A passive fusion communication method and platform in a cabin

Abstract

The present invention discloses a method and platform for passive fusion communication in a cabin, and relates to the field of fusion communication technology. The method includes: collecting information on passive and active devices in the target cabin, constructing an active device layer and a passive device layer; generating heterogeneous communication tasks and heterogeneous communication links; evaluating heterogeneous communication tasks, and determining communication task measurement coefficients; predicting the communication quality of the heterogeneous communication tasks; judging whether the heterogeneous communication quality prediction results meet the heterogeneous communication quality multi-feature constraints; strengthening the heterogeneous communication links, establishing heterogeneous communication strengthening links, and executing the heterogeneous communication tasks. The present invention solves the technical problems in the prior art that cross-layer communication coordination of active and passive devices in the cabin is difficult, and communication quality is difficult to accurately guarantee and optimize, and achieves the technical effect of optimizing the communication links in the cabin and accurately predicting and strengthening the communication quality, and ensuring efficient and stable execution of heterogeneous communication tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of fusion communication, and in particular to a method and platform for in-cabin passive fusion communication. Background Art

[0002] In the field of modern in-cabin communications, with the continuous development of technology, active devices and passive devices are widely used and coexist. Active devices such as various sensors and controllers can actively transmit and receive signals and have strong data processing capabilities; passive devices such as reflectors and sensors rely on external signal stimulation to respond. However, existing technologies face many challenges in in-cabin communications. On the one hand, due to the differences in the characteristics of active and passive devices, it is difficult to achieve effective integration and coordination between the two when building communication links. The processing of cross-layer communication requests lacks systematicity and efficiency, and it is impossible to accurately generate adapted heterogeneous communication tasks and links. On the other hand, the evaluation and guarantee of communication quality is not accurate and perfect, and it is difficult to dynamically predict and optimize the communication quality according to different communication task requirements. As a result, in a complex in-cabin environment, the stability, reliability and effectiveness of communication are difficult to meet the growing communication needs, and signal interruption, transmission delay, data error and other problems are prone to occur, which seriously affects the normal operation of the in-cabin system and the efficiency of task execution.

[0003] In the existing technology, cross-layer communication coordination between active and passive devices in the cabin is difficult, and the communication quality is difficult to accurately guarantee and optimize. Summary of the invention

[0004] The present application provides a method and platform for in-cabin passive fusion communication, which is used to solve the technical problems in the prior art that cross-layer communication coordination between active and passive devices in the cabin is difficult, and communication quality is difficult to accurately ensure and optimize.

[0005] In view of the above problems, the present application provides a method and platform for in-cabin passive fusion communication.

[0006] In a first aspect of the present application, a method for passive fusion communication in a cabin is provided, the method comprising:

[0007] Collect information on active and passive devices of the target cabin, and construct an active device layer and a passive device layer, wherein the active device layer includes multiple active devices, and the passive device layer includes multiple passive devices; monitor the active and passive fusion communication requests of the active device layer and the passive device layer according to the communication request multi-factor, and generate heterogeneous communication tasks and heterogeneous communication links; evaluate the heterogeneous communication tasks according to the multi-element indicators of communication task evaluation, and determine the communication task measurement coefficient; based on the heterogeneous communication link, drive the communication evaluation gating component and the communication quality evaluation component according to the communication task measurement coefficient, predict the communication quality of the heterogeneous communication task, and obtain the heterogeneous communication quality prediction result; judge whether the heterogeneous communication quality prediction result meets the heterogeneous communication quality multi-feature constraint; if the heterogeneous communication quality prediction result does not meet the heterogeneous communication quality multi-feature constraint, strengthen the heterogeneous communication link based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the heterogeneous communication quality multi-feature constraint, and establish a heterogeneous communication strengthening link; execute the heterogeneous communication task based on the heterogeneous communication strengthening link.

[0008] A second aspect of the present application provides an in-cabin passive fusion communication platform, the platform comprising:

[0009] An equipment layer construction module, the equipment layer construction module is used to collect information about passive and passive equipment of the target cabin, and construct an active equipment layer and a passive equipment layer, wherein the active equipment layer includes multiple active equipment, and the passive equipment layer includes multiple passive equipment; a heterogeneous communication task generation module, the heterogeneous communication task generation module is used to monitor the active and passive equipment layers for passive and passive fusion communication requests according to multiple factors of communication requests, and generate heterogeneous communication tasks and heterogeneous communication links; a communication task measurement coefficient determination module, the communication task measurement coefficient determination module is used to evaluate the heterogeneous communication task according to multiple indicators of communication task evaluation, and determine the communication task measurement coefficient; a prediction result acquisition module, the prediction result acquisition module is based on the heterogeneous communication link, according to the communication task measurement coefficient, drives the communication evaluation The invention relates to a communication quality evaluation component and a communication quality evaluation component, which are used to predict the communication quality of the heterogeneous communication task and obtain the heterogeneous communication quality prediction result; a multi-feature constraint judgment module, which is used to judge whether the heterogeneous communication quality prediction result satisfies the multi-feature constraint of the heterogeneous communication quality; a heterogeneous communication enhanced link establishment module, which is used to strengthen the heterogeneous communication link based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the multi-feature constraint of the heterogeneous communication quality if the heterogeneous communication quality prediction result does not satisfy the multi-feature constraint of the heterogeneous communication quality, and establish a heterogeneous communication enhanced link; a heterogeneous communication task execution module, which is used to execute the heterogeneous communication task based on the heterogeneous communication enhanced link.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Collect the information of passive and non-passive devices in the target cabin, and construct the active and passive device layers; monitor the active and passive fusion communication requests of the active and passive device layers according to the communication request multi-factors, and generate heterogeneous communication tasks and heterogeneous communication links; evaluate the heterogeneous communication tasks according to the multi-element indicators of communication task evaluation, and determine the communication task measurement coefficient; drive the communication evaluation gating component and the communication quality evaluation component to predict the communication quality of the heterogeneous communication tasks and obtain the heterogeneous communication quality prediction results; judge whether the heterogeneous communication quality prediction results meet the heterogeneous communication quality multi-feature constraints; if the heterogeneous communication quality prediction results do not meet the heterogeneous communication quality multi-feature constraints, strengthen the heterogeneous communication links and establish heterogeneous communication strengthening links; execute the heterogeneous communication tasks based on the heterogeneous communication strengthening links. The technical effect of optimizing the communication links in the cabin and accurately predicting and strengthening the communication quality, and ensuring the efficient and stable execution of heterogeneous communication tasks is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a method for in-cabin passive fusion communication provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a passive fusion communication platform in a cabin provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: device layer construction module 10, heterogeneous communication task generation module 20, communication task measurement coefficient determination module 30, prediction result acquisition module 40, multi-feature constraint judgment module 50, heterogeneous communication enhanced link establishment module 60, heterogeneous communication task execution module 70. DETAILED DESCRIPTION

[0016] The present application provides an in-cabin passive fusion communication and platform, which is used to solve the technical problems in the prior art that cross-layer communication coordination between active and passive devices in the cabin is difficult, and communication quality is difficult to accurately ensure and optimize.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a method for passive fusion communication in a cabin, the method comprising:

[0019] Step S100: Collect information on active and passive devices of the target cabin, and construct an active device layer and a passive device layer, wherein the active device layer includes a plurality of active devices, and the passive device layer includes a plurality of passive devices.

[0020] Specifically, comprehensive equipment information is collected for the target cabin (taking the cabin as an example) to construct the active equipment layer and the passive equipment layer. First, a variety of professional detection and identification technologies, such as sensor scanning, equipment interface detection, and communication protocol analysis, are used to conduct detailed inspections of various types of equipment in the cabin. For active equipment, such as shipboard communication equipment, navigation systems, surveillance cameras, etc., detailed equipment information is obtained by detecting its power supply module, signal transmitting and receiving device, and equipment operating status parameters, including equipment type, equipment model, equipment performance parameters (such as communication frequency band, transmission power, data processing capacity, etc.), equipment location information, and equipment operating status (whether it is working normally, whether it is in standby or dormant state, etc.). These active devices have their own independent power supply and can actively send and receive signals to the outside. They play a leading role in cabin communication and information interaction. They constitute the active equipment layer and provide a stable signal source and powerful information processing capabilities for the entire cabin communication system. For passive devices, such as passive sensors (such as temperature sensors, pressure sensors, etc.), RFID tags, etc., by detecting their physical properties (such as the sensor's sensing element, the tag's reflective characteristics), signal modulation methods, and interaction mechanisms with external devices, relevant information is collected, covering the functional attributes of the device, sensing range, data transmission mode, and distribution location in the cabin. Although passive devices do not have their own power supply, they can achieve information transmission through external energy excitation (such as radio frequency signal excitation). They constitute the passive device layer, which can supplement and enrich the information perception and data collection functions in the cabin in specific scenarios. Through the comprehensive collection and integration of these passive device information, a layered and functionally complementary active and passive device layer is constructed, laying a solid foundation for subsequent passive and active fusion communications, ensuring that under different communication needs and environmental conditions, the advantages of various types of equipment can be fully utilized to achieve efficient and reliable cabin communication and information interaction.

[0021] Step S200: According to the communication request multi-factor, the active device layer and the passive device layer are monitored for passive fusion communication requests, and heterogeneous communication tasks and heterogeneous communication links are generated.

[0022] Specifically, in order to realize passive and passive fusion communication in the cabin, it is necessary to first clarify the communication request multi-factors, which include the communication request source (i.e. the specific device or node that initiates the communication request, such as an active sensor or passive tag reader in the cabin), the communication request target (clarifies the specific device to which the information is to be transmitted, which may be another active device or a processing system associated with a passive device), the communication request type (including data query, control command issuance, status report upload, etc.) and the communication request task (describes in detail the specific business content of this communication request, such as obtaining environmental parameters of a specific area or performing specific operations on a certain device). Based on these multi-factors, advanced monitoring technology and algorithms are used to carry out comprehensive monitoring of the active device layer (including many devices such as communication base stations and active sensors that can actively transmit and receive signals) and the passive device layer (such as passive sensors, RFID tags, etc. that require external energy to communicate). By real-time monitoring of the communication port status of each device in the device layer, the signal transmission and reception status, and the interactive signals between devices, when a signal that meets the communication request multi-factor conditions is detected, cross-layer communication request identification is immediately performed. If a data collection request is issued by an active device, its goal is to obtain environmental data monitored by passive sensors. After the request is identified across the active and passive device layers, a heterogeneous communication task is generated. Subsequently, the communication link characteristics of the heterogeneous communication task are identified based on the topological structure of the device layer, the device communication capability, and the signal transmission characteristics. Taking into account factors such as the distance between devices, signal interference, transmission loss, and device compatibility, a heterogeneous communication link connecting the communication request source and target is constructed to ensure that information can be accurately and efficiently transmitted between different types of devices, providing a reliable connection foundation for integrated communications in the cabin.

[0023] Step S300: Evaluate the heterogeneous communication task according to the communication task evaluation multivariate index to determine the communication task measurement coefficient.

[0024] Specifically, the heterogeneous communication tasks are comprehensively evaluated through the communication task evaluation multi-element indicators, and then the communication task measurement coefficient is determined. The communication task evaluation multi-element indicators include three key aspects: communication urgency, communication criticality and communication coverage. Communication urgency is used to measure the urgency of heterogeneous communication tasks in the time dimension. For example, in certain emergency situations in the cabin (such as fire alarm signal transmission, equipment failure emergency notification, etc.), the urgency of related communication tasks is extremely high and needs to be prioritized to ensure cabin safety and normal equipment operation; communication criticality reflects the importance of the task to the operation of the entire cabin system. For example, communication tasks involving core equipment control instructions and key data transmission (such as navigation system data update, etc.) have a high level of criticality and have a significant impact on the overall performance of the system; communication coverage mainly considers the scope of the communication task. For example, the communication task of full cabin broadcast notification has a wide coverage, while the parameter adjustment task for individual equipment has a relatively narrow coverage. For each heterogeneous communication task, a detailed evaluation is carried out based on these multi-element indicators to obtain the communication task urgency coefficient, communication task criticality coefficient and communication task coverage coefficient. Afterwards, by evaluating the importance of multiple indicators for communication task evaluation, multiple indicator importance coefficients are obtained. These coefficients reflect the relative importance of each indicator in the overall evaluation. Then, based on these indicator importance coefficients, reasonable weights are allocated to communication urgency, communication criticality, and communication coverage, and a communication task evaluation weighted network is constructed. Finally, the communication task urgency coefficient, communication task criticality coefficient, and communication task coverage coefficient are input into the constructed communication task evaluation weighted network. After weighted calculation and comprehensive analysis, the communication task measurement coefficient is finally determined. This measurement coefficient can accurately quantify the comprehensive characteristics of heterogeneous communication tasks, and provide an important reference for subsequent communication quality prediction, link optimization, and other operations, ensuring the efficiency and reliability of passive fusion communication in the cabin.

[0025] Step S400: Based on the heterogeneous communication link and according to the communication task measurement coefficient, a communication evaluation gating component and a communication quality evaluation component are driven to perform communication quality prediction on the heterogeneous communication task to obtain a heterogeneous communication quality prediction result.

[0026] Specifically, based on the constructed heterogeneous communication link, a modeling operation is performed to construct a heterogeneous communication link model that can accurately reflect the link characteristics and communication process. This model covers key elements such as active devices, passive devices in the link, the connection relationship between them, and the signal transmission method, providing a basic framework for subsequent simulation execution. Then, based on the heterogeneous communication link model, the heterogeneous communication task is simulated and executed to reproduce the actual communication process in the simulation environment, thereby obtaining heterogeneous communication simulation data. These simulation data contain various parameters in the signal transmission process, such as signal strength, transmission delay, bit error rate, etc., which are important bases for evaluating communication quality. Subsequently, the previously determined communication task measurement coefficient is input into the communication evaluation gating component, which contains a communication evaluation gating network. The communication task measurement coefficient is used as an input signal to activate the communication evaluation gating network so that it adjusts its internal state according to the characteristics of the coefficient, and then outputs the communication evaluation gate activation coefficient. This activation coefficient can dynamically control the working mode and sensitivity of the subsequent communication quality evaluation component. The communication quality evaluation component consists of a communication effectiveness evaluation unit, a communication reliability evaluation unit, and a communication delay evaluation unit. Based on the communication evaluation gated activation coefficient, the communication effectiveness evaluation unit analyzes the heterogeneous communication simulation data. The unit contains multiple communication effectiveness evaluation models. The activation coefficient first activates the features of these models, screens out the models that match the current task and link characteristics, and then inputs the simulation data into these activated models to obtain multiple communication effectiveness evaluation coefficients. Finally, the centralized values ​​of these coefficients are calculated to generate the communication effectiveness prediction coefficient, which is used to measure the performance of the communication task in terms of information transmission accuracy and completeness. At the same time, the communication reliability evaluation unit works based on the activation coefficient and simulation data. After the multiple communication reliability evaluation models in the unit are activated, the simulation data is processed to evaluate the communication reliability from the perspectives of signal stability and anti-interference ability, and the communication reliability prediction coefficient is output, which reflects the ability of the communication task to maintain stable communication in a complex cabin environment. In addition, the multiple communication delay evaluation models in the communication delay evaluation unit also calculate the communication delay prediction coefficient based on the simulation data under the action of the activation coefficient to characterize the characteristics of the communication task in terms of information transmission speed. Finally, the communication effectiveness prediction coefficient, communication reliability prediction coefficient and communication delay prediction coefficient are integrated and output together to form the heterogeneous communication quality prediction result. This result comprehensively reflects the expected communication quality of the heterogeneous communication task under the current heterogeneous communication link and communication task measurement coefficient, and provides a key basis for subsequent judgment of whether the communication quality meets the requirements and link strengthening operations, ensuring that the passive fusion communication in the cabin can meet the expected performance standards.

[0027] Step S500: determining whether the heterogeneous communication quality prediction result satisfies the heterogeneous communication quality multi-feature constraint.

[0028] Specifically, it is necessary to make a comprehensive and detailed judgment on the heterogeneous communication quality prediction results to determine whether they meet the multi-feature constraints of heterogeneous communication quality, which mainly cover communication effectiveness constraints, communication reliability constraints and communication delay constraints. The communication effectiveness constraint focuses on the accuracy and integrity of information transmission during the communication process, that is, to determine whether the information can be accurately transmitted from the source to the target during the execution of the heterogeneous communication task, and to ensure that the information content is not lost, tampered with or misinterpreted. For example, for the communication task of transmitting the operating parameters of key equipment in the cabin, the accuracy of the data is required to meet extremely high standards, otherwise it may lead to misjudgment of the equipment status and affect the normal operation of the cabin system. The communication reliability constraint mainly focuses on the stability and anti-interference ability of the communication link in the complex cabin environment. There are various potential interference sources in the cabin, such as electromagnetic interference, physical vibration, etc. The communication link needs to maintain a stable connection state in such an environment to ensure that the signal can be transmitted continuously and reliably. This means that in the prediction results, the signal strength fluctuation should be within an acceptable range, and communication interruption or frequent disconnection should be avoided as much as possible to ensure the continuity and stability of communication. Especially for communication tasks with extremely high reliability requirements such as emergency alarm signal transmission, any communication interruption may cause serious consequences. The communication delay constraint puts forward requirements on the speed of information transmission, that is, the time from the initiation of the communication task to the successful delivery of the information to the target end must be controlled within a certain range. In the cabin environment, many communication tasks have high real-time requirements, such as real-time monitoring video data transmission, real-time control command issuance, etc. Excessive delay will cause information lag, affecting the timeliness of decision-making and the accuracy of operation. Therefore, it is necessary to judge whether the delay in the heterogeneous communication quality prediction results meets the real-time requirements of the corresponding communication task to ensure that the information can be transmitted in time to meet the communication speed requirements of various business scenarios in the cabin. By comparing and comprehensively judging the constraints of the three main aspects of communication effectiveness, reliability and delay one by one, it is determined whether the prediction results of heterogeneous communication quality meet the multi-feature constraints of heterogeneous communication quality as a whole, providing key basis for subsequent link enhancement or task execution decisions.

[0029] Step S600: If the heterogeneous communication quality prediction result does not meet the heterogeneous communication quality multi-feature constraint, the heterogeneous communication link is strengthened based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the heterogeneous communication quality multi-feature constraint to establish a heterogeneous communication strengthened link.

[0030] Specifically, when it is determined that the heterogeneous communication quality prediction result does not meet the heterogeneous communication quality multi-feature constraints, the heterogeneous communication link is strengthened to establish a heterogeneous communication strengthened link. First, a comprehensive communication configuration optimization is performed based on the current heterogeneous communication link, and the performance characteristics, location distribution, and utilization of communication resources of each active device and passive device in the link are comprehensively considered to construct a communication link optimization space containing multiple possible optimization schemes. This space covers many potential optimization directions such as adjusting the device transmission power, optimizing the signal transmission frequency band, improving the device connection method, and replanning the communication route. The first optimized communication link is extracted from the communication link optimization space as a preliminary attempt. Based on this first optimized communication link, the communication evaluation gating component and the communication quality evaluation component are driven by the communication task measurement coefficient again to perform a new round of communication quality prediction on the heterogeneous communication task, thereby obtaining the first link communication quality prediction result. This step aims to evaluate whether the communication link after preliminary optimization can meet the communication quality multi-feature constraints. Then, the communication quality prediction result of the first link is judged to check whether it meets the heterogeneous communication quality multi-feature constraints. If the result meets the constraint conditions, it indicates that the first optimized communication link has good communication performance and can be directly added to the heterogeneous communication enhanced link as part of the final enhanced link. However, if the communication quality prediction result of the first link still does not meet the constraint, it is necessary to continue to search for the optimization space of the communication link based on the communication task measurement coefficient, communication evaluation gating component and communication quality evaluation component according to the multi-feature constraint of heterogeneous communication quality. In this process, the optimization direction is constantly adjusted, different communication link configuration schemes are reselected and evaluated, and the above link extraction, quality prediction and judgment steps are repeated until a communication link that can fully meet the multi-feature constraint of heterogeneous communication quality is found and established as a heterogeneous communication enhanced link. This enhanced link is optimized and screened by comprehensively considering various factors, and has better communication performance. It can effectively improve the quality and reliability of passive and passive fusion communication in the cabin, ensure that heterogeneous communication tasks can be smoothly and efficiently executed in a complex cabin environment, and meet the strict requirements of various services in the cabin for communication.

[0031] Step S700: Execute the heterogeneous communication task based on the heterogeneous communication enhanced link.

[0032] Specifically, once the heterogeneous communication enhanced link is determined, the execution of the heterogeneous communication task is carried out based on it. The heterogeneous communication enhanced link is obtained through optimization and screening of the previous series of steps. It comprehensively considers multiple factors such as communication task measurement coefficient, communication evaluation gating component, communication quality evaluation component and heterogeneous communication quality multi-feature constraints, and has the ability to ensure high-quality communication in the complex environment of the cabin. Based on this link, a more stable and efficient communication connection can be established between active devices and passive devices. For active devices, such as communication base stations and active sensors, they will accurately send communication signals according to the parameters and configurations determined by the enhanced link to ensure that the information can be transmitted to the target device in the best way. Passive devices, such as passive sensors and RFID tags, can receive external energy excitation more effectively with the support of the enhanced link, and accurately modulate and reflect the data collected by themselves back to the active device or other related processing nodes. During the entire communication task execution process, whether it is data collection, device control instruction transmission or status information reporting and other heterogeneous communication tasks, they can achieve fast, accurate and reliable information transmission under the protection of the enhanced link. At the same time, during the execution of the mission, the status of the communication link will be continuously monitored to promptly detect and handle possible abnormal situations such as signal interference and equipment failure, ensuring that the heterogeneous communication mission can be carried out continuously and stably, thereby meeting various business needs in the cabin and ensuring the normal operation of the cabin system and the effective realization of various functions.

[0033] In a possible implementation, step S400 further includes:

[0034] Step S410: Modeling is performed based on the heterogeneous communication link to construct a heterogeneous communication link model. Step S420: Based on the heterogeneous communication link model, the heterogeneous communication task is simulated to obtain heterogeneous communication simulation data. Step S430: The communication task measurement coefficient is input into the communication evaluation gating component to obtain a communication evaluation gating activation coefficient, wherein the communication evaluation gating component includes a communication evaluation gating network. Step S440: The communication quality evaluation component includes a communication effectiveness evaluation unit, a communication reliability evaluation unit and a communication delay evaluation unit. Step S450: Based on the communication evaluation gating activation coefficient, the heterogeneous communication simulation data is evaluated according to the communication effectiveness evaluation unit, the communication reliability evaluation unit and the communication delay evaluation unit to generate the heterogeneous communication quality prediction result.

[0035] Specifically, modeling work is carried out based on heterogeneous communication links. This process requires comprehensive consideration of the various characteristics of active and passive devices in the link, including the signal transmission power, frequency range, modulation mode, etc. of active devices, as well as the reflection characteristics, induction sensitivity and other parameters of passive devices. At the same time, it is also necessary to deeply analyze the connection relationship between devices, whether it is the transmission loss characteristics of wired connections, or the signal propagation path, channel characteristics and interference factors of wireless connections. Combined with the specific environmental conditions in the cabin, such as the impact of electromagnetic environment and spatial layout on signal propagation, a heterogeneous communication link model is constructed using communication theory, signal processing technology and network analysis methods. This model can accurately describe the entire process of signals being sent from active devices, interacting with passive devices through transmission media, and finally reaching the target device, providing a solid theoretical framework for subsequent simulation operations.

[0036] Based on the constructed heterogeneous communication link model, the heterogeneous communication task is simulated. In the simulation environment, the active equipment is driven to generate corresponding communication signals in strict accordance with the parameter settings and process requirements of the actual task, and the signal transmission process in the link model is simulated. This process carefully records the changes in the signal in each link, such as the attenuation of signal strength, the degree of signal distortion, the interference during transmission, and the response time of the device to the signal, etc. It also covers the accuracy and integrity of data transmission. These data together constitute the heterogeneous communication simulation data, which provides a data basis for subsequent quality evaluation.

[0037] The communication task measurement coefficient is input into the communication evaluation gating component. The communication evaluation gating network in this component is composed of many neuron nodes and connection weights. After the communication task measurement coefficient is input, each neuron node compares and calculates based on the received signal strength and the preset threshold. If the signal strength exceeds the threshold, the neuron is activated and the activation signal is transmitted to the next layer of nodes according to the connection weight. Through layer-by-layer transmission and weighted processing, the network dynamically adjusts the internal state according to the characteristics of the coefficient, and finally outputs the communication evaluation gating activation coefficient. This coefficient can intelligently adjust the working mode and sensitivity of subsequent communication quality evaluation components based on key factors such as the importance and urgency of the communication task.

[0038] The entire communication quality evaluation system consists of three key parts: the communication effectiveness evaluation unit, the communication reliability evaluation unit, and the communication delay evaluation unit. The communication effectiveness evaluation unit focuses on evaluating the accuracy and integrity of information transmission during the communication process. It has built multiple communication effectiveness evaluation models based on different principles and algorithms. These models can conduct in-depth analysis of heterogeneous communication simulation data from multiple dimensions such as signal strength, signal-to-noise ratio, and bit error rate. Through precise calculation and data processing, it determines whether the information can be accurately transmitted from the source to the target, and whether there is information loss, tampering, or incorrect parsing during the transmission process, thereby determining the performance level of the communication task in terms of information transmission effectiveness and generating the corresponding communication effectiveness evaluation coefficient.

[0039] The communication reliability evaluation unit mainly evaluates the stability and anti-interference ability of the communication link in a complex cabin environment. The unit contains multiple communication reliability evaluation models based on signal stability theory, anti-interference technology, and equipment failure prediction models. These models can conduct detailed analysis of signal fluctuations, interference signal characteristics, and equipment operation status data in heterogeneous communication simulation data, and comprehensively evaluate whether the communication link can maintain a stable connection state in the face of various interference factors (such as electromagnetic interference, physical vibration, etc.), ensuring that the signal can be transmitted continuously and reliably, and then generate multiple communication reliability evaluation coefficients to fully reflect the reliability characteristics of the communication task.

[0040] The communication delay evaluation unit focuses on the speed characteristics of information transmission. It is equipped with multiple communication delay evaluation models based on time series analysis, queuing theory and other theories. These models can accurately extract key information such as signal sending timestamps, receiving timestamps and processing time of each node in heterogeneous communication simulation data. Through complex calculations and analysis, they can accurately evaluate the time delay from the initiation of the communication task to the successful delivery of the information to the target end, including the delays generated by each link in the transmission process, such as the delay in signal transmission between devices, the time consumption of the device processing the signal, etc., thereby generating a communication delay evaluation coefficient, which intuitively reflects the performance of the communication task in terms of delay. These three units work together and complement each other, and together constitute a comprehensive and complete communication quality evaluation component, which provides a solid foundation and reliable guarantee for the accurate prediction of heterogeneous communication quality.

[0041] Based on the communication evaluation gate activation coefficient, the communication effectiveness evaluation unit, the communication reliability evaluation unit and the communication delay evaluation unit evaluate the heterogeneous communication simulation data. The communication effectiveness evaluation unit selects the adaptation model from multiple models built based on different algorithms and theories according to the activation coefficient, analyzes the key parameters such as signal strength and bit error rate in the simulation data, generates multiple communication effectiveness evaluation coefficients, and obtains the communication effectiveness prediction coefficient by comprehensive calculation. The communication reliability evaluation unit activates multiple models that consider factors such as signal stability, anti-interference ability and equipment failure rate by means of the activation coefficient, deeply analyzes the simulation data, generates multiple communication reliability evaluation coefficients, and obtains the communication reliability prediction coefficient after integration. The communication delay evaluation unit processes the time delay related data in the simulation data using multiple models built based on theories such as time series analysis according to the activation coefficient, generates multiple communication delay evaluation coefficients, and determines the communication delay prediction coefficient after summarizing and optimizing. Finally, the communication effectiveness prediction coefficient, communication reliability prediction coefficient and communication delay prediction coefficient are organically combined to generate the heterogeneous communication quality prediction result, which provides a key basis for the performance evaluation and optimization of the entire heterogeneous communication system.

[0042] In a possible implementation, step S450 further includes:

[0043] Step S451: Based on the communication evaluation gated activation coefficient and the heterogeneous communication simulation data, according to the communication effectiveness evaluation unit, a communication effectiveness prediction coefficient is obtained. Step S452: Based on the communication evaluation gated activation coefficient and the heterogeneous communication simulation data, according to the communication reliability evaluation unit, a communication reliability prediction coefficient is obtained, wherein the communication reliability evaluation unit includes a plurality of communication reliability evaluation models. Step S453: Based on the communication evaluation gated activation coefficient and the heterogeneous communication simulation data, according to the communication delay evaluation unit, a communication delay prediction coefficient is obtained, wherein the communication delay evaluation unit includes a plurality of communication delay evaluation models. Step S454: Output the communication effectiveness prediction coefficient, the communication reliability prediction coefficient and the communication delay prediction coefficient as the heterogeneous communication quality prediction result.

[0044] Specifically, the communication effectiveness evaluation unit works based on the communication evaluation gate activation coefficient and heterogeneous communication simulation data to obtain the communication effectiveness prediction coefficient. The communication evaluation gate activation coefficient, as a key guiding parameter, acts on multiple communication effectiveness evaluation models inside the communication effectiveness evaluation unit. These models are constructed based on different signal processing algorithms and communication theories, covering a variety of technical means such as signal strength analysis, bit error rate detection, and signal modulation and demodulation accuracy evaluation. When the communication evaluation gate activation coefficient is input, it will screen and feature activate multiple internal evaluation models based on the communication task feature information carried by the coefficient, such as the urgency and importance of the task, and the requirements for data accuracy. Only those models that best match the current communication task features will be activated. Subsequently, the heterogeneous communication simulation data is input into these activated communication effectiveness evaluation models. The simulation data contains rich information, such as the actual strength changes of the signal during transmission, the bit errors during data transmission, the frequency characteristics of the signal, and the proportional relationship between the signal and noise. Each activated model will conduct in-depth analysis and processing of these data according to its own algorithm logic. After each activated model completes the analysis of the heterogeneous communication simulation data and outputs the corresponding effectiveness evaluation coefficient, the communication effectiveness evaluation unit will comprehensively process these multiple communication effectiveness evaluation coefficients, and use the weighted average method to assign corresponding weights to each coefficient according to the importance of the effectiveness aspect represented by each coefficient and the emphasis of the communication task on this aspect. Then, each coefficient is multiplied by its corresponding weight and added together, and finally a communication effectiveness prediction coefficient that can comprehensively and accurately reflect the performance of the current heterogeneous communication task in terms of information transmission accuracy and completeness is obtained.

[0045] The communication reliability evaluation unit operates under the drive of the communication evaluation gate activation coefficient and heterogeneous communication simulation data. The multiple communication reliability evaluation models in this unit are constructed based on the principle of signal stability, advanced anti-interference technology and accurate equipment failure prediction methods. The communication evaluation gate activation coefficient selects the model that best matches the current task from many models and activates it according to the specific requirements of the communication task. Then, the heterogeneous communication simulation data is input into these activated models. The model extracts important information such as the fluctuation characteristics of the signal in the complex cabin environment, the degree of influence of various interference sources on the signal, and the equipment operation status parameters from the data. For example, the model based on signal stability evaluates the stability of the signal by calculating the variance or standard deviation of the signal strength to generate a stability evaluation coefficient; the anti-interference model obtains the anti-interference ability evaluation coefficient by analyzing the power ratio or spectrum overlap between the interference signal and the effective signal; the equipment failure prediction model predicts the probability of equipment failure based on the equipment's operating parameters such as temperature, current, voltage, and historical failure data, and converts it into an equipment reliability evaluation coefficient. Finally, the corresponding weights are assigned to each model according to its relative importance in the overall reliability evaluation, such as the signal stability weight is set to 0.4, the anti-interference ability weight is set to 0.4, and the equipment failure impact weight is set to 0.2. The indicators, coefficients, and degree values ​​obtained by each model are multiplied by the corresponding weights and added together. The final weighted sum is used to obtain the communication reliability prediction coefficient, which can comprehensively reflect the reliability of communication tasks in complex environments and provide key support for the prediction results of heterogeneous communication quality.

[0046] The communication delay evaluation unit generates the communication delay prediction coefficient by relying on the communication evaluation gate activation coefficient and heterogeneous communication simulation data. The multiple communication delay evaluation models in the unit are constructed based on professional theories such as time series analysis and queuing theory, which can accurately analyze and evaluate the time delay characteristics in the communication process. The communication evaluation gate activation coefficient activates the corresponding communication delay evaluation model based on factors such as the urgency of the communication task, the amount of data, and the link bandwidth. After the heterogeneous communication simulation data is input into the activated model, the model extracts key time information such as the signal sending timestamp, receiving timestamp, processing time of each intermediate node, and queue waiting time from the data. For example, the model based on time series analysis models and predicts the historical communication delay data, and combines the time information in the current simulation data to calculate the trend evaluation coefficient of the communication delay; the queuing theory model evaluates the queue waiting time of the data at each node according to parameters such as the service rate of each node in the link, the data arrival rate, and the queue length, and generates the queue delay evaluation coefficient. Finally, by optimizing and summarizing these different types of communication delay evaluation coefficients, the communication delay prediction coefficient is determined, which can accurately reflect the speed performance and time delay of the communication task during the information transmission process.

[0047] The communication effectiveness prediction coefficient, communication reliability prediction coefficient and communication delay prediction coefficient are integrated and output by the system to form the heterogeneous communication quality prediction results. The communication effectiveness prediction coefficient accurately reflects the accuracy and completeness of information transmission in the heterogeneous communication link. It comprehensively considers the influence of multiple factors such as the maintenance level of signal strength, the control of bit error rate and the accuracy of signal modulation and demodulation on the quality of information transmission. The communication reliability prediction coefficient comprehensively reflects the stability and anti-interference ability of the communication link in the complex cabin environment. It integrates key elements such as the stability assessment of signal fluctuations, the degree of signal quality assurance under various interference factors, and the potential impact of potential equipment failures on communication reliability. The communication delay prediction coefficient accurately indicates the time delay characteristics experienced by the information from the sender to the receiver, covering the comprehensive performance of the signal transmission delay at each node, the processing waiting time, and the queuing delay caused by factors such as link bandwidth and data volume. By organically combining these three coefficients that quantify the communication quality from different core dimensions, the generated heterogeneous communication quality prediction results can fully and accurately present the expected communication quality status of the heterogeneous communication task under the current specific communication link conditions and task requirements, providing an extremely critical and reliable basis and decision-making reference for subsequent judgment of whether the communication meets the actual application needs, formulating targeted link optimization strategies, and ensuring the efficient and stable operation of the passive fusion communication system in the entire cabin.

[0048] In a possible implementation, step S451 further includes:

[0049] Step S4511: The communication effectiveness evaluation unit includes multiple communication effectiveness evaluation models. Step S4512: Based on the communication evaluation gate activation coefficient, the communication effectiveness evaluation unit is feature activated to obtain multiple activated communication effectiveness evaluation models. Step S4513: The heterogeneous communication simulation data is input into the multiple activated communication effectiveness evaluation models to obtain multiple communication effectiveness evaluation coefficients. Step S4514: The centralized value of the multiple communication effectiveness evaluation coefficients is calculated to generate the communication effectiveness prediction coefficient.

[0050] Specifically, the communication effectiveness evaluation unit has built multiple communication effectiveness evaluation models. These models are based on different communication principles and technologies and evaluate communication effectiveness from multiple dimensions. For example, some models focus on the monitoring and analysis of signal strength. By fitting and judging the change curve of signal strength in heterogeneous communication simulation data, they measure whether the attenuation of the signal during transmission is within an acceptable range; some models focus on the statistics and analysis of bit error rate, accurately calculating the proportion of erroneous code elements in the data transmission process to evaluate the accuracy of information transmission; and some models focus on the accuracy of signal modulation and demodulation, and quantify the errors in the modulation and demodulation process to ensure that the signal can be correctly encoded and decoded.

[0051] The communication evaluation gate activation coefficient plays a key role in screening and activation. Assuming that the communication evaluation gate activation coefficient is a specific value, such as 3, then three communication effectiveness evaluation models will be accurately activated from the numerous models in the communication effectiveness evaluation unit, thereby obtaining three activated communication effectiveness evaluation models. These activated models are determined based on the communication task characteristics and requirements implied by the activation coefficient, and can analyze heterogeneous communication simulation data more targeted. For example, if the current communication task has high requirements for the stability of signal strength, then the probability of activation of models related to signal strength monitoring will increase; if more attention is paid to the accuracy of information transmission, the bit error rate statistical model may be activated first.

[0052] The heterogeneous communication simulation data is input into the above-mentioned multiple activated communication effectiveness evaluation models. These models use their unique algorithms and logics to deeply analyze the simulation data. Taking the signal strength monitoring model as an example, it will extract the signal strength values ​​at different times and locations in the simulation data, compare them with the preset standard signal strength threshold, calculate the degree of deviation between the signal strength and the ideal value, and then obtain a coefficient reflecting the effectiveness of the signal strength; the bit error rate statistical model will compare the data sequence in the simulation data bit by bit, count the ratio of the number of error code elements to the total number of code elements, and generate the bit error rate effectiveness coefficient; the signal modulation and demodulation accuracy model will analyze the characteristic differences between the modulated signal and the original signal in the simulation data, as well as the degree of restoration of the demodulated signal and the original transmitted signal, and obtain the modulation and demodulation effectiveness coefficient, etc. In this way, each activation model will output a corresponding communication effectiveness evaluation coefficient, thereby obtaining multiple communication effectiveness evaluation coefficients.

[0053] Finally, it is necessary to comprehensively process these multiple communication effectiveness evaluation coefficients to generate a communication effectiveness prediction coefficient. Using weighted average, the corresponding weight is assigned according to the importance of the effectiveness aspect represented by each coefficient in the overall communication task, and the coefficient is multiplied by the weight and then summed to obtain the communication effectiveness prediction coefficient. In this way, the communication effectiveness prediction coefficient finally generated can comprehensively and accurately reflect the overall performance of the current heterogeneous communication task in terms of information transmission effectiveness, and provide an important component for the generation of subsequent heterogeneous communication quality prediction results, so as to more accurately evaluate the performance of the communication system and make targeted optimization and adjustments.

[0054] In a possible implementation, step S600 further includes:

[0055] Step S610: Optimize the communication configuration according to the heterogeneous communication link and establish a communication link optimization space. Step S620: Extract the first optimized communication link according to the communication link optimization space. Step S630: Based on the first optimized communication link, drive the communication evaluation gating component and the communication quality evaluation component according to the communication task measurement coefficient, predict the communication quality of the heterogeneous communication task, and obtain the first link communication quality prediction result. Step S640: Determine whether the first link communication quality prediction result meets the heterogeneous communication quality multi-feature constraint. Step S650: If the first link communication quality prediction result meets the heterogeneous communication quality multi-feature constraint, add the first optimized communication link to the heterogeneous communication enhanced link. Step S660: If the first link communication quality prediction result does not meet the heterogeneous communication quality multi-feature constraint, continue to optimize the communication link optimization space according to the heterogeneous communication quality multi-feature constraint based on the communication task measurement coefficient, the communication evaluation gating component and the communication quality evaluation component until the heterogeneous communication enhanced link is obtained.

[0056] Specifically, the heterogeneous communication links are deeply analyzed and their components are comprehensively considered. For active devices, the flexible adjustment range of their transmission power, the multiple choices of signal modulation methods, and the adaptable range of data transmission rate are accurately detected. For passive devices, the focus is on their signal reflection characteristics, the difference in inductive sensitivity to signals in different frequency bands, and the influence of the relative position relationship with active devices in the spatial layout on the signal propagation path. Based on these elements, mathematical modeling and simulation technology are used to construct a communication configuration optimization model. With transmission power, modulation method, transmission rate, passive device layout position, etc. as variables, optimization goals such as maximizing signal strength, minimizing interference, and minimizing transmission delay are set, while combining constraints such as physical limitations of equipment and electromagnetic compatibility. By solving the model, multiple solutions that meet the constraints and approach the optimization goals are generated in the multidimensional variable space. Each solution corresponds to a unique communication link configuration scheme. These schemes together constitute the communication link optimization space, which covers many optimized communication links with potential optimization value, laying a solid foundation for the subsequent screening and strengthening of communication links.

[0057] A comprehensive scan and analysis of the communication link optimization space is conducted. This optimization space covers many potential communication link configurations, each of which is defined by multiple key parameters, such as the transmit power settings of different active devices, the frequency bands selected for signal transmission, the connection topology between devices, and the coding and modulation methods for data transmission. Comprehensive consideration is given to the estimation of the signal transmission capacity of each link configuration under ideal conditions, including the maximum bandwidth that can be achieved theoretically, the expected attenuation of the signal at the preset transmission distance, and the connection stability inferred based on the device characteristics and connection methods. At the same time, combined with historical data or simulation experiment results, a weighted evaluation is conducted on the performance of each link in different interference scenarios, such as the link anti-interference capability score in areas with strong electromagnetic interference, and the link congestion response capability rating during high-traffic data transmission. By using a comprehensive evaluation model constructed by multi-factor weighted summation or based on the hierarchical analysis method, each potential link is quantitatively scored, and finally the link with the highest comprehensive score is selected from many candidate links as the first optimized communication link. Under the current evaluation system, this link is considered to have the most balanced and excellent potential in terms of communication effectiveness, reliability and stability, providing an initial optimal link foundation for subsequent communication quality prediction.

[0058] Relying on the extracted first optimized communication link, the communication evaluation gating component and the communication quality evaluation component are driven to work with the help of the communication task measurement coefficient, so as to predict the communication quality of the heterogeneous communication task and obtain the first link communication quality prediction result. The communication task measurement coefficient reflects the key attributes of the communication task such as urgency, importance and scope, which can guide the neural network structure or logic judgment module in the communication evaluation gating component to dynamically adjust the evaluation process according to these attributes. For example, for urgent and important communication tasks, the weight of the evaluation path related to communication reliability and timeliness will be increased. The communication quality evaluation component conducts an in-depth analysis of the simulated transmission data of the heterogeneous communication task on the first optimized communication link based on the activated and adjusted evaluation mechanism, and conducts a comprehensive evaluation and prediction from multiple dimensions, including communication effectiveness (such as whether the signal strength is sufficient to ensure accurate reception of information, whether the bit error rate is within an acceptable range, etc.), communication reliability (such as the stability of the signal under complex environmental interference, the potential impact of equipment failure on communication, etc.) and communication delay (such as whether the transmission time of the signal from the transmitter to the receiver meets the task requirements), and finally obtains the prediction result of the communication quality of the first link, which will intuitively reflect the quality level of this optimized link in the current communication task scenario.

[0059] The prediction results of the first link communication quality are carefully compared and judged with the multi-feature constraints of heterogeneous communication quality. These multi-feature constraints cover the core requirements of communication quality. For example, in terms of communication effectiveness, the lowest signal strength threshold and the highest bit error rate upper limit are set to ensure that information can be transmitted accurately; in terms of communication reliability, the maximum allowed number or probability of signal interruption within a certain time period and the tolerable range of equipment failure rate are stipulated to ensure the stability of the communication link; in terms of communication delay, the maximum acceptable transmission delay time of different types of communication tasks is clarified to meet real-time requirements. By comparing each indicator in the prediction results of the first link communication quality with these constraints one by one, it is determined whether the link can meet the strict quality requirements of heterogeneous communication tasks as a whole.

[0060] If the prediction result of the communication quality of the first link is determined to meet the multi-feature constraints of heterogeneous communication quality, this indicates that the currently selected first optimized communication link has met the expected standards and requirements in all key dimensions of communication quality, has good performance and adaptability, and can effectively support the smooth implementation and successful completion of heterogeneous communication tasks. At this time, this first optimized communication link is formally included in the heterogeneous communication enhanced link set, making it an important part of building the final heterogeneous communication link system, providing a solid and reliable link guarantee for the efficient execution of subsequent heterogeneous communication tasks.

[0061] On the contrary, if it is found that the prediction result of the first link communication quality fails to meet the multi-feature constraints of heterogeneous communication quality, it means that the current first optimized communication link still has deficiencies and defects in some aspects and cannot fully adapt to the strict requirements of heterogeneous communication tasks. In this case, based on the task-oriented information provided by the communication task measurement coefficient, with the help of the flexible control ability of the communication evaluation gating component and the powerful analysis and evaluation function of the communication quality evaluation component, according to the standards and goals set by the multi-feature constraints of heterogeneous communication quality, a new round of optimization exploration work is carried out in the communication link optimization space again. In this process, the optimization strategy and search direction will be adjusted in a targeted manner according to the problems and deficiencies exposed in the prediction results of the first link communication quality. For example, if the communication effectiveness does not meet the standard, it may focus on in-depth exploration in aspects such as signal transmission power adjustment, signal transmission frequency band optimization, and signal processing algorithm improvement; if there are problems with communication reliability, it will focus on optimizing equipment redundant configuration, upgrading anti-interference technology, and improving equipment fault detection and repair mechanisms; for the shortcomings in communication delay, it will consider optimizing link routing selection, improving equipment data processing speed, and improving data transmission protocols. By continuously repeating the above-mentioned process of communication quality prediction, judgment and optimization, continuous iterative optimization is performed until one or more heterogeneous communication enhanced links that can fully meet the multi-feature constraints of heterogeneous communication quality are successfully found, thereby ensuring that the heterogeneous communication system can always complete various heterogeneous communication tasks with efficient, stable and reliable communication link configuration in the complex and changeable cabin environment, and achieve high-quality communication services and information transmission goals.

[0062] In a possible implementation, step S300 further includes:

[0063] Step S310: The communication task evaluation multivariate indicators include communication urgency, communication criticality and communication impact breadth. Step S320: Evaluate the heterogeneous communication task according to the communication task evaluation multivariate indicators to obtain the communication task urgency coefficient, communication task criticality coefficient and communication task breadth coefficient. Step S330: Perform importance evaluation according to the communication task evaluation multivariate indicators to obtain multiple indicator importance coefficients. Step S340: Assign weights to the communication task evaluation multivariate indicators according to the multiple indicator importance coefficients to construct a communication task evaluation weighted network. Step S350: Input the communication task urgency coefficient, the communication task urgency coefficient and the communication task breadth coefficient into the communication task evaluation weighted network to generate the communication task measurement coefficient.

[0064] Specifically, it is clear that the multiple indicators for evaluating communication tasks include communication urgency, communication criticality, and communication reach. Communication urgency is used to measure the urgency of heterogeneous communication tasks in the time dimension, such as whether the task has strict time limits and whether information transmission needs to be completed in a very short time. For example, if some real-time monitoring data transmission tasks cannot be delivered in time, it will lead to misjudgment of the cabin conditions or delay important decisions. Communication criticality focuses on the importance of the task to the overall system operation. If the communication task involves the transmission of control instructions for critical systems, such as parameter adjustment instructions for life support systems, then its criticality is extremely high. Once there is a problem with communication, it may endanger the safety and normal operation of the entire cabin. The reach of communication considers the scope of the communication task. For example, some broadcast information notification tasks may involve many devices and personnel in the cabin, and their reach is relatively large.

[0065] According to the above-mentioned communication task evaluation multivariate indicators, the heterogeneous communication tasks are evaluated, and then the communication task urgency coefficient, communication task criticality coefficient and communication task breadth coefficient are obtained. For the evaluation of communication urgency, the time threshold can be set and compared with the actual time required for the task. If the task requires the transmission to be completed within 10 seconds, and the actual time it may take is 8 seconds, then the communication task urgency coefficient can be set to 0.8. The closer the coefficient is to 1, the more urgent the task is. The determination of the communication task criticality coefficient can be assigned according to the importance level of the task-associated system. For example, the criticality coefficient of the task associated with the first-level important system is 0.9, and the criticality coefficient of the task associated with the second-level important system is 0.7. The communication task breadth coefficient can be calculated based on the proportion of the number of equipment or personnel involved in the task to the total number of equipment or personnel in the cabin. If 50% of the equipment is involved, the communication task breadth coefficient may be 0.5.

[0066] The importance of multiple indicators for communication task evaluation is evaluated to obtain the importance coefficients of multiple indicators. This process can be done by using the analytic hierarchy process, which constructs a judgment matrix and solves it. The importance coefficient of the indicator of communication criticality is 0.5, the importance coefficient of the indicator of communication urgency is 0.3, and the importance coefficient of the indicator of communication coverage is 0.2.

[0067] According to the importance coefficients of multiple indicators, weights are assigned to the multivariate indicators of communication task evaluation, thereby constructing a weighted network for communication task evaluation. Taking the indicator importance coefficients just obtained as an example, the weight of communication criticality is 0.5, the weight of communication urgency is 0.3, and the weight of communication reach is 0.2. When constructing a weighted network for communication task evaluation, these weights are assigned to the corresponding indicator nodes. The nodes in the network represent communication urgency, communication criticality, and communication reach, respectively. The connection weights between nodes are the assigned weight values, so that the network can perform comprehensive calculations on the input coefficients based on the weights.

[0068] Finally, the communication task urgency coefficient, communication task criticality coefficient, and communication task breadth coefficient obtained previously are input into the communication task evaluation weighted network. The network will perform weighted summation operations on these coefficients according to the weights of each indicator. For example, if the communication task urgency coefficient is 0.6, the communication task criticality coefficient is 0.8, and the communication task breadth coefficient is 0.4, then the communication task measurement coefficient = 0.6×0.3+0.8×0.5+0.4×0.2=0.66. The communication task measurement coefficient generated in this way can comprehensively reflect the characteristics of heterogeneous communication tasks in terms of urgency, criticality, and breadth, and provide important decision-making basis for subsequent communication link optimization, task scheduling, etc.

[0069] In a possible implementation, step S200 further includes:

[0070] Step S210: The communication request multi-factors include a communication request source, a communication request target, a communication request type, and a communication request task. Step S220: Based on the communication request multi-factors, the communication request of the active device layer and the passive device layer is monitored to obtain multiple communication requests. Step S230: Based on the active device layer and the passive device layer, the multiple communication requests are identified as cross-layer communication requests to generate the heterogeneous communication task. Step S240: Based on the active device layer and the passive device layer, the communication link feature of the heterogeneous communication task is identified to construct the heterogeneous communication link.

[0071] Specifically, the multiple factors of communication requests include the communication request source, communication request target, communication request type and communication request task. The communication request source refers to the specific device or node that initiates the communication request, which can be a sensor or controller in the active device layer, or a specific reflective or inductive element in the passive device layer. The communication request target is the destination to which the communication information is expected to be delivered. It is also distributed in the active device layer and the passive device layer, such as another sensor, actuator or data processing center. Communication request types include data transmission, command issuance, status feedback and other forms. Different types of requests differ in terms of data volume, transmission frequency and real-time requirements. The communication request task further refines the purpose and content of the request, such as transmitting environmental monitoring data, sending device control instructions or feedback on system operation status.

[0072] Based on these communication request multi-factors, comprehensive communication request monitoring is carried out on the active device layer and the passive device layer. Through the monitoring modules or software programs deployed in the devices of each layer, the signals or events that meet the characteristics of the communication request multi-factors are captured in real time. For example, the monitoring module in the active device layer will detect a data transmission request issued by a sensor, which contains information such as the active device identification (communication request source), the target data processing center identification (communication request target), the data transmission type (communication request type) and the environmental monitoring data collection task (communication request task); in the passive device layer, the signal changes caused by the passive sensing element due to environmental changes are monitored, which are also associated with specific communication request sources, targets, types and task information. These multiple communication requests from different layers are summarized and collected to form a series of communication request sequences to be processed.

[0073] Based on the architectural characteristics of the active device layer and the passive device layer, the cross-layer communication request identification work is carried out for the collected multiple communication requests. Since the communication requests may be triggered and transmitted between the active device and the passive device, it is necessary to identify the related requests that cross different layers. For example, the state change of a certain sensing element in the passive device layer triggers the data collection request of the active device layer, and the data collected by the active device layer needs to be transmitted to the signal processing node of another passive device layer for preliminary processing before being fed back to the control center of the active device layer. By analyzing the cross-layer relationship between the source and target of the communication request, the correlation of the request type, and the coherence of the task, these interrelated communication requests are integrated to generate a complete heterogeneous communication task. This heterogeneous communication task clarifies a series of communication processes and task requirements from the initial communication request source to the final communication request target, covering the collaborative interaction process of active and passive devices.

[0074] Still based on the structure and characteristics of the active device layer and the passive device layer, the generated heterogeneous communication task is identified for communication link characteristics, and then a heterogeneous communication link is constructed. The location distribution of each device node involved in the task in the active device layer and the passive device layer, the connection mode between the devices (wired connection, wireless connection and its specific communication protocol and interface), the signal transmission path (including the reflection, refraction or induction transmission path through the passive device) and possible signal attenuation, interference and other influencing factors are analyzed. According to these identified communication link characteristics, appropriate active devices, passive devices and the connection combination between them are selected, and the specific route and transfer node of signal transmission are determined, so as to build a heterogeneous communication link that can meet the needs of heterogeneous communication tasks. This link must not only ensure that the communication request can be accurately and efficiently transmitted between the active device layer and the passive device layer, but also take into account the stability, reliability and adaptability of the link to different communication request types and tasks, laying the foundation for subsequent communication quality evaluation and optimization.

[0075] Embodiment 2 is based on the same inventive concept as the passive fusion communication method in the cabin in the above embodiment. Figure 2 As shown, the present application provides a passive fusion communication platform in the cabin, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. The platform includes:

[0076] The device layer construction module 10 is used to collect the passive and passive device information of the target cabin, and construct the active device layer and the passive device layer, wherein the active device layer includes multiple active devices, and the passive device layer includes multiple passive devices. The heterogeneous communication task generation module 20 is used to monitor the active and passive fusion communication requests of the active device layer and the passive device layer according to the communication request multi-factor, and generate heterogeneous communication tasks and heterogeneous communication links. The communication task measurement coefficient determination module 30 is used to evaluate the heterogeneous communication task according to the multi-element indicators of communication task evaluation, and determine the communication task measurement coefficient. The prediction result acquisition module 40, based on the heterogeneous communication link and the communication task measurement coefficient, drives the communication evaluation gating component and the communication quality evaluation component to predict the communication quality of the heterogeneous communication task and obtain the heterogeneous communication quality prediction result. A multi-feature constraint judgment module 50, the multi-feature constraint judgment module 50 is used to judge whether the heterogeneous communication quality prediction result meets the heterogeneous communication quality multi-feature constraint. A heterogeneous communication enhanced link establishment module 60, the heterogeneous communication enhanced link establishment module 60 is used to strengthen the heterogeneous communication link based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the heterogeneous communication quality multi-feature constraint if the heterogeneous communication quality prediction result does not meet the heterogeneous communication quality multi-feature constraint, and establish a heterogeneous communication enhanced link. A heterogeneous communication task execution module 70, the heterogeneous communication task execution module 70 executes the heterogeneous communication task based on the heterogeneous communication enhanced link.

[0077] Further, the prediction result acquisition module 40 also includes: a heterogeneous communication link model construction unit, the heterogeneous communication link model construction unit performs modeling based on the heterogeneous communication link to construct a heterogeneous communication link model. A heterogeneous communication simulation data acquisition unit, the heterogeneous communication simulation data acquisition unit simulates the execution of the heterogeneous communication task based on the heterogeneous communication link model to obtain heterogeneous communication simulation data. A communication evaluation gated activation coefficient acquisition unit, the communication evaluation gated activation coefficient acquisition unit is used to input the communication task measurement coefficient into the communication evaluation gated component to obtain the communication evaluation gated activation coefficient, wherein the communication evaluation gated component includes a communication evaluation gated network. The communication quality evaluation component includes a communication effectiveness evaluation unit, a communication reliability evaluation unit and a communication delay evaluation unit. A heterogeneous communication quality prediction result generation unit, the heterogeneous communication quality prediction result generation unit evaluates the heterogeneous communication simulation data based on the communication evaluation gated activation coefficient according to the communication effectiveness evaluation unit, the communication reliability evaluation unit and the communication delay evaluation unit to generate the heterogeneous communication quality prediction result.

[0078] Further, the heterogeneous communication quality prediction result generating unit also includes: a communication validity prediction coefficient acquisition unit, the communication validity prediction coefficient acquisition unit obtains the communication validity prediction coefficient based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data according to the communication validity evaluation unit. A communication reliability prediction coefficient acquisition unit, the communication reliability prediction coefficient acquisition unit obtains the communication reliability prediction coefficient based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data according to the communication reliability evaluation unit, wherein the communication reliability evaluation unit includes multiple communication reliability evaluation models. A communication delay prediction coefficient acquisition unit, the communication delay prediction coefficient acquisition unit obtains the communication delay prediction coefficient based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data according to the communication delay evaluation unit, wherein the communication delay evaluation unit includes multiple communication delay evaluation models. A quality prediction result output unit, the quality prediction result output unit is used to output the communication validity prediction coefficient, the communication reliability prediction coefficient and the communication delay prediction coefficient as the heterogeneous communication quality prediction result.

[0079] Furthermore, the communication effectiveness prediction coefficient acquisition unit also includes: the communication effectiveness evaluation unit includes multiple communication effectiveness evaluation models. A communication effectiveness evaluation model activation unit, the communication effectiveness evaluation model activation unit performs feature activation on the communication effectiveness evaluation unit based on the communication evaluation gated activation coefficient to obtain multiple activated communication effectiveness evaluation models. A communication effectiveness evaluation coefficient acquisition unit, the communication effectiveness evaluation coefficient acquisition unit is used to input the heterogeneous communication simulation data into the multiple activated communication effectiveness evaluation models to obtain multiple communication effectiveness evaluation coefficients. A validity prediction coefficient generation unit, the validity prediction coefficient generation unit is used to calculate the centralized value of the multiple communication effectiveness evaluation coefficients to generate the communication effectiveness prediction coefficient.

[0080] Furthermore, the heterogeneous communication enhanced link establishment module 60 also includes: a communication link optimization space establishment unit, the communication link optimization space establishment unit is used to optimize the communication configuration according to the heterogeneous communication link and establish a communication link optimization space. A first optimized communication link extraction unit, the first optimized communication link extraction unit is used to extract a first optimized communication link according to the communication link optimization space. A first link communication quality prediction result acquisition unit, the first link communication quality prediction result acquisition unit is based on the first optimized communication link, according to the communication task measurement coefficient, drives the communication evaluation gating component and the communication quality evaluation component, performs communication quality prediction on the heterogeneous communication task, and obtains the first link communication quality prediction result. A quality prediction result judgment unit, the quality prediction result judgment unit is used to judge whether the first link communication quality prediction result meets the heterogeneous communication quality multi-feature constraint. A first optimized communication link adding unit, the first optimized communication link adding unit is used to add the first optimized communication link to the heterogeneous communication enhanced link if the first link communication quality prediction result meets the heterogeneous communication quality multi-feature constraint. A communication link optimization space optimization unit, wherein the communication link optimization space optimization unit is used to continue optimizing the communication link optimization space according to the heterogeneous communication quality multi-feature constraints based on the communication task measurement coefficient, the communication evaluation gating component and the communication quality evaluation component if the communication quality prediction result of the first link does not meet the heterogeneous communication quality multi-feature constraints, until the heterogeneous communication enhanced link is obtained.

[0081] Further, the communication task measurement coefficient determination module 30 also includes: the communication task evaluation multivariate indicators include communication urgency, communication criticality and communication impact breadth. A heterogeneous communication task evaluation unit, the heterogeneous communication task evaluation unit is used to evaluate the heterogeneous communication task according to the communication task evaluation multivariate indicators, and obtain the communication task urgency coefficient, communication task criticality coefficient and communication task breadth coefficient. An indicator importance coefficient acquisition unit, the indicator importance coefficient acquisition unit is used to evaluate the importance according to the communication task evaluation multivariate indicators, and obtain multiple indicator importance coefficients. A weighted network construction unit, the weighted network construction unit is used to assign weights to the communication task evaluation multivariate indicators according to the multiple indicator importance coefficients, and construct a communication task evaluation weighted network. A communication task measurement coefficient generation unit, the communication task measurement coefficient generation unit is used to input the communication task urgency coefficient, the communication task urgency coefficient and the communication task breadth coefficient into the communication task evaluation weighted network to generate the communication task measurement coefficient.

[0082] Further, the heterogeneous communication task generation module 20 also includes: the communication request multi-factors include a communication request source, a communication request target, a communication request type and a communication request task. A communication request acquisition unit, wherein the communication request acquisition unit monitors the communication requests of the active device layer and the passive device layer based on the communication request multi-factors to obtain multiple communication requests. A heterogeneous communication task generation unit, wherein the heterogeneous communication task generation unit identifies cross-layer communication requests for the multiple communication requests based on the active device layer and the passive device layer to generate the heterogeneous communication task. A heterogeneous communication link construction unit, wherein the heterogeneous communication link construction unit identifies communication link characteristics for the heterogeneous communication task based on the active device layer and the passive device layer to construct the heterogeneous communication link.

[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0084] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A passive fusion communication method in a cabin, characterized in that: The method comprises: Collect information about active and passive devices of the target cabin, and construct an active device layer and a passive device layer, wherein the active device layer includes a plurality of active devices, and the passive device layer includes a plurality of passive devices; According to the communication request multi-factor, the active device layer and the passive device layer are monitored for passive fusion communication requests, and heterogeneous communication tasks and heterogeneous communication links are generated; Evaluate the heterogeneous communication task according to the communication task evaluation multivariate index to determine the communication task measurement coefficient; Based on the heterogeneous communication link, according to the communication task measurement coefficient, driving the communication evaluation gating component and the communication quality evaluation component to perform communication quality prediction on the heterogeneous communication task to obtain a heterogeneous communication quality prediction result; Determining whether the heterogeneous communication quality prediction result satisfies the heterogeneous communication quality multi-feature constraint; If the heterogeneous communication quality prediction result does not satisfy the heterogeneous communication quality multi-feature constraint, the heterogeneous communication link is strengthened based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the heterogeneous communication quality multi-feature constraint, and a heterogeneous communication strengthening link is established; Execute the heterogeneous communication task based on the heterogeneous communication enhanced link; Based on the heterogeneous communication link, according to the communication task measurement coefficient, driving the communication evaluation gating component and the communication quality evaluation component, performing communication quality prediction on the heterogeneous communication task, and obtaining the heterogeneous communication quality prediction result, including: Modeling is performed based on the heterogeneous communication link to construct a heterogeneous communication link model; Based on the heterogeneous communication link model, simulating and executing the heterogeneous communication task to obtain heterogeneous communication simulation data; Inputting the communication task measurement coefficient into the communication evaluation gating component to obtain a communication evaluation gating activation coefficient, wherein the communication evaluation gating component includes a communication evaluation gating network; The communication quality evaluation component includes a communication effectiveness evaluation unit, a communication reliability evaluation unit and a communication delay evaluation unit; Based on the communication evaluation gate activation coefficient, the heterogeneous communication simulation data is evaluated according to the communication effectiveness evaluation unit, the communication reliability evaluation unit and the communication delay evaluation unit to generate the heterogeneous communication quality prediction result.

2. The method for in-cabin passive fusion communication according to claim 1, characterized in that: Based on the communication evaluation gate activation coefficient, the heterogeneous communication simulation data is evaluated according to the communication effectiveness evaluation unit, the communication reliability evaluation unit and the communication delay evaluation unit to generate the heterogeneous communication quality prediction result, including: Based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data, obtaining a communication effectiveness prediction coefficient according to the communication effectiveness evaluation unit; Based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data, a communication reliability prediction coefficient is obtained according to the communication reliability evaluation unit, wherein the communication reliability evaluation unit includes a plurality of communication reliability evaluation models; Based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data, a communication delay prediction coefficient is obtained according to the communication delay evaluation unit, wherein the communication delay evaluation unit includes a plurality of communication delay evaluation models; The communication effectiveness prediction coefficient, the communication reliability prediction coefficient and the communication delay prediction coefficient are output as the heterogeneous communication quality prediction result.

3. The method for in-cabin passive fusion communication as claimed in claim 2, characterized in that: Based on the communication evaluation gate activation coefficient and the heterogeneous communication simulation data, obtaining a communication effectiveness prediction coefficient according to the communication effectiveness evaluation unit includes: The communication effectiveness evaluation unit includes a plurality of communication effectiveness evaluation models; Performing feature activation on the communication effectiveness evaluation unit based on the communication evaluation gated activation coefficient to obtain a plurality of activated communication effectiveness evaluation models; Inputting the heterogeneous communication simulation data into the multiple activated communication effectiveness evaluation models to obtain multiple communication effectiveness evaluation coefficients; The centralized value of the plurality of communication effectiveness evaluation coefficients is calculated to generate the communication effectiveness prediction coefficient.

4. The method for in-cabin passive fusion communication as claimed in claim 1, characterized in that: If the heterogeneous communication quality prediction result does not satisfy the heterogeneous communication quality multi-feature constraint, the heterogeneous communication link is strengthened based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the heterogeneous communication quality multi-feature constraint, and a heterogeneous communication strengthening link is established, including: Optimize the communication configuration according to the heterogeneous communication link and establish a communication link optimization space; Extracting a first optimized communication link according to the communication link optimization space; Based on the first optimized communication link, according to the communication task measurement coefficient, driving the communication evaluation gating component and the communication quality evaluation component, performing communication quality prediction on the heterogeneous communication task, and obtaining a first link communication quality prediction result; Determining whether the first link communication quality prediction result meets the heterogeneous communication quality multi-feature constraint; If the communication quality prediction result of the first link meets the multi-feature constraint of the heterogeneous communication quality, adding the first optimized communication link to the heterogeneous communication enhanced link; If the communication quality prediction result of the first link does not meet the multi-feature constraint of the heterogeneous communication quality, based on the communication task measurement coefficient, the communication evaluation gating component and the communication quality evaluation component, continue to optimize the communication link optimization space according to the multi-feature constraint of the heterogeneous communication quality until the heterogeneous communication enhanced link is obtained.

5. The method for in-cabin passive fusion communication as claimed in claim 1, characterized in that: The heterogeneous communication task is evaluated according to the communication task evaluation multivariate index to determine the communication task measurement coefficient, including: The communication task evaluation multi-element indexes include communication urgency, communication criticality and communication coverage; Evaluate the heterogeneous communication task according to the communication task evaluation multivariate index to obtain the communication task urgency coefficient, the communication task key coefficient and the communication task breadth coefficient; Performing importance evaluation according to the communication task evaluation multivariate indicators to obtain importance coefficients of multiple indicators; According to the plurality of index importance coefficients, weights are assigned to the communication task evaluation multivariate indexes to construct a communication task evaluation weighted network; The communication task urgency coefficient, the communication task urgency coefficient and the communication task breadth coefficient are input into the communication task evaluation weighted network to generate the communication task measurement coefficient.

6. The method for in-cabin passive fusion communication as claimed in claim 1, characterized in that: According to the communication request multi-factor, the active device layer and the passive device layer are monitored for passive fusion communication requests, and heterogeneous communication tasks and heterogeneous communication links are generated, including: The communication request multi-factors include a communication request source, a communication request target, a communication request type and a communication request task; Monitoring the communication requests of the active device layer and the passive device layer based on the communication request multiple factors to obtain multiple communication requests; Based on the active device layer and the passive device layer, performing cross-layer communication request identification on the multiple communication requests to generate the heterogeneous communication task; Based on the active device layer and the passive device layer, communication link characteristics of the heterogeneous communication task are identified to construct the heterogeneous communication link.

7. A passive fusion communication platform in the cabin, characterized in that: The platform is used to execute a method for in-cabin passive fusion communication according to any one of claims 1 to 6, and the platform comprises: A device layer construction module, wherein the device layer construction module is used to collect information about active and passive devices of the target cabin and construct an active device layer and a passive device layer, wherein the active device layer includes a plurality of active devices and the passive device layer includes a plurality of passive devices; A heterogeneous communication task generation module, wherein the heterogeneous communication task generation module is used to monitor the active device layer and the passive device layer for passive or passive fusion communication requests according to multiple factors of communication requests, and generate heterogeneous communication tasks and heterogeneous communication links; A communication task measurement coefficient determination module, wherein the communication task measurement coefficient determination module is used to evaluate the heterogeneous communication task according to the communication task evaluation multivariate index to determine the communication task measurement coefficient; A prediction result acquisition module, which drives a communication evaluation gating component and a communication quality evaluation component based on the heterogeneous communication link and the communication task measurement coefficient, performs communication quality prediction on the heterogeneous communication task, and obtains a heterogeneous communication quality prediction result; A multi-feature constraint judgment module, the multi-feature constraint judgment module is used to judge whether the heterogeneous communication quality prediction result satisfies the heterogeneous communication quality multi-feature constraint; A heterogeneous communication enhanced link establishment module, wherein the heterogeneous communication enhanced link establishment module is used to enhance the heterogeneous communication link based on the communication task measurement coefficient, the communication evaluation gating component, the communication quality evaluation component and the heterogeneous communication quality multi-feature constraint if the heterogeneous communication quality prediction result does not meet the heterogeneous communication quality multi-feature constraint, and establish a heterogeneous communication enhanced link; A heterogeneous communication task execution module, wherein the heterogeneous communication task execution module executes the heterogeneous communication task based on the heterogeneous communication enhanced link; The prediction result acquisition module also includes: a heterogeneous communication link model construction unit, which performs modeling based on the heterogeneous communication link to construct a heterogeneous communication link model; a heterogeneous communication simulation data acquisition unit, which simulates the execution of the heterogeneous communication task based on the heterogeneous communication link model to obtain heterogeneous communication simulation data; a communication evaluation gated activation coefficient acquisition unit, which is used to input the communication task measurement coefficient into the communication evaluation gated component to obtain the communication evaluation gated activation coefficient, wherein the communication evaluation gated component includes a communication evaluation gated network; the communication quality evaluation component includes a communication effectiveness evaluation unit, a communication reliability evaluation unit and a communication delay evaluation unit; a heterogeneous communication quality prediction result generation unit, which evaluates the heterogeneous communication simulation data based on the communication evaluation gated activation coefficient according to the communication effectiveness evaluation unit, the communication reliability evaluation unit and the communication delay evaluation unit to generate the heterogeneous communication quality prediction result.

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