CN114240155B - Method, device and computer equipment for evaluating health of equipment in computer room - Google Patents
Method, device and computer equipment for evaluating health of equipment in computer room Download PDFInfo
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Abstract
本申请涉及大数据技术领域，特别是涉及一种机房设备健康度的评估方法、装置、计算机设备、存储介质和计算机程序产品。所述方法包括：获取身份标识，身份标识表征已监控系统中注册成功。基于身份标识，从监控系统处获取与目标设备对应的多个类别的设备监控数据；多个类别的设备监控数据至少包括与目标设备对应的设备相关数据，以及目标设备相关的关联设备的关联设备数据，设备相关数据包括目标设备的运行状态数据。基于各设备监控数据分别进行异常判定，得到与各设备监控数据分别对应的异常判定结果。基于异常判定结果确定目标设备的健康等级，健康等级用于表征目标设备的健康程度，大大增加了对运行中目标设备监控的有效性。
The present application relates to the field of big data technology, and in particular to a method, device, computer equipment, storage medium and computer program product for evaluating the health of equipment in a computer room. The method comprises: obtaining an identity identifier, the identity identifier representing successful registration in the monitoring system. Based on the identity identifier, multiple categories of equipment monitoring data corresponding to the target device are obtained from the monitoring system; the multiple categories of equipment monitoring data at least include equipment-related data corresponding to the target device, and associated equipment data of associated equipment related to the target device, and the equipment-related data include operating status data of the target device. Based on the monitoring data of each device, an abnormality determination is performed respectively to obtain an abnormality determination result corresponding to the monitoring data of each device. Based on the abnormality determination result, the health level of the target device is determined, and the health level is used to characterize the health of the target device, which greatly increases the effectiveness of monitoring the target device in operation.
Description
Technical Field
The application relates to the technical field of big data, in particular to a method and a device for evaluating the health degree of equipment in a machine room, computer equipment, a storage medium and a computer program product.
Background
With the development of big data technology, the number of data and the data scale running in the data center tend to rise year by year, which correspondingly increases the workload and the operation and maintenance requirements of the operation and maintenance management of the data center. During operation of a data center, a DCIM (DATA CENTER Infrastructure management ) system is often required to monitor the data center.
The DCIM system can monitor alarm information related to the state of the equipment only when the equipment fails and display the alarm information to a technician, so that the technician can check and maintain the failed equipment. Therefore, the DCIM system cannot evaluate the state of the running device, i.e., cannot evaluate the health of the running device, and thus, it is difficult to effectively monitor the running device, i.e., there is a problem that the effectiveness of monitoring the running device is low.
Disclosure of Invention
Based on this, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for evaluating the health of equipment in a machine room in order to solve the above-mentioned technical problems.
In a first aspect, the application provides a method for evaluating the health degree of equipment in a machine room. The method comprises the following steps:
Acquiring an identity, wherein the identity represents successful registration in a monitored system;
acquiring a plurality of categories of equipment monitoring data corresponding to target equipment from the monitoring system based on the identity, wherein the plurality of categories of equipment monitoring data at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment;
respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data;
and determining a health grade of the target equipment based on the abnormality determination result, wherein the health grade is used for representing the health degree of the target equipment.
In a second aspect, the application further provides a device for evaluating the health degree of the equipment in the machine room. The device comprises:
the first acquisition module is used for acquiring an identity, and the identity characterizes successful registration in the monitored system;
The system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring equipment monitoring data of a plurality of categories corresponding to target equipment from a monitoring system based on the identity, the equipment monitoring data of the plurality of categories at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment;
the judging module is used for respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data;
and the determining module is used for determining the health grade of the target equipment based on the abnormality determination result, wherein the health grade is used for representing the health degree of the target equipment.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Acquiring an identity, wherein the identity represents successful registration in a monitored system;
acquiring a plurality of categories of equipment monitoring data corresponding to target equipment from the monitoring system based on the identity, wherein the plurality of categories of equipment monitoring data at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment;
respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data;
and determining a health grade of the target equipment based on the abnormality determination result, wherein the health grade is used for representing the health degree of the target equipment.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring an identity, wherein the identity represents successful registration in a monitored system;
acquiring a plurality of categories of equipment monitoring data corresponding to target equipment from the monitoring system based on the identity, wherein the plurality of categories of equipment monitoring data at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment;
respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data;
and determining a health grade of the target equipment based on the abnormality determination result, wherein the health grade is used for representing the health degree of the target equipment.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
Acquiring an identity, wherein the identity represents successful registration in a monitored system;
acquiring a plurality of categories of equipment monitoring data corresponding to target equipment from the monitoring system based on the identity, wherein the plurality of categories of equipment monitoring data at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment;
respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data;
and determining a health grade of the target equipment based on the abnormality determination result, wherein the health grade is used for representing the health degree of the target equipment.
The method, the device, the computer equipment, the storage medium and the computer program product for evaluating the health degree of the equipment in the machine room acquire the identity mark, and the identity mark represents successful registration in the monitored system. Based on the identity, a plurality of categories of device monitoring data corresponding to the target device are acquired from the monitoring system. The plurality of classes of device monitoring data includes at least device-related data corresponding to the target device, and associated device data of associated devices associated with the target device, the device-related data including operational status data of the target device. And respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data. In this way, based on a plurality of abnormality determination results corresponding to the target device and abnormality determination results corresponding to the target device-associated device centering around the target device, a more comprehensive abnormality determination result corresponding to the operating target device can be obtained. A health level of the target device is determined based on the anomaly determination, the health level being used to characterize the health of the target device. Therefore, based on a more comprehensive abnormality judgment result, the target equipment in operation can be monitored more effectively, and the effectiveness of monitoring the target equipment in operation is greatly improved.
Drawings
FIG. 1 is an application environment diagram of a method for evaluating the health of equipment in a machine room in one embodiment;
FIG. 2 is a flow chart of a method for evaluating the health of equipment in a machine room in one embodiment;
FIG. 3 is a flowchart illustrating a step of determining an abnormality determination result according to an embodiment;
Fig. 4 is a flowchart of a method for evaluating the health of equipment in a machine room according to another embodiment;
FIG. 5 is a block diagram of an apparatus for evaluating health of equipment in a machine room in one embodiment;
Fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for evaluating the health degree of the equipment in the machine room, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. Wherein the monitoring system 102 communicates with the computer device 104 via a network. The data storage system may store data that computer device 104 needs to process. The data storage system may be integrated on the computer device 104 or may be located on a cloud or other network server. The computer device 104 obtains an identity that characterizes the success of the registration in the monitored system 102. Based on the identity, a plurality of categories of device monitoring data corresponding to the target device are obtained from the monitoring system 102. The plurality of classes of device monitoring data includes at least device-related data corresponding to the target device, and associated device data of associated devices associated with the target device, the device-related data including operational status data of the target device. Based on the respective device monitoring data, abnormality determination is performed, and the computer device 104 obtains abnormality determination results corresponding to the respective device monitoring data. The computer device 104 determines a health level of the target device based on the anomaly determination, the health level being used to characterize the health of the target device. Wherein the monitoring system 102 may be a terminal. The terminal can be, but not limited to, various personal computers, notebook computers, tablet computers, and internet of things devices, which can be smart televisions, intelligent air conditioners, and the like. The computer device 104 may be a terminal or a server, and the server may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a method for evaluating the health degree of equipment in a machine room is provided, and the method is applied to the computer equipment in fig. 1 for illustration, and includes the following steps:
step S202, an identity is obtained, and the identity characterization is successfully registered in the monitoring system.
The monitoring system is a DCIM system (DATA CENTER Infrastructure management ), and the DCIM system is used for carrying out centralized management such as centralized monitoring, capacity planning and the like on the data center equipment by combining information technology and equipment management.
Specifically, the computer device obtains an identity sent by the monitoring system. For example, the identity may be KeyID identities corresponding to the Http protocol.
Step S204, based on the identity, acquiring a plurality of types of device monitoring data corresponding to the target device from the monitoring system. The plurality of classes of device monitoring data includes at least device-related data corresponding to the target device, and associated device data of associated devices associated with the target device, the device-related data including operational status data of the target device.
Specifically, based on the identity, the computer device sends a plurality of interface requests carrying the identity to the monitoring system. And if the monitoring system determines that the identity is effective, responding to a plurality of interface requests, and acquiring a plurality of types of equipment monitoring data corresponding to the target equipment from the monitoring system. The plurality of classes of device monitoring data includes at least device-related data corresponding to the target device, and associated device data of associated devices associated with the target device, the device-related data including operational status data of the target device. Wherein the device-related data includes a plurality of different kinds of operation state data. Such as power distribution cabinet voltage operational status data, power distribution cabinet current operational status data, power distribution cabinet frequency operational status data, and the like.
Step S206, respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data.
Specifically, the computer device performs corresponding abnormality determination on each piece of equipment related data through an mining algorithm based on each piece of equipment related data corresponding to the target equipment, and obtains abnormality determination results corresponding to each piece of equipment related data. And the computer equipment carries out corresponding abnormality judgment on the associated equipment data through an excavation algorithm based on the associated equipment data corresponding to the target equipment, and obtains abnormality judgment results corresponding to the associated equipment data respectively.
The mining algorithm can be a Mahout algorithm, the algorithm is a distributed computing framework based on Hadoop machine learning and data mining, a classical algorithm for mining a large amount of data is packaged in the framework, and massive data can be analyzed conveniently and rapidly. The associated equipment data may be data corresponding to equipment such as an incoming line, a bypass, a UPS, and a parallel machine of a UPS (Uninterruptible Power System, uninterruptible power supply).
Step S208, determining a health level of the target device based on the abnormality determination result, the health level being used to characterize the health degree of the target device.
Specifically, the computer device obtains the abnormality determination results corresponding to the respective associated device data, and determines the health level of the target device based on the respective abnormality determination results. And each abnormal judgment result can be characterized by characters or words, so that whether the result is abnormal or not is not limited.
In the method for evaluating the health degree of the equipment in the machine room, the identity is obtained, and the identity characterizes successful registration in the monitored system. Based on the identity, a plurality of categories of device monitoring data corresponding to the target device are acquired from the monitoring system. The plurality of classes of device monitoring data includes at least device-related data corresponding to the target device, and associated device data of associated devices associated with the target device, the device-related data including operational status data of the target device. And respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data. In this way, based on a plurality of abnormality determination results corresponding to the target device and abnormality determination results corresponding to the target device-associated device centering around the target device, a more comprehensive abnormality determination result corresponding to the operating target device can be obtained. A health level of the target device is determined based on the anomaly determination, the health level being used to characterize the health of the target device. Therefore, based on a more comprehensive abnormality judgment result, the target equipment in operation can be monitored more effectively, and the effectiveness of monitoring the target equipment in operation is greatly improved.
In one embodiment, as shown in fig. 3, the device related data further includes cable status data, life cycle data, and failure frequency data of the target device, the abnormality determination results include a first determination result, a second determination result, a third determination result, a fourth determination result, and a fifth determination result, and the abnormality determination is performed based on each device monitoring data, to obtain an abnormality determination result corresponding to each device monitoring data, respectively, including:
Step S302, comparing the operation state data of the target equipment with corresponding operation threshold values to obtain a first judgment result.
Specifically, the computer device obtains operating state data of the target device in different operating states. The computer equipment compares the operation state data in the corresponding operation state with the corresponding operation threshold value to obtain a first state judgment result of the corresponding operation state. The computer device determines a first determination result based on the plurality of first state determination results.
For example, a computer device obtains power distribution cabinet voltage operational status data, power distribution cabinet current operational status data, and power distribution cabinet frequency operational status data. And the computer equipment compares the voltage operation state data of the power distribution cabinet with a voltage operation threshold value to obtain a first state judgment result of the voltage operation state. And comparing the current operation state data of the power distribution cabinet with a current operation threshold value by the computer equipment to obtain a first state judgment result of the current operation state. And comparing the frequency operation state data of the power distribution cabinet with a frequency operation threshold value by the computer equipment to obtain a first state judgment result of the frequency operation state. The computer device determines a first determination result based on the first state determination results for the respective operating states.
Step S304, comparing the sensing temperature in the cable status data with the reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second determination result based on the sensing temperature result and the partial discharge detection result.
Specifically, the computer device obtains a sensing temperature of the cable condition data inside the target device, and compares the sensing temperature with a reference temperature to obtain a sensing temperature result. The sensing temperature result is used for representing whether the sensing temperature of the cable is abnormal or not. And the computer equipment performs external partial discharge detection on the cable to obtain a partial discharge detection result. The partial discharge detection result is used for representing whether the partial discharge is abnormal or not. The computer equipment synthesizes the sensing temperature result and the partial discharge detection result to obtain a comprehensive result. The computer device determines a second decision result based on the composite result.
Step S306, comparing the life cycle data with the reference cycle to obtain a third determination result.
Specifically, the computer device obtains life cycle data of the operation of the target device, and compares the life cycle data with a reference cycle to obtain a third determination result. The third determination is used to characterize whether the target device lifecycle data is abnormal.
Step S308, comparing the fault frequency data with a reference fault frequency to obtain a fourth judging result.
Specifically, the computer device obtains the fault frequency data of the target device, and compares the fault frequency data with a reference fault frequency to obtain a fourth determination result. The fourth determination result is used for representing whether the failure frequency of the target equipment is abnormal.
Step S310, determining a fifth determination result based on whether the alarm information exists in the associated device data.
Specifically, the computer device acquires the associated device data and determines whether alarm information exists in the associated device data. The computer device determines a fifth determination result based on the determination result. If the alarm information exists, the computer equipment determines that the fifth judging result is abnormal related equipment data, and if the alarm information does not exist, the computer equipment determines that the fifth alarm information is normal related equipment data.
In this embodiment, the operation state data of the target device is compared with a corresponding operation threshold value, so as to obtain a first determination result. And comparing the sensing temperature in the cable condition data with a reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judging result based on the sensing temperature result and the partial discharge detection result. And comparing the life cycle data with a reference cycle to obtain a third judging result. And comparing the fault frequency data with the reference fault frequency to obtain a fourth judging result. And determining a fifth judging result based on whether alarm information exists in the associated equipment data. In this way, based on the first determination result, the second determination result, the third determination result, and the fourth determination result corresponding to the target device, and the fifth determination result of the associated device associated with the target device, a more comprehensive abnormality determination result corresponding to the target device in operation can be obtained, greatly increasing the reliability of the evaluation of the target device.
In one embodiment, the determining the health level of the target device based on the abnormality determination result includes directly determining the health level of the target device as a device pre-warning level if the first determination result is abnormal. If the first judging result is normal and the second judging result is abnormal, determining that the health grade of the target equipment is the equipment early warning grade.
Specifically, if the first determination result is abnormal, the health level of the target device is directly determined to be the device early warning level. If the first judging result is normal, the computer equipment determines a second judging result, and if the first judging result is normal and the second judging result is abnormal, the health grade of the target equipment is determined to be the equipment early warning grade.
In this embodiment, if the first determination result is abnormal, the health level of the target device is directly determined to be the device early warning level. Thus, the monitoring level of the target equipment can be directly and rapidly determined to be the equipment early warning level. If the first judging result is normal and the second judging result is abnormal, determining that the health grade of the target equipment is the equipment early warning grade. Therefore, whether the first judging result represents abnormality or not can directly and rapidly determine the equipment early warning grade, so that the health condition of the running equipment can be timely and rapidly judged, and the judging efficiency and accuracy are improved.
In one embodiment, the determining the health level of the target device based on the abnormal determination result includes determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is abnormal, and determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is abnormal.
Specifically, if the first determination result is normal, the computer device determines a second determination result, and if the first determination result is normal and the second determination result is normal, the computer device determines a third determination result. And if the first judging result is normal, the second judging result is normal, and the third judging result is abnormal, determining that the health grade of the target equipment is the equipment attention grade. If the first judgment result is normal, the second judgment result is normal, and the third judgment result is normal, the computer equipment determines a fourth judgment result. And if the first judging result is normal, the second judging result is normal, the third judging result is normal, and the fourth judging result is abnormal, determining that the monitoring level of the target equipment is the equipment attention level. If the first judgment result is normal, the second judgment result is normal, the third judgment result is normal, and the fourth judgment result is normal, the computer equipment determines a fifth judgment result. And if the first judging result is normal, the second judging result is normal, the third judging result is normal, the fourth judging result is normal, and the fifth judging result is abnormal, determining that the monitoring level of the target equipment is the equipment attention level.
In the present embodiment, the determination that the target device health level is the device attention level is determined based on the results of each of the third determination result, the fourth determination result, and the fifth determination result on the basis of the first determination result being normal and the second determination result being normal. The health level of the target equipment is further determined on the premise of ensuring that the running state of the target equipment is normal, namely, on the premise of ensuring that the target equipment is not the health level of equipment early warning, so that the health level of the target equipment can be followed in real time, and the target equipment can be timely and effectively detected, so that the condition that the health level of the target equipment is converted from the equipment attention level to the equipment early warning is avoided.
In one embodiment, the determining the health level of the target device based on the abnormal determination result includes determining the health level of the target device as a device health level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is normal.
The first judgment result, the second judgment result, the third judgment result and the fourth judgment result are all judgment results corresponding to the equipment related data of the running target equipment. The fifth determination result is a determination result corresponding to associated device data, which is data corresponding to associated devices related to the running target device.
Specifically, based on each determination result, if the first determination result is normal and the second determination result is normal, the computer device determines a third determination result, and if the first determination result is normal and the second determination result is normal and the third determination result is normal, the computer device determines a fourth determination result. If the first judgment result is normal, the second judgment result is normal, the third judgment result is normal, and the fourth judgment result is normal, the computer equipment determines a fifth judgment result. If the first determination result is normal, the second determination result is normal, the third determination result is normal, the fourth determination result is normal, and the fifth determination result is normal, the computer device determines that the health level of the target device is a device health level.
In this embodiment, the determination that the target device health level is the device health level is based on whether the first determination result is normal and the second determination result is normal, and whether the third determination result, the fourth determination result, and the fifth determination result are all normal is sequentially determined. If the third judging result, the fourth judging result and the fifth judging result are all normal in sequence, the equipment health grade is directly determined. That is, the device health level can reflect the situation that both the device-related data and the associated device data are normal, that is, the health degree of the target device in operation can be accurately and clearly determined to be healthy.
In one embodiment, the obtaining the identity comprises initiating a registration request to the monitoring system, the registration request being used to instruct the monitoring system to perform identity verification, and registering after the identity verification is passed. And after successful registration, receiving the identity identification fed back by the monitoring system.
Specifically, the computer device sends a registration request to the monitoring system, and if the monitoring system verifies that the registration is successful, the computer device receives the identity sent by the monitoring system. The registration request is used for indicating the monitoring system to perform identity verification, and registering is performed after the identity verification is passed. The registration may be identity registration or account registration. For example, the computer device sends a registration request to the DCIM system, and if the DCIM system verifies that the registration is successful, the computer device obtains the identity of KeyID sent by the DCIM system.
In this embodiment, a registration request is initiated to the monitoring system, where the registration request is used to instruct the monitoring system to perform authentication, and after the authentication passes, registration is performed. And after successful registration, receiving the identity identification fed back by the monitoring system. Therefore, based on the identity, the safety of data transmission can be ensured when the monitoring system is in data docking.
In order to better understand the technical solution of the present application, a more detailed embodiment is provided for describing, as shown in fig. 4, that the computer device sends a registration request to the monitoring system, and if the monitoring system verifies that the registration is successful, the computer device receives the identity sent by the monitoring system. Based on the identity, the computer device sends a plurality of interface requests carrying the identity to the monitoring system. And if the monitoring system determines that the identity is effective, responding to a plurality of interface requests, and acquiring a plurality of types of equipment monitoring data corresponding to the target equipment from the monitoring system. The computer device performs mining analysis on the monitoring data of each device based on a Mahout algorithm. Specifically, the computer compares each operation state data (i.e., operation parameters) in the target device in operation with a corresponding threshold value based on the algorithm to obtain each first state judgment result, and determines a first judgment result based on a plurality of first state judgment results. If the first determination result is abnormal (i.e. abnormal), determining the health level of the target device as the device early warning. If the first judging result is normal, the computer equipment acquires the sensing temperature of the cable condition data in the target equipment, and compares the sensing temperature with the reference temperature to obtain a sensing temperature result. The sensing temperature result is used for representing whether the sensing temperature of the cable is abnormal or not. And the computer equipment performs external partial discharge detection on the cable to obtain a partial discharge detection result. The partial discharge detection result is used for representing whether the partial discharge is abnormal or not. The computer equipment synthesizes the sensing temperature result and the partial discharge detection result to obtain a comprehensive result. The computer device determines a second decision result based on the composite result. If the second determination result is abnormal (i.e. abnormal), the computer equipment determines that the health grade of the target equipment is equipment early warning. If the second judging result is normal, the computer equipment acquires the life cycle data of the operation of the target equipment, and compares the life cycle data with a reference cycle to obtain a third judging result. The third determination is used to characterize whether the target device lifecycle data is abnormal. If the third determination result is abnormal (i.e., abnormal), the computer device determines that the health level of the target device is a device concern. If the third judging result is normal, the computer equipment acquires the fault frequency data of the target equipment, and compares the fault frequency data with the reference fault frequency to obtain a fourth judging result. The fourth determination result is used for representing whether the failure frequency of the target equipment is abnormal. If the fourth determination result is abnormal (i.e., abnormal), the computer device determines that the health level of the target device is a device concern. If the fourth judging result is normal, the computer equipment acquires the associated equipment data and judges whether alarm information exists in the associated equipment data. the computer device determines a fifth determination result based on the determination result. If the alarm information exists, the computer equipment determines that the fifth judging result is abnormal in the related equipment data, and the computer equipment determines that the health grade of the target equipment is the equipment attention. If the alarm information does not exist, the computer equipment determines that the fifth alarm information is normal in the data of the associated equipment, and the computer equipment determines that the health grade of the target equipment is equipment health.
In this embodiment, based on the identity, a plurality of types of device monitoring data corresponding to the target device are obtained from the monitoring system. The plurality of classes of device monitoring data includes at least device-related data corresponding to the target device, and associated device data of associated devices associated with the target device, the device-related data including operational status data of the target device. And respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data. In this way, based on a plurality of abnormality determination results corresponding to the target device and abnormality determination results corresponding to the target device-associated device centering around the target device, a more comprehensive abnormality determination result corresponding to the operating target device can be obtained. A health level of the target device is determined based on the anomaly determination, the health level being used to characterize the health of the target device. Therefore, based on a more comprehensive abnormality judgment result, the target equipment in operation can be monitored more effectively, and the effectiveness of monitoring the target equipment in operation is greatly improved. In addition, the intelligent degree of the monitoring system is further improved while the comprehensive monitoring of the equipment in the machine room is realized, so that the health management of the data center by technicians is facilitated, the control right of the technicians on the equipment of the data center is greatly improved, and a new approach is provided for the operation and maintenance management of the ultra-large data center.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an evaluation device for the machine room equipment health degree, which is used for realizing the evaluation method for the machine room equipment health degree. The implementation scheme of the solution provided by the device is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the device for evaluating the health degree of one or more machine room devices provided below can be referred to the limitation of the method for evaluating the health degree of the machine room devices hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 5, there is provided an apparatus for evaluating the health of equipment in a machine room, where the apparatus 500 includes a first obtaining module 502, a second obtaining module 504, a determining module 506, and a determining module 508, where:
the first obtaining module 502 is configured to obtain an identity, where the identity characterizes successful registration in the monitored system.
And the second obtaining module 504 is configured to obtain, from the monitoring system, a plurality of types of device monitoring data corresponding to the target device based on the identity, where the plurality of types of device monitoring data at least include device-related data corresponding to the target device and associated device data of associated devices related to the target device, and the device-related data includes operation state data of the target device.
And a determining module 506, configured to perform an anomaly determination based on each piece of equipment monitoring data, and obtain an anomaly determination result corresponding to each piece of equipment monitoring data.
A determining module 508 is configured to determine a health level of the target device based on the anomaly determination result, where the health level is used to characterize a health level of the target device.
In one embodiment, the determining module 506 is configured to compare the operation status data of the target device with a corresponding operation threshold value to obtain a first determination result. And comparing the sensing temperature in the cable condition data with a reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judging result based on the sensing temperature result and the partial discharge detection result. And comparing the life cycle data with a reference cycle to obtain a third judging result. And comparing the fault frequency data with the reference fault frequency to obtain a fourth judging result. And determining a fifth judging result based on whether alarm information exists in the associated equipment data.
In one embodiment, the determining module 508 is configured to directly determine that the health level of the target device is a device early warning level if the first determination result is abnormal. If the first judging result is normal and the second judging result is abnormal, determining that the health grade of the target equipment is the equipment early warning grade.
In one embodiment, the determining module 508 is configured to determine that the health level of the target device is the device attention level if the first determination result is normal, the second determination result is normal, and the third determination result is abnormal. And if the first judging result is normal, the second judging result is normal, the third judging result is normal, and the fourth judging result is abnormal, determining that the health grade of the target equipment is the equipment attention grade. And if the first judging result is normal, the second judging result is normal, the third judging result is normal, the fourth judging result is normal, and the fifth judging result is abnormal, determining that the health grade of the target equipment is the equipment attention grade.
In one embodiment, the determining module 508 is configured to determine that the health level of the target device is the device health level if the first determination result is normal, the second determination result is normal, the third determination result is normal, the fourth determination result is normal, and the fifth determination result is normal.
In one embodiment, the first obtaining module 502 is configured to initiate a registration request to the monitoring system, where the registration request is used to instruct the monitoring system to perform authentication, and perform registration after the authentication passes. And after successful registration, receiving the identity identification fed back by the monitoring system.
All or part of each module in the evaluation device for the health degree of the equipment in the machine room can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing evaluation data of the health degree of the equipment in the machine room. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for evaluating the health of equipment in a machine room.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided that includes a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of obtaining an identity that characterizes a successful registration in a monitoring system. And acquiring a plurality of types of equipment monitoring data corresponding to the target equipment from the monitoring system based on the identity mark, wherein the equipment monitoring data of the plurality of types at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment. And respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data. A health level of the target device is determined based on the anomaly determination, the health level being used to characterize the health of the target device.
In one embodiment, the processor when executing the computer program further performs the step of comparing the operational status data of the target device with a corresponding operational threshold to obtain a first determination result. And comparing the sensing temperature in the cable condition data with a reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judging result based on the sensing temperature result and the partial discharge detection result. And comparing the life cycle data with a reference cycle to obtain a third judging result. And comparing the fault frequency data with the reference fault frequency to obtain a fourth judging result. And determining a fifth judging result based on whether alarm information exists in the associated equipment data.
In one embodiment, the processor when executing the computer program further performs the step of directly determining that the health level of the target device is a device pre-warning level if the first determination result is abnormal. If the first judging result is normal and the second judging result is abnormal, determining that the health grade of the target equipment is the equipment early warning grade.
In one embodiment, the processor when executing the computer program further performs the steps of determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is abnormal, determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is abnormal, and determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is abnormal.
In one embodiment, the processor when executing the computer program further performs the step of determining that the health level of the target device is a device health level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is normal.
In one embodiment, the processor when executing the computer program further performs the step of initiating a registration request to the monitoring system, the registration request being for instructing the monitoring system to perform authentication, and registering after the authentication is passed. And after successful registration, receiving the identity identification fed back by the monitoring system.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor performs the steps of obtaining an identity characterizing a successful registration in a monitoring system. And acquiring a plurality of types of equipment monitoring data corresponding to the target equipment from the monitoring system based on the identity mark, wherein the equipment monitoring data of the plurality of types at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment. And respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data. A health level of the target device is determined based on the anomaly determination, the health level being used to characterize the health of the target device.
In an embodiment the computer program when executed by the processor further realizes the step of comparing the operational status data of the target device with a corresponding operational threshold value resulting in a first decision result. And comparing the sensing temperature in the cable condition data with a reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judging result based on the sensing temperature result and the partial discharge detection result. And comparing the life cycle data with a reference cycle to obtain a third judging result. And comparing the fault frequency data with the reference fault frequency to obtain a fourth judging result. And determining a fifth judging result based on whether alarm information exists in the associated equipment data.
In one embodiment, the computer program when executed by the processor further performs the step of directly determining that the health level of the target device is a device pre-warning level if the first determination is abnormal. If the first judging result is normal and the second judging result is abnormal, determining that the health grade of the target equipment is the equipment early warning grade.
In one embodiment, the computer program when executed by the processor further performs the steps of determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is abnormal, determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is abnormal, and determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is abnormal.
In one embodiment, the computer program when executed by the processor further performs the step of determining that the health level of the target device is a device health level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is normal.
In one embodiment, the computer program when executed by the processor further performs the step of initiating a registration request to the monitoring system, the registration request being for instructing the monitoring system to perform authentication, and registering after the authentication is passed. And after successful registration, receiving the identity identification fed back by the monitoring system.
In one embodiment, a computer program product is provided that includes a computer program that when executed by a processor performs the steps of obtaining an identity that characterizes a successful registration in a monitoring system. And acquiring a plurality of types of equipment monitoring data corresponding to the target equipment from the monitoring system based on the identity mark, wherein the equipment monitoring data of the plurality of types at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data of the target equipment. And respectively carrying out abnormality judgment based on the equipment monitoring data to obtain abnormality judgment results respectively corresponding to the equipment monitoring data. A health level of the target device is determined based on the anomaly determination, the health level being used to characterize the health of the target device.
In an embodiment the computer program when executed by the processor further realizes the step of comparing the operational status data of the target device with a corresponding operational threshold value resulting in a first decision result. And comparing the sensing temperature in the cable condition data with a reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judging result based on the sensing temperature result and the partial discharge detection result. And comparing the life cycle data with a reference cycle to obtain a third judging result. And comparing the fault frequency data with the reference fault frequency to obtain a fourth judging result. And determining a fifth judging result based on whether alarm information exists in the associated equipment data.
In one embodiment, the computer program when executed by the processor further performs the step of directly determining that the health level of the target device is a device pre-warning level if the first determination is abnormal. If the first judging result is normal and the second judging result is abnormal, determining that the health grade of the target equipment is the equipment early warning grade.
In one embodiment, the computer program when executed by the processor further performs the steps of determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is abnormal, determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is abnormal, and determining that the health level of the target device is a device attention level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is abnormal.
In one embodiment, the computer program when executed by the processor further performs the step of determining that the health level of the target device is a device health level if the first determination result is normal and the second determination result is normal and the third determination result is normal and the fourth determination result is normal and the fifth determination result is normal.
In one embodiment, the computer program when executed by the processor further performs the step of initiating a registration request to the monitoring system, the registration request being for instructing the monitoring system to perform authentication, and registering after the authentication is passed. And after successful registration, receiving the identity identification fed back by the monitoring system.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. The method for evaluating the health degree of the equipment in the machine room is characterized by comprising the following steps of:
acquiring an identity, wherein the identity characterization is successfully registered in a monitoring system;
Acquiring a plurality of types of equipment monitoring data corresponding to target equipment from the monitoring system based on the identity mark, wherein the plurality of types of equipment monitoring data at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data, cable condition data, life cycle data and failure frequency data of the target equipment;
Comparing the running state data of the target equipment with corresponding running threshold values to obtain a first judging result;
Comparing the sensing temperature in the cable condition data with a reference temperature to obtain a sensing temperature result, performing external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judgment result based on the sensing temperature result and the partial discharge detection result;
comparing the life cycle data with a reference cycle to obtain a third judging result;
Comparing the fault frequency data with a reference fault frequency to obtain a fourth judging result;
determining a fifth judging result based on whether alarm information exists in the associated equipment data;
the health grade of the target device is determined based on an abnormal judgment result, wherein the health grade is used for representing the health degree of the target device, and the abnormal judgment result comprises the first judgment result, the second judgment result, the third judgment result, the fourth judgment result and the fifth judgment result.
2. The method of claim 1, wherein the determining the health level of the target device based on the anomaly determination result comprises:
if the first judging result is abnormal, directly determining that the health grade of the target equipment is an equipment early warning grade;
and if the first judging result is normal and the second judging result is abnormal, determining the health grade of the target equipment as the equipment early warning grade.
3. The method of claim 1, wherein the determining the health level of the target device based on the anomaly determination result comprises:
if the first judging result is normal, the second judging result is normal, and the third judging result is abnormal, determining that the health grade of the target equipment is the equipment attention grade;
If the first judgment result is normal, the second judgment result is normal, the third judgment result is normal, and the fourth judgment result is abnormal, determining that the health level of the target device is a device attention level;
And if the first judgment result is normal, the second judgment result is normal, the third judgment result is normal, the fourth judgment result is normal, and the fifth judgment result is abnormal, determining that the health grade of the target device is a device attention grade.
4. The method of claim 1, wherein the determining the health level of the target device based on the anomaly determination result comprises:
and if the first judging result is normal, the second judging result is normal, the third judging result is normal, the fourth judging result is normal, and the fifth judging result is normal, determining that the health grade of the target equipment is the equipment health grade.
5. The method according to any one of claims 1 to 4, wherein the obtaining an identity comprises:
Initiating a registration request to the monitoring system, wherein the registration request is used for indicating the monitoring system to perform identity verification, and registering after the identity verification is passed;
and after successful registration, receiving the identity identification fed back by the monitoring system.
6. An evaluation device for the health degree of equipment in a machine room, which is characterized by comprising:
the first acquisition module is used for acquiring an identity, and the identity characterizes successful registration in the monitored system;
The system comprises a first acquisition module, a second acquisition module and a monitoring system, wherein the first acquisition module is used for acquiring equipment monitoring data of a plurality of categories corresponding to target equipment from the monitoring system based on the identity, the equipment monitoring data of the plurality of categories at least comprise equipment related data corresponding to the target equipment and associated equipment data of associated equipment related to the target equipment, and the equipment related data comprise running state data, cable condition data, life cycle data and failure frequency data of the target equipment;
The system comprises a judging module, a third judging module, a fourth judging module, a fifth judging module, a third judging module, a fourth judging module, a third judging module and a fourth judging module, wherein the judging module is used for comparing the running state data of the target equipment with the corresponding running threshold value to obtain a first judging result, comparing the sensing temperature in the cable condition data with the reference temperature to obtain a sensing temperature result, carrying out external partial discharge detection on the cable to obtain a partial discharge detection result, and determining a second judging result based on the sensing temperature result and the partial discharge detection result;
The determining module is used for determining the health grade of the target equipment based on an abnormal judging result, wherein the health grade is used for representing the health degree of the target equipment, and the abnormal judging result comprises the first judging result, the second judging result, the third judging result, the fourth judging result and the fifth judging result.
7. The apparatus of claim 6, wherein the first obtaining module is further configured to initiate a registration request to the monitoring system, the registration request being configured to instruct the monitoring system to perform authentication, perform registration after the authentication is passed, and receive an identity fed back by the monitoring system after the registration is successful.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.
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