Remote monitoring filtering cabinet system and method based on Internet of Things technology

Abstract

The invention discloses a remote monitoring filter cabinet system and method based on the Internet of Things technology, environmental parameters in a filter cabinet are monitored in real time through a data acquisition module, data are ensured to be timely and accurate, a data filter module filters original data by using a specific filter calculation formula, noise is effectively eliminated, data reliability is improved, and real-time monitoring of the filter cabinet is realized. Wherein parameters alpha, beta and the like can be flexibly adjusted to meet system requirements, the Internet of Things communication module realizes real-time remote transmission of filtered data, so that a manager can know the operation state of the filtering cabinet at any time, the remote monitoring center analyzes the received data, establishes a fault prediction model by mining historical data, predicts the equipment fault trend, and realizes the real-time monitoring of the operation state of the filtering cabinet. The fault rate is reduced, when abnormal data is detected, an alarm mechanism is automatically triggered, a manager is reminded in time, in addition, the manager can remotely access real-time data, historical records and system configuration through the user interaction module, and comprehensive remote monitoring and management are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring filter cabinets, and in particular to a remote monitoring filter cabinet system and method based on Internet of Things technology. Background Art

[0002] In the context of the rapid development of industrial automation and Internet of Things technology, higher requirements are placed on the remote monitoring and management of filter cabinets. Traditional filter cabinet monitoring systems mostly use local monitoring methods, which have problems such as poor data real-time performance, limited monitoring range, and high maintenance costs. In order to solve these problems, a remote monitoring filter cabinet system based on Internet of Things technology came into being.

[0003] However, existing IoT-based remote monitoring systems still have some shortcomings in data processing. For example, the original data signals often contain a large amount of noise and interference. If these noise and interference are not effectively filtered, they will seriously affect the accuracy and reliability of the data, and thus affect subsequent analysis and control decisions. In addition, when processing complex and changeable signals, existing filtering algorithms often find it difficult to achieve ideal filtering effects, and parameter adjustments are not flexible enough to be adaptively adjusted according to actual needs. Summary of the Invention

[0004] In view of this, the present invention proposes a remote monitoring filter cabinet system and method based on Internet of Things technology, which can effectively solve the defects of the existing technology such as low data accuracy and reliability, and insufficient flexibility in parameter adjustment, which makes it difficult to achieve an ideal filtering effect.

[0005] The technical solution of the present invention is achieved as follows:

[0006] The remote monitoring filter cabinet system based on Internet of Things technology includes:

[0007] The data acquisition module is used to monitor the environmental parameters in the filter cabinet in real time and transmit the collected raw data signals to the data filtering module;

[0008] The data filtering module is used to filter the collected raw data signal according to the filtering calculation formula. The calculation formula is:

[0009]

[0010] Among them, the F filtered (t) represents the data signal after filtering, F raw (t) represents the original data signal, Represents the original data signal F at time point τ raw(t) is the rate of change, α, β and γ are adjustable parameters preset according to system requirements, which are used to adjust the filtering effect and response speed, and τ is the virtual time variable used for integral calculation;

[0011] IoT communication module, used to transmit filtered data in real time to a remote monitoring center for analysis and control;

[0012] A remote monitoring center analyzes the filtered data received to predict equipment failure trends and automatically triggers an alarm mechanism when abnormal data is detected;

[0013] The user interaction module is used for users to remotely access the real-time data, historical records and system configuration of the filter cabinet to achieve remote monitoring and management.

[0014] As a further optional solution of the remote monitoring filter cabinet system based on Internet of Things technology, the data acquisition module monitors the environmental parameters in the filter cabinet in real time and transmits the collected raw data signals to the data filtering module, specifically including:

[0015] According to the temperature sensor, humidity sensor, air pressure sensor and electromagnetic interference sensor installed in the filter cabinet, the environmental parameters in the filter cabinet are collected, and the environmental parameters include temperature data, humidity data, air pressure data and electromagnetic interference intensity data;

[0016] Calculate the environmental condition index inside the filter cabinet based on the weights of temperature data, humidity data, air pressure data, and electromagnetic interference intensity data;

[0017] The environmental condition index is transmitted to the data filtering module.

[0018] As a further optional solution for the remote monitoring filter cabinet system based on Internet of Things technology, the environmental condition index in the filter cabinet is calculated based on the weights of temperature data, humidity data, air pressure data and electromagnetic interference intensity data. The specific formula is:

[0019]

[0020] Among them, Q represents the environmental condition index, T min ,T max are the minimum and maximum values of temperature; H min

[0021] ,H max is the minimum and maximum value of humidity; P min ,P max is the reference value and maximum value of air pressure; EMI min ,EMI max are the minimum and maximum values of electromagnetic interference intensity, and w1, w2, w3, and w4 are weight coefficients.

[0022] As a further optional solution for the remote monitoring filter cabinet system based on Internet of Things technology, the Internet of Things communication module transmits the filtered data to the remote monitoring center in real time for analysis and control, specifically including:

[0023] Encapsulate the filtered temperature data, humidity data, air pressure data, electromagnetic interference intensity data, and environmental condition index into a data packet;

[0024] According to the communication protocol, the encapsulated data packets are sent to the remote monitoring center in real time.

[0025] As a further optional solution for the remote monitoring filter cabinet system based on Internet of Things technology, the remote monitoring center includes a data analysis module and an early warning module. The data analysis module uses big data analysis and machine learning algorithms to analyze the received filtered data to predict equipment failure trends. The early warning module automatically triggers an alarm mechanism when abnormal data is detected, and sends early warning information to management personnel via SMS, email or instant messaging software.

[0026] As a further optional solution of the remote monitoring filter cabinet system based on Internet of Things technology, the system also includes an adaptive adjustment module for dynamically adjusting the parameters α, β and γ of the calculation formula in the data filtering module according to the feedback from the remote monitoring center.

[0027] A remote monitoring filter cabinet method based on Internet of Things technology, specifically comprising:

[0028] The data acquisition module installed in the filter cabinet is used to monitor the environmental parameters in the filter cabinet in real time, and transmit these raw data signals to the data filtering module;

[0029] In the data filtering module, the received raw data signal is filtered, and the filtering calculation formula is:

[0030]

[0031] Among them, the F filtered (t) represents the data signal after filtering, F raw (t) represents the original data signal, Represents the original data signal F at time point τ raw (t) is the rate of change, α, β and γ are adjustable parameters preset according to system requirements, which are used to adjust the filtering effect and response speed, and τ is the virtual time variable used for integral calculation;

[0032] The filtered data is transmitted to the remote monitoring center in real time for analysis and control based on the Internet of Things communication module;

[0033] Analyze the filtered data received by the remote monitoring center to predict equipment failure trends and automatically trigger an alarm mechanism when abnormal data is detected;

[0034] Users can remotely access the real-time data, historical records and system configuration of the filter cabinet based on the user interaction module to achieve remote monitoring and management.

[0035] As a further optional solution of the method for remotely monitoring the filter cabinet based on the Internet of Things technology, the method further includes:

[0036] The parameters α, β and γ of the calculation formula in the data filtering module are dynamically adjusted according to the feedback from the remote monitoring center.

[0037] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the above-mentioned method for remotely monitoring a filter cabinet based on Internet of Things technology.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for remotely monitoring a filter cabinet based on Internet of Things technology.

[0039] The beneficial effects of the present invention are as follows: the data acquisition module can monitor the environmental parameters in the filter cabinet in real time, such as temperature, humidity, air pressure, etc., to ensure the timeliness and accuracy of the data; the data filtering module filters the original data signal through a specific calculation formula, effectively eliminates noise and interference, and improves the reliability and accuracy of the data; the parameters such as α and β in the formula can be preset and adjusted according to system requirements to achieve flexible filtering effect and response speed; the Internet of Things communication module transmits the filtered data to the remote monitoring center in real time, realizing remote access and real-time monitoring of the data, which helps managers to understand the operating status of the filter cabinet in a timely manner and take necessary measures in a timely manner; the remote monitoring center analyzes the received filtered data to predict equipment failure trends; by mining and analyzing historical data, a fault prediction model can be established to discover potential faults in advance and reduce the equipment failure rate; when the remote monitoring center detects abnormal data, it automatically triggers an alarm mechanism to remind managers to deal with it in time; at the same time, managers can remotely access the real-time data, historical records and system configuration of the filter cabinet through the user interaction module to realize remote monitoring and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a schematic diagram of the composition of the remote monitoring filter cabinet system based on the Internet of Things technology of the present invention;

[0042] Figure 2 Schematic diagram of the process of the remote monitoring filter cabinet method based on the Internet of Things technology of the present invention;

[0043] Figure 3 A schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0044] The following is a clear and complete description of 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 making creative efforts are within the scope of protection of the present invention.

[0045] refer to Figures 1 to 3 , the remote monitoring filter cabinet system based on Internet of Things technology includes:

[0046] The data acquisition module is used to monitor the environmental parameters in the filter cabinet in real time and transmit the collected raw data signals to the data filtering module;

[0047] The data filtering module is used to filter the collected raw data signal according to the filtering calculation formula. The calculation formula is:

[0048]

[0049] Among them, the F filtered (t) represents the data signal after filtering, F raw (t) represents the original data signal, Represents the original data signal F at time point τ raw (t) is the rate of change, α, β and γ are adjustable parameters preset according to system requirements, which are used to adjust the filtering effect and response speed, and τ is the virtual time variable used for integral calculation;

[0050] IoT communication module, used to transmit filtered data in real time to a remote monitoring center for analysis and control;

[0051] A remote monitoring center analyzes the filtered data received to predict equipment failure trends and automatically triggers an alarm mechanism when abnormal data is detected;

[0052] The user interaction module is used for users to remotely access the real-time data, historical records and system configuration of the filter cabinet to achieve remote monitoring and management.

[0053] In this embodiment, the data acquisition module can monitor the environmental parameters in the filter cabinet in real time, such as temperature, humidity, and air pressure, to ensure the timeliness and accuracy of the data. The data filtering module filters the raw data signal using a specific calculation formula to effectively eliminate noise and interference, thereby improving the reliability and accuracy of the data. Parameters such as α and β in the formula can be preset and adjusted according to system requirements to achieve flexible filtering effects and response speeds. The Internet of Things communication module transmits the filtered data to the remote monitoring center in real time, enabling remote access and real-time monitoring of the data. This helps managers to promptly understand the operating status of the filter cabinet and take necessary measures in a timely manner. The remote monitoring center analyzes the received filtered data to predict equipment failure trends. By mining and analyzing historical data, a fault prediction model can be established to detect potential faults in advance and reduce equipment failure rates. When the remote monitoring center detects abnormal data, an alarm mechanism is automatically triggered to remind managers to deal with it in a timely manner. At the same time, managers can remotely access the real-time data, historical records, and system configuration of the filter cabinet through the user interaction module to achieve remote monitoring and management.

[0054] Preferably, the data acquisition module monitors the environmental parameters in the filter cabinet in real time and transmits the collected raw data signals to the data filtering module, specifically including:

[0055] According to the temperature sensor, humidity sensor, air pressure sensor and electromagnetic interference sensor installed in the filter cabinet, the environmental parameters in the filter cabinet are collected, and the environmental parameters include temperature data, humidity data, air pressure data and electromagnetic interference intensity data;

[0056] Calculate the environmental condition index inside the filter cabinet based on the weights of temperature data, humidity data, air pressure data, and electromagnetic interference intensity data;

[0057] The environmental condition index is transmitted to the data filtering module.

[0058] In this embodiment, the temperature sensors, humidity sensors, air pressure sensors, and electromagnetic interference sensors installed in the filter cabinet can monitor various environmental parameters in the filter cabinet in real time. This comprehensive perception capability helps to promptly detect and respond to changes in the environment in the filter cabinet, ensuring the stable operation of the equipment. The environmental condition index in the filter cabinet is calculated based on the weights of the temperature data, humidity data, air pressure data, and electromagnetic interference intensity data. This weighted calculation method can comprehensively consider the impact of various environmental parameters on the operating status of the filter cabinet, thereby more accurately reflecting the overall environmental condition of the filter cabinet. Through the real-time monitoring and weighted calculation of the data acquisition module, the data transmitted to the data filtering module can be ensured to be more accurate and reliable, which helps to improve the accuracy of subsequent data filtering, transmission, and analysis, thereby providing users with more accurate environmental parameter information. At the same time, before transmitting the environmental condition index to the data filtering module, the data acquisition module has completed the collection and weighted calculation of the original data. This preprocessing step helps to reduce the burden on the data filtering module and improve the processing efficiency of the entire system.

[0059] Preferably, the environmental condition index in the filter cabinet is calculated based on the weights of the temperature data, humidity data, air pressure data and electromagnetic interference intensity data. The specific formula is:

[0060]

[0061] Among them, Q represents the environmental condition index, T min ,T max are the minimum and maximum values of temperature; H min

[0062] ,H max is the minimum and maximum value of humidity; P min ,P max is the reference value and maximum value of air pressure; EMI min ,EMI max are the minimum and maximum values of electromagnetic interference intensity, and w1, w2, w3, and w4 are weight coefficients.

[0063] In this embodiment, by introducing the concepts of minimum and maximum values, the technical solution can ensure that the actual variation range of each environmental parameter is taken into account when calculating the environmental condition index, which helps to improve the accuracy and reliability of the data. The environmental condition index, as a comprehensive indicator, can simplify the monitoring process of environmental parameters in the filter cabinet. Managers only need to pay attention to changes in the environmental condition index to understand the overall environmental conditions in the filter cabinet, thereby reducing monitoring costs and improving work efficiency. The weight coefficient can be adjusted according to actual conditions to adapt to different filter cabinets and application scenarios. This flexibility enables the technical solution to be more widely used in various environmental conditions, improving its practicality and applicability.

[0064] Preferably, the Internet of Things communication module transmits the filtered data to a remote monitoring center in real time for analysis and control, specifically including:

[0065] Encapsulate the filtered temperature data, humidity data, air pressure data, electromagnetic interference intensity data, and environmental condition index into a data packet;

[0066] According to the communication protocol, the encapsulated data packets are sent to the remote monitoring center in real time.

[0067] In this embodiment, the Internet of Things communication module first encapsulates the filtered temperature data, humidity data, air pressure data, electromagnetic interference intensity data, and environmental condition index to form a data packet in a unified format. This encapsulation method not only ensures the integrity and consistency of the data, but also helps to improve the efficiency and reliability of data transmission. Through encapsulation, the risk of data loss and damage during transmission can be reduced, thereby ensuring that the remote monitoring center can receive complete and accurate data. This technical solution emphasizes real-time transmission of data. Through the Internet of Things communication module, the encapsulated data packet can be sent to the remote monitoring center in real time according to the communication protocol. This real-time guarantee is crucial for remote monitoring because it ensures that managers can understand the current status of the filter cabinet in a timely manner and make quick responses and decisions. The Internet of Things communication module sends data packets based on a specific communication protocol. This protocol support ensures the compatibility of data between different devices and systems, allowing the remote monitoring center to smoothly receive and parse data packets. At the same time, the selection of communication protocol also takes into account the security and reliability of data, providing additional protection for data transmission.

[0068] Preferably, the remote monitoring center includes a data analysis module and an early warning module. The data analysis module uses big data analysis and machine learning algorithms to analyze the received filtered data to predict equipment failure trends. The early warning module automatically triggers an alarm mechanism when abnormal data is detected, and sends early warning information to management personnel via SMS, email or instant messaging software.

[0069] In this embodiment, the data analysis module uses big data analysis and machine learning algorithms to conduct an in-depth analysis of the received filtered data. This analysis method can mine the potential patterns and trends in the data, thereby achieving accurate prediction of equipment failure trends. By discovering abnormal conditions and potential failures of equipment in advance, the data analysis module provides valuable decision-making support for managers, helping to reduce equipment failure rates and extend equipment service life; the early warning module can automatically trigger an alarm mechanism when abnormal data is detected. This automated mechanism ensures the timeliness and accuracy of the early warning, avoiding delays and false alarms caused by manual intervention. At the same time, the early warning module sends early warning information to managers through various methods such as text messages, emails or instant messaging software, ensuring multi-channel transmission and reception of information. This efficient information transmission method helps managers respond quickly to abnormal situations. , take necessary measures to ensure the safe operation of the equipment; through the collaborative work of the data analysis module and the early warning module, the remote monitoring center realizes the intelligent management and decision support of the filter cabinet. The data analysis and fault prediction results provided by the data analysis module, and the real-time early warning information provided by the early warning module, together constitute a comprehensive monitoring and evaluation of the operating status of the filter cabinet. This intelligent management method not only improves management efficiency, but also reduces management costs; the fault prediction results provided by the data analysis module help managers to formulate maintenance plans in advance and optimize maintenance strategies. By performing preventive maintenance on equipment on a regular basis, the equipment failure rate can be reduced, downtime can be reduced, and thus maintenance costs can be reduced. At the same time, the real-time early warning information provided by the early warning module also helps managers to take necessary maintenance measures in a timely manner to avoid excessive wear and damage to equipment due to long-term operation.

[0070] Preferably, the system further comprises an adaptive adjustment module for dynamically adjusting the parameters α, β and γ of the calculation formula in the data filtering module according to the feedback from the remote monitoring center.

[0071] In this embodiment, the adaptive adjustment module can dynamically adjust the calculation formula parameters in the data filtering module based on the real-time feedback from the remote monitoring center. This dynamic adjustment mechanism ensures that the system can be optimally configured according to the current environmental parameters and equipment status, thereby improving the overall performance and accuracy of the system. By continuously optimizing the calculation formula, the system can better adapt to different working environments and equipment conditions, and achieve more accurate data filtering and processing; through the adaptive adjustment module, the system can respond more quickly to small changes in equipment status, thereby preventing failures before they occur. When the remote monitoring center detects an abnormal trend or potential failure, the adaptive adjustment module can immediately adjust the data filtering module. parameters to more sensitively capture and process relevant data, so as to take measures in advance to avoid the occurrence of failures or mitigate their impact; the introduction of the adaptive adjustment module reduces the need for manual adjustment of parameters, reduces the frequency and cost of manual intervention, and managers can focus more on other important tasks without having to frequently manually adjust system parameters, which not only improves work efficiency but also reduces system maintenance costs; the design of the adaptive adjustment module enables the system to more easily adapt to future changes and needs. As the working environment and equipment conditions change, the system can adjust parameters to maintain optimal performance. This flexibility and scalability ensure that the system can operate stably in the long term to meet changing needs.

[0072] A remote monitoring filter cabinet method based on Internet of Things technology, specifically comprising:

[0073] The data acquisition module installed in the filter cabinet is used to monitor the environmental parameters in the filter cabinet in real time, and transmit these raw data signals to the data filtering module;

[0074] In the data filtering module, the received raw data signal is filtered, and the filtering calculation formula is:

[0075]

[0076] Among them, the F filtered (t) represents the data signal after filtering, F raw (t) represents the original data signal, Represents the original data signal F at time point τ raw (t) is the rate of change, α, β and γ are adjustable parameters preset according to system requirements, which are used to adjust the filtering effect and response speed, and τ is the virtual time variable used for integral calculation;

[0077] The filtered data is transmitted to the remote monitoring center in real time for analysis and control based on the Internet of Things communication module;

[0078] Analyze the filtered data received by the remote monitoring center to predict equipment failure trends and automatically trigger an alarm mechanism when abnormal data is detected;

[0079] Users can remotely access the real-time data, historical records and system configuration of the filter cabinet based on the user interaction module to achieve remote monitoring and management.

[0080] Preferably, the method further comprises:

[0081] The parameters α, β and γ of the calculation formula in the data filtering module are dynamically adjusted according to the feedback from the remote monitoring center.

[0082] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the above-mentioned method for remotely monitoring a filter cabinet based on Internet of Things technology.

[0083] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for remotely monitoring a filter cabinet based on Internet of Things technology.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote monitoring filter cabinet system based on Internet of Things technology, characterized in that: include: The data acquisition module is used to monitor the environmental parameters in the filter cabinet in real time and transmit the collected raw data signals to the data filtering module; The data filtering module is used to filter the collected raw data signal according to the filtering calculation formula. The calculation formula is: Among them, the F filtered (t) represents the data signal after filtering, F raw (t) represents the original data signal, Represents the original data signal F at time point τ raw (t) is the rate of change, α, β and γ are adjustable parameters preset according to system requirements, which are used to adjust the filtering effect and response speed, and τ is the virtual time variable used for integral calculation; IoT communication module, used to transmit filtered data in real time to a remote monitoring center for analysis and control; A remote monitoring center analyzes the filtered data received to predict equipment failure trends and automatically triggers an alarm mechanism when abnormal data is detected; The user interaction module is used for users to remotely access the real-time data, historical records and system configuration of the filter cabinet to achieve remote monitoring and management.

2. The remote monitoring filter cabinet system based on Internet of Things technology according to claim 1 is characterized in that: The data acquisition module monitors the environmental parameters in the filter cabinet in real time and transmits the collected raw data signals to the data filtering module, specifically including: According to the temperature sensor, humidity sensor, air pressure sensor and electromagnetic interference sensor installed in the filter cabinet, the environmental parameters in the filter cabinet are collected, and the environmental parameters include temperature data, humidity data, air pressure data and electromagnetic interference intensity data; Calculate the environmental condition index inside the filter cabinet based on the weights of temperature data, humidity data, air pressure data, and electromagnetic interference intensity data; The environmental condition index is transmitted to the data filtering module.

3. The remote monitoring filter cabinet system based on Internet of Things technology according to claim 2 is characterized in that: The environmental condition index in the filter cabinet is calculated based on the weights of temperature data, humidity data, air pressure data, and electromagnetic interference intensity data. The specific formula is: Among them, Q represents the environmental condition index, T min ,T max are the minimum and maximum values of temperature; H min ,H max is the minimum and maximum value of humidity; P min ,P max is the reference value and maximum value of air pressure; EMI min ,EMI max are the minimum and maximum values of electromagnetic interference intensity, and w1, w2, w3, and w4 are weight coefficients.

4. The remote monitoring filter cabinet system based on Internet of Things technology according to claim 3 is characterized in that: The IoT communication module transmits the filtered data to the remote monitoring center in real time for analysis and control, specifically including: Encapsulate the filtered temperature data, humidity data, air pressure data, electromagnetic interference intensity data, and environmental condition index into a data packet; According to the communication protocol, the encapsulated data packets are sent to the remote monitoring center in real time.

5. The remote monitoring filter cabinet system based on Internet of Things technology according to claim 4 is characterized in that: The remote monitoring center includes a data analysis module and an early warning module. The data analysis module uses big data analysis and machine learning algorithms to analyze the received filtered data to predict equipment failure trends. The early warning module automatically triggers an alarm mechanism when abnormal data is detected and sends early warning information to management personnel via text message, email or instant messaging software.

6. The remote monitoring filter cabinet system based on Internet of Things technology according to claim 5 is characterized in that: The system further comprises an adaptive adjustment module for dynamically adjusting parameters α, β and γ of the calculation formula in the data filtering module according to feedback from the remote monitoring center.

7. A remote monitoring filter cabinet method based on Internet of Things technology, characterized in that: Specifically include: The data acquisition module installed in the filter cabinet is used to monitor the environmental parameters in the filter cabinet in real time, and transmit these raw data signals to the data filtering module; In the data filtering module, the received raw data signal is filtered, and the filtering calculation formula is: Among them, the F filtered (t) represents the data signal after filtering, F raw (t) represents the original data signal, Represents the original data signal F at time point τ raw (t) is the rate of change, α, β and γ are adjustable parameters preset according to system requirements, which are used to adjust the filtering effect and response speed, and τ is the virtual time variable used for integral calculation; The filtered data is transmitted to the remote monitoring center in real time for analysis and control based on the Internet of Things communication module; Analyze the filtered data received by the remote monitoring center to predict equipment failure trends and automatically trigger an alarm mechanism when abnormal data is detected; Users can remotely access the real-time data, historical records and system configuration of the filter cabinet based on the user interaction module to achieve remote monitoring and management.

8. The method for remotely monitoring a filter cabinet based on Internet of Things technology according to claim 7, characterized in that: The method further comprises: The parameters α, β and γ of the calculation formula in the data filtering module are dynamically adjusted according to the feedback from the remote monitoring center.

9. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for remotely monitoring a filter cabinet based on Internet of Things technology described in any one of claims 7 to 8 are implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for remotely monitoring a filter cabinet based on Internet of Things technology as recited in any one of claims 7 to 8.

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