Multi-source sensor data sharing system and method under weak signal

Abstract

The invention belongs to the technical field of industrial Internet of Things, and discloses a multi-source sensor data sharing system and method under weak signals. The method comprises the steps of obtaining a power plant data set; processing the power plant data set, and performing time-space fusion processing to obtain a power plant data sequence; compressing the power plant data sequence to generate a plurality of data packets; optimizing the plurality of data packets to generate lightweight data packets; performing protocol encapsulation on the lightweight data packet to obtain transmission data; performing channel allocation on the transmission data to obtain a transmission channel data stream; acquiring the state of the mobile terminal equipment; performing dynamic energy efficiency analysis on the state of the mobile terminal equipment, and constructing an energy efficiency model; the energy efficiency model is evaluated and analyzed, a transmission strategy decision is made, and an environment transmission strategy is constructed; real-time dynamic scheduling control is carried out on adaptive transmission channel data streams based on an environment transmission strategy, a power plant multi-source sensor data sharing model is constructed, and the reliability and efficiency of data transmission are improved.

Description

Technical Field

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

[0002] With the deepening of the concept of industrial intelligent manufacturing, the electric power industry is undergoing a critical stage of digital transformation. As the core facility of energy production, the operation safety, efficiency optimization and predictive maintenance of power plants are highly dependent on the real-time monitoring system built by multi-source sensor networks. At present, a typical power plant usually deploys more than 10,000 measurement points, covering multiple parameters such as temperature, pressure, vibration, flow, liquid level, etc., forming a huge and complex sensor ecosystem. These sensors can be divided into two categories according to the deployment method: fixed online sensors and mobile measurement sensors, which together constitute the "nervous system" of the power plant's operating status.

[0003] However, the complex physical environment unique to power plants - heavy concrete walls, large metal equipment, high-voltage transformers, and dense electromagnetic interference sources - usually causes serious attenuation and instability in signal propagation, which is particularly evident in key areas such as boiler areas, turbine rooms, and underground pipe galleries. Measured data show that the signal strength in these areas is generally low and fluctuates violently. Traditional transmission solutions perform poorly in such weak signal environments, with a high data packet loss rate, and cannot meet the continuity requirements of industrial-grade monitoring. At the same time, existing data processing methods are insufficient in processing high-frequency vibration noise, temperature drift, and transient interference in power plant environments, resulting in quality risks at the source of the data. In particular, when mobile measurement personnel need to conduct inspections in the plant area and transmit back equipment status data, due to the lack of targeted weak signal transmission mechanisms, key parameters are often unable to be transmitted back to the control center in a timely manner, missing the opportunity for early warning of 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, which not only leads to a sharp increase in battery energy consumption and the inability to maintain work during the inspection cycle, but also causes more interference problems due to high-power transmission. The traditional fixed-priority data transmission strategy ignores the dynamic changes in the value of different data under different working conditions. For example, during the equipment start-up and shutdown phase, the importance of vibration and temperature data is significantly higher than that during the stable operation period, but they fail to obtain priority protection for transmission resources. Especially during the full-load operation of the power plant, the peak of the communication network load and the sharp increase in the amount of equipment monitoring data occur simultaneously. There is a lack of a channel resource allocation mechanism that can sense the network congestion state and dynamically adjust it, resulting in even early warning data facing the risk of transmission delays or loss. Existing technologies separate the data collection, transmission and processing links, and fail to establish a weak signal environment data sharing framework that runs through the entire process, resulting in the inability to achieve end-to-end global optimization, which restricts the digital and intelligent transformation of power plants.

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

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for sharing multi-source sensor data under weak signals, comprising: Step S1: Acquire 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 spatiotemporal fusion serialization processing to obtain a 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.

[0006] Preferably, step S1 specifically comprises the following steps: 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.

[0007] Preferably, 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.

[0008] Preferably, 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.

[0009] Preferably, step S3 specifically comprises the following steps: 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.

[0010] Preferably, 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.

[0011] Preferably, 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.

[0012] Preferably, 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.

[0013] Preferably, 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.

[0014] A multi-source sensor data sharing system under weak signal conditions, which is used to implement the multi-source sensor data sharing method under weak signal conditions, comprises: 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; 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.

[0015] The technical effects and advantages of the multi-source sensor data sharing system and method under weak signal of the present invention are as follows: The present invention significantly improves the quality and availability of data in a weak signal environment by performing signal quality evaluation and enhancement processing on multi-source sensor data of a power plant, and reduces the influence of signal interference and noise on data acquisition. The low-power coding compression technology is adopted to greatly reduce the data transmission load and extend the battery life of the mobile terminal device; the reliability and integrity of data transmission in a weak signal environment are improved by optimizing the bit error rate; a dedicated transmission protocol encapsulation mechanism is designed in view of the characteristics of the weak signal environment, and the transmission success rate of data in a weak signal environment is significantly improved by combining the adaptive channel allocation technology. By performing dynamic energy efficiency analysis on the mobile terminal device, an accurate energy efficiency model is constructed, which provides a reliable basis for transmission strategy decision-making and realizes the efficient utilization of energy resources. Based on the energy efficiency model of the terminal device and the communication quality evaluation results, the optimal transmission strategy can be automatically determined to adapt to the changes in different signal environments and ensure the efficiency and reliability of data transmission. According to the real-time changes in the signal quality level, the data transmission strategy can be dynamically adjusted to optimize resource utilization while ensuring the transmission of key data and improve the overall transmission efficiency. By performing priority classification and signal environment classification on the data, refined transmission control is realized to ensure that the most critical data can still be transmitted in the worst signal environment. Then, a complete data sharing model was built, covering the entire process from data collection, processing, transmission to application, improving the reliability and efficiency of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the steps of a method for sharing multi-source sensor data under weak signals of the present invention; Figure 2 Detailed implementation flow chart of step S1 of the present invention; Figure 3 The figure is a schematic diagram of a multi-source sensor data sharing system under weak signals of the present invention. DETAILED DESCRIPTION

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

[0018] See also Figure 1 to Figure 2 As shown, this embodiment provides a method for sharing multi-source sensor data under weak signals, comprising the following steps: Step S1: Acquire 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 spatiotemporal fusion serialization processing to obtain a 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.

[0019] The present invention improves the quality of multi-source sensor data in power plants through signal quality evaluation and enhancement processing and time-space fusion serialization processing, providing a reliable data basis for subsequent analysis. After obtaining the multi-source sensor data sequence of the power plant, the characteristics and change laws of the sensor data can be better understood, providing a basis for subsequent processing and analysis, generating multiple lightweight data packets to reduce data transmission load and improve transmission efficiency, while retaining important information, improving the reliability of data packet transmission through bit error rate optimization, providing better data support for subsequent tasks, weak signal transmission protocol encapsulation to adapt data to weak signal environments, improving data transmission success rate, adaptive channel allocation to increase the flexibility of data transmission, and improving data transmission efficiency in weak signal environments, dynamic energy efficiency analysis to gain an in-depth understanding of the energy utilization of mobile terminal devices, and constructing Build a terminal equipment energy efficiency model to provide a basis for subsequent analysis and transmission strategy decisions. The establishment of the terminal equipment energy efficiency model helps power plant operation and maintenance personnel better understand the equipment energy consumption and communication characteristics, and provide support for data transmission decisions. Adaptive transmission strategy decisions can timely identify and respond to transmission challenges in weak signal environments, protect the integrity of data transmission, and build a weak signal environment transmission strategy. According to the terminal equipment energy efficiency model and communication quality assessment results, formulate corresponding transmission strategies to improve data transmission efficiency. Real-time dynamic scheduling and control based on the weak signal environment transmission strategy can achieve real-time monitoring and adjustment of data transmission, improve data transmission success rate and system adaptability, build a power plant multi-source sensor data sharing model to comprehensively manage and optimize power plant data transmission, and improve data sharing efficiency and adaptability to weak signal environments.

[0020] In the embodiment of the present invention, refer to Figure 1, 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: Step S1: Acquire 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 spatiotemporal fusion serialization processing to obtain a multi-source sensor data sequence of the power plant; In this embodiment, various types of industrial data are collected and integrated from various sensors deployed in various production links 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, 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 power plant multi-source sensor data set. Signal quality evaluation and enhancement processing are performed for low-quality data points or noise data in the power plant multi-source sensor data set. A quality evaluation 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 basic data for subsequent processing. The enhanced multi-source sensor data is subjected to spatiotemporal fusion serialization processing according to timestamp and spatial position information. The spatiotemporal fusion serialization method adopts a fusion algorithm based on spatiotemporal correlation, such as spatiotemporal sequence fusion. Through spatiotemporal fusion serialization, these multi-source sensor data are integrated into a number of spatiotemporal serialized data sequences. These data sequences reflect the distribution and change law of the power plant sensor data in the time and space dimensions.

[0021] 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; In this embodiment, a low-power compression method is used to encode the multi-source sensor data sequence of the power plant obtained above, and a compression model based on a lightweight algorithm is used. Different encoding structures are defined according to the characteristics of each type of sensor data. By training the compression model, the original high-dimensional multi-source sensor data is compressed and encoded into a lightweight data packet, and low-power encoding is performed on different types of sensor data to generate multiple lightweight data packets. The multiple lightweight data packets generated above are input into a bit error rate optimization framework. This framework uses a forward error correction coding structure to learn the association and redundancy characteristics between multiple data packets. During the optimization process, multiple bit error rate evaluation functions are defined, such as bit error rate, packet loss rate, transmission delay, etc., and end-to-end bit error rate optimization training is performed. Finally, a set of optimized multimodal lightweight data packets, i.e., optimized lightweight data packets, are obtained.

[0022] 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; In this embodiment, the optimized lightweight data packet is taken as input and processed by a weak signal transmission protocol encapsulation module. This weak signal transmission protocol encapsulation module is based on low-power wide area network technology and 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 the two. Through this weak signal transmission protocol encapsulation, a weak signal transmission data is obtained, which includes a weak signal adaptive representation of the original multi-source sensor data. The weak signal transmission data is further subjected to a series of adaptive channel allocation processing. 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.

[0023] 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; In this embodiment, the equipment operation status information is collected and integrated from the mobile terminal devices used by the power plant operation and maintenance personnel. Based on the collected mobile terminal equipment status information, the dynamic energy efficiency characteristics of the equipment are extracted using machine learning and data mining methods. The characteristics include the power status of the equipment, such as battery power, discharge rate, charging cycle, etc., the operating status of the equipment, such as CPU occupancy, memory usage, communication module power consumption, etc., and the communication behavior of the equipment, such as data transmission frequency, signal strength, communication protocol type, etc. Integrating these dynamic energy efficiency characteristics into a terminal equipment energy efficiency model can comprehensively describe the energy efficiency characteristics of each mobile terminal device.

[0024] 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; In this embodiment, the terminal device energy efficiency model constructed above is input into a communication quality assessment and analysis module based on machine learning. This analysis module will refer to the known weak signal communication characteristics to compare and analyze the energy efficiency model of each terminal device. The results of the communication quality assessment 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 strategy to dynamically evaluate the transmission capacity of the device. Based on the transmission capacity assessment results, a targeted weak signal environment transmission strategy is formulated.

[0025] 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.

[0026] In this embodiment, based on the adaptive transmission channel data flow, the overall architecture of the power plant multi-source sensor data sharing 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 connected to the power plant data sharing framework. According to different signal quality levels, corresponding transmission scheduling, resource allocation and other decision rules are formulated. These rules are mapped to the 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 in the data management system of the power plant. For each sensor data transmission request, the framework will perform real-time scheduling control based on 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 power plant multi-source sensor data sharing model is constructed.

[0027] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: 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.

[0028] In this embodiment, various types of industrial-related data, including temperature data, pressure data, flow data, etc., are collected from various sensors deployed in various production links of the power plant. The collected raw data are reasonably classified and archived to establish a multi-source sensor data set of the power plant. The sensor type of the data set is identified by a machine learning method, and the data is divided into online sensor data and mobile sensor data. For online sensor data, data features are extracted by data mining technology; for mobile sensor data, signal features are extracted by signal processing technology. The time series in the online sensor data is analyzed to detect whether there are abnormal data points. Methods based on time series analysis, such as outlier detection and signal mutation detection, are used to identify abnormal data points in the data. 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 sensor data, a signal enhancement algorithm is used for optimization, weak signal data is enhanced, interference data is suppressed, and signal loss is compensated. The signal-enhanced mobile sensor data and the filtered online sensor data are subjected to spatiotemporal fusion serialization processing. Through spatiotemporal correlation analysis, the association relationship between different sensor data is established, the spatiotemporal fusion of data is realized, and the multi-source sensor data sequence of the power plant is obtained.

[0029] In this embodiment, 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.

[0030] 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 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, generate data representing the signal quality level, that is, signal quality grading data; Specifically, the calculation formula for evaluating the signal-to-noise ratio is: ; 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).

[0031] The calculation formula for the signal quality indicator is: ; where 、 and are weight coefficients, \(\sigma_s\) is the standard deviation of the signal amplitude, \(\mu_s\) 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.

[0032] The generation rule for the signal quality grading data is: When \(Q\geq Q_{high}\), the signal quality is high level; When \(Q_{medium}\leq Q < Q_{high}\), the signal quality is medium level; When \(Q < Q_{medium}\), the signal quality is low level.

[0033] Where \(Q_{high}\) and \(Q_{medium}\) are the preset upper threshold and lower threshold of the signal quality.

[0034] 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, adopt mild enhancement; for medium-quality signals, adopt moderate enhancement; for low-quality signals, adopt intensive enhancement to generate parameterized enhancement data; further analyze the parameterized enhancement data to identify weak signal data points. These weak signal data points 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 and optimization processing. For data with weak signal amplitude, use amplitude enhancement technology to improve the signal intensity. For data with low signal-to-noise ratio, use noise reduction technology to improve the signal quality. For data with unstable signals, use smoothing technology to improve the signal stability.

[0035] In this embodiment, the detailed implementation steps of step S2 include: 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.

[0036] In this embodiment, for the aforementioned obtained power plant multi-source sensor data sequence, the data sequence is divided into different priority levels according to the business importance, timeliness and security level of the data. For example, alarm data, key equipment status data, etc. are divided into high priority, and historical trend data, environmental monitoring data, etc. are divided into low priority, etc., to generate data sequences of different priorities. For data sequences of different priorities, data mining and machine learning methods are used to analyze their importance characteristics, including the criticality of the data, timeliness requirements, business impact range, etc., to obtain multiple data priority characteristics. Based on these data priority characteristics, a low-power coding compression method is used to compress the data. For data of 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, and the transmission bit error rate in a weak signal environment is estimated through simulation or historical data analysis to obtain a data packet error risk assessment value. According to the error risk assessment value, the lightweight data packets are processed with redundant coding, such as check bits, error correction codes, etc. More redundant protection is added to high-risk data packets, and redundant protection is reduced for low-risk data packets, generating multiple redundant protection lightweight data packets. These redundant protection lightweight data packets are further optimized based on the error risk assessment value, and the redundant code rate, encoding method and data block size are adjusted to minimize the transmission overhead while ensuring transmission reliability, and finally generate optimized lightweight data packets.

[0037] In this embodiment, 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.

[0038] In this embodiment, the optimized lightweight data packet is used as input and processed by the weak signal transmission protocol encapsulation module. The module uses a transmission protocol designed 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, slicing processing, checksum generation, etc., so that the data adapts to the transmission characteristics of the weak signal environment and obtains weak signal transmission data. The encapsulated weak signal transmission data is subjected to transmission channel quality detection, and the quality status of the currently available channels is evaluated by sending a detection packet or analyzing historical transmission data, including signal strength, interference level, bandwidth capacity, etc., to obtain the channel quality evaluation result. Based on the channel quality evaluation results, the 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), and the availability, stability and transmission capacity of each channel are comprehensively considered to form an available channel resource pool. Adaptively allocate the available channel resource pool, and intelligently allocate transmission channels according to the priority, size and timeliness requirements of the data packet, as well as the quality status of each channel. High-priority data is allocated to high-quality channels, and low-priority data is allocated to ordinary channels, forming an adaptive transmission channel data flow.

[0039] In this embodiment, 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.

[0040] In this embodiment, the working status of the device is monitored in real time through the status monitoring module embedded in the mobile terminal device, including battery power, CPU usage, memory occupancy, communication module status, sensor working status, etc., to obtain the status information of the mobile terminal device. For the collected mobile terminal device status information, the battery power-related data, including the current power percentage, discharge rate, charging status, battery health, etc., are analyzed to generate the terminal device power status data. The terminal device power status data is deeply analyzed to study the impact of different operations and communication behaviors on power consumption, identify energy efficiency bottlenecks and optimization space, and generate dynamic energy efficiency characteristics. Based on the dynamic energy efficiency characteristics, machine learning methods (such as regression analysis, neural networks, etc.) are used to fit the energy efficiency mode of the device, establish a model that can predict energy consumption under different operating conditions, and construct a terminal device energy efficiency model.

[0041] In this embodiment, 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.

[0042] In this embodiment, by analyzing the time series changes of the power status data of the terminal device, the time points when the power obviously decreases are identified. These time points usually correspond to high-energy consumption operations or communication behaviors, and these power consumption time points are extracted as the focus of analysis. Based on the extracted power consumption time points, combined with the device status information in the same time period, the energy consumption rate of the device in different working states is calculated, including the energy consumption rate in the idle state, the energy consumption rate of data processing, the energy consumption rate of communication transmission, etc. Pay special attention to the energy consumption of the device when performing data communication, count the energy consumption value of each communication activity (sending and receiving data), and analyze the energy consumption differences under different communication protocols and different signal strength conditions. Combined with the calculated energy consumption rate, evaluate the energy efficiency ratio of each communication (transmission data volume / energy consumption value), analyze the energy efficiency performance of different communication strategies, and obtain communication energy efficiency characteristic data.

[0043] The energy consumption rate is calculated as: ; Where ER is the energy 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 impact factor ( ), T is the current temperature, Tref is the reference temperature 25 。 C), Fage is the equipment aging factor ( ), t is the equipment usage time, tref is the expected life of the equipment), ERb is the benchmark energy consumption rate, which reflects the inherent energy consumption characteristics of the equipment.

[0044] The calculation formula of communication energy efficiency ratio is: ; Where EER is the communication energy efficiency ratio, D is the amount of data transmitted, EC is the communication energy 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, and Fsi is the signal quality impact 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).

[0045] By analyzing the current battery power level and historical discharge curve, the remaining power status of the device is identified and the remaining working capacity of the device is evaluated. Based on the identified remaining power and calculated energy consumption rate, the endurance of the device in different working modes is predicted to obtain device endurance prediction data.

[0046] The prediction formula for battery life is: ; Where Tre is the estimated remaining working time, Erem is the remaining power, ERcu is the current energy consumption rate, and Fbat is the battery health status factor ( , Ncy is the number of battery charge and discharge cycles, Nmax is the battery design cycle life), FT is the temperature influence factor, Fwo is the expected workload change factor (predicted based on historical usage patterns), a1 is the nonlinear adjustment coefficient (usually 0.1-0.3), E_full is the full charge value, It is the nonlinear correction term of the charge, reflecting the characteristics of the battery discharge curve.

[0047] By integrating the device life 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, and provide a basis for subsequent transmission strategy decisions.

[0048] In this embodiment, 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.

[0049] In this embodiment, based on the terminal equipment energy efficiency model constructed above, the power level that the device can use for data transmission under the current power state is evaluated, and the remaining power of the device, the expected working time and the requirements of key tasks are taken into consideration to calculate a reasonable transmission power allocation plan and extract the available transmission power data. The signal strength data collected by the mobile terminal device is spatially analyzed, and a signal coverage heat map in the power plant area is constructed in combination with the movement trajectory and historical signal data of the device. The heat map shows the signal strength distribution at different locations, including signal strong areas, weak areas and blind areas. The signal coverage heat map is combined with the available transmission power data for analysis to evaluate the communication quality in different areas and at different power levels, taking into account the signal propagation loss, interference level and communication protocol characteristics, and obtaining the communication quality evaluation results.

[0050] Based on the communication quality evaluation results, a machine learning model is used to predict the data transmission success rate under different transmission strategies. The transmission strategy evaluation indicators are generated by considering factors such as packet size, transmission timing, transmission power, and protocol selection. 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 power, etc., and the output is the predicted transmission success rate. According to the transmission strategy evaluation indicators, a decision tree or reinforcement learning algorithm is used to formulate an adaptive transmission strategy. The optimal transmission parameters and methods are selected for different signal environments and device states, and a transmission strategy for a weak signal environment is constructed.

[0051] In this embodiment, the transmission power is evaluated using the following formula: ; 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 data priority dynamic coefficient (urgent data = 1.3, critical data = 1.1, regular data = 0.9, low priority data = 0.7), and b is the adjustment index (usually 0.7-1.2, reflecting the nonlinear relationship between power and power).

[0052] The communication quality evaluation adopts the following calculation formula: ; Among them, Q_comm is the communication quality assessment value, RSSI_norm is the normalized received signal strength index, SNR_norm is the normalized signal-to-noise ratio, BER_est is the estimated bit error rate, w1, w2, w3 are the weight coefficients of each item, and the sum of the three is 1.

[0053] In this embodiment, 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 is specifically as follows: identifying the signal quality level of the current communication environment based on the weak signal environment transmission strategy; 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 transmission priority processing; When the signal quality level of the communication environment is in the low-quality signal area, the power plant data sharing scheduling framework performs emergency data compression transmission processing.

[0054] 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, which includes a data acquisition layer, a data processing layer, a transmission control layer and an application service layer. Each layer interacts through a standard interface to form a complete data sharing framework. The weak signal environment transmission strategy constructed above is mapped to the data sharing framework, and specific scheduling rules are defined 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 scheduling framework.

[0055] The constructed power plant data sharing dispatch framework implements real-time dynamic dispatch control, and automatically adjusts the data transmission strategy according to the real-time status of the current communication environment. Specifically, based on the signal quality evaluation method in the weak signal environment transmission strategy, the signal quality of the current communication environment is monitored and analyzed in real time, and the signal quality is divided into three levels: high-quality signal area (high signal strength, low interference), medium-quality signal area (medium signal strength, some interference) and low-quality signal area (weak signal strength, severe interference).

[0056] When it is detected that the power plant is currently in a high-quality signal area, the power plant data sharing scheduling framework adopts a full data high-frequency transmission strategy, namely: Allows transmission of all types of sensor data, including high-priority and low-priority data; uses higher data sampling rates and transmission frequencies; uses lower data compression ratios to retain more original data details; and transmits multiple data streams simultaneously to fully utilize bandwidth resources.

[0057] When it is detected that the current signal quality is medium, the power plant data sharing scheduling framework switches to the key data priority transmission strategy, that is: Prioritize the transmission of high-priority critical production data and safety monitoring data; reduce data sampling rate and transmission frequency to reduce transmission load; use moderate data compression to balance data quality and transmission efficiency; cache or downsample non-critical data to reduce transmission volume; When it is detected that the power plant is currently in a low-quality signal area, the power plant data sharing scheduling framework initiates an emergency data compression transmission strategy, namely: Only transmit emergency data directly related to safe production, significantly reduce the sampling rate, retain only data at key time points, use a data compression algorithm with a high compression ratio to minimize the amount of transmitted data, enable multiple transmission protection mechanisms such as data redundancy encoding, multi-channel parallel transmission, etc., store non-emergency data locally, and wait to enter a better signal area before transmitting.

[0058] Through this real-time dynamic scheduling control based on signal quality level, the transmission strategy can be adaptively adjusted to maximize the use of limited communication resources while ensuring the timely transmission of key data, improve overall transmission efficiency and reliability, and ultimately build a comprehensive power plant multi-source sensor data sharing model.

[0059] The present invention significantly improves the quality and availability of data in a weak signal environment by performing signal quality evaluation and enhancement processing on multi-source sensor data of a power plant, and reduces the influence of signal interference and noise on data acquisition. The low-power coding compression technology is adopted to greatly reduce the data transmission load and extend the battery life of the mobile terminal device; the reliability and integrity of data transmission in a weak signal environment are improved by optimizing the bit error rate; a dedicated transmission protocol encapsulation mechanism is designed in view of the characteristics of the weak signal environment, and the transmission success rate of data in a weak signal environment is significantly improved by combining the adaptive channel allocation technology. By performing dynamic energy efficiency analysis on the mobile terminal device, an accurate energy efficiency model is constructed, which provides a reliable basis for transmission strategy decision-making and realizes the efficient utilization of energy resources. Based on the energy efficiency model of the terminal device and the communication quality evaluation results, the optimal transmission strategy can be automatically determined to adapt to the changes in different signal environments and ensure the efficiency and reliability of data transmission. According to the real-time changes in the signal quality level, the data transmission strategy can be dynamically adjusted to optimize resource utilization while ensuring the transmission of key data and improve the overall transmission efficiency. By performing priority classification and signal environment classification on the data, refined transmission control is realized to ensure that the most critical data can still be transmitted in the worst signal environment. Then, a complete data sharing model was built, covering the entire process from data collection, processing, transmission to application, improving the reliability and efficiency of data transmission.

[0060] 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: 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; 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 to achieve data transmission between modules.

[0061] 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.

[0062] 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.

[0063] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0064] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0065] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0066] In the description of the present invention, "several" means one or more than one, and "a large number" means two or more than two.

[0067] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0068] The formulas in this manual are all dimensionless and calculated numerically. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0069] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 time-space fusion serialization is performed 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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