CN113835387A - Operation and maintenance management method, system and medium - Google Patents
Operation and maintenance management method, system and medium Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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- H—ELECTRICITY
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Abstract
The embodiment of the invention discloses an operation and maintenance management method, a system and a medium, wherein the method comprises the following steps: collecting service data and alarm data based on a data acquisition interface; obtaining predicted service data based on an alarm prediction model; comparing the predicted service data with the service data based on a service alarm model, and generating an alarm if the predicted service data exceeds a set threshold; based on an alarm automatic matching model, performing identification recovery alarm processing according to the alarm data; and acquiring environment characteristic data based on the inspection robot, and determining whether to alarm or not according to the environment characteristic data. The embodiment of the invention can improve the operation and maintenance efficiency.
Description
Technical Field
The present invention relates to the field of operation and maintenance technologies, and in particular, to an operation and maintenance management method, system, and medium.
Background
The current operation and maintenance status of the current equipment mainly has the following problems:
1. human input is not matched with equipment growth
The current operation and maintenance management scale is not the operation and maintenance category of a few years ago, and the current scale is often developed by modes of thousands of devices, multi-distributed deployment, clustering, operation and the like. So that the number of network components is large, the models are complicated, and the positions are scattered. Therefore, the number of the operation and maintenance personnel is not increased in a counter-observation mode no matter the number of the operation and maintenance personnel is managed or the complexity of the operation and maintenance personnel is increased, the operation and maintenance personnel are tired all the year round, a lot of difficulties are brought to companies and individuals, and time and labor are wasted in management.
2. Emergency reaction operation and maintenance
At present, in the operation and maintenance process from a central machine room or in China, most of operation and maintenance personnel are just in a passive and low-efficiency manual fire fighting state and are just required to patrol and examine according to rules. The working mode is inevitably driven by fault alarm, and personnel passively solve the problem. The fault is processed after the alarm is sent out, so that the effect of getting half the result is caused, and larger economic loss is caused.
3. Massive data, null filtering
Admittedly, operation and maintenance technology is developing all the time, but operation and maintenance personnel do not liberate both hands, probably because the automation level of system is limited, can not select useful information from mass data automatically and lead to.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides an operation and maintenance management method which can reduce operation and maintenance time and improve operation and maintenance efficiency.
The invention also provides an operation and maintenance management system using the operation and maintenance management method.
The invention also provides a computer readable storage medium for implementing the operation and maintenance management method.
The operation and maintenance management method according to the embodiment of the first aspect of the invention comprises the following steps: collecting service data and alarm data based on a data acquisition interface; obtaining predicted service data based on an alarm prediction model; comparing the predicted service data with the service data based on a service alarm model, and generating an alarm if the predicted service data exceeds a set threshold; based on an alarm automatic matching model, performing identification recovery alarm processing according to the alarm data; and acquiring environment characteristic data based on the inspection robot, and determining whether to alarm or not according to the environment characteristic data.
The operation and maintenance management method provided by the embodiment of the invention at least has the following beneficial effects: the embodiment of the invention introduces an alarm prediction model, a service alarm model, an alarm automatic matching model and the like, and can greatly improve the operation and maintenance capability of the current system. The operation and maintenance time is reduced, the operation and maintenance efficiency is improved, and therefore the human resource cost is reduced. According to the embodiment of the invention, the environmental characteristic data is collected by the inspection robot, so that a large amount of waste of manpower and material resources is avoided.
According to some embodiments of the invention, the method further comprises: and obtaining the fault occurrence rate based on a system firmware fault prediction model, and determining whether to alarm or not according to the fault occurrence rate.
According to some embodiments of the invention, the system firmware failure prediction model performing firmware failure prediction comprises: acquiring basic information of various firmware, wherein the basic information comprises: firmware name, purchase date, use date, firmware life and failure rate; setting an aging threshold value for each firmware according to the basic information, wherein the unit is day; and establishing a scanning thread, and generating a probability fault alarm according to different aging thresholds.
According to some embodiments of the present invention, the alarm prediction model adopts a Prophet model, and obtaining the predicted service data based on the alarm prediction model includes: establishing a Prophet model, trying parameters of the Prophet model, and evaluating the alarm prediction model according to a simulation result; inputting historical service data into the alarm prediction model, and predicting and analyzing the trend of the next day; and visually feeding back the predicted service data.
According to some embodiments of the invention, comparing the predicted traffic data and the traffic data based on a traffic alarm model, and generating an alarm if a set threshold is exceeded comprises: establishing a business alarm model and a model alarm threshold library; the formula of the service alarm model is as follows: (x) ExpA-B × 100/B, where ExpA represents predicted data, B represents real-time data, and f (x) represents a comparison result between the predicted traffic data and the traffic data; and generating an alarm for the service data of which f (x) is greater than the corresponding threshold value according to the threshold value of the model alarm threshold value library.
According to some embodiments of the present invention, performing an identity recovery alarm process according to the alarm data based on an alarm auto-matching model comprises: establishing an alarm knowledge model; when an alarm is generated, the alarm is stored in an alarm index table as a new alarm and is marked as a new alarm; when the alarm is recovered, searching an alarm model corresponding to the alarm according to the alarm knowledge model, determining the associated alarm according to the alarm model, searching the unrecovered alarm in the memory according to the associated alarm, and identifying the unrecovered alarm recovery.
According to some embodiments of the present invention, collecting environmental characteristic data based on the inspection robot and determining whether to alarm according to the environmental characteristic data includes: according to a preset inspection task, the inspection robot executes an automatic inspection task; when an automatic inspection task is executed, the inspection robot carries out remote online monitoring through detecting the environment and equipment of a machine room of the device to obtain the environmental characteristic data; the detection means comprises at least one of: the system comprises a thermal infrared imager, a high-definition camera and a sound acquisition device; the environmental characteristic data comprises at least one of: the temperature of the machine room, the humidity of the machine room, the picture of a front panel of the equipment and the state of an alarm lamp; and acquiring the state of the warning lamp, and generating an alarm if the warning lamp is on.
The operation and maintenance management system according to the second aspect of the embodiment of the invention comprises: the data center is used for collecting and storing service data, alarm data and environment characteristic data; the data analysis center is used for analyzing, processing and visually displaying the service data, the alarm data and the environment characteristic data; the control center is used for remotely controlling the inspection robot and controlling the inspection robot to realize automatic inspection; and the inspection robot is used for acquiring environmental characteristic data according to the inspection task set by the control center.
The operation and maintenance management system provided by the embodiment of the invention at least has the following beneficial effects: the embodiment of the invention introduces an alarm prediction model, a service alarm model, an alarm automatic matching model and the like, and can greatly improve the operation and maintenance capability of the current system. The operation and maintenance time is reduced, the operation and maintenance efficiency is improved, and therefore the human resource cost is reduced. According to the embodiment of the invention, the environmental characteristic data is collected by the inspection robot, so that a large amount of waste of manpower and material resources is avoided.
According to some embodiments of the invention, the control center comprises: the remote control module is used for remotely controlling the inspection robot to move through a keyboard and/or a mouse and realizing the remote inspection of the inspection robot by operating a detection device on the inspection robot; the automatic inspection module is used for creating an inspection task or a preset inspection task started at regular time; the real-time image data monitoring module is used for receiving environmental characteristic data obtained by the inspection robot through a detection device; wherein the detection means comprises at least one of: thermal infrared imager, high definition digtal camera and sound collection system.
The computer-readable storage medium according to an embodiment of the third aspect of the invention has stored thereon a computer program which, when executed by a processor, performs the method of any of the embodiments of the first aspect of the invention.
All the advantages of the first aspect of the present invention are achieved because the computer-readable storage medium of the embodiment of the present invention stores the computer-executable instructions for executing the operation and maintenance management method according to any one of the first aspect of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a diagram illustrating an embodiment of the present invention for analyzing alarms based on model-based data implementation;
FIG. 2 is a block schematic diagram of modules of a system of an embodiment of the invention;
FIG. 3 is a diagram illustrating firmware failure prediction based on a system firmware failure prediction model according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating automatic alarm matching elimination and alarm tagging based on an alarm knowledge model according to an embodiment of the present invention;
FIG. 5 is a flow chart illustrating a method according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and more than, less than, more than, etc. are understood as excluding the present number, and more than, less than, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
According to the automatic intelligent analysis operation and maintenance system, the inspection robot is introduced, various sensor devices of a machine room are added, the operation parameters of the devices are collected from multiple directions, and a multi-angle analysis comprehensive analysis algorithm is adopted, so that the problems of difficulty in data collection, difficulty in operation and maintenance analysis, difficulty in fault prediction and the like are solved, and the purpose of intelligent operation and maintenance is achieved.
Referring to fig. 1, in the embodiment of the present invention, a prediction algorithm library and a knowledge model library are established, and a prediction model adopts historical experience and existing feature information to provide constructive early warning information to a user in time; in addition, a set of model algorithms for automatically eliminating alarms is also designed, namely, a set of automatic alarm matching models is designed by utilizing the existing alarm information and integrating artificial experience knowledge, and alarm information and associated alarms generated by the system are automatically combined in real time, so that the operation and maintenance of the whole system are facilitated for a user.
Referring to fig. 2, a system of an embodiment of the present invention includes: the data center is used for collecting and storing service data, alarm data and environment characteristic data; the data analysis center is used for analyzing, processing and visually displaying the service data, the alarm data and the environmental characteristic data; the control center is used for remotely controlling the inspection robot and controlling the inspection robot to realize automatic inspection; and the inspection robot is used for acquiring environmental characteristic data according to the inspection task set by the control center.
In some embodiments, the operation and maintenance management system according to the embodiments of the present invention includes several types of data, such as service data, alarm data, external environment feature data, and basic data. The service data is stored into different data structures according to different service interface types, and is stored into the data center after being formatted; alarm data is collected and stored to a data center in real time by adopting an alarm standard interface through a transmission line; and the external environment characteristic data adopts an intelligent operation and maintenance inspection robot, a cruising route is preset to inspect the machine room at regular time, and the positions and the running states of various production equipment are identified, collected and stored to perform primary processing. The method is characterized by collecting machine room environment data such as temperature, humidity, cleanliness and air speed regularly, analyzing the environmental condition distribution of a machine room, counting indexes such as utilization rate, forming comprehensive evaluation of the overall running state of equipment and the machine room, providing timely and effective data for operation and maintenance of a data center, realizing unmanned and intelligent inspection reports of the machine room, and transmitting scanned data to the data analysis center in real time. And based on the data, establishing a data analysis center at the back end, and analyzing the current alarm and historical data through a data model so as to generate a prediction analysis result and remind a user.
In some embodiments, the system of the embodiments of the present invention includes an acquisition and entry module for three types of data, namely, alarm data, critical business data, and environmental characteristic data. The modules all adopt standard data acquisition interfaces. The alarm data and the key service characteristic data are directly collected from a standard interface of front-end special equipment, the standard collection interface is adopted, and after the alarm data and the key service characteristic data are collected, the alarm data and the key service characteristic data are transmitted through an Ethernet port and stored in a background to be used as original data of model prediction; the environmental characteristics adopt an intelligent robot, the function is automatically collected, the data is converted into characteristic data after scanning, the data comprises the temperature, the humidity and the like of a machine room, and a front panel picture, a power supply warning lamp, a hard disk warning lamp and the like of the equipment.
In some embodiments, the system of embodiments of the present invention includes a key base library, and the key base library field information includes the following: equipment number, equipment name, starting service time, average service life, fault type and other parameters. The key basic library establishment mainly comprises modes such as import, input and the like. The association base library can be established by means of user import or interface input.
In some embodiments, a data analysis center of an embodiment of the present invention includes an alarm prediction model. The model adopts a Prophet model which is a decomposable time series model, is based on an open source library of a decomposable (trend + season + holiday) model, performs high-precision time series prediction on simpler and more intuitive parameters, and supports the influence of self-defined season and holiday factors. It is described as follows:
the first step is as follows: establishing a time sequence model according to the time sequence by using historical service data as a basis;
the second step is that: model evaluation, namely, performing various attempts on parameters, and evaluating a more appropriate model according to a simulation effect; presenting problems, presenting potential causes with larger errors to an analyst for manual intervention;
the third step: and predictive analysis, namely, importing historical data and predictive analyzing the trend of the next day.
The fourth step: and feeding back the whole prediction result in a visualization mode.
The above is based on the Prophet model to make corresponding prediction, and certainly, the Prophet can also predict a service system by using a seasonal trend model (seaquality model) aiming at the service of the network, so that the hidden fault of the system can be more accurately found early, and the early processing fault can be better found early.
The above model mainly completes the prediction of business data, and enables the customer to know the future or the upcoming result in the next week. According to the results, a visualization result and a super-threshold business alarm are formed through a threshold value comparison analysis model, and the model is called a business alarm model.
In some embodiments, a data analysis center of an embodiment of the present invention includes a traffic alarm model.
The first step is as follows: and comparing with the real-time data to establish a service alarm model. And establishing a model alarm threshold library, and comparing the predicted data with the real-time data.
(x) 100/B, if the value of f (x) is greater than the threshold, an alarm is generated.
The second step is that: and generating an alarm for the data exceeding the threshold value by n% according to the business alarm model, and pushing the alarm to the page.
Referring to fig. 3, in some embodiments, a data analysis center according to an embodiment of the present invention includes a failure prediction model of system firmware, and a central idea of the data analysis center is to predict and remind a user to change or increase an operation and maintenance frequency in time by using characteristics of existing firmware, such as firmware age, failure rate, and the like. The model is combined with historical fault information, is analyzed and weighted after being processed, and is combined with a model algorithm of manual intervention. The method comprises the following implementation steps:
the first step is as follows: the central idea of the system firmware prediction model is to record the name, purchase date, use date, firmware life, failure rate, etc. of each firmware based on the basic characteristics of the existing firmware.
The second step is that: each firmware is set with its aging threshold in days.
The third step: and establishing a scanning thread, generating an alarm of periodic probability fault according to different threshold values, and reminding a user of replacing the firmware.
The early warning of the above processes can remind the user of operation and maintenance in time, and key parts can be replaced in time, so that the effect is good. Economic losses due to equipment failure can be reduced, and after a period of observation and application, the economic losses can be reduced by about 5%.
The data analysis center of the embodiment of the invention mainly comprises a monitoring workstation, a server, a switch, wireless communication equipment and safety equipment, completes storage and analysis of data acquired by front-end equipment, and completes presentation and display of an interface by combining the prediction model.
In some embodiments, the control center of the inspection robot in the embodiments of the present invention includes a remote control module, an automatic inspection module, a real-time image data monitoring module, a state monitoring module, and the like. Its main functions include:
(1) remote control module: the operation and maintenance personnel in the center remotely control the motion of the robot through a keyboard and a mouse, and realize the remote control inspection of the robot by adopting the focusing of a high-definition camera, the operation of an online thermal infrared imager and the like.
(2) Automatic module of patrolling: and the operation and maintenance personnel manually create or regularly start the established inspection task. When the robot executes the automatic inspection task, the optimal stop point for completing the inspection task is automatically calculated, and the corresponding inspection task is executed.
(3) The real-time image data monitoring module: the control robot is matched with remote control and automatic inspection functions to realize remote online monitoring of the primary equipment of the transformer substation by operation personnel through non-contact detection of a thermal infrared imager, a high-definition camera sound acquisition device and the like carried on a vehicle.
The inspection robot finishes the inspection of the robot to the machine room environment, the inspection content can include the temperature, the humidity and the like of the machine room, the state of the server is periodically inspected, for example, the information of the warning lamp of the front panel of the server can be scanned, and the warning is preliminarily generated according to the information of the warning lamp. The intelligent robot transmits data to the background through a safe transmission line through a wireless network.
The former machine room inspection depends on a manual mode, periodic inspection is adopted, manual observation is carried out, and a large amount of manpower and material resources are wasted. Therefore, aiming at the current network operation and maintenance mode, the artificial intelligent mode inspection is promoted as soon as possible, and particularly, since 2020, epidemic outbreaks have been promoted in all places to be used for remote office, so that the remote operation and maintenance of central machine rooms and remote machine rooms are also trending in the future.
Referring to fig. 4, the data analysis center according to the embodiment of the present invention includes an alarm automatic elimination model. The central idea is as follows: establishing a model base for alarm and alarm recovery, and when an alarm is generated, storing the system as a new alarm into an alarm index table and marking the new alarm; when the alarm is recovered, firstly, the model searches the alarm model which can recover the alarm, judges which alarms can be recovered by the alarm or report according to the model, and then searches the unrecovered alarms in the memory to mark that the alarm is recovered. After the system alarms are subjected to alarm recovery and combination processing, the system can automatically complete alarm recovery and elimination, reduce the alarm display quantity of the interface, provide a clean interface for a user and reduce the operation and maintenance workload.
Referring to fig. 5, an operation and maintenance management method is further provided in the embodiments of the present invention, where a prediction algorithm library and a knowledge model library are used, the operation and maintenance management method in the embodiments of the present invention includes: collecting service data and alarm data based on a data acquisition interface; obtaining predicted service data based on an alarm prediction model; comparing and predicting the service data and the service data based on the service alarm model, and generating an alarm if the predicted service data and the service data exceed a set threshold; based on the alarm automatic matching model, carrying out identification recovery alarm processing according to the alarm data; and acquiring environment characteristic data based on the inspection robot, and determining whether to alarm or not according to the environment characteristic data.
In some embodiments, the method of embodiments of the present invention further comprises: and obtaining the fault occurrence rate based on a system firmware fault prediction model, and determining whether to alarm according to the fault occurrence rate.
In some embodiments, the system firmware failure prediction model performing firmware failure prediction comprises: acquiring basic information of various firmware, including: firmware name, purchase date, use date, firmware life and failure rate; setting an aging threshold value for each firmware according to the basic information, wherein the unit is day; and establishing a scanning thread, and generating a probability fault alarm according to different aging thresholds.
In some embodiments, the alarm prediction model adopts a Prophet model, and obtaining the predicted service data based on the alarm prediction model includes: establishing a Prophet model, trying parameters of the Prophet model, and evaluating the alarm prediction model according to a simulation result; inputting historical service data into an alarm prediction model, and predicting and analyzing the trend of the next day; and visually feeding back and predicting the business data.
In some embodiments, comparing the predicted traffic data to the traffic data based on the traffic alarm model, and generating the alarm if the set threshold is exceeded comprises: establishing a business alarm model and a model alarm threshold library; the formula of the service alarm model is as follows: (x) ExpA-B × 100/B, where ExpA represents predicted data, B represents real-time data, and f (x) represents a comparison result between predicted service data and service data; and generating an alarm for the service data of which f (x) is greater than the corresponding threshold value according to the threshold value of the model alarm threshold value library.
In some embodiments, performing the identity recovery alarm processing based on the alarm data based on the automatic alarm matching model includes: establishing an alarm knowledge model; when the alarm is generated, the alarm is stored in an alarm index table as a new alarm and is marked as the new alarm; when the alarm is recovered, searching an alarm model corresponding to the alarm according to the alarm knowledge model, determining the associated alarm according to the alarm model, searching the unrecovered alarm in the memory according to the associated alarm, and identifying the unrecovered alarm recovery.
In some embodiments, collecting environmental characteristic data based on the inspection robot and determining whether to alarm according to the environmental characteristic data includes: according to a preset inspection task, the inspection robot executes an automatic inspection task; when an automatic inspection task is executed, the inspection robot carries out remote online monitoring through detecting the environment and equipment of a machine room of the device to obtain environment characteristic data; the detection means comprise at least one of: the system comprises a thermal infrared imager, a high-definition camera and a sound acquisition device; the environmental characteristic data includes at least one of: the temperature of the machine room, the humidity of the machine room, the picture of a front panel of the equipment and the state of an alarm lamp; and acquiring the state of the warning lamp, and generating an alarm if the warning lamp is on.
Although specific embodiments have been described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are equally within the scope of this disclosure. For example, any of the functions and/or processing capabilities described in connection with a particular device or component may be performed by any other device or component. In addition, while various illustrative implementations and architectures have been described in accordance with embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications of the illustrative implementations and architectures described herein are also within the scope of the present disclosure.
Certain aspects of the present disclosure are described above with reference to block diagrams and flowchart illustrations of systems, methods, systems, and/or computer program products according to example embodiments. It will be understood that one or more blocks of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by executing computer-executable program instructions. Also, according to some embodiments, some blocks of the block diagrams and flow diagrams may not necessarily be performed in the order shown, or may not necessarily be performed in their entirety. In addition, additional components and/or operations beyond those shown in the block diagrams and flow diagrams may be present in certain embodiments.
Accordingly, blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, can be implemented by special purpose hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special purpose hardware and computer instructions.
Program modules, applications, etc. described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.
The software components may be encoded in any of a variety of programming languages. An illustrative programming language may be a low-level programming language, such as assembly language associated with a particular hardware architecture and/or operating system platform. Software components that include assembly language instructions may need to be converted by an assembler program into executable machine code prior to execution by a hardware architecture and/or platform. Another exemplary programming language may be a higher level programming language, which may be portable across a variety of architectures. Software components that include higher level programming languages may need to be converted to an intermediate representation by an interpreter or compiler before execution. Other examples of programming languages include, but are not limited to, a macro language, a shell or command language, a job control language, a scripting language, a database query or search language, or a report writing language. In one or more exemplary embodiments, a software component containing instructions of one of the above programming language examples may be executed directly by an operating system or other software component without first being converted to another form.
The software components may be stored as files or other data storage constructs. Software components of similar types or related functionality may be stored together, such as in a particular directory, folder, or library. Software components may be static (e.g., preset or fixed) or dynamic (e.g., created or modified at execution time).
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. An operation and maintenance management method is characterized by comprising the following steps:
collecting service data and alarm data based on a data acquisition interface;
obtaining predicted service data based on an alarm prediction model;
comparing the predicted service data with the service data based on a service alarm model, and generating an alarm if the predicted service data exceeds a set threshold;
based on an alarm automatic matching model, performing identification recovery alarm processing according to the alarm data;
and acquiring environment characteristic data based on the inspection robot, and determining whether to alarm or not according to the environment characteristic data.
2. The operation and maintenance management method according to claim 1, further comprising: and obtaining the fault occurrence rate based on a system firmware fault prediction model, and determining whether to alarm or not according to the fault occurrence rate.
3. The operation and maintenance management method according to claim 2, wherein the performing firmware fault prediction by the system firmware fault prediction model comprises:
acquiring basic information of various firmware, wherein the basic information comprises: firmware name, purchase date, use date, firmware life and failure rate;
setting an aging threshold value for each firmware according to the basic information, wherein the unit is day;
and establishing a scanning thread, and generating a probability fault alarm according to different aging thresholds.
4. The operation and maintenance management method according to claim 1, wherein the alarm prediction model adopts a Prophet model, and obtaining the predicted service data based on the alarm prediction model comprises:
establishing a Prophet model, trying parameters of the Prophet model, and evaluating the alarm prediction model according to a simulation result;
inputting historical service data into the alarm prediction model, and predicting and analyzing the trend of the next day;
and visually feeding back the predicted service data.
5. The operation and maintenance management method according to claim 1, wherein comparing the predicted service data with the service data based on a service alarm model, and generating an alarm if a set threshold is exceeded comprises:
establishing a business alarm model and a model alarm threshold library;
the formula of the service alarm model is as follows: (x) ExpA-B100/B, where ExpA represents predicted data, B represents real-time data, and f (x) represents the comparison result between the predicted service data and the service data;
and generating an alarm for the service data of which f (x) is greater than the corresponding threshold value according to the threshold value of the model alarm threshold value library.
6. The operation and maintenance management method according to claim 1, wherein performing identification recovery alarm processing according to the alarm data based on an alarm automatic matching model comprises:
establishing an alarm knowledge model;
when an alarm is generated, the alarm is stored in an alarm index table as a new alarm and is marked as a new alarm;
when the alarm is recovered, searching an alarm model corresponding to the alarm according to the alarm knowledge model, determining the associated alarm according to the alarm model, searching the unrecovered alarm in the memory according to the associated alarm, and identifying the unrecovered alarm recovery.
7. The operation and maintenance management method according to claim 1, wherein the collecting of environmental characteristic data based on the inspection robot and the determining whether to alarm according to the environmental characteristic data comprises:
according to a preset inspection task, the inspection robot executes an automatic inspection task;
when an automatic inspection task is executed, the inspection robot carries out remote online monitoring through detecting the environment and equipment of a machine room of the device to obtain the environmental characteristic data;
the detection means comprises at least one of: the system comprises a thermal infrared imager, a high-definition camera and a sound acquisition device;
the environmental characteristic data comprises at least one of: the temperature of the machine room, the humidity of the machine room, the picture of a front panel of the equipment and the state of an alarm lamp;
and acquiring the state of the warning lamp, and generating an alarm if the warning lamp is on.
8. An operation and maintenance management system using the method of any one of claims 1 to 7, comprising:
the data center is used for collecting and storing service data, alarm data and environment characteristic data;
the data analysis center is used for analyzing, processing and visually displaying the service data, the alarm data and the environment characteristic data;
the control center is used for remotely controlling the inspection robot and controlling the inspection robot to realize automatic inspection;
and the inspection robot is used for acquiring environmental characteristic data according to the inspection task set by the control center.
9. The operation and maintenance management system according to claim 8, wherein the control center comprises:
the remote control module is used for remotely controlling the inspection robot to move through a keyboard and/or a mouse and realizing the remote inspection of the inspection robot by operating a detection device on the inspection robot;
the automatic inspection module is used for creating an inspection task or a preset inspection task started at regular time;
the real-time image data monitoring module is used for receiving environmental characteristic data obtained by the inspection robot through a detection device;
wherein the detection means comprises at least one of: thermal infrared imager, high definition digtal camera and sound collection system.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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