Radio frequency monitoring data analysis system and method for industrial Internet of Things

Abstract

The present invention relates to the technical field of industrial Internet of Things, and in particular to a radio frequency monitoring data analysis system and method for the industrial Internet of Things. The present invention comprehensively considers multi-dimensional information such as equipment operation anomalies in the next cycle and the current cycle, and the cosine similarity between the equipment and the radio frequency signal, avoiding the one-sidedness of single-factor judgment, and more comprehensively and accurately evaluating signal anomalies and frequency band interference. Based on the interference analysis results, the receiving frequency band corresponding to the minimum abnormal interference analysis result is selected as the transmission frequency band of the equipment in the future cycle. This can effectively reduce interference in the signal transmission process, improve the signal quality and stability of the signal receiving end, reduce the difficulty of signal denoising, and ensure the accuracy of signal transmission. Reasonable frequency band selection helps the industrial Internet of Things system to operate more stably and reduce problems such as signal loss and errors caused by interference.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things, and in particular to a radio frequency monitoring data analysis system and method for the industrial Internet of Things. Background Art

[0002] The Industrial Internet of Things (IIoT), a product of the deep integration of industrial and IoT technologies, is driving the rapid development of intelligent and automated industrial production. Within IIoT systems, wireless communication between devices is a critical link in enabling data transmission and interaction. Through wireless communication, various sensors, actuators, and other devices can collect and transmit various production process data, such as temperature, pressure, and flow, in real time, while also receiving control commands, enabling precise control and optimization of industrial production processes. With the continuous development of the IIoT, an increasing number of devices are connected to the network, and the demand for wireless communication is also growing. To meet the demands of simultaneous communication among a large number of devices, it is necessary to fully utilize limited spectrum resources and rationally select signal transmission frequency bands to ensure efficient and stable data transmission. When analyzing signal interference, existing methods often struggle to accurately correlate interfering devices with RF signals. Traditional correlation methods primarily rely on simple signal feature matching, ignoring the complex relationship between device operating status and signal characteristics. For example, in industrial environments, different types of devices may generate similar RF signals, making it difficult to accurately identify the source of interference based solely on signal characteristics. This inaccurate association will result in the subsequent interference analysis and treatment measures being less targeted and unable to effectively solve the signal interference problem. When selecting the transmission frequency band of the equipment, the existing methods are often based on experience or simple signal strength comparison, and lack in-depth analysis and scientific evaluation of the frequency band interference situation. This method is prone to selecting frequency bands with potential interference, resulting in a decline in signal transmission quality, signal loss, errors and other problems. At the same time, because the future operation of the equipment and the dynamic changes in frequency band interference are not taken into account, the selected frequency band may not be able to meet the signal transmission requirements in a short period of time, and the frequency band needs to be frequently changed, which increases the complexity and instability of the system. In order to solve the above problems, this application designs a radio frequency monitoring data analysis system and method for the industrial Internet of Things. Summary of the Invention

[0003] In order to overcome the technical problems existing in the prior art, the present invention provides a radio frequency monitoring data analysis system and method for industrial Internet of Things.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for analyzing radio frequency monitoring data for the industrial Internet of Things, comprising the following steps:

[0006] Obtain the strength information of radio frequency signals in each frequency band and the transmission frequency information of the corresponding signals in the industrial Internet of Things scenario, and at the same time obtain the operation status of interference equipment in the industrial Internet of Things scenario;

[0007] Analyze device types based on the transmission status of corresponding transmission signals, predict device operation based on device type operation, and analyze future RF information transmission of interfering devices based on the predicted results of interfering device operation in the scenario and the RF transmission status of the devices;

[0008] Conduct future interference analysis on corresponding frequency bands based on the future RF information transmission of the interfering equipment;

[0009] The interference analysis results of each frequency band are used to select the frequency band for future transmission of the equipment.

[0010] In one implementation of the present invention, the strength information of the radio frequency signals in each frequency band and the transmission frequency information of the corresponding signals are obtained through the corresponding radio frequency signal acquisition terminal. The operation status of the interference device includes the standard signal transmission frequency of other interference devices running in the scene and the strength information reflecting the communication quality of the device. The signal transmission anomaly is evaluated by the signal deviation. It also includes the operating voltage, operating current, temperature and other data reflecting the operation quality of the device. The abnormal operation of the device will have a negative impact on the corresponding signal transmission. The abnormal development trend of the signal transmission is evaluated by the operation trend of the device, which provides the original data for subsequent interference analysis and equipment classification, and can detect signal anomalies in real time.

[0011] In one implementation of the present invention, analyzing the device type based on the transmission status of the corresponding transmission signal includes the following specific steps:

[0012] S21. Obtain the spectrum of the corresponding radio frequency signal, and simultaneously obtain the standard signal transmission frequency and strength information corresponding to each interfering device in the scene, as well as the operating voltage, operating current, and temperature of the interfering device to reflect the operating quality of the device;

[0013] S22. Perform a similarity analysis of the time length based on the spectrum of the corresponding radio frequency signal, the standard signal transmission frequency and strength information of each interfering device in the scene, obtain the corresponding interfering device with the greatest similarity to the radio frequency signal of the corresponding scene, and set it as the corresponding interfering device of the spectrum signal, wherein the similarity analysis method is: obtain the frequency and strength of the radio frequency signal in each time period of the corresponding time length, and at the same time obtain the standard signal transmission frequency and strength information of each interfering device in the scene of each time period of the corresponding time length, import the frequency and strength of the radio frequency signal at the corresponding moment, and the standard signal transmission frequency and strength information of each interfering device into the cosine similarity calculation formula to calculate the corresponding cosine similarity, calculate the cosine similarity between the radio frequency signal and the standard signal of each interfering device, match the device most likely to cause interference, and accurately associate the signal source and the device through the similarity of time domain and frequency domain characteristics;

[0014] S23: Obtain the corresponding interference device of each corresponding radio frequency signal, and simultaneously obtain the cosine similarity between the corresponding interference device and the corresponding radio frequency signal.

[0015] In one implementation of the present invention, the device operation prediction based on the device type operation status includes the following specific steps:

[0016] S24. Obtaining operation data of the device in the preceding period that caused the interference, and performing an operation anomaly assessment based on the operation data of the interfering device. The operation anomaly assessment formula is: a normalized deviation between the device operation data and the device operation standard data, followed by a weighted summation of the normalized deviations of different types of operation data, integrating the weighted summation result over the time length, and then dividing it by the corresponding standard time length to obtain the operation anomaly of the device in the preceding period;

[0017] S25, based on the operational anomaly of the interference device in the previous cycle, the environmental data corresponding to the location of the interference device, and the operational planning of the interference device in the next cycle, predict the operational anomaly of the interference device in the next cycle, and use a deep learning neural network to predict the future situation based on the operational anomaly of the interference device in the previous cycle, the environmental data corresponding to the location of the interference device, and the operational planning of the interference device in the next cycle. The specific method is: obtain the operational anomaly of the interference device in the previous cycle, the environmental data corresponding to the location of the interference device, and the operational planning of the interference device in the next cycle, and predict the operational anomaly of the interference device in the next cycle. The operation anomaly of the equipment in the previous cycle, the environmental data of the location of the corresponding interference equipment, and the operation plan of the interference equipment in the next cycle are used to predict the operation anomaly of the interference equipment in the next cycle. The training set with a weight and bias of 75% and the test set with a weight and bias of 25% are used. The 75% weight and bias training set are input into the deep learning neural network model for training to obtain the initial deep learning neural network model. The initial deep learning neural network model is tested using the 25% weight and bias test set, and the output of the initial deep learning neural network model that meets the maximum preset accuracy of the operation anomaly judgment of the interference equipment in the next cycle is used as the deep learning prediction model.

[0018] In one implementation of the present invention, the analysis of the future radio frequency information transmission of the interference device includes the following specific contents:

[0019] S26. Obtain the predicted interference device operation abnormality in the next period, the interference device operation abnormality in the current period, and the cosine similarity between the corresponding interference device and the corresponding radio frequency signal in the current period;

[0020] S27. Based on the abnormal operation of the interference device in the next period, the abnormal operation of the interference device in the current period, and the cosine similarity between the corresponding interference device and the corresponding radio frequency signal in the current period, perform an abnormal analysis of the signal generation in the next period. The formula for the abnormal analysis of the signal generation in the next period is: , where r is the coefficient of influence of device abnormality on RF signal transmission, cx is the cosine similarity between the interference device and the corresponding RF signal in this period, kz is the operation abnormality of the interference device in the next period, and ks is the operation abnormality of the interference device in this period. This step comprehensively considers the operation abnormality of the interference device in the next period, the operation abnormality of the interference device in this period, and the cosine similarity between the interference device and the corresponding RF signal in this period. The operation abnormality information reflects the working status of the device itself, while the cosine similarity reflects the correlation between the interference device and the RF signal. By combining this multi-dimensional information, the abnormality of the signal in the next period can be more comprehensively and accurately determined. This analysis avoids the potential bias associated with relying solely on a single factor. It also considers the abnormal operating conditions of the device in both the current and next cycles, capturing trends in abnormal device status. If the device's abnormal status changes between cycles, this change is likely to impact signal abnormalities. By comparing the abnormal conditions between the two cycles, this step can more sensitively detect abnormal signal changes caused by changes in device status, helping to identify potential signal issues in advance. Introducing the cosine similarity between the interfering device and the corresponding RF signal in the current cycle allows the inherent connection between the device and the RF signal to be incorporated into the analysis process. Different device operating conditions may have varying degrees of impact on the RF signal, and cosine similarity quantifies the extent of this impact.

[0021] In one implementation of the present invention, the future interference analysis of corresponding frequency bands based on the future radio frequency information transmission of the interfering device includes the following specific contents:

[0022] Obtain the operation plan of the interference device in the next cycle, the receiving frequency band of the corresponding signal receiving end, and the abnormal analysis results of the signal in the next cycle. Based on the operation plan of the interference device in the next cycle and the abnormal analysis results of the signal in the next cycle, perform abnormal interference analysis on each frequency band in the next cycle. The abnormal interference analysis formula for the frequency band in the next cycle is: , T is the cycle length, nc is the number of radio frequency signals generated by the interference device in the IoT scenario of the corresponding frequency band, Ki is the abnormal analysis result of the i-th signal in the next cycle, Hit is the planned transmission frequency of the i-th signal at time t, which can be obtained by the planning method, where the signal corresponds to the device, the i-th signal is the signal generated by the i-th device, Hm is the frequency standard value, Qit is the planned transmission intensity of the i-th signal at time t, where the transmission intensity is the signal strength after the corresponding interference transmission radio frequency signal is attenuated to the signal receiving end, Qm is the intensity standard value, and dt is the time integral. is the frequency impact weight, For intensity influence weighting, in IoT scenarios, the number of RF signals generated by interfering devices is a key factor influencing frequency band interference. The greater the number of signals, the greater the potential for mutual interference. For example, within a limited frequency band, if a large number of interfering devices transmit simultaneously, the signals within the band become very complex, prone to signal overlap and interference. Comparing the planned transmission frequency with the standard frequency value can reveal any deviations in the signal frequency. Frequency deviations can cause the signal to deviate from its intended frequency band, thereby interfering with signals in other bands.

[0023] In one implementation of the present invention, the selection of a frequency band for future periodic transmission of the device based on the interference analysis results corresponding to each frequency band includes the following specific contents:

[0024] The abnormal interference analysis results of all receiving frequency bands of the signal receiving end in the future cycle are obtained, and the receiving frequency band corresponding to the minimum abnormal interference analysis result is selected as the transmission frequency band of the device in the future cycle. The signal transmitting end transmits the signal to the signal receiving end through the transmission frequency band of the device in the future cycle in the next cycle.

[0025] In a second aspect, the present invention further provides a radio frequency monitoring data analysis system for the industrial Internet of Things, comprising:

[0026] The data acquisition module is used to obtain the strength information of the radio frequency signals in each frequency band in the industrial Internet of Things scenario and the transmission frequency information of the corresponding signals, and at the same time obtain the operation status of the interference equipment in the industrial Internet of Things scenario;

[0027] The RF information transmission analysis module analyzes the device type by analyzing the transmission status of the corresponding transmission signal, predicts the device operation based on the device type, and analyzes the future RF information transmission of the interfering device based on the predicted results of the interfering device operation in the scenario and the device RF transmission status;

[0028] The frequency band interference analysis module performs future interference analysis on corresponding frequency bands based on the future RF information transmission of the interfering equipment;

[0029] The transmission frequency band selection module selects the transmission frequency band of the device in the future period according to the interference analysis results of each frequency band.

[0030] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a radio frequency monitoring data analysis method for the industrial Internet of Things by calling the computer program stored in the memory.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a radio frequency monitoring data analysis method for the industrial Internet of Things.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0033] When analyzing the future radio frequency information transmission of the interference equipment and the interference analysis of the corresponding frequency bands in the future, the present invention comprehensively considers multi-dimensional information such as the equipment operation abnormalities in the next cycle and the current cycle, the cosine similarity between the equipment and the radio frequency signal, etc., avoids the one-sidedness of single-factor judgment, and more comprehensively and accurately evaluates the signal abnormalities and frequency band interference. Based on the interference analysis results, the receiving frequency band corresponding to the minimum abnormal interference analysis result is selected as the transmission frequency band of the equipment in the future cycle, which can effectively reduce the interference in the signal transmission process, improve the signal quality and stability of the signal receiving end, reduce the difficulty of signal denoising, and ensure the accuracy of signal transmission. Reasonable frequency band selection helps the industrial Internet of Things system to operate more stably, reduce problems such as signal loss and errors caused by interference, improve the reliability and availability of the entire system, and reduce maintenance costs and production losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0035] Figure 1 Schematic diagram of the overall process of an embodiment of the method of the present invention;

[0036] Figure 2 This is a schematic diagram of the S2 process of an embodiment of the method of the present invention;

[0037] Figure 3 A schematic diagram of a neural network model construction process according to an embodiment of the method of the present invention;

[0038] Figure 4 Schematic diagram of the structure of the system embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0042] Example 1

[0043] like Figure 1 and Figure 2 As shown, this embodiment provides a radio frequency monitoring data analysis method for the industrial Internet of Things, which specifically includes the following steps:

[0044] S1. Obtain the strength information of the radio frequency signals in each frequency band and the transmission frequency information of the corresponding signals in the industrial Internet of Things scenario, and at the same time obtain the operating status of the interfering equipment in the industrial Internet of Things scenario;

[0045] In this embodiment, the strength information of the radio frequency signal in each frequency band and the transmission frequency information of the corresponding signal are obtained through a corresponding radio frequency signal acquisition terminal, such as a spectrum analyzer and spectrum analysis software, to obtain the signal strength information of the signal in each frequency band at the corresponding position of the signal receiving end and the transmission frequency information of the corresponding signal. The operating status of the interfering device includes the standard signal transmission frequency and strength information of other interfering devices in the scene, which reflects the communication quality of the device. When a safe device is operating, its radio frequency signal is transmitted according to the specified frequency and strength, while the frequency and strength of the signal of an abnormal device may deviate. The signal deviation is used to evaluate the signal transmission anomaly. The operating voltage, operating current, temperature, and other data reflecting the operating quality of the device are also included. Abnormal device operation can negatively impact the corresponding signal transmission. The development trend of signal transmission anomalies is evaluated based on the device operation trend, providing raw data for subsequent interference analysis and device classification. Signal anomalies (such as frequency offset and sudden strength changes) can be detected in real time, indicating potential interference or device failure, identifying which frequency bands are crowded or idle in the current environment, and optimizing spectrum resource allocation.

[0046] S2. Analyze the device type based on the transmission status of the corresponding transmission signal, predict the device operation based on the device type operation status, and analyze the future RF information transmission status of the interfering device based on the interference device operation prediction results and the device RF transmission status in the scenario;

[0047] In this embodiment, analyzing the device type based on the transmission status of the corresponding transmission signal includes the following specific steps:

[0048] S21. Obtain the spectrum of the corresponding radio frequency signal, and simultaneously obtain the standard signal transmission frequency and strength information corresponding to each interfering device in the scene, as well as the operating voltage, operating current, and temperature of the interfering device to reflect the operating quality of the device;

[0049] S22. Perform a similarity analysis of the time length based on the spectrum of the corresponding radio frequency signal, the standard signal transmission frequency and strength information of each interfering device in the scene, obtain the corresponding interfering device with the greatest similarity to the radio frequency signal of the corresponding scene, and set it as the corresponding interfering device of the spectrum signal, wherein the similarity analysis method is: obtain the frequency and strength of the radio frequency signal in each time period of the corresponding time length, and at the same time obtain the standard signal transmission frequency and strength information of each interfering device in the scene of each time period of the corresponding time length, import the frequency and strength of the radio frequency signal at the corresponding moment, and the standard signal transmission frequency and strength information of each interfering device into the cosine similarity calculation formula to calculate the corresponding cosine similarity, calculate the cosine similarity between the radio frequency signal and the standard signal of each interfering device, match the device most likely to cause interference, and accurately associate the signal source and the device through the similarity of time domain and frequency domain characteristics;

[0050] S23. Obtain the corresponding interference device of each corresponding radio frequency signal, and simultaneously obtain the cosine similarity between the corresponding interference device and the corresponding radio frequency signal;

[0051] In this embodiment, performing device operation prediction based on device type operation status includes the following specific steps:

[0052] S24. Obtaining operation data of the device in the preceding period that caused the interference, and performing an operation anomaly assessment based on the operation data of the interfering device. The operation anomaly assessment formula is: a normalized deviation between the device operation data and the device operation standard data, followed by a weighted summation of the normalized deviations of different types of operation data, integrating the weighted summation result over the time length, and then dividing it by the corresponding standard time length to obtain the operation anomaly of the device in the preceding period;

[0053] S25. Predicting the operational anomaly of the interference device in the next cycle based on the operational anomaly of the interference device in the previous cycle, the environmental data corresponding to the location of the interference device, and the operational plan of the interference device in the next cycle. It is feasible to use a deep learning neural network to predict the operational anomaly of the interference device in the next cycle based on the operational anomaly of the interference device in the previous cycle, the environmental data corresponding to the location of the interference device, and the operational plan of the interference device in the next cycle. The following analysis is conducted from multiple aspects: The operational anomaly of the interference device is affected by a combination of multiple factors. The operational anomaly in the previous cycle reflects the historical status and potential fault hazards of the device itself; environmental conditions (such as temperature, humidity, electromagnetic interference intensity, etc.) will affect the normal operation of the device; and the operational plan of the next cycle (such as operating time, power setting, etc.) will also bring different workloads to the device. Deep learning neural networks have powerful nonlinear modeling capabilities and can automatically learn complex mapping relationships from these multi-source data to predict the operational anomaly of the next cycle. This involves multimodal data such as operational anomaly data, environmental data, and operational plan data. Deep learning neural networks can process different types of data. For example, they can integrate and process numerical environmental parameters, operational anomaly records in time series form, and operational plan information in text or numerical form. Through appropriate network architecture design, the data features of these different modalities can be effectively integrated to improve the accuracy of prediction. The abnormal operation data of the previous cycle is essentially a time series data, which contains the status information of the equipment changing over time. The recurrent neural network and its variants in deep learning can well process time series data and capture the long-term dependencies in the data, such as Figure 3 As shown, the specific method of using the past operating status of the equipment to predict the future situation is as follows: obtain the operating anomaly of the previous cycle of the historical interference equipment, the environmental situation data corresponding to the location of the interference equipment, and the operating plan of the next cycle of the interference equipment to predict the operating anomaly of the interference equipment in the next cycle, and divide the operating anomaly of the previous cycle of the interference equipment, the environmental situation data corresponding to the location of the interference equipment, and the operating plan of the next cycle of the interference equipment into a 75% weight and bias training set and a 25% weight and bias test set; input the 75% weight and bias training set into the deep learning neural network model for training to obtain an initial deep learning neural network model; use the 25% weight and bias test set to test the initial deep learning neural network model, and output the initial deep learning neural network model output that meets the maximum preset accuracy rate of the operation anomaly judgment of the interference equipment in the next cycle as the deep learning prediction model;

[0054] In this embodiment, the analysis of the future radio frequency information transmission of the interfering device includes the following specific contents:

[0055] S26. Obtain the predicted interference device operation abnormality in the next period, the interference device operation abnormality in the current period, and the cosine similarity between the corresponding interference device and the corresponding radio frequency signal in the current period;

[0056] S27. Based on the abnormal operation of the interference device in the next period, the abnormal operation of the interference device in the current period, and the cosine similarity between the corresponding interference device and the corresponding radio frequency signal in the current period, perform an abnormal analysis of the signal generation in the next period. The formula for the abnormal analysis of the signal generation in the next period is: , where r is the coefficient of influence of device abnormality on RF signal transmission, cx is the cosine similarity between the interference device and the corresponding RF signal in this period, kz is the operation abnormality of the interference device in the next period, and ks is the operation abnormality of the interference device in this period. This step comprehensively considers the operation abnormality of the interference device in the next period, the operation abnormality of the interference device in this period, and the cosine similarity between the interference device and the corresponding RF signal in this period. The operation abnormality information reflects the working status of the device itself, while the cosine similarity reflects the correlation between the interference device and the RF signal. By combining this multi-dimensional information, the abnormality of the signal in the next period can be more comprehensively and accurately determined. This analysis avoids the bias that can arise from relying solely on a single factor. It also considers the device operating anomalies in both the current and next cycles, capturing trends in device anomaly status. If the device anomaly status changes between cycles, this change is likely to impact signal anomalies. By comparing the anomaly status between the two cycles, this step can more sensitively detect signal anomalies caused by device status changes, helping to identify potential signal issues in advance. The cosine similarity between the interfering device and the corresponding RF signal in the current cycle is introduced to incorporate the inherent connection between the device and the RF signal into the analysis process. Different device operating states may have varying degrees of impact on the RF signal, and cosine similarity quantifies the extent of this impact. This coefficient allows for a more precise assessment of the specific contribution of device anomalies to signal anomalies, improving analysis accuracy. The formula includes the coefficient r for the impact of device anomalies on RF signal transmission, which can be adjusted based on actual conditions. Different application scenarios, device types, and RF environments may cause device anomalies to have varying degrees of impact on signal transmission. By adjusting the value of r, the analysis method can be better adapted to various practical situations, enhancing its versatility and flexibility. Device anomalies can cause fluctuations in the frequency and intensity of interfering RF signals. This makes it difficult for signal receivers to distinguish whether the interfering RF signals are from stored interfering devices, making accurate denoising difficult. Therefore, the selection of transmission frequency bands is necessary. However, existing technologies can easily denoise interfering RF signals that do not fluctuate.

[0057] S3. Conduct future interference analysis on corresponding frequency bands based on the future radio frequency information transmission of the interference device;

[0058] In this embodiment, based on the future radio frequency information transmission of the interfering device, future interference analysis of each corresponding frequency band is performed, including the following specific contents:

[0059] Obtain the operation plan of the interference device in the next cycle, the receiving frequency band of the corresponding signal receiving end, and the abnormal analysis results of the signal in the next cycle. Based on the operation plan of the interference device in the next cycle and the abnormal analysis results of the signal in the next cycle, perform abnormal interference analysis on each frequency band in the next cycle. The abnormal interference analysis formula for the frequency band in the next cycle is: , T is the cycle length, nc is the number of radio frequency signals generated by the interference device in the IoT scenario of the corresponding frequency band, Ki is the abnormal analysis result of the i-th signal in the next cycle, Hit is the planned transmission frequency of the i-th signal at time t, which can be obtained by the planning method, where the signal corresponds to the device, the i-th signal is the signal generated by the i-th device, Hm is the frequency standard value, Qit is the planned transmission intensity of the i-th signal at time t, where the transmission intensity is the signal strength after the corresponding interference transmission radio frequency signal is attenuated to the signal receiving end, Qm is the intensity standard value, and dt is the time integral. is the frequency impact weight, Intensity influences weighting. In IoT scenarios, the number of RF signals generated by interfering devices is a significant factor influencing frequency band interference. The greater the number of signals, the greater the potential for mutual interference. For example, within a limited frequency band, if a large number of interfering devices transmit simultaneously, the signals within the band become highly complex, prone to signal overlap and interference. Comparing the planned transmission frequency with the standard frequency value can reveal signal frequency deviations. Frequency deviations can cause signals to deviate from their intended frequency band, potentially interfering with signals in other bands. For example, when a signal's transmission frequency exceeds its specified frequency band range, it can interfere with signals in adjacent bands. Therefore, considering frequency deviations can more accurately assess the impact of signal frequency on abnormal interference within a band. Comparing the planned transmission intensity with the standard intensity value can reveal signal strength deviations. Excessively strong signal strength can mask other normal signals, while too weak can cause unstable signal transmission. For example, a signal with excessively strong transmission intensity can create strong interference within the band, impacting the reception of other signals. Therefore, incorporating signal strength deviations into the formula can reflect the impact of signal strength on abnormal interference within a band. Interference conditions vary over time, and signal transmission conditions may differ at different times. By integrating time, we can comprehensively consider the interference of signals throughout the entire cycle. For example, within a cycle, a signal may have abnormal transmission frequency or intensity during certain time periods. By integrating, we can accumulate the abnormalities in these time periods and more comprehensively evaluate the interference impact of the signal on the frequency band throughout the cycle. Frequency impact weight and intensity impact weight: Different factors may have different degrees of influence on abnormal interference in the frequency band. Frequency and intensity are two important characteristics of signals, and their impact on frequency band interference may have different weights. This formula comprehensively considers multiple factors such as the number of interfering device signals, signal anomaly analysis results, signal frequency and intensity, and can more comprehensively and accurately evaluate the abnormal interference situation of each frequency band in the next cycle. Compared with the single-factor evaluation method, it can capture more complex interference situations and avoid evaluation errors caused by ignoring certain important factors.

[0060] It should be noted in this embodiment that the setting parameters (such as the influence coefficient and the influence weight, etc.) in this embodiment are obtained by experiments conducted by those skilled in the art based on historical data. A specific example of the experimental method is as follows: historically obtaining the strength information of radio frequency signals in each frequency band in the industrial Internet of Things scenario and the transmission frequency information of the corresponding signal, and simultaneously obtaining the operation status of the interference device in the industrial Internet of Things scenario, and substituting them into the steps of this embodiment to select the future periodic transmission frequency band, and simultaneously obtaining the factual judgment result of whether the future periodic transmission frequency band meets the actual requirements, and substituting the factual result and the selection prediction result into the fitting software for iterative fitting of the data, and outputting the setting parameter value of this embodiment that meets the maximum judgment accuracy. The fitting software is preferably MATLAB software;

[0061] S4. Select the frequency band for future transmission of the device based on the interference analysis results of each frequency band;

[0062] In this embodiment, the selection of the transmission frequency band includes the following specific contents:

[0063] Obtain the abnormal interference analysis results of all receiving frequency bands of the signal receiving end in the future cycle, select the receiving frequency band corresponding to the minimum abnormal interference analysis result as the device's transmission frequency band in the future cycle, and the signal transmitting end transmits the signal to the signal receiving end in the next cycle using the device's transmission frequency band in the future cycle;

[0064] It should be noted in this embodiment that this embodiment has the following benefits and advantages. When analyzing the future radio frequency information transmission of the interfering device and the interference analysis of the corresponding frequency bands in the future, multi-dimensional information such as the device operation abnormalities in the next cycle and the current cycle, the cosine similarity between the device and the radio frequency signal, etc. is comprehensively considered, avoiding the one-sidedness of single-factor judgment, and more comprehensively and accurately evaluating the signal abnormalities and frequency band interference. Based on the interference analysis results, the receiving frequency band corresponding to the minimum abnormal interference analysis result is selected as the transmission frequency band of the device in the future cycle, which can effectively reduce the interference in the signal transmission process, improve the signal quality and stability of the signal receiving end, reduce the difficulty of signal denoising, and ensure the accuracy of signal transmission. Reasonable frequency band selection helps the industrial Internet of Things system to operate more stably, reduce signal loss and errors caused by interference, improve the reliability and availability of the entire system, and reduce maintenance costs and production losses.

[0065] Example 2

[0066] like Figure 4 As shown, this embodiment provides a radio frequency monitoring data analysis system for the industrial Internet of Things, including: a data acquisition module for obtaining the strength information of radio frequency signals in each frequency band in the industrial Internet of Things scenario and the transmission frequency information of the corresponding signals, and at the same time obtaining the operation status of interference devices in the industrial Internet of Things scenario;

[0067] The RF information transmission analysis module analyzes the device type by analyzing the transmission status of the corresponding transmission signal, predicts the device operation based on the device type, and analyzes the future RF information transmission of the interfering device based on the predicted results of the interfering device operation in the scenario and the device RF transmission status;

[0068] The frequency band interference analysis module performs future interference analysis on corresponding frequency bands based on the future RF information transmission of the interfering equipment;

[0069] The transmission frequency band selection module selects the transmission frequency band of the device in the future period according to the interference analysis results of each frequency band. The specific content of each step in this embodiment has been specifically described in the above method embodiment.

[0070] Example 3

[0071] An electronic device according to an embodiment of the present invention includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes a radio frequency monitoring data analysis method for the Industrial Internet of Things by calling the computer program stored in the memory. It should be noted that all computer programs of the radio frequency monitoring data analysis method for the Industrial Internet of Things are implemented in the C language.

[0072] Example 4

[0073] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0074] When the computer program runs on a computer device, the computer device executes the above-mentioned radio frequency monitoring data analysis method for the industrial Internet of Things.

[0075] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0076] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0077] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0079] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0080] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0081] Throughout this specification, references to terms such as "one embodiment," "example," and "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0082] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A radio frequency monitoring data analysis method for industrial Internet of Things, characterized by: The steps include: Obtain the strength information of radio frequency signals in each frequency band and the transmission frequency information of the corresponding signals in the industrial Internet of Things scenario, and at the same time obtain the operation status of interference equipment in the industrial Internet of Things scenario; Analyze device types based on the transmission status of corresponding transmission signals, predict device operation based on device type operation, and analyze future RF information transmission of interfering devices based on the predicted results of interfering device operation in the scenario and the RF transmission status of the devices; Analysis of future radio frequency information transmission of the interference device, including the following specific contents: Obtain the predicted interference device operation anomaly in the next period, the interference device operation anomaly in the current period, and the cosine similarity between the corresponding interference device and the corresponding RF signal in the current period; An analysis of the next cycle signal generation anomaly is performed based on the next cycle interference device operation anomaly, the current cycle interference device operation anomaly, and the cosine similarity between the corresponding interference device and the corresponding RF signal in the current cycle. The next cycle signal generation anomaly analysis formula is: , where r is the coefficient of influence of equipment abnormality on RF signal transmission, cx is the cosine similarity between the corresponding interference device and the corresponding RF signal in this cycle, kz is the abnormal operation of the interference device in the next cycle, and ks is the abnormal operation of the interference device in this cycle. Conduct future interference analysis on corresponding frequency bands based on the future RF information transmission of the interfering equipment; The interference analysis results of each frequency band are used to select the frequency band for future transmission of the equipment.

2. The method for analyzing radio frequency monitoring data for industrial Internet of Things according to claim 1, characterized in that: The analysis of the device type based on the transmission status of the corresponding transmission signal includes the following specific steps: Obtain the spectrum of the corresponding radio frequency signal, and at the same time obtain the standard signal transmission frequency and strength information corresponding to each interfering device in the scene, as well as the operating voltage, operating current, and temperature of the interfering device to reflect the operating quality of the device; Based on the spectrum of the radio frequency signal corresponding to the time length, the standard signal transmission frequency and strength information of each interfering device in the scene, a similarity analysis of the time length is performed to obtain the corresponding interfering device with the greatest similarity to the radio frequency signal of the corresponding scene, and set it as the corresponding interfering device of the spectrum signal. The similarity analysis method is as follows: obtaining the frequency and strength of the radio frequency signal in each time period of the corresponding time length, and at the same time obtaining the standard signal transmission frequency and strength information of each interfering device in the scene of each time period of the corresponding time length, importing the frequency and strength of the radio frequency signal at the corresponding moment, and the standard signal transmission frequency and strength information of each interfering device into the cosine similarity calculation formula to calculate the corresponding cosine similarity, calculating the cosine similarity between the radio frequency signal and the standard signal of each interfering device, and matching the device most likely to cause interference; The corresponding interference generating device of each corresponding radio frequency signal is obtained, and the cosine similarity between the corresponding interference generating device and the corresponding radio frequency signal is obtained.

3. The method for analyzing radio frequency monitoring data for industrial Internet of Things according to claim 2, characterized in that: The device operation prediction based on the device type operation status includes the following specific steps: Obtain the operating data of the device in the previous period that caused the interference, and perform an operational anomaly assessment based on the operating data of the interfering device. The operational anomaly assessment formula is: the normalized deviation between the device operating data and the device operating standard data, and then perform a weighted summation of the normalized deviations of different types of operating data. The weighted summation result is integrated over the time length and then divided by the corresponding standard time length to obtain the operational anomaly of the device in the previous period; The operation abnormality of the interference device in the next cycle is predicted based on the operation abnormality of the interference device in the previous cycle, the environmental condition data corresponding to the location of the interference device and the operation plan of the interference device in the next cycle.

4. The method for analyzing radio frequency monitoring data for industrial Internet of Things according to claim 3, characterized in that: The interference analysis of each frequency band in the future based on the future radio frequency information transmission of the interference device includes the following specific contents: Obtain the operation plan of the interference device in the next cycle, the receiving frequency band of the corresponding signal receiving end, and the abnormal analysis results of the signal in the next cycle. Based on the operation plan of the interference device in the next cycle and the abnormal analysis results of the signal in the next cycle, perform abnormal interference analysis on each frequency band in the next cycle. The abnormal interference analysis formula for the frequency band in the next cycle is: , T is the cycle length, nc is the number of RF signals generated by the interfering devices in the IoT scenario of the corresponding frequency band, Ki is the abnormal analysis result of the i-th signal in the next cycle, Hit is the planned transmission frequency of the i-th signal at time t, where the signal corresponds to the device, the i-th signal is the signal generated by the i-th device, Hm is the frequency standard value, Qit is the planned transmission intensity of the i-th signal at time t, Qm is the intensity standard value, and dt is the time integral. is the frequency impact weight, The intensity influence weight.

5. The method for analyzing radio frequency monitoring data for industrial Internet of Things according to claim 1, characterized in that: The selection of the device's future periodic transmission frequency band based on the interference analysis results for each frequency band includes the following specific contents: The abnormal interference analysis results of all receiving frequency bands of the signal receiving end in the future cycle are obtained, and the receiving frequency band corresponding to the minimum abnormal interference analysis result is selected as the transmission frequency band of the device in the future cycle. The signal transmitting end transmits the signal to the signal receiving end through the transmission frequency band of the device in the future cycle in the next cycle.

6. The method for analyzing radio frequency monitoring data for industrial Internet of Things according to claim 1, characterized in that: The strength information of the radio frequency signals in each frequency band and the transmission frequency information of the corresponding signals are obtained through the corresponding radio frequency signal acquisition terminal. The operation status of the interference device includes the standard signal transmission frequency of other interference devices running in the scene and the strength information reflecting the communication quality of the device, and also includes the operating voltage, operating current and temperature of the interference device reflecting the operation quality of the device.

7. A radio frequency monitoring data analysis system for industrial Internet of Things, which is used to implement the radio frequency monitoring data analysis method for industrial Internet of Things according to any one of claims 1 to 6, characterized in that: The system comprises: The data acquisition module is used to obtain the strength information of the radio frequency signals in each frequency band in the industrial Internet of Things scenario and the transmission frequency information of the corresponding signals, and at the same time obtain the operation status of the interference equipment in the industrial Internet of Things scenario; The RF information transmission analysis module analyzes the device type by analyzing the transmission status of the corresponding transmission signal, predicts the device operation based on the device type, and analyzes the future RF information transmission of the interfering device based on the predicted results of the interfering device operation in the scenario and the device RF transmission status; The frequency band interference analysis module performs future interference analysis on corresponding frequency bands based on the future RF information transmission of the interfering equipment; The transmission frequency band selection module selects the transmission frequency band of the device in the future period according to the interference analysis results of each frequency band.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the radio frequency monitoring data analysis method for industrial Internet of Things according to any one of claims 1 to 6 by calling the computer program stored in the memory.

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