Intelligent control method and system for network transmission terminal

Abstract

The embodiment of the invention provides a network transmission terminal intelligent control method and system, and relates to the technical field of network transmission terminal intelligent control, and the method comprises the steps: obtaining terminal operation data, external environment data and user flow data; performing correlation analysis on the terminal operation data, the external environment data and the user flow data to obtain a correlation analysis result to identify an evaluation deviation of a channel evaluation model; generating calibration parameters for calibrating the channel evaluation model according to the correlation analysis result; applying the calibration parameter to the channel evaluation model to correct the evaluation deviation of the channel evaluation model to obtain a channel evaluation result; and adjusting the transmission power according to the channel evaluation result. According to the invention, the stability and reliability of network transmission can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network transmission terminal intelligent control, and particularly relates to a network transmission terminal intelligent control method and system. BACKGROUND

[0002] In the related art, in a modern wireless communication network, network transmission terminals play a crucial role in complex wireless environments, responsible for efficient and stable data transmission. In order to cope with the inherent uncertainty of wireless channels, such as signal attenuation, multipath effects and various interferences, these terminals usually integrate advanced intelligent control methods. The core of these methods is to be able to assess the channel quality in real time, and dynamically adjust the transmission parameters according to the assessment results, among which the adjustment of transmission power is the key link to ensure the reliability of the communication link. However, in practical applications, especially in scenarios where the environment and user behavior patterns are constantly evolving, these intelligent control methods may face severe challenges. For example, when the urban environment of the network transmission terminal deployment area gradually changes, such as the appearance of new building groups, and the user traffic pattern changes significantly, the calculation model used by the terminal to assess the channel quality begins to show its limitations. The model was mainly trained and optimized based on open or only a small number of shielding environment features at the beginning of the design. In the face of complex multipath propagation and deep fading phenomena brought by new building groups, the accuracy of the model's assessment of the actual channel quality begins to decline. This decline in assessment accuracy leads to a deviation in the information fed back to the transmission power adjustment strategy, which can no longer accurately reflect the current wireless environment conditions. SUMMARY

[0003] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application proposes a network transmission terminal intelligent control method and system, aiming to improve the stability and reliability of network transmission.

[0004] In a first aspect, an embodiment of the present application provides a network transmission terminal intelligent control method, comprising: obtaining terminal running data, external environment data and user traffic data; performing correlation analysis on the terminal running data, the external environment data and the user traffic data to obtain a correlation analysis result to identify an evaluation deviation of a channel evaluation model; generating a calibration parameter for calibrating the channel evaluation model according to the correlation analysis result; applying the calibration parameter to the channel evaluation model to correct the evaluation deviation of the channel evaluation model to obtain a channel evaluation result; adjusting the transmission power according to the channel evaluation result.

[0005] According to some embodiments of the present application, the correlation analysis on the terminal running data, the external environment data, and the user traffic data obtains a correlation analysis result to identify evaluation bias of a channel evaluation model, including: For the high real-time data stream identified as the industrial Internet of Things, preset frequency monitoring logic is started and the error code rate and the packet loss rate are monitored within a first preset time window; A micro-interruption event alarm is generated according to the error code rate and the packet loss rate; The geographic position of the terminal and a three-dimensional model of a high-reflective building in geographic information system data are obtained; The direction of arrival of the active reflective signal is deduced reversely according to the geographic position and the three-dimensional model; It is judged whether the direction of arrival of the reflective signal is consistent with a theoretical path from the terminal to the preset high-reflective building and then reflected to the base station; It is judged whether the reflective signal is synchronized in time with the micro-interruption event alarm; When the direction of arrival of the reflective signal is consistent with the theoretical path and the reflective signal is synchronized in time with the micro-interruption event alarm, a high-risk mirror reflection path is obtained; According to the high-risk mirror reflection path, the terminal running data, the external environment data, and the user traffic data, a correlation analysis result is obtained to identify evaluation bias of a channel evaluation model.

[0006] According to some embodiments of the present application, the calibration parameters for calibrating the channel evaluation model are generated according to the correlation analysis result, including: The calibration parameters of the high-risk mirror reflection path are obtained; According to the calibration parameters and the correlation analysis result, the evaluation of the signal component of the high-risk mirror reflection path on the contribution of the overall channel quality is reduced, and the calibration parameters for calibrating the channel evaluation model are generated.

[0007] According to some embodiments of the present application, the high-risk mirror reflection path is obtained by the following steps: The spatial direction of arrival and the signal polarization state of the received signal are obtained and the potential mirror reflection signal is marked; The direction of arrival of the potential mirror reflection signal is compared with the geometric reflection path of the high-reflective structure in the geographic information system data, and the high-risk mirror reflection path is marked.

[0008] According to some embodiments of the present application, according to the calibration parameters and the correlation analysis result, the evaluation of the signal component of the high-risk mirror reflection path on the contribution of the overall channel quality is reduced, and the calibration parameters for calibrating the channel evaluation model are generated, including: evaluate the instantaneous interruption characteristics of each of the high-risk specular reflection paths; identify the types of industrial internet of things services transmitted through the high-risk specular reflection paths, and assign service priorities according to the requirements of the industrial internet of things services for latency and reliability; generate independent path reliability reduction factors according to the instantaneous interruption characteristics of each of the high-risk specular reflection paths and the service priorities; reduce the evaluation of the contribution of signal components of the high-risk specular reflection paths to the overall channel quality according to the independent path reliability reduction factors, the calibration parameters, and the correlation analysis results, and generate calibration parameters for calibrating the channel evaluation model.

[0009] According to some embodiments of the present application, the evaluation of the instantaneous interruption characteristics of each of the high-risk specular reflection paths comprises: continuously monitor the received signal strength, signal phase, and signal polarization state of the high-risk specular reflection paths; real-time acquisition of environmental parameters of the area where the terminal is located; correlation analysis of the received signal strength, signal phase, signal polarization state, and environmental parameters, to identify the nonlinear coupling relationship between the changes in the environmental parameters and the received signal strength, signal phase, and signal polarization state; According to the nonlinear coupling relationship, dynamically adjust the detection threshold and evaluation weight for evaluating the instantaneous interruption characteristics of each of the high-risk specular reflection paths to obtain the adjusted detection threshold and evaluation weight; Based on the adjusted detection threshold and evaluation weight, evaluate the instantaneous interruption characteristics of each of the high-risk specular reflection paths.

[0010] According to some embodiments of the present application, the identification of the types of industrial internet of things services transmitted through the high-risk specular reflection paths, and the assignment of service priorities according to the requirements of the industrial internet of things services for latency and reliability, comprises: identify multiple industrial internet of things services transmitted through the high-risk specular reflection paths and analyze the dependency relationship between the multiple industrial internet of things services; analyze the competitive relationship between the multiple industrial internet of things services; According to the dependency relationship, the priority of a service that is a prerequisite for dependency is raised to be higher than or equal to the priority of a service that depends on it, to obtain the raised priority; According to the competitive relationship, perform resource allocation coordination to obtain the coordinated resource allocation result; According to the raised priority and the coordinated resource allocation result, assign service priorities.

[0011] According to some embodiments of the present application, the analyzing the dependency relationship between the plurality of industrial Internet of Things services comprises: continuously monitoring data flow direction and execution status of control instructions of the industrial Internet of Things services; real-time obtaining device state information, production process configuration information and historical service dependency graph in the industrial Internet of Things system; correlation analysis of the data flow direction, the execution status of the control instructions, the device state information and the production process configuration information to identify the dynamically changing dependency relationship between services; obtaining a target service dependency graph according to the dependency relationship; analyzing the dependency relationship between the plurality of industrial Internet of Things services according to the target service dependency graph.

[0012] According to some embodiments of the present application, the step of obtaining a target service dependency graph comprises one of: when a new dependency relationship is identified, updating the historical service dependency graph to obtain a target service dependency graph; when an existing dependency relationship is invalidated, removing the invalidated dependency relationship from the historical service dependency graph to obtain a target service dependency graph.

[0013] In a second aspect, the embodiments of the present application provide a network transmission terminal intelligent control system, comprising: an acquisition module configured to acquire terminal running data, external environment data and user traffic data; an analysis module configured to perform correlation analysis on the terminal running data, the external environment data and the user traffic data to obtain a correlation analysis result to identify evaluation deviation of a channel evaluation model; a generation module configured to generate a calibration parameter for calibrating the channel evaluation model according to the correlation analysis result; a correction module configured to apply the calibration parameter to the channel evaluation model to correct evaluation deviation of the channel evaluation model to obtain a corrected channel evaluation result; an adjustment module configured to adjust transmission power according to the corrected channel evaluation result.

[0014] According to the technical scheme of the embodiment of the present application, at least the following beneficial effects are achieved: the present application discloses a network transmission terminal intelligent control method, by acquiring terminal running data, external environment data and user traffic data, and performing correlation analysis on these multi-source data, the evaluation deviation of the channel evaluation model can be identified. On this basis, calibration parameters are generated according to the correlation analysis results, and are applied to the channel evaluation model, so as to correct the evaluation deviation of the model and obtain more accurate channel evaluation results. Finally, the transmission power is adjusted according to the corrected channel evaluation results. The method effectively solves the problem that the accuracy of the channel evaluation model decreases and the transmission power adjustment strategy is no longer optimal due to environmental changes and user behavior pattern evolution in the prior art. By introducing multi-source data correlation analysis and model calibration mechanism, the present application can significantly improve the accuracy of channel evaluation, avoid excessive or unnecessary power adjustment, thereby reducing terminal energy consumption, prolonging equipment life, and reducing radio frequency noise and electromagnetic interference, effectively solving the specific technical difficulties of unstable transmission performance, increased energy consumption and difficult fault diagnosis in the prior art, and realizing more stable, efficient and reliable network transmission.

[0015] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings are included to provide a further understanding of the technical scheme of the present application, and constitute a part of the specification, and are used together with the embodiments of the present application to explain the technical scheme of the present application, and do not constitute a limitation on the technical scheme of the present application.

[0017] Figure 1 A flowchart of a network transmission terminal intelligent control method provided by an embodiment of the present application is shown in the figure; Figure 2 A schematic diagram of a network transmission terminal intelligent control system provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical method and advantages of the present application more clear and explicit, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and do not limit the present application.

[0019] It should be noted that the meaning of multiple (or multiple) involved in the description of the embodiments of the present application is two or more, greater than, less than, more than, etc. is not included in the number, above, below, within, etc. is understood to include the number. If there is a description of "first", "second", etc. is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of the indicated technical features.

[0020] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association between the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" and similar expressions mean any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can mean: a alone, b alone, c alone, a and b exist at the same time, a and c exist at the same time, b and c exist at the same time, or a and b and c exist at the same time, where a, b, and c can be single or multiple.

[0021] In the description of the present application, unless otherwise explicitly limited, the words such as setting, installation, connection, etc. should be broadly understood, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0022] Based on the above situation, the present application proposes a network transmission terminal intelligent control method and system, aiming to improve the stability and reliability of network transmission.

[0023] The network transmission terminal intelligent control method provided by the embodiments of the present application can be applied in a terminal, can also be applied in a server end, and can also be software running in a terminal or a server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc. The server end can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content distribution network (Content Delivery Network, CDN) and big data and artificial intelligence platform. The software can be an application that implements the network transmission terminal intelligent control method, but is not limited to the above forms.

[0024] The embodiments of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices. It should be noted that in various specific embodiments of the present application, when relevant processing is required in relation to data related to the characteristics of an object (e.g. a user, etc.) such as attribute information or a set of attribute information of the object, permission or consent of the corresponding object is obtained first, and the collection, use and processing of such data comply with relevant laws and standards. In addition, when the attribute information of the object is required by the embodiments of the present application, separate permission or separate consent of the corresponding object is obtained through a pop-up window or by jumping to a confirmation page, and after obtaining the separate permission or separate consent of the corresponding object, the necessary related data of the object for enabling the embodiments of the present application to operate normally is obtained.

[0025] Referring to Figure 1 , Figure 1 A flowchart of a network transmission terminal intelligent control method provided by an embodiment of the present application is shown. The network transmission terminal intelligent control method provided by an embodiment of the present application includes but is not limited to steps S110 to S150, and each step will be introduced in turn.

[0026] Step S110, obtaining terminal running data, external environment data and user traffic data; Step S120, performing correlation analysis on the terminal running data, the external environment data and the user traffic data to obtain a correlation analysis result to identify an evaluation deviation of a channel evaluation model; Step S130, generating a calibration parameter for calibrating the channel evaluation model according to the correlation analysis result; Step S140, applying the calibration parameter to the channel evaluation model to correct the evaluation deviation of the channel evaluation model to obtain a channel evaluation result; Step S150, adjusting transmission power according to the channel evaluation result.

[0027] It should be noted that "terminal running data" refers to various types of data generated by the network transmission terminal during operation, such as device CPU utilization, memory usage, battery level, temperature, signal reception strength (RSSI), signal-to-noise ratio (SNR), bit error rate (BER), data throughput, connection state, working mode, etc. These data reflect the internal working state and performance of the terminal. "External environment data" refers to relevant data of the physical environment in which the terminal is located, such as geographic location information, weather conditions, distribution and height of surrounding buildings, vegetation coverage, electromagnetic interference sources, activities of other wireless devices, etc. These data affect the propagation characteristics of wireless signals. "User traffic data" refers to data traffic information generated by terminal users when using network services, such as upload / download rate, packet size, service type (such as video streaming, voice call, web browsing, Internet of Things data, etc.), traffic priority, user behavior pattern, etc. These data reflect the user's demand and usage habits for network resources. "Channel evaluation model" refers to a mathematical model or algorithm used to predict or estimate the quality of a wireless channel. The model is usually trained based on historical data and pre-set propagation characteristics, aiming to output channel attenuation, multipath effect, interference level, etc. evaluation results according to input parameters (such as distance, frequency, environment type, etc.). "Evaluation deviation" refers to the difference between the channel evaluation results output by the channel evaluation model and the actual channel conditions. When the environment changes or the model parameters are no longer applicable, the evaluation deviation will increase, causing the model to be unable to accurately reflect the true channel quality. "Calibration parameters" refer to numerical values or sets used to adjust or optimize the internal parameters of the channel evaluation model. By applying calibration parameters, the model can better adapt to the current environment, reduce evaluation deviation, and improve evaluation accuracy.

[0028] In an embodiment, the acquisition of terminal operation data can be achieved in multiple ways. For example, the operating system or firmware inside the terminal can integrate a data collection module, which periodically reads performance indicators such as CPU utilization, memory usage, signal strength, bit error rate, and stores these data in local log files or reports them to the control unit through a specific interface. Another way is to deploy a lightweight agent program on the terminal, which is responsible for real-time monitoring of various operating parameters of the terminal, and sends the data to the central processing server in a packaged form according to the preset frequency. The acquisition of external environment data depends on various sensors and external information sources. The terminal can be equipped with a GPS module to obtain accurate geographic location information, combined with pre-stored geographic information system (GIS) data, the distribution of buildings and topography around the terminal can be inferred. In addition, real-time weather data can be obtained by interfacing with a weather service. For electromagnetic environment data, the terminal can be equipped with a spectrum sensing module to scan the signal strength and interference level of the surrounding frequency band. The acquisition of user traffic data is usually completed by network side equipment or the terminal itself. For example, the base station or core network equipment can monitor the data transmission volume, service type and QoS (Quality of Service) parameters of each terminal. On the terminal side, the application program or operating system can record the user's data usage, including upload / download traffic, application types accessed, session duration, etc. These data can be aggregated and reported to the control system.

[0029] In an embodiment, the implementation of the correlation analysis can be diversified. Preliminary screening and matching are performed by a preset rule engine. For example, when the bit error rate in the terminal running data continues to rise, while the external environment data shows that the terminal is located in an area with high-rise buildings, and the user traffic data shows that there are a large number of high-bandwidth services, the system can preliminarily judge that there may be channel evaluation deviation. A multiple-input multiple-output (MIMO) neural network model can also be constructed, with terminal running data, external environment data, and user traffic data as input features, and the actual deviation of the channel evaluation model as the output label for training. Through historical data training, the model can learn the complex nonlinear relationship between different data dimensions, so as to accurately predict or identify the potential deviation of the channel evaluation model when new data is input. If the correlation analysis result indicates that the channel evaluation model has a fixed deviation pattern in a specific scenario, an optimization algorithm can be used to dynamically generate calibration parameters. For example, a loss function can be defined, which measures the difference between the evaluation results of the channel evaluation model before and after applying the calibration parameters and the actual channel conditions. Then, through gradient descent or other optimization algorithms, the internal parameters of the channel evaluation model or external calibration factors are iteratively adjusted to minimize the loss function. These adjusted parameters or external factors are the calibration parameters. For example, when the correlation analysis result shows that in the high-speed mobile scenario, the Doppler effect causes the channel evaluation model to inaccurately estimate the channel coherence time, a calibration factor that adjusts the Doppler spread parameter can be generated. Then, the application method of the calibration parameter depends on its generation method and the structure of the channel evaluation model. If the calibration parameter is a simple compensation factor, it can be directly added to or multiplied by the output result of the channel evaluation model. If the calibration parameter is an internal parameter adjustment of the channel evaluation model, the parameters need to be updated to the model through the API or configuration interface of the model. For example, if the calibration parameter is the coefficient of the multipath fading model, the model will use these new coefficients for calculation each time the channel evaluation is performed. By applying the calibration parameter, the channel evaluation model can more accurately reflect the current wireless channel conditions, thereby obtaining the corrected channel evaluation result. It can be based on the target received signal strength or target signal-to-noise ratio. If the corrected channel evaluation result shows that the current channel attenuation is large, in order to ensure that the receiving end can reach the preset minimum received signal strength, the terminal will correspondingly increase the transmission power. Conversely, if the channel quality is good, the transmission power can be reduced to save energy and reduce interference to other devices. The specific adjustment algorithm can be a proportional-integral-derivative (PID) controller or a power control strategy based on a lookup table. For example, a power adjustment table can be preset, which maps different channel evaluation results (such as signal-to-noise ratio, bit error rate level, etc.) to corresponding transmission power levels. When the channel evaluation result is "poor signal-to-noise ratio", the terminal will look up the table and select a higher transmission power level for transmission.

[0030] It should be noted that the scheme of the present application can capture the micro-interruption events in the channel in time by introducing a fine monitoring mechanism for high real-time data flow of industrial Internet of Things. At the same time, combined with the geographical position information of the terminal and the three-dimensional model of the high-reflective building, the potential mirror reflection signal is spatially deduced and verified. More importantly, by associating the spatially consistent reflection signal with the time-synchronized micro-interruption event alarm, the present application can accurately identify those "high-risk mirror reflection paths" that not only exist but also indeed cause the channel quality to decline. This identification method combining time synchronization and spatial consistency makes the evaluation deviation of the channel evaluation model more accurately attributed to specific physical phenomena, thereby overcoming the limitations of traditional correlation analysis in accurately identifying the cause of instantaneous channel interruption in complex environments, and further providing more accurate input for subsequent channel calibration. Through the above technical scheme, the present application can accurately identify the high-risk channel interruption path caused by mirror reflection for high real-time data flow of industrial Internet of Things. This significantly improves the accuracy and fineness of the evaluation deviation identification of the channel evaluation model, especially in complex multipath environments, which can effectively distinguish between general channel fading and instantaneous channel deterioration caused by specific physical structures. Thus, it provides a solid foundation for generating more accurate calibration parameters subsequently, thereby ensuring the transmission reliability and real-time performance of the key services of industrial Internet of Things, avoiding improper transmission power adjustment due to inaccurate channel evaluation, and thereby improving the intelligent control level of the entire network transmission system.

[0031] In an embodiment, assume that in a smart factory environment, an industrial robot is performing a high-precision assembly task, and its control data stream is identified as a high real-time data stream of industrial IoT. When the robot communicates with the base station, complex specular reflection can occur due to the presence of a large amount of metal walls and large equipment in the factory. The system will start the preset frequency monitoring logic for the data stream of the industrial robot, and continuously monitor its bit error rate and packet loss rate within the first preset time window. Once the bit error rate or packet loss rate is suddenly increased and exceeds the preset threshold, the system will immediately generate a micro outage event alarm, marking that the channel quality may have decreased near this time point. At the same time, the system will obtain the real-time geographic location information of the industrial robot, and combine it with the three-dimensional model of the high-reflective building such as the metal wall in the geographic information system data of the factory building. Based on these information, the system performs reverse deduction on the theoretical signal path from the base station to the metal wall and then to the industrial robot, and predicts the arrival direction of the reflected signal. Subsequently, the system compares whether the arrival direction of the actually received reflected signal is consistent with the theoretically deduced arrival direction. For example, if a strong reflected signal from a certain angle is actually detected, and the angle is consistent with the theoretical path from the base station to the robot via a certain metal wall, it is preliminarily considered that there is a potential specular reflection. Further, the system judges whether the occurrence or intensity change of the reflected signal is synchronized in time with the previously generated micro outage event alarm. For example, if the occurrence time of the strong reflected signal is highly consistent with the occurrence time of the micro outage event alarm, it can be confirmed that the specific reflection path is a high-risk specular reflection path. Finally, the system inputs the identified high-risk specular reflection path information, together with the operation data of the industrial robot, the factory environment data (such as temperature, humidity, equipment running state), and the user traffic data, into the correlation analysis model.

[0032] It should be noted that obtaining the calibration parameters of the high-risk mirror reflection path refers to obtaining calibration data or model parameters related to the characteristics of the high-risk mirror reflection path identified in the above correlation analysis. These parameters can include but are not limited to reflection intensity, time delay, attenuation factor, polarization change, etc., and the purpose is to quantify the specific impact of the path on signal transmission quality. Among them, the calibration parameters can be pre-stored in the system, or dynamically generated by real-time monitoring and analysis of the characteristics of the high-risk mirror reflection path. Further, according to the calibration parameters and the correlation analysis results, reducing the evaluation of the signal component of the high-risk mirror reflection path on the overall channel quality contribution refers to special processing of the signal component from the high-risk mirror reflection path when comprehensively evaluating the channel quality. For example, according to the obtained calibration parameters, a lower weight can be given to these signal components, or a reduction factor can be introduced in the channel evaluation model to reduce its positive contribution to the overall channel quality evaluation, or even be regarded as an interference source for suppression. The purpose is to more accurately reflect the actual channel quality and avoid overly optimistic or inaccurate channel evaluation results due to the existence of high-risk mirror reflection paths. The scheme of the present application obtains the calibration parameters of the high-risk mirror reflection path when generating the calibration parameters, and based on these parameters and the correlation analysis results, actively reduces the contribution of the signal components of these high-risk paths to the overall channel quality evaluation. It is precisely because high-risk mirror reflection paths often bring negative effects such as signal fading and multipath interference, if their signal components are not distinguished and included in the overall channel quality evaluation, it may cause the channel evaluation model to deviate from the actual channel condition. By reducing its contribution, the present application can more accurately reflect the true quality of the channel, so that the subsequent transmission power adjustment is more accurate and effective, solving the problem that the above channel evaluation may not be accurate enough.

[0033] In an embodiment, assume that an industrial IoT terminal is transmitting critical production control data in a certain industrial park. By the above method, the system identifies a high-risk specular reflection path caused by a nearby highly reflective building, leading to a micro-outage event. To more accurately calibrate the channel estimation model, the application first obtains the calibration parameters of the high-risk specular reflection path, and determines that the path will cause an average reduction of 5 dB in signal strength and introduce a 20 ns time delay under certain environmental conditions through historical data analysis or real-time measurement. When generating the calibration parameters for calibrating the channel estimation model, the system will reduce the weight of the signal component of the high-risk specular reflection path in the overall channel quality evaluation from the default 1.0 to 0.2 according to these calibration parameters and the correlation analysis results, or directly introduce a -5 dB attenuation factor in the channel estimation model specifically for the signal component of the path. Thus, even if the signal strength of the path is high, its positive contribution to the overall channel quality will be significantly reduced, thereby avoiding the channel estimation model from giving an overly optimistic channel quality due to misjudgment, ensuring the accuracy of subsequent transmission power adjustment, and effectively improving the reliability of industrial IoT data transmission.

[0034] It should be noted that obtaining the spatial direction of arrival and the polarization state of the received signal means performing spatial spectrum estimation and polarization analysis on the received wireless signal through the array antenna or multi-antenna system equipped by the terminal to determine the direction from which the signal arrives and the vibration direction of its electromagnetic wave. For example, the spatial direction of arrival of the signal can be accurately estimated by using multiple signal classification (MUSIC) algorithm, estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm or other beamforming techniques. At the same time, by analyzing the polarization components of the received signal, its polarization state can be determined, such as linear polarization, circular polarization or elliptical polarization. These information helps to distinguish direct signals, diffuse reflection signals and specular reflection signals, thereby preliminarily identifying potential specular reflection signals. Among them, comparing the direction of arrival of the potential specular reflection signal with the geometric reflection path of the highly reflective structure in the geographic information system data to mark the high-risk specular reflection path means comparing the spatial direction of arrival of the identified potential specular reflection signal with the theoretical geometric reflection path calculated from the three-dimensional model of the highly reflective building or structure pre-stored in the geographic information system data. The geographic information system data can contain detailed information such as the height, location and material properties of urban buildings, and the theoretical reflection path from the base station to the highly reflective structure to the terminal can be simulated by ray tracing and other techniques. When the direction of arrival of the potential specular reflection signal is highly consistent with a certain theoretical geometric reflection path, it can be marked as a high-risk specular reflection path.

[0035] In one embodiment, assume a network transmission terminal equipped with an eight-element uniform linear array antenna operates in an urban environment. When the terminal receives signals from a base station, its antenna array captures a composite signal including a direct signal and multiple reflected signals. First, the terminal's signal processing unit processes the received signal using array signal processing techniques, such as subspace decomposition-based algorithms, to estimate the spatial direction of arrival (e.g., azimuth and elevation) and its corresponding signal polarization state for each arriving signal. For example, if a signal is detected arriving from a certain direction with a polarization state significantly different from that of the direct signal, it can be preliminarily labeled as a potential specular reflection signal. Subsequently, the terminal compares the directions of arrival of these potential specular reflection signals with pre-loaded geographic information system data. This geographic information system data contains precise three-dimensional models and geometric information of all highly reflective buildings (e.g., glass-walled buildings, metal-structured buildings, etc.) in the terminal's operating area. By calculating the theoretical geometric reflection paths from the base station to these highly reflective buildings and then to the terminal, a series of expected reflection signal directions of arrival can be obtained. If the direction of arrival of a potential specular reflection signal highly matches that of one of the theoretical geometric reflection paths within a pre-set error range, the potential specular reflection signal is confirmed as a "high-risk specular reflection path" caused by that highly reflective building. For example, if a potential reflection signal arrives from the northwest direction with a certain elevation angle, and the geographic information system data shows that there is a high-rise glass-walled building in that direction, whose geometric reflection path also points to that direction, then this reflection signal is labeled as a high-risk specular reflection path.

[0036] It should be noted that the instantaneous interruption characteristic refers to the degree and frequency of signal quality degradation or interruption due to factors such as signal fluctuations, fading, or interference of the specular reflection path within a short period of time. This evaluation can be based on continuous monitoring and correlation analysis of received signal strength, signal phase, signal polarization state, and environmental parameters, aiming to quantify the dynamic reliability performance of each specific reflection path. Secondly, deep packet inspection (DPI) or classification based on port, protocol, etc. information can be performed on the data stream to determine its industrial internet of things service, such as real-time control, sensor data acquisition, video monitoring, etc. Subsequently, according to the strict requirements of different services on latency (e.g., millisecond-level response) and reliability (e.g., 99.999% availability), each service is assigned a corresponding priority, for example, the real-time control service is set to the highest priority.

[0037] Again, according to the instantaneous interruption characteristic and the traffic priority of each high-risk mirror reflection path, an independent path reliability reduction factor is generated. The reduction factor is a quantitative value indicating how the signal component contribution of this particular high-risk mirror reflection path should be adjusted in channel assessment. For example, for a path with poor instantaneous interruption characteristic (i.e. unstable) and carrying high-priority traffic, its reduction factor can be large, meaning that its signal component contribution to the overall channel quality should be significantly reduced; on the contrary, for a relatively stable path carrying low-priority traffic, its reduction factor can be small. Finally, by introducing an independent path reliability reduction factor, fine and dynamic adjustment of the signal component contribution of each high-risk mirror reflection path can be achieved, so that the generated calibration parameters can more accurately reflect the actual channel conditions and better serve the performance requirements of different traffics.

[0038] In an embodiment, assume that in an industrial IoT scenario, e.g. an automated factory workshop, there is a network transmission terminal whose communication with the base station is affected by a specular reflection signal generated by a large metal structure (e.g. a mechanical arm or a large equipment on the automated production line), the reflection path has been identified as a high-risk specular reflection path. The system first continuously monitors the received signal strength, signal phase and signal polarization state of the high-risk specular reflection path, and combines environmental parameters such as the real-time location information of mobile devices (such as AGV trolleys, forklifts) in the workshop to evaluate the instantaneous interruption characteristics of the path. For example, when an AGV trolley passes near the reflection path, it may cause instantaneous fading or phase change of the reflection signal, and the system identifies that the path has a high risk of instantaneous interruption in a certain time period. At the same time, the system identifies that the industrial IoT traffic transmitted through the high-risk specular reflection path includes real-time instruction data for controlling the mechanical arm (with extremely high requirements for latency and reliability) and sensor data for environmental temperature and humidity monitoring (with relatively low requirements for latency and reliability). According to these requirements, the system assigns the mechanical arm control instruction data as high-priority traffic and the environmental monitoring data as low-priority traffic, and then the system generates independent path reliability reduction factors according to the evaluated instantaneous interruption characteristics and the assigned traffic priorities. For example, when the high-risk specular reflection path has poor instantaneous interruption characteristics and is transmitting high-priority mechanical arm control instructions, the system generates a larger reduction factor to significantly reduce the contribution of the path signal component to the overall channel quality evaluation. Conversely, if the path is relatively stable or only transmits low-priority environmental monitoring data, a smaller reduction factor will be generated. Finally, these independent path reliability reduction factors are combined with existing calibration parameters and correlation analysis results to more accurately reduce the evaluation of the signal component of the high-risk specular reflection path to the overall channel quality contribution, thereby generating calibration parameters for calibrating the channel evaluation model. In this way, the channel evaluation model can more accurately reflect the actual channel conditions and dynamically adjust the transmission power according to the priority of different traffic to ensure the stability and reliability of high-priority traffic while optimizing the overall network resource utilization efficiency.

[0039] It should be noted that continuous monitoring of the received signal strength, signal phase, and signal polarization state of high-risk specular reflection paths refers to the continuous sampling and measurement of signals from specific high-risk specular reflection paths by the receiver of a network transmission terminal or base station. Received signal strength reflects the signal's energy level, signal phase carries sensitive information about signal propagation distance and environmental changes, and signal polarization state is closely related to the physical characteristics of the reflecting surface and the signal propagation direction. Continuous monitoring of these parameters provides the foundational data for subsequent correlation analysis. Real-time acquisition of environmental parameters in the terminal's location can be understood as collecting real-time information related to the signal propagation environment through various sensors or external data sources. For example, these environmental parameters may include, but are not limited to, temperature, humidity, rainfall, wind speed, atmospheric pressure, and the presence of moving obstacles (such as vehicles or pedestrians). Changes in these environmental parameters may directly or indirectly affect the signal propagation characteristics, thereby affecting the instantaneous interruption characteristics of the specular reflection path. Correlation analysis is performed on received signal strength, signal phase, signal polarization state, and environmental parameters to identify the nonlinear coupling relationship between changes in environmental parameters and received signal strength, signal phase, and signal polarization state. Specifically, this involves using machine learning algorithms (e.g., neural networks, support vector machines, or decision trees) or statistical analysis methods to deeply mine multidimensional data. The aim is to reveal how subtle changes in environmental parameters nonlinearly affect the received signal quality and characteristics. For example, increased humidity may lead to accelerated signal attenuation, or micro-vibrations on highly reflective building surfaces at specific wind speeds may cause rapid signal phase drift. Identifying this nonlinear coupling relationship is crucial for achieving dynamic adaptive assessment. Finally, based on adjusted detection thresholds and assessment weights, the instantaneous interruption characteristics of each high-risk specular reflection path are evaluated. This means that the final judgment and quantification of instantaneous interruptions are based on dynamically adjusted assessment standards that better reflect current environmental conditions. This assessment can more accurately reflect the true reliability of high-risk specular reflection paths, avoiding misjudgments or omissions.

[0040] In one embodiment, an Industrial Internet of Things (IIoT) terminal is located near a highly reflective building, and its data stream is transmitted through a high-risk specular reflection path. To accurately assess the transient interruption characteristics of this path, the system first continuously monitors the received signal strength, signal phase, and signal polarization state of the path. Simultaneously, environmental sensors deployed near the terminal acquire real-time environmental parameters such as temperature, humidity, and wind speed. When a sudden increase in ambient humidity is detected, the system activates a correlation analysis module. This module, based on a pre-trained machine learning model, identifies a non-linear coupling relationship between increased humidity and decreased received signal strength. Specifically, the model may find that when humidity exceeds a certain threshold, signal attenuation accelerates at a faster rate. Based on this identified non-linear coupling relationship, the system dynamically adjusts the detection threshold for assessing transient interruption characteristics. For example, in low humidity conditions, a 5dB decrease in signal strength might be considered a potential interruption; however, in high humidity environments, due to generally increased signal attenuation, the system might adjust the detection threshold to only consider an 8dB decrease in signal strength as a potential interruption to avoid false alarms. Simultaneously, the evaluation weights are adjusted accordingly. For example, in high wind speeds, the weight of signal phase fluctuations may be increased because wind can cause slight movements of the reflective surface, resulting in drastic phase changes. Based on these dynamically adjusted detection thresholds and evaluation weights, the system assesses the transient interruption characteristics of high-risk specular reflection paths. For instance, if in a high-humidity, high-wind-speed environment, the received signal strength drops by 7 dB and the signal phase fluctuates drastically, the system can accurately determine that the path is experiencing a transient interruption event based on the adjusted thresholds and weights. This dynamically adjusted evaluation method enables the system to more accurately identify and quantify transient interruptions.

[0041] It's important to clarify that identifying multiple Industrial IoT (IIoT) services transmitted via high-risk specular reflection paths and analyzing their dependencies means the system needs to accurately identify all IIoT service instances currently in use or likely to use this high-risk specular reflection path for data transmission. These services might include real-time control command transmission, sensor data acquisition, video surveillance streaming, and device status reporting. After identifying these services, further analysis of their logical relationships is needed—that is, whether the completion of one service depends on the output or status of another. This dependency can be an upward data flow dependency or a control logic dependency, aiming to ensure the correct order and integrity of service execution. Analyzing the competitive relationships between multiple IIoT services can be understood as assessing potential conflicts arising when different IIoT services share limited network resources (e.g., bandwidth, processing power, transmission time slots). For example, when multiple high-real-time services simultaneously need to transmit data via the same high-risk specular reflection path, they will compete for limited channel resources. The purpose is to identify these points of competition to provide a basis for subsequent resource allocation coordination, avoiding resource contention that could lead to performance degradation. Resource allocation coordination based on competition, resulting in a coordinated resource allocation outcome, refers to the system's optimized allocation of shared resources according to preset strategies or algorithms after identifying competitive relationships between services. For example, scheduling algorithms based on priority, fairness, or maximizing overall system throughput can be used to allocate corresponding bandwidth, latency guarantees, and other resources to each competing service. The aim is to minimize resource conflicts and improve resource utilization efficiency while satisfying service dependencies. Therefore, allocating service priorities based on the improved priorities and coordinated resource allocation results means that the final service priority allocation comprehensively considers the priorities adjusted for service dependencies and the results of resource competition coordination. This final priority guides network transmission terminals in how to schedule and process data streams from different services during actual operation, aiming to ensure the smooth execution of critical services while optimizing overall system performance.

[0042] It's important to clarify that continuously monitoring the data flow and control command execution status of Industrial IoT (IIoT) operations refers to the system uninterruptedly tracking the data packet transmission paths, data volume, transmission latency, and the sending, receiving, and execution feedback of key control commands between IIoT devices. The purpose is to obtain the real-time context of business operations, providing foundational data for subsequent dependency identification. Real-time acquisition of device status information, production process configuration information, and historical business dependency maps within the IIoT system can be understood as the system collecting real-time data on device operation status (e.g., online status, fault codes, load rate) through various channels such as sensors, SCADA systems, and MES systems; the current production line's process flow settings; and previously accumulated business dependency models. The aim is to provide multi-dimensional and comprehensive contextual information to aid in understanding business behavior. Correlation analysis of data flow, control command execution status, device status information, and production process configuration information identifies dynamically changing dependencies between businesses. Specifically, this involves using big data analytics and machine learning technologies to cross-reference and identify patterns in the massive amounts of data acquired through real-time monitoring. For example, when a device malfunctions, observing changes in its data flow and control command execution status, and combining this with production process configuration, helps determine which upstream or downstream businesses will be affected, thereby identifying new or changing dependencies. The goal is to uncover hidden, dynamically changing business relationships from complex data. Further, based on these dependencies, a target business dependency graph is obtained. This involves integrating the identified dynamically changing dependencies into the existing business dependency model to form an up-to-date business dependency graph reflecting the current system state. The aim is to provide a clear and visually representable business dependency structure, facilitating subsequent analysis and decision-making. Finally, based on the target business dependency graph, the dependencies between various industrial IoT businesses are analyzed. This means that based on the updated business dependency graph, the system can more accurately assess the degree of mutual influence, critical paths, and potential bottlenecks between different businesses. The goal is to provide a precise basis for the rational allocation of business priorities and the optimization of resource configuration.

[0043] It's important to clarify that "new dependencies" refer to interactions between business processes not yet recorded in the current historical business dependency graph, discovered during continuous monitoring of data flow and control command execution status in the Industrial Internet of Things (IIoT) business, combined with correlation analysis of equipment status information and production process configuration information. New dependencies arise when a new production task is introduced or a new data exchange link is established between existing devices. The "historical business dependency graph" can be understood as a graph-like data structure pre-stored by the system, reflecting known dependencies between IIoT businesses. This graph dynamically evolves with business operations and environmental changes, serving as the foundation for analyzing business dependencies. "Update the historical business dependency graph" means adding newly identified dependencies to the historical business dependency graph to ensure its integrity and real-time performance. This can be represented by adding new nodes or edges to the graph. "Existing dependencies becoming invalid" means that during business operations, due to factors such as production process adjustments, equipment shutdowns, communication link interruptions, or changes in business logic, some dependencies already recorded in the historical business dependency graph no longer hold true or have practical significance. For example, when a piece of equipment is replaced or a production step is canceled, its associated dependencies become invalid. "Removing invalid dependencies from the historical business dependency graph" means deleting dependencies that are no longer valid from the historical business dependency graph to avoid performing business dependency analysis based on outdated information, thereby improving the accuracy of the analysis. This can be achieved by deleting the corresponding nodes or edges in the graph.

[0044] See Figure 2 , Figure 2 This is a schematic diagram of a network transmission terminal intelligent control system provided in one embodiment of this application. The network transmission terminal intelligent control system 200 includes: The acquisition module 210 is used to acquire terminal operating data, external environment data, and user traffic data. Analysis module 220 is used to perform correlation analysis on terminal operation data, external environment data and user traffic data, and obtain correlation analysis results to identify the evaluation bias of the channel evaluation model. The generation module 230 is used to generate calibration parameters for calibrating the channel evaluation model based on the correlation analysis results. The correction module 240 is used to apply the calibration parameters to the channel evaluation model to correct the evaluation bias of the channel evaluation model and obtain the corrected channel evaluation result. The adjustment module 250 is used to adjust the transmission power based on the corrected channel assessment results.

[0045] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0046] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0047] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A network transmission terminal intelligent control method, characterized in that, The method comprises the following steps: acquiring terminal running data, external environment data, and user traffic data; performing correlation analysis on the terminal running data, the external environment data, and the user traffic data to obtain a correlation analysis result to identify evaluation deviation of a channel evaluation model; generating a calibration parameter for calibrating the channel evaluation model according to the correlation analysis result; applying the calibration parameter to the channel evaluation model to correct the evaluation deviation of the channel evaluation model to obtain a channel evaluation result; adjusting transmission power according to the channel evaluation result.

2. The method of claim 1, wherein, The correlation analysis on the terminal running data, the external environment data, and the user traffic data to obtain a correlation analysis result to identify evaluation deviation of a channel evaluation model comprises: starting preset frequency monitoring logic and monitoring bit error rate and packet loss rate within a first preset time window for high real-time data flow identified as industrial Internet of Things; generating a micro-interruption event alarm according to the bit error rate and the packet loss rate; acquiring geographical position of a terminal and a three-dimensional model of a high-reflective building in geographic information system data; performing reverse deduction on a direction of arrival of an active reflective signal according to the geographical position and the three-dimensional model; judging whether the direction of arrival of the reflective signal is consistent with a theoretical path from the terminal to a preset high-reflective building and then to a base station; judging whether the reflective signal is synchronized in time with the micro-interruption event alarm; when the direction of arrival of the reflective signal is consistent with the theoretical path and the reflective signal is synchronized in time with the micro-interruption event alarm, obtaining a high-risk mirror reflection path; obtaining a correlation analysis result to identify evaluation deviation of a channel evaluation model according to the high-risk mirror reflection path, the terminal running data, the external environment data, and the user traffic data.

3. The method of claim 2, wherein, The generation of a calibration parameter for calibrating the channel evaluation model according to the correlation analysis result comprises: acquiring a calibration parameter of a high-risk mirror reflection path; reducing evaluation of a signal component of the high-risk mirror reflection path on contribution to overall channel quality according to the calibration parameter and the correlation analysis result to generate a calibration parameter for calibrating the channel evaluation model.

4. The method of claim 3, wherein, The high-risk mirror reflection path is obtained through the following steps: acquiring a spatial direction of arrival of a received signal and a signal polarization state and marking a potential mirror reflection signal; comparing the direction of arrival of the potential mirror reflection signal with a geometric reflection path of a high-reflective structure in the geographic information system data to mark a high-risk mirror reflection path.

5. The method of claim 4, wherein, The reduction of evaluation of a signal component of the high-risk mirror reflection path on contribution to overall channel quality according to the calibration parameter and the correlation analysis result to generate a calibration parameter for calibrating the channel evaluation model comprises: evaluating instantaneous interruption characteristics of each high-risk mirror reflection path; identifying a type of industrial Internet of Things service transmitted through the high-risk mirror reflection path and assigning a service priority according to a requirement of the industrial Internet of Things service on latency and reliability; generating independent path reliability reduction factors according to the instantaneous interruption characteristics of each of the high-risk mirror reflection paths and the service priorities; reducing the evaluation of the signal component of the high-risk mirror reflection path on the overall channel quality contribution according to the independent path reliability reduction factors, the calibration parameters and the correlation analysis results, and generating calibration parameters for calibrating the channel evaluation model.

6. The method of claim 5, wherein, The evaluation of the instantaneous interruption characteristics of each of the high-risk mirror reflection paths comprises: continuously monitoring the received signal strength, signal phase and signal polarization state of the high-risk mirror reflection path; real-time acquisition of environmental parameters of the area where the terminal is located; correlation analysis of the received signal strength, signal phase, signal polarization state and environmental parameters to identify the nonlinear coupling relationship between the changes in the environmental parameters and the received signal strength, signal phase and signal polarization state; according to the nonlinear coupling relationship, dynamically adjusting the detection threshold and evaluation weight for evaluating the instantaneous interruption characteristics of each of the high-risk mirror reflection paths to obtain the adjusted detection threshold and evaluation weight; based on the adjusted detection threshold and evaluation weight, evaluating the instantaneous interruption characteristics of each of the high-risk mirror reflection paths.

7. The method of claim 5, wherein, The identification of the type of industrial internet of things service transmitted through the high-risk mirror reflection path and the allocation of service priority according to the requirements of the industrial internet of things service on delay and reliability comprises: identifying multiple industrial internet of things services transmitted through the high-risk mirror reflection path and analyzing the dependency relationship between the multiple industrial internet of things services; analyzing the competition relationship between the multiple industrial internet of things services; according to the dependency relationship, the priority of the service as the prerequisite for dependency is raised to be higher than or equal to the priority of the service dependent on the prerequisite to obtain the raised priority; according to the competition relationship, resource allocation coordination is carried out to obtain the coordinated resource allocation result; according to the raised priority and the coordinated resource allocation result, the service priority is allocated.

8. The method of claim 7, wherein, The analysis of the dependency relationship between the multiple industrial internet of things services comprises: continuously monitoring the data flow direction and execution state of the control instruction of the industrial internet of things service; real-time acquisition of device state information, production process configuration information and historical service dependency graph in the industrial internet of things system; correlation analysis of the data flow direction, the execution state of the control instruction, the device state information and the production process configuration information to identify the dynamically changing dependency relationship between services; according to the dependency relationship, a target service dependency graph is obtained; according to the target service dependency graph, the dependency relationship between the multiple industrial internet of things services is analyzed.

9. The method of claim 8, wherein, The step of obtaining the target service dependency graph comprises one of: when a new dependency relationship is identified, updating the historical service dependency graph to obtain the target service dependency graph; when it is identified that the original dependency relationship is invalid, removing the invalid dependency relationship from the historical service dependency graph to obtain the target service dependency graph.

10. A network transmission terminal intelligent control system, characterized by, comprises: An acquisition module is configured to acquire terminal running data, external environment data, and user traffic data; An analysis module is configured to perform correlation analysis on the terminal running data, the external environment data, and the user traffic data to obtain a correlation analysis result to identify an evaluation deviation of a channel evaluation model; A generation module is configured to generate a calibration parameter for calibrating the channel evaluation model according to the correlation analysis result; A correction module is configured to apply the calibration parameter to the channel evaluation model to correct the evaluation deviation of the channel evaluation model to obtain a corrected channel evaluation result; An adjustment module is configured to adjust transmission power according to the corrected channel evaluation result.

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