CN115204674A - Running state evaluation method and system, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a running state evaluation method and system, electronic equipment and a storage medium, wherein the method comprises the following steps: predicting the total business data amount and transaction increment information of future time according to the historical business data amount and business change condition of the system; according to the predicted total business data amount and transaction increment information, combining with the resource information of the current system to obtain the running state index parameter of the system; determining the running state of the system according to the running state index parameter of the system; therefore, the system operation state can be simulated and predicted in advance through calculation of corresponding operation index parameters, early warning is carried out before the system operation pressure appears, and emergency response processing is carried out through modes of dynamic scheduling and expansion of system resources, plan adjustment of database execution and the like, so that stable operation of the system is ensured.
Description
Technical Field
The invention belongs to the technical field of system operation and maintenance, and particularly relates to an operation state evaluation method and system, electronic equipment and a storage medium.
Background
When a business system of a bank is just put into production and brought on line, because software and hardware resources are sufficient, the possibility of system failure is low, but with the popularization and application of the business system, the system stock business data and the transaction volume can rapidly rise, and the operation pressure of the corresponding business system is increased.
The stable operation of the banking system is a key basis for maintaining good customer experience, although all banks currently monitor the operation condition of the banking system in real time, because the risk cannot be controlled and eliminated in advance in time, once the banking system breaks down abnormally, a large amount of manpower and material resources still need to be spent for remediation afterwards, so that the normal use of service personnel and customers is influenced, and the reputation of the banking system can be adversely affected.
Disclosure of Invention
In view of the above, the present invention provides an operation state evaluation method and system, an electronic device, and a storage medium, which are used for performing simulation prediction on an operation state of a system in advance through calculation of corresponding operation index parameters, and performing early warning before an operation pressure of the system occurs.
The first aspect of the present application discloses an operation state evaluation method, including:
predicting the total business data amount and transaction increment information of future time according to the historical business data amount and business change condition of the system;
obtaining an operation state index parameter of the system according to the predicted total business data amount and transaction increment information by combining with the current resource information of the system;
and determining the running state of the system according to the running state index parameter of the system.
Optionally, in the operation state evaluation method, the predicting a total amount of service data and transaction increment information at a future time according to a historical service data amount and a service change condition of the system includes:
inputting the historical business data volume and the business change condition into a transaction volume prediction model;
and predicting by the transaction amount prediction model to obtain the total business data amount and transaction increment information of the future time.
Optionally, in the method for evaluating an operating state, the obtaining an index parameter of the operating state of the system according to the predicted total amount of the service data and the transaction increment information and by combining the current resource information of the system includes:
inputting the total business data, transaction increment information and the current resource information of the system into an operation state evaluation model;
the running state evaluation model evaluates the system to obtain running state index parameters of the system;
wherein the operating condition index parameters include: the system comprises a batch task execution time index and an execution window matching degree index, a transaction response time index and a response threshold value or fusing threshold value matching index of online transaction, and a memory and I/O (input/output) resource occupation and risk early warning value matching index of the system.
Optionally, in the operation state evaluation method, a process of constructing the transaction amount prediction model includes:
taking the historical data volume of the service of the system and the historical daily service volume data as a first training sample;
inputting the first training sample into a first training model for training, and predicting the total business data amount and transaction increment information;
and when the first training model meets a first preset convergence condition, taking the training model as the transaction amount prediction model.
Optionally, in the method for evaluating an operating state, when the first training model satisfies a first preset convergence condition, after the training model is used as the transaction amount prediction model, the method further includes:
and optimizing and retraining the transaction amount prediction model according to the daily actual business data total amount and the transaction increment information.
Optionally, in the operation state evaluation method, a process of constructing an operation state evaluation model includes:
the total business data amount and transaction increment information predicted by the transaction amount prediction model and the resource information of the system are used as second training samples;
inputting the second training sample into a second training model for training, and evaluating the running state index parameter of the system;
and when the second training model meets a second preset convergence condition, taking the training model as the running state evaluation model.
Optionally, in the method for evaluating an operating state, when the second training model satisfies a second preset convergence condition, after the training model is used as the operating state evaluation model, the method further includes:
and optimizing and retraining the operation state evaluation model according to the operation state index parameters of the daily actual system.
A second aspect of the present application discloses an operation state evaluation system, including:
the prediction module is used for predicting the total business data amount and the transaction increment information at the future time according to the historical business data amount and the business change condition of the system;
the evaluation module is used for obtaining an operation state index parameter of the system according to the predicted total business data amount and transaction increment information by combining with the current resource information of the system; and determining the running state of the system according to the running state index parameter of the system.
A third aspect of the present application discloses an electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the operation state evaluation method according to any one of the first aspects of the present application.
A fourth aspect of the present application discloses a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for estimating an operating state according to any one of the first aspect of the present application.
From the above technical solution, the method for evaluating an operation state provided by the present invention includes: predicting the total business data amount and transaction increment information of future time according to the historical business data amount and business change condition of the system; according to the predicted total business data amount and transaction increment information, combining with the resource information of the current system to obtain the running state index parameter of the system; determining the running state of the system according to the running state index parameter of the system; therefore, the system operation state can be simulated and predicted in advance through calculation of corresponding operation index parameters, early warning is carried out before the system operation pressure appears, and emergency response processing is carried out through modes of dynamic scheduling and expansion of system resources, plan adjustment of database execution and the like, so that stable operation of the system is ensured.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions in the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of an operation status evaluation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for evaluating an operating condition according to an embodiment of the present invention;
FIG. 3 is a flow chart of another method for evaluating an operating condition according to an embodiment of the present invention;
FIG. 4 is a flow chart of another method for evaluating operating conditions according to an embodiment of the present invention;
FIG. 5 is a flow chart of another method for evaluating operating conditions according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an operating condition evaluation system according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The embodiment of the application provides an operation state evaluation method, which is used for solving the problem that the stable operation of a banking system in the prior art is the key basis for maintaining good customer experience, although the operation condition of the banking system is monitored in real time by all banks at present, because the risk cannot be controlled and eliminated in time in advance, once the banking system breaks down abnormally, a large amount of manpower and material resources are still needed to be spent for carrying out post-incident remediation, the normal use of business personnel and customers is influenced, and the reputation of the banking system can be adversely affected.
Referring to fig. 1, the operation state evaluation method includes:
s101, predicting the total business data amount and transaction increment information of future time according to the historical business data amount and the business change condition of the system.
That is to say, the historical business data volume and the business change situation have a certain regular relationship with the business data total volume and the transaction increment information in the future time, so that the future data can be predicted through the historical data.
That is, the total amount of business data and transaction increment information that will occur can be predicted based on the historical business data amount and business change situation.
The prediction process may be to obtain a corresponding functional relationship according to the historical data, and then obtain the prediction data according to the functional relationship.
Of course, the training may be performed according to historical data to obtain a corresponding prediction model, and then the corresponding prediction data may be obtained according to the prediction model.
The specific prediction process is not described herein any more, and the above description is only an example, and is within the scope of the present application.
And S102, obtaining an operation state index parameter of the system according to the predicted total business data amount and the transaction increment information and by combining the resource information of the current system.
It should be noted that, the running state index parameters of the system are related to the total amount of the service data, the transaction increment information and the resource information of the current system; therefore, the operation state index parameter of the system can be evaluated by the above three parameters.
Specifically, the system operation status is closely related to the corresponding stock business data and the incremental concurrent transaction amount, and when the total quantity of the business data is small, the transactions are few, and the concurrence is few, the system operation pressure is small, and the operation is relatively stable; when the total amount of the business data is large, the transactions are increased and high concurrence is caused, the response pressure of the system is increased steeply, and the running health state of the system is impacted correspondingly.
Wherein the operation state index parameter may include: the method comprises the steps of matching indexes of batch task execution time and an execution window of the system, matching indexes of transaction response time and a response threshold or a fusing threshold of online transaction, and matching indexes of memory and I/O (input/output) resource occupation and risk early warning values of the system.
Of course, the operation index parameter may also be other values, which are not described herein any more, and all are within the protection scope of the present application depending on the actual situation.
S103, determining the running state of the system according to the running state index parameter of the system.
It should be noted that the rated resources of the system are fixed. Therefore, the operation state of the system can be determined according to the operation state index parameter of the system.
The operation state of the system can be stable operation, operation impact, faults and the like, and is not described in detail here any more, and the system is determined according to actual conditions and is within the protection scope of the application.
The operation state index parameters of the above example are taken as an example for explanation:
(1) Matching degree indexes of system batch task execution time and execution window are as follows: namely, the business system sets corresponding online batch operation based on different business scenes in the process of job design, for example, online business acceptance is carried out in the daytime, corresponding data batch processing is carried out at night, the batch operation has a corresponding execution time window, when the total amount of data is accumulated and the increment is sharply increased, the batch operation cannot be completed in the execution window, the normal response of scheduling execution and associated online transaction of subsequent operation on time can be directly influenced, and further the system operation fault is caused.
(2) The transaction response time of the online transaction and the response threshold or the matching index of the fusing threshold, namely, each online transaction is provided with corresponding transaction waiting response time or emergency fusing threshold, along with the increment of the traffic and the occupation and consumption of system resources, under high concurrent access, the response efficiency of the system online transaction is reduced, once the maximum response threshold is reached (namely, the transaction does not respond), transaction congestion is caused, the system connection resources are occupied and the system is crashed easily under the condition of no fusing mechanism, and a large number of transaction failures occur under the condition of the fusing mechanism, so that the normal operation of the system is influenced.
(3) The index matching between the system memory, the I/O resource occupation and the risk early warning value is combined, that is, the data volume and the resource occupation condition of the current service system are combined, whether the resources such as the system memory reach the preset risk early warning value or not and whether the conditions such as the memory resource occupation or overflow and the like occur or not when the predicted transaction volume is reached are judged, and whether stable operation support can be provided continuously or not is judged.
In the embodiment, the total amount of the business data and the transaction increment information at the future time are predicted according to the historical business data volume and the business change condition of the system; according to the predicted total business data amount and transaction increment information, combining with the resource information of the current system to obtain the running state index parameter of the system; determining the running state of the system according to the running state index parameter of the system; therefore, the system operation state can be simulated and predicted in advance through calculation of corresponding operation index parameters, early warning is carried out before the system operation pressure appears, and emergency response processing is carried out through modes of dynamic scheduling and expansion of system resources, plan adjustment of database execution and the like, so that stable operation of the system is ensured.
In practical applications, referring to fig. 2, the step S102 of predicting the total amount of service data and transaction increment information at a future time according to the historical service data amount and service change condition of the system includes:
s201, inputting historical business data volume and business change conditions into a transaction volume prediction model.
It should be noted that the transaction amount prediction model is constructed in advance, and the specific construction process thereof is described in detail in the following related embodiments, which are not described herein any more, and are all within the protection scope of the present application.
S202, predicting by using a transaction amount prediction model to obtain the total business data amount and transaction increment information of future time.
That is, the input of the transaction amount prediction model is historical business data amount and business change condition, and the output of the transaction amount prediction model is business data amount and transaction increment information; furthermore, the data prediction of the future time is realized through the transaction amount prediction model.
In practical applications, referring to fig. 3, the step S103 of obtaining the operation state index parameter of the system according to the predicted total amount of the service data and the transaction increment information and by combining the resource information of the current system includes:
s301, inputting the total business data, the transaction increment information and the resource information of the current system into the operation state evaluation model.
It should be noted that the operation state evaluation model is constructed in advance, and the specific construction process thereof is described in detail in the following related embodiments, which are not described herein any more, and are all within the protection scope of the present application.
And S302, evaluating the system by the running state evaluation model to obtain running state index parameters of the system.
Wherein, the operation state index parameter comprises: the method comprises the steps of matching indexes of batch task execution time and an execution window of the system, matching indexes of transaction response time and a response threshold or a fusing threshold of online transaction, and matching indexes of memory and I/O (input/output) resource occupation and risk early warning values of the system.
That is, the input of the operation state evaluation model is the total amount of the service data, the transaction increment information and the resource information of the current system, and the output of the operation state evaluation model is the operation state index parameter of the system; and then, the evaluation of the system running state is realized through the running state evaluation model.
Of course, it is not excluded that the operation state evaluation model directly outputs the operation state of the system, and the specific implementation process is not described herein any more, and is within the scope of the present application depending on the actual situation.
In practical application, referring to fig. 4, the process of constructing the transaction amount prediction model includes:
s401, using the historical data volume of the system and the historical daily traffic volume data as a first training sample.
The first training samples may include a test sample and a training sample, which are specifically in proportion, and are not described herein any more, and may be determined according to actual situations, all of which are within the scope of the present application.
S402, inputting the first training sample into a first training model for training, and predicting the total business data amount and the transaction increment information.
And S403, when the first training model meets a first preset convergence condition, taking the training model as a trading volume prediction model.
Namely, a transaction amount prediction model is established, and the prediction model is trained by taking the service historical data amount and the historical daily service amount data of the system as input.
In practical applications, when the first training model satisfies the first preset convergence condition in step S403, after the training model is used as the transaction amount prediction model, the method further includes:
and optimizing and retraining the transaction amount prediction model according to the daily actual business data total amount and the transaction increment information.
In practical applications, referring to fig. 5, the process of constructing the operation state estimation model includes:
s501, the total amount of business data and transaction increment information predicted by the transaction amount prediction model and the resource information of the system are used as second training samples.
And S502, inputting the second training sample into a second training model for training, and evaluating the running state index parameter of the system.
And S503, when the second training model meets a second preset convergence condition, taking the training model as an operation state evaluation model.
That is, an operation state evaluation model is established, the operation state evaluation model takes the output of the transaction amount prediction model as input, and meanwhile, the historical daily operation condition of the system is obtained by combining the operation report of the service system, wherein the historical daily operation condition comprises the following steps: the method comprises the steps of training an operation state evaluation model by taking historical operation conditions as output, wherein the batch task execution time, the response time of each online transaction, the use change condition of a system memory, the transaction peak value resource consumption condition, the database peak value DDL and the like are used.
The specific contents of the first predetermined convergence condition and the second predetermined convergence condition are not described herein any more, and are all within the protection scope of the present application depending on the actual situation.
The first preset convergence condition and the second preset convergence condition may be the same or different, and are not described herein any more, and are within the protection scope of the present application depending on the actual situation.
In practical applications, in step S503, when the second training model satisfies the second preset convergence condition, after the training model is used as the running state estimation model, the method further includes:
and optimizing and retraining the operation state evaluation model according to the operation state index parameters of the daily actual system.
After the construction of the two models is completed, the output result of the traffic prediction model and the current state of the system can be used as the input of the operation state evaluation model, so that the operation condition of the system can be evaluated in advance according to the service change prediction condition, the risk caused by unstable operation of the system is avoided before the actual service occurs, and the stable operation of the system is ensured.
In the application process, the prediction model and the intelligent evaluation model need to be optimized and retrained according to daily actual traffic and system operation conditions, and model prediction is guaranteed to be actively adapted to actual production changes.
In the embodiment, the operation condition of the system is evaluated and predicted, so that the early discovery and early elimination of the operation risk of the system are realized. Firstly, establishing a transaction amount prediction model, and training the prediction model by taking the service historical data amount and the historical daily service amount data of the system as input; secondly, establishing an operation state evaluation model, taking the service historical data volume and the service volume of the system as input, simultaneously obtaining the historical daily operation condition of the system (including batch task execution time, response time of each online transaction, system memory use change condition, transaction peak resource consumption condition, database peak value DDL and the like) by combining the operation report of the service system, and training the evaluation model by taking the historical operation condition as output; and finally, the output result of the traffic prediction model and the current state of the system are used as the input of the intelligent evaluation model, so that the operation condition of the system can be evaluated in advance according to the service change prediction condition, the risk caused by unstable operation of the system is avoided before the actual service occurs, and the stable operation of the system is ensured. In the application process, the prediction model and the intelligent evaluation model need to be optimized and retrained according to daily actual traffic and system operation conditions, and model prediction is guaranteed to be actively adapted to actual production changes.
Another embodiment of the present application provides an operation state evaluation system.
Referring to fig. 6, the operation state evaluation system includes:
and the prediction module 101 is configured to predict the total amount of service data and transaction increment information at a future time according to the historical service data amount and the service change condition of the system.
Specifically, a service data volume, a transaction volume and peak concurrency prediction model is established by taking service historical data volume and historical daily service volume data of the system as input, and then the total service data volume and transaction increment information of future time are predicted through the service data volume, transaction volume and peak concurrency prediction model.
The evaluation module 102 is configured to obtain an operating state index parameter of the system according to the predicted total amount of the business data and the transaction increment information in combination with resource information of the current system; and determining the running state of the system according to the running state index parameter of the system.
The method comprises the steps of taking the service historical data volume and the service volume of a system as input, simultaneously obtaining the historical daily operating conditions (including batch task execution time, the response time of each branch online transaction, the use change condition of a system memory, the transaction peak resource consumption condition, the database peak value DDL and the like) of the system by combining the operating report of the service system, establishing an operating state evaluation model, and intelligently evaluating the operating condition of the service system according to the predicted output of the service volume and the current condition of the system to eliminate the operating risk of the system in advance; and then, the evaluation of the running state of the system is realized according to the running state evaluation model.
In practical application, the method further comprises the following steps: and an optimization module.
The optimization model optimizes and retrains the prediction model and the intelligent evaluation model according to daily actual traffic and system operation conditions, and ensures that model prediction is actively adapted to actual production changes.
In this embodiment, the prediction module 101 predicts the total amount of service data and transaction increment information at a future time according to the historical service data volume and service change condition of the system; the evaluation module 102 obtains an operation state index parameter of the system according to the predicted total business data amount and the transaction increment information by combining with the resource information of the current system; determining the running state of the system according to the running state index parameter of the system; therefore, the system operation state can be simulated and predicted in advance through calculation of corresponding operation index parameters, early warning is carried out before the system operation pressure appears, and emergency response processing is carried out through modes of dynamic scheduling and expansion of system resources, plan adjustment of database execution and the like, so that stable operation of the system is ensured.
Another embodiment of the present application provides a storage medium, on which a computer program is stored, wherein when being executed by a processor, the computer program implements the operation state evaluation method according to any one of the above embodiments.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable 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. 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the computer readable medium of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device.
Another embodiment of the present invention provides an electronic device, as shown in fig. 7, including:
one or more processors 601.
A storage device 602 having one or more programs stored thereon.
The one or more programs, when executed by the one or more processors 601, cause the one or more processors 601 to implement the operation state evaluation method as in any one of the above embodiments.
In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Features described in the embodiments in the present specification may be replaced with or combined with each other, and the same and similar portions among the embodiments may be referred to each other, and each embodiment is described with emphasis on differences from other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An operation state evaluation method, comprising:
predicting the total business data amount and transaction increment information of future time according to the historical business data amount and the business change condition of the system;
obtaining an operation state index parameter of the system according to the predicted total business data amount and transaction increment information by combining with the current resource information of the system;
and determining the running state of the system according to the running state index parameter of the system.
2. The method for evaluating an operating state according to claim 1, wherein the predicting of the total amount of service data and the transaction increment information at a future time according to the historical service data amount and the service change condition of the system comprises:
inputting the historical business data volume and the business change condition into a transaction volume prediction model;
and predicting by the transaction amount prediction model to obtain the total business data amount and transaction increment information of the future time.
3. The method according to claim 1, wherein the obtaining an operation status index parameter of the system according to the predicted total amount of the service data and the transaction increment information in combination with the current resource information of the system comprises:
inputting the total business data, transaction increment information and the current resource information of the system into an operation state evaluation model;
the running state evaluation model evaluates the system to obtain running state index parameters of the system;
wherein the operating condition index parameters include: the system comprises a batch task execution time index and a matching degree index of an execution window, a transaction response time index and a response threshold value or a matching index of a fusing threshold value of online transaction, and a memory and I/O resource occupation and risk early warning value matching index of the system.
4. The operation state evaluation method according to claim 2, wherein the process of constructing the transaction amount prediction model includes:
taking the historical data volume of the service of the system and the historical daily service volume data as a first training sample;
inputting the first training sample into a first training model for training, and predicting the total business data amount and transaction increment information;
and when the first training model meets a first preset convergence condition, taking the training model as the transaction amount prediction model.
5. The operating condition evaluation method according to claim 4, wherein when the first training model satisfies a first preset convergence condition, the method further includes, after using the training model as the transaction amount prediction model:
and optimizing and retraining the transaction amount prediction model according to the daily actual business data total amount and the transaction increment information.
6. The operating condition evaluation method according to claim 3, wherein the process of constructing the operating condition evaluation model includes:
the total business data amount and transaction increment information predicted by the transaction amount prediction model and the resource information of the system are used as second training samples;
inputting the second training sample into a second training model for training, and evaluating the running state index parameters of the system;
and when the second training model meets a second preset convergence condition, taking the training model as the running state evaluation model.
7. The operating state estimating method according to claim 6, wherein when the second training model satisfies a second preset convergence condition, the method further includes, after using the training model as the operating state estimating model:
and optimizing and retraining the operation state evaluation model according to the operation state index parameters of the daily actual system.
8. An operation state evaluation system characterized by comprising:
the prediction module is used for predicting the total business data amount and transaction increment information at the future time according to the historical business data amount and the business change condition of the system;
the evaluation module is used for obtaining the running state index parameters of the system according to the predicted total business data amount and transaction increment information and by combining the current resource information of the system; and determining the running state of the system according to the running state index parameter of the system.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the operational state assessment method of any of claims 1-7.
10. A storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the operation state evaluation method according to any one of claims 1 to 7.
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| CN117390079A (en) * | 2023-09-05 | 2024-01-12 | 西安易诺敬业电子科技有限责任公司 | Data processing method and system for data center |
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| CN117390079A (en) * | 2023-09-05 | 2024-01-12 | 西安易诺敬业电子科技有限责任公司 | Data processing method and system for data center |
| CN117390079B (en) * | 2023-09-05 | 2024-10-15 | 西安易诺敬业电子科技有限责任公司 | Data processing method and system for data center |
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