A full-factor discretization simulation calculation method for emergency communication in buried space

Abstract

This invention discloses a full-element discretization simulation calculation method for emergency communication in buried spaces, comprising: uniformly dividing the target buried space into cubic grids, and defining a unified data structure for each cubic grid; for each cubic grid, calculating the atmospheric environment distribution vector, electromagnetic signal distribution vector, communication signal distribution vector, and network coverage vector at the center point, and merging and storing the calculation results in the grid's data structure to form a full-element discretization environment model; based on the full-element discretization environment model, performing functional simulation on the communication link between any two communication nodes in the simulation scenario, evaluating the reachability and effectiveness of the link in turn, and establishing or rejecting the communication connection based on the evaluation results; mapping the simulation object to the corresponding grid position in the discretization environment model, obtaining the environmental element vector of the position, and performing iteration and state updates; this invention improves simulation accuracy.

Description

Technical Field

[0001] This invention relates to the field of simulation communication technology, and in particular to a full-element discretization simulation calculation method for emergency communication in buried spaces. Background Technology

[0002] In emergency search and rescue operations in buried spaces, reliable communication is the lifeline for the success of the rescue operation. To conduct rehearsals, training, and scheme evaluations before the operation, high-fidelity communication simulations of complex underground or buried environments are required. However, traditional communication simulation technologies suffer from the following problems: high computational complexity, making it difficult to meet real-time requirements; the effectiveness of emergency communication is not solely determined by the communication equipment itself, but is influenced by a combination of factors including spatial structure (such as tunnel curvature and obstacle shielding), electromagnetic environment (such as multi-source interference), and even atmospheric conditions (such as humidity and dust). Existing technologies lack a unified framework to effectively integrate and couple these heterogeneous factors, leading to significant deviations between simulation results and actual operational environments; traditional simulation models are usually customized for specific scenarios or equipment, and when the rescue scenario changes (such as spatial structural deformation) or new equipment and interference sources need to be added, the models are difficult to adapt and expand quickly, requiring complex remodeling work and failing to meet the dynamic needs of emergency response. The aforementioned problems make it difficult for existing simulation systems to provide efficient and realistic communication support simulations in command and decision-making, tactical simulations, and personnel training for emergency rescue in buried spaces, thus hindering the improvement of rescue effectiveness. Summary of the Invention

[0003] Purpose of the Invention: The purpose of this invention is to provide a full-element discretization simulation calculation method for emergency communication in buried spaces, solving the problem of how to quickly and efficiently simulate complex buried scenarios involving multiple dimensions such as space, electromagnetics, environment, and business under limited computing resources, so as to meet the real-time requirements of large-scale practical simulation; solving how to transform the continuous real physical environment (space, electromagnetic wave propagation, etc.) into a standardized, computable discrete model, and efficiently solve the connectivity, quality, and interference status of communication links on this model; and solving how to design a simulation mechanism that can dynamically balance computational overhead and simulation realism according to the scale and accuracy requirements of the simulation task, and can quickly adapt to dynamically changing rescue scenarios.

[0004] Technical solution: The present invention provides a full-element discretization simulation calculation method for emergency communication in buried spaces, comprising the following steps: Step 1: Divide the target burial space into cubic units of a preset size into a uniform grid, generating multiple adjacent cubic grids, and define a unified data structure for each cubic grid to store the geometric information of the grid and the distribution vector set of various environmental elements within it. Step 2: For each cubic grid, calculate the atmospheric environment distribution vector, electromagnetic signal distribution vector, communication signal distribution vector, and network coverage vector at the center point, and merge and store the calculation results into the grid's data structure to form a full-element discretized environment model; Step 3: Based on the full-element discretized environment model, perform functional simulation on the communication link between any two communication nodes in the simulation scenario, evaluate the reachability and effectiveness of the link in turn, and establish or reject the communication connection based on the evaluation results. Step 4: Map the simulation object to the corresponding grid position in the discretized environment model. Dynamically select the single-point matching or multi-point fitting adaptation mode according to the accuracy and efficiency requirements of the simulation task, obtain the environmental element vector of the position, and drive the behavior iteration and state update of the simulation object.

[0005] Furthermore, in step 2, the electromagnetic signal distribution vector is calculated as follows: traverse all electromagnetic signal sources in the simulation scene, determine whether each signal source covers the center point of the current grid, and if it does, calculate the energy distribution of the signal source at the center point by combining the signal source attributes, spatial propagation path and physical attributes of the current grid, and finally merge the energy distribution vectors of all effective signal sources to form the comprehensive electromagnetic environment distribution vector of the current grid.

[0006] Furthermore, in step 2, the calculation of the communication signal distribution vector is as follows: Based on the electromagnetic signal distribution vector, the signal strength of each communication signal source at the current grid center point is calculated, and the electromagnetic interference effect is superimposed to generate a set of communication signal vectors at the current point.

[0007] Furthermore, in step 2, the calculation of the network coverage vector is as follows: traverse all network signal sources in the simulation scenario, determine whether each signal source covers the current grid center point, and if it does, calculate the signal energy distribution and superimpose the electromagnetic interference effect to generate the network coverage vector set at the current point.

[0008] Furthermore, step 3 is as follows: Based on the distance between communication nodes, spatial medium properties, and electromagnetic interference, quickly calculate the signal quality parameters of the receiving end; compare the calculated quality parameters with the service thresholds in the pre-built knowledge base to determine whether the link meets the service requirements.

[0009] Furthermore, in step 3, if the communication node cannot establish a valid link with the network signal source, the system automatically marks the node's network status as abnormal and triggers it to execute a preset adaptive behavior strategy, including channel switching, relay search, or operation mode switching.

[0010] Furthermore, in step 4, the single-point matching mode is as follows: directly select the cube grid that is closest to the center point of the simulation object in Euclidean distance, and use the pre-stored environmental element vector in the grid as the input of the simulation object; the multi-point fitting mode is as follows: extract the environmental element vectors of multiple neighboring grids around the center point of the simulation object, perform interpolation calculation with distance as weight, and generate the fitted environmental vector.

[0011] Furthermore, in step 4, the simulation object iteratively updates its communication connectivity, network status, and operational behavior based on the acquired environmental element vectors, thereby achieving closed-loop simulation of environmental perception and behavioral response.

[0012] The present invention discloses a full-element discretization simulation system for emergency communication in buried spaces, comprising: Environmental Discretization Modeling Module: This module is used to divide a continuous three-dimensional burial space into multiple uniform cubic grids and define a unified data structure for each grid. Full-element distribution vector calculation module: used to calculate the distribution vectors of atmospheric environment, electromagnetic signals, communication signals and network coverage in parallel at the center point of each cubic grid, and generate a full-element discretized environment model; Communication simulation and calculation module: used to perform functional simulation of the reachability and effectiveness of communication links based on the aforementioned full-element discretized environment model; Dynamic Adaptation and Behavior Iteration Module: This module maps simulation objects to corresponding grid positions in the discretized environment model, dynamically selects the adaptation mode to obtain environmental element vectors, and drives the state and behavior updates of simulation objects. System Data Management Module: Used to coordinate the execution order and data interaction of the above modules, and to centrally manage simulation data assets.

[0013] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. This invention establishes the foundation for parallel computing by discretizing continuous space into standard cubic grids, enabling quantitative analysis of all environmental elements, including atmosphere, electromagnetics, and communication, to be performed simultaneously on all grid cells. The discretized model uniformly represents the influence of each element as an attribute vector of the grid center point, achieving structured integration and collaborative solution of multi-physics data. This provides standardized data support for building a high-fidelity, scalable digital twin environment and reduces the complexity of system-level simulation to a manageable scale linearly related to the number of grid cells. 2. This invention constructs a highly efficient functional simulation computation chain based on energy relationships and a priori knowledge base. This computation chain is key to achieving efficient and accurate simulation. It completes the most computationally expensive part of the simulation offline and embeds it in the priori knowledge base; during online simulation, only lightweight energy calculation and fast table lookups are required. This method achieves an order-of-magnitude improvement in computational efficiency while strictly ensuring the reliability of simulation results, making it possible to perform second / millisecond-level real-time performance evaluation of large-scale, multi-node complex communication networks, thoroughly meeting the real-time requirements of practical training and rapid scheme derivation. 3. This invention designs a dynamic adaptation mechanism, which endows the simulation system with flexibility and adaptability. During initial exploration or large-scale simulations, single-point matching can be used to quickly obtain trend results with minimal overhead. During critical node analysis or detailed equipment evaluation, multi-point fitting can be switched to obtain computational accuracy close to continuous space. This flexible computing capability allows the same system to flexibly serve simulation tasks of different resolutions and scales, greatly expanding the system's application scope and practical value. Simultaneously, this mechanism enables the system to adapt to future growth in computing resources, continuously improving simulation accuracy by adjusting the fitting range. Attached Figure Description

[0014] Figure 1 This is a flowchart of the present invention; Figure 2 This is a flowchart of the full-element emergency communication support simulation calculation and construction process of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0016] like Figure 1 As shown, embodiments of the present invention provide a full-element discretization simulation calculation method for emergency communication in buried spaces, as detailed below: Step 1: Discretization Modeling of the Buried Space Environment: The buried space environment consists of elements such as space, electromagnetic, environmental, and operational factors. This includes the following steps: Step 11: Divide the three-dimensional burial space into grids: Determine the target three-dimensional space range for simulation, use a fixed size (e.g., a cube with a side length of 1 meter) as the basic unit, divide the space into uniform grids, and generate a set of adjacent cube grids; where each cube grid is the smallest unit for spatial discretization.

[0017] Step 12: Calculate the grid center point: For each generated cubic grid, calculate the three-dimensional spatial coordinates (denoted as center) of the geometric center point. These center point coordinates can be calculated using the simple Euler equation and serve as the reference position for subsequent calculations of the distribution vectors of various environmental elements.

[0018] Step 13: Data Structure Definition and Attribute Association: Define a unified data structure to represent the entire discretized environment. This structure is named... It is a set where each element corresponds to a cube grid and contains the following key information:

[0019] in: This represents the cubic grid itself, corresponding to the unit in the aforementioned rasterized model; This represents the calculated coordinates of the center point of the cube grid. This represents a vector set used to store and associate the distribution of all key environmental elements within the cubic grid. Since the grid is arranged according to fixed size and rules, its spatial topology (such as the 26 adjacency relationships between each grid cell, including top, bottom, left, right, front, and back) is naturally determined.

[0020] Step 2: Calculate the distribution vector set of various environmental elements based on the cube center point: For each cube grid defined in Step 1, calculate the distribution vector set eleM of various environmental elements at the center point of the cube grid. eleM includes: atmospheric environment vector, communication signal vector, network coverage vector and electromagnetic signal vector.

[0021] The distribution vector at that point is calculated based on the relationship between the feature object and the center point, such as positional distance and angle.

[0022] in: This represents the atmospheric environment distribution vector. It is a set of electromagnetic signal distribution vectors; : is the set of communication signal distribution vectors, and T represents the transpose.

[0023] like Figure 2 As shown, calculating each cubic grid involves the following steps: Step 21: Calculate the atmospheric environment distribution vector Based on the meteorological element objects defined in the simulation task, at the center point of the cubic grid, the influence vector of each meteorological element is summed. This includes the following steps: Step 211: Obtain the information of the set meteorological objects: Traverse and obtain the current simulation task The set of all meteorological element objects that need to be included in the calculation is denoted as ; This represents the i-th meteorological element object (such as temperature, humidity, wind speed, concentration of harmful gases, etc.), which includes attributes such as the type, intensity, spatial location, and range of influence of the meteorological element; this meteorological element belongs to the set of all meteorological element objects that need to be calculated in the current simulation task. Meteorological elements include: temperature, humidity, wind speed, concentration of harmful gases, etc. Each element contains attribute parameters such as its type, intensity, spatial location and range of influence.

[0024] Step 212: Calculate the individual meteorological vector: For each acquired meteorological element object Based on its relationship with the center point of the current cube grid Relative spatial relationships (such as distance and direction) are calculated using predefined vector calculation functions. Solve for this single meteorological element in The influence vector at the point. The calculation formula is: ; in, The function is used to calculate a single meteorological element. The influence vector at the center point.

[0025] Step 213: Vector Merging and Output: The individual influence vectors calculated for all meteorological element objects are summed to form the final comprehensive atmospheric environment distribution vector. and use it as the set of distribution vectors of the elements. An important component. The merging formula is: ; Step 22: Calculate the electromagnetic signal distribution vector and communication signal distribution vector By systematically traversing and calculating the center point of each electromagnetic signal source in the scene at the current cubic grid, The energy distribution at a point is ultimately combined to form the comprehensive electromagnetic environment distribution vector at that point. Then, the set of communication signal distribution vectors is calculated. Specifically, it includes the following steps: Step 221: Obtain Electromagnetic Signal Sources: Traverse and obtain the set of all electromagnetic signal source objects that need to participate in the calculation in the current simulation scene, denoted as Each electromagnetic signal source object It includes attributes such as spatial location, transmission power, operating frequency band, signal modulation method, and effective coverage area.

[0026] Step 222: Determine whether the current location is within the effective coverage area of ​​the electromagnetic signal source: For each acquired electromagnetic signal source... Determine the center point of the current cube grid. Is it within the effective coverage area of ​​the signal source? If Dot at If the signal source is within the coverage area, proceed to step 223; if it is not within the coverage area, determine that the signal source has no effect on the current point, skip the subsequent calculation, and directly proceed to step 224 to check whether the calculation of all electromagnetic signal sources has been completed.

[0027] Step 223: Calculate the energy distribution vector information of the current electromagnetic signal source at the current location. For the current point determined through step 222 Electromagnetic signal sources that produce effective effects Based on electromagnetic wave propagation models, signal sources and Considering the relative positions of the points, spatial medium attenuation (using the physical properties of the grid), and possible interference mechanisms, calculate the signal source's position. The energy distribution intensity and other relevant parameters (such as interference-to-signal ratio) generated at the point. For each electromagnetic signal source in the scene. Calculate the state of the signal after it reaches the center point.

[0028] The calculation considers the following factors: distance attenuation: based on the Euclidean distance between the signal source and the center point, the fundamental signal strength attenuation is calculated; spatial medium influence: combined with the physical properties of the cubic grid (such as dielectric constant, water content coefficient), the signal penetration attenuation is calculated; electromagnetic interference analysis: based on the electromagnetic environment vector at the current point (… ), calculate the interference-to-signal ratio of the interference signal to the communication signal.

[0029] The free space path loss formula is as follows:

[0030] in, This represents the path loss in free space, measured in decibels (dB). The distance to be transmitted is expressed in kilometers (km) or meters (m), and in this invention it specifically refers to the spatial Euclidean distance between the signal source and the center point of the grid. Indicates the signal frequency, with the unit being megahertz (MHz); The wavelength of the signal is expressed in meters (m) and satisfies the following relationship. c is the speed of light.

[0031] Formula for superposition of multiple signal energy (interference superposition): Since the parameters of each electromagnetic signal source are usually characterized in logarithmic units (dBm), when performing energy superposition, they must first be transformed to the linear domain (mW or W) to satisfy the energy conservation criterion. The specific calculation formula is as follows:

[0032] in, This represents the total received power. Let be the value of the i-th signal source in the logarithmic domain. After completing the linear domain summation operation, can be adjusted according to engineering needs. Then convert back to the logarithmic field (dBm) for storage, that is:

[0033] The above conversion ensures the accuracy of physical calculations while also taking into account the data storage and output habits of the simulation system.

[0034] The formula for calculating the signal-to-interference-plus-noise ratio (SINR) is as follows:

[0035] in, The received power (linear value) of the useful signal. The total electromagnetic interference power (linear value) calculated in step 225 is denoted as , and the system thermal noise power is denoted as . In the simulation implementation, The calculation results are expressed in logarithmic units (dB) and are used for subsequent comparison with the prior knowledge base threshold.

[0036] Medium attenuation formula:

[0037] in, This represents the dielectric penetration attenuation, expressed in decibels (dB). This represents the attenuation coefficient, expressed in decibels per meter (dB / m). It indicates the depth of penetration into a medium, and the unit is meters (m).

[0038] For a single electromagnetic signal source The energy distribution results are formatted into a standardized distribution vector unit. The vector unit records the signal source identifier and its energy state at the current point, and stores it temporarily. ; in, Represents the three-dimensional spatial coordinates of the center point of the cube grid currently being calculated; This represents the p-th electromagnetic signal source. Represents the electromagnetic wave propagation model. This indicates the spatial medium attenuation (using grid physical properties) and possible interference mechanisms of the current electromagnetic signal source, among other influencing parameters.

[0039] Step 224: Determine whether the calculation of all electromagnetic signal sources in the scene has been completed: If there are still uncalculated electromagnetic signal sources, return to step 222 and process the next signal source; if all signal sources have been calculated, proceed to step 225.

[0040] Step 225: Merge electromagnetic distribution vectors A single distributed vector unit that calculates and records all valid electromagnetic signal sources. Perform vector merging (such as vector summation or rule-based aggregation) to form the center point of the current cube grid. The comprehensive electromagnetic environment distribution vector at the location This vector characterizes the original electromagnetic environment at that location. ; Where p represents the index of the electromagnetic signal source, and its value ranges from 1 to q; Option 1: Vector summation and merging Applicable scenarios: When all electromagnetic signal sources operate in the same or similar frequency bands, and it is necessary to evaluate the total electromagnetic energy intensity at that point.

[0041] The merging process involves converting the received power of each signal source from the logarithmic domain (dBm) to the linear domain (mW or W) for superposition, then converting it back to the logarithmic domain, and simultaneously aggregating the attribute information of each signal source.

[0042] Option 2: Aggregate by Rules Applicable scenarios: When there are multiple electromagnetic signal sources operating in different frequency bands or with different signal systems (such as UHF communication signals, VHF interference sources, radar pulse signals, etc.), and it is necessary to evaluate the differentiated impact of each signal source on the subsequent communication link.

[0043] Merging process: The independent information of each signal source is preserved, and the signals are classified and organized according to preset rules.

[0044] The specific calculation steps are as follows: Step 225-1: Preserve the independent information of each signal source. Instead of superimposing the energy of each signal source, preserve the independent information of each source. Retained as an independent unit.

[0045] Step 225-2: Organize by category according to rules.

[0046] The signal sources are grouped according to preset classification rules. This invention supports one or more of the following classification rules: (A1) Grouped by frequency band Based on the operating frequency band of the signal source, it is divided into different frequency band groups (such as HF, VHF, UHF, L-band, S-band, etc.). Within each group, the energy superposition method of Scheme 1 is used to merge the signals, while maintaining independence between groups.

[0047] (A2) Grouping by signal type Grouping based on signal source type (e.g., communication signals, jamming signals, radar signals, broadcast signals).

[0048] (A3) Grouping by priority Signals are grouped according to the preset priorities of the simulation task (such as friendly signals, neutral signals, and enemy interference). High-priority signal sources are stored separately, while low-priority signal sources can be selectively merged or discarded. Within low-priority groups, mode one merging is used.

[0049] (A4) Filter by impact threshold Only signal sources with received power above a preset threshold are retained; those below the threshold are considered ambient noise background and are not recorded separately.

[0050] Step 225-3: Record the adopted classification rules as rule context for subsequent simulation steps to query.

[0051] Step 225-4: Format the output vector in Mode 2 as follows ; in The grouped set of signal source information For the rule context, For timestamps.

[0052] Step 226: Based on the electromagnetic signal distribution vector Calculate the communication signal distribution vector : The energy distribution of communication signals is calculated and extracted based on the distance relationship between the signal source and the center of the current cube. Information such as the current signal type and intensity is recorded at this center point. When multiple communication signals exist in the scene, the interference ratio is calculated based on the electromagnetic interference calculation formula. This forms a vector set of available communication signals at the current location.

[0053] ; ; in, This indicates that the j-th communication signal source is located at the center point of the current cube. The communication signal vector generated at the location; This indicates a specific object instance of a communication signal source, including but not limited to source coordinates, communication type, strength, modulation, and time / frequency. Represents the three-dimensional spatial coordinates of the center point of the cube grid currently being calculated; This represents the set of electromagnetic signal distribution vectors at the current center point of the cube.

[0054] Step 23: Calculate the network coverage vector Network coverage vector calculation is a method used to quantify the coverage status of various network signal sources (such as communication base stations and network access points) at specific discrete locations within a buried space. The specific implementation includes the following steps: Step 231: Obtain the deployment locations of network equipment: Traverse and obtain the set of all deployed network signal source objects in the current simulation scenario, denoted as... Each network signal source object It includes attributes such as its spatial location, transmission power, operating frequency band, network identifier, coverage radius, and signal modulation scheme.

[0055] Step 232: Determine if the current location is within the effective coverage area of ​​the network equipment: for each network signal source Determine the center point of the current cube grid. Whether it is within its effective coverage area. If Dot at If the signal source is within the coverage area, then proceed to step 233; otherwise, determine that the signal source does not effectively cover the current point, skip the subsequent calculations, and directly proceed to step 236.

[0056] Step 233: Calculate the energy distribution of the current network at the current location: For network signal sources that have passed the validity determination... Based on the electromagnetic wave propagation model, the signal is calculated in... Energy distribution at a point. The calculation needs to consider the following factors: distance attenuation: based on... and Calculate path loss using the Euclidean distance between points; calculate additional signal attenuation after penetrating the ruins structure by considering the physical properties of the current cubic grid (such as dielectric constant and water content). Distance attenuation (path loss): Refer to Wen Yinghong, *Theory of Radio Wave Propagation*; Spatial medium attenuation: Refer to Wen Yinghong, *Theory of Radio Wave Propagation*. Step 234: Calculate the interference of the current electromagnetic signal on the network: based on the electromagnetic signal distribution vector at the current point. Analyze the effects of other electromagnetic sources The impact of network signal interference, calculating the network signal in Point superposition electromagnetic interference The subsequent signal. That is: ; ; in, This indicates the location of the k-th network signal source at the center of the currently calculated cubic grid. The network coverage vector generated at that location, This represents the k-th network signal source object itself. This represents the three-dimensional spatial coordinates of the center point of the cube grid currently being calculated. Indicates the current center point of the cube. The set of electromagnetic signal distribution vectors at a given location.

[0057] Step 235: Record the vector information of the current network coverage: The calculation results from steps 233 and 234 are structured and encapsulated to form a vector information system targeting the network topology. Network coverage vector This vector contains information such as signal strength, network load, and signal-to-interference ratio, and is temporarily stored.

[0058] The received power calculated in step 233 The signal-to-interference-plus-noise ratio calculated in step 234 Perform structured associations to form a network of signal sources. Network coverage vector This vector does not represent a numerical superposition of the results from the two steps; instead, it encapsulates them as complementary fields within the same data structure. The specific data structure definition is as follows: ;

[0059] in, The received power of the network signal (unit: dBm) is derived from the calculation results in step 233; The signal-to-interference-plus-noise ratio (SIR) of the network signal (unit: dB) is derived from the calculation results in step 234; Network identifiers representing network signal sources; Indicates the operating frequency band; Indicates the signal modulation scheme; Represents a timestamp, used for timing alignment in dynamic simulations.

[0060] The vector It is temporarily stored as the basic data unit for merging into a comprehensive network coverage distribution vector set in subsequent step 237.

[0061] Step 236: Determine if all network source calculations are complete: Determine if the coverage calculation for all network signal sources in the scenario has been completed. If there are still uncalculated network signal sources, return to step 232 to process the next signal source; if all calculations are complete, proceed to step 237.

[0062] Step 237: Merge network coverage distribution vectors: merge the coverage vectors corresponding to all valid network signal sources. The vectors are merged to form a comprehensive network coverage distribution set for the current point. and treat it as a set of feature distribution vectors. An important component.

[0063] Step 24: Generate full-element downhole environment output: For each cubic grid to be calculated, after completing the calculation of all spatial cubic grids (Steps 21-23), the system merges the output results of the distributed computing nodes and updates the element distribution vector set. Synchronize to a global hash table structure with geocoding as the key. In this process, a unified update and output of the full-element downhole environment model is achieved, providing a complete discretized data foundation for subsequent communication simulation and situation analysis.

[0064] Step 3: Based on the discretized environment model constructed above, perform functional simulation of the communication link: based on the discretized environment model constructed in Step 1 ( ) and the total element distribution vector calculated in step 2 ( This method utilizes a high-efficiency functional simulation computation chain to quickly calculate the reachability and effectiveness of links between any two communication nodes, simulating communication states such as congestion, interference, and obstruction. The steps include: Step 31: Simulation Input Parameter Preparation: Determine the two ends of the communication link to be evaluated (the local machine and the communication object), and configure a set of simulation input parameters for each node, including: Relative positional relationship of the communication objects: The spatial geometric relationship between the two parties (i.e., the local machine and the communication object), mainly including their absolute coordinates in three-dimensional space, and the relative parameters derived from these coordinates, such as straight-line distance, relative azimuth angle, elevation angle, etc. Motion state information of the communication object: Mainly including the object's velocity, direction of motion (heading angle), and acceleration, etc. This information is used to predict the positional changes of the communication object within the simulation step cycle, thereby realizing the simulation of the dynamic link. Situational information set: Mainly refers to the set of distribution vectors of various environmental elements in which the simulation object is located, calculated from the data. For example, the set of environmental vectors of its location obtained through a dynamic adaptation mechanism. This includes: electromagnetic situation: This refers to the strength, source, and interference level of all electromagnetic signals present in the surrounding environment; communication relationship status: and This includes the strength and quality of all detectable communication and networking signals; atmospheric environmental conditions: This refers to the potential impact of current temperature, humidity, and harmful gas concentrations on communication and equipment.

[0065] Status information set: This mainly refers to the inherent, real-time changing attribute parameters of the communication object. Examples include: communication equipment operating parameters such as transmit power, receive sensitivity, current operating frequency band, signal system (modulation and coding scheme), and power consumption; mission status: the current stage or execution mode of the rescue mission undertaken by the simulation object; communication link status: whether it is currently in a communication connection state, which object it is connected to, and the real-time quality parameters of the link (such as signal-to-noise ratio and bit error rate); and network status: whether it is connected to a network, network identifier (ID), and IP address.

[0066] In the context of element coupling, during the communication link calculation (step 32), the input to the simulation calculation chain is no longer a single data point, but rather includes quantified data of multiple environmental elements. This input data originates from the set of full-element distribution vectors calculated in step 2. Specifically, this includes: Spatial structural elements: represented by the physical properties of the grid where the node is located (such as the dielectric coefficient), used to calculate signal attenuation when penetrating obstacles such as walls and ruins; Electromagnetic environment elements: represented by... This is used to calculate interference from other signal sources; atmospheric environmental factors: through This is to reflect what might affect signal propagation (such as the effect of humidity on specific frequency bands). Here, the communication object refers to the simulated communication object (i.e., the simulated object, such as rescue equipment or personnel).

[0067] Step 32: Based on the comparison of energy and prior knowledge base, execute the communication link calculation chain: input the parameters of both nodes, their relative positional relationship, and environmental element vectors into the pre-constructed functional simulation calculation chain (corresponding function). The calculation is performed within a prior knowledge base (rather than real-time physical modeling) and comprehensively evaluates the following factors: path loss: calculating basic signal attenuation based on the distance between nodes and spatial geometry; medium penetration attenuation: calculating additional signal loss when the signal passes through the ruins structure, considering the physical properties (medium coefficient, water content coefficient) of the grid where the node is located; electromagnetic interference analysis: based on the positions of both parties... Vector, calculate key quality parameters such as the interference-to-signal ratio of the interference signal to the communication signal. (Steps 321-325); In this process, the relative positional relationship between the local machine and the communication object, as well as communication / networking parameters, are used as preconditions. The spatial environment and electromagnetic interference are used as conditional inputs. Based on these inputs, the reachability and effectiveness of the communication signal between the two endpoints are calculated using a combination of energy relationships and prior knowledge comparisons, resulting in corresponding quantification results and state judgments. This includes the following aspects: Link reachability judgment: Whether a basic communication connection can be established between the two communication nodes at this simulation step. Link effectiveness (quality) parameters: A series of quantitative indicators used to describe the communication quality even if the link is reachable. These parameters are derived based on "energy relationships" (physical layer signal calculations) and "prior knowledge comparisons" (matching with a priori knowledge base), including: Signal-to-interference-plus-noise ratio (SINNR): Characterizing the ratio of the useful signal strength to the sum of noise and interference, a key physical layer indicator for measuring link quality. Bit error rate or packet loss rate: Characterizing the reliability of communication, i.e., the accuracy of data transmission. Equivalent data rate: The theoretical data transmission rate that the link can support under a given modulation and coding scheme and current channel conditions.

[0068] The prior knowledge base stores prior rules or data used to determine the quality of communication links. For example, it may include the mapping relationship between signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) under different communication systems, as well as the minimum quality thresholds required for various services (such as voice and video). The computational chain can quickly determine the validity of the link by querying this knowledge base.

[0069] Step 321: For the communication link between the sending node Tx and the receiving node Rx, the received signal power... ; Step 322: Electromagnetic environment distribution vector based on the location of the receiving node Rx Calculate the total interference power of all interference signals at point Rx. ; Step 323: Based on the above calculation results, calculate the signal-to-interference-plus-noise ratio (SINR) at the receiving node; Step 324: Construction of the prior knowledge base (completed offline).

[0070] The prior knowledge base is a pre-built offline dataset that stores the mapping relationship between SINR and key link quality parameters under different communication systems. The knowledge base construction process is as follows: For the target communication system (such as QPSK, 16QAM, 64QAM, etc.), high-precision simulation methods (such as Monte Carlo simulation) are used to calculate the corresponding bit error rate (BER) and packet loss rate (PER) under different SINR conditions; the mapping relationship between SINR and BER / PER is stored in the form of a lookup table, denoted as: Simultaneously, store the minimum quality thresholds required for different service types (such as voice, video, and data): .

[0071] Step 325: Online table lookup mapping.

[0072] Using the SINR value calculated in step 323 as the query key, perform the following mapping operation in the prior knowledge base: 1) Obtain BER / PER by looking up a table: Based on the current operating frequency band and modulation scheme of the communication link, look up the entry in the corresponding lookup table that is closest to SINR to obtain the current bit error rate (BER) and packet loss rate (PER); 2) Equivalent data rate determination: Based on the SINR value and combined with the adaptive modulation and coding (AMC) rules, determine the theoretical equivalent data rate that the current link can support.

[0073] Step 33: Link Status Determination and Relationship Establishment: The output of Step 32 is used for threshold determination, including reachability calculation and validity calculation. Reachability calculation is fundamental, primarily determining whether the signal strength exceeds physical layer thresholds such as receiver sensitivity, addressing the "present or absent" problem. Validity calculation focuses on whether the link can stably and reliably support upper-layer applications (such as clear voice, high-definition video, and reliable data return) in real-world environments with noise, interference, and attenuation.

[0074] Effectiveness assessment is achieved by calculating a series of key, quantifiable communication quality parameters. The calculated quality parameters (such as SINR and BER) are compared with pre-stored service thresholds in a prior knowledge base. If the link quality parameters (such as signal-to-noise ratio and bit error rate) meet the preset service thresholds, it is determined that the two parties can establish a valid communication link, and the system establishes a "communication relationship" to support subsequent data interaction; if the service thresholds are not met, the communication link is determined to be invalid.

[0075] The link quality parameters (including SINR, BER, PER, and DataRate) output in step 32 are subjected to threshold determination, specifically including two levels: reachability determination and validity determination. The thresholds are derived from a pre-built prior knowledge base and are preset for different service types (voice, video, data, and emergency commands).

[0076] Step 331: Link reachability determination Reachability determination addresses the "existence" problem, specifically whether a basic physical connection can be established between two communication nodes. The signal-to-interference-plus-noise ratio (SINR) (in dB) calculated in step 32 is obtained; the prior knowledge base is queried to obtain the minimum SINR threshold required for the current service type (e.g., voice, video, data, emergency commands). (Unit: dB); Judgment condition: If If the link is reachable, then the link is considered reachable; otherwise, the link is considered unreachable.

[0077] It should be noted that SINR already comprehensively reflects the relationship between signal power, interference power, and noise power. Therefore, using SINR to compare with the threshold covers the basic requirements of both "sufficient signal strength" and "sufficient signal quality", and there is no need to compare power and sensitivity separately.

[0078] Step 332: Link Validity Determination The validity determination is used to solve the "good or bad" problem, that is, for reachable links, to further evaluate whether their communication quality can stably and reliably support upper-layer applications (such as clear voice, high-definition video, and reliable data backhaul).

[0079] Obtain the bit error rate (BER) (or packet loss rate PER) calculated in step 32; query the prior knowledge base to obtain the maximum bit error rate threshold that can be tolerated for the current business type. Judgment condition: If If the link is valid, it is considered to be effective; otherwise, the link quality is considered insufficient.

[0080] Step 333: Comprehensive Judgment and Relationship Establishment; Based on the results of the accessibility and validity judgments, perform the following actions (as shown in Table 1). Table 1 shows the results of the combined accessibility and effectiveness assessments. ; If the link is determined to be valid, the system establishes a "communication relationship" between the two communicating parties and records the link parameters (SINR, BER, DataRate, etc.) for subsequent data interaction simulation. If the link is determined to be invalid or of insufficient quality, the system refuses to establish a connection and transmits the status information to step 34, triggering network status synchronization and adaptive behavior simulation.

[0081] Step 34: Network Status Synchronization and Behavior Simulation: For network connection scenarios, if a node cannot establish an effective link with the network signal source (such as a base station), the system automatically marks the node's "network location" in its task status as abnormal (such as "offline" or "restricted access"). This status change triggers the node behavior policy library, driving it to execute preset adaptive actions (such as channel switching, relay search, or autonomous operation mode switching) to simulate the intelligent response of real equipment in a complex electromagnetic environment.

[0082] Step 4: Dynamic Adaptation of Simulation Objects to Environmental Effects: To address the issues of accurate positioning and environmental impact calculation of simulation objects (such as rescue equipment and personnel) in a discretized environmental model, two dynamic adaptation mechanisms are proposed. Based on its center point coordinates, the simulation object quickly retrieves the distribution vector of environmental elements at its location from the global environmental hash table using an efficient indexing method, and uses this vector as input for its behavior and state iteration. This includes the following steps: Step 41: Simulation Object Location Mapping and Key-Value Encoding: Based on the spatial center point coordinates of the simulation object and according to the preset geocoding principle, generate a unique geocoding key value (Key); use this key value as an index in the global environment model organized based on a hash table structure (…). This allows for fast location and retrieval of cube objects.

[0083] Based on the spatial center point coordinates of the simulation object, and according to the preset geocoding principles, a unique geocoding key is generated; this key is then used as an index in the global environment model organized based on a hash table structure. This implementation achieves fast location and retrieval of cube objects within a specific framework. The specific implementation includes the following sub-steps: Step 411: Mapping coordinates to raster Let the coordinates of the spatial center point of the simulation object be... (Unit: meters), preset grid side length L (unit: meters, e.g., L=1). Then the index coordinates of the corresponding cubic grid cell. Calculate using the following formula: , ,

[0084] in, , , The minimum coordinate values ​​(spatial origin) of the simulation space in three dimensions; To ensure that coordinates falling on the grid boundary belong to the grid with the smaller index, the floor function is used.

[0085] Step 412: Generating Geocoding Key Values 3D index coordinates The key is encoded as a one-dimensional integer value for fast indexing in the hash table. In one example, linear encoding could be used.

[0086]

[0087] Where i represents the raster index in the X-axis direction (0, 1, 2, ...), j represents the raster index in the Y-axis direction (0, 1, 2, ...), and k represents the raster index in the Z-axis direction (0, 1, 2, ...). This represents the total number of grid cells on the Y-axis. This represents the total number of grid cells on the Z-axis.

[0088] This encoding method ensures that each 3D index coordinate maps to a unique key value, and the encoded key value is... It is continuously distributed within a range, making it suitable as an index for arrays or hash tables.

[0089] Step 413: Hash Table Retrieval Using the key value generated in step 412 as an index, in the global environment model (Using a hash table structure) to perform fast retrieval with O(1) time complexity:

[0090] in The complete data structure for the corresponding cubic raster includes the raster's geometric information. ) and its total element distribution vector set .like If the key value does not exist (i.e., the coordinates are outside the preset space range), then an empty value is returned and boundary exception handling is triggered.

[0091] Step 414 (optional): Exception handling If the coordinates of the simulated object exceed the preset simulation space range, the system will record a warning log, indicating "The simulated object exceeds the boundary of the environment model". Depending on the simulation task configuration, the following processing methods can be selected: ignore the environmental effects of the object (marked as "outside the boundary"); map the object to the nearest boundary grid; terminate the current simulation step and report an error.

[0092] Step 42: Dynamic Adaptation Mode Selection Mechanism: Based on the comprehensive requirements of accuracy and performance for the current simulation task, the system dynamically selects one of the following two adaptation modes: Single-point matching mode: suitable for scenarios prioritizing computational efficiency or small object size; Multi-point fitting mode: suitable for scenarios requiring high simulation accuracy or needing to reflect continuous changes in the environmental space. The system dynamically selects either single-point matching mode or multi-point fitting mode based on the comprehensive requirements of accuracy and performance for the current simulation task. The selection mechanism is based on the following judgment dimensions and rules.

[0093] The selection of the mode is based on a comprehensive evaluation of the following four dimensions to determine which adaptation mode to adopt (as shown in Table 2).

[0094] Table 2 Comprehensive evaluation across four dimensions ; The mode should be selected according to the following priority order: Rule 1: Explicitly specify by task configuration (highest priority) If the adaptation mode is explicitly specified in the configuration file of the simulation task, directly adopt the specified mode: If the task configuration is specified as the single-point matching mode, select the single-point matching mode; If the task configuration is specified as the multi-point fitting mode, select the multi-point fitting mode.

[0095] Rule 2: Automatically judge according to the object size If the mode is not explicitly specified for the task, judge according to the relationship between the size of the simulation object and the side length of the grid: If the object size D_obj ≤ L (side length of the grid), select the single-point matching mode. At this time, the object is within one grid, and the accuracy of single-point matching is sufficient; If the object size D_obj > L (side length of the grid), select the multi-point fitting mode. At this time, the object spans multiple grids, and multi-point fitting is required to reflect the spatial variation of the environment.

[0096] Among them, the object size D_obj is defined as the maximum circumscribed diameter of the object in the three-dimensional space (unit: meter), and the grid side length L is the preset side length of the cubic grid in step 1 (unit: meter).

[0097] Rule 3: Automatically judge according to the accuracy requirement; if none of the above rules are triggered, judge according to the preset accuracy requirement: if the accuracy requirement Acc_req of the task configuration ≥ L (allowable error is greater than or equal to the side length of the grid), select the single-point matching mode; if the accuracy requirement Acc_req of the task configuration < L (sub-grid-level accuracy is required), select the multi-point fitting mode. Among them, the accuracy requirement Acc_req is a simulation task configuration parameter, representing the maximum allowable value of the position error (unit: meter).

[0098] Rule 4: Dynamically switch according to the simulation stage: For long-running simulation tasks, the system supports dynamically switching the adaptation mode at different stages (as shown in Table 3).

[0099] Table 3 Dynamically switch the adaptation mode at different stages ; Step 43: Obtain and calculate the environmental element vector: For the single-point matching mode, directly select the cube with the closest Euclidean distance to the center point of the simulation object, and the pre-calculated element distribution vector set at its center point ( This serves as the comprehensive environmental input for the object. For multi-point fitting mode, the feature distribution vectors of multiple neighboring cubes around the object's center point are extracted, and interpolation is performed using distance as weight to generate a more accurate environmental impact vector. A preferred interpolation method is the inverse distance weighted average method. This method calculates weights based on the distance between the simulation object and the center points of each neighboring cube; the closer the distance, the greater the weight. Then, the weighted sum of each feature vector is performed to obtain the fitted comprehensive environmental vector.

[0100] Step 44: Environmental Effects Drive Behavior Iteration: Input the environmental element vectors (such as communication signal strength and electromagnetic interference level) obtained in Step 43 into the state calculation and behavior decision module of the simulation object; the object iterates its operating state (such as communication connectivity and network status) based on the environmental input and triggers adaptive behaviors (such as channel switching and path replanning) to realize the closed-loop simulation of "environmental perception-behavior response".

Claims

1. A full-element discretization simulation calculation method for emergency communication in buried spaces, characterized in that, Includes the following steps: Step 1: Divide the target burial space into cubic units of a preset size into a uniform grid, generating multiple adjacent cubic grids, and define a unified data structure for each cubic grid to store the geometric information of the grid and the distribution vector set of various environmental elements within it. Step 2: For each cubic grid, calculate the atmospheric environment distribution vector, electromagnetic signal distribution vector, communication signal distribution vector, and network coverage vector at the center point, and merge and store the calculation results into the grid's data structure to form a full-element discretized environment model; Step 3: Based on the full-element discretized environment model, perform functional simulation on the communication link between any two communication nodes in the simulation scenario, evaluate the reachability and effectiveness of the link in turn, and establish or reject the communication connection based on the evaluation results. Step 4: Map the simulation object to the corresponding grid position in the discretized environment model. Dynamically select the single-point matching or multi-point fitting adaptation mode according to the accuracy and efficiency requirements of the simulation task, obtain the environmental element vector of the position, and drive the behavior iteration and state update of the simulation object.

2. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 1, characterized in that, In step 2, the electromagnetic signal distribution vector is calculated as follows: all electromagnetic signal sources in the simulation scene are traversed, and it is determined whether each signal source covers the center point of the current grid. If it covers, the energy distribution of the signal source at the center point is calculated by combining the signal source attributes, spatial propagation path and physical attributes of the current grid. Finally, the energy distribution vectors of all effective signal sources are merged to form the comprehensive electromagnetic environment distribution vector of the current grid.

3. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 2, characterized in that, In step 2, the specific calculation of the communication signal distribution vector is as follows: Based on the electromagnetic signal distribution vector, the signal strength of each communication signal source at the current grid center point is calculated, and the electromagnetic interference effect is superimposed to generate the communication signal vector set at the current point.

4. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 2, characterized in that, In step 2, the network coverage vector is calculated as follows: traverse all network signal sources in the simulation scenario, determine whether each signal source covers the current grid center point, and if it does, calculate the signal energy distribution and superimpose the electromagnetic interference effect to generate the network coverage vector set at the current point.

5. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 1, characterized in that, Step 3 is as follows: Based on the distance between communication nodes, spatial medium properties, and electromagnetic interference, quickly calculate the signal quality parameters of the receiving end; compare the calculated quality parameters with the service thresholds in the pre-built knowledge base to determine whether the link meets the service requirements.

6. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 1, characterized in that, In step 3, if a communication node cannot establish a valid link with the network signal source, the system automatically marks the node's network status as abnormal and triggers it to execute a preset adaptive behavior strategy, including channel switching, relay search, or operation mode switching.

7. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 1, characterized in that, In step 4, the single-point matching mode is as follows: directly select the cube grid that is closest to the center point of the simulation object in Euclidean distance, and use the pre-stored environmental element vector in the grid as the input of the simulation object; the multi-point fitting mode is as follows: extract the environmental element vectors of multiple neighboring grids around the center point of the simulation object, perform interpolation calculation with distance as weight, and generate the fitted environmental vector.

8. The full-element discretization simulation calculation method for emergency communication in buried spaces according to claim 1, characterized in that, In step 4, the simulation object iteratively updates its communication connectivity, network status, and operational behavior based on the acquired environmental element vectors, thereby achieving closed-loop simulation of environmental perception and behavioral response.

9. A full-element discretized simulation system for emergency communication in buried spaces, characterized in that, include: Environmental Discretization Modeling Module: This module is used to divide a continuous three-dimensional burial space into multiple uniform cubic grids and define a unified data structure for each grid. Full-element distribution vector calculation module: used to calculate the distribution vectors of atmospheric environment, electromagnetic signals, communication signals and network coverage in parallel at the center point of each cubic grid, and generate a full-element discretized environment model; Communication simulation and calculation module: used to perform functional simulation of the reachability and effectiveness of communication links based on the full-element discretized environment model; Dynamic Adaptation and Behavior Iteration Module: This module maps simulation objects to corresponding grid positions in the discretized environment model, dynamically selects the adaptation mode to obtain environmental element vectors, and drives the state and behavior updates of simulation objects. System Data Management Module: Used to coordinate the execution order and data interaction of the above modules, and to centrally manage simulation data assets.

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