Digital monitoring platform and monitoring method for axial flow fan, terminal and storage medium

Abstract

The invention belongs to the technical field of industrial monitoring equipment, and particularly relates to a digital monitoring platform and method for an axial flow fan, a terminal and a storage medium, and the platform comprises a data collection layer, a data transmission layer, a data processing layer and an application service layer. According to the invention, through various monitoring sensors in the data acquisition layer, the operation data of the axial flow fan can be comprehensively acquired, including but not limited to key parameters such as temperature, pressure, humidity, vibration, current and voltage; the data preprocessing module ensures the accuracy and integrity of the data, and provides a reliable basis for subsequent data analysis. According to the intelligent management, through real-time monitoring display and data analysis reports provided by the application service layer, the operation state of the axial flow fan can be mastered in real time, and adjustment and optimization can be made in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial monitoring equipment, and in particular relates to a digital monitoring platform, a monitoring method, a terminal and a storage medium for an axial flow fan. Background Art

[0002] With the advent of the Industrial 4.0 era, the manufacturing industry is undergoing unprecedented changes, and equipment digitalization has become a key factor in promoting enterprise transformation and upgrading and improving production efficiency. As an important part of industrial equipment, the operating status of axial flow fans directly affects the stability and efficiency of the entire production line. However, the traditional axial flow fan equipment management model faces many challenges, such as low efficiency, high maintenance costs, slow fault response, etc. These problems seriously restrict the production efficiency and competitiveness of enterprises.

[0003] In the existing technology, although some equipment monitoring and data analysis technologies have been applied to the management of axial flow fans, these technologies are often single-function and cannot meet the needs of enterprises for comprehensive monitoring and intelligent management of equipment. For example, some simple sensors can only monitor certain basic operating parameters of the equipment, but cannot comprehensively evaluate the overall operating status of the equipment; although some data analysis technologies can process large amounts of data, they lack the professionalism and intelligence for axial flow fan equipment and cannot accurately predict the risk of equipment failure. Summary of the invention

[0004] In view of the defects in the prior art that traditional axial flow fan monitoring equipment has a single function and cannot comprehensively evaluate the overall operating status of the equipment, the present invention provides a digital monitoring platform, monitoring method, terminal and storage medium for an axial flow fan to solve the above technical problems.

[0005] In a first aspect, the present invention provides a digital monitoring platform for an axial flow fan, including a data acquisition layer, a data transmission layer, a data processing layer and an application service layer; The data acquisition layer is used to obtain the operating data of the axial flow fan from multiple monitoring sensors and pre-process the collected operating data of the axial flow fan; it includes several monitoring sensor interface modules, data acquisition modules and data pre-processing modules; The data transmission layer is used to encrypt the axial fan operation data preprocessed in the data acquisition layer and transmit it to the data processing layer; it includes a communication module and an encryption module; The data processing layer is used to decrypt the received encrypted information and analyze the decrypted axial flow fan operation data based on the pre-stored early warning model to determine whether the axial flow fan has a failure risk and issue an early warning when it is determined that there is a failure risk; it includes a decryption module, a data analysis and early warning module, and a data storage module; The application service layer is used to provide users with real-time monitoring display, data analysis reports and remote control interfaces; it includes a monitoring interface, a data analysis report generation module and a remote control module.

[0006] A further improvement of the technical solution is that the data acquisition module includes a temperature sensor, a pressure sensor, a humidity sensor, a vibration sensor, a current sensor and a voltage sensor.

[0007] A further improvement of the technical solution is that the data preprocessing module includes a data de-noising unit, a data filtering unit and a data format conversion unit.

[0008] A further improvement of the technical solution is that the communication protocol adopted by the data transmission layer includes at least one of 4G, 5G, MQTT, CoAP, Modbus, S7 and RestAPI.

[0009] In a second aspect, the present invention provides a monitoring method of a digital monitoring platform for an axial flow fan based on any one of the above, comprising: Acquire the operating data of the axial flow fan from multiple monitoring sensors and pre-process the collected operating data of the axial flow fan; at the same time, display the monitored operating data of the axial flow fan in real time; The axial fan operation data pre-processed in the data acquisition layer is encrypted and transmitted to the data processing layer; The data processing layer decrypts the received encrypted information, and analyzes the decrypted axial flow fan operation data based on the pre-stored early warning model to determine whether the axial flow fan has a failure risk, and issues an early warning when it is determined that there is a failure risk; Generate and display analysis reports based on the analysis results, and at the same time, maintain communication with external devices through the remote control module.

[0010] A further improvement of the technical solution is that the training method of the early warning model includes: Collect historical operation data of axial flow fan equipment, and set a fault label for the collected historical operation data; Preprocess the historical operation data, including data cleaning and normalization; integrate the preprocessed historical operation data into the training data set D; The information entropy of the training data set D is calculated using the information entropy calculation formula; Calculate the information gain of each axial flow fan attribute A to the training data set D according to the calculated information entropy, and use the attribute with the largest information gain as the partition attribute of the current node; The partition attribute is used as the judgment condition of the current node, and the child nodes are partitioned according to the different values ​​of the attribute; The information gain calculation is recursively performed on each child node until the stopping condition is met to form the initial decision tree model; the stopping condition is that the samples in the child nodes all belong to the same class or the number of samples in the child nodes is less than the preset threshold or the attribute set is empty; Use the training data set D to train the initial decision tree model to obtain a prediction model; The obtained prediction model is verified by cross-validation method, and the parameters of the prediction model are adjusted according to the verification results. The parameters of the prediction model include the depth of the decision tree and the number of leaf node samples; The trained prediction model is deployed to the data processing layer for real-time analysis of the decrypted axial flow fan operation data.

[0011] A further improvement of this technical solution is that the information entropy calculation formula is: ; in, is the information entropy of the training data set D; is the proportion of samples belonging to the kth category in the sample set; y is the total number of categories.

[0012] A further improvement of this technical solution is that the calculation formula of information gain is: ; in, is the information gain of attribute A on training data set D; is the sample subset with attribute value v after being divided by attribute A; V is the number of all possible values ​​of attribute A.

[0013] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0014] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0015] The beneficial effects of the present invention are: Comprehensive monitoring and intelligent management: Through the various monitoring sensors in the data acquisition layer, the operating data of the axial flow fan can be fully obtained, including but not limited to key parameters such as temperature, pressure, humidity, vibration, current and voltage; the data preprocessing module ensures the accuracy and integrity of the data, providing a reliable basis for subsequent data analysis. Intelligent management provides real-time monitoring display and data analysis reports through the application service layer, so that enterprises can grasp the operating status of the axial flow fan in real time and make timely adjustments and optimizations.

[0016] Fault warning and rapid response: The warning model in the data processing layer can intelligently evaluate the operating status of the axial flow fan based on historical operating data and advanced algorithms, and timely discover potential fault risks. Once a fault risk is determined, the platform will immediately issue a warning and maintain communication with external devices through the remote control module so that the company can take quick measures to avoid the occurrence or expansion of the fault.

[0017] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 The figure is a schematic block diagram of a monitoring platform according to an embodiment of the present invention.

[0020] Figure 2 The present invention is a schematic flow chart of a method according to an embodiment of the present invention.

[0021] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention.

[0022] 110 is a data acquisition layer, 111 is a monitoring sensor interface module, 112 is a data acquisition module, 113 is a data preprocessing module, 120 is a data transmission layer, 121 is a communication module, 122 is an encryption module, 130 is a data processing layer, 131 is a decryption module, 132 is a data analysis and early warning module, 133 is a data storage module, 140 is an application service layer, 141 is a monitoring interface, 142 is a data analysis report generation module, and 143 is a remote control module. DETAILED DESCRIPTION

[0023] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in this specific embodiment. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this patent.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0025] The key terms appearing in the present invention are explained below.

[0026] 4G (The 4th Generation mobile communication technology) is the abbreviation of the fourth generation of mobile communication technology. It integrates 3G and WLAN and can quickly transmit data, high-quality audio, video and images. Compared with previous generations of communication technology, 4G has faster communication speed, wider network spectrum, higher communication quality, stronger intelligence, and can provide users with richer wireless services. At present, 4G technology has been widely used in mobile terminal devices such as smart phones and tablets.

[0027] 5G (5th generation mobile networks) is the fifth generation of mobile communication technology and the latest generation of cellular mobile communication technology. Compared with 4G, 5G has higher data transmission rate, lower network latency, larger connection capacity and wider coverage. 5G technology is designed to meet the explosive growth of mobile Internet traffic in the future and the large-scale connection needs of IoT devices, providing strong support for emerging application scenarios such as autonomous driving, telemedicine, and smart cities.

[0028] MQTT (Message Queuing Telemetry Transport) is a lightweight, publish / subscribe-based message transmission protocol that runs on the TCP / IP protocol family and provides stable communication services for small devices or low-bandwidth, unreliable or high-latency networks. The MQTT protocol is easy to implement, supports multiple message formats, supports QoS (quality of service) levels, supports offline message storage and retransmission, and is widely used in the field of the Internet of Things.

[0029] CoAP (The Constrained Application Protocol) is a network application protocol designed for small devices. It is based on the REST architectural style and supports a simple request / response model. The CoAP protocol is designed to enable IoT devices to communicate using an interactive model similar to the Web, while taking into account the resource constraints of IoT devices (such as processing power, storage space, network bandwidth, etc.). The CoAP protocol supports operations such as discovering resources, reading resources, writing resources, and observing resources, which facilitates interoperability between IoT devices.

[0030] Modbus is a serial communication protocol for communication between electronic controllers, invented by Modicon. It has become an industrial standard communication protocol and is widely used between industrial electronic equipment. The Modbus protocol supports multiple transmission media (such as RS-232, RS-422, RS-485 and Ethernet, etc.), and provides multiple function codes (such as reading coils, reading discrete inputs, writing single coils, writing multiple coils, etc.) to meet the needs of different application scenarios.

[0031] S7 is a protocol related to Siemens S7 series PLC. Although S7 is not the full name of a standard communication protocol, we understand it as the communication protocol supported by Siemens S7 series programmable logic controller (PLC). S7 series PLC is an important product in Siemens industrial automation field. They support multiple communication protocols (such as MPI, PROFIBUS, PROFINET, Ethernet, etc.) to achieve communication with other PLCs, human-machine interfaces (HMI), host computers and other devices. These communication protocols provide strong support for the wide application of S7 series PLC.

[0032] RestAPI (Representational State Transfer Application Programming Interface) is an application programming interface (API) based on the REST (representational state transfer) architectural style. It uses the HTTP protocol for communication and follows a series of design principles (such as statelessness, caching, layered systems, etc.) to achieve loosely coupled communication between clients and servers. RestAPI supports multiple data types (such as JSON, XML, etc.) and provides a variety of HTTP methods (such as GET, POST, PUT, DELETE, etc.) to meet the needs of different application scenarios. RestAPI has become one of the mainstream communication methods between modern Web services and applications.

[0033] like Figure 1As shown, the present invention provides a digital monitoring platform for axial flow fans, including a data acquisition layer, a data transmission layer, a data processing layer and an application service layer; wherein the data acquisition layer is used to obtain the operation data of the axial flow fan from multiple monitoring sensors, and pre-process the collected operation data of the axial flow fan; including several monitoring sensor interface modules, data acquisition modules and data pre-processing modules; the data transmission layer is used to encrypt the operation data of the axial flow fan pre-processed in the data acquisition layer and transmit it to the data processing layer; including a communication module and an encryption module; the data processing layer is used to decrypt the received encrypted information, and analyze the operation data of the axial flow fan obtained after decryption based on the pre-stored early warning model, determine whether the axial flow fan has a fault risk, and issue an early warning when it is determined that there is a fault risk; including a decryption module, a data analysis and early warning module and a data storage module; the application service layer is used to provide users with real-time monitoring display, data analysis report and remote control interface; including a monitoring interface, a data analysis report generation module and a remote control module. Among them, the data storage module is used to store the operation data of the axial flow fan received by the data processing layer.

[0034] Specifically, the data acquisition module includes a temperature sensor, a pressure sensor, a humidity sensor, a vibration sensor, a current sensor and a voltage sensor. Among them, the temperature sensor: a high-precision digital temperature sensor (such as DS18B20) is selected, which has the characteristics of small size, high precision and strong anti-interference ability. It communicates with the main control chip of the data acquisition module through a single bus interface to collect environmental temperature data in real time. Pressure sensor: a piezoresistive pressure sensor (such as the MPX series) is used, which can convert pressure changes into changes in resistance values, and then convert them into voltage or current signal output through a circuit. The sensor has the advantages of a wide measurement range, good stability and fast response speed. Humidity sensor: a capacitive humidity sensor (such as HS1101) is selected, and its capacitance value changes with the change of environmental humidity. The environmental humidity data can be obtained by measuring the change of capacitance value. The sensor has the characteristics of high sensitivity, wide measurement range and low power consumption. Vibration sensor: a piezoelectric vibration sensor (such as the acceleration sensor ADXL345) is used, which can convert vibration signals into electrical signals for output. The sensor has the characteristics of high measurement accuracy, fast response speed and strong impact resistance. Current sensor: Hall current sensor (such as HCS series) is selected. Based on the Hall effect principle, it can measure the current size in a non-contact manner. This sensor has the advantages of wide measurement range, good linearity, and good isolation effect. Voltage sensor: A voltage divider voltage sensor is used to convert high voltage into low voltage signal output through the principle of resistor voltage divider. This sensor has the characteristics of simple structure, low cost, and moderate measurement accuracy.

[0035] In addition, the data preprocessing module includes a data de-noising unit, a data filtering unit and a data format conversion unit.

[0036] In addition, the communication protocol adopted by the data transmission layer includes at least one of 4G, 5G, MQTT, CoAP, Modbus, S7 and RestAPI.

[0037] like Figure 2 As shown, the present invention provides a monitoring method of a digital monitoring platform of an axial flow fan based on any one of the above, comprising: Step 210, acquiring the operating data of the axial flow fan from a plurality of monitoring sensors, and preprocessing the acquired operating data of the axial flow fan; at the same time, displaying the monitored operating data of the axial flow fan in real time; Step 220, encrypting the axial flow fan operation data preprocessed in the data acquisition layer and transmitting it to the data processing layer; Step 230: the data processing layer decrypts the received encrypted information, and analyzes the decrypted axial flow fan operation data based on a pre-stored early warning model to determine whether the axial flow fan has a failure risk, and issues an early warning when it is determined that there is a failure risk; Step 240, generating and displaying an analysis report based on the analysis results, and at the same time, maintaining communication with external devices through a remote control module.

[0038] Among them, the training method of the early warning model includes: S231, collecting historical operation data of the axial flow fan equipment, and setting a fault label for the collected historical operation data; S232, preprocessing the historical operation data, including data cleaning and normalization; integrating the preprocessed historical operation data into a training data set D; S233, calculating the information entropy of the training data set D using the information entropy calculation formula; S234, calculating the information gain of each axial flow fan attribute A with respect to the training data set D according to the calculated information entropy, and taking the attribute with the largest information gain as the partition attribute of the current node; S235, using the partition attribute as the judgment condition of the current node, and partitioning the child nodes according to different values ​​of the attribute; S236, recursively perform information gain calculation on each child node until a stopping condition is met to form an initial decision tree model; the stopping condition is that the samples in the child nodes all belong to the same class or the number of samples in the child nodes is less than a preset threshold or the attribute set is empty; S237, using the training data set D to train the initial decision tree model to obtain a prediction model; S238, using a cross-validation method to validate the obtained prediction model, and adjusting the parameters of the prediction model according to the validation results, the parameters of the prediction model including the depth of the decision tree and the number of leaf node samples; S239. Deploy the trained prediction model to the data processing layer for real-time analysis of the decrypted axial flow fan operation data.

[0039] Specifically, the information entropy calculation formula is: ; in, is the information entropy of the training data set D; is the proportion of samples belonging to the kth category in the sample set; y is the total number of categories.

[0040] Furthermore, the calculation formula of information gain is: ; in, is the information gain of attribute A on training data set D; is the sample subset with attribute value v after being divided by attribute A; V is the number of all possible values ​​of attribute A.

[0041] In addition, the method further includes: setting a warning threshold (such as a failure probability exceeding 50%) on the leaf node of the decision tree. When the real-time collected axial flow fan operation data is input into the prediction model, if the result corresponding to the leaf node indicates that there is a failure risk (i.e., the failure probability exceeds the threshold), a warning is triggered.

[0042] Figure 3 A schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention is provided. The terminal 300 can be used to execute the monitoring method of the digital monitoring platform of the axial flow fan based on any one of the above items provided in an embodiment of the present invention.

[0043] The terminal 300 may include: a processor 310, a memory 320 and a communication module 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0044] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0045] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0046] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0047] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0048] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0049] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0050] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0051] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0052] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0053] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A digital monitoring platform for axial flow fans, characterized in that: It includes data collection layer, data transmission layer, data processing layer and application service layer; The data acquisition layer is used to obtain the operating data of the axial flow fan from multiple monitoring sensors and pre-process the collected operating data of the axial flow fan; it includes several monitoring sensor interface modules, data acquisition modules and data pre-processing modules; The data transmission layer is used to encrypt the axial fan operation data preprocessed in the data acquisition layer and transmit it to the data processing layer; it includes a communication module and an encryption module; The data processing layer is used to decrypt the received encrypted information and analyze the decrypted axial flow fan operation data based on the pre-stored early warning model to determine whether the axial flow fan has a failure risk and issue an early warning when it is determined that there is a failure risk; It includes decryption module, data analysis and early warning module and data storage module; The application service layer is used to provide users with real-time monitoring display, data analysis reports and remote control interfaces; It includes monitoring interface, data analysis report generation module and remote control module.

2. The digital monitoring platform for axial flow fans according to claim 1 is characterized in that: The data acquisition module includes a temperature sensor, a pressure sensor, a humidity sensor, a vibration sensor, a current sensor and a voltage sensor.

3. The digital monitoring platform for axial flow fans according to claim 1 is characterized in that: The data preprocessing module includes a data de-noising unit, a data filtering unit and a data format conversion unit.

4. The digital monitoring platform for axial flow fans according to claim 1, characterized in that: The communication protocol adopted by the data transmission layer includes at least one of 4G, 5G, MQTT, CoAP, Modbus, S7 and RestAPI.

5. A monitoring method based on a digital monitoring platform of an axial flow fan according to any one of claims 1 to 4, characterized in that: include: Acquire the operating data of the axial flow fan from multiple monitoring sensors, and pre-process the acquired operating data of the axial flow fan; At the same time, the monitored axial flow fan operation data is displayed in real time; The axial fan operation data pre-processed in the data acquisition layer is encrypted and transmitted to the data processing layer; The data processing layer decrypts the received encrypted information, and analyzes the decrypted axial flow fan operation data based on the pre-stored early warning model to determine whether the axial flow fan has a failure risk, and issues an early warning when it is determined that there is a failure risk; Generate and display analysis reports based on the analysis results, and at the same time, maintain communication with external devices through the remote control module.

6. The monitoring method according to claim 5, characterized in that: The training methods of the early warning model include: Collect historical operation data of axial flow fan equipment, and set a fault label for the collected historical operation data; Preprocess the historical operation data, including data cleaning and normalization; integrate the preprocessed historical operation data into the training data set D; The information entropy of the training data set D is calculated using the information entropy calculation formula; Calculate the information gain of each axial flow fan attribute A to the training data set D according to the calculated information entropy, and use the attribute with the largest information gain as the partition attribute of the current node; The partition attribute is used as the judgment condition of the current node, and the child nodes are partitioned according to the different values ​​of the attribute; The information gain calculation is recursively performed on each child node until the stopping condition is met to form the initial decision tree model; the stopping condition is that the samples in the child nodes all belong to the same class or the number of samples in the child nodes is less than the preset threshold or the attribute set is empty; Use the training data set D to train the initial decision tree model to obtain a prediction model; The obtained prediction model is verified by cross-validation method, and the parameters of the prediction model are adjusted according to the verification results. The parameters of the prediction model include the depth of the decision tree and the number of leaf node samples; The trained prediction model is deployed to the data processing layer for real-time analysis of the decrypted axial flow fan operation data.

7. The monitoring method according to claim 6, characterized in that: The information entropy calculation formula is: ； in, is the information entropy of the training data set D; is the proportion of samples belonging to the kth category in the sample set; y is the total number of categories.

8. The monitoring method according to claim 7, characterized in that: The calculation formula of information gain is: ； in, is the information gain of attribute A on training data set D; is the sample subset with attribute value v after being divided by attribute A; V is the number of all possible values ​​of attribute A.

9. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 5 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 5 to 8 is implemented.

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