A Multi-source Sensor Data Sharing System and Method under Weak Signals

Abstract

The present invention belongs to the technical field of industrial Internet of Things. The present invention discloses a multi-source sensor data sharing system and method under weak signals. The method includes: obtaining a power plant data set; processing the power plant data set and performing spatio-temporal fusion processing to obtain a power plant data sequence; compressing the power plant data sequence to generate multiple data packets; optimizing the multiple data packets to generate lightweight data packets; performing protocol encapsulation on the lightweight data packets to obtain transmission data; performing channel allocation on the transmission data to obtain a transmission channel data stream; obtaining the status of a mobile terminal device; performing dynamic energy efficiency analysis on the status of the mobile terminal device to construct an energy efficiency model; evaluating and analyzing the energy efficiency model and making a transmission strategy decision to construct an environmental transmission strategy; based on the environmental transmission strategy, performing real-time dynamic scheduling control on the adaptive transmission channel data stream to construct a multi-source sensor data sharing model for a power plant, improving the reliability and efficiency of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things. More specifically, the present invention relates to a multi-source sensor data sharing system and method under weak signals. Background Art

[0002] With the deep promotion of the concept of industrial intelligent manufacturing, the power energy industry is undergoing a crucial stage of digital transformation. As the core facility for energy production, the operation safety, efficiency optimization, and predictive maintenance of power plants highly rely on the real-time monitoring system constructed by multi-source sensor networks. Currently, a typical power plant usually deploys more than ten thousand measuring points, covering various types of parameters such as temperature, pressure, vibration, flow rate, and liquid level, forming a huge and complex sensor ecosystem. These sensors can be divided into two major categories according to the deployment method: fixed online sensors and mobile measurement sensors, jointly constituting the "nervous system" of the power plant operation status.

[0003] However, for the unique complex physical environment of power plants - thick concrete walls, large metal equipment, high-voltage transformers, and dense electromagnetic interference sources, it usually causes severe attenuation and instability of signal propagation, which is particularly obvious in key areas such as the boiler area, turbine hall, and underground pipe gallery. Measured data shows that the signal strength in these areas is generally low and fluctuates violently. Traditional transmission schemes perform poorly in such weak signal environments, with a high data packet loss rate, and cannot meet the continuity requirements of industrial-level monitoring. At the same time, the existing data processing methods have insufficient capabilities in dealing with high-frequency vibration noise, temperature drift, and transient interference in the power plant environment, resulting in quality hidden dangers at the data source. Especially when mobile measurement personnel need to conduct inspections in the plant area and transmit equipment status data back, due to the lack of a targeted weak signal transmission mechanism, key parameters often cannot be transmitted back to the control center in a timely manner, missing the early warning opportunity for equipment abnormalities. In addition, the power management of on-site equipment in power plants is extensive, and mobile terminals blindly increase the transmission power in low-signal coverage areas, not only causing a sharp increase in battery energy consumption and being unable to maintain operation during the inspection cycle, but also causing more interference problems due to high-power transmission. The traditional fixed-priority data transmission strategy even ignores the dynamic changes in the value of different data under different working conditions. For example, during the start-up and shutdown stages of equipment, the importance of vibration and temperature data is significantly higher than that during the stable operation period, but they fail to obtain priority guarantee of transmission resources. Especially during the full-load operation of power plants, the peak load of the communication network occurs simultaneously with a sharp increase in the amount of equipment monitoring data. Without a channel resource allocation mechanism that can sense the network congestion status and dynamically adjust, even warning data faces the risk of transmission delay or loss. The existing technologies process the data acquisition, transmission, and processing links separately, and fail to establish a data sharing framework for weak signal environments throughout the whole process, resulting in the inability to achieve end-to-end global optimization and restricting the digital and intelligent transformation process of power plants.

[0004] In view of this, the present invention proposes a multi-source sensor data sharing system and method under weak signals to solve the above problems. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solutions: A multi-source sensor data sharing method under weak signals, including:

[0006] Step S1: Obtain the multi-source sensor data set of the power plant; perform signal quality evaluation and enhancement processing on the multi-source sensor data set of the power plant, and perform spatio-temporal fusion serialization processing to obtain the multi-source sensor data sequence of the power plant;

[0007] Step S2: Perform low-power coding compression on the multi-source sensor data sequence of the power plant to generate a plurality of lightweight data packets; perform bit error rate optimization on the plurality of lightweight data packets to generate optimized lightweight data packets;

[0008] Step S3: Encapsulate the optimized lightweight data packets with a weak signal transmission protocol to obtain weak signal transmission data; perform adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream;

[0009] Step S4: Obtain the status information of the mobile terminal device; perform dynamic energy efficiency analysis on the status information of the mobile terminal device to construct an energy efficiency model of the terminal device;

[0010] Step S5: Perform communication quality evaluation and analysis on the energy efficiency model of the terminal device, and make an adaptive transmission strategy decision to construct a transmission strategy under a weak signal environment;

[0011] Step S6: Based on the transmission strategy under the weak signal environment, perform real-time dynamic scheduling control on the adaptive transmission channel data stream to construct a multi-source sensor data sharing model of the power plant.

[0012] Preferably, the specific steps of step S1 are:

[0013] Step S11: Obtain the multi-source sensor data set of the power plant;

[0014] Step S12: Perform sensor type identification and classification processing on the multi-source sensor data set of the power plant to obtain on-line sensor data and mobile measurement sensor data;

[0015] Step S13: Perform data quality monitoring on the on-line sensor data to identify abnormal data points;

[0016] Step S14: Perform adaptive filtering processing on the on-line sensor data according to the abnormal data points to obtain filtered on-line sensor data;

[0017] Step S15: Perform signal enhancement and optimization on the mobile measurement sensor data to generate enhanced mobile measurement sensor data;

[0018] Step S16: Perform spatio-temporal fusion serialization processing on the filtered online sensor data and the enhanced mobile measurement sensor data to obtain the power plant multi-source sensor data sequence.

[0019] Preferably, the specific steps of step S15 are as follows:

[0020] Perform signal strength analysis on the mobile measurement sensor data to obtain signal strength characteristics;

[0021] Perform signal-to-noise ratio evaluation based on the signal strength characteristics to obtain signal quality indicators;

[0022] Perform threshold analysis on the signal quality indicators to generate signal quality grading data;

[0023] Perform parametric signal enhancement processing on the mobile measurement sensor data based on the signal quality grading data to generate parametric enhancement data;

[0024] Perform weak signal identification and compensation on the mobile measurement sensor data based on the parametric enhancement data, and mark the weak signal data points;

[0025] Perform enhancement optimization processing on the weak signal data points to generate enhanced mobile measurement sensor data.

[0026] Preferably, the specific steps of step S2 are as follows:

[0027] Step S21: Perform priority grading on the power plant multi-source sensor data sequence to generate data sequences with different priorities;

[0028] Step S22: Perform data importance analysis on the data sequences with different priorities to obtain multiple data priority characteristics;

[0029] Step S23: Perform low-power coding compression based on multiple data priority characteristics to generate multiple lightweight data packets;

[0030] Step S24: Perform transmission error rate evaluation on the multiple lightweight data packets to obtain a data packet error risk assessment value;

[0031] Step S25: Perform redundant coding processing on the multiple lightweight data packets to generate multiple redundant protection lightweight data packets;

[0032] Step S26: Perform error rate optimization on the multiple redundant protection lightweight data packets based on the data packet error risk assessment value to generate optimized lightweight data packets.

[0033] Preferably, the specific steps of step S3 are as follows:

[0034] Step S31: Encapsulate the optimized lightweight data packet with a weak signal transmission protocol to obtain weak signal transmission data;

[0035] Step S32: Detect the transmission channel quality of the weak signal transmission data to obtain a channel quality evaluation result;

[0036] Step S33: Analyze the multi-channel resources based on the channel quality evaluation result to obtain an available channel resource pool;

[0037] Step S34: Perform adaptive channel allocation on the available channel resource pool to obtain an adaptive transmission channel data stream.

[0038] Preferably, the specific steps of Step S4 are as follows:

[0039] Step S41: Monitor the working state of the mobile terminal device to obtain mobile terminal device status information;

[0040] Step S42: Analyze the power consumption of the mobile terminal device status information to generate terminal device power status data;

[0041] Step S43: Perform dynamic energy efficiency analysis on the terminal device power status data to generate dynamic energy efficiency characteristics;

[0042] Step S44: Fit the energy efficiency mode to the dynamic energy efficiency characteristics to construct a terminal device energy efficiency model.

[0043] Preferably, the specific steps of Step S43 are as follows:

[0044] Extract multiple power consumption time points based on the terminal device power status data;

[0045] Calculate the energy consumption rate of the mobile terminal device status information according to the multiple power consumption time points to obtain the energy consumption rate;

[0046] Perform communication energy consumption statistics on the terminal device power status data to obtain the energy consumption value of each communication;

[0047] Analyze the communication efficiency of the energy consumption value of each communication according to the energy consumption rate to obtain communication energy efficiency characteristic data;

[0048] Identify the remaining battery power of the device based on the terminal device power status data;

[0049] Perform device battery life prediction analysis based on the remaining battery power of the device to obtain device battery life prediction data;

[0050] Perform dynamic energy efficiency analysis on the device battery life prediction data and the communication energy efficiency characteristic data to generate dynamic energy efficiency characteristics.

[0051] Preferably, the specific steps of Step S5 are as follows:

[0052] Step S51: Evaluate the transmission power of the terminal device energy efficiency model and extract available transmission power data;

[0053] Step S52: Analyze the signal coverage area of the mobile terminal device status information to obtain a signal coverage heat map;

[0054] Step S53: Based on the signal coverage heat map, conduct a communication quality evaluation and analysis of the available transmission power data to obtain a communication quality evaluation result;

[0055] Step S54: Predict the transmission success rate of the communication quality evaluation result and generate a transmission strategy evaluation index;

[0056] Step S55: Based on the transmission strategy evaluation index, make an adaptive transmission strategy decision and construct a transmission strategy in a weak signal environment.

[0057] Preferably, the specific steps of Step S6 are as follows:

[0058] Step S61: Construct a data sharing architecture for the adaptive transmission channel data stream to generate a data sharing framework;

[0059] Step S62: Based on the transmission strategy in the weak signal environment, map the transmission scheduling rules to the data sharing framework to construct a power plant data sharing scheduling framework;

[0060] Step S63: Conduct real-time dynamic scheduling control on the power plant data sharing scheduling framework to construct a power plant multi-source sensor data sharing model;

[0061] The real-time dynamic scheduling control is specifically: Based on the transmission strategy in the weak signal environment, identify the signal quality level of the current communication environment;

[0062] The signal quality levels include: high-quality signal area, medium-quality signal area, and low-quality signal area;

[0063] When the signal quality level of the communication environment is a high-quality signal area, the power plant data sharing scheduling framework performs high-frequency transmission processing of all data;

[0064] When the signal quality level of the communication environment is a medium-quality signal area, the power plant data sharing scheduling framework performs priority transmission processing of key data;

[0065] When the signal quality level of the communication environment is a low-quality signal area, the power plant data sharing scheduling framework performs compressed transmission processing of emergency data.

[0066] A multi-source sensor data sharing system under weak signals, which is used to implement the multi-source sensor data sharing method under weak signals, includes:

[0067] A data acquisition module, configured to obtain a multi-source sensor data set of a power plant; perform signal quality assessment and enhancement processing on the multi-source sensor data set of the power plant, and perform spatio-temporal fusion serialization processing to obtain a multi-source sensor data sequence of the power plant;

[0068] A compression and optimization module, configured to perform low-power coding compression on the multi-source sensor data sequence of the power plant to generate a plurality of lightweight data packets; perform bit error rate optimization on the plurality of lightweight data packets to generate optimized lightweight data packets;

[0069] A channel allocation module, configured to encapsulate the optimized lightweight data packets with a weak signal transmission protocol to obtain weak signal transmission data; perform adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream;

[0070] A device fitting module, configured to obtain the status information of a mobile terminal device; perform dynamic energy efficiency analysis on the status information of the mobile terminal device to construct an energy efficiency model of the terminal device;

[0071] A strategy fitting module, configured to perform communication quality assessment and analysis on the energy efficiency model of the terminal device, and perform adaptive transmission strategy decision-making to construct a weak signal environment transmission strategy;

[0072] A comprehensive scheduling module, based on the weak signal environment transmission strategy, performs real-time dynamic scheduling control on the adaptive transmission channel data stream to construct a multi-source sensor data sharing model of the power plant, and each module is connected by wired and / or wireless means.

[0073] The technical effects and advantages of a multi-source sensor data sharing system and method under weak signals of the present invention:

[0074] By performing signal quality assessment and enhancement processing on multi-source sensor data in power plants, the present invention significantly improves the quality and usability of data in weak signal environments, and reduces the impact of signal interference and noise on data acquisition. By adopting low-power coding and compression technology, the data transmission load is greatly reduced, and the battery life of mobile terminal devices is extended; through bit error rate optimization, the reliability and integrity of data transmission in weak signal environments are improved; in view of the characteristics of weak signal environments, a dedicated transmission protocol encapsulation mechanism is designed, combined with adaptive channel allocation technology, significantly improving the transmission success rate of data in weak signal environments. By performing dynamic energy efficiency analysis on mobile terminal devices, an accurate energy efficiency model is constructed, providing a reliable basis for transmission strategy decision-making and achieving efficient utilization of energy resources. Based on the energy efficiency model of terminal devices and the communication quality assessment results, the optimal transmission strategy can be automatically determined, adapting to changes in different signal environments and ensuring the efficiency and reliability of data transmission. According to the real-time changes in signal quality levels, the data transmission strategy can be dynamically adjusted, while ensuring the transmission of critical data, optimizing resource utilization, and improving the overall transmission efficiency. Through priority classification of data and signal environment classification, refined transmission control is achieved, ensuring that the most critical data can still be transmitted in the most adverse signal environments. Subsequently, a complete data sharing model is constructed, covering the entire process from data acquisition, processing, transmission to application, improving the reliability and efficiency of data transmission. Description of the Drawings

[0075] Figure 1 It is a schematic flowchart of the steps of a method for sharing multi-source sensor data under weak signals according to the present invention;

[0076] Figure 2 It is a schematic flowchart of the detailed implementation steps of step S1 of the present invention;

[0077] Figure 3 It is a schematic diagram of a system for sharing multi-source sensor data under weak signals according to the present invention. Detailed Implementation Modes

[0078] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0079] Please refer to Figures 1 to 2 As shown, this embodiment provides a method for sharing multi-source sensor data under weak signals, including the following steps:

[0080] Step S1: Obtain the multi-source sensor dataset of the power plant; perform signal quality evaluation and enhancement processing on the multi-source sensor dataset of the power plant, and perform spatio-temporal fusion serialization processing to obtain the multi-source sensor data sequence of the power plant;

[0081] Step S2: Perform low-power coding compression on the multi-source sensor data sequence of the power plant to generate multiple lightweight data packets; perform bit error rate optimization on the multiple lightweight data packets to generate optimized lightweight data packets;

[0082] Step S3: Perform weak signal transmission protocol encapsulation on the optimized lightweight data packets to obtain weak signal transmission data; perform adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream;

[0083] Step S4: Obtain the status information of the mobile terminal device; perform dynamic energy efficiency analysis on the status information of the mobile terminal device to construct an energy efficiency model of the terminal device;

[0084] Step S5: Perform communication quality evaluation and analysis on the energy efficiency model of the terminal device, and make an adaptive transmission strategy decision to construct a weak signal environment transmission strategy;

[0085] Step S6: Based on the weak signal environment transmission strategy, perform real-time dynamic scheduling control on the adaptive transmission channel data stream to construct a multi-source sensor data sharing model of the power plant.

[0086] Through signal quality assessment and enhancement processing and spatio-temporal fusion serialization processing, the present invention improves the quality of multi-source sensor data in power plants, provides a reliable data basis for subsequent analysis. After obtaining the multi-source sensor data sequence of the power plant, it can better understand the characteristics and variation laws of the sensor data, provide a basis for subsequent processing and analysis, generate multiple lightweight data packets to reduce the data transmission load, improve the transmission efficiency, and at the same time retain important information. By optimizing the bit error rate, the reliability of data packet transmission is improved, providing better data support for subsequent tasks. The weak signal transmission protocol encapsulation adapts the data to the weak signal environment, improving the data transmission success rate. The adaptive channel allocation increases the flexibility of data transmission, improving the data transmission efficiency in the weak signal environment. The dynamic energy efficiency analysis deeply understands the energy utilization of mobile terminal devices, constructs an energy efficiency model of the terminal device, providing a basis for subsequent analysis and transmission strategy decision-making. The establishment of the terminal device energy efficiency model helps the power plant operation and maintenance personnel better understand the device energy consumption situation and communication characteristics, providing support for data transmission decision-making. The adaptive transmission strategy decision timely identifies and responds to the transmission challenges in the weak signal environment, protecting the integrity of data transmission. The transmission strategy in the weak signal environment is constructed according to the terminal device energy efficiency model and the communication quality assessment results, improving the data transmission efficiency. The real-time dynamic scheduling control realizes the real-time monitoring and adjustment of data transmission according to the transmission strategy in the weak signal environment, improving the data transmission success rate and the adaptability of the system. The multi-source sensor data sharing model of the power plant is constructed to comprehensively manage and optimize the data transmission of the power plant, improving the data sharing efficiency and the adaptability to the weak signal environment.

[0087] In the embodiment of the present invention, refer to Figure 1 , which is a schematic flow chart of the steps of a method for sharing multi-source sensor data under weak signals of the present invention. In this example, the steps of the method for sharing multi-source sensor data under weak signals include:

[0088] Step S1: Obtain the multi-source sensor data set of the power plant; perform signal quality assessment and enhancement processing on the multi-source sensor data set of the power plant, and perform spatio-temporal fusion serialization processing to obtain the multi-source sensor data sequence of the power plant;

[0089] In this embodiment, various types of industrial data are collected and integrated from multiple sensors deployed in each production link of the power plant. These data include: temperature sensor data (such as boiler temperature monitoring), pressure sensor data (such as pipeline pressure monitoring), flow sensor data (such as water flow and air flow monitoring), vibration sensor data (such as equipment vibration monitoring), position sensor data (such as valve opening monitoring), etc. These heterogeneous multi-source sensor data are integrated into a comprehensive multi-source sensor dataset of the power plant. For the low-quality data points or noise data existing in the multi-source sensor dataset of the power plant, signal quality assessment and enhancement processing are carried out. A quality assessment method based on signal strength or a noise detection model based on machine learning is used to clean and optimize the data. The high-quality data after enhancement processing is retained as the basic data for subsequent processing. For the enhanced multi-source sensor data, spatio-temporal fusion serialization processing is carried out according to the timestamp and spatial position information. The method of spatio-temporal fusion serialization adopts a fusion algorithm based on spatio-temporal correlation, such as spatio-temporal sequence fusion, etc. Through spatio-temporal fusion serialization, these multi-source sensor data are integrated into several spatio-temporal serialized data sequences, and these data sequences reflect the distribution and change rules of the power plant sensor data in the time and space dimensions.

[0090] Step S2: Perform low-power coding compression on the multi-source sensor data sequence of the power plant to generate multiple lightweight data packets; optimize the bit error rate of the multiple lightweight data packets to generate optimized lightweight data packets;

[0091] In this embodiment, for the multi-source sensor data sequence of the power plant obtained above, a low-power compression method is used for coding. A compression model based on a lightweight algorithm is used. Different coding structures are defined according to the characteristics of various types of sensor data. By training the compression model, the original high-dimensional multi-source sensor data is compressed and encoded into lightweight data packets. The low-power coding is performed separately on different types of sensor data to generate multiple lightweight data packets. The multiple lightweight data packets generated above are input into an error rate optimization framework. This framework adopts a forward error correction coding structure to learn the association and redundancy characteristics between multiple data packets. During the optimization process, various error rate evaluation functions are defined, such as bit error rate, packet loss rate, transmission delay, etc., to perform end-to-end error rate optimization training. Finally, a set of optimized multi-modal lightweight data packets, that is, optimized lightweight data packets, are obtained.

[0092] Step S3: Package the optimized lightweight data packets with a weak signal transmission protocol to obtain weak signal transmission data; perform adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream;

[0093] In this embodiment, the optimized lightweight data packet is used as the input and processed by a weak signal transmission protocol encapsulation module. Based on the low-power wide-area network technology, this weak signal transmission protocol encapsulation module encapsulates the original lightweight data packet into a protocol format suitable for transmission in a weak signal environment. Each data unit in the weak signal transmission protocol corresponds to a part of the original lightweight data packet, but there is a complex mapping relationship between them. Through this weak signal transmission protocol encapsulation, a weak signal transmission data is obtained, which contains a weak signal adaptation representation of the original multi-source sensor data. The weak signal transmission data is further processed through a series of adaptive channel allocation processes. Through these complex adaptive channel allocation transformations, the weak signal transmission data is further optimized and protected to generate an adaptive transmission channel data stream.

[0094] Step S4: Obtain the mobile terminal device status information; perform dynamic energy efficiency analysis on the mobile terminal device status information, and construct a terminal device energy efficiency model;

[0095] In this embodiment, the device operation status information is collected and integrated from the mobile terminal devices used by the power plant operation and maintenance personnel. For the collected mobile terminal device status information, machine learning and data mining methods are used to extract the dynamic energy efficiency characteristics of the devices. The characteristics include the power status of the devices, such as battery power, discharge rate, charging cycle, etc., the operation status of the devices, such as CPU occupancy rate, memory usage rate, communication module power consumption, etc., and the communication behavior of the devices, such as data sending frequency, signal strength, communication protocol type, etc. Integrating these dynamic energy efficiency characteristics into a terminal device energy efficiency model can comprehensively describe the energy efficiency characteristics of each mobile terminal device.

[0096] Step S5: Conduct communication quality evaluation and analysis on the terminal device energy efficiency model, and make an adaptive transmission strategy decision to construct a weak signal environment transmission strategy;

[0097] In this embodiment, the previously constructed terminal device energy efficiency model is input into a communication quality evaluation and analysis module based on machine learning. This analysis module will conduct a comparative analysis on the energy efficiency model of each terminal device by referring to the known weak signal communication characteristics. The results of the communication quality evaluation and analysis are input into an adaptive transmission strategy decision model. This transmission strategy decision model will comprehensively consider factors such as the terminal device energy efficiency model, communication quality characteristics, and power plant operation safety strategies to dynamically evaluate the transmission capabilities of the devices. According to the transmission capability evaluation results, a targeted weak signal environment transmission strategy is formulated.

[0098] Step S6: Based on the weak signal environment transmission strategy, perform real-time dynamic scheduling and control on the adaptive transmission channel data stream to construct a power plant multi-source sensor data sharing model.

[0099] In this embodiment, based on the adaptive transmission channel data stream, the overall architecture of the multi-source sensor data sharing in the power plant is defined. This architecture includes: a data transmission module, a channel management module, an energy efficiency control module, etc. Through the collaborative work of these functional modules, a complete power plant data sharing framework is constructed. This framework provides basic support for subsequent power plant data sharing. The obtained weak signal environment transmission strategy is docked with the power plant data sharing framework. According to the different signal quality levels, corresponding decision rules such as transmission scheduling and resource allocation are formulated, and these rules are mapped into the respective functional modules of the power plant data sharing framework to construct a power plant-level data sharing scheduling framework. This data sharing scheduling framework realizes refined power plant data transmission control and resource scheduling. The constructed data sharing scheduling framework is deployed into the power plant's data management system. For each transmission request of sensor data, the framework will perform real-time scheduling control according to the dynamic signal quality evaluation results. The means of scheduling control include: dynamic bandwidth allocation, transmission priority adjustment, transmission timing selection, etc. Through this dynamic scheduling control mechanism, a comprehensive multi-source sensor data sharing model for the power plant is constructed.

[0100] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0101] Step S11: Obtain the multi-source sensor data set of the power plant;

[0102] Step S12: Perform sensor type identification and classification processing on the multi-source sensor data set of the power plant to obtain online sensor data and mobile measurement sensor data;

[0103] Step S13: Monitor the data quality of the online sensor data and identify abnormal data points;

[0104] Step S14: Perform adaptive filtering processing on the online sensor data according to the abnormal data points to obtain filtered online sensor data;

[0105] Step S15: Perform signal enhancement and optimization on the mobile measurement sensor data to generate enhanced mobile measurement sensor data;

[0106] Step S16: Perform spatio-temporal fusion serialization processing on the filtered online sensor data and the enhanced mobile measurement sensor data to obtain the multi-source sensor data sequence of the power plant.

[0107] In this embodiment, various types of industrial-related data, including temperature data, pressure data, flow data, etc., are collected from multiple sensors deployed in each production link of the power plant. The collected raw data is reasonably classified and archived to establish a multi-source sensor dataset for the power plant. The machine learning method is used to identify the sensor types in the dataset, and the data is divided into online sensor data and mobile measurement sensor data. For the online sensor data, data features are extracted through data mining techniques; for the mobile measurement sensor data, signal features are extracted using signal processing techniques. The time series in the online sensor data is analyzed to detect whether there are abnormal data points. Using methods based on time series analysis, such as outlier detection and signal mutation detection, the abnormal data points in the data are identified. For the identified abnormal data points, an adaptive filtering model (such as Kalman filtering, median filtering, wavelet transform, etc.) is used to filter the abnormal data. The results of the filtering process are filled into the original online sensor data to repair the abnormal data points and obtain the filtered online sensor data. For the mobile measurement sensor data, a signal enhancement algorithm is used for optimization, the weak signal data is enhanced, the interference data is suppressed, and the signal loss is compensated. The signal-enhanced mobile measurement sensor data and the filtered online sensor data are subjected to spatio-temporal fusion serialization processing. Through spatio-temporal correlation analysis, the correlation relationship between different sensor data is established to achieve the spatio-temporal fusion of data and obtain the multi-source sensor data sequence of the power plant.

[0108] In this embodiment, the specific steps of step S15 are as follows:

[0109] Perform signal strength analysis on the mobile measurement sensor data to obtain signal strength characteristics;

[0110] Evaluate the signal-to-noise ratio based on the signal strength characteristics to obtain a signal quality index;

[0111] Perform threshold analysis on the signal quality index to generate signal quality grading data;

[0112] Perform parameterized signal enhancement processing on the mobile measurement sensor data based on the signal quality grading data to generate parameterized enhanced data;

[0113] Perform weak signal identification and compensation on the mobile measurement sensor data based on the parameterized enhanced data, and mark the weak signal data points;

[0114] Perform enhancement and optimization processing on the weak signal data points to generate enhanced mobile measurement sensor data.

[0115] In this embodiment, for the mobile measurement sensor data of the power plant, signal processing technology is used to analyze the signal intensity, calculate the amplitude, duration, change trend, etc. of the signal, generate features representing the signal intensity, that is, signal intensity features. Based on the signal intensity features, signal analysis technology is used to evaluate the signal-to-noise ratio, calculate indicators such as the ratio of the signal to noise and spectral characteristics, and generate indicators representing the signal quality, that is, signal quality indicators; for the signal quality indicators, evaluate their relationship with the preset threshold, and analyze the stability and reliability of the signal, and generate data representing the signal quality level, that is, signal quality classification data;

[0116] Specifically, the calculation formula for signal-to-noise ratio evaluation is:

[0117] ; where P_s is the signal power, P_n is the noise power; SNR is the signal-to-noise ratio, and the unit is decibel (dB).

[0118] The calculation formula for the signal quality indicator is:

[0119] ; where 、 and are weight coefficients, σ_s is the standard deviation of the signal amplitude, μ_s is the average value of the signal amplitude, N_v is the number of effective sampling points, and N_t is the total number of sampling points.

[0120] The generation rule for the signal quality classification data is:

[0121] When Q≥Q_high, the signal quality is high level;

[0122] When Q_medium≤Q<Q_high, the signal quality is medium level;

[0123] When Q<Q_medium, the signal quality is low level.

[0124] Where Q_high and Q_medium are the preset upper threshold and lower threshold of the signal quality.

[0125] Apply the signal quality grading data to the mobile measurement sensor data. According to different quality levels, adopt a parameterized signal enhancement method. For high-quality signals, use mild enhancement; for medium-quality signals, use moderate enhancement; for low-quality signals, use intense enhancement to generate parameterized enhanced data. Further analyze the parameterized enhanced data to identify weak signal data points, which may come from areas at the edge of signal coverage, signal occlusion areas, or areas with severe interference. Mark these weak signal data points. For the marked weak signal data points, adopt targeted enhancement optimization processing. For data with weak signal amplitudes, use amplitude enhancement technology to increase the signal strength; for data with low signal-to-noise ratios, use noise reduction technology to improve the signal quality; for data with unstable signals, use smoothing technology to improve the signal stability.

[0126] In this embodiment, the detailed implementation steps of step S2 include:

[0127] Step S21: Perform priority grading on the power plant multi-source sensor data sequence to generate data sequences with different priorities;

[0128] Step S22: Analyze the data importance of the data sequences with different priorities to obtain multiple data priority features;

[0129] Step S23: Perform low-power coding compression based on multiple data priority features to generate multiple lightweight data packets;

[0130] Step S24: Evaluate the transmission error rate of the multiple lightweight data packets to obtain a data packet error risk assessment value;

[0131] Step S25: Perform redundant coding processing on the multiple lightweight data packets to generate multiple redundant protection lightweight data packets;

[0132] Step S26: Optimize the error rate of the multiple redundant protection lightweight data packets based on the data packet error risk assessment value to generate optimized lightweight data packets.

[0133] In this embodiment, for the obtained multi-source sensor data sequence of the power plant, according to the business importance, timeliness, and security level of the data, the data sequence is divided into different priority levels. For example, alarm data, key equipment status data, etc. are divided into high-priority levels, and historical trend data, environmental monitoring data, etc. are divided into low-priority levels, etc., to generate data sequences with different priorities. For data sequences with different priorities, data mining and machine learning methods are used to analyze their importance features, including the criticality of the data, timeliness requirements, business impact scope, etc., to obtain multiple data priority features. Based on these data priority features, a low-power coding and compression method is used to compress the data. For data with different priorities, different compression ratios and compression algorithms are used, such as differential coding, run-length coding, Huffman coding, etc., to generate multiple lightweight data packets. For the generated multiple lightweight data packets, the transmission bit error rate is evaluated. Through simulation or historical data analysis, the transmission bit error rate in a weak signal environment is estimated to obtain the bit error risk assessment value of the data packet. According to the bit error risk assessment value, redundant coding processing is performed on the lightweight data packets, such as parity bits, error correction codes, etc.; more redundant protection is added for high-risk data packets, and redundant protection is reduced for low-risk data packets, to generate multiple lightweight data packets with redundant protection. For these lightweight data packets with redundant protection, further optimization is performed based on the bit error risk assessment value, adjusting the redundant code rate, coding method, and data block size, to minimize the transmission overhead while ensuring the transmission reliability, and finally generate optimized lightweight data packets.

[0134] In this embodiment, the specific steps of step S3 are as follows:

[0135] Step S31: Encapsulate the optimized lightweight data packet with a weak signal transmission protocol to obtain weak signal transmission data;

[0136] Step S32: Detect the transmission channel quality of the weak signal transmission data to obtain the channel quality assessment result;

[0137] Step S33: Analyze the multi-channel resources based on the channel quality assessment result to obtain an available channel resource pool;

[0138] Step S34: Perform adaptive channel allocation on the available channel resource pool to obtain an adaptive transmission channel data stream.

[0139] In this embodiment, the optimized lightweight data packet is used as the input and processed by the weak signal transmission protocol encapsulation module. This module uses a transmission protocol designed specifically for weak signal environments, such as LoRa, NBIoT, or an improved low-power Bluetooth protocol, to encapsulate the data packet. The encapsulation process includes adding a protocol header, fragmenting, generating a check code, etc., to make the data adapt to the transmission characteristics of the weak signal environment, obtaining the weak signal transmission data. For the encapsulated weak signal transmission data, the transmission channel quality is detected. By sending detection packets or analyzing historical transmission data, the quality status of the currently available channels is evaluated, including signal strength, interference level, bandwidth capacity, etc., to obtain the channel quality evaluation result. Based on the channel quality evaluation result, various communication channel resources available in the power plant environment are analyzed, including wireless channels (such as Wi-Fi, cellular networks, dedicated wireless networks) and wired channels (such as industrial Ethernet, fieldbus). Considering the availability, stability, and transmission capacity of each channel comprehensively, an available channel resource pool is formed. An adaptive allocation is performed on the available channel resource pool. According to the priority, size, and timeliness requirements of the data packet, as well as the quality status of each channel, the transmission channel is intelligently allocated. High-priority data is allocated to high-quality channels, and low-priority data is allocated to ordinary channels, forming an adaptive transmission channel data stream.

[0140] In this embodiment, the specific steps of step S4 are as follows:

[0141] Step S41: Monitor the working state of the mobile terminal device to obtain the mobile terminal device status information;

[0142] Step S42: Analyze the power of the mobile terminal device status information to generate the terminal device power status data;

[0143] Step S43: Perform dynamic energy efficiency analysis on the terminal device power status data to generate dynamic energy efficiency characteristics;

[0144] Step S44: Fit the energy efficiency mode to the dynamic energy efficiency characteristics to construct the terminal device energy efficiency model.

[0145] In this embodiment, the state monitoring module embedded in the mobile terminal device is used to monitor the working state of the device in real time, including battery power, CPU usage rate, memory occupancy, communication module state, sensor working state, etc., to obtain the state information of the mobile terminal device. For the collected state information of the mobile terminal device, the data related to the battery power is analyzed emphatically, including the current power percentage, discharge rate, charging state, battery health, etc., to generate the power state data of the terminal device. The power state data of the terminal device is analyzed deeply to study the influence of different operations and communication behaviors on the power consumption, identify the energy efficiency bottleneck and optimization space, and generate dynamic energy efficiency characteristics. Based on the dynamic energy efficiency characteristics, machine learning methods (such as regression analysis, neural network, etc.) are used to fit the energy efficiency pattern of the device, establish a model that can predict the energy consumption under different operating conditions, and construct the energy efficiency model of the terminal device.

[0146] In this embodiment, the specific steps of step S43 are as follows:

[0147] Extract multiple power consumption time points based on the power state data of the terminal device;

[0148] Calculate the energy consumption rate of the mobile terminal device state information according to the multiple power consumption time points to obtain the energy consumption rate;

[0149] Perform communication energy consumption statistics on the power state data of the terminal device to obtain the energy consumption value of each communication;

[0150] Analyze the communication energy efficiency of the energy consumption value of each communication according to the energy consumption rate to obtain the communication energy efficiency characteristic data;

[0151] Identify the remaining battery power of the device based on the power state data of the terminal device;

[0152] Perform prediction analysis on the device's battery life based on the remaining battery power of the device to obtain the device battery life prediction data;

[0153] Perform dynamic energy efficiency analysis on the device battery life prediction data and the communication energy efficiency characteristic data to generate dynamic energy efficiency characteristics.

[0154] In this embodiment, by analyzing the time series changes of the power status data of the terminal device, the time points with significant power decline are identified. These time points usually correspond to high-energy-consuming operations or communication behaviors. The power consumption time points are extracted as the key points for analysis. Based on the extracted power consumption time points and combined with the device status information in the same time period, the power consumption rates of the device in different working states are calculated, including the power consumption rate in the idle state, the power consumption rate in data processing, the power consumption rate in communication transmission, etc. Particular attention is paid to the power consumption during data communication of the device. The power consumption values of each communication activity (sending and receiving data) are statistically analyzed, and the power consumption differences under different communication protocols and different signal strength conditions are analyzed. Combining the calculated power consumption rates, the energy efficiency ratio of each communication (transmission data volume / power consumption value) is evaluated, the energy efficiency performance of different communication strategies is analyzed, and the communication energy efficiency characteristic data are obtained.

[0155] The calculation formula for the power consumption rate is:

[0156] ; where ER is the power consumption rate, ΔE is the power change value, Δt is the time interval, Ws is the weight coefficient of the current working state (such as communication state = 1.2, data processing = 1.0, idle state = 0.8), FT is the temperature influence factor ( ), T is the current temperature, Tref is the reference temperature of 25 。 °C), Fage is the device aging factor ( ), t is the device usage time, tref is the device expected life), and ERb is the reference power consumption rate, reflecting the inherent power consumption characteristics of the device.

[0157] The calculation formula for the communication energy efficiency ratio is:

[0158] ; where EER is the communication energy efficiency ratio, D is the transmission data volume, EC is the communication power consumption value, Wp is the data priority weight (urgent data = 1.5, ordinary data = 1.0, non-critical data = 0.8), Psu is the transmission success rate, Fsi is the signal quality influence factor ( , SNR is the current signal-to-noise ratio, SNRmax is the maximum reference signal-to-noise ratio), and Fpr is the transmission protocol efficiency factor (determined according to different protocol characteristics, such as LoRa = 1.2, NB-IoT = 1.0, Wi-Fi = 0.9).

[0159] By analyzing the current battery power level and the historical discharge curve, the remaining power status of the device is identified, and the remaining working ability of the device is evaluated. Based on the identified remaining power and the calculated power consumption rate, the battery life of the device in different working modes is predicted, and the device battery life prediction data are obtained.

[0160] The prediction formula for the battery life is:

[0161] ; where Tre is the predicted remaining working time, Erem is the remaining battery power, ERcu is the current energy consumption rate, Fbat is the battery health status factor ( , Ncy is the number of battery charge and discharge cycles, Nmax is the designed cycle life of the battery), FT is the temperature impact factor, Fwo is the expected workload change factor (predicted based on historical usage patterns), a1 is the non-linear adjustment coefficient (usually taken as 0.1 - 0.3), E_full is the full battery power value, is the non-linear correction term for battery power, reflecting the characteristics of the battery discharge curve.

[0162] Based on the comprehensive device endurance prediction data and communication energy efficiency characteristic data, a dynamic energy efficiency characteristic model of the device is constructed. This model can describe the energy efficiency performance of the device under different working conditions and communication environments, providing a basis for subsequent transmission strategy decisions.

[0163] In this embodiment, the specific steps of step S5 are as follows:

[0164] Step S51: Evaluate the transmission power of the terminal device energy efficiency model and extract available transmission power data;

[0165] Step S52: Analyze the signal coverage area of the mobile terminal device status information to obtain a signal coverage heat map;

[0166] Step S53: Based on the signal coverage heat map, conduct a communication quality evaluation and analysis of the available transmission power data to obtain a communication quality evaluation result;

[0167] Step S54: Predict the transmission success rate of the communication quality evaluation result and generate a transmission strategy evaluation index;

[0168] Step S55: Based on the transmission strategy evaluation index, make an adaptive transmission strategy decision and construct a transmission strategy in a weak signal environment.

[0169] In this embodiment, based on the previously constructed terminal device energy efficiency model, evaluate the power level available for data transmission of the device under the current battery state. Considering the remaining battery power, expected working time, and critical task requirements of the device, calculate a reasonable transmission power allocation scheme and extract available transmission power data. Conduct a spatial distribution analysis of the signal strength data collected by the mobile terminal device, and combine the device's movement trajectory and historical signal data to construct a signal coverage heat map within the power plant area. This heat map shows the signal strength distribution at different locations, including strong signal areas, weak signal areas, and blind spots. Combine the signal coverage heat map with the available transmission power data for analysis, evaluate the communication quality at different regions and different power levels, considering signal propagation loss, interference level, and communication protocol characteristics, to obtain a communication quality evaluation result.

[0170] Based on the communication quality assessment results, a machine learning model is used to predict the data transmission success rate under different transmission strategies. Considering factors such as packet size, transmission timing, transmission power, and protocol selection, transmission strategy evaluation metrics are generated. The transmission success rate prediction model uses a random forest algorithm trained with historical transmission data. The input features include signal strength, transmission power, packet size, current battery level, etc., and the output is the predicted transmission success rate. According to the transmission strategy evaluation metrics, a decision tree or reinforcement learning algorithm is used to formulate an adaptive transmission strategy. For different signal environments and device states, the optimal transmission parameters and methods are selected to construct a transmission strategy for weak signal environments.

[0171] In this embodiment, the transmission power evaluation uses the following formula:

[0172] ; where P_ava is the available transmission power, P_max is the maximum transmission power of the device, P_ther is the maximum power under thermal constraints, D_pri is the dynamic coefficient of data priority (emergency data = 1.3, critical data = 1.1, regular data = 0.9, low-priority data = 0.7), and b is the adjustment exponent (usually taken as 0.7 - 1.2, reflecting the non-linear relationship between battery level and power).

[0173] The communication quality assessment uses the following calculation formula:

[0174] ; where Q_comm is the communication quality assessment value, RSSI_norm is the normalized received signal strength indicator, SNR_norm is the normalized signal-to-noise ratio, BER_est is the estimated bit error rate, w1, w2, and w3 are the weight coefficients of each item, and the sum of the three is 1.

[0175] In this embodiment, the specific steps of step S6 are as follows:

[0176] Step S61: Construct a data sharing architecture for the adaptive transmission channel data stream to generate a data sharing framework;

[0177] Step S62: Map the transmission scheduling rules to the data sharing framework based on the transmission strategy for weak signal environments to construct a power plant data sharing scheduling framework;

[0178] Step S63: Perform real-time dynamic scheduling control on the power plant data sharing scheduling framework to construct a power plant multi-source sensor data sharing model;

[0179] The real-time dynamic scheduling control is specifically: based on the transmission strategy for weak signal environments, identify the signal quality level of the current communication environment;

[0180] The signal quality levels include: high-quality signal area, medium-quality signal area, and low-quality signal area;

[0181] When the signal quality level of the communication environment is in the high-quality signal area, the power plant data sharing and scheduling framework performs high-frequency transmission processing of all data;

[0182] When the signal quality level of the communication environment is in the medium-quality signal area, the power plant data sharing and scheduling framework performs priority transmission processing of key data;

[0183] When the signal quality level of the communication environment is in the low-quality signal area, the power plant data sharing and scheduling framework performs compressed transmission processing of emergency data.

[0184] In this embodiment, based on the adaptive transmission channel data stream obtained above, the overall architecture of power plant data sharing is designed and constructed. This architecture includes a data acquisition layer, a data processing layer, a transmission control layer, and an application service layer. Each layer interacts through standard interfaces to form a complete data sharing framework. Map the weak signal environment transmission strategy constructed above into the data sharing framework, and define specific scheduling rules for the transmission control layer in the framework, including data priority determination rules, channel selection rules, transmission timing decision rules, and power control rules, to construct a power plant data sharing and scheduling framework.

[0185] Implement real-time dynamic scheduling control on the constructed power plant data sharing and scheduling framework, and automatically adjust the data transmission strategy according to the real-time status of the current communication environment. Specifically, first, based on the signal quality evaluation method in the weak signal environment transmission strategy, monitor and analyze the signal quality of the current communication environment in real time, and divide the signal quality into three levels: high-quality signal area (high signal strength, low interference), medium-quality signal area (medium signal strength, certain interference), and low-quality signal area (weak signal strength, severe interference).

[0186] When it is detected that the current is in the high-quality signal area, the power plant data sharing and scheduling framework adopts a high-frequency transmission strategy for all data, that is:

[0187] Allow the transmission of all types of sensor data, including high-priority and low-priority data; adopt a higher data sampling rate and transmission frequency; use a lower data compression ratio to retain more original data details; transmit multiple data streams simultaneously to make full use of bandwidth resources.

[0188] When it is detected that the current is in the medium-quality signal area, the power plant data sharing and scheduling framework switches to a key data priority transmission strategy, that is:

[0189] Prioritize the transmission of critical production data and safety monitoring data with high priority; reduce the data sampling rate and transmission frequency to reduce the transmission load; adopt medium-level data compression to balance data quality and transmission efficiency; cache or downsample non-critical data to reduce the amount of data transmitted;

[0190] When it is detected that the current location is in a low-quality signal area, the power plant data sharing scheduling framework activates an emergency data compression and transmission strategy, namely:

[0191] Only transmit emergency data directly related to safe production, significantly reduce the sampling rate, only retain data at critical time points, adopt a data compression algorithm with a high compression ratio, minimize the amount of transmitted data to the greatest extent, enable multiple transmission protection mechanisms, such as data redundancy coding, multi-channel parallel transmission, etc., and locally store non-emergency data and wait to transmit it after entering a better signal area.

[0192] Through this real-time dynamic scheduling and control based on the signal quality level, it is possible to adaptively adjust the transmission strategy, while ensuring the timely transmission of critical data, maximize the utilization of limited communication resources, improve the overall transmission efficiency and reliability, and ultimately construct a comprehensive power plant multi-source sensor data sharing model.

[0193] The present invention significantly improves the quality and usability of data in a weak signal environment and reduces the impact of signal interference and noise on data acquisition by performing signal quality assessment and enhancement processing on multi-source sensor data in a power plant. By adopting low-power coding and compression technology, the data transmission load is significantly reduced, and the battery life of mobile terminal devices is extended; through bit error rate optimization, the reliability and integrity of data transmission in a weak signal environment are improved; aiming at the characteristics of a weak signal environment, a dedicated transmission protocol encapsulation mechanism is designed, combined with adaptive channel allocation technology, significantly improving the transmission success rate of data in a weak signal environment. By performing dynamic energy efficiency analysis on mobile terminal devices, an accurate energy efficiency model is constructed, providing a reliable basis for transmission strategy decision-making and realizing the efficient utilization of energy resources. Based on the energy efficiency model of the terminal device and the communication quality assessment results, it is possible to automatically decide the optimal transmission strategy, adapt to changes in different signal environments, and ensure the efficiency and reliability of data transmission. According to the real-time change of the signal quality level, the data transmission strategy can be dynamically adjusted, while ensuring the transmission of critical data, optimizing resource utilization, and improving the overall transmission efficiency. Through the priority classification of data and the classification of signal environments, refined transmission control is achieved, ensuring that the most critical data can still be transmitted in the worst signal environment. Subsequently, a complete data sharing model is constructed, covering the entire process from data acquisition, processing, transmission to application, improving the reliability and efficiency of data transmission.

[0194] The above describes the multi-source sensor data sharing method under weak signal in the embodiment of the present application. The following describes the multi-source sensor data sharing system under weak signal in the embodiment of the present application. Figure 3 , is a schematic diagram of a multi-source sensor data sharing system under weak signal of the present invention. An embodiment of the multi-source sensor data sharing system under weak signal in the present application includes:

[0195] The data acquisition module is used to obtain the multi-source sensor data set of the power plant; perform signal quality assessment and enhancement processing on the multi-source sensor data set of the power plant, and perform spatiotemporal fusion serialization processing to obtain the multi-source sensor data sequence of the power plant;

[0196] The compression optimization module is used to perform low-power encoding compression on the data sequence of the multi-source sensors of the power plant to generate multiple lightweight data packets; the bit error rate of multiple lightweight data packets is optimized to generate optimized lightweight data packets;

[0197] The channel allocation module is used to perform weak signal transmission protocol encapsulation on the optimized lightweight data packet to obtain weak signal transmission data; perform adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream;

[0198] The device fitting module is used to obtain the status information of the mobile terminal device; perform dynamic energy efficiency analysis on the status information of the mobile terminal device and build an energy efficiency model for the terminal device;

[0199] The strategy fitting module is used to evaluate and analyze the communication quality of the terminal equipment energy efficiency model, make adaptive transmission strategy decisions, and build a weak signal environment transmission strategy;

[0200] The integrated dispatching module performs real-time dynamic dispatching and control of the adaptive transmission channel data stream based on the weak signal environment transmission strategy, builds a multi-source sensor data sharing model for the power plant, and connects each module via wired and / or wireless means to achieve data transmission between modules.

[0201] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0202] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0203] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0204] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0205] In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0206] In the description of the present invention, the meaning of "several" is one or more, and the meaning of "a large number" is two or more.

[0207] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the said embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0208] For the formulas in this specification, the dimensional quantities are removed and only the numerical values are calculated. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula closest to the actual situation. The preset parameters and threshold values in the formulas are set by those skilled in the art according to the actual situation.

[0209] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for sharing multi-source sensor data under weak signals, characterized in that: include: Step S1: Acquire a multi-source sensor data set of a power plant; The signal quality of the multi-source sensor data set of the power plant is evaluated and enhanced, and spatiotemporal fusion serialization is performed according to the timestamp and spatial location information to obtain the multi-source sensor data sequence of the power plant; Step S2: performing low-power encoding compression on the data sequence of the multi-source sensors of the power plant to generate multiple lightweight data packets; performing bit error rate optimization on the multiple lightweight data packets to generate optimized lightweight data packets; Step S3: performing weak signal transmission protocol encapsulation on the optimized lightweight data packet to obtain weak signal transmission data; performing adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream; Step S4: obtaining mobile terminal device status information; performing dynamic energy efficiency analysis on the mobile terminal device status information and constructing a terminal device energy efficiency model; Step S5: Perform communication quality evaluation and analysis on the energy efficiency model of the terminal device, make adaptive transmission strategy decisions, and build a weak signal environment transmission strategy; Step S6: Based on the weak signal environment transmission strategy, the adaptive transmission channel data stream is dynamically dispatched and controlled in real time to build a multi-source sensor data sharing model for the power plant.

2. The method for sharing multi-source sensor data under weak signal conditions according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Acquire a multi-source sensor data set of a power plant; Step S12: performing sensor type identification and classification processing on the multi-source sensor data set of the power plant to obtain online sensor data and mobile sensor data; Step S13: monitoring the data quality of the online sensor data and identifying abnormal data points; Step S14: performing adaptive filtering on the online sensor data according to the abnormal data points to obtain filtered online sensor data; Step S15: performing signal enhancement optimization on the mobile sensor data to generate enhanced mobile sensor data; Step S16: Perform spatiotemporal fusion serialization processing on the filtered online sensor data and the enhanced mobile sensor data to obtain a power plant multi-source sensor data sequence.

3. The method for sharing multi-source sensor data under weak signal conditions according to claim 2, characterized in that: The specific steps of step S15 are: Perform signal strength analysis on mobile sensor data to obtain signal strength characteristics; The signal-to-noise ratio is evaluated based on the signal strength characteristics to obtain the signal quality index; Perform threshold analysis on signal quality indicators to generate signal quality grading data; Performing parametric signal enhancement processing on the mobile sensor data based on the signal quality classification data to generate parametric enhanced data; Based on the parameterized enhanced data, weak signal identification and compensation are performed on the mobile sensor data, and weak signal data points are marked; Weak signal data points are enhanced and optimized to generate enhanced mobile sensor data.

4. The method for sharing multi-source sensor data under weak signal conditions according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Prioritize the data sequences of the multi-source sensors of the power plant to generate data sequences of different priorities; Step S22: performing data importance analysis on data sequences of different priorities to obtain multiple data priority features; Step S23: performing low-power coding compression based on multiple data priority features to generate multiple lightweight data packets; Step S24: evaluating the transmission bit error rate of multiple lightweight data packets to obtain a data packet error risk evaluation value; Step S25: performing redundant encoding processing on the plurality of lightweight data packets to generate a plurality of redundantly protected lightweight data packets; Step S26: Optimizing the bit error rates of multiple redundant protection lightweight data packets based on the data packet error risk assessment value to generate an optimized lightweight data packet.

5. The method for sharing multi-source sensor data under weak signal conditions according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: encapsulating the optimized lightweight data packet using a weak signal transmission protocol to obtain weak signal transmission data; Step S32: performing transmission channel quality detection on weak signal transmission data to obtain a channel quality assessment result; Step S33: performing multi-channel resource analysis on the channel quality assessment result to obtain an available channel resource pool; Step S34: Adaptively allocate channels to the available channel resource pool to obtain an adaptive transmission channel data stream.

6. The method for sharing multi-source sensor data under weak signal conditions according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: monitoring the working status of the mobile terminal device and obtaining the status information of the mobile terminal device; Step S42: Performing power analysis on the mobile terminal device status information to generate terminal device power status data; Step S43: performing dynamic energy efficiency analysis on the power status data of the terminal device to generate dynamic energy efficiency characteristics; Step S44: performing energy efficiency pattern fitting on the dynamic energy efficiency characteristics to construct an energy efficiency model for the terminal device.

7. The method for sharing multi-source sensor data under weak signal conditions according to claim 6, characterized in that: The specific steps of step S43 are: Extract multiple power consumption time points based on the power status data of the terminal device; Calculating the energy consumption rate of the mobile terminal device status information according to multiple power consumption time points to obtain the energy consumption rate; Perform communication energy consumption statistics on the power status data of the terminal equipment to obtain the energy consumption value of each communication; Perform communication efficiency analysis on the energy consumption value of each communication according to the energy consumption rate to obtain communication energy efficiency characteristic data; Identify the remaining power of the device based on the power status data of the terminal device; Perform device life prediction analysis based on the remaining power of the device to obtain device life prediction data; Perform dynamic energy efficiency analysis on the device life prediction data and communication energy efficiency characteristic data to generate dynamic energy efficiency characteristics.

8. The method for sharing multi-source sensor data under weak signal conditions according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: Evaluate the transmission power of the terminal equipment energy efficiency model and extract available transmission power data; Step S52: Analyze the signal coverage area of ​​the mobile terminal device status information to obtain a signal coverage heat map; Step S53: performing communication quality evaluation and analysis on the available transmission power data based on the signal coverage heat map to obtain a communication quality evaluation result; Step S54: predicting the transmission success rate of the communication quality evaluation result and generating a transmission strategy evaluation index; Step S55: Make an adaptive transmission strategy decision based on the transmission strategy evaluation index and construct a weak signal environment transmission strategy.

9. The method for sharing multi-source sensor data under weak signal conditions according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: constructing a data sharing architecture for the adaptive transmission channel data stream to generate a data sharing framework; Step S62: Mapping the transmission scheduling rules of the data sharing framework based on the weak signal environment transmission strategy to build a power plant data sharing scheduling framework; Step S63: Perform real-time dynamic dispatch control on the power plant data sharing dispatch framework to build a power plant multi-source sensor data sharing model; The real-time dynamic scheduling control specifically includes: identifying the signal quality level of the current communication environment based on the weak signal environment transmission strategy; The signal quality levels include: high-quality signal area, medium-quality signal area and low-quality signal area; When the signal quality level of the communication environment is in the high-quality signal area, the power plant data sharing scheduling framework performs high-frequency transmission processing of the full amount of data; When the signal quality level of the communication environment is in the medium quality signal area, the power plant data sharing scheduling framework performs key data priority transmission processing; When the signal quality level of the communication environment is in a low-quality signal area, the power plant data sharing scheduling framework performs emergency data compression transmission processing.

10. A multi-source sensor data sharing system under weak signal, which is used to implement the multi-source sensor data sharing method under weak signal according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, used to obtain multi-source sensor data sets of power plants; The signal quality of the multi-source sensor data set of the power plant is evaluated and enhanced, and time-space fusion serialization is performed to obtain the multi-source sensor data sequence of the power plant; The compression optimization module is used to perform low-power encoding compression on the data sequence of the multi-source sensors of the power plant to generate multiple lightweight data packets; the bit error rate of multiple lightweight data packets is optimized to generate optimized lightweight data packets; The channel allocation module is used to perform weak signal transmission protocol encapsulation on the optimized lightweight data packet to obtain weak signal transmission data; perform adaptive channel allocation on the weak signal transmission data to obtain an adaptive transmission channel data stream; The device fitting module is used to obtain the status information of the mobile terminal device; perform dynamic energy efficiency analysis on the status information of the mobile terminal device and build an energy efficiency model for the terminal device; The strategy fitting module is used to evaluate and analyze the communication quality of the terminal equipment energy efficiency model, make adaptive transmission strategy decisions, and build a weak signal environment transmission strategy; The integrated dispatching module performs real-time dynamic dispatching and control of the adaptive transmission channel data stream based on the weak signal environment transmission strategy, builds a multi-source sensor data sharing model for the power plant, and connects each module via wired and / or wireless means.

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