Fire-fighting smoke exhaust fan remote control method based on data analysis

Abstract

The invention discloses a fire-fighting smoke exhaust fan remote control method based on data analysis, and relates to the technical field of data analysis, and the method comprises the following steps: obtaining fan parameters and power supply parameters, carrying out the analysis to obtain a fan operation state value and a power supply operation state value, and sending a fault early warning signal; and respectively training a first machine learning model used for predicting the operation state value of the fan and a second machine learning model used for predicting the operation state value of the power supply. Value intervals of fan and power supply parameters are analyzed, whether current parameter values are in a normal range or not or whether potential abnormal conditions exist or not can be determined, a machine learning model is trained and tested, training is carried out based on historical data and monitoring data so as to learn and predict possible running states in the future, and the working efficiency is improved. According to the method, the analysis precedence and intelligence are improved, possible faults can be prevented, the maintenance plan can be optimized, the reliability and efficiency of equipment can be improved, and the decision-making process is more scientific and accurate based on data analysis and machine learning.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a remote control method for a fire smoke exhaust fan based on data analysis. Background Art

[0002] In recent years, with the popularization of the Internet of Things and sensor technology, real-time data analysis has become increasingly important. Timely understanding and responding to data changes in order to take timely actions has prompted the advancement of real-time data processing and analysis technology.

[0003] At present, a Chinese invention patent with publication number CN 115382139 B discloses a fire-fighting fan capable of spraying water mist and a control method thereof. The method can reduce smoke temperature and achieve dust removal and purify smoke by spraying water mist on the fan blades and motor. The motor and blades are cooled by water, which not only has a better cooling effect, but also can prevent the motor surface from being contaminated by dust. However, the related technology does not predict the state of the fan, lacks foresight, does not collect and analyze comprehensive data, lacks comprehensiveness, and is prone to cause fire-fighting errors or delays. Summary of the invention

[0004] The technical problem solved by the present invention is that the related technology does not predict the status of the fan, lacks foresight, does not collect and analyze comprehensive data, lacks comprehensiveness, and is prone to cause fire-fighting errors or delays.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, a remote control method for a fire smoke exhaust fan based on data analysis comprises the following steps: Step S1, obtaining fan parameters and power supply parameters; Step S2, calling the fan database, respectively obtaining the value interval corresponding to the fan parameter and the value interval corresponding to the power supply parameter, analyzing the fan parameter and the value interval corresponding to the fan parameter to obtain the fan operation state value, analyzing the power supply parameter and the value interval corresponding to the power supply parameter to obtain the power supply operation state value; Step S3, sending a fault warning signal according to the fan operation status value and the power supply operation status value; Step S4, respectively train a first machine learning model for predicting the wind turbine operating status value and a second machine learning model for predicting the power supply operating status value, and respectively test the first machine learning model and the second machine learning model.

[0006] As a preferred solution of the method for remote control of a fire smoke exhaust fan based on data analysis described in the present invention, step S1 includes the following sub-steps: Step S11, turn on the smoke simulator and start the smoke exhaust fan; Step S12, obtaining wind turbine parameters and power supply parameters within a first time period, wherein the wind turbine parameters include wind speed, wind volume and wind pressure, and the power supply parameters include current and voltage.

[0007] As a preferred solution of the method for remote control of a fire smoke exhaust fan based on data analysis described in the present invention, step S2 includes the following sub-steps: Step S21, calling the wind turbine database, respectively obtaining the value interval corresponding to the wind speed, the value interval corresponding to the wind volume, and the value interval corresponding to the wind pressure, which are recorded as the first value interval, the second value interval, and the third value interval, respectively, and respectively obtaining the value interval corresponding to the current and the value interval corresponding to the voltage, which are recorded as the fourth value interval and the fifth value interval, respectively; Step S22, analyzing the fan parameter and the value interval corresponding to the fan parameter to obtain the fan operation state value, and analyzing the power supply parameter and the value interval corresponding to the power supply parameter to obtain the power supply operation state value.

[0008] As a preferred solution of a remote control method for a fire smoke exhaust fan based on data analysis described in the present invention, the analysis logic of the fan operation status value is as follows: respectively calculating the average value of the first value interval, the average value of the second value interval and the average value of the third value interval, performing a ratio operation on the wind speed and the average value of the first value interval to obtain a first ratio, performing a ratio operation on the air volume and the average value of the second value interval to obtain a second ratio, performing a ratio operation on the wind pressure and the average value of the third value interval to obtain a third ratio, performing a multivariate regression analysis on the first ratio, the second ratio, the third ratio and the fan operation status value to obtain the fan operation status value; The analysis logic of the power supply operation status value is: respectively calculate the average value of the fourth value interval and the average value of the fifth value interval, perform a ratio operation on the current and the average value of the fourth value interval to obtain a fourth ratio, perform a ratio operation on the voltage and the average value of the fifth value interval to obtain a fifth ratio, perform a multivariate regression analysis on the fourth ratio, the fifth ratio and the power supply operation status value to obtain the power supply operation status value.

[0009] As a preferred solution of the remote control method of a fire smoke exhaust fan based on data analysis described in the present invention, the calculation expression of the fan operation status value is:

[0010] in, is the fan operating status value, , and are constants, is the first ratio, is the second ratio, is the third ratio; The calculation expression of the data power supply operation status value is: ; in, is the power supply operation status value, and are constants, is the fourth ratio, The fifth ratio.

[0011] As a preferred solution of the method for remote control of a fire smoke exhaust fan based on data analysis described in the present invention, step S3 includes the following sub-steps: Step S31, setting the first value as a standard fan operation state value, setting the second value as a standard power supply operation state value, and obtaining the fan operation state value and the power supply operation state value; Step S32, receiving a first trigger condition, identifying the first trigger condition, obtaining a first identification result, and recording data information corresponding to the first identification result, receiving a second trigger condition, identifying the second trigger condition, obtaining a second identification result, and recording data information corresponding to the second identification result; The first trigger condition is that the fan operation state value is less than a first value, and the second trigger condition is that the power supply operation state value is less than a second value; Step S32: sending a fan fault warning signal and a power supply operation fault warning signal respectively according to the data information corresponding to the first recognition result and the data information corresponding to the second recognition result.

[0012] As a preferred solution of the method for remote control of a fire smoke exhaust fan based on data analysis described in the present invention, step S4 includes the following sub-steps: Step S41, taking the fan operation state value, wind volume, wind speed and wind pressure as a first sample set, and dividing the first sample set into a first training set and a first test set according to a certain ratio; Step S42, training a first machine learning model for predicting the wind turbine operating state value, and testing the first machine learning model until the accuracy reaches a first accuracy threshold; Step S43, taking the power supply operation state value, current and voltage as a second sample set, and dividing the second sample set into a second training set and a second test set according to a certain ratio; Step S44, training a second machine learning model for predicting the power supply operation status value, and testing the second machine learning model until the accuracy reaches a second accuracy threshold.

[0013] As a preferred solution of the method for remote control of a fire smoke exhaust fan based on data analysis described in the present invention, wherein: the first machine learning model takes the predicted fan operation state value as the output, takes the actual fan operation state value as the output target, and takes reducing the difference between the predicted fan operation state value and the actual fan operation state value as the training target; The second machine learning model uses the predicted power supply operating status value as the output quantity and the actual power supply operating status value as the output target, and uses reducing the difference between the predicted power supply operating status value and the actual power supply operating status value as the training goal.

[0014] In a second aspect, the present invention provides an electronic device comprising a memory, a processor and storage in the memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in any one of the methods described above are executed.

[0015] In a third aspect, the present invention provides a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps in any one of the methods described above are executed.

[0016] The beneficial effects of the present invention are as follows: by acquiring fan parameters and power supply parameters, which are necessary basic data for subsequent analysis and prediction, analyzing the value ranges of fan and power supply parameters, it is helpful to determine whether the current parameter values ​​are within the normal range or whether there are potential abnormal conditions, training and testing machine learning models, and training based on historical data and monitoring data to learn and predict possible future operating states, thereby improving the foresight and intelligence of the analysis, helping to prevent possible failures, optimizing maintenance plans, and improving equipment reliability and efficiency. Based on data analysis and machine learning, the decision-making process is made more scientific and precise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A basic flow chart of a method for remotely controlling a fire smoke exhaust fan based on data analysis is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0019] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a remote control method for a fire smoke exhaust fan based on data analysis, comprising the following steps: Step S1, obtaining fan parameters and power supply parameters; Step S2, calling the fan database, respectively obtaining the value interval corresponding to the fan parameter and the value interval corresponding to the power supply parameter, analyzing the fan parameter and the value interval corresponding to the fan parameter to obtain the fan operation state value, analyzing the power supply parameter and the value interval corresponding to the power supply parameter to obtain the power supply operation state value; Step S3, sending a fault warning signal according to the fan operation status value and the power supply operation status value; Step S4, respectively train a first machine learning model for predicting the wind turbine operating status value and a second machine learning model for predicting the power supply operating status value, and respectively test the first machine learning model and the second machine learning model.

[0020] The present invention obtains fan parameters and power supply parameters, which are necessary basic data for subsequent analysis and prediction, and analyzes the value ranges of the fan and power supply parameters, which helps to determine whether the current parameter values ​​are within the normal range or whether there are potential abnormal conditions. The machine learning model is trained and tested based on historical data and monitoring data to learn and predict possible future operating states, thereby improving the foresight and intelligence of the analysis, helping to prevent possible failures, optimizing maintenance plans, and improving equipment reliability and efficiency. Based on data analysis and machine learning, the decision-making process is made more scientific and accurate.

[0021] The step S1 includes the following sub-steps: Step S11, turn on the smoke simulator and start the smoke exhaust fan; Step S12, obtaining wind turbine parameters and power supply parameters within a first time period, wherein the wind turbine parameters include wind speed, wind volume and wind pressure, and the power supply parameters include current and voltage.

[0022] By obtaining wind turbine parameters and power supply parameters, these parameters are necessary basic data for subsequent analysis and prediction.

[0023] The step S2 includes the following sub-steps: Step S21, calling the wind turbine database, respectively obtaining the value interval corresponding to the wind speed, the value interval corresponding to the wind volume, and the value interval corresponding to the wind pressure, which are recorded as the first value interval, the second value interval, and the third value interval, respectively, and respectively obtaining the value interval corresponding to the current and the value interval corresponding to the voltage, which are recorded as the fourth value interval and the fifth value interval, respectively; Step S22, analyzing the fan parameter and the value interval corresponding to the fan parameter to obtain the fan operation state value, and analyzing the power supply parameter and the value interval corresponding to the power supply parameter to obtain the power supply operation state value.

[0024] Analyzing the value ranges of fan and power supply parameters helps determine whether the current parameter values ​​are within the normal range or whether there are potential abnormalities.

[0025] The analysis logic of the fan operation status value is as follows: respectively calculating the average value of the first value interval, the average value of the second value interval and the average value of the third value interval, performing a ratio operation on the wind speed and the average value of the first value interval to obtain a first ratio, performing a ratio operation on the wind volume and the average value of the second value interval to obtain a second ratio, performing a ratio operation on the wind pressure and the average value of the third value interval to obtain a third ratio, performing a multivariate regression analysis on the first ratio, the second ratio, the third ratio and the fan operation status value to obtain the fan operation status value; The analysis logic of the power supply operation status value is: respectively calculate the average value of the fourth value interval and the average value of the fifth value interval, perform a ratio operation on the current and the average value of the fourth value interval to obtain a fourth ratio, perform a ratio operation on the voltage and the average value of the fifth value interval to obtain a fifth ratio, perform a multivariate regression analysis on the fourth ratio, the fifth ratio and the power supply operation status value to obtain the power supply operation status value.

[0026] Quantifying the power supply operating status and fan operating status makes data analysis clearer and simpler.

[0027] The calculation expression of the fan operation status value is: ; in, is the fan operating status value, , and are constants, is the first ratio, is the second ratio, is the third ratio; The calculation expression of the data power supply operation status value is: ; in, is the power supply operation status value, and are constants, is the fourth ratio, The fifth ratio.

[0028] The step S3 includes the following sub-steps: Step S31, setting the first value as a standard fan operation state value, setting the second value as a standard power supply operation state value, and obtaining the fan operation state value and the power supply operation state value; Step S32, receiving a first trigger condition, identifying the first trigger condition, obtaining a first identification result, and recording data information corresponding to the first identification result, receiving a second trigger condition, identifying the second trigger condition, obtaining a second identification result, and recording data information corresponding to the second identification result; The first trigger condition is that the fan operation state value is less than a first value, and the second trigger condition is that the power supply operation state value is less than a second value; Step S32: sending a fan fault warning signal and a power supply operation fault warning signal respectively according to the data information corresponding to the first recognition result and the data information corresponding to the second recognition result.

[0029] The data is screened according to the first trigger condition and the second trigger condition, which improves the speed of data calculation, reduces the data storage capacity of the computing system, is beneficial to the long-term stable operation of the system, and improves the computing efficiency.

[0030] The step S4 includes the following sub-steps: Step S41, taking the fan operation state value, wind volume, wind speed and wind pressure as a first sample set, and dividing the first sample set into a first training set and a first test set according to a certain ratio; Step S42, training a first machine learning model for predicting the wind turbine operating state value, and testing the first machine learning model until the accuracy reaches a first accuracy threshold; Step S43, taking the power supply operation state value, current and voltage as a second sample set, and dividing the second sample set into a second training set and a second test set according to a certain ratio; Step S44, training a second machine learning model for predicting the power supply operation status value, and testing the second machine learning model until the accuracy reaches a second accuracy threshold.

[0031] Train and test machine learning models based on historical data and monitoring data to learn and predict possible future operating states, improving the foresight and intelligence of analysis.

[0032] The first machine learning model uses the predicted wind turbine operating state value as an output, the actual wind turbine operating state value as an output target, and reduces the difference between the predicted wind turbine operating state value and the actual wind turbine operating state value as a training goal; The second machine learning model uses the predicted power supply operating status value as the output quantity and the actual power supply operating status value as the output target, and uses reducing the difference between the predicted power supply operating status value and the actual power supply operating status value as the training goal.

[0033] Help prevent possible failures, optimize maintenance plans, improve equipment reliability and efficiency, and make the decision-making process more scientific and accurate based on data analysis and machine learning.

[0034] The present invention obtains fan parameters and power supply parameters, which are necessary basic data for subsequent analysis and prediction, and analyzes the value ranges of the fan and power supply parameters, which helps to determine whether the current parameter values ​​are within the normal range or whether there are potential abnormal conditions. The machine learning model is trained and tested based on historical data and monitoring data to learn and predict possible future operating states, thereby improving the foresight and intelligence of the analysis, helping to prevent possible failures, optimizing maintenance plans, and improving equipment reliability and efficiency. Based on data analysis and machine learning, the decision-making process is made more scientific and accurate.

[0035] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0036] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A remote control method for fire smoke exhaust fans based on data analysis, characterized in that: The following steps are involved: Step S1, obtaining fan parameters and power supply parameters; Step S2, calling the fan database, respectively obtaining the value interval corresponding to the fan parameter and the value interval corresponding to the power supply parameter, analyzing the fan parameter and the value interval corresponding to the fan parameter to obtain the fan operation state value, analyzing the power supply parameter and the value interval corresponding to the power supply parameter to obtain the power supply operation state value; Step S3, sending a fault warning signal according to the fan operation status value and the power supply operation status value; Step S4, respectively train a first machine learning model for predicting the wind turbine operating status value and a second machine learning model for predicting the power supply operating status value, and respectively test the first machine learning model and the second machine learning model.

2. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The step S1 includes the following sub-steps: Step S11, turn on the smoke simulator and start the smoke exhaust fan; Step S12, obtaining wind turbine parameters and power supply parameters within a first time period, wherein the wind turbine parameters include wind speed, wind volume and wind pressure, and the power supply parameters include current and voltage.

3. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The step S2 includes the following sub-steps: Step S21, calling the wind turbine database, respectively obtaining the value interval corresponding to the wind speed, the value interval corresponding to the wind volume, and the value interval corresponding to the wind pressure, which are recorded as the first value interval, the second value interval, and the third value interval, respectively, and respectively obtaining the value interval corresponding to the current and the value interval corresponding to the voltage, which are recorded as the fourth value interval and the fifth value interval, respectively; Step S22, analyzing the fan parameter and the value interval corresponding to the fan parameter to obtain the fan operation state value, and analyzing the power supply parameter and the value interval corresponding to the power supply parameter to obtain the power supply operation state value.

4. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The analysis logic of the fan operation status value is as follows: respectively calculating the average value of the first value interval, the average value of the second value interval and the average value of the third value interval, performing a ratio operation on the wind speed and the average value of the first value interval to obtain a first ratio, performing a ratio operation on the wind volume and the average value of the second value interval to obtain a second ratio, performing a ratio operation on the wind pressure and the average value of the third value interval to obtain a third ratio, performing a multivariate regression analysis on the first ratio, the second ratio, the third ratio and the fan operation status value to obtain the fan operation status value; The analysis logic of the power supply operation status value is: respectively calculate the average value of the fourth value interval and the average value of the fifth value interval, perform a ratio operation on the current and the average value of the fourth value interval to obtain a fourth ratio, perform a ratio operation on the voltage and the average value of the fifth value interval to obtain a fifth ratio, perform a multivariate regression analysis on the fourth ratio, the fifth ratio and the power supply operation status value to obtain the power supply operation status value.

5. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The calculation expression of the fan operation status value is: ； in, is the fan operating status value, , and are constants, is the first ratio, is the second ratio, is the third ratio; The calculation expression of the data power supply operation status value is: ； in, is the power supply operation status value, and are constants, is the fourth ratio, The fifth ratio.

6. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The step S3 includes the following sub-steps: Step S31, setting the first value as a standard fan operation state value, setting the second value as a standard power supply operation state value, and obtaining the fan operation state value and the power supply operation state value; Step S32, receiving a first trigger condition, identifying the first trigger condition, obtaining a first identification result, and recording data information corresponding to the first identification result, receiving a second trigger condition, identifying the second trigger condition, obtaining a second identification result, and recording data information corresponding to the second identification result; The first trigger condition is that the fan operation state value is less than a first value, and the second trigger condition is that the power supply operation state value is less than a second value; Step S32: sending a fan fault warning signal and a power supply operation fault warning signal respectively according to the data information corresponding to the first recognition result and the data information corresponding to the second recognition result.

7. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The step S4 includes the following sub-steps: Step S41, taking the fan operation state value, wind volume, wind speed and wind pressure as a first sample set, and dividing the first sample set into a first training set and a first test set according to a certain ratio; Step S42, training a first machine learning model for predicting the wind turbine operating state value, and testing the first machine learning model until the accuracy reaches a first accuracy threshold; Step S43, taking the power supply operation state value, current and voltage as a second sample set, and dividing the second sample set into a second training set and a second test set according to a certain ratio; Step S44, training a second machine learning model for predicting the power supply operation status value, and testing the second machine learning model until the accuracy reaches a second accuracy threshold.

8. A fire smoke exhaust fan remote control method based on data analysis as claimed in claim 1, characterized in that: The first machine learning model uses the predicted wind turbine operating state value as an output, the actual wind turbine operating state value as an output target, and reduces the difference between the predicted wind turbine operating state value and the actual wind turbine operating state value as a training goal; The second machine learning model uses the predicted power supply operating status value as the output quantity and the actual power supply operating status value as the output target, and uses reducing the difference between the predicted power supply operating status value and the actual power supply operating status value as the training goal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the remote control method for a fire smoke exhaust fan based on data analysis as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the remote control method of a fire smoke exhaust fan based on data analysis according to any one of claims 1 to 8 is implemented.

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