An Internet of Things-based machine room equipment state monitoring method, device and medium

Abstract

The application discloses a kind of based on Internet of Things machine room equipment state monitoring method, equipment and medium, method includes: acquisition module obtains the state data in machine room;Data processing module is preprocessed to state data, determine the type identification corresponding to the state data after pre-processing, according to type identification determines corresponding detection procedure, and according to detection procedure state data is analyzed to determine the danger signal in machine room;Data processing module generates positioning request according to danger signal, and according to positioning request to acquisition module is started, to make acquisition module enter positioning mode, determine the dangerous position in machine room by acquisition module under positioning mode;Early warning module obtains danger signal and dangerous position, and according to danger signal and dangerous position are reminded.The application uses Internet of Things technology, realizes the automation and intelligentization of data acquisition, processing, early warning, improves the stability and reliability of system.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a method, device, and medium for monitoring the status of data center equipment based on IoT. Background Technology

[0002] With the rapid development of information technology, data centers and server rooms, as core infrastructure supporting the operation of various information systems, have become particularly important in terms of stability and reliability. However, the numerous critical devices operating within server rooms, such as servers, network equipment, and cooling systems, inevitably experience various malfunctions during long-term high-load operation, such as overheating, abnormal vibration, and component aging. If these problems are not detected and addressed in a timely manner, they will seriously affect the normal operation of the server room, and may even lead to data loss and system paralysis. Summary of the Invention

[0003] To address the aforementioned issues, this application proposes an IoT-based method for monitoring the status of data center equipment. This method is applied to an IoT-based data center equipment status monitoring system, which includes a data acquisition module, a data processing module, and an early warning module. The method comprises: the data acquisition module acquiring status data within the data center and sending the status data to a pre-set data processing module; the types of status data include, but are not limited to, temperature, vibration, smoke, and noise; the data processing module preprocessing the status data, determining a type identifier corresponding to the preprocessed status data, determining a corresponding detection program based on the type identifier, and analyzing the status data according to the detection program to identify danger signals within the data center; the data processing module generating a location request based on the danger signal and activating the data acquisition module according to the location request to put the data acquisition module into a location mode, and determining dangerous locations within the data center through the data acquisition module in location mode; and the early warning module acquiring the danger signal and the dangerous location, and issuing an alert based on the danger signal and the dangerous location.

[0004] In one example, the acquisition module includes a transmission device and multiple sensor groups, each sensor group consisting of multiple sensors. The types of the sensor groups include, but are not limited to, temperature sensor groups, vibration sensor groups, smoke sensor groups, and noise sensor groups. The multiple sensor groups are located at different positions within the computer room. Before the acquisition module acquires the status data within the computer room, the method further includes: initializing the acquisition module to put it into acquisition mode, thereby acquiring data from within the computer room through the acquisition module in acquisition mode to obtain the status data.

[0005] In one example, the method further includes: determining a first sensor corresponding to the type, activating multiple first sensors according to the acquisition mode, and acquiring data from the computer room using the multiple first sensors to obtain the status data.

[0006] In one example, after the acquisition module enters the positioning mode, the method further includes: activating all sensors of the sensor group according to the positioning mode, so as to locate the danger signal through all the sensors, thereby obtaining the danger location.

[0007] In one example, determining the hazardous location within the computer room specifically includes: determining the data type corresponding to the hazardous signal; determining the corresponding sensor group based on the data type; activating all sensors in the sensor group; synchronously collecting data from all sensors based on the hazardous signal to obtain a positioning signal group, the positioning signal group including multiple positioning signals, each including signal transmission time and signal retrieval time; determining a distance matrix between all sensors and the transmission location of the hazardous signal based on the positioning signal group; establishing a corresponding type of spatial coordinate system for the computer room based on the multiple sensor groups; and determining corresponding coordinate points in the corresponding type of spatial coordinate system based on the distance matrix, so as to determine the hazardous location based on the coordinate points.

[0008] In one example, the system further includes a remote controller, and the method further includes: determining a corresponding radiation range based on the type, and determining a danger range based on the danger location and the radiation range; determining the location information of all equipment in the computer room, and determining whether any equipment is within the danger range based on the location information; if any equipment is within the danger range, then powering off the equipment via the remote controller.

[0009] In one example, before analyzing the status data according to the detection procedure, the method further includes: acquiring historical status data of the computer room; dividing the historical status data according to the type to obtain multi-dimensional historical data; extracting features from the multi-dimensional historical data to obtain a feature set; establishing a status monitoring model based on the feature set; and determining the detection procedure based on the status monitoring model.

[0010] In one example, the method further includes: splitting the state monitoring model according to the type to obtain multiple sub-state monitoring modules; determining the type identifier corresponding to the state data, and determining the corresponding single or multiple sub-state monitoring modules according to the type identifier; if the type identifier corresponds to a single sub-state monitoring module, then determining the detection program according to the single sub-state monitoring module; if the type identifier corresponds to multiple sub-state monitoring modules, then determining the detection program according to the multiple sub-state monitoring modules.

[0011] On the other hand, this application also proposes an IoT-based data center equipment status monitoring device, applied in an IoT-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, and an early warning module. The device includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor. These instructions, when executed by the at least one processor, enable the IoT-based data center equipment status monitoring device to perform the following: the data acquisition module acquires status data within the data center and sends the status data to a pre-set data processing module. The type of the status data includes, but is not limited to, […]. Limited by temperature, vibration, smoke, and noise; the data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data according to the detection program to identify danger signals within the computer room; the data processing module generates a location request based on the danger signal and activates the acquisition module according to the location request to put the acquisition module into location mode, and determines the dangerous location within the computer room through the acquisition module in location mode; the early warning module acquires the danger signal and the dangerous location, and issues a warning based on the danger signal and the dangerous location.

[0012] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, applied in an Internet of Things-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, and an early warning module. The computer-executable instructions are configured as follows: the data acquisition module acquires status data within the data center and sends the status data to a pre-set data processing module. The types of status data include, but are not limited to, temperature, vibration, smoke, and noise. The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data according to the detection program to identify danger signals within the data center. The data processing module generates a location request based on the danger signal and activates the data acquisition module according to the location request, enabling the data acquisition module to enter a location mode. The data acquisition module in location mode determines dangerous locations within the data center. The early warning module acquires the danger signal and the dangerous location and issues an alert based on the danger signal and the dangerous location.

[0013] This application utilizes a multi-type sensor array to collect real-time data on various conditions within the computer room, including temperature, vibration, smoke, and noise, enabling comprehensive monitoring of the computer room environment. Upon detecting a danger signal, it can quickly locate the danger point and provide timely alerts via an early warning module, shortening emergency response time. By synchronously collecting data in positioning mode using the sensor array and employing algorithms to accurately calculate the danger location, the accuracy and reliability of the positioning are improved. Simultaneously, a status monitoring model built from historical data can intelligently analyze current status data and accurately identify potential hazards. The system can automatically determine the danger zone based on danger signals and radiation range, and remotely control the power-off of equipment within the danger zone, effectively preventing the accident from escalating and reducing losses. This application breaks down the status monitoring model into multiple sub-modules, flexibly selecting corresponding detection programs based on data type, improving data processing efficiency and system scalability. Employing IoT technology, it automates and intelligently manages data acquisition, processing, and early warning, reducing the need for manual intervention and improving system stability and reliability. Furthermore, this application can be widely applied to status monitoring of various computer room equipment, possessing high practical value and promising prospects for widespread adoption. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0015] Figure 1 This is a flowchart illustrating an IoT-based data center equipment status monitoring method according to an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of an IoT-based data center equipment status monitoring device in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0019] like Figure 1 As shown, to solve the above problems, this application provides an IoT-based data center equipment status monitoring method, applied in an IoT-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, an early warning module, and a remote controller. The method includes:

[0020] S101. The acquisition module obtains the status data in the computer room and sends the status data to the pre-set data processing module. The types of status data include, but are not limited to, temperature, vibration, smoke, and noise.

[0021] The data acquisition module comprehensively and meticulously monitors the internal environment of the computer room, capturing and recording real-time status data including temperature, vibration, smoke concentration, and noise. The acquired data is transmitted to a pre-configured data processing module via transmission equipment, where it is quickly filtered, integrated, and preliminarily analyzed, providing solid data support for subsequent fault early warning, performance optimization, and resource scheduling.

[0022] In one embodiment, the data acquisition module comprises a transmission device and multiple sensor groups. These sensor groups are numerous and categorized by function, including temperature sensor groups, vibration sensor groups, smoke sensor groups, and noise sensor groups. Each sensor group contains multiple sensors of the same type, ensuring comprehensive data acquisition while improving system reliability and accuracy. Before executing the data acquisition task, the acquisition module is initialized. During initialization, the module is set to acquisition mode, which is its fundamental state for performing data acquisition tasks. In acquisition mode, the sensor groups continuously monitor various status data within the computer room and transmit this data in real-time to the subsequent data processing module via the transmission device, providing timely and accurate information support for the computer room's operation and maintenance management. Each sensor group is positioned in different locations within the computer room according to its type. For example, all smoke sensors in the smoke sensor group are installed on the ceiling of the computer room, and all noise sensors in the noise sensor group are installed on various walls and ceilings of the computer room.

[0023] In one embodiment, after initializing the acquisition module and confirming its entry into acquisition mode, the first sensor corresponding to the required monitoring type is determined. These first sensors are selected from their respective sensor groups, representing the main data acquisition points for their respective monitoring types. Once the first sensors for each type are determined, the system activates them according to the acquisition mode settings to ensure that the sensors can accurately and stably begin data acquisition. Subsequently, multiple first sensors work together to perform basic data acquisition on the environment within the computer room. Temperature sensors monitor temperature changes within the computer room, vibration sensors capture vibrations from equipment operation, smoke sensors detect potential smoke threats, and noise sensors assess noise levels within the computer room. The data collected by these sensors collectively constitutes the computer room's status data, providing crucial information for subsequent data processing and analysis.

[0024] S102. The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data based on the detection program to determine the danger signals in the computer room.

[0025] After receiving status data from the acquisition module, the data processing module first performs a series of preprocessing operations to improve data accuracy and usability, including data cleaning, compression, and conversion. After preprocessing, the module assigns a type identifier to each set of status data. These type identifiers correspond to the type of status data and provide reference information for subsequent data analysis. Next, the module selects the corresponding detection program from a pre-set detection program library based on the type identifier. These detection programs are specifically designed for particular types of data, and the module performs in-depth analysis of the preprocessed status data according to the selected program to determine if any danger signals exist in the data center. Once a danger signal is detected, the module immediately triggers an alarm mechanism, notifying relevant personnel to take timely measures to ensure the safe and stable operation of the data center.

[0026] In one embodiment, before analyzing the real-time acquired status data according to the detection procedure, the system acquires historical status data of the data center. This data records the data center's operation over a past period and contains rich information and hazard analysis experience. The data processing module divides the historical data into multi-dimensional historical datasets based on pre-defined types, such as temperature, vibration, smoke, and noise. Feature extraction is then performed on this multi-dimensional historical data. Feature extraction is a crucial step in data mining and machine learning, extracting key information useful for subsequent analysis from raw data. Through feature extraction, a feature set is obtained, containing various key features that describe the data center's status. Based on this feature set, the data processing module builds a status monitoring model for real-time monitoring and evaluation of the data center's status. This model can predict the data center's operating status and identify potential hazard signals based on the input status data. During model building, the module uses historical data to train and validate the model to ensure its accuracy and reliability. After the status monitoring model is built, the data processing module determines the detection procedure based on this model. The detection procedure is the concrete implementation of the model, containing algorithms for analyzing status data, identifying hazard signals, and rules for determining whether a hazard exists. During real-time data acquisition, the data processing module processes and analyzes the status data according to the instructions of the detection program to promptly detect and address potential hazards in the computer room.

[0027] In one embodiment, to improve processing flexibility and efficiency in practical applications of the status monitoring model, the model is split according to data type. Specifically, the data processing module divides the status monitoring model into multiple sub-status monitoring modules based on the type of status data. Each sub-status monitoring module focuses on processing a specific type of data and possesses corresponding analytical capabilities and detection rules. After real-time status data is collected and preprocessed, the data processing module first determines the type identifier corresponding to this data.

[0028] The data processing module determines the corresponding single or multiple sub-status monitoring modules based on the type identifier. In some cases, the status data may contain only one type of data, corresponding to only one sub-status monitoring module. In this case, the module directly calls the detection program in that sub-module for analysis. In other cases, the status data may contain multiple types of data, requiring multiple sub-status monitoring modules to work collaboratively to comprehensively assess the data center status. For cases corresponding to only one sub-status monitoring module, the data processing module directly analyzes the status data based on the detection program in that sub-module. These detection programs may include feature extraction methods, analysis algorithms, and hazard signal identification rules for specific data types. For cases requiring multiple sub-status monitoring modules to work collaboratively, the data processing module first calls the detection programs in each sub-module to perform preliminary analysis of the status data. Then, it comprehensively evaluates these preliminary analysis results to determine the overall operating status and potential hazard signals within the data center. During this process, the module may use a pre-set fusion algorithm to integrate the output results of different sub-modules and arrive at a final judgment and recommendation. In this way, the data processing module can process the status data within the data center more efficiently and accurately identify potential hazard signals, providing strong protection for the safe and stable operation of the data center.

[0029] S103. The data processing module generates a location request based on the danger signal and starts the acquisition module based on the location request, so that the acquisition module enters the location mode and determines the dangerous location in the computer room through the acquisition module in the location mode.

[0030] When the data processing module detects a hazard signal within the computer room using the status monitoring model, further action is required to pinpoint the hazard's exact location. First, the data processing module generates a location request based on the hazard signal. This request includes crucial information such as the type of hazard to be located, its potential impact range, and priority. Based on this location request, the data processing module activates the acquisition module, putting it into location mode. Location mode requires the acquisition module to monitor environmental changes within the computer room more precisely, paying particular attention to the specific location and related areas of the hazard signal.

[0031] In location mode, the data acquisition module utilizes all its internal sensor arrays to collect more intensive and precise data from the data center. These sensors, based on the indications in the location request, prioritize monitoring potentially hazardous areas and transmit the collected data to the data processing module in real time. This location process not only helps data center maintenance personnel quickly and accurately pinpoint hazards but also provides them with timely guidance, enabling them to rapidly take measures to eliminate dangers and ensure the safe and stable operation of the data center.

[0032] In one embodiment, after the acquisition module enters the positioning mode, it activates all sensors in the sensor group according to the positioning mode to locate the danger signal through all sensors, thereby obtaining the danger location.

[0033] In one embodiment, the specific processing flow for determining hazardous signals within a computer room first identifies the data type corresponding to the hazardous signal, such as temperature, vibration, smoke, and noise. Based on the data type, the corresponding sensor group to be activated can be determined; these sensor groups are specifically designed to detect this type of data. After determining the sensor group, all sensors within that group are activated to synchronously collect hazardous signals from multiple angles and dimensions to obtain more comprehensive and accurate data. Through synchronous collection, the location signals received by each sensor can be acquired, including key information such as signal emission time and signal retrieval time. Next, a location signal group can be constructed based on these location signals. Each location signal in the location signal group represents the correlation between the sensor and the location of the hazardous signal emission. By calculating the difference between the signal emission time and retrieval time, combined with the sensor's location information, the distance between each sensor and the hazardous signal source can be preliminarily estimated, thereby establishing a distance matrix.

[0034] Based on data from multiple sensor arrays, a corresponding spatial coordinate system is established for the data center. The type of spatial coordinate system depends on the specific layout of the data center and the configuration of the sensor arrays. For example, if the data center layout is relatively regular and the sensors are evenly distributed, a Cartesian coordinate system can be used; if the data center layout is complex, a polar coordinate system or other types of coordinate systems may be required. For another example, if the data type is smoke, a planar coordinate system is established using the data center ceiling, without considering the vertical height of the data center; if the data type is noise, a three-dimensional coordinate system is established using the three-dimensional space of the data center. After establishing the spatial coordinate system, the corresponding coordinate points are determined in the coordinate system based on the distance matrix calculated earlier. These coordinate points represent the estimates of the location of the hazard signal emitted by each sensor, determining the most probable location of the hazard signal, i.e., the danger location. The entire process, through precise data acquisition, spatial modeling, and data analysis, achieves rapid and accurate location of hazard signals within the data center, providing strong support for the safe operation and maintenance of the data center.

[0035] S104. The warning module acquires the danger signal and the danger location, and issues a warning based on the danger signal and the danger location.

[0036] Upon receiving a danger signal and its identified location, the early warning module immediately activates an alert mechanism. This module communicates the danger information to data center administrators through various means, including audible and visual alarms, SMS messages, and emails. The alert not only includes the specific type and severity of the danger signal but also clearly indicates its exact location, enabling administrators to respond quickly and take appropriate measures. This timely early warning mechanism helps ensure data center security, reduce potential losses, and improve overall operational efficiency.

[0037] In one embodiment, during the processing of hazard signals, the system determines the possible radiation range based on the type of hazard signal. This radiation range is an area potentially affected by the hazard, estimated based on historical data and professional expertise. For example, after a smoke hazard occurs in the computer room, the smoke will rapidly spread from its source. Based on the typical rate of smoke diffusion, the radiation range of this smoke hazard is pre-determined. Next, the system queries and determines the location information of all equipment in the computer room. This location information can be obtained through the equipment's built-in network positioning function, or it can be determined through the computer room layout diagram and records in the equipment management system. Based on this location information, the system can determine which equipment is located within the pre-determined hazard range. If any equipment is found to be within the hazard range, to prevent further damage or safety accidents, the system will immediately power off these devices via a remote controller. A remote controller is a device capable of remotely controlling the on / off status of equipment, allowing computer room administrators to quickly perform necessary operations on the equipment without being physically present. Through this process, the system can quickly and effectively protect the safety of equipment and personnel in the computer room when a hazard is detected.

[0038] like Figure 2 As shown in the figure, this application embodiment also provides an IoT-based data center equipment status monitoring device, applied in an IoT-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, and an early warning module. The device includes:

[0039] At least one processor; and,

[0040] A memory that is communicatively connected to at least one processor; wherein,

[0041] The memory stores instructions that can be executed by at least one processor, which enables an IoT-based data center equipment status monitoring device to perform the following:

[0042] The acquisition module obtains the status data in the computer room and sends the status data to a pre-set data processing module. The types of status data include, but are not limited to, temperature, vibration, smoke, and noise.

[0043] The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data based on the detection program to determine the danger signals in the computer room.

[0044] The data processing module generates a location request based on the danger signal and activates the acquisition module based on the location request to put the acquisition module into location mode. The acquisition module in location mode determines the dangerous location in the computer room.

[0045] The warning module acquires the danger signal and the danger location, and issues a warning based on the danger signal and the danger location.

[0046] This application also provides a non-volatile computer storage medium storing computer-executable instructions, applied in an Internet of Things-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, and an early warning module. The computer-executable instructions are configured as follows:

[0047] The acquisition module obtains the status data in the computer room and sends the status data to a pre-set data processing module. The types of status data include, but are not limited to, temperature, vibration, smoke, and noise.

[0048] The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data based on the detection program to determine the danger signals in the computer room.

[0049] The data processing module generates a location request based on the danger signal and activates the acquisition module based on the location request to put the acquisition module into location mode. The acquisition module in location mode determines the dangerous location in the computer room.

[0050] The warning module acquires the danger signal and the danger location, and issues a warning based on the danger signal and the danger location.

[0051] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0052] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0053] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0054] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0055] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0056] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0057] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0062] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0063] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0064] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0065] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for monitoring the status of data center equipment based on the Internet of Things, characterized in that, This method is applied in an IoT-based data center equipment status monitoring system, which includes a data acquisition module, a data processing module, and an early warning module. The acquisition module obtains the status data in the computer room and sends the status data to a pre-set data processing module. The types of status data include temperature, vibration, smoke, and noise. The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data based on the detection program to determine the danger signals in the computer room. The data processing module generates a location request based on the danger signal and activates the acquisition module based on the location request to put the acquisition module into location mode. The acquisition module in location mode determines the dangerous location in the computer room. The early warning module acquires the danger signal and the danger location, and issues a warning based on the danger signal and the danger location; The acquisition module includes a transmission device and multiple sensor groups. Each sensor group consists of multiple sensors. The types of sensor groups include temperature sensor groups, vibration sensor groups, smoke sensor groups, and noise sensor groups. The multiple sensor groups are set in different locations within the computer room. Before the acquisition module obtains the status data in the computer room, the method further includes: The acquisition module is initialized to enter the acquisition mode, thereby acquiring data from the computer room through the acquisition module in the acquisition mode to obtain the status data. The method further includes: collecting data from the computer room using the acquisition module in the acquisition mode; Identify the first sensor corresponding to the type, and activate multiple first sensors according to the acquisition mode; The status data is obtained by collecting data from the computer room using multiple first sensors. After the acquisition module enters the positioning mode, the method further includes: According to the positioning mode, all sensors in the sensor group are activated to locate the danger signal through all the sensors, thereby obtaining the danger location; Identifying hazardous locations within the computer room specifically includes: Determine the data type corresponding to the danger signal, determine the corresponding sensor group based on the data type, and activate all sensors in the sensor group; The location signal group is obtained by synchronously collecting data from all the sensors based on the danger signal. The location signal group includes multiple location signals, and each location signal includes the signal emission time and the signal retrieval time. The distance matrix between all the sensors and the emission location of the danger signal is determined based on the location signal group. A spatial coordinate system of a corresponding type is established for the computer room based on the multiple sensor groups, and a corresponding coordinate point is determined in the corresponding spatial coordinate system based on the distance matrix, so as to determine the dangerous location based on the coordinate point; The system also includes a remote controller, and the method further includes: The corresponding radiation range is determined according to the type, and the danger range is determined according to the danger location and the radiation range; Determine the location information of all equipment in the computer room, and determine whether any equipment is within the danger zone based on the location information; If any equipment is within the danger zone, the equipment will be powered off via the remote controller. Before analyzing the state data according to the detection procedure, the method further includes: The historical status data of the data center is obtained, and the historical status data is divided according to the type to obtain multi-dimensional historical data; Feature extraction is performed on the multi-dimensional historical data to obtain a feature set. A status monitoring model is established based on the feature set, and the detection procedure is determined based on the status monitoring model. The status monitoring model is split according to the type to obtain multiple sub-status monitoring modules; Determine the type identifier corresponding to the status data, and determine the corresponding single or multiple sub-status monitoring modules based on the type identifier; If the type identifier corresponds to a single sub-state monitoring module, then the detection procedure is determined based on the single sub-state monitoring module; If the type identifier corresponds to multiple sub-state monitoring modules, then the detection procedure is determined based on the multiple sub-state monitoring modules.

2. A data center equipment status monitoring device based on the Internet of Things, characterized in that, This system is applied in an IoT-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, and an early warning module. The equipment includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the IoT-based data center equipment status monitoring device to perform the following: The acquisition module obtains the status data in the computer room and sends the status data to a pre-set data processing module. The types of status data include temperature, vibration, smoke, and noise. The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data based on the detection program to determine the danger signals in the computer room. The data processing module generates a location request based on the danger signal and activates the acquisition module based on the location request to put the acquisition module into location mode. The acquisition module in location mode determines the dangerous location in the computer room. The early warning module acquires the danger signal and the danger location, and issues a warning based on the danger signal and the danger location; The acquisition module includes a transmission device and multiple sensor groups. Each sensor group consists of multiple sensors. The types of sensor groups include temperature sensor groups, vibration sensor groups, smoke sensor groups, and noise sensor groups. The multiple sensor groups are set in different locations within the computer room. Before the acquisition module obtains the status data within the computer room... The acquisition module is initialized to enter the acquisition mode, thereby acquiring data from the computer room through the acquisition module in the acquisition mode to obtain the status data. Data is collected from the computer room using the acquisition module in the acquisition mode. Identify the first sensor corresponding to the type, and activate multiple first sensors according to the acquisition mode; The status data is obtained by collecting data from the computer room using multiple first sensors. After the acquisition module enters the positioning mode According to the positioning mode, all sensors in the sensor group are activated to locate the danger signal through all the sensors, thereby obtaining the danger location; Identifying hazardous locations within the computer room specifically includes: Determine the data type corresponding to the danger signal, determine the corresponding sensor group based on the data type, and activate all sensors in the sensor group; The location signal group is obtained by synchronously collecting data from all the sensors based on the danger signal. The location signal group includes multiple location signals, and each location signal includes the signal emission time and the signal retrieval time. The distance matrix between all the sensors and the emission location of the danger signal is determined based on the location signal group. A spatial coordinate system of a corresponding type is established for the computer room based on the multiple sensor groups, and a corresponding coordinate point is determined in the corresponding spatial coordinate system based on the distance matrix, so as to determine the dangerous location based on the coordinate point; The system also includes a remote controller. The corresponding radiation range is determined according to the type, and the danger range is determined according to the danger location and the radiation range; Determine the location information of all equipment in the computer room, and determine whether any equipment is within the danger zone based on the location information; If any equipment is within the danger zone, the equipment will be powered off via the remote controller. Before analyzing the status data according to the detection procedure. The historical status data of the data center is obtained, and the historical status data is divided according to the type to obtain multi-dimensional historical data; Feature extraction is performed on the multi-dimensional historical data to obtain a feature set. A status monitoring model is established based on the feature set, and the detection procedure is determined based on the status monitoring model. The status monitoring model is split according to the type to obtain multiple sub-status monitoring modules; Determine the type identifier corresponding to the status data, and determine the corresponding single or multiple sub-status monitoring modules based on the type identifier; If the type identifier corresponds to a single sub-state monitoring module, then the detection procedure is determined based on the single sub-state monitoring module; If the type identifier corresponds to multiple sub-state monitoring modules, then the detection procedure is determined based on the multiple sub-state monitoring modules.

3. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, This system is applied in an IoT-based data center equipment status monitoring system. The system includes a data acquisition module, a data processing module, and an early warning module. The computer-executable instructions are set as follows: The acquisition module obtains the status data in the computer room and sends the status data to a pre-set data processing module. The types of status data include temperature, vibration, smoke, and noise. The data processing module preprocesses the status data, determines the type identifier corresponding to the preprocessed status data, determines the corresponding detection program based on the type identifier, and analyzes the status data based on the detection program to determine the danger signals in the computer room. The data processing module generates a location request based on the danger signal and activates the acquisition module based on the location request to put the acquisition module into location mode. The acquisition module in location mode determines the dangerous location in the computer room. The early warning module acquires the danger signal and the danger location, and issues a warning based on the danger signal and the danger location; The acquisition module includes a transmission device and multiple sensor groups. Each sensor group consists of multiple sensors. The types of sensor groups include temperature sensor groups, vibration sensor groups, smoke sensor groups, and noise sensor groups. The multiple sensor groups are set in different locations within the computer room. Before the acquisition module obtains the status data within the computer room... The acquisition module is initialized to enter the acquisition mode, thereby acquiring data from the computer room through the acquisition module in the acquisition mode to obtain the status data. Data is collected from the computer room using the acquisition module in the acquisition mode. Identify the first sensor corresponding to the type, and activate multiple first sensors according to the acquisition mode; The status data is obtained by collecting data from the computer room using multiple first sensors. After the acquisition module enters the positioning mode According to the positioning mode, all sensors in the sensor group are activated to locate the danger signal through all the sensors, thereby obtaining the danger location; Identifying hazardous locations within the computer room specifically includes: Determine the data type corresponding to the danger signal, determine the corresponding sensor group based on the data type, and activate all sensors in the sensor group; The location signal group is obtained by synchronously collecting data from all the sensors based on the danger signal. The location signal group includes multiple location signals, and each location signal includes the signal emission time and the signal retrieval time. The distance matrix between all the sensors and the emission location of the danger signal is determined based on the location signal group. A spatial coordinate system of a corresponding type is established for the computer room based on the multiple sensor groups, and a corresponding coordinate point is determined in the corresponding spatial coordinate system based on the distance matrix, so as to determine the dangerous location based on the coordinate point; The system also includes a remote controller. The corresponding radiation range is determined according to the type, and the danger range is determined according to the danger location and the radiation range; Determine the location information of all equipment in the computer room, and determine whether any equipment is within the danger zone based on the location information; If any equipment is within the danger zone, the equipment will be powered off via the remote controller. Before analyzing the status data according to the detection procedure. The historical status data of the data center is obtained, and the historical status data is divided according to the type to obtain multi-dimensional historical data; Feature extraction is performed on the multi-dimensional historical data to obtain a feature set. A status monitoring model is established based on the feature set, and the detection procedure is determined based on the status monitoring model. The status monitoring model is split according to the type to obtain multiple sub-status monitoring modules; Determine the type identifier corresponding to the status data, and determine the corresponding single or multiple sub-status monitoring modules based on the type identifier; If the type identifier corresponds to a single sub-state monitoring module, then the detection procedure is determined based on the single sub-state monitoring module; If the type identifier corresponds to multiple sub-state monitoring modules, then the detection procedure is determined based on the multiple sub-state monitoring modules.

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