of time window expansions, is the initial time deviation threshold, To retrieve the priority strategy, is the time domain buffer, For persistent history storage;

[0147] 3.4, Cross-protocol similarity calculation: Compare the field similarities of candidate traffic packets and calculate the comprehensive matching score;

[0148] Scoring model:

[0149] ;

[0150] ;

[0151] in, To trace back the similarity score, is the time similarity weight, is the call identification weight, is the timestamp difference, is the time attenuation coefficient, is the Levenshtein edit distance, is the media packet call identifier, is the call identifier of the traffic packet, The maximum length of the call identifier is fixed at 32.

[0152] 3.5, Dynamic Window Expansion Decision: Determine whether the tracing limit has been reached and automatically expand the spatiotemporal search range;

[0153] Scaling strategy:

[0154] ;

[0155] in, is the number of extensions after update, is the current number of expansions, is the maximum number of expansions, To trace back the similarity score, is the retroactive matching threshold, is the current maximum similarity score;

[0156] 3.6, Result Feedback and Recording: Return the best matching result to the cross-protocol correlation engine module and record the unmatched data features;

[0157] Feedback Protocol:

[0158] ;

[0159] in, To provide feedback on the traceability results, Standardize the set of fields for media packets, Standardize the field set for the best matching traffic packet, To trace back the similarity score, is the retroactive matching threshold, Marks an empty collection;

[0160] Module 4, the Intelligent Scheduling Controller, integrates successful correlation and retroactive remediation data streams, calculates a multi-dimensional emergency index, and divides the processing queues into three levels. It then dynamically allocates resources, handles queue overflow risks, and ultimately outputs the scheduling results to the elastic load balancing module and generates a system status report.

[0161] The following steps are performed through this module:

[0162] 4.1, Multi-source data integration input: Receive the association success data stream and the retrospective remediation data stream, and merge them into the input queue;

[0163] Receive the successful association data stream from the cross-protocol correlation engine module:

[0164] ;

[0165] Receive the traceability remediation data flow from the reverse traceability service module:

[0166] ;

[0167] Merge input queues:

[0168] ;

[0169] in, is the similarity threshold, is the retroactive matching threshold, is the data set that is successfully associated. Data collection for retroactive remediation, is the total data set after merging, For the association results, To provide feedback on the traceability results, is the comprehensive similarity score, To score the similarity for retrospective;

[0170] 4.2, Multi-dimensional Urgency Assessment: Calculate the composite urgency index of the data packet and dynamically adjust the weight according to business rules;

[0171] Evaluation formula:

[0172] ;

[0173] ;

[0174] in, is the composite emergency index, is the timestamp difference, is the current system time, is the corrected timestamp, is the comprehensive similarity score, is the queue depth monitoring value, is the safe queue depth threshold, is the timeliness weight, is the associated credibility weight, is the channel priority weight;

[0175] 4.3, Dynamic division of three-level queues: divide the processing queues according to the emergency index and set queue capacity warning lines;

[0176] Division rules:

[0177] ;

[0178] in, is the composite emergency index, is the emergency queue threshold, and the maximum capacity of the emergency queue is , is the hysteresis queue threshold, Indicates the queue type;

[0179] 4.4, Elastic resource allocation: Monitor the load status of each queue and dynamically adjust the computing resource allocation ratio;

[0180] Resource allocation formula:

[0181] ;

[0182] in, is the resource allocation ratio of queue j, is the current backlog of queue j, is the current backlog of queue i, is the smoothing factor, is the average urgency index of queue j, is the average urgency index of queue i, is the queue index, where 1 = emergency level, 2 = normal level, and 3 = delayed level;

[0183] 4.5, Exception handling and degradation: Detect queue overflow risks and trigger resource borrowing mechanisms;

[0184] Overflow determination conditions:

[0185] ;

[0186] Secondment Rules:

[0187] ;

[0188] in, is the current backlog of the emergency queue, is the current backlog of the normal queue, is the maximum capacity of the emergency queue, is the amount of resources that can be seconded, is the loanable ratio of the normal queue, is the maximum borrowing coefficient;

[0189] 4.6, Output Control and Feedback: Push the scheduling results to the elastic load balancing module and generate a system health status report;

[0190] Output protocol:

[0191] ;

[0192] in, For the final scheduling output result, is the data set in queue j, is the resource allocation ratio of queue j, is the total computing resources of the cluster, is the minimum resource requirement for a single task, is the queue index;

[0193] The scheduling result consists of the following parts:

[0194] Priority marking: three-level processing markings of urgent / normal / delayed;

[0195] Target node allocation: a list of message queue (MQ) cluster nodes specified for each data packet;

[0196] Resource quota: computing resource allocation plan such as number of CPU cores and memory size;

[0197] Timeliness constraint: maximum allowable processing delay threshold (e.g., ≤50ms);

[0198] Routing path: Transmission link planning for cross-protocol associated data;

[0199] Module 5, Elastic Load Balancing: This module collects cluster node status and quantifies their health in real time, performs quantized resource allocation, detects abnormal nodes, triggers circuit breakers, dynamically migrates traffic, and distributes data to message queue (MQ) cluster nodes. It also generates resource reports and provides load status feedback.

[0200] The following steps are performed through this module:

[0201] 5.1. Multi-dimensional load status collection: Real-time collection of message queue (MQ) cluster node status indicators and receiving queue status feedback from the intelligent scheduling controller module;

[0202] Acquisition parameters:

[0203] ;

[0204] in, is the CPU usage of node i, is the CPU resource used by node i, is the total CPU resource of node i, is the memory usage of node i, is the memory resource used by node i, is the total memory resource of node i, is the queue utilization rate of node i, is the real-time depth of the three-level queue, is the maximum capacity of the emergency queue, is the node index number;

[0205] 5.2, Node health quantification: Calculate the node comprehensive load index and classify the node health level;

[0206] Health calculation:

[0207] ;

[0208] Weight adjustment:

[0209] ;

[0210] in, is the health index of node i, is the CPU weight benchmark value, is the memory weight benchmark value, is the queue weight (dynamically adjusted), is the CPU usage of node i, is the memory usage of node i, is the queue utilization rate of node i, is the current backlog of the emergency queue, is the maximum capacity of the emergency queue;

[0211] 5.3,Quantized resource allocation: Abstract node resources into quantum units and allocate processing quantum according to health;

[0212] Allocation calculation:

[0213] ;

[0214] ;

[0215] in, is the quantum number assigned to node i, is the health index of node i, is the health index of node j, is the total quantum number to be distributed, is the total number of cluster nodes, The node index for summation, is the total data set after merging;

[0216] 5.4, Abnormal node fuse detection: Detect potential overloaded nodes and trigger the fuse protection mechanism;

[0217] Circuit breaker determination:

[0218] ;

[0219] in, is the node index number, is the CPU usage of node i, is the memory usage of node i, is the queue utilization rate of node i, is the CPU fuse threshold, is the memory fuse threshold, The queue circuit breaker threshold.

[0220] 5.5, Dynamic Traffic Redistribution: Calculate the amount of traffic migration between nodes and implement a smooth migration strategy;

[0221] Migration Computing:

[0222] ;

[0223] ;

[0224] in, is the quantum number that migrates from node i to node j, is the current quantum number of node i, is the current quantum number of node j, For the ideal distribution amount, is the health index of node i, is the sum of the health of all nodes, is the total quantum number, is the migration damping coefficient;

[0225] 5.6, Data distribution and monitoring feedback: Distribute data to MQ cluster nodes according to quantum allocation, generate resource allocation reports and feedback status;

[0226] Distribution strategy:

[0227] ;

[0228] Feedback format:

[0229] ;

[0230] in, is the feedback result set, is the node identifier, is the health index of node i, is the quantum number assigned to node i, is the total number of cluster nodes, is the node index number, is the data set distributed to node i, is a consistent hashing function, is the total number of cluster nodes.

[0231] Based on the above system and its architecture, the following example is given - emergency response of urban emergency dispatch system.

[0232] Scenario Background: A multi-vehicle pileup occurs on the road. Witnesses and in-vehicle emergency communications use devices from different operators to call the 110 emergency number and the 120 emergency number. The ambulance dispatch system of a nearby hospital receives multiple emergency calls simultaneously.

[0233] System operation process:

[0234] Data reception and buffering:

[0235] Multiple voice collection devices connected to base stations near the accident site began recording emergency calls and continuously sending traffic and on-site audio and video data via UDP broadcasts;

[0236] The time domain buffer management module detected that a device had a clock failure and its media packet timestamp was 300 milliseconds earlier than the gateway system time;

[0237] The system automatically stores "advanced" data in the future buffer, waiting to be released when the real-time processing window arrives;

[0238] Cross-protocol association processing:

[0239] Incoming call packets from some calling numbers arrive late due to network jitter;

[0240] The correlation engine finds that the corresponding on-site video package has arrived in advance and immediately starts the reverse tracing service;

[0241] By comparing features such as the incoming call number and the geographic information of the accident section, the delayed call packet was successfully associated with the earlier-arriving video stream.

[0242] Intelligent priority determination:

[0243] If the system identifies multiple incoming calls describing the same incident, they will automatically merge them into one emergency incident;

[0244] Based on keywords such as "multiple injuries" and "vehicle fire" in the call content, the incident was upgraded to a red emergency level;

[0245] Prioritize bandwidth resources to transmit on-site fire video to the fire command center;

[0246] Dynamic load scheduling:

[0247] Emergency dispatch terminals from several nearby hospitals simultaneously initiated resource requests, and the load balancing module detected a surge in CPU usage at the second hospital node.

[0248] Automatically switch some video streams to backup cloud nodes for processing, ensuring uninterrupted transmission of emergency instructions;

[0249] Intelligently allocate the task of transferring the wounded based on the number of idle ambulances in each hospital;

[0250] Closed-loop emergency response:

[0251] A complete on-site assessment report (including voice recording, injured person location, and preliminary injury assessment) is generated within 2 minutes of the accident;

[0252] The system automatically pushes resource demand forecasts to the Municipal Emergency Management Bureau, triggering the reserve force support mechanism;

[0253] After the first aid is completed, all relevant data packets are automatically archived to form a complete emergency incident handling knowledge base.

[0254] The above describes the embodiments of the present invention, but the present invention is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms under the guidance of the present invention, all of which are protected by the present invention.

Claims

1. A real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch, characterized by: include: Time Domain Buffer Management Module: This module receives and verifies the time deviation of UDP packets, stores advanced data in the future buffer, processes real-time data directly, and performs historical compensation alignment on lagging data. It also regularly clears expired caches and merges and outputs standardized data streams to downstream processing modules. Cross-protocol correlation engine module: This module parses and standardizes traffic and media data packets, builds a spatiotemporal joint index, calculates field similarity, and establishes candidate matching pairs. It then dynamically verifies time window deviations and outputs correlation results or triggers a tracing process. Reverse tracing service module: This module receives uncorrelated media packets and generates tracing query keys, performs multi-level searches in the real-time buffer and historical storage, calculates similarity and dynamically expands the time window, and returns the best matching result to the correlation module, or records unmatched data features. Intelligent Scheduling Controller Module: This module integrates successful correlation and retroactive remediation data streams, calculates a multi-dimensional emergency index, and divides the processing queues into three levels. It then dynamically allocates resources, handles queue overflow risks, and ultimately outputs scheduling results to the elastic load balancing module and generates a system status report. Elastic load balancing module: By collecting cluster node status and quantifying health in real time, it performs quantized resource allocation, detects abnormal nodes, triggers circuit breakers, dynamically migrates traffic, and distributes data to MQ cluster nodes. It also generates resource reports and provides load status feedback.

2. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 1, characterized in that: The steps performed in the time domain buffer management module are as follows: UDP data packet reception and timestamp extraction: Receive UDP broadcast messages sent by the voice collector, parse the timestamp field in the message header, and obtain the gateway system time; Time window classification decision: classify data packets according to clock deviation and assign them to corresponding processing channels; Future buffer storage management: advanced packets are organized and stored by time slots, and a ring buffer pointer is maintained; History compensator time domain alignment: aligns the time scale of the delayed packet with the historical data, and uses linear interpolation to compensate for clock deviation; Regular cleaning service: Eliminate expired data during each cleaning cycle to maintain the effectiveness of the buffer space; Data flow control: Merges and processes data streams and outputs them to the cross-protocol correlation engine module.

3. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 2, characterized in that: The steps performed in the cross-protocol correlation engine module are as follows: Dual-protocol data input and standardization: Receives regularized data streams from the time-domain buffer management module, parses traffic packets and media packets, and extracts standardized fields. Spatiotemporal index construction: Build a joint index for the two types of data and generate spatiotemporal hash keys; Correlation matrix generation: Calculate cross-protocol field similarity and establish a set of candidate matching pairs; Timing tolerance verification: Checks whether the timestamp deviation is within the permitted range and dynamically adjusts the time window; Association decision and output: Perform final matching based on similarity scores, output association results, or trigger tracing.

4. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 3, characterized in that: The steps performed in the reverse tracing service module are as follows: Trigger conditions and input preprocessing: Receive uncorrelated media packets output by the cross-protocol correlation engine module and extract key traceability fields; Retrospective query key generation: Build a composite index key and define the traceable time range; Multi-level buffer search: prioritizes querying the real-time buffer of the time domain buffer management module and expands the search history storage area; Cross-protocol similarity calculation: compares the field similarity of candidate traffic packets and calculates the comprehensive matching score; Dynamic window expansion decision: determines whether the tracing limit has been reached and automatically expands the spatiotemporal search range; Result feedback and recording: Return the best matching result to the cross-protocol correlation engine module and record the unmatched data features.

5. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 4, characterized in that: The steps performed in the intelligent scheduling controller module are as follows: Multi-source data integration input: Receives the correlation success data stream and the retrospective remediation data stream, and merges them into the input queue; Multi-dimensional urgency assessment: Calculates the composite urgency index of a data packet and dynamically adjusts the weight based on business rules; Dynamic three-level queue division: divide the processing queue according to the emergency index and set the queue capacity warning line; Flexible resource allocation: monitor the load status of each queue and dynamically adjust the computing resource allocation ratio; Exception handling and degradation: Detect queue overflow risks and trigger resource borrowing mechanisms; Output control and feedback: Push scheduling results to the elastic load balancing module to generate a system health status report.

6. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 5, characterized in that: The steps performed in the elastic load balancing module are as follows: Multi-dimensional load status collection: real-time collection of message queue cluster node status indicators, and receiving queue status feedback from the intelligent scheduling controller module; Node health quantification: calculate the node's comprehensive load index and classify the node's health level; Quantum resource allocation: abstract node resources into quantum units and allocate processing quantum according to health; Abnormal node circuit breaker detection: Detects potential overloaded nodes and triggers circuit breaker protection mechanisms; Dynamic traffic redistribution: Calculates the amount of traffic migration between nodes and implements a smooth migration strategy; Data distribution and monitoring feedback: Distribute data to MQ cluster nodes according to quantum allocation, generate resource allocation reports and feedback status.

7. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 6, characterized in that: In the step of generating the correlation matrix, for any traffic packet i and media packet j, the calculation formula of the elements in the correlation matrix is as follows: ； in Channel similarity: ； in, is the call identification weight, is the channel number weight, is the exact matching function, is the channel similarity calculation function, is the call identifier of the i-th traffic packet, is the call identifier of the j-th media packet, The element in the i-th row and j-th column of the association matrix represents the similarity score between the i-th traffic packet and the j-th media packet. Indicates the timestamp of the i-th traffic packet, Indicates the timestamp of the j-th media packet, Indicates the channel number of the i-th traffic packet, Indicates the channel number of the jth traffic packet, Half the width of the immediate window.

8. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 7, characterized in that: In associating decision and output, the decision rule formula is as follows: ； The output format is as follows: ； in, represents the element in row i and column j of the incidence matrix, is the similarity threshold, Standardize the field set for traffic packets, Standardize the set of fields for media packets, Output of the association result, The empty collection marker.

9. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 8, characterized in that: In the traceback query key generation, the key generation formula is as follows: ； ； in, is the generated traceback query key, is the number of time window extensions, the initial value is 0, is the maximum time deviation threshold, is a hash function, is the string concatenation operator, is the media packet call identifier, The corrected timestamp of the media packet.

10. The real-time collection and intelligent distribution system for digital trunk calls for emergency dispatch according to claim 9, characterized in that: In the intelligent scheduling controller module, the scheduling results include priority tags, target node allocation, resource quotas, timeliness constraints and routing paths.

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