Intelligent early warning system for data traffic

Abstract

The invention discloses an intelligent early warning system for data traffic, relates to the technical field of traffic early warning, and evaluates the state of equipment by calculating the performance reduction coefficient of the equipment, the use frequency change coefficient and the electromagnetic interference coefficient. Specifically, the system obtains the current performance index and the historical performance index of the equipment, the current use frequency and the historical average use frequency of the equipment, and the electromagnetic interference data of the equipment in the operation period, so as to calculate the corresponding coefficient. And according to the coefficients, the system obtains a coefficient needing to be adjusted, compares the coefficient with a preset adjustment threshold value, and judges whether an early warning threshold value of data traffic monitoring needs to be adjusted or not. And if the threshold adjustment is necessary, the system performs data traffic early warning according to the new threshold, and timely identifies the abnormal condition of the equipment, so that the equipment fault risk is reduced. The mechanism can ensure that the equipment can be processed in time under abnormal conditions, so that production stagnation, data loss or safety accidents are prevented, and the stability and the safety of equipment operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic early warning, and in particular to a data traffic intelligent early warning system. Background Art

[0002] The data flow warning of the equipment is to monitor and analyze the data flow generated by the equipment during operation, set a certain threshold, so that when the data flow of the equipment fluctuates abnormally, an alarm can be issued in time, thereby providing early warning signals to equipment maintenance personnel to help them determine whether the equipment has potential failures or abnormalities. This mechanism is usually based on factors such as the historical data flow trend of the equipment, equipment performance indicators, and environmental conditions, and sets reasonable early warning thresholds to ensure that the equipment operates within the normal load range and can quickly detect possible failures or performance degradation. For example, when the data flow of the equipment exceeds the preset threshold, the system will automatically issue an early warning to indicate that the equipment may have problems such as overload, abnormal behavior, or system vulnerabilities, thereby avoiding production interruptions, data loss, or safety hazards caused by equipment failures.

[0003] However, with the long-term use of the equipment, factors such as the equipment's hardware performance, working environment, and frequency of use will change, and the original warning threshold may lose its adaptability; if it is not possible to promptly determine whether it is necessary to adjust the warning threshold related to the equipment's data traffic monitoring and analysis, the system may be unable to effectively identify abnormal conditions in the equipment's operation, thereby increasing the risk of equipment failure, delaying fault handling time, and may even lead to production stagnation, data loss, or security incidents. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and provide a data traffic intelligent early warning system.

[0005] The present invention proposes a data traffic intelligent early warning system, the system comprising:

[0006] Equipment performance degradation module: obtains the current performance index value and historical performance index value of the equipment, and calculates the equipment performance degradation coefficient according to the current performance index value and historical performance index value;

[0007] Frequency of use change module: obtains the frequency of use of the device in the current period and the average frequency of use of the device in the historical period, and calculates the frequency of use change coefficient based on the frequency of use of the device in the current period and the average frequency of use of the device in the historical period;

[0008] Electromagnetic interference module: obtains the time interval from the start of the device operation to the current time, obtains the electromagnetic interference data of the device within the time interval, and calculates the electromagnetic interference coefficient based on the electromagnetic interference data;

[0009] Early warning module: obtains the coefficient that needs to be adjusted based on the equipment performance degradation coefficient, the frequency of use change coefficient and the electromagnetic interference coefficient; compares the coefficient that needs to be adjusted with the preset threshold of the coefficient that needs to be adjusted, and determines whether it is necessary to adjust the early warning threshold related to the data flow monitoring and analysis of the equipment based on the comparison result; and issues an early warning for the data flow of the equipment based on the adjusted early warning threshold.

[0010] Optionally, calculating the device performance degradation coefficient according to the indicator value of the current performance indicator and the historical performance indicator includes:

[0011] Get the current performance index value of each device and the corresponding historical performance index value, and calculate the performance degradation value of each performance index value. The calculation formula is: Where P is the performance degradation value, YA and YB are the current performance index value and the corresponding historical performance index value respectively;

[0012] The equipment performance degradation coefficient is calculated based on the performance degradation value of each performance indicator value. The calculation formula is: In the formula, DF is the equipment performance degradation coefficient, i represents the order number of the performance index, and n represents the total number of performance indexes; P i is the performance degradation value of the i-th performance indicator, ω i Indicates the preset weight value of the i-th performance indicator.

[0013] Optionally, calculating the usage frequency change coefficient according to the usage frequency of the device in the current period and the average usage frequency of the device in the historical period includes:

[0014] Obtain the usage frequency of the device in the current period and the average usage frequency of the device in the historical period, and divide the usage frequency of the device in the current period by the average usage frequency of the device in the historical period to obtain the frequency change value S. In the formula, HJ and HU are the usage frequency of the equipment in the current period and the average usage frequency of the equipment in the historical period respectively;

[0015] Calculate the frequency variation coefficient. The calculation formula is: PY=S-1, where PY is the frequency variation coefficient.

[0016] Optionally, calculating the electromagnetic interference coefficient according to the electromagnetic interference data includes:

[0017] Obtain the time interval from the start of the operation of the device to the current moment, and obtain the electromagnetic interference signal to which the device is subjected within the time interval, and mark it as B(t), where t=1, 2, 3, 4, ..., Z, where Z is a positive integer and represents the number of electromagnetic interference signals;

[0018] Morlet wavelet is used to perform continuous wavelet transform on the electromagnetic interference signal B(t) at each moment. The electromagnetic interference signal is decomposed in the time domain and frequency domain through continuous wavelet transform (CWT). Continuous wavelet transform is realized through convolution, and its calculation formula is as follows: Where x(t) is the input signal, ψ(t) is the selected wavelet basis function, a is the scale parameter used to adjust the scale of the wavelet basis function; b is the translation parameter used to adjust the position of the wavelet basis function; t represents the time coordinate of the signal, that is, the position on the time axis;

[0019] For each scale a and translation b, the result calculated using wavelet transform is called wavelet coefficient;

[0020] Extract the energy characteristics of the wavelet coefficients. According to the calculated wavelet coefficients, further extract the energy characteristics, calculate the energy of the wavelet coefficients at each scale, and then perform weighted summation of the energies at different scales to obtain the comprehensive energy characteristics Ez. The calculation formula is as follows: Where E(a) is the energy at scale a, CWT(a,b) is the wavelet coefficient;

[0021] Calculate the electromagnetic interference index at each moment, the calculation expression is: BK(t) = Ez, where BK(t) is the electromagnetic interference index;

[0022] The electromagnetic interference index BK(t) at different times is added together to obtain the electromagnetic interference index. The calculation formula is: Where JM is the electromagnetic interference coefficient.

[0023] Optionally, the coefficients to be adjusted obtained according to the equipment performance degradation coefficient, the use frequency change coefficient and the electromagnetic interference coefficient include:

[0024]

[0025] Where GHT is the coefficient that needs to be adjusted, DF, PY and JM are the equipment performance degradation coefficient, the frequency of use change coefficient and the electromagnetic interference coefficient respectively, α, β, γ are the preset proportional coefficients of DF, PY and JM respectively, and α, β, γ are all greater than 0.

[0026] Optionally, the coefficient to be adjusted is compared with a preset threshold value of the coefficient to be adjusted, and it is determined whether the data flow monitoring and analysis related warning thresholds of the device need to be adjusted according to the comparison result. The thresholds include:

[0027] If the coefficient to be adjusted is not less than the preset threshold value of the coefficient to be adjusted, the warning threshold value related to the data flow monitoring and analysis of the device needs to be adjusted;

[0028] If the coefficient to be adjusted is less than the preset threshold value of the coefficient to be adjusted, there is no need to adjust the warning threshold value related to the data flow monitoring and analysis of the device.

[0029] Beneficial effects of the present invention:

[0030] The present invention proposes a data flow intelligent early warning system, which calculates the equipment performance degradation coefficient by obtaining the current performance index value and the historical performance index value of the equipment; obtains the use frequency of the equipment in the current period and the average use frequency of the equipment in the historical period to calculate the use frequency change coefficient; obtains the electromagnetic interference data of the equipment in the time interval from the start of operation to the current moment to calculate the electromagnetic interference coefficient; obtains the required adjustment coefficient according to the equipment performance degradation coefficient, the use frequency change coefficient and the electromagnetic interference coefficient; compares the required adjustment coefficient with the preset required adjustment coefficient threshold, and judges whether it is necessary to adjust the warning threshold related to the data flow monitoring and analysis of the equipment according to the comparison result; and warns the data flow of the equipment according to the warning threshold related to the data flow monitoring and analysis of the adjusted equipment; can timely judge whether it is necessary to adjust the warning threshold related to the data flow monitoring and analysis of the equipment, ensure that abnormal conditions in the operation of the equipment can be effectively identified, thereby reducing the risk of equipment failure, ensuring that the failure processing time can be timely, and will not cause production stagnation, data loss or security incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Figure 1 The figure is a framework diagram of a data traffic intelligent early warning system. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0035] The embodiment of the present invention provides a data traffic intelligent early warning system. Figure 1 , Figure 1 A framework diagram of a data traffic intelligent early warning system provided by an embodiment of the present invention. The system includes:

[0036] Equipment performance degradation module: obtains the current performance index value and historical performance index value of the equipment, and calculates the equipment performance degradation coefficient according to the current performance index value and historical performance index value;

[0037] Frequency of use change module: obtains the frequency of use of the device in the current period and the average frequency of use of the device in the historical period, and calculates the frequency of use change coefficient based on the frequency of use of the device in the current period and the average frequency of use of the device in the historical period;

[0038] Electromagnetic interference module: obtains the time interval from the start of the device to the current time, obtains the electromagnetic interference data of the device within the time interval, and calculates the electromagnetic interference coefficient based on the electromagnetic interference data;

[0039] Early warning module: obtains the coefficient that needs to be adjusted based on the equipment performance degradation coefficient, the frequency of use change coefficient and the electromagnetic interference coefficient; compares the coefficient that needs to be adjusted with the preset threshold of the coefficient that needs to be adjusted, and determines whether it is necessary to adjust the early warning threshold related to the data flow monitoring and analysis of the equipment based on the comparison result; and issues an early warning on the data flow of the equipment based on the early warning threshold related to the data flow monitoring and analysis of the adjusted equipment.

[0040] Based on a data traffic intelligent early warning system provided by an embodiment of the present invention, through the above method, it can timely determine whether it is necessary to adjust the early warning threshold related to the data traffic monitoring and analysis of the equipment, thereby ensuring that abnormal conditions in the operation of the equipment can be effectively identified, thereby reducing the risk of equipment failure and ensuring that the fault processing time can be timely without causing production stagnation, data loss or security incidents.

[0041] In one embodiment, calculating the device performance degradation coefficient according to the indicator value of the current performance indicator and the historical performance indicator includes:

[0042] Get the current performance index value of each device and the corresponding historical performance index value, and calculate the performance degradation value of each performance index value. The calculation formula is: Where P is the performance degradation value, YA and YB are the current performance index value and the corresponding historical performance index value respectively;

[0043] The equipment performance degradation coefficient is calculated based on the performance degradation value of each performance indicator value. The calculation formula is: In the formula, DF is the equipment performance degradation coefficient, i represents the order number of the performance index, and n represents the total number of performance indexes; P i is the performance degradation value of the i-th performance indicator, ω i Indicates the preset weight value of the i-th performance indicator.

[0044] It should be noted that the preset weight value of each performance indicator is set by professionals according to the actual situation, and no specific restrictions or elaborations are made; in addition, performance indicators can cover multiple dimensions, such as CPU usage, memory occupancy, disk read and write rate, network bandwidth, temperature, power consumption, etc. Specific performance indicators will vary according to the type of equipment and application scenarios. For example, for servers, the focus may be on the use of CPU and memory; for storage devices, more attention may be paid to the utilization of disk read and write rates and storage capacity; for industrial equipment, the operating temperature and power consumption of the equipment may also be included. When obtaining the current value of each performance indicator of the device, real-time data can be obtained through hardware monitoring tools, the performance monitoring interface of the operating system, or the embedded control system of the device; historical performance indicator values ​​usually come from the log records of the device, the data storage of the monitoring platform, or the historical performance data collected regularly, which are generally stored in the central database, cloud platform, or storage system of the local device. By comparing the current and historical performance data, it is possible to identify whether the device has a trend of performance degradation, and then determine whether the warning threshold needs to be adjusted.

[0045] It should be noted that when the device performance degradation coefficient is larger, it means that it is more necessary to adjust the warning threshold related to the data flow monitoring and analysis of the device, and to issue a warning for the data flow of the device according to the warning threshold related to the data flow monitoring and analysis of the adjusted device. The reason is that the larger the device performance degradation coefficient is, the more it means that the processing power, storage capacity or other key performance indicators of the device have significantly decreased, which may lead to abnormal changes in the data flow pattern. For example, the device may experience data transmission lag due to insufficient processing power, or data packet loss due to increased storage pressure. At this time, the original warning threshold may not accurately reflect the actual operating status of the device, and it is easy to ignore potential failure risks. By adjusting the warning threshold according to the device performance degradation coefficient, abnormal data flow fluctuations can be captured more sensitively, so that measures can be taken in advance to avoid service interruption or data loss caused by device performance degradation, and ensure the stability and reliability of the device in long-term operation.

[0046] In one implementation, the benefit of analyzing the equipment performance degradation coefficient to determine the need to adjust the warning threshold related to the equipment data flow monitoring and analysis, and issuing a warning for the equipment data flow according to the warning threshold related to the adjusted equipment data flow monitoring and analysis is: by dynamically adjusting the warning threshold to make it more consistent with the actual performance status of the equipment, potential problems can be discovered in time when the equipment performance is degraded, thereby reducing the possibility of false alarms and missed alarms; this flexible warning mechanism can provide earlier warnings in the process of gradual decline in equipment performance, help managers take preventive maintenance measures, avoid system downtime or data loss caused by equipment failure, and improve the operating efficiency and reliability of the equipment. At the same time, it can also optimize resource utilization, reduce unnecessary warnings, and ensure that the warning system always maintains efficient and accurate monitoring capabilities under different equipment states.

[0047] In one embodiment, calculating the usage frequency change coefficient according to the usage frequency of the device in the current period and the average usage frequency of the device in the historical period includes:

[0048] Obtain the usage frequency of the device in the current period and the average usage frequency of the device in the historical period, and divide the usage frequency of the device in the current period by the average usage frequency of the device in the historical period to obtain the frequency change value S. In the formula, HJ and HU are the usage frequency of the equipment in the current period and the average usage frequency of the equipment in the historical period respectively;

[0049] Calculate the frequency of use change coefficient, the calculation formula is: PY = S-1, where PY is the frequency of use change coefficient;

[0050] It should be noted that the usage frequency of the equipment in the current period refers to the number of times or duration that the equipment actually performs tasks or is used in a specific period of time. For example, for production equipment, the usage frequency may be the number of hours the equipment runs in a certain day or month; for network equipment, it may be the number of data packets or connection requests processed by the equipment. The average usage frequency of the equipment in the historical period refers to the average usage frequency of the equipment in the past period (such as the past month or year). This indicator is usually calculated based on the data collected by the equipment in the historical records, reflecting the normal working mode of the equipment. In terms of acquisition methods, the usage frequency of the current period can be collected through the real-time monitoring system, operation log or sensor data of the equipment. For example, the usage time of production equipment can be recorded by the running time of the equipment; the data traffic or number of requests of network equipment can be obtained through network traffic monitoring tools. The average usage frequency in the historical period is usually stored in the long-term monitoring database of the equipment and can be calculated through regular data collection or historical logs. These data help analyze the usage pattern of the equipment and provide a basis for optimizing warning thresholds and maintenance decisions.

[0051] It should be noted that when the frequency of use change coefficient is larger, it means that it is more necessary to adjust the warning threshold related to the data flow monitoring and analysis of the device, and to warn the data flow of the device according to the warning threshold related to the data flow monitoring and analysis of the adjusted device. The reason is that when the frequency of use change coefficient is larger, it means that the frequency of use of the device has changed significantly, which means that the load, work intensity or data flow demand of the device has also changed accordingly. For example, if the frequency of use of the device increases significantly, the device may need to process more data, resulting in increased fluctuations in data flow. The traditional warning threshold may not accurately reflect this change, which may easily lead to delayed warnings or ignore potential problems. On the contrary, if the frequency of use of the device decreases significantly, the data flow may decrease, and an excessively high warning threshold may lead to unnecessary warnings. By adjusting the warning threshold, it can ensure that the data flow monitoring matches the actual use of the device, so as to identify potential risks more timely and accurately, and avoid affecting the normal operation of the device and the effective use of resources due to delayed warnings or excessive warnings.

[0052] In one implementation, the benefit of analyzing the usage frequency change coefficient to determine the need to adjust the warning threshold related to the data flow monitoring and analysis of the device, and warning the data flow of the device according to the warning threshold related to the adjusted data flow monitoring and analysis of the device is that it can help ensure that the warning system is always consistent with the actual workload of the device. When the usage frequency of the device changes, adjusting the warning threshold helps to accurately capture the flow fluctuation caused by the load change, and avoid false alarms or missed alarms caused by the original threshold not adapting to the new working state. For example, when the usage frequency increases, the data flow of the device may increase. If the warning threshold is not adjusted, the abnormal growth of data flow may not be discovered in time, thereby missing the warning signal; conversely, when the usage frequency decreases, the flow load of the device decreases, and an excessively high warning threshold may lead to overreaction, resulting in unnecessary warnings. By dynamically adjusting the warning threshold, the flexibility and sensitivity of the monitoring system can be improved, so that the system can make accurate flow warnings according to the actual usage of the device, ensure the efficient operation of the device and respond to potential risks in a timely manner.

[0053] In one embodiment, calculating the electromagnetic interference coefficient according to the electromagnetic interference data includes:

[0054] Obtain the time interval from the start of the operation of the device to the current moment, and obtain the electromagnetic interference signal to which the device is subjected within the time interval, and mark it as B(t), where t=1, 2, 3, 4, ..., Z, where Z is a positive integer and represents the number of electromagnetic interference signals;

[0055] Morlet wavelet is used to perform continuous wavelet transform on the electromagnetic interference signal B(t) at each moment. Through continuous wavelet transform (CWT), the electromagnetic interference signal is decomposed in the time domain and frequency domain. Continuous wavelet transform is realized through convolution, and its calculation formula is as follows: Where x(t) is the input signal, ψ(t) is the selected wavelet basis function, a is the scale parameter used to adjust the scale of the wavelet basis function; b is the translation parameter used to adjust the position of the wavelet basis function; t represents the time coordinate of the signal, that is, the position on the time axis;

[0056] For each scale a and translation b, the result calculated using wavelet transform is called wavelet coefficient;

[0057] Extract the energy characteristics of the wavelet coefficients. According to the calculated wavelet coefficients, further extract the energy characteristics, calculate the energy of the wavelet coefficients at each scale, and then perform weighted summation of the energies at different scales to obtain the comprehensive energy characteristics Ez. The calculation formula is as follows: Where E(a) is the energy at scale a, CWT(a,b) is the wavelet coefficient;

[0058] Calculate the electromagnetic interference index at each moment, and the calculation expression is: BK(t)=Ez, where BK(t) is the electromagnetic interference index;

[0059] The electromagnetic interference index BK(t) at different times is added together to obtain the electromagnetic interference index. The calculation formula is: Where JM is the electromagnetic interference coefficient.

[0060] It should be noted that electromagnetic interference signals can be collected through special electromagnetic interference sensors or signal receivers. These sensors can capture and record the electromagnetic interference intensity of the surrounding environment in real time during the operation of the equipment; these sensors can be installed in key parts of the equipment to monitor the changes in the electromagnetic field in real time and send signal data to the central processing system for storage and processing. At the same time, the operating time and related working status of the equipment also need to be obtained through the equipment's operating records. These data are usually recorded by the equipment's control system or status monitoring module, and provide timestamps and operating status basis for subsequent wavelet transforms and electromagnetic interference index calculations; through the cooperation of these devices and monitoring systems, the impact of electromagnetic interference on the equipment can be accurately obtained and analyzed.

[0061] It should be noted that when the electromagnetic interference coefficient is larger, it means that it is more necessary to adjust the warning threshold related to the data flow monitoring and analysis of the device, and to warn the data flow of the device according to the warning threshold related to the data flow monitoring and analysis of the adjusted device. The reason is that when the electromagnetic interference coefficient is larger, it means that the electromagnetic interference to the device is stronger, which may cause the fluctuation or error of the internal signal of the device to increase, thereby affecting the data transmission and processing capacity of the device. Electromagnetic interference may not only cause abnormal fluctuations in data flow, but also cause problems such as decreased processing speed, storage errors or communication failure of the device. Therefore, if the electromagnetic interference coefficient is too large, the original warning threshold may not be able to capture the potential anomalies caused by interference in time, thereby missing the best time for fault warning. By adjusting the warning threshold related to data flow monitoring and analysis, the working state of the device under electromagnetic interference can be more accurately reflected, so that the warning system can detect abnormal fluctuations in data flow in advance when the device is interfered with, and issue warnings in time to avoid more serious failures or performance degradation caused by interference.

[0062] In one implementation, the benefits of analyzing the electromagnetic interference coefficient to determine whether it is necessary to adjust the warning threshold related to the data flow monitoring and analysis of the device, and warning the data flow of the device according to the warning threshold related to the data flow monitoring and analysis of the adjusted device are: analyzing the electromagnetic interference coefficient to determine whether it is necessary to adjust the warning threshold related to the data flow monitoring and analysis of the device can help identify and respond to abnormal data fluctuations caused by electromagnetic interference, thereby improving the accuracy and reliability of the early warning system. As the electromagnetic interference coefficient increases, the interference risk faced by the device also increases, and the original early warning threshold may not be able to capture the abnormal performance of the device in this case in time. Therefore, adjusting the early warning threshold can make the monitoring system more sensitive, respond quickly to potential data flow problems caused by electromagnetic interference, and avoid data loss, processing delays or performance degradation caused by interference. By dynamically adjusting the threshold, it can adapt to changes in the operating environment of the device in real time, so that the early warning system can still effectively identify and prevent potential failures in an interference environment, ensure the stability of the equipment and the integrity of the data, thereby reducing the risk of equipment failure and maintenance costs.

[0063] In one embodiment, the coefficient to be adjusted is obtained according to the equipment performance degradation coefficient, the frequency of use change coefficient and the electromagnetic interference coefficient, including:

[0064]

[0065] Where GHT is the coefficient that needs to be adjusted, DF, PY and JM are the equipment performance degradation coefficient, the frequency of use change coefficient and the electromagnetic interference coefficient respectively, α, β, γ are the preset proportional coefficients of DF, PY and JM respectively, and α, β, γ are all greater than 0.

[0066] It should be noted that α, β, and γ are set by professionals according to actual conditions. Generally, the sum of α, β, and γ is 1. For example, α, β, and γ can be 0.2, 0.4, and 0.4 respectively. They can also be other numbers without specific limitations. In addition, before calculating the adjustment coefficient, it is necessary to remove the unit of the equipment performance degradation coefficient, the frequency change coefficient, and the electromagnetic interference coefficient.

[0067] In one embodiment, the coefficient that needs to be adjusted is compared with a preset threshold value of the coefficient that needs to be adjusted, and it is determined whether the data flow monitoring and analysis related warning thresholds of the device need to be adjusted according to the comparison result. The thresholds include:

[0068] If the coefficient to be adjusted is not less than the preset threshold value of the coefficient to be adjusted, the warning threshold value related to the data flow monitoring and analysis of the device needs to be adjusted;

[0069] If the coefficient to be adjusted is less than the preset threshold value of the coefficient to be adjusted, there is no need to adjust the warning threshold value related to the data flow monitoring and analysis of the device.

[0070] It should be noted that the preset adjustment coefficient threshold is set by professionals based on actual conditions and will not be limited or elaborated on in detail.

[0071] It should be noted that by comparing the coefficient that needs to be adjusted with the preset threshold value of the coefficient that needs to be adjusted, it can be determined in the following way whether it is necessary to adjust the warning threshold value related to the data flow monitoring and analysis of the device. First, the coefficient that needs to be adjusted for the device in the current working environment and use state is obtained. The coefficient comprehensively considers factors such as the electromagnetic interference, frequency of use, and performance degradation of the device. If the coefficient is greater than or equal to the preset threshold value of the coefficient that needs to be adjusted, it means that the risk or change faced by the device is large, and the original warning threshold value may no longer be applicable. It must be adjusted to ensure that the monitoring system can reflect the current operating status of the device in a timely manner. Therefore, in this case, it is necessary to adjust the warning threshold value related to the data flow monitoring and analysis of the device. On the contrary, if the coefficient that needs to be adjusted is less than the preset threshold value, it means that the state change of the device is small, the existing warning threshold value is still applicable, and the monitoring system does not need to be adjusted. Through this comparison and judgment method, the warning threshold setting can be dynamically optimized to ensure that the warning system of the device can always play the maximum efficiency in different working environments and states, thereby improving the operation safety and reliability of the equipment.

[0072] When adjusting the warning thresholds related to the data flow monitoring and analysis of the device, the size of the coefficient and the current state of the device can be adjusted as needed, combined with different adjustment strategies. If the adjustment coefficient needs to be greater than or equal to the preset threshold, the actual data flow pattern of the device can be evaluated first, and the existing warning threshold can be compared with the actual working conditions of the device to identify the range of abnormal fluctuations. Based on this information, the sensitivity of the warning threshold can be increased or decreased. For example, in the case of strong electromagnetic interference, the data flow threshold can be appropriately increased to avoid false alarms. At the same time, when the performance of the device is significantly reduced, the threshold can be lowered to enhance the sensitivity to abnormal data fluctuations. In addition, a dynamic adjustment mechanism can be introduced to automatically update the warning threshold based on real-time data flow and device status changes, so that the system can adapt to changes in the device and the working environment in real time. In this process, the adjustment range needs to be set according to the stability requirements of the device and the historical data trend to ensure that potential faults or performance problems can be responded to in a timely manner without causing too many false alarms.

[0073] According to the adjusted device data traffic monitoring and analysis related warning thresholds, the device data traffic is warned:

[0074] According to the warning thresholds related to the data flow monitoring and analysis of the adjusted equipment, the system can monitor the data flow of the equipment in real time and automatically trigger a warning when the flow fluctuation exceeds the newly set threshold. This process includes continuous monitoring of the current data flow of the equipment to determine whether it exceeds the adjusted threshold range. If it exceeds, it means that the equipment may face the risk of failure or performance abnormality. The system will issue an alarm according to the preset rules and notify relevant personnel to conduct further investigation or take preventive measures. For example, the system can notify the operation and maintenance personnel by email, SMS or instant message to remind them to check the operating status of the equipment or take necessary repair measures. In addition, the system can also record the detailed information of each warning, including the triggering reason, the change of the warning threshold and the processing result, so as to conduct subsequent data analysis and optimization adjustment. This warning mechanism based on dynamically adjusted warning thresholds helps to improve the monitoring accuracy of the equipment, reduce the probability of failure, and improve the efficiency and response speed of equipment maintenance.

[0075] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A data traffic intelligent early warning system, characterized in that: The following steps are involved: Equipment performance degradation module: obtains the current performance index value and historical performance index value of the equipment, and calculates the equipment performance degradation coefficient according to the current performance index value and historical performance index value; Frequency of use change module: obtains the frequency of use of the device in the current period and the average frequency of use of the device in the historical period, and calculates the frequency of use change coefficient based on the frequency of use of the device in the current period and the average frequency of use of the device in the historical period; Electromagnetic interference module: obtains the time interval from the start of the device to the current time, obtains the electromagnetic interference data of the device within the time interval, and calculates the electromagnetic interference coefficient based on the electromagnetic interference data; Early warning module: obtains the adjustment coefficient according to the equipment performance degradation coefficient, usage frequency change coefficient and electromagnetic interference coefficient; The coefficient to be adjusted is compared with the preset threshold value of the coefficient to be adjusted, and based on the comparison result, it is determined whether it is necessary to adjust the warning threshold value related to the data flow monitoring and analysis of the device; and a warning is issued for the data flow of the device based on the adjusted warning threshold value.

2. According to claim 1, a data traffic intelligent early warning system is characterized in that: The device performance degradation coefficient is calculated based on the current performance index and the historical performance index, including: Get the current performance index value of each device and the corresponding historical performance index value, and calculate the performance degradation value of each performance index value. The calculation formula is: Where P is the performance degradation value, YA and YB are the current performance index value and the corresponding historical performance index value respectively; The equipment performance degradation coefficient is calculated based on the performance degradation value of each performance indicator value. The calculation formula is: In the formula, DF is the equipment performance degradation coefficient, i represents the order number of the performance index, and n represents the total number of performance indexes; P i is the performance degradation value of the i-th performance indicator, ω i Indicates the preset weight value of the i-th performance indicator.

3. According to claim 1, a data traffic intelligent early warning system is characterized in that: The frequency of use change coefficient is calculated based on the frequency of use of the equipment in the current period and the average frequency of use of the equipment in the historical period, including: Obtain the usage frequency of the device in the current period and the average usage frequency of the device in the historical period, and divide the usage frequency of the device in the current period by the average usage frequency of the device in the historical period to obtain the frequency change value S. In the formula, HJ and HU are the usage frequency of the equipment in the current period and the average usage frequency of the equipment in the historical period respectively; Calculate the frequency variation coefficient. The calculation formula is: PY=S-1, where PY is the frequency variation coefficient.

4. According to claim 1, a data traffic intelligent early warning system is characterized in that: Calculation of electromagnetic interference coefficient based on electromagnetic interference data includes: Obtain the time interval from the start of the operation of the device to the current moment, and obtain the electromagnetic interference signal to which the device is subjected within the time interval, and mark it as B(t), where t=1, 2, 3, 4, ..., Z, where Z is a positive integer and represents the number of electromagnetic interference signals; Morlet wavelet is used to perform continuous wavelet transform on the electromagnetic interference signal B(t) at each moment. Through continuous wavelet transform (CWT), the electromagnetic interference signal is decomposed in the time domain and frequency domain. Continuous wavelet transform is realized through convolution, and its calculation formula is as follows: Where x(t) is the input signal, ψ(t) is the selected wavelet basis function, a is the scale parameter used to adjust the scale of the wavelet basis function; b is the translation parameter used to adjust the position of the wavelet basis function; t represents the time coordinate of the signal, that is, the position on the time axis; For each scale a and translation b, the result calculated using wavelet transform is called wavelet coefficient; Extract the energy characteristics of the wavelet coefficients. According to the calculated wavelet coefficients, further extract the energy characteristics, calculate the energy of the wavelet coefficients at each scale, and then perform weighted summation of the energies at different scales to obtain the comprehensive energy characteristics Ez. The calculation formula is as follows: Where E(a) is the energy at scale a, CWT(a,b) is the wavelet coefficient; Calculate the electromagnetic interference index at each moment, and the calculation expression is: BK(t)=Ez, where BK(t) is the electromagnetic interference index; The electromagnetic interference index BK(t) at different times is added together to obtain the electromagnetic interference index. The calculation formula is: Where JM is the electromagnetic interference coefficient.

5. According to claim 1, a data traffic intelligent early warning system is characterized in that: The coefficients that need to be adjusted based on the equipment performance degradation coefficient, frequency of use change coefficient and electromagnetic interference coefficient include: Where GHT is the coefficient that needs to be adjusted, DF, PY and JM are the equipment performance degradation coefficient, the frequency of use change coefficient and the electromagnetic interference coefficient respectively, α, β, γ are the preset proportional coefficients of DF, PY and JM respectively, and α, β, γ are all greater than 0.

6. The data traffic intelligent early warning system according to claim 1 is characterized in that: Compare the coefficient that needs to be adjusted with the preset threshold value of the coefficient that needs to be adjusted, and determine whether the device needs to be adjusted based on the comparison result. The warning thresholds related to data flow monitoring and analysis include: If the coefficient to be adjusted is not less than the preset threshold value of the coefficient to be adjusted, the warning threshold value related to the data flow monitoring and analysis of the device needs to be adjusted; If the coefficient to be adjusted is less than the preset threshold value of the coefficient to be adjusted, there is no need to adjust the warning threshold value related to the data flow monitoring and analysis of the device.

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