Electrical equipment remote monitoring and adjusting method and system based on wireless communication

Abstract

The invention relates to the field of remote monitoring, in particular to an electrical equipment remote monitoring and adjusting method and system based on wireless communication, and the method comprises the steps: receiving monitoring data of target electrical equipment in real time; judging whether interference signals exist or not according to the monitoring data; when an interference signal exists in the monitoring data, extracting change characteristics of the interference signal; analyzing an interference reason for generating the interference signal according to the change characteristics of the interference signal; predicting an interference parameter corresponding to the interference signal according to the interference signal and the interference reason; and generating a compensation parameter in combination with the interference parameter and the interference reason, and adjusting the monitoring data according to the compensation parameter. Through detailed analysis and prediction of interference signals, the system can predict specific parameters of the interference signals, and a compensation strategy is generated according to the parameters. The predictive capability helps the system to take necessary measures before interference occurs, and adverse effects of interference signals on equipment operation are prevented.

Description

Technical Field

[0001] The present application relates to the field of remote monitoring technology, and in particular to a method and system for remote monitoring and adjusting electrical equipment based on wireless communication. Background Art

[0002] Wireless communication for remote monitoring of electrical equipment refers to the use of wireless communication technology to remotely monitor and manage electrical equipment. It combines modern wireless communication technology, sensor technology, data transmission technology and intelligent control systems to achieve real-time monitoring, data collection and fault diagnosis of electrical equipment through wireless networks.

[0003] In the prior art, wireless sensor networks often rely on the stability of wireless signals, but in actual applications, signals may be affected by factors such as surrounding equipment, weather changes, and physical obstacles, resulting in signal attenuation, data loss, or transmission errors. The prior art often uses signal quality detection (such as RSSI or SNR) to evaluate the strength of the signal, and then takes a direct adjustment method to adjust the acquisition position or number of times of the monitoring end. However, these methods lack the ability to locate interference sources and analyze the degree of impact. When these factors are not fully considered, signal quality problems that should have been identified and corrected may be ignored, resulting in a significant reduction in the accuracy of monitoring data. For example, in a wireless sensor network, if the signal quality is interfered with or attenuated, the data received by the sensor may be distorted or even lost, thereby affecting the system's accurate reflection of the environmental status. Due to the shortcomings of the prior art in locating interference sources, the system usually... For example, the reliability of data may be improved by increasing the acquisition frequency or increasing the number of data retransmissions, but this approach is often difficult to solve the problem of signal interference or weak signals, and is prone to information loss or delay. This data loss not only affects real-time monitoring, but may also lead to the loss of key data, thereby affecting the accuracy of system decision-making. For example, in environmental monitoring or electrical health monitoring scenarios, the lack of key data may lead to misjudgment of environmental changes or electrical health status.

[0004] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0005] In order to solve one or several problems in the prior art, the main purpose of the present application is to provide a method and system for remote monitoring and adjustment of electrical equipment based on wireless communication.

[0006] In order to achieve the above-mentioned invention object, the present application proposes a method for remote monitoring and adjustment of electrical equipment based on wireless communication, the method comprising: Receive monitoring data of target electrical equipment in real time; Determining whether there is an interference signal according to the monitoring data; When there is an interference signal in the monitoring data, extracting a change feature of the interference signal; Analyzing the interference cause of the interference signal according to the change characteristics of the interference signal; Predicting interference parameters corresponding to the interference signal according to the interference signal and the interference cause; Combining the interference parameter and the interference cause, a compensation parameter is generated, and the monitoring data is adjusted by the compensation parameter.

[0007] The present application also provides a wireless communication-based remote monitoring and adjustment system for electrical equipment, including: A receiving module, used for receiving monitoring data of target electrical equipment in real time; A judgment module, used to judge whether there is an interference signal according to the monitoring data; An extraction module, used for extracting the change characteristics of the interference signal when the monitoring data contains the interference signal; An analysis module, used for analyzing the interference cause of the interference signal according to the change characteristics of the interference signal; A prediction module, used to predict interference parameters corresponding to the interference signal according to the interference signal and the interference cause; A generating module is used to generate a compensation parameter based on the interference parameter and the interference cause, and adjust the monitoring data with the compensation parameter.

[0008] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0009] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0010] The remote monitoring and adjustment method and system of electrical equipment based on wireless communication of the embodiment of the present application can quickly obtain the operating status of the equipment, detect abnormalities or faults in time by receiving the monitoring data of the target electrical equipment in real time, and can accurately determine whether there is an interference signal in the monitoring data, and further analyze the cause of the interference by extracting the change characteristics of the interference signal. Through accurate interference signal analysis, the system can distinguish between normal fluctuations and interference, thereby avoiding wrong diagnosis and unnecessary adjustments. Through detailed analysis and prediction of interference signals, the system can foresee the specific parameters of interference signals and generate compensation strategies based on these parameters. This predictive ability helps the system take necessary measures before interference occurs to prevent interference signals from having an adverse effect on equipment operation. Combined with interference parameters and interference causes, the system can generate compensation parameters and adjust monitoring data, and correct data errors caused by interference in real time. This compensation mechanism effectively eliminates the impact of interference, ensures the accuracy and reliability of equipment operation data, and avoids wrong decisions or misoperations caused by interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of a method for remote monitoring and adjusting electrical equipment based on wireless communication according to an embodiment of the present application; Figure 2 A flowchart of a method for remote monitoring and adjusting electrical equipment based on wireless communication according to an embodiment of the present application; Figure 3 This is a schematic block diagram of the structure of a remote monitoring and adjustment system for electrical equipment based on wireless communication according to an embodiment of the present application; Figure 4 A schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0012] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Reference Figure 1 In an embodiment of the present application, a method for remote monitoring and adjustment of electrical equipment based on wireless communication is provided, the method comprising: S1. Receive monitoring data of target electrical equipment in real time; S2. judging whether there is an interference signal according to the monitoring data; S3. When there is an interference signal in the monitoring data, extracting the change characteristics of the interference signal; S4. Analyzing the interference cause of the interference signal according to the change characteristics of the interference signal; S5. predicting interference parameters corresponding to the interference signal according to the interference signal and the interference cause; S6. Generate a compensation parameter based on the interference parameter and the interference cause, and adjust the monitoring data with the compensation parameter.

[0015] As described in the above steps S1-S3, the status monitoring data of electrical equipment (such as power equipment, household appliances, etc.) is received in real time through wireless communication. The monitoring data may include information such as voltage, current, temperature, frequency, etc. The wireless communication method may be Wi-Fi, Bluetooth, ZigBee, etc., which can remotely transmit data to the monitoring platform or control terminal. The purpose of selecting real-time data reception is to be able to respond quickly and make adjustments or interventions when electrical equipment fails or is abnormal. Real-time monitoring ensures that the status data of the equipment is fed back in time, which helps to detect potential problems or failures early and improve the safety and operating efficiency of electrical equipment. Provide a data basis for subsequent interference signal analysis and adjustment to ensure the effectiveness of remote control. During the operation of electrical equipment, interference signals may be caused by environmental factors (such as electromagnetic interference) or internal equipment failures (such as noise, poor contact, etc.). These interference signals may affect the normal operation of the equipment and the accuracy of the monitoring data. This feature indicates that the system needs to analyze the data received in real time through algorithms (such as filtering, pattern recognition or statistical analysis, etc.) to determine whether there are abnormal fluctuations or interference signals. Interference signals usually appear as sudden changes or periodic fluctuations that are significantly different from normal working conditions. By judging the presence of interference signals, the system can identify abnormal data fluctuations, thereby distinguishing the normal operating state of the equipment from the state of external interference. Avoiding incorrect diagnosis and unnecessary operation adjustments ensures the accuracy of the monitoring system. Once the system detects the presence of an interference signal, it is necessary to further analyze the interference signal. At this time, it is key to extract the changing characteristics of the interference signal (such as amplitude changes, frequency fluctuations, duration, etc.). By analyzing these changing characteristics, the system can identify the type of interference signal and its laws, thereby providing clues for subsequent analysis of the cause of the interference. Extracting the changing characteristics of the interference signal helps to identify the specific pattern of interference and lays the foundation for further diagnosis of the cause, source and scope of influence of the interference. This analysis can improve the system's ability to identify and respond to different types of interference.

[0016] As described in the above steps S4-S6, based on the change characteristics of the extracted interference signal, the system can analyze the cause of the interference signal through a variety of methods. This involves the analysis of the time-frequency characteristics of the signal, the analysis of the working status of the equipment, the monitoring of environmental factors, etc. For example, if the interference signal fluctuates within a specific frequency range, it may be due to interference from some wireless communication equipment; if the interference is intermittent, it may be due to poor contact of the electrical equipment or power supply noise. By analyzing the cause of the interference, it is possible to effectively distinguish whether it is external interference or a problem with the equipment itself, thereby providing a basis for the subsequent location and treatment of the interference source. This step helps to accurately identify the root cause of the problem, thereby avoiding misdiagnosis and ineffective interference suppression measures. Once the cause of the interference is analyzed, the system can use this information to predict the parameters of the interference signal (such as intensity, frequency, duration, etc.), that is, predict the specific impact of the interference. The prediction can be based on historical data, modeling analysis, or the physical characteristics of the cause of the interference. For example, if the cause of the interference is that the operating frequency of a certain device coincides with the interference signal, the specific frequency of the interference can be predicted. By predicting the specific parameters of the interference signal, the system can prepare countermeasures in advance and optimize the interference suppression strategy. This predictive capability improves the predictability of the system, allowing the interference signal to be mitigated or corrected before it actually occurs, thereby improving the stability and response speed of the system. Generating compensation parameters means calculating a set of compensation values ​​based on the analysis results, combined with the parameters and causes of the interference, to offset the impact of the interference. Compensation parameters can be obtained in a variety of ways, such as: removing interference components through filtering algorithms; restoring interfered data through signal conditioning algorithms; predicting and compensating for the impact of interference through machine learning models. Adjusting monitoring data means using compensation parameters to correct the received interference data so that it can be restored to the data when the equipment is operating normally as much as possible. The process of generating compensation parameters and adjusting monitoring data can effectively eliminate the impact of interference and ensure the accuracy and reliability of equipment operation data. This mechanism can correct data in real time, avoid misjudgments or invalid adjustments caused by interference, and improve the accuracy and stability of the entire monitoring system.

[0017] As mentioned above, by receiving the monitoring data of the target electrical equipment in real time, the system can quickly obtain the equipment operation status, detect abnormalities or faults in time, and accurately determine whether there are interference signals in the monitoring data, and further analyze the cause of the interference by extracting the change characteristics of the interference signal. Through accurate interference signal analysis, the system can distinguish between normal fluctuations and interference, thereby avoiding wrong diagnosis and unnecessary adjustments. Through detailed analysis and prediction of interference signals, the system can foresee the specific parameters of interference signals and generate compensation strategies based on these parameters. This predictive ability helps the system take necessary measures before interference occurs to prevent interference signals from having an adverse effect on equipment operation. Combining interference parameters and interference causes, the system can generate compensation parameters and adjust monitoring data to correct data errors caused by interference in real time. This compensation mechanism effectively eliminates the impact of interference, ensures the accuracy and reliability of equipment operation data, and avoids wrong decisions or misoperations caused by interference.

[0018] Reference Figure 2 In one embodiment, the method for analyzing the interference cause of the interference signal according to the change characteristics of the interference signal includes: S41, obtaining a change characteristic of the interference signal, and performing frequency domain characteristic analysis on the change characteristic; S42, determining the signal change frequency of the change characteristic according to the result of the frequency domain characteristic analysis; S43, performing time domain feature analysis on the change feature; S44, extracting the fluctuation characteristics of the change characteristics on the time axis according to the result of the time domain characteristic analysis; S45, determining the change amplitude and duration of the change feature according to the fluctuation feature of the change feature on the time axis; S46. Determine interference causes based on the frequency domain feature analysis and the time domain feature analysis, wherein the interference causes include external device interference, electrical device interference, and environmental interference.

[0019] As described in the above steps, frequency domain analysis is the process of converting a signal from the time domain to the frequency domain, which can reveal the strength of different frequency components in the signal. For interference signals, the frequency domain feature analysis can identify the spectral distribution of the signal. For example, periodic interference signals often appear as strong peaks at certain specific frequencies in the frequency domain, while random noise usually appears as a uniform distribution of the spectrum. Frequency domain analysis can use mathematical tools such as Fourier transform to convert the signal from the fluctuation characteristics of the time domain to frequency components. This analysis can help identify the frequency characteristics of the interference signal and thus infer its source, such as the external power frequency, the inherent frequency of the device, etc. Frequency domain analysis can help identify the periodic characteristics of the interference signal. If a peak of a specific frequency appears in the spectrum of the interference signal, it may be caused by a specific device or an external source (such as power supply, wireless communication, etc.). This step helps to determine the rough type of interference (for example, whether it is caused by electrical equipment or external equipment). From the results of the frequency domain feature analysis, the frequency of signal changes can be directly extracted. This step is a further application of frequency domain analysis, the purpose of which is to identify the main frequency components of the interference signal from the spectrum. The identification of the changing frequency helps to determine the nature of the interference signal. For example, if the main frequency component of the signal is 50 Hz or 60 Hz, it may be caused by power supply interference; if the frequency is higher, it may be related to interference in the radio frequency band. After determining the frequency of change, the possible source of the interference signal can be quickly locked. This step helps to clarify the frequency characteristics of the interference, thereby providing clear clues for subsequent interference source analysis. The results of this step can help narrow the scope of the interference source, such as determining whether it is an external electrical device, the frequency of the adjacent power grid, or the operating frequency of the device itself. Time domain analysis observes and analyzes the changes in the signal over time. The main purpose of this step is to further reveal the characteristics of the interference signal by examining the volatility of the signal over time. Unlike frequency domain analysis, time domain analysis focuses on the instantaneous changes of the signal, such as amplitude changes, waveform shape, etc. Time domain feature analysis can be performed through statistical indicators (such as mean, standard deviation, peak value, etc.) or by directly observing the change pattern of the waveform. Through this process, it can be obtained whether the interference signal is sudden (such as instantaneous impact interference) or continuous (such as periodic vibration or current fluctuation). Time domain analysis can reveal the temporal characteristics of interference signals, such as whether they have a certain pattern (e.g., periodicity, short-term bursts, etc.), or whether the interference signal is stable in time. With this information, it is possible to determine the mechanism of interference generation, whether it is caused by continuous external device interference or by instability within the device (e.g., current fluctuations, vibrations, etc.). The results of this step help to complement the results of frequency domain analysis and further refine the identification of interference sources. In time domain feature analysis, fluctuation features refer to the fluctuation pattern of the signal in time, such as fluctuations in amplitude, periodic changes, etc. The purpose of this step is to extract specific parameters related to fluctuations from the time domain signal, such as fluctuation amplitude, duration, peak interval, etc.This analysis can help distinguish different types of interference. For example, interference signals caused by equipment failure may appear as large sudden changes, while external environmental interference may appear as relatively stable periodic fluctuations. Extracting fluctuation characteristics helps to identify the rules and characteristics of interference in more detail, and thus further infer the cause of interference. If the fluctuation amplitude is large and the duration is short, it may indicate that the interference source is a transient impact (such as electrical equipment failure, switching transient interference, etc.). If the fluctuation is small and the duration is long, it may be external environmental interference. This step is a further quantification of the fluctuation characteristics, and the intensity and persistence of the interference are determined by analyzing the amplitude and duration of the interference signal. Amplitude and duration are usually important indicators for analyzing the type of interference. For example, large fluctuations may indicate high interference intensity, and long duration may indicate that the interference source is more persistent or stable. These parameters can further infer possible sources of interference (such as equipment failure, external electrical equipment, etc.). This step provides specific quantitative data about the interference signal, which can help determine the characteristics of the interference source. If the amplitude is large and the duration is long, it may be a sign of equipment failure or electrical equipment instability; if the amplitude is small and the duration is short, it may be caused by external environmental factors (such as short-term external electrical interference). Through this step, some interference sources can be effectively excluded, helping to narrow the scope of troubleshooting. The analysis results of the frequency domain and time domain characteristics are combined to finally determine the specific cause of the interference. This step combines the frequency characteristics with the time characteristics to judge the possibility of the interference source in combination with the actual situation. For example, if the frequency domain analysis shows that the interference signal has a specific frequency, and the time domain analysis shows that its fluctuation amplitude is small and the duration is long, it may be environmental interference (such as electromagnetic wave propagation, temperature change, etc.); if the time domain analysis shows sudden fluctuations, it may be related to equipment failure. By comprehensively analyzing the characteristics of the frequency domain and time domain, the cause of interference can be accurately identified. This provides a direct basis for subsequent interference resolution. For example, external equipment interference may need to be resolved through shielding, filtering, etc., while electrical equipment interference may require inspection and adjustment of the equipment. The final identification of the cause of interference helps to take corresponding compensatory measures, such as adjusting system settings, replacing faulty components, or improving environmental conditions, thereby improving the stability and anti-interference ability of the system.

[0020] In one embodiment, the method of predicting an interference parameter corresponding to the interference signal according to the interference signal and the interference cause includes: According to the interference cause, identifying the interference type of the interference signal; Preprocessing the interference signal and extracting interference features; Constructing a correlation model, inputting the interference characteristics and interference types into the correlation model, predicting interference parameters corresponding to the interference signal through the correlation model, and outputting the predicted results; Compare and analyze the predicted interference parameters with the preset actual interference parameters, and calculate the correlation coefficient between the predicted interference parameters and the preset actual interference parameters; Determining whether the predicted result is within a preset range according to the correlation coefficient; When the predicted result is within a preset range, the predicted result is judged to be a credible result; Based on the result of the judgment, the predicted result is used as an interference parameter.

[0021] As mentioned above, the types of interference signals may come from a variety of sources, such as electromagnetic interference, temperature fluctuations, mechanical vibrations, etc. By identifying the interference causes of interference signals, it can help determine their characteristics. For example, some types of interference signals may be periodic, while other types may be transient. Understanding the type of interference is crucial for subsequent interference analysis and compensation. After identifying the type of interference, it is possible to accurately prescribe the right medicine and choose the appropriate treatment method. For example, if the interference type is electromagnetic interference, filtering or shielding technology may be required, while if it is vibration interference caused by equipment failure, the mechanical structure may need to be adjusted. Accurately identifying the type of interference helps the accuracy of subsequent analysis and avoids wrong judgments and excessive intervention. Interference signals often contain unnecessary information such as noise and background fluctuations, so preprocessing the interference signals is a necessary step. Preprocessing usually includes technical means such as filtering, denoising, and smoothing, which aim to remove irrelevant parts of the interference signal and retain key information. Extracting interference features is to extract key indicators that can characterize the nature of interference from the signal, such as frequency, amplitude, waveform, etc. Through preprocessing and feature extraction, the efficiency and accuracy of subsequent analysis can be greatly improved. After eliminating noise, the characteristics of the remaining signal can better reflect the actual situation of the interference. The optimization of this link enables the system to more intelligently identify and distinguish different types of interference. The construction of the association model is to predict the specific parameters of the interference signal (such as frequency, amplitude, phase, etc.) by modeling the relationship between the interference characteristics and the interference type. The association model may include regression models, neural networks, support vector machines, etc. Through the prediction of the association model, the system can realize real-time prediction of the interference signal and quickly provide accurate interference parameters. The prediction results enable the system to take preventive or compensatory measures for interference in advance, and by comparing with the preset actual interference parameters, the accuracy of the prediction can be evaluated. The correlation coefficient (such as the Pearson correlation coefficient) is a statistic that measures the strength of the linear relationship between two variables. If the correlation between the predicted result and the actual result is strong, it means that the model prediction is valid, otherwise, the model needs to be recalibrated. By calculating the correlation coefficient, the system can quantify the credibility of the prediction result. A higher correlation coefficient indicates that the prediction result is close to the actual interference situation, which enhances the reliability of the system. If the correlation coefficient is low, the system can identify inaccurate prediction results, thereby triggering adjustment strategies to avoid incorrect interference compensation or decision-making. If the correlation coefficient is high and the predicted interference parameter is within a preset allowable range (for example, within the error range), the prediction result can be considered valid. If it exceeds this range, it may mean that the forecast has a large deviation and needs to be re-analyzed or corrected. Prevent misjudgment. The preset range is usually set based on historical data or the fault tolerance of the equipment. This judgment mechanism ensures that when the forecast results meet expectations, the forecast data can be effectively applied for subsequent operations.If the prediction result is judged to be credible, the system can take corresponding adjustment measures based on these interference parameters to ensure the stable operation of the equipment. If the prediction result is unreliable, it may trigger re-prediction or other corrective measures to avoid misoperation and potential risks.

[0022] In one embodiment, after the step of adjusting the monitoring data by the compensation parameter, the method further comprises: Acquire the signal change frequency, change amplitude and duration of the change characteristics; Determining whether the conditions for adjusting the monitoring end are met according to the signal change frequency, change amplitude and duration of the change characteristics; quantifying the signal change frequency, change amplitude and duration of the change feature, and converting the signal change frequency, change amplitude and duration of the change feature into an influence degree value for evaluating the influence degree of the interference parameter; When the impact degree value is greater than a preset degree threshold, it is determined that the condition for adjusting the monitoring end is met; Based on the judgment result, a simulation model is constructed, and the data quality of the monitoring data collected by the monitoring terminal under different adjustment parameters is predicted by the simulation model; quantifying the data quality to obtain quality coefficients of different adjustment parameters; The adjustment parameter with the largest quality coefficient is selected to adjust the monitoring end.

[0023] As mentioned above, the frequency of signal change: refers to the number of times the signal changes in a unit of time, reflecting the speed of signal fluctuations. Changes with a higher frequency usually mean that there is a rapidly changing interference or noise in the system. The amplitude of change: refers to the range of signal changes, reflecting the strength of signal fluctuations. Changes with a larger amplitude usually indicate that the signal strength or fluctuation is larger, which may be related to the strength of the interference source. Duration: refers to the time span of signal changes. Changes with a longer duration may indicate some long-term interference or trends. The purpose of collecting these features is to fully understand the dynamic characteristics of the signal, so that the interference source can be better identified and adjusted. Frequency, amplitude and duration are key factors in interference analysis, which can help determine the type of interference and the severity of the interference. The purpose of this judgment is to determine whether the current signal is in an abnormal or undesirable state based on the obtained signal characteristics, and then decide whether the working parameters of the monitoring end need to be adjusted. For example, if the signal frequency is too high, the amplitude is too large, or the duration is too long, it may indicate that the signal is abnormal or interfered. At this time, the monitoring end needs to be adjusted to improve data quality. By comprehensively considering frequency, amplitude and duration, the signal state can be dynamically identified, and the adjustment of the monitoring end will only be triggered when the signal changes significantly. This can avoid frequent and meaningless adjustments and improve the stability and efficiency of the system. Quantifying these features is to facilitate comparison and evaluation of the impact under different change conditions. By converting frequency, amplitude and duration into numerical form, a weight value can be assigned to each change feature, thereby forming a comprehensive impact value. The impact value can be regarded as a measure of the impact of interference or noise on the monitoring data, which can help determine the severity of the interference and whether the interference parameters need to be adjusted. Quantification and conversion into impact values ​​make the assessment of interference more objective and standardized, which is convenient for subsequent automatic decision-making. This judgment logic is based on a threshold judgment mechanism, that is, only when the impact of the interference exceeds a certain predetermined threshold, will the adjustment of the monitoring end be triggered. The threshold can be a pre-set standard to determine whether the change in the signal has reached the level that requires action. If the impact is low, there is no need to adjust the monitoring end. This judgment mechanism has an "intelligent" filtering function, which can avoid invalid adjustments and reduce unnecessary calculations and interference. It can ensure that adjustments are only made when interference or noise seriously affects the data quality, thereby improving the efficiency of the system. The simulation model helps predict which adjustments can effectively improve the data quality by simulating the impact of different adjustment parameters on the quality of monitoring data. Simulation models are usually based on historical data and mathematical models of system behavior. They can evaluate the effects of different parameters without actually adjusting the hardware. By predicting the effects of different adjustment schemes through simulation, a lot of experimental and debugging time can be saved. This method is efficient and accurate, avoids excessive tentative adjustments, and helps select the optimal adjustment strategy.The quantification of data quality is to evaluate the accuracy, stability, integrity and other aspects of the quality of monitoring data through some kind of standardization or indicator system. The quality coefficient is usually a numerical value that can comprehensively reflect the quality of data, and can be compared according to different adjustment parameters to help determine which parameter can provide the best data quality. Quantifying data quality and obtaining the quality coefficient can help make clearer decisions. By comparing the quality coefficients of different adjustment parameters, the system can select the best adjustment strategy to achieve the best data quality. The final adjustment selection is based on the quality coefficient. Selecting the adjustment parameter with the largest quality coefficient means selecting the parameter that can bring the highest data quality. This selection method is to improve the performance of the entire monitoring system by evaluating the effects of different solutions and selecting the most effective solution. By selecting the optimal adjustment parameters, the work efficiency and data quality of the monitoring end can be maximized. Doing so helps optimize system performance, reduce errors, and improve data reliability.

[0024] In a specific embodiment, wireless signals in the monitoring area are collected and changes in signal frequency are analyzed. By monitoring the frequency of signal changes in a unit time, it is identified whether the signal is in a state of frequent fluctuations. Frequently fluctuating signals may indicate the presence of interference sources. By calculating the amplitude of the change in signal strength, the intensity of signal fluctuations is analyzed. Large amplitude changes may be caused by electromagnetic interference (EMI) or external interference sources, indicating that the signal quality may be seriously affected. The duration of signal changes is analyzed to evaluate the persistence of the changes. If the duration of signal fluctuations is long, it may indicate that the system is exposed to an interference environment for a long time, affecting the stability of the system. The correlation between signal frequency, amplitude and duration is analyzed by algorithm, and the root cause of the signal change is determined by combining historical data and known interference source patterns. For example, high-frequency, large-amplitude and long-duration signal changes may be related to equipment failure or external electromagnetic interference. High-frequency, small-amplitude and short-duration signal changes may be caused by short-term instability of wireless signal transmission or external noise. The identified signal change pattern is trained using a model (such as a machine learning or statistical regression model) to help automatically identify the root cause of the current signal problem. The analysis results are converted into a quantitative impact value. The impact degree value is obtained by weighted summing of the signal change characteristics (such as frequency, amplitude, and duration). The larger the impact degree value, the more serious the signal quality problem. According to the relationship between the signal change characteristics and the interference source, a simulation model or a prediction model (such as a support vector machine, a neural network, etc.) is used to predict the specific impact parameters of the interference. For example, if the interference source is electromagnetic interference, it may affect the quality and stability of the received signal. Compare the impact parameters under different interference modes, quantify the impact, and decide whether to make adjustments based on the impact degree value. Set a preset threshold. If the impact degree value is greater than the threshold, it is judged that the system has a more serious interference or signal quality problem, and then the monitoring end needs to be adjusted. If the impact degree value is less than the threshold, no adjustment is made and the current monitoring end configuration is maintained. Based on historical data and signal change characteristics, a simulation model is constructed to predict the impact of different adjustment schemes on the quality of monitoring data. The simulation model predicts the quality of the adjusted monitoring data based on the known interference type, signal change characteristics, and adjustment parameters. Compare the effects of different adjustment schemes and evaluate the quality coefficient of each scheme (such as signal quality, data error rate, and other indicators). Select the best adjustment scheme, that is, the scheme that can significantly improve data quality without causing excessive interference. Select the adjustment scheme with the largest quality coefficient. The scheme with a higher quality coefficient has the best optimization effect on the system and can improve the accuracy and stability of the monitoring end data. Apply the adjustment parameters of the scheme to the monitoring end and adjust them to ensure that the monitoring end works in the optimized state. After the adjustment, continue to monitor the quality of the wireless signal and monitoring data. If the signal quality is detected to be disturbed or deteriorated again, repeat the above steps to perform interference analysis, cause prediction and adjustment optimization.According to new signal change characteristics and interference patterns, the simulation model and prediction model are adjusted to adapt to the ever-changing wireless signal environment.

[0025] In one embodiment, after the step of generating compensation parameters by combining the interference parameters and the interference causes, the method further includes: Based on the timestamp, analyzing whether the receiving time of the monitoring data is consistent with the preset receiving time; If the receiving time of the monitoring data is inconsistent with the preset receiving time, extract the delay characteristics of the monitoring data; generating delay compensation according to the delay characteristics; The delay compensation, interference parameters and monitoring data are combined to generate compensation parameters.

[0026] As mentioned above, the timestamp is used to record the exact time when the data is received. The reception time of the monitoring data is compared with the preset reception time to determine whether the data transmission is carried out according to the scheduled plan and whether there is any delay. Signals in wireless communication may cause deviations in the reception time due to network congestion, signal attenuation, interference, etc. When the reception time is inconsistent with the preset time, it means that there may be a non-negligible delay, which requires compensation. Timing deviation may cause data to be unable to be accurately synchronized, especially in time-sensitive application scenarios (such as real-time monitoring, event response, etc.), the timing accuracy of the data is crucial. By checking the reception time, it can ensure that the system finds possible time problems and avoids analysis errors caused by delays. Timely detection of the deviation of the reception time provides a basis for subsequent delay compensation. This step is designed to ensure that the system can automatically identify and compensate for errors caused by delays. By checking the reception time, the system can ensure the consistency of each monitoring data in time, which helps the accuracy of subsequent analysis and decision-making. The early warning mechanism of delay compensation can effectively avoid error processing caused by inconsistent reception time and ensure long-term stable operation of the system. This includes the deviation size and change trend of the received data relative to the preset time. The delay of wireless signals may be caused by multiple factors. During the monitoring process, timely acquisition and analysis of delay characteristics can help diagnose problems in the data transmission chain, especially the source of network delay (such as multi-hop transmission, routing selection, etc.). By extracting delay characteristics, the system can more accurately calculate the parameters required for delay compensation, thereby achieving targeted compensation. Accurately extracting delay characteristics can improve the accuracy of delay compensation, avoid excessive or insufficient compensation, and ensure the synchronization of monitoring data with the scheduled time. Delay compensation is based on the size and change law of delay characteristics, and compensates for the impact of delay on monitoring data by adjusting the data processing and analysis process. For example, the delay value is calculated using the timestamp difference, and the original monitoring data is "time corrected" during the data analysis process. The delay compensation method can be based on linear regression, interpolation or other more complex algorithm models, with the goal of restoring the timing of the data to make it consistent with the actual scheduled time. By generating delay compensation, the data can be restored to the original scheduled reception time point, thereby reducing the data deviation caused by delay. Delay compensation enables the system to adapt to delay changes under different network conditions and ensure the efficiency and accuracy of data processing in various network environments. Delay compensation can correct the errors caused by delay, thereby improving the timeliness and accuracy of data processing and ensuring the reliability of subsequent analysis results. Delay compensation reduces the data synchronization error caused by delay and can effectively improve the quality of the final monitoring data. The final compensation parameters combine delay compensation, interference parameters and monitoring data. Delay compensation and interference parameters are usually predicted and generated by different algorithms or models, while monitoring data provides actual measurement values.Combining these factors to form the final compensation parameters can fully optimize the interference and delay of wireless signals. This process combines the characteristics of delay, interference and the data itself to ensure that the compensation scheme not only corrects the delay problem, but also takes into account the impact of signal interference. Through this comprehensive adjustment, it can be ensured that the parameter adjustment of the monitoring end can adapt to different network environments and signal conditions. By combining delay compensation, interference parameters and monitoring data, the status and environment of wireless signals can be more comprehensively evaluated, thereby generating more accurate and multi-dimensional compensation parameters. Not only delay is compensated, but the impact of interference is also taken into account, making the compensation more comprehensive and effective, and maximizing the quality of monitoring data.

[0027] In one embodiment, the method of generating delay compensation according to the delay characteristic includes: Extracting the delay amplitude of the monitoring data when receiving according to the delay characteristics; Generating delay compensation amounts at different time points according to the delay amplitude and timestamp; Based on the delay compensation amounts at different time points, a delay compensation is generated to correct the receiving time of the monitoring data.

[0028] As mentioned above, the delay amplitude refers to the change in the delay size during the transmission process of data from the sender to the receiver. In actual systems, factors such as network transmission, signal processing, and hardware response may cause the delay to fluctuate in different time periods. By extracting the "delay amplitude", the delay fluctuation characteristics of the network or device at different time points can be obtained. Due to the volatility of delay, a single delay value is not enough to reflect the entire transmission process, so extracting amplitude information helps to compensate for delay more accurately. Delay is a dynamic process, and its changes are usually affected by factors such as network load and transmission medium. By extracting the delay amplitude, a comprehensive information about delay fluctuations can be obtained, providing accurate input for subsequent compensation strategies. By combining the delay amplitude and timestamp, the delay compensation amount at a specific time point can be determined. The timestamp marks the exact moment when the data is received. After combining the delay amplitude, the compensation amount that should be made at that moment can be calculated. The delay compensation amount is usually a correction for the time difference to ensure that the data reception time meets expectations. The delay compensation amount is generated to correct the data according to the specific delay fluctuation situation. This can eliminate the time error caused by delay and ensure that the received data can match the real time of sending data, thereby improving the timing accuracy of the system. Delay compensation is not only based on the static delay value, but also needs to consider the change of delay. Therefore, by combining the delay amplitude and timestamp to generate the compensation amount for a specific moment, a more accurate and dynamic correction can be achieved, avoiding the time error caused by delay fluctuation. According to the delay compensation amount generated at multiple time points, a delay compensation value can be calculated comprehensively, and then the receiving time of the received monitoring data can be corrected. After the final delay compensation is generated, the system can accurately calibrate the timestamp of the received data to make it closer to the real sending time. Especially in systems with high precision requirements (such as real-time monitoring, synchronous data analysis, etc.). Delay fluctuations not only affect the receiving time in a short time, but may also accumulate over time. Therefore, by comprehensively considering the delay compensation amount at different time points, the time accumulation error can be effectively eliminated and a comprehensive correction of the receiving time can be provided.

[0029] In one embodiment, the interference parameter includes: signal strength attenuation value, signal distortion or signal delay time. The signal strength attenuation value refers to the degree of signal strength reduction caused by factors such as distance, environmental obstacles, interference, etc. during propagation. The attenuation value is usually expressed in decibels (dB). Signal attenuation directly affects the communication quality. When the signal strength attenuation is large, the receiving end may not be able to receive the signal correctly, or noise and distortion may occur, resulting in data transmission errors or instability. Common factors: propagation path, obstacles (such as buildings, trees, etc.), air humidity, weather conditions, etc. Signal distortion refers to the change in shape, amplitude or frequency of the signal during transmission, resulting in the difference between the received signal and the original signal. Distortion is usually manifested as waveform distortion, frequency offset, phase distortion, etc. Signal distortion will cause the received signal to be inaccurate, and the original information may not be restored, thereby affecting the demodulation and correctness of the data. High distortion will lead to more data errors and even communication interruption. Common factors: nonlinear effects, uneven frequency response, mismatch in modulation and demodulation, etc. Signal delay time refers to the time difference experienced by the signal from the transmitter to the receiver. It is usually composed of factors such as propagation speed, network equipment processing time, and routing forwarding time. Delay time has a greater impact on applications with high real-time requirements (such as voice calls, video conferencing, online games, etc.). Higher delay will cause signal transmission lags and affect user experience. For example, in voice communication, high delay will cause echoes or delays during conversations. Common factors: network bandwidth, routing path, server processing power, transmission medium, etc. These interference parameters are all key factors affecting the quality of signal transmission. By controlling or compensating for these interference parameters, the transmission quality of the signal can be optimized and the performance and reliability of the system can be improved. In practical applications, it is usually necessary to combine various technical means (such as signal processing, error correction, channel estimation, etc.) to reduce the negative impact of these interference parameters.

[0030] Reference Figure 3 In an embodiment of the present application, a remote monitoring and adjustment system for electrical equipment based on wireless communication is also provided, comprising: Receiving module 1, used for receiving monitoring data of target electrical equipment in real time; A judgment module 2 is used to judge whether there is an interference signal according to the monitoring data; An extraction module 3, used for extracting the change characteristics of the interference signal when there is an interference signal in the monitoring data; An analysis module 4 is used to analyze the interference cause of the interference signal according to the change characteristics of the interference signal; Prediction module 5, used for predicting interference parameters corresponding to the interference signal according to the interference signal and the interference cause; The generating module 6 is used to generate a compensation parameter based on the interference parameter and the interference cause, and adjust the monitoring data with the compensation parameter.

[0031] As described above, it can be understood that the various components of the electrical equipment remote monitoring and adjustment system based on wireless communication proposed in this application can realize the functions of any of the electrical equipment remote monitoring and adjustment methods based on wireless communication described above, and the specific structure will not be repeated.

[0032] Reference Figure 4 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a remote monitoring and adjustment method for electrical equipment based on wireless communication is implemented.

[0033] The above-mentioned processor executes the above-mentioned electrical equipment remote monitoring and adjustment method based on wireless communication, including: receiving monitoring data of the target electrical equipment in real time; judging whether there is an interference signal based on the monitoring data; when there is an interference signal in the monitoring data, extracting the change characteristics of the interference signal; analyzing the interference cause of the interference signal based on the change characteristics of the interference signal; predicting the interference parameter corresponding to the interference signal based on the interference signal and the interference cause; generating compensation parameters based on the interference parameters and the interference cause, and adjusting the monitoring data by the compensation parameters.

[0034] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for remote monitoring and adjustment of electrical equipment based on wireless communication is implemented, including the following steps: receiving monitoring data of the target electrical equipment in real time; determining whether there is an interference signal based on the monitoring data; when an interference signal exists in the monitoring data, extracting the change characteristics of the interference signal; analyzing the interference cause of the interference signal based on the change characteristics of the interference signal; predicting the interference parameter corresponding to the interference signal based on the interference signal and the interference cause; generating compensation parameters based on the interference parameters and the interference cause, and adjusting the monitoring data by the compensation parameters.

[0035] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0036] 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, device, article or method 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, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0037] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for remote monitoring and adjustment of electrical equipment based on wireless communication, characterized in that: The method comprises: Receive monitoring data of target electrical equipment in real time; Determining whether there is an interference signal according to the monitoring data; When there is an interference signal in the monitoring data, extracting a change feature of the interference signal; Analyzing the interference cause of the interference signal according to the change characteristics of the interference signal; Predicting interference parameters corresponding to the interference signal according to the interference signal and the interference cause; Combining the interference parameter and the interference cause, a compensation parameter is generated, and the monitoring data is adjusted by the compensation parameter.

2. The method for remote monitoring and adjustment of electrical equipment based on wireless communication according to claim 1, characterized in that: The method of predicting an interference parameter corresponding to the interference signal according to the interference signal and the interference cause comprises: According to the interference cause, identifying the interference type of the interference signal; Preprocessing the interference signal and extracting interference features; Constructing a correlation model, inputting the interference characteristics and interference types into the correlation model, predicting interference parameters corresponding to the interference signal through the correlation model, and outputting the predicted results; Compare and analyze the predicted interference parameters with the preset actual interference parameters, and calculate the correlation coefficient between the predicted interference parameters and the preset actual interference parameters; Determining whether the predicted result is within a preset range according to the correlation coefficient; When the predicted result is within a preset range, the predicted result is judged to be a credible result; Based on the result of the judgment, the predicted result is used as an interference parameter.

3. The method for remote monitoring and adjustment of electrical equipment based on wireless communication according to claim 1, characterized in that: The method of analyzing the interference cause of the interference signal according to the change characteristics of the interference signal includes: Acquire the change characteristics of the interference signal, and perform frequency domain characteristic analysis on the change characteristics; According to the results of the frequency domain characteristic analysis, the signal change frequency of the changing characteristic is determined; Performing time domain feature analysis on the change features; According to the results of time domain feature analysis, the fluctuation characteristics of the change characteristics on the time axis are extracted; Determining the change amplitude and duration of the change feature according to the fluctuation characteristics of the change feature on the time axis; Based on the frequency domain feature analysis and the time domain feature analysis, the interference cause is determined, wherein the interference cause includes external device interference, electrical device interference and environmental interference.

4. The method for remote monitoring and adjusting electrical equipment based on wireless communication according to claim 3, characterized in that: After the step of adjusting the monitoring data by the compensation parameter, the method further comprises: Acquire the signal change frequency, change amplitude and duration of the change characteristics; Determining whether the conditions for adjusting the monitoring end are met according to the signal change frequency, change amplitude and duration of the change characteristics; quantifying the signal change frequency, change amplitude and duration of the change feature, and converting the signal change frequency, change amplitude and duration of the change feature into an influence degree value for evaluating the influence degree of the interference parameter; When the impact degree value is greater than a preset degree threshold, it is determined that the condition for adjusting the monitoring end is met; Based on the judgment result, a simulation model is constructed, and the data quality of the monitoring data collected by the monitoring terminal under different adjustment parameters is predicted by the simulation model; quantifying the data quality to obtain quality coefficients of different adjustment parameters; The adjustment parameter with the largest quality coefficient is selected to adjust the monitoring end.

5. The method for remote monitoring and adjustment of electrical equipment based on wireless communication according to claim 1, characterized in that: After the step of generating compensation parameters by combining the interference parameters and interference causes, the method further includes: Based on the timestamp, analyzing whether the receiving time of the monitoring data is consistent with the preset receiving time; If the receiving time of the monitoring data is inconsistent with the preset receiving time, extract the delay characteristics of the monitoring data; generating delay compensation according to the delay characteristics; The delay compensation, interference parameters and monitoring data are combined to generate compensation parameters.

6. The method for remote monitoring and adjusting electrical equipment based on wireless communication according to claim 5, characterized in that: The method of generating delay compensation according to the delay characteristic comprises: Extracting the delay amplitude of the monitoring data when receiving according to the delay characteristics; Generating delay compensation amounts at different time points according to the delay amplitude and timestamp; Based on the delay compensation amounts at different time points, a delay compensation is generated to correct the receiving time of the monitoring data.

7. The method for remote monitoring and adjusting electrical equipment based on wireless communication according to claim 5, characterized in that: The interference parameters include: signal strength attenuation value, signal distortion or signal delay time.

8. A remote monitoring and adjustment system for electrical equipment based on wireless communication, characterized in that: include: A receiving module, used for receiving monitoring data of target electrical equipment in real time; A judgment module, used to judge whether there is an interference signal according to the monitoring data; An extraction module, used for extracting the change characteristics of the interference signal when the monitoring data contains the interference signal; An analysis module, used for analyzing the interference cause of the interference signal according to the change characteristics of the interference signal; A prediction module, used to predict interference parameters corresponding to the interference signal according to the interference signal and the interference cause; A generating module is used to generate a compensation parameter based on the interference parameter and the interference cause, and adjust the monitoring data with the compensation parameter.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images