Method for multi-channel dynamic allocation of intercom handset groups and related devices

Abstract

The application provides a method for multi-channel dynamic allocation of a talkgroup and related equipment thereof, which is used for reducing terminal energy consumption and improving the reliability and spectrum utilization of group communication. The method comprises the following steps: obtaining communication awareness data of each terminal in the talkgroup, statistically analyzing the communication awareness data to obtain a demand probability distribution of the talkgroup, performing channel utility and device communication resource evaluation processing on the communication awareness data to obtain communication constraint data of each terminal, performing local conflict identification between terminals according to the demand probability distribution and the communication constraint data to obtain a resource conflict data set, and performing resource coordination optimization allocation of channels and time on the talkgroup according to the resource conflict data set and the communication constraint data to generate resource allocation data.

Description

Technical Field

[0001] This application relates to the field of channel allocation technology, and in particular to a method for dynamic multi-channel allocation of walkie-talkie groups and related equipment. Background Technology

[0002] The core requirements for communication in walkie-talkie groups are "low latency, determinism, and reliability," while also considering multicast / broadcast characteristics and limited terminal resources. Specifically, in push-to-talk (PTT) scenarios, uplink handshakes, channel switching, and intra-group broadcasts must be completed within milliseconds to tens of milliseconds to avoid voice interruptions. Because walkie-talkies are often half-duplex one-to-many communications, the system must ensure priority transmission and low packet loss for key members (such as group leaders and emergency responders) while guaranteeing fair access for ordinary members. Mobility and heterogeneous deployments require channel allocation to adapt to path loss and transient interference caused by displacement. In addition, terminals in the field or with limited power are sensitive to energy consumption; prolonged high-frequency sensing or excessive switching can significantly shorten battery life. Therefore, while ensuring real-time performance, the system must minimize terminal power consumption and reduce control signaling overhead to improve overall reliability and availability.

[0003] Currently, common intercom group channel allocation methods in practice are mostly simplified engineering strategies or coarse allocation based on centralized control. Traditional methods include static allocation (reserving fixed channels for different groups or cells), centralized assignment triggered periodically or by events by the base station / dispatch center, and the use of simple carrier sense / contention mechanisms (such as CSMA or listen-before-send) on shared channels. In some private networks or vehicle-mounted scenarios, time slot reservation or routing scheduling mechanisms based on traffic priority are also used. These methods are simple to implement, easy to deploy, or easy to integrate with existing base station systems, but most of them are relatively coarse in terms of spatiotemporal resolution, sensing granularity, and consideration of terminal capabilities. In addition, for the sake of universality and stability, many systems tend to adopt longer sensing / scheduling cycles and conservative interference protection rules.

[0004] The existing methods lack fine-grained adaptation to spatiotemporal dynamics and terminal capabilities, resulting in low spectrum utilization efficiency, large reconfiguration delay, and high terminal power consumption. Summary of the Invention

[0005] This application provides a method for dynamic multi-channel allocation of walkie-talkie groups and related equipment, which can improve the reliability and spectrum utilization of group communication while reducing terminal power consumption.

[0006] In a first aspect, this application provides a method for dynamic multi-channel allocation of walkie-talkie groups, the method comprising: The communication sensing data of each terminal in the intercom group is acquired, and the communication sensing data is statistically analyzed to obtain the demand probability distribution of the intercom group. The communication sensing data is processed to evaluate channel utility and device communication resources to obtain communication constraint data for each terminal. Based on the demand probability distribution and the communication constraint data, local conflicts are identified between the terminals to obtain a resource conflict dataset. Based on the resource conflict dataset and the communication constraint data, the intercom group is allocated resource collaboratively in terms of channel and time to generate resource allocation data.

[0007] In one possible implementation, acquiring communication sensing data from each terminal within the intercom group and performing statistical analysis on the communication sensing data to obtain the demand probability distribution of the intercom group includes: The communication perception data and historical session data of each terminal in the intercom group are obtained according to the preset scanning method. The communication sensing data is spatiotemporally aligned and fused to obtain channel measurement data of the communication network. The channel measurement data is then spatially interpolated based on the location information of each terminal to obtain regional channel quality data. Based on the historical session data and the regional channel quality data, a statistical analysis is performed to obtain the demand probability distribution of the intercom group.

[0008] In one possible implementation, the process of evaluating the channel utility and device communication resources of the communication-sensing data to obtain communication constraint data for each terminal includes: Based on the preset interference weighting coefficients between channels and the regional channel quality data, the path loss and location interference intensity of the candidate channels between terminals are calculated to predict the signal-to-interference-plus-noise ratio of each candidate channel. According to a preset nonlinear mapping rule, the signal-to-interference-plus-noise ratio is mapped to the communication utility value of candidate channels between each terminal; Based on the remaining battery power of each terminal in the communication sensing data, calculate the remaining communication energy value of each terminal; Based on the hardware specifications and interface load status in the communication sensing data, an interface mutual exclusion analysis is performed to obtain the wireless operating status of each terminal. Based on the wireless operating status and the operating load data in the communication sensing data, a transmission delay analysis is performed to obtain the communication delay constraint value of each terminal.

[0009] In one possible implementation, the step of identifying local conflicts between the terminals based on the demand probability distribution and the communication constraint data to obtain a resource conflict dataset includes: Potential communication datasets whose probability values ​​reach a preset activation threshold are selected from the demand probability distribution; wherein each potential communication dataset includes a source terminal and a target terminal set; Based on the communication utility value between the source terminal and the target terminal set, candidate channels between the source terminal and the target terminal are selected to form a high-utility channel group corresponding to the potential communication dataset; Based on the location information of each terminal in the potential communication dataset and the high-efficiency channel group, a potential communication connection graph is constructed. Local conflict is then identified in the potential communication connection graph using a preset local conflict detection method based on the communication constraint data to obtain local conflict data. Based on the preset regional weight coefficients and the local conflict data, regional conflict statistical analysis is performed on the potential communication connection graph to extract the resource conflict dataset.

[0010] In one possible implementation, the step of performing channel and time-based resource collaborative optimization allocation on the intercom group based on the resource conflict dataset and the communication constraint data, and generating resource allocation data, includes: Based on the resource conflict dataset and the communication constraint data, recommended channels are allocated to each region in the potential communication connection graph, and the communication activities of the recommended channels are allocated in the time domain according to the communication delay constraint value, generating the transmission time window corresponding to each recommended channel; Based on the recommended channel, the transmission time window, and the remaining communication energy value, the channel selection and transmission power timing adjustment of each terminal are performed to obtain the channel identifier and power strategy of each terminal. Based on the channel identifiers and power policies of all terminals in the intercom group, a global consistency check is performed to obtain the resource allocation data of channel, time, and power for each terminal in the intercom group in the next scheduling cycle.

[0011] In one possible implementation, the step of identifying local conflicts in the potential communication connection graph based on the communication constraint data using a preset local conflict detection method to obtain local conflict data includes: Based on the location information of each terminal in the potential communication connection diagram and the spectral distance between each channel in the high-efficiency channel group, the interference coupling value between each channel is calculated. When the interference coupling value reaches the preset spatial spectrum conflict threshold, the corresponding channel in the potential communication connection diagram is extracted as the channel conflict data. Based on the device identifiers of the source terminal and target terminal corresponding to each channel in the potential communication connection diagram, the reflection mutual exclusion of the source terminal and the reception conflict of the target terminal between each potential communication are identified to obtain the execution conflict data. Based on the device identifier of the target terminal and the communication delay constraint value, an intersection conflict analysis of the target terminals between potential communications is performed to obtain the public terminal conflict data.

[0012] Secondly, this application provides a multi-channel dynamic allocation device for a group of walkie-talkie phones, the device comprising: The perception and statistics module is used to acquire communication perception data of each terminal in the intercom group, and to perform statistical analysis on the communication perception data to obtain the demand probability distribution of the intercom group. The constraint evaluation module is used to evaluate the channel utility and device communication resources of the communication sensing data to obtain the communication constraint data of each terminal. The conflict identification module is used to identify local conflicts between the terminals based on the demand probability distribution and the communication constraint data, and to obtain a resource conflict dataset. The collaborative optimization module is used to perform collaborative optimization allocation of channel and time resources for the intercom group based on the resource conflict dataset and the communication constraint data, and generate resource allocation data.

[0013] In summary, this application includes at least the following beneficial technical effects: 1. Sparse trigger-based sensing and background patrol are used instead of continuous full-spectrum scanning to reduce the number of terminal spectrum scans and reports, thereby reducing power consumption and interference with main services.

[0014] 2. The hierarchical primary and backup coordination mechanism, which includes coarse allocation at the central level and fine-tuning at the terminal level, local incremental reconfiguration, and protection window mechanism, enables fast scheduling convergence, controllable control signaling and computational burden, and rapid local recovery in the event of node failure or anomaly, thereby reducing the risk of network-wide disturbance.

[0015] 3. By using offline coupling coefficients, online channel utility estimation, candidate channel screening based on location information and coupling coefficients, and local conflict identification and regional optimization allocation for high-probability needs, co-channel / adjacent-channel conflicts and receiver listening conflicts are reduced, thereby improving PDR and overall spectrum efficiency. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for dynamic multi-channel allocation of walkie-talkie groups provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a multi-channel dynamic allocation device for a group of walkie-talkies provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the computing device provided in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. With the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0018] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the description of embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to those processes, methods, products, or apparatuses.

[0019] like Figure 1 The diagram shown is a flowchart illustrating the multi-channel dynamic allocation method for walkie-talkie groups provided in this embodiment of the application. The multi-channel dynamic allocation method for walkie-talkie groups provided in this embodiment includes the following steps.

[0020] Step S1: Obtain communication sensing data of each terminal in the intercom group, and perform statistical analysis on the communication sensing data to obtain the demand probability distribution of the intercom group.

[0021] It should be understood that communication sensing data is a multi-source raw data set acquired by each terminal in the intercom group through various acquisition modes, used to characterize the terminal's own state and the characteristics of the wireless environment. This application embodiment relies on an intelligent sensing agent deployed on each intercom terminal. This agent does not collect data at a fixed period or uniform frequency, but dynamically adjusts its acquisition behavior according to the terminal's real-time communication status and network environment, thereby minimizing the terminal's energy consumption and RF resource usage while ensuring sufficient sensing information. Specifically, the intelligent sensing agent executes acquisition operations in parallel using three different trigger modes. The first mode is event-triggered fine scanning. When the terminal detects a user pressing the PTT button in preparation, or when the terminal is receiving a critical group voice stream, the sensing agent is immediately triggered. Within a very short time window (e.g., 50 milliseconds), it performs high-precision signal strength sampling on the currently used channel and a few adjacent channels (e.g., ±2 channels), and simultaneously records the channel occupancy rate. This focused scanning can acquire high-quality interference information most directly related to the upcoming or ongoing communication activity, avoiding energy consumption on scanning distant frequency bands unrelated to the current activity. The second mode is low-power periodic background patrol scanning. When the terminal is idle (e.g., without transmitting or receiving for a long time), the perception agent performs a simplified full-channel scan at a lower frequency (e.g., once every 10 seconds). This scan has a shorter sampling duration and lower resolution. Its core objective is to discover newly emerging unknown interference sources in the environment or to perceive long-term trend changes in the quality of each channel, providing baseline data for the system's global environmental awareness. The third mode is command-based cooperative scanning. When the system determines through analysis that the interference situation in a specific area is unclear or abnormal, it can use control signaling to instruct some terminals in that area to perform a supplementary measurement on a specified set of channels during their idle periods, thereby obtaining a more accurate perception of the local environment. The three modes described above together constitute a flexible sensing system capable of peacetime patrols, wartime focus, and on-demand coordination. The ultimately collected communication sensing data includes, but is not limited to: instantaneous signal strength values ​​for each channel, channel occupancy percentage, terminal remaining battery percentage, current transmit power level, available wireless interface types and status, processor load, and traffic metadata generated each time a user presses the PTT button. This metadata includes key information such as source device identifier, target group identifier, call initiation time, and voice segment duration. Simultaneously, historical session data for each terminal is acquired. This historical session data refers to the time-series collection of all traffic metadata recorded and stored by the system over a continuous period of operation. This data records the communication patterns and behavioral regularities between terminals within a group, such as the frequency of communication initiated by a specific terminal to a specific group within a specific time period, the duration distribution of each communication, and the time interval characteristics between communication activities.This historical session data is typically maintained by the system in this application or periodically archived and updated by a cloud server.

[0022] After obtaining the aforementioned communication sensing data and historical session data, since the data reported by each terminal comes from different geographical locations, different collection times, and uses different collection modes (event-triggered, background patrol, or collaborative scanning), these data are not synchronized in the time dimension and are scattered in the spatial dimension. Without unified alignment and fusion, a consistent understanding of the entire network's wireless environment cannot be formed. Therefore, spatiotemporal alignment and fusion are performed on the obtained communication sensing data. Specifically, after receiving the sensing data reported by each terminal, the system of this application first unifies all data to the same time base based on the high-precision timestamp contained in the data packet (obtained through network time protocol or synchronized with the clock of this application's system). Subsequently, the system of this application summarizes all terminal-reported data received in each preset sensing period. Based on spatiotemporal alignment, the system of this application performs data fusion processing, integrating multiple measurement values ​​from different terminals but for the same channel, similar times, and adjacent locations, eliminating obviously abnormal outliers, and generating a fused channel measurement value for each discrete sampling point through weighted averaging or median filtering. Ultimately, a set of discrete channel measurement data for the communication network with geographic location tags is obtained. This channel measurement data includes the signal strength and occupancy of which channels were measured at which locations and at what times. However, this data is still discrete point information and has not yet formed a complete description of the continuous space.

[0023] The area covered by the intercom group is a continuous two-dimensional space, while discrete sampling points cannot fully reflect the wireless environment of every location within the area, especially when the interference source has local characteristics. Relying solely on sampling point data may miss crucial information. Therefore, using the channel measurement data obtained above, the channel measurement data for any unknown point within the area is estimated, thereby expanding the discrete point-like information into continuous area-like information. Specifically, the system maintains the approximate current location information of each terminal. This location information can be obtained through signal arrival time difference estimation or a simplified RSSI positioning method, without relying on high-precision GPS. For each channel to be evaluated, the system uses the fused measurement value of that channel at all terminals as a known point and applies a spatial interpolation algorithm. One feasible interpolation method is the inverse distance weighted interpolation method, whose core idea is that the estimated value of an unknown point is obtained by weighting the measurement values ​​of surrounding known points according to the inverse of the distance; the closer the known point, the greater its influence on the estimated value. Through this interpolation process, the system of this application generates the predicted signal strength and occupancy rate of the channel for each grid point or each area of ​​interest within the network coverage area, thereby forming a regional channel quality data that reflects the spatial distribution characteristics of interference. The regional channel quality data is presented in the form of a heatmap, which can clearly distinguish whether it is background noise covering the entire area or a local interference source affecting only a certain corner.

[0024] The dynamic channel allocation in this application requires predicting which terminals are most likely to initiate communication in the near future, thus reserving or coordinating resources in advance for these high-probability communication activities, rather than passively responding to every sudden communication request. Therefore, statistical analysis is needed based on the obtained regional channel quality data combined with historical session data. The resulting demand probability distribution is a quantitative prediction of future communication needs. Specifically, the system first performs time-series statistical analysis on historical session data to extract the pattern characteristics of each terminal initiating communication with each target group. For example, analysis may reveal that a team leader issues instructions to all team members every 5 minutes, or that two team members frequently engage in private calls within a specific time period. This statistical analysis can employ simple frequency statistics methods. Simultaneously, the system introduces the current regional channel quality data as context-aware input. The logic is that communication needs depend not only on historical habits but also on the current environmental quality: when the quality of a channel deteriorates significantly, terminals may be more inclined to initiate communication to report anomalies or increase communication activity on channels with better quality. Therefore, the statistical analysis needs to integrate historical pattern characteristics with the current regional channel quality data. For example, if historical data shows that the probability of a terminal initiating communication with a group during a working period is 0.3, while current regional channel quality data shows that the interference level at the terminal's location is significantly lower than average, the system can appropriately increase the predicted probability. Through the above fusion analysis, the system calculates an initiation probability within a future preset time period (e.g., the next 2-second cycle) for the communication needs between each source terminal and each possible target group. These probability values ​​are organized into a two-dimensional matrix with source terminals as rows and target groups as columns, ultimately forming the demand probability distribution of the intercom group.

[0025] Step S2: Evaluate the channel utility and device communication resources of the communication sensing data to obtain communication constraint data for each terminal.

[0026] To transform communication-sensing data into quantitative constraint indicators (i.e., communication constraint data) that can directly support resource allocation decisions, step S2 of this application characterizes the terminal's communication capabilities and limitations in the current environment from three key dimensions: link quality, energy budget, and real-time performance. It should be understood that the obtained communication constraint data is not a single numerical value, but rather a multi-dimensional set of parameters composed of communication utility value, remaining communication energy value, and communication delay constraint value.

[0027] Since transmitted signals attenuate with increasing distance during propagation in space, the degree of attenuation depends on the straight-line distance between terminals and the environmental type (e.g., open ground, inside a building). First, based on preset inter-channel interference weighting coefficients and the regional channel quality data obtained in step S1, path loss and location interference intensity are calculated for each pair of terminal candidate channels to predict the signal-to-interference-plus-noise ratio (SNR) of each candidate channel. The inter-channel interference weighting coefficient is a parameter matrix obtained in advance through offline calibration or theoretical modeling. It quantifies the signal leakage intensity caused by spectral overlap between any two different channels. For example, the interference weighting coefficient between channel 1 and channel 2 can be 0.8, and the interference weighting coefficient between channel 1 and channel 6 can be 0. The regional channel quality data provides the background interference intensity present on each channel at a specific geographical location. This interference may originate from other intercom devices, Wi-Fi devices, or industrial interference sources. In this embodiment, the location interference intensity calculation uses the interference thermal value obtained through spatial interpolation in step S1 at the location of the receiving terminal as an estimate of external interference. Subtracting path loss from the transmit power, and then subtracting the sum of background noise at the receiver and coupling interference from other channels, yields the expected signal-to-interference-plus-noise ratio (SNR) at the receiver. The predicted SNR directly reflects the theoretical margin by which the receiver can correctly resolve the signal from noise and interference during a single communication on that channel.

[0028] Since the signal-to-interference-plus-noise ratio (SINR) is a physical quantity measured in decibels, while a higher value generally indicates better signal quality, the correlation between SINR and successful communication is not linear. For example, when the SINR is below a certain threshold, communication almost inevitably fails; when the SINR is above another threshold, the success rate is close to 100%; and in the intermediate region, the success rate increases rapidly with increasing SINR. Furthermore, after obtaining the predicted SINR for each candidate channel, this application maps it to a communication utility value between each terminal according to a preset nonlinear mapping rule. The nonlinear mapping rule used in this application can be an S-shaped function curve obtained through actual measurement or simulation, or a pre-built lookup table. By inputting the predicted SINR value into this nonlinear mapping rule, a communication utility value between 0 and 1 is output. The communication utility value represents the probability that packets can be successfully received when communicating on that channel, transforming the physical layer's signal quality indicator into a performance indicator understandable by the link layer.

[0029] It should be understood that existing conventional walkie-talkie terminals rely on battery power, and the remaining battery power directly determines the length of time the terminal can continue to operate and the number of high-power transmissions it can support. Therefore, it is necessary to calculate the remaining communication energy value of each terminal based on the remaining battery power of each terminal in the communication sensing data. The method used in this application embodiment to calculate the remaining communication energy value combines the terminal's power consumption data, converting the remaining battery power into the talk time that can be supported at a specific transmission power. Specifically, the system of this application pre-stores the instantaneous power consumption data of each type of terminal at different transmission power levels, such as the percentage of power consumed in 1 second of transmission at the highest power. Dividing the current remaining battery power by this power consumption value yields the theoretical number of seconds that the terminal can continue to transmit under the current power configuration. The obtained remaining communication energy value reflects the energy budget of the corresponding terminal. When allocating channels and power subsequently, it is necessary to prioritize assigning high-energy-consuming communication tasks to terminals with sufficient energy to avoid critical communication interruptions due to battery depletion.

[0030] In this application, the intercom terminal has multiple built-in wireless interfaces, such as a main intercom radio frequency module, a Bluetooth module, or a Wi-Fi module. When these interfaces operate simultaneously, there may be limitations due to frequency interference or hardware resource sharing. For example, when the main radio frequency is transmitting on a certain channel, Bluetooth scanning may be interfered with due to frequency proximity, or the processor may be unable to handle two high-load tasks simultaneously. This application employs interface mutual exclusion analysis to identify these hardware-level limitations. Based on the currently active interface types (i.e., hardware specifications), operating frequencies, and load states (i.e., interface load states) reported by the terminal, it determines which interface combinations can operate in parallel and which must be mutually exclusive. The wireless operating status obtained from the interface mutual exclusion analysis clarifies the set of interfaces that the terminal can simultaneously activate at the current moment and their respective availability. Based on this, and combined with the processor's operating load data from the communication sensing data reported by each terminal, a transmission delay analysis is performed. When the processor load is too high, operations such as channel switching and voice coding will generate additional processing delays, causing the actual start time of transmission to be later than the planned time. The transmission delay analysis employed in this application estimates the time offset required from receiving the transmission command to actually starting transmission by matching the current load with preset historical processing delay data. This offset is the communication delay constraint value. The communication delay constraint value determines the size of the protection interval that needs to be reserved between the transmission time windows allocated to different terminals to avoid time collisions caused by differences in terminal processing speeds.

[0031] The communication utility value obtained above quantifies the link quality, the remaining communication energy value quantifies the energy budget, and the communication delay constraint value quantifies the real-time limitation, enabling resource allocation decisions to make globally optimal arrangements while fully considering link quality, energy consumption, and timing requirements.

[0032] Step S3: Based on the demand probability distribution and the communication constraint data, perform local conflict identification between the terminals to obtain a resource conflict dataset.

[0033] First, potential communication datasets with probability values ​​reaching a preset activation threshold need to be selected from the demand probability distribution obtained in step S1. The demand probability distribution is a two-dimensional matrix with source terminals as rows and target groups as columns. Each element in the matrix represents the probability that a specific source terminal will initiate communication with a specific target group within a preset time period (e.g., the next 2-second cycle). For example, based on statistical analysis of historical session data, the probability of a team leader issuing an instruction to the entire team may be as high as 0.9, while the probability of an ordinary team member initiating a private call to another team member may be only 0.1. Since there may be hundreds of possible communication activities in the communication network at the same time, but the probability of most of these activities occurring is extremely low, if subsequent conflict identification and resource allocation are performed for every possible communication activity, it will lead to an explosive increase in computational scale, and a large amount of computational resources will be wasted on events that are unlikely to occur. Therefore, this application uses a preset activation threshold (e.g., 0.3) to filter communication activities on the communication network. Only communication activities with a corresponding probability value that reaches or exceeds this threshold will be included in the subsequent consideration. The threshold filtering operation significantly reduces the number of activities that need to be processed, allowing the system to focus limited computing resources on the most likely communication needs. Each potential communication activity in the filtered potential communication dataset is recorded as a data structure containing at least two core elements: the source terminal, i.e., the terminal about to initiate the communication; and the target terminal set, i.e., the expected recipients of this communication. For group calls, the target terminal set may contain dozens or even hundreds of member terminals. It should be understood that the aforementioned source terminal and target terminal set are represented by device-specific identifier codes.

[0034] For each selected potential communication activity, there is a corresponding communication utility value between the source terminal and each target terminal in the target terminal set on each available channel. However, in actual communication, when a source terminal sends voice to a group containing multiple target terminals, it needs to select a channel that all target terminals can receive. This means that the selected channel must have an acceptable communication utility value at all target terminals. Therefore, for each potential communication activity, the system of this application needs to traverse all available channels and calculate a comprehensive index, such as a minimum or weighted average, of the communication utility value between the channel and all target terminals to characterize the overall suitability of the channel for the group's communication. Subsequently, the system of this application will sort all channels according to the comprehensive index and select the top N channels as the most efficient channel group for the potential communication activity. Here, N is a preset positive integer (e.g., 2 or 3). By setting N to provide a limited number of alternative channels for each communication activity, sufficient scheduling flexibility is ensured for subsequent conflict identification, while avoiding uncontrolled conflict scale due to too many alternative channels.

[0035] Next, a potential communication connection graph needs to be constructed based on the location information of each terminal in the potential communication dataset and the high-efficiency channel group. The resulting potential communication connection graph is a graph structure with potential communication activities as nodes. Each node represents a selected communication activity that may occur in the future, and the node is attached with the source terminal identifier, target terminal set, and high-efficiency channel group information for that activity. The edges between nodes in the graph represent a conflict between two potential communication activities; that is, if these two activities occur simultaneously, it may lead to a significant deterioration or even failure of communication quality for one or both. By constructing the potential communication connection graph, implicit and dispersed conflict risks are made explicit and structured, enabling the system to analyze competitive relationships in the network using graph theory.

[0036] Furthermore, conflict relationships in the potential communication connection graph are identified through a preset local conflict detection method. The local conflict data obtained in this application includes, but is not limited to, channel conflict data, execution conflict data, and public-end conflict data. The local conflict detection method employed in this application specifically includes the following three parallel operations: The first dimension involves calculating the interference coupling value between channels based on the location information of each terminal in the potential communication connection diagram and the spectral distance between channels in the high-efficiency channel group. When this interference coupling value reaches a preset spatial spectral conflict threshold, the corresponding channel in the potential communication connection diagram is extracted as channel conflict data. Specifically, for two potential communication activities A and B, the candidate channel numbers they plan to use are first obtained, denoted as Ca and Cb, respectively. Based on the spectral interval of Ca and Cb, the corresponding interference weight coefficient β(Ca,Cb) is retrieved from the interference weight coefficient matrix between channels preset in step S2. This coefficient is a value between 0 and 1, representing that when the two channels completely overlap in the spectrum (i.e., the same channel), the interference weight coefficient is 1. As the spectral distance increases, the coupling coefficient gradually decreases, and when the spectral distance exceeds 5 channels, the interference weight coefficient approaches 0. However, this interference weight coefficient only reflects the interference potential in the spectrum and does not consider spatial factors. Even if the two channels have completely overlapping spectra, if the transmitter and receiver are far apart, the interference signal may have attenuated to a negligible level by the time it reaches the receiver. Therefore, spatial location information needs to be introduced for correction. Based on the terminal location information obtained in step S1, the source terminal location of activity A and the target terminal location of activity B, as well as the source terminal location of activity B and the target terminal location of activity A, are obtained. Whether interference can actually cause a conflict depends on the distance between the interference source (the opposing source terminal) and the interfered object (our own target terminal). Taking the interference of activity A on activity B as an example, the Euclidean distance D(a,b) between the source terminal of activity A and the target terminal of activity B needs to be calculated. Based on this distance and the propagation characteristics of wireless signals in space, the path loss L(d) is estimated. The path loss used in this embodiment can be calculated using a simplified logarithmic distance formula: L(d)=L0+10.n.log10[D(a,b) / D0], where L0 is the path loss at the reference distance d0, and n is the path loss exponent (depending on the environment, such as 2 in open areas, 3 or 4 inside buildings). This formula can convert the distance into a path loss value in decibels. Subsequently, by combining the transmit power Pa of the source terminal A and the background noise N0 at the target terminal B, the actual interference intensity Iab caused by the transmission of activity A to the receiver of activity B is estimated. The interference intensity estimation formula used in this embodiment is: Iab = Pa - L[D(a,b)]. The actual interference intensity Iab reflects the absolute intensity of the interference signal from activity A at the target terminal of activity B. By comparing the actual interference intensity Iab with the background noise N0 at the target terminal of activity B, a signal-to-interference ratio (SIR) index can be obtained. However, in order to make a unified comparison with a preset threshold, this SIR index needs to be fused with the interference weighting coefficient β(Ca,Cb) to form a comprehensive interference coupling value γab.The fusion method used in this embodiment is: γab = β(Ca,Cb) × [Iab / (Iab+N0)]. When the interference signal strength is much greater than the background noise, [Iab / (Iab+N0)] approaches 1, and the interference coupling value is mainly determined by the interference weighting coefficient. When the interference signal strength is much less than the background noise, [Iab / (Iab+N0)] approaches 0. Even if the spectrum completely overlaps, the interference coupling value is very small, meaning that the spatial distance has attenuated the interference to a harmless level. In this way, the obtained interference coupling value γab reflects both the degree of spectrum overlap and the degree of spatial proximity, and is a comprehensive index between 0 and 1. Similarly, the interference coupling value γba of activity B to activity A is calculated in the above way. Since the interference may be asymmetrical (i.e., A interferes strongly with B, but B interferes weakly with A), it is necessary to calculate the interference in both directions separately. The maximum value between γba and γab is taken as the comprehensive interference coupling value γ between the two potential communication activities. Finally, γ is compared with the preset spatial spectrum conflict threshold γ1. If γ≥γ1, it is determined that there is a spatial spectrum conflict between the two activities. A conflict edge needs to be established between their corresponding nodes, and the conflict type is marked as channel conflict. At the same time, the value of γ is recorded as the strength weight of the conflict edge, thus forming the corresponding channel conflict data.

[0037] The second dimension involves identifying source terminal reflection mutual exclusion and target terminal reception conflicts between potential communications based on the device identifiers of the source and target terminals corresponding to each channel in the potential communication connection diagram, thereby obtaining execution conflict data. Source terminal reflection mutual exclusion means that the same source terminal cannot simultaneously initiate two different communication activities because a walkie-talkie terminal is a half-duplex device, capable of transmitting only one voice channel at a time. If two potential communication activities have the same source terminal identifier, they constitute an absolute conflict regardless of the channel they plan to use. Target terminal reception conflict means that the same target terminal cannot simultaneously receive voice streams from two different source terminals because a single radio frequency device can only tune to one channel for reception at a time. If the target terminal sets of two potential communication activities intersect, and the intersection is not empty, then these two activities conflict for the common receiving terminal within the intersection.

[0038] The third dimension involves analyzing the intersection and conflict of target terminals between potential communications based on the device identifier of the target terminal and the communication delay constraint value, obtaining common-end conflict data. This further deepens the understanding of target terminal reception conflicts. Even if two sets of target terminals overlap, if the two activities are staggered in time and the time interval is large enough to allow the common receiving terminal to complete reception sequentially without overlap, the conflict can be resolved. Therefore, the communication delay constraint value obtained in step S2 is introduced, which quantifies the time offset required from receiving the transmission command to actually starting transmission. By comparing the expected occurrence times of the two activities with the communication delay constraint value, it can be determined whether the common receiving terminal has the ability to receive sequentially in time. If the times cannot be staggered, it is determined to be a common-end conflict.

[0039] Finally, based on the completion of the above three types of conflict detection, the remaining communication energy value needs to be converted into a conflict intensity weight according to a preset conversion coefficient. This conflict intensity weight, along with channel conflict data, execution conflict data, and public-end conflict data, is used to mark conflicts in the potential communication connection graph, generating local conflict data. The remaining communication energy value, derived from step S2, reflects the length of time the terminal can continue operating. The significance of converting the remaining energy value into a conflict intensity weight is that when two communication activities conflict, the system needs to decide which activity to prioritize. A reasonable decision-making principle is to prioritize protecting communications initiated by terminals whose energy is about to run out, because once these terminals run out of power, they will completely lose their communication capabilities. Therefore, through a preset conversion coefficient, such as mapping the reciprocal of the remaining energy value to a weight value, the terminal with lower remaining energy is assigned a higher weight for its participating conflict activity. This weight value is attached to the corresponding conflict edge. Ultimately, each node in the potential communication connection graph carries detailed information about its communication activities, and each edge carries a conflict type identifier (such as spatial spectrum conflict, execution conflict, public-end conflict) and a conflict intensity weight. This data structure is called local conflict data.

[0040] After obtaining local conflict data, it is necessary to perform regional conflict statistical analysis on the local conflict data according to preset regional weight coefficients to extract resource conflict datasets. Local conflict data may contain dozens of nodes and hundreds of edges, but not all regions in the graph have the same level of tension. Some regions may have dense nodes and dense edge connections, forming core regions with highly concentrated conflicts, i.e., key conflict clusters; while other regions may have sparse nodes and fewer conflicts. The regional conflict statistical analysis operation used in this application is precisely to identify these key conflict clusters so that the most intractable conflicts can be prioritized in subsequent resource allocation. Specifically, the system runs a graph density-based clustering algorithm or a maximal clique search heuristic algorithm on the local conflict data to find subsets of nodes in the graph whose edge connection density or the sum of conflict weights exceeds the preset regional weight coefficients. Each such subset is defined as a key conflict cluster, and all communication activities within the cluster and their conflict relationships are extracted. Simultaneously, detailed information on all conflict edges in the graph, including conflict type, conflict intensity weight, and the terminals and channels involved in the conflict, is also recorded. All this information, including the list of key conflict clusters, detailed descriptions of activities within each key conflict cluster, and conflict edge data for the entire graph, is packaged together into a structured data object, namely, a resource conflict dataset. This dataset clearly reveals which regions and communication activities in the network have the most intense resource competition under the current predicted demand, providing precise, key-conflict-focused input for the coordinated optimization allocation of channels and time in step S4.

[0041] Step S4: Based on the resource conflict dataset and the communication constraint data, perform resource collaborative optimization allocation of the intercom group in terms of channel and time to generate resource allocation data.

[0042] It should be understood that the resource conflict dataset obtained in step S3 above contains several identified key conflict clusters. These clusters represent the areas with the most intensive communication activity and the most intense competition in the network, such as a scenario where multiple members in a group simultaneously plan to initiate a call. If these clusters are not prioritized and all activities are allowed to compete freely, frequent collisions and retransmissions will inevitably occur. Therefore, the system in this application first focuses on these key conflict clusters and selects a recommended channel from the communication utility values ​​generated in step S2 for each potential communication activity within the cluster. The selection principle adopted in this application is to maximize the overall communication utility while ensuring that there is no spectral space conflict between all activities within the cluster. For example, in a key conflict cluster containing three members planning to call their respective targets simultaneously, the system in this application may recommend channel 1 for member A, channel 6 for member B, and channel 11 for member C, because these three channels are orthogonal channels with zero interference weight coefficient between them, which can ensure that there will be no spectral conflict even if they are transmitted simultaneously. After assigning recommended channels to each activity, the system in this application further introduces staggered scheduling in the time dimension. Since the recommended channels may be the same or adjacent (e.g., channel 1 and channel 2 still have a certain degree of interference coupling), even if orthogonal channels are selected, collisions may still occur at the receiving end if multiple activities transmit simultaneously. Therefore, based on the communication delay constraint value obtained in step S2, a non-overlapping transmission time window needs to be allocated to each activity using the same or adjacent recommended channels. The communication delay constraint value quantifies the time offset required from the terminal receiving the transmission command to the actual start of transmission, and this offset varies depending on the terminal hardware performance and processing load. When dividing the time window, the system of this application needs to take into account the delay constraint values ​​of different terminals to ensure that the length of the time window allocated to an activity is sufficient to cover its voice packet transmission time plus its own transmission delay, while reserving sufficient guard intervals between adjacent windows to prevent time overlap caused by differences in terminal response speeds. Finally, a set of corresponding transmission time windows is generated for each recommended channel, and each window is bound to a specific potential communication activity, specifying the time period during which the activity can use the channel for transmission.

[0043] The recommended channels and transmission time windows obtained above constitute a global scheduling framework. However, this framework is based on the average state of all terminals and the conflict graph analysis in step S3, and does not fully consider the local real-time status of each terminal at the current moment. Therefore, each terminal needs to perform local fine-tuning after receiving the cluster-level coordination plan issued by the system of this application. The terminal first checks the channel allocated to it by the system of this application. If the communication utility value of the channel has significantly decreased in the terminal's latest local real-time perception, for example, due to a sudden drop in signal-to-noise ratio caused by sudden interference, the terminal can make limited adjustments within a preset set of alternative channels. The set of alternative channels usually consists of other channels in the high-utility channel group associated with the terminal's communication activities, and these channels do not have a strong conflict relationship with the original channels allocated by the system of this application, to ensure that the adjustment does not introduce new global conflicts. For example, this application's system recommends channel 3 for terminal A. However, terminal A detects a strong pulse interference on channel 3 at the current moment through local sensing. Therefore, it can switch to channel 4 from the alternative channel group, provided that the interference weight coefficient of channel 4 is lower than that of channel 3, and channel 4 is not occupied by other activities within the same time window. Simultaneously with channel adjustment, the terminal also needs to adjust the timing of its transmission power based on the transmission time window and its remaining communication energy value. The transmission time window defines the earliest and latest times when an activity can begin transmission. The terminal needs to calculate the precise local transmission time based on its own communication delay constraint value to ensure that its transmission behavior strictly falls within this window. The remaining communication energy value guides the selection of transmission power: if the terminal has sufficient remaining energy and the current channel quality is average, the terminal can choose a higher power level to improve the communication success rate; if the terminal's remaining energy is already at a low level and the current channel quality is good, the terminal can choose a lower power level to extend its battery life. Through the aforementioned local adjustments, each terminal ultimately determines its final channel identifier (which may be the same as or different from the original recommended channel after minor adjustments), precise transmission time (calibrated with microsecond-level precision), and transmission power level (wherein, the transmission time and transmission power level together constitute the power strategy), and packages and reports this information back to the application system.

[0044] Finally, a global consistency check needs to be performed based on the channel identifiers and power policies of all terminals in the intercom group to obtain the resource allocation data for channel, time, and power of each terminal in the intercom group in the next scheduling cycle. After receiving the local policy data of all terminals, the system cannot immediately issue it as the final instruction, but must perform a global check. The purpose of the check is to ensure that although the terminal improves its own communication quality or saves energy during the local fine-tuning process, it does not accidentally introduce new, global resource conflicts. For example, if terminal A adjusts its channel from 3 to 4 allocated by the system, but terminal B also adjusts its channel from 5 to 4 during its local fine-tuning, and both are within the same transmission time window, this constitutes a new co-channel conflict. The system needs to summarize the final channel, final transmission time, and power of all terminals, rerun a simplified conflict detection, and check against the resource conflict dataset constructed in step S3 to see if there are any new conflict edges. If a conflict is detected, the system needs to conduct a quick negotiation with the relevant terminals, requiring one of them to fall back to the original recommended channel or adjust the transmission time. Once all terminals' local policies have passed consistency verification, the system in this application integrates these policies into a global resource allocation scheme. This scheme is a structured data table, where each row corresponds to a terminal, and each column specifies the channel identifier that the terminal should use in the next scheduling cycle, the precise time when it should start transmitting (or the duration of continuous listening), and the transmission power level that should be adopted. This global resource allocation scheme is encapsulated as resource allocation data and broadcast to all terminals in the group through the control channel. It becomes the communication instruction that all terminals must jointly follow in the next scheduling cycle, thereby ensuring orderly and conflict-free communication of the entire group in a multi-channel environment.

[0045] Please see Figure 2 , Figure 2 This is a schematic diagram of a multi-channel dynamic allocation device for a group of walkie-talkies provided in an embodiment of this application. Figure 2 As shown, the multi-channel dynamic allocation device 2 for walkie-talkie groups includes: a perception and statistics module 21, a constraint evaluation module 22, a conflict identification module 23, and a collaborative optimization module 24.

[0046] The perception and statistics module 21 is used to acquire the communication perception data of each terminal in the intercom group, and to perform statistical analysis on the communication perception data to obtain the demand probability distribution of the intercom group. The constraint evaluation module 22 is used to evaluate the channel utility and device communication resources of the communication sensing data to obtain the communication constraint data of each terminal. The conflict identification module 23 is used to identify local conflicts between the terminals based on the demand probability distribution and the communication constraint data, and obtain a resource conflict dataset. The collaborative optimization module 24 is used to perform collaborative optimization allocation of channel and time resources for the intercom group based on the resource conflict dataset and the communication constraint data, and generate resource allocation data.

[0047] In one possible implementation, the sensing statistics module 21 is used for: The communication perception data and historical session data of each terminal in the intercom group are obtained according to the preset scanning method. The communication sensing data is spatiotemporally aligned and fused to obtain channel measurement data of the communication network. The channel measurement data is then spatially interpolated based on the location information of each terminal to obtain regional channel quality data. Based on the historical session data and the regional channel quality data, a statistical analysis is performed to obtain the demand probability distribution of the intercom group.

[0048] In one possible implementation, the constraint evaluation module 22 is used to: Based on the preset interference weighting coefficients between channels and the regional channel quality data, the path loss and location interference intensity of the candidate channels between terminals are calculated to predict the signal-to-interference-plus-noise ratio of each candidate channel. According to a preset nonlinear mapping rule, the signal-to-interference-plus-noise ratio is mapped to the communication utility value of candidate channels between each terminal; Based on the remaining battery power of each terminal in the communication sensing data, calculate the remaining communication energy value of each terminal; Based on the hardware specifications and interface load status in the communication sensing data, an interface mutual exclusion analysis is performed to obtain the wireless operating status of each terminal. Based on the wireless operating status and the operating load data in the communication sensing data, a transmission delay analysis is performed to obtain the communication delay constraint value of each terminal.

[0049] In one possible implementation, the conflict identification module 23 is used for: Potential communication datasets whose probability values ​​reach a preset activation threshold are selected from the demand probability distribution; wherein each potential communication dataset includes a source terminal and a target terminal set; Based on the communication utility value between the source terminal and the target terminal set, candidate channels between the source terminal and the target terminal are selected to form a high-utility channel group corresponding to the potential communication dataset; Based on the location information of each terminal in the potential communication dataset and the high-efficiency channel group, a potential communication connection graph is constructed. Local conflict is then identified in the potential communication connection graph using a preset local conflict detection method based on the communication constraint data to obtain local conflict data. Based on the preset regional weight coefficients and the local conflict data, regional conflict statistical analysis is performed on the potential communication connection graph to extract the resource conflict dataset.

[0050] In one possible implementation, the collaborative optimization module 24 is used for: Based on the resource conflict dataset and the communication constraint data, recommended channels are allocated to each region in the potential communication connection graph, and the communication activities of the recommended channels are allocated in the time domain according to the communication delay constraint value, generating the transmission time window corresponding to each recommended channel; Based on the recommended channel, the transmission time window, and the remaining communication energy value, the channel selection and transmission power timing adjustment of each terminal are performed to obtain the channel identifier and power strategy of each terminal. Based on the channel identifiers and power policies of all terminals in the intercom group, a global consistency check is performed to obtain the resource allocation data of channel, time, and power for each terminal in the intercom group in the next scheduling cycle.

[0051] In one possible implementation, the conflict identification module 23 is further configured to: Based on the location information of each terminal in the potential communication connection diagram and the spectral distance between each channel in the high-efficiency channel group, the interference coupling value between each channel is calculated. When the interference coupling value reaches the preset spatial spectrum conflict threshold, the corresponding channel in the potential communication connection diagram is extracted as the channel conflict data. Based on the device identifiers of the source terminal and target terminal corresponding to each channel in the potential communication connection diagram, the reflection mutual exclusion of the source terminal and the reception conflict of the target terminal between each potential communication are identified to obtain the execution conflict data. Based on the device identifier of the target terminal and the communication delay constraint value, an intersection conflict analysis of the target terminals between potential communications is performed to obtain the public terminal conflict data.

[0052] The perception statistics module 21, constraint evaluation module 22, conflict identification module 23, and collaborative optimization module 24 can all be implemented in software or hardware. For example, the implementation of the perception statistics module 21 will be described below. Similarly, the implementation of the constraint evaluation module 22, conflict identification module 23, and collaborative optimization module 24 can refer to the implementation of the perception statistics module 21.

[0053] As an example of a software functional unit, the awareness and statistics module 21 may include code running on a computing instance. The computing instance may include at least one of a physical host (computing device), a virtual machine, or a container. Further, the aforementioned computing instance may be one or more. For example, the awareness and statistics module 21 may include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers used to run the code may be distributed in the same region or in different regions. Further, the multiple hosts / virtual machines / containers used to run the code may be distributed in the same availability zone (AZ) or in different AZs, each AZ including one or more geographically proximate data centers. Typically, a region may include multiple AZs.

[0054] Similarly, multiple hosts / virtual machines / containers used to run this code can be distributed within the same Virtual Private Cloud (VPC) or across multiple VPCs. Typically, a VPC is set up within a region. Communication between two VPCs within the same region, as well as between VPCs in different regions, requires a communication gateway to be set up within each VPC to enable interconnection between VPCs.

[0055] As an example of a hardware functional unit, the perception and statistics module 21 may include at least one computing device, such as a server. Alternatively, the perception and statistics module 21 may also be a device implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). The PLD may be implemented using a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), generic array logic (GAL), or any combination thereof.

[0056] The multiple computing devices included in the perception and statistics module 21 can be distributed in the same region or in different regions. Similarly, the multiple computing devices included in the perception and statistics module 21 can be distributed in the same Availability Zone (AZ) or in different AZs. Likewise, the multiple computing devices included in the perception and statistics module 21 can be distributed in the same Virtual Private Cloud (VPC) or in multiple VPCs. These multiple computing devices can be any combination of computing devices such as servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0057] See Figure 3 As shown, Figure 3 This is a schematic diagram of a computing device provided in this application. The computing device 100 includes: a processor 104, a communication interface 108, a bus 102, and a memory 106. The processor 104, the communication interface 108, and the memory 106 communicate via the bus 102. In practical applications, communication can also be achieved through other means such as wireless transmission; however, this is not limited here.

[0058] The computing device 100 may be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device 100.

[0059] The processor 104 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0060] The communication interface 108 uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between the computing device 100 and other devices or communication networks.

[0061] Bus 102 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus 102 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 102 may include a path for transmitting information between various components of the computing device 100 (e.g., memory 106, processor 104, communication interface 108).

[0062] Memory 106 may include volatile memory, such as random access memory (RAM). Memory 106 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0063] The memory 106 stores executable program code, and the processor 104 executes the executable program code to implement the functions of the aforementioned perception statistics module 21, constraint evaluation module 22, conflict identification module 23 and collaborative optimization module 24 respectively, thereby realizing the multi-channel dynamic allocation method for walkie-talkie groups. That is, the memory 106 stores instructions for executing the multi-channel dynamic allocation method for walkie-talkie groups.

[0064] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute a multi-channel dynamic allocation method for a group of walkie-talkie phones, or instruct the computing device to execute a multi-channel dynamic allocation method for a group of walkie-talkie phones.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic multi-channel allocation of walkie-talkie groups, characterized in that, The method includes: The communication sensing data of each terminal in the intercom group is acquired, and the communication sensing data is statistically analyzed to obtain the demand probability distribution of the intercom group. The communication sensing data is processed to evaluate channel utility and device communication resources to obtain communication constraint data for each terminal. Based on the demand probability distribution and the communication constraint data, local conflicts are identified between the terminals to obtain a resource conflict dataset. Based on the resource conflict dataset and the communication constraint data, the intercom group is allocated resource collaboratively in terms of channel and time to generate resource allocation data.

2. The multi-channel dynamic allocation method for walkie-talkie phone groups according to claim 1, characterized in that, The step of acquiring communication sensing data from each terminal within the intercom group and performing statistical analysis on the communication sensing data to obtain the demand probability distribution of the intercom group includes: The communication perception data and historical session data of each terminal in the intercom group are obtained according to the preset scanning method. The communication sensing data is spatiotemporally aligned and fused to obtain channel measurement data of the communication network. The channel measurement data is then spatially interpolated based on the location information of each terminal to obtain regional channel quality data. Based on the historical session data and the regional channel quality data, a statistical analysis is performed to obtain the demand probability distribution of the intercom group.

3. The multi-channel dynamic allocation method for walkie-talkie groups according to claim 2, wherein the communication constraint data includes communication utility value, remaining communication energy value, and communication delay constraint value, characterized in that, The process of evaluating the channel utility and device communication resources of the communication sensing data to obtain communication constraint data for each terminal includes: Based on the preset interference weighting coefficients between channels and the regional channel quality data, the path loss and location interference intensity of the candidate channels between terminals are calculated to predict the signal-to-interference-plus-noise ratio of each candidate channel. According to a preset nonlinear mapping rule, the signal-to-interference-plus-noise ratio is mapped to the communication utility value of candidate channels between each terminal; Based on the remaining battery power of each terminal in the communication sensing data, calculate the remaining communication energy value of each terminal; Based on the hardware specifications and interface load status in the communication sensing data, an interface mutual exclusion analysis is performed to obtain the wireless operating status of each terminal. Based on the wireless operating status and the operating load data in the communication sensing data, a transmission delay analysis is performed to obtain the communication delay constraint value of each terminal.

4. The multi-channel dynamic allocation method for walkie-talkie phone groups according to claim 3, characterized in that, The step of identifying local conflicts between the terminals based on the demand probability distribution and the communication constraint data to obtain a resource conflict dataset includes: Potential communication datasets whose probability values ​​reach a preset activation threshold are selected from the demand probability distribution; wherein each potential communication dataset includes a source terminal and a target terminal set; Based on the communication utility value between the source terminal and the target terminal set, candidate channels between the source terminal and the target terminal are selected to form a high-utility channel group corresponding to the potential communication dataset; Based on the location information of each terminal in the potential communication dataset and the high-efficiency channel group, a potential communication connection graph is constructed. Local conflict is then identified in the potential communication connection graph using a preset local conflict detection method based on the communication constraint data to obtain local conflict data. Based on the preset regional weight coefficients and the local conflict data, regional conflict statistical analysis is performed on the potential communication connection graph to extract the resource conflict dataset.

5. The multi-channel dynamic allocation method for walkie-talkie phone groups according to claim 4, characterized in that, The step of performing channel and time-based resource collaborative optimization allocation for the intercom group based on the resource conflict dataset and the communication constraint data, and generating resource allocation data, includes: Based on the resource conflict dataset and the communication constraint data, recommended channels are allocated to each region in the potential communication connection graph, and the communication activities of the recommended channels are allocated in the time domain according to the communication delay constraint value, generating the transmission time window corresponding to each recommended channel; Based on the recommended channel, the transmission time window, and the remaining communication energy value, the channel selection and transmission power timing adjustment of each terminal are performed to obtain the channel identifier and power strategy of each terminal. Based on the channel identifiers and power policies of all terminals in the intercom group, a global consistency check is performed to obtain the resource allocation data of channel, time, and power for each terminal in the intercom group in the next scheduling cycle.

6. The multi-channel dynamic allocation method for walkie-talkie phone groups according to claim 4, wherein the local conflict data includes channel conflict data, execution conflict data, and public terminal conflict data, characterized in that, The step of identifying local conflicts in the potential communication connection graph based on the communication constraint data using a preset local conflict detection method to obtain local conflict data includes: Based on the location information of each terminal in the potential communication connection diagram and the spectral distance between each channel in the high-efficiency channel group, the interference coupling value between each channel is calculated. When the interference coupling value reaches the preset spatial spectrum conflict threshold, the corresponding channel in the potential communication connection diagram is extracted as the channel conflict data. Based on the device identifiers of the source terminal and target terminal corresponding to each channel in the potential communication connection diagram, the reflection mutual exclusion of the source terminal and the reception conflict of the target terminal between each potential communication are identified to obtain the execution conflict data. Based on the device identifier of the target terminal and the communication delay constraint value, an intersection conflict analysis of the target terminals between potential communications is performed to obtain the public terminal conflict data.

7. A multi-channel dynamic allocation device for a group of walkie-talkie phones, applied to the multi-channel dynamic allocation method for a group of walkie-talkie phones as described in claim 1, characterized in that, The device includes: The perception and statistics module is used to acquire communication perception data of each terminal in the intercom group, and to perform statistical analysis on the communication perception data to obtain the demand probability distribution of the intercom group. The constraint evaluation module is used to evaluate the channel utility and device communication resources of the communication sensing data to obtain the communication constraint data of each terminal. The conflict identification module is used to identify local conflicts between the terminals based on the demand probability distribution and the communication constraint data, and to obtain a resource conflict dataset. The collaborative optimization module is used to perform collaborative optimization allocation of channel and time resources for the intercom group based on the resource conflict dataset and the communication constraint data, and generate resource allocation data.

8. A computing device, characterized in that, The computing device includes: At least one processor; and, A memory and a communication interface that are communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the multi-channel dynamic allocation method for walkie-talkie groups according to any one of claims 1 to 6 by executing the instructions stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-channel dynamic allocation method for a group of walkie-talkie phones according to any one of claims 1 to 6.

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