CN117667593A - Interface monitoring method, device, equipment and medium based on deep learning - Google Patents
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Abstract
The invention discloses an interface monitoring method, device, equipment and medium based on deep learning. The interface monitoring method based on deep learning comprises the following steps: acquiring the use data of the whole process interface in real time; carrying out aggregation treatment on the data used by the whole flow interface to obtain the data to be predicted of each interface to be monitored; inputting the data to be predicted of each interface to be monitored into an interface performance prediction model based on deep learning to obtain interface performance prediction data of each interface to be monitored; and generating interface monitoring evaluation data based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored. The technical scheme of the embodiment of the invention can improve the investigation effect of the software problem and realize the full mining of the acquired data during the software monitoring.
Description
Technical Field
The present invention relates to the field of software operation monitoring technologies, and in particular, to an interface monitoring method, device, equipment, and medium based on deep learning.
Background
As the time of use becomes longer, the scale of the software becomes more complex and uncontrollable, and software operation becomes extremely difficult, which is particularly a problem in operation of financial software systems where stability is emphasized. When the problem occurs in the practical use process of the software and needs to be repaired, the problem can be found and solved by personnel which are rich in experience and are familiar enough with the history project, however, because the flow of project personnel and the coding capability of coding personnel are uneven, many old problems are not solved, new problems are generated, the reliability of the software is not ensured, and only when the problem occurs in the practical application, the problem is solved by removing errors by people, the unpredictability leads the user experience to be poor, and the continuous usability of the software is challenged.
In the use process of the software, a large amount of data can be generated, and the data can directly or indirectly reflect the running efficiency of the software, and at present, a software or a module of the software is mainly used as a monitoring unit to monitor the current running condition of the software, but the following problems exist in the method: on one hand, the dimension of the monitoring unit is large, and even if abnormality of software or a software module is perceived from the data, the problem is not conducive to the investigation and the solution; on the other hand, the value of the data collected by the software monitoring is not fully mined, and the future operation and maintenance work and improvement direction of the software cannot be guided.
Disclosure of Invention
The invention provides an interface monitoring method, device, equipment and medium based on deep learning, which are used for solving the problems of poor checking effect and insufficient mining of the collected data in the existing software monitoring.
According to an aspect of the present invention, there is provided an interface monitoring method based on deep learning, including:
acquiring the use data of the whole process interface in real time;
carrying out aggregation treatment on the data used by the whole flow interface to obtain the data to be predicted of each interface to be monitored;
inputting the data to be predicted of each interface to be monitored into an interface performance prediction model based on deep learning to obtain interface performance prediction data of each interface to be monitored;
and generating interface monitoring evaluation data based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored.
According to another aspect of the present invention, there is provided an interface monitoring apparatus based on deep learning, including:
the data acquisition module is used for acquiring the use data of the whole-flow interface in real time;
the data aggregation processing module is used for carrying out aggregation processing on the data used by the whole flow interface to obtain the data to be predicted of each interface to be monitored;
the interface performance prediction module is used for inputting the data to be predicted of each interface to be monitored into the interface performance prediction model based on deep learning to obtain the interface performance prediction data of each interface to be monitored;
and the monitoring evaluation module is used for generating interface monitoring evaluation data based on the whole flow interface use data and the interface performance prediction data of each interface to be monitored.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the deep learning based interface monitoring method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the deep learning based interface monitoring method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the full-flow interface use data are acquired in real time, so that the full-flow interface use data are aggregated to obtain the to-be-predicted data of each to-be-monitored interface, the to-be-predicted data of each to-be-monitored interface are input into the interface performance prediction model based on deep learning, the interface performance prediction data of each to-be-monitored interface are obtained, and the interface monitoring evaluation data are generated further based on the full-flow interface use data and the interface performance prediction data of each to-be-monitored interface. The logic inside the software or the software module is connected in series by the interfaces, the working condition of the interfaces directly reflects the operation efficiency of the software or the software module, the scheme can rapidly locate and solve problems when detecting the problems, and excavate the future availability information of the interfaces by means of a deep learning technology, thereby providing help and guidance for the future improvement of operation and maintenance personnel on the system, helping to discover and avoid the problems in advance, realizing the full utilization of the acquired data during monitoring, solving the problems of poor investigation effect of the problems existing in the current software monitoring and insufficient excavation of the acquired data during monitoring, improving the investigation effect of the software problems, and realizing the full excavation of the acquired data during the software monitoring.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an interface monitoring method based on deep learning according to a first embodiment of the present invention;
fig. 2 is a flowchart of an interface monitoring method based on deep learning according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an interface monitoring device based on deep learning according to a third embodiment of the present invention;
fig. 4 shows a schematic diagram of the structure of an electronic device that may be used to implement an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "target" and "initial" and the like in the description of the present invention and the claims and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a deep learning-based interface monitoring method according to an embodiment of the present invention, where the method may be performed by a deep learning-based interface monitoring device, and the deep learning-based interface monitoring device may be implemented in hardware and/or software, and the deep learning-based interface monitoring device may be configured in an electronic device. As shown in fig. 1, the method includes:
step 110, acquiring the full-flow interface use data in real time.
The full-flow interface usage data may be data describing a full-flow interface usage condition in the software system. The full-flow interface does not refer to an interface used in a narrow inter-system call, but refers to program code or logic used in data transfer and interaction between system internal modules (or functions). The full flow interface usage data may include, but is not limited to, response time, call times, update time of the interface, interface failure conditions, etc. of each interface in the full flow interface.
In the embodiment of the invention, the full-flow interface use data of the software system can be acquired in real time.
And 120, aggregating the data used by the whole process interfaces to obtain the data to be predicted of each interface to be monitored.
Wherein the aggregation process may be based on known data aggregation operations. The aggregation process may include, but is not limited to, a maximum statistics, an interface failure count per unit time, and the like. The interface to be monitored may be all or part of the full flow interface of the software system. The data to be predicted may be an aggregate result of the full-flow interface usage data.
In the embodiment of the invention, the data statistics rule can be determined based on software monitoring requirements (can be set according to requirements), and the interface to be monitored is determined based on the whole-flow interface, so that the data used by the whole-flow interface is aggregated based on the data statistics rule by taking the interface as a unit, and the data to be predicted of each interface to be monitored is obtained.
And 130, inputting the data to be predicted of each interface to be monitored into an interface performance prediction model based on deep learning to obtain interface performance prediction data of each interface to be monitored.
The interface performance prediction model may be a deep learning model that predicts interface performance. The interface performance prediction data may be a prediction result of data to be predicted of the interface to be monitored by the interface performance prediction model.
In the embodiment of the invention, the data to be predicted of each interface to be monitored can be input into the interface performance prediction model based on deep learning, so that the working performance of each interface to be monitored is analyzed and predicted through the interface performance prediction model, and the interface performance prediction data of each interface to be monitored is obtained.
And 140, generating interface monitoring evaluation data based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored.
The interface monitoring evaluation data may be evaluation data for performing real-time dimension and prediction dimension on the interface performance of the interface to be monitored.
In the embodiment of the invention, the real-time operation performance of each interface to be monitored can be determined based on the use data of the whole process interface, and then the interface monitoring evaluation data is determined according to the real-time operation performance of each interface to be monitored and the interface performance prediction data of each interface to be monitored.
According to the technical scheme, the full-flow interface use data are acquired in real time, so that the full-flow interface use data are aggregated to obtain the to-be-predicted data of each to-be-monitored interface, the to-be-predicted data of each to-be-monitored interface are input into the interface performance prediction model based on deep learning, the interface performance prediction data of each to-be-monitored interface are obtained, and the interface monitoring evaluation data are generated further based on the full-flow interface use data and the interface performance prediction data of each to-be-monitored interface. The logic inside the software or the software module is connected in series by the interfaces, the working condition of the interfaces directly reflects the operation efficiency of the software or the software module, the scheme can rapidly locate and solve problems when detecting the problems, and excavate the future availability information of the interfaces by means of a deep learning technology, thereby providing help and guidance for the future improvement of operation and maintenance personnel on the system, helping to discover and avoid the problems in advance, realizing the full utilization of the acquired data during monitoring, solving the problems of poor investigation effect of the problems existing in the current software monitoring and insufficient excavation of the acquired data during the monitoring, improving the investigation effect of the software problems, and improving the full excavation of the acquired data during the software monitoring.
Example two
Fig. 2 is a flowchart of an interface monitoring method based on deep learning according to a second embodiment of the present invention, where the present embodiment is implemented based on the foregoing embodiment, and a specific optional implementation manner of aggregating data used by a full-flow interface to obtain data to be predicted of each interface to be monitored is provided. As shown in fig. 2, the method includes:
step 210, acquiring the full-flow interface use data in real time.
And 220, counting interface response time evaluation values, interface fault times within a preset time period, throughput and percentile values according to the whole process interface usage data by taking the interface as a unit to obtain to-be-predicted data of each to-be-monitored interface.
The interface response time evaluation value may be a numerical value for evaluating the interface response condition. The interface response time assessment values may include, but are not limited to, response time maxima, response time minima, and response time averages. The preset time period may be a preset time period. Optionally, the preset time period may be 10 minutes or 30 minutes, and may be specifically set according to needs. Throughput is used to denote the number of requests processed per unit time. The percentile value may be a value of the data corresponding to a percentile when a group of data is sorted from small to large and a corresponding cumulative percentile is calculated.
In the embodiment of the invention, the interface response time evaluation value, the interface fault times within a preset time period, the throughput and the percentile value statistics can be carried out on the data used by the whole flow interface by taking the interface as a unit, so as to obtain the data to be predicted of each interface to be monitored.
And 230, inputting the data to be predicted of each interface to be monitored into an interface performance prediction model based on deep learning to obtain the interface performance prediction data of each interface to be monitored.
In an optional embodiment of the present invention, before inputting the data to be predicted of each interface to be monitored into the interface performance prediction model based on deep learning, the method may further include: acquiring historical use data of a full-flow interface and a pre-training deep learning model; wherein the pre-training deep learning model comprises a GRU model; and determining an interface performance prediction model based on the deep learning according to the historical usage data of the whole-flow interface and the pre-training deep learning model.
The historical usage data of the full-flow interface can be data describing the historical usage situation of the full-flow interface in the software system.
In the embodiment of the invention, the history use data of the whole-flow interface can be obtained, and the GRU (Gated Recurrent Unit, door control circulating unit) model is used as a pre-training deep learning model, so that the history use data of the whole-flow interface is used for training the pre-training deep learning model, and the pre-training deep learning model which is used for completing training is used as an interface performance prediction model based on deep learning.
Compared with a standard RNN (Recurrent Neural Network, cyclic neural network) model, the GRU model can effectively relieve the problems of gradient disappearance and gradient explosion, has a structure which is simpler than an LSTM (Long Short-Term Memory) model, has fewer parameters and is easier to converge, has a better effect in capturing a Long-Term dependent task in a sequence mode, and accords with the time sequence characteristic of interface data.
In an alternative embodiment of the present invention, determining a deep learning based interface performance prediction model based on full flow interface historical usage data and a pre-trained deep learning model may include: carrying out data preprocessing on historical use data of the whole process interface to obtain an initial sample set; dividing the initial sample set into a target training set, a target verification set and a target test set; and determining an interface performance prediction model based on deep learning according to the target training set, the target verification set, the target test set and the pre-training deep learning model.
The data preprocessing may include, but is not limited to, data cleaning, data labeling, normalization, denoising, data dimension reduction, and the like. The initial sample set may be a data preprocessing result of the full flow interface historical usage data. The target training set may be a subset of the initial sample set that trains the pre-trained deep learning model. The target validation set may be a subset of the initial sample set that validates the pre-trained deep learning model. The target test set may be a subset of the initial sample set that tests the pre-trained deep learning model.
In the embodiment of the invention, the historical usage data of the whole process interface can be subjected to data preprocessing to obtain an initial sample set, the initial sample set is divided according to the preset sample set dividing proportion to obtain a target training set, a target verification set and a target test set, so that a pre-training deep learning model is subjected to model training based on the target training set, the pre-training deep learning model is further verified by the target verification set, the model is prevented from being fitted, the pre-training deep learning model which is completed to be trained is screened according to the verification effect, and the screened model is tested by the target test set, so that the model meeting the test requirement is used as a final interface performance prediction model based on deep learning, and the model effect is real and accurate.
In an optional embodiment of the present invention, after obtaining the interface performance prediction data of each interface to be monitored, the method may further include: transmitting the data to be predicted of each interface to be monitored to a display module so as to display the data to be predicted of each interface to be monitored through the display module; and/or storing the data to be predicted of each interface to be monitored according to the data statistics period.
The display module may be any module having a display function. The data statistics period may be a preset time period. Illustratively, the data statistics period may be a day or a week, etc., and may be set by itself.
In the embodiment of the invention, the data to be predicted of each interface to be monitored can be transmitted to the display module, and the data to be predicted of each interface to be monitored is displayed after the display module receives the data to be predicted of each interface to be monitored; and/or collecting the data to be predicted of each interface to be monitored according to a preset sampling frequency, and storing the data to be predicted of each interface to be monitored in one data statistics period after the data statistics period is reached.
And 240, generating interface monitoring evaluation data based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored.
In an alternative embodiment of the present invention, after generating the interface monitoring evaluation data, it may further include: and sending the interface monitoring evaluation data to a display module, and generating fault interface alarm data when the existence of the fault interface is determined based on the interface monitoring evaluation data.
The fault interface alarm data may be alarm data generated by the fault interface determined based on the interface monitoring evaluation data.
In the embodiment of the invention, the interface monitoring evaluation data can be sent to the display module for operation and maintenance personnel to know the operation condition and the operation prediction condition of the interface in real time, analyze the interface monitoring evaluation data, generate fault interface alarm data and timely inform the operation and maintenance personnel to timely maintain the interface if the fault interface exists in the full-flow interface.
In an alternative embodiment of the present invention, generating interface monitoring evaluation data based on the full-flow interface usage data and the interface performance prediction data of each interface to be monitored may include: determining real-time monitoring evaluation data of each interface to be monitored according to the using data of the whole process interface; and determining interface monitoring evaluation data according to the real-time monitoring evaluation data of each interface to be monitored and the interface performance prediction data of each interface to be monitored.
The real-time monitoring evaluation data may describe the current operation condition of each interface to be monitored.
In the embodiment of the invention, the use data of the whole flow interface can be analyzed to obtain the real-time monitoring evaluation data of each interface to be monitored, and the real-time monitoring evaluation data of each interface to be monitored and the interface performance prediction data of each interface to be monitored are used as the interface monitoring evaluation data. The performance of the interface is analyzed by predicting both dimensions in real time and in the future.
According to the technical scheme, the full-flow interface use data are obtained in real time, so that interface response time evaluation values, interface fault times in a preset time period, throughput and percentile values are counted according to the full-flow interface use data, interface response time evaluation values, interface fault times in a preset time period and percentile values are used as units, to-be-predicted data of each interface to be monitored are obtained, to-be-predicted data of each interface to be monitored are input into an interface performance prediction model based on deep learning, to-be-predicted data of each interface to be monitored are obtained, and interface monitoring evaluation data are generated based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored. The logic inside the software or the software module is connected in series by the interfaces, the working condition of the interfaces directly reflects the operation efficiency of the software or the software module, the scheme can rapidly locate and solve problems when detecting the problems, and excavate the future availability information of the interfaces by means of a deep learning technology, thereby providing help and guidance for the future improvement of operation and maintenance personnel on the system, helping to discover and avoid the problems in advance, realizing the full utilization of the acquired data during monitoring, reducing the performance influence on the original software monitoring system as much as possible, realizing the full decoupling with the original software system, solving the problems of poor investigation effect of the problems existing in the existing software monitoring and insufficient excavation of the acquired data during the monitoring, improving the investigation effect of the software problems, and realizing the full excavation of the acquired data during the software monitoring.
Example III
Fig. 3 is a schematic structural diagram of an interface monitoring device based on deep learning according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
a data acquisition module 310, configured to acquire the full-flow interface usage data in real time;
the data aggregation processing module 320 is configured to aggregate the data used by the full-flow interfaces to obtain to-be-predicted data of each to-be-monitored interface;
the interface performance prediction module 330 is configured to input data to be predicted of each interface to be monitored into an interface performance prediction model based on deep learning, so as to obtain interface performance prediction data of each interface to be monitored;
the monitoring evaluation module 340 is configured to generate interface monitoring evaluation data based on the full-flow interface usage data and the interface performance prediction data of each interface to be monitored.
According to the technical scheme, the full-flow interface use data are acquired in real time, so that the full-flow interface use data are aggregated to obtain the to-be-predicted data of each to-be-monitored interface, the to-be-predicted data of each to-be-monitored interface are input into the interface performance prediction model based on deep learning, the interface performance prediction data of each to-be-monitored interface are obtained, and the interface monitoring evaluation data are generated further based on the full-flow interface use data and the interface performance prediction data of each to-be-monitored interface. The logic inside the software or the software module is connected in series by the interfaces, the working condition of the interfaces directly reflects the operation efficiency of the software or the software module, the scheme can rapidly locate and solve problems when detecting the problems, and excavate the future availability information of the interfaces by means of a deep learning technology, thereby providing help and guidance for the future improvement of operation and maintenance personnel on the system, helping to discover and avoid the problems in advance, realizing the full utilization of the acquired data during monitoring, solving the problems of poor investigation effect of the problems existing in the current software monitoring and insufficient excavation of the acquired data during monitoring, improving the investigation effect of the software problems, and realizing the full excavation of the acquired data during the software monitoring.
Optionally, the data aggregation processing module 320 is configured to count, according to the full-flow interface usage data, interface response time evaluation values, interface failure times within a preset time period, throughput and percentile values by using an interface as a unit, to obtain to-be-predicted data of each to-be-monitored interface.
Optionally, the interface monitoring device based on deep learning further comprises an interface performance prediction model determining module, which is used for acquiring historical use data of the whole-flow interface and a pre-training deep learning model; wherein the pre-training deep learning model comprises a gate control loop unit GRU model; and determining the interface performance prediction model based on the deep learning according to the historical usage data of the full-flow interface and the pre-training deep learning model.
Optionally, the interface performance prediction model determining module is configured to perform data preprocessing on the historical usage data of the full-flow interface to obtain an initial sample set; dividing the initial sample set into a target training set, a target verification set and a target test set; and determining the interface performance prediction model based on the deep learning according to the target training set, the target verification set, the target test set and the pre-training deep learning model.
Optionally, the deep learning interface monitoring device may further include a data post-processing module, configured to transmit data to be predicted of each interface to be monitored to a display module, so as to display the data to be predicted of each interface to be monitored through the display module; and/or storing the data to be predicted of each interface to be monitored according to the data statistics period.
Optionally, the deep learning interface monitoring device may further include an early warning data generating module, configured to send interface monitoring evaluation data to the display module, and generate fault interface warning data when determining that a fault interface exists based on the interface monitoring evaluation data.
Optionally, the monitoring evaluation module 340 is configured to determine real-time monitoring evaluation data of each interface to be monitored according to the usage data of the full-flow interface, and determine performance prediction data of each interface to be monitored according to the performance prediction data of each interface; and determining the interface monitoring evaluation data according to the real-time monitoring evaluation data of each interface to be monitored and the performance prediction data of each interface to be monitored.
In a specific example, the data acquisition module, the data aggregation processing module, the display module, the interface performance prediction module, the storage module, and the data preprocessing module may form an interface monitoring prediction system based on deep learning. The data acquisition module and the data aggregation processing module are in an online real-time running state. The display module, the prediction module, the storage module and the data preprocessing module are in an offline timing running state. The interface monitoring and predicting system based on deep learning is divided into two parts of online real-time execution and offline timing operation, and has stronger engineering practice significance. The data acquisition module is a module built by a stress framework (such as a cluster deployment framework), and has low delay, lower memory consumption and better expandability. The low delay is used for not affecting or seldom affecting the response time of the interface, and the better expandability is used for expanding new functions for the module under the condition that the monitored software system does not modify the module source code and even does not know the module source code. The data acquisition module and the data aggregation processing module can be persisted to a database to provide a data basis for other module calls.
The interface monitoring device based on the deep learning provided by the embodiment of the invention can execute the interface monitoring method based on the deep learning provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the deep learning based interface monitoring method.
In some embodiments, the deep learning based interface monitoring method may be implemented as a computer program tangibly embodied on a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the deep learning based interface monitoring method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the deep learning based interface monitoring method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. An interface monitoring method based on deep learning is characterized by comprising the following steps:
acquiring the use data of the whole process interface in real time;
carrying out aggregation treatment on the data used by the full-flow interfaces to obtain to-be-predicted data of each to-be-monitored interface;
inputting the data to be predicted of each interface to be monitored into an interface performance prediction model based on deep learning to obtain interface performance prediction data of each interface to be monitored;
and generating interface monitoring evaluation data based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored.
2. The method of claim 1, wherein the aggregating the full-flow interface usage data to obtain to-be-predicted data for each to-be-monitored interface comprises:
and according to the using data of the whole flow interface, counting the interface response time evaluation value, the interface fault times within a preset time period, the throughput and the percentile value by taking the interface as a unit to obtain the data to be predicted of each interface to be monitored.
3. The method of claim 1, further comprising, prior to said inputting the data to be predicted for each of said interfaces to be monitored into a deep learning based interface performance prediction model:
acquiring historical use data of a full-flow interface and a pre-training deep learning model; wherein the pre-training deep learning model comprises a gate control loop unit GRU model;
and determining the interface performance prediction model based on the deep learning according to the historical usage data of the full-flow interface and the pre-training deep learning model.
4. The method of claim 3, wherein said determining said deep learning based interface performance prediction model from said full flow interface historical usage data and said pre-trained deep learning model comprises:
performing data preprocessing on the historical use data of the full-flow interface to obtain an initial sample set;
dividing the initial sample set into a target training set, a target verification set and a target test set;
and determining the interface performance prediction model based on the deep learning according to the target training set, the target verification set, the target test set and the pre-training deep learning model.
5. The method of claim 1, further comprising, after said obtaining the interface performance prediction data for each of said interfaces to be monitored:
transmitting the data to be predicted of each interface to be monitored to a display module, so as to display the data to be predicted of each interface to be monitored through the display module; and/or the number of the groups of groups,
and storing the data to be predicted of each interface to be monitored according to the data statistics period.
6. The method of claim 5, further comprising, after the generating interface monitor and assessment data:
and sending interface monitoring evaluation data to the display module, and generating fault interface alarm data when the existence of the fault interface is determined based on the interface monitoring evaluation data.
7. The method of claim 1, wherein generating interface monitoring evaluation data based on the full-flow interface usage data and interface performance prediction data for each of the interfaces to be monitored comprises:
determining real-time monitoring evaluation data of each interface to be monitored according to the using data of the whole process interface;
and determining the interface monitoring evaluation data according to the real-time monitoring evaluation data of each interface to be monitored and the interface performance prediction data of each interface to be monitored.
8. An interface monitoring device based on deep learning, which is characterized by comprising:
the data acquisition module is used for acquiring the use data of the whole-flow interface in real time;
the data aggregation processing module is used for carrying out aggregation processing on the data used by the full-flow interfaces to obtain to-be-predicted data of each to-be-monitored interface;
the interface performance prediction module is used for inputting the data to be predicted of each interface to be monitored into the interface performance prediction model based on deep learning to obtain the interface performance prediction data of each interface to be monitored;
and the monitoring evaluation module is used for generating interface monitoring evaluation data based on the full-flow interface use data and the interface performance prediction data of each interface to be monitored.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the deep learning based interface monitoring method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the deep learning based interface monitoring method of any one of claims 1-7 when executed.
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