Operation and maintenance index data simulation method and device, equipment and storage medium

Abstract

The invention discloses an operation and maintenance index data simulation method, device and equipment and a storage medium, and the method comprises the steps that push object information and an index list are read from a global configuration file, and the index list comprises index basic information and control parameters; index classification is determined according to the index basic information, and the index classification comprises a periodic type, a saturation type, an error number and a stationary type; calling a simulation mode according to the index classification, and generating simulation operation and maintenance index data based on the control parameters through the simulation mode; and pushing the simulation operation and maintenance index data by referring to the pushing object information. According to the method, the index list is obtained through the configuration of the user, various operation and maintenance index sample data are simulated according to the index list, and pushing is performed according to the pushing object information read from the configuration, so that the problem that data accumulation needs to be performed for a long time due to insufficient data samples in various machine learning and deep learning algorithms is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an operation and maintenance indicator data simulation method, device, equipment and storage medium. Background Art

[0002] With the development of intelligence in the field of operation and maintenance, more and more operation and maintenance monitoring methods have emerged. For example, when indicator anomaly detection is required, various manufacturers use various machine learning and deep learning algorithms to detect anomalies in time series indicator data. In order to make the model trained by the algorithm more accurate, we often need a large amount of training sample data. Taking the most popular 1-minute interval indicator in the industry as an example, in order to enable the algorithm to learn as many data features as possible, we often need to accumulate training samples for more than one week. In addition, some indicators with seasonality, such as business call volume data with a monthly cycle and call volume data with a weekly cycle, require a longer data cycle to collect data samples in order to train a model that fits the real data better.

[0003] However, in the early stages of a project, users are often eager to understand the characteristics and detection effects of each algorithm, so they do not want to wait for a week or even a month to accumulate data samples. In addition, in order to verify the detection effects of each algorithm on operation and maintenance indicators and the effectiveness of the alarm strategy, in the past, fault points were designed and injected into the test / production environment to simulate faults, and then fault data was collected for verification. The entire process is relatively complicated, the workload is large, and there is a possibility of contaminating production data. Therefore, there is no efficient and accurate way to obtain operation and maintenance indicator sample data in the existing technology. Summary of the invention

[0004] The present invention provides an operation and maintenance indicator data simulation method to achieve efficient and accurate simulation of operation and maintenance indicator data.

[0005] According to a first aspect of the present invention, there is provided a method for simulating operation and maintenance indicator data, comprising: reading push object information and an indicator list from a global configuration file, wherein the indicator list includes basic indicator information and control parameters;

[0006] Determining an indicator classification according to the indicator basic information, wherein the indicator classification includes periodicity, saturation, number of errors and stability;

[0007] Calling a simulation method according to the indicator classification, and generating simulated operation and maintenance indicator data based on the control parameters through the simulation method;

[0008] The simulated operation and maintenance indicator data is pushed with reference to the push object information.

[0009] According to another aspect of the present invention, there is provided an operation and maintenance indicator data simulation device, comprising: a global configuration file reading module, used to read push object information and an indicator list from a global configuration file, wherein the indicator list includes basic indicator information and control parameters;

[0010] An indicator classification determination module, used to determine the indicator classification according to the indicator basic information, wherein the indicator classification includes stable type, periodic type, saturation and number of errors;

[0011] A simulation operation and maintenance indicator data generation module, used to call a simulation method according to the indicator classification, and generate simulation operation and maintenance indicator data based on the control parameters through the simulation method;

[0012] The simulated operation and maintenance indicator data pushing module is used to push the simulated operation and maintenance indicator data with reference to the push object information.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention obtains an indicator list through the user's configuration, simulates various types of operation and maintenance indicator sample data according to the indicator list, and pushes them according to the push object information read from the configuration, thereby solving the problem that various machine learning and deep learning algorithms require a long time to accumulate data due to insufficient data samples.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a method for simulating operation and maintenance indicator data according to Embodiment 1 of the present invention;

[0022] Figure 2 This is an example of periodic simulation operation and maintenance indicator data provided according to the first embodiment of the present invention;

[0023] Figure 3 This is an example of error number simulation operation and maintenance indicator data provided according to the first embodiment of the present invention;

[0024] Figure 4 This is an example of saturation simulation operation and maintenance indicator data provided according to the first embodiment of the present invention;

[0025] Figure 5 This is an example of stability simulation operation and maintenance indicator data provided according to the first embodiment of the present invention;

[0026] Figure 6 is a flow chart of a method for simulating operation and maintenance indicator data according to Embodiment 2 of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of an operation and maintenance indicator data simulation device provided according to Embodiment 3 of the present invention;

[0028] Figure 8 It is a schematic diagram of the structure of an electronic device provided by Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 A flowchart of a method for simulating operation and maintenance indicator data is provided for the first embodiment of the present invention. This embodiment is applicable to the case of simulating operation and maintenance indicator data. The method can be executed by an operation and maintenance indicator data simulation device, which can be implemented in the form of hardware and / or software. Figure 1 As shown, the method includes:

[0033] Step S101, read push object information and indicator list from the global configuration file.

[0034] Optionally, the push object information and indicator list are read from the global configuration file, including: obtaining a pre-set global configuration file; reading the push object information from a first storage location in the global configuration file, wherein the push object includes a target address and a subject; reading the indicator list from a second storage location in the global configuration file, wherein the indicator basic information includes the indicator name, monitoring object name, time interval and service level.

[0035] Specifically, the user pre-configures the relevant information of the operation and maintenance indicator data that needs to be modeled, and saves it in the global configuration file. When receiving the user's data simulation instruction, the global configuration file can be read from the local, and the push object information and indicator list contained in the global configuration file can be extracted, and the push object information and indicator list are respectively stored in different locations in the global configuration file. The specific storage location is not limited in this embodiment, as long as it can be stored independently without confusion and overlap, it is within the protection scope of this application.

[0036] Among them, the push object mainly includes the target address and topic, and the target address can specifically be a kafka address; the indicator list includes basic indicator information and control parameters, and the basic indicator information includes indicator name, monitoring object name, time interval and service level, wherein the service level can include primary classification and secondary classification, which are used to indicate the level of the indicator and distinguish data by type for easy management. Control parameters are personalized parameters in the process of controlling the data model. In this implementation, the specific content contained in the indicator list is not included, and the basic information and control parameters corresponding to multiple indicators can be included in the indicator list. The following Table 1 is an example of an indicator list:

[0037] Table 1

[0038]

[0039]

[0040] Due to space limitations, Table 1 only uses configuration information of two indicators as an example for explanation, and in actual applications, the configured indicator data contained in the indicator list is not limited.

[0041] Step S102, determining the indicator classification according to the indicator basic information.

[0042] Optionally, determining the indicator classification according to the indicator basic information includes: extracting the indicator name in the indicator basic information; querying the classification list according to the indicator name, and obtaining the indicator classification corresponding to the indicator name, wherein the classification list includes the correspondence between the indicator name and the indicator classification.

[0043] Specifically, the indicator classification in this implementation includes periodic type, saturation, number of errors and stable type, and multiple indicator names may be included under each indicator classification. For example, the periodic indicator classification includes the recharge service call volume, the saturation indicator classification includes the service success rate, the error number indicator classification includes the success rate, and the stable indicator classification includes the CPU usage rate. Of course, this implementation is only an example, and does not limit the specific indicators included in each indicator classification, and pre-establishes the corresponding relationship between the indicator name and the indicator classification, and saves it in the classification list. The following Table 2 is an example of the classification list:

[0044] Table 2

[0045]

[0046] Due to space limitations, Table 2 only provides examples and does not limit the number of indicator names included in each indicator category.

[0047] It is worth mentioning that in the present implementation mode, periodic data is also common in operation and maintenance scenarios. For example, the call volume of a certain operator's recharge service is basically based on a daily cycle and has a certain periodicity. The calls will increase gradually after work during the day, and will gradually decrease during the lunch break. The business volume will continue to increase in the afternoon, and then gradually decrease from afternoon to early morning, presenting a hump-shaped data. It is difficult to simulate this type of data because it is not a standard normal distribution. The trend of data growth in the morning and data decline in the afternoon will be different, and it is not completely symmetrical. In addition, due to the characteristics of lunch break and nighttime, it is difficult to simulate this type of data through mathematical functions. Saturation data is also common in operation and maintenance scenarios, such as service success rate and business response rate. Taking a stable service success rate as an example, the service success rate may be a data that fluctuates between 95-100. Although the data fluctuates in a range, the success rate of 100% may account for 90%, the success rate between 99%-100% accounts for 9%, and the remaining 1% of the data is 90%-99%. Therefore, when simulating this type of data, it is necessary to Consider the weights of different numerical ranges. In operation and maintenance work, we usually form an "error number" indicator for many error logs, which reflects the number of anomalies in the logs of the current monitored object or the number of business call failures. This is also one of the golden indicators in operation and maintenance monitoring. In real operation and maintenance scenarios, a large number of indicators are indicators of stable trends, such as the CPU usage of the host and the average time required for business calls when resources are sufficient. The characteristic of this type of data is that it fluctuates within a fixed value range. For example, the average call time of business A may fluctuate between 10-30ms or 50-100ms. When the value deviates significantly from this range, it may be accompanied by the occurrence of failures.

[0048] Step S103, calling a simulation method according to the indicator classification, and generating simulated operation and maintenance indicator data based on control parameters through the simulation method.

[0049] Optionally, simulated operation and maintenance indicator data are generated based on the control parameters through a simulation method, including: when the control parameter is a fault data control parameter, fault simulation operation and maintenance indicator data are generated based on the fault data control parameter through a simulation method; when the control parameter is a normal data control parameter, normal simulation operation and maintenance indicator data are generated based on the normal data control parameter through a simulation method.

[0050] Specifically, when the indicator is classified as periodic indicator data according to the basic information of the indicator, the simulation method corresponding to the periodic indicator data will be called, and the periodic simulation operation and maintenance indicator data will be generated based on the control parameters through the called simulation method. In this embodiment, the production data generated in the actual production process of the monitoring object corresponding to the indicator name will be collected, and the baseline of the data sample will be generated based on the real data and saved in the specified file. Based on the above baseline, a trend chart roughly close to the production environment can be constructed. If only the baseline data is pushed, it is still idealized and can only be guaranteed to be equivalent to the real data trend. However, to simulate periodic indicator data that is closer to the actual data, the participation of control parameters is required. In this embodiment, normal data simulation can be performed and fault data simulation can be performed. It can be specifically limited according to the specific content of the control parameters. In this embodiment, the simulation of normal periodic indicator data is used as an example for explanation. The normal data control parameters include noise ratio: 0.001, noise value interval: 1000-10000, amplification factor: 5, random value in the interval: randm, and baseline value file path: X. In this implementation, the baseline value is first extracted from the specified file according to the baseline value file path X, cut into 5-minute time periods, and the mean of the baseline value is calculated. The mean is multiplied by the amplification factor and then by the interval random value to obtain the adjusted baseline value. Then, noise is generated in the noise value interval according to the noise ratio, and the noise is added to the adjusted baseline value to generate periodic simulation operation and maintenance indicator data, such as Figure 2 What is shown is an example of periodic simulation operation and maintenance indicator data. Of course, this embodiment is only an example for illustration and does not limit the specific generation method of periodic simulation operation and maintenance indicator data.

[0051] Among them, when the indicator classification is determined as error number indicator data according to the basic information of the indicator, the simulation method corresponding to the error number indicator data will be called, and the error number simulation operation and maintenance indicator data will be generated based on the control parameters through the called simulation method. For this type of indicator data, under normal circumstances, 0 is normal, and the proportion of 0 value is the largest, followed by a large proportion of data with small fluctuations, and then accompanied by a certain amount of noise. In this implementation, the simulation of normal indicator data of the error number is used as an example for explanation. The normal data control parameters include noise ratio: 0.001; noise interval: 100, which can be set according to the actual business volume, generally not greater than the business volume; the basic value is 0, indicating that there is no error; the proportion of basic value: 0.9; the numerical interval of small fluctuations: [0,5], indicating that it jumps between 0-5; the fluctuation ratio: 0.099, indicating that 9.9% of the data will jump slightly within the interval; the total number of simulated points: 10080. According to the above data control parameters, the error number simulation operation and maintenance indicator data can be simulated, such as Figure 3The example shown is an error number simulation operation and maintenance indicator data. Of course, this embodiment is only an example for illustration, and does not limit the specific generation method of the error number simulation operation and maintenance indicator data.

[0052] Among them, when the indicator classification is determined to be saturation indicator data according to the indicator basic information, the simulation method corresponding to the saturation indicator data will be called, and the saturation simulation operation and maintenance indicator data will be generated based on the control parameters through the called simulation method. This type of data is similar to the characteristics of the above-mentioned error number indicator, but a reverse process is done, that is, 100 is normal under normal circumstances, and the proportion of 100 values ​​is the largest, followed by a large proportion of small fluctuations, and then accompanied by a certain amount of noise. In this implementation, the simulation of normal saturation indicator data is used as an example for explanation. The normal data control parameters include noise ratio: 0.001; noise interval: 100, which can be set according to the actual business volume, generally not greater than the business volume; the basic value is 100, indicating success; the proportion of basic value: 0.9; the numerical interval of small fluctuations: [90,100], indicating that it jumps between 90-100; the oscillation ratio: 0.099, indicating that 9.9% of the data will jump slightly within the interval; the total number of simulated points: 10080. As can be seen, the basic value and oscillation ranges in the control parameters of the saturation index data and the error number index data are different, but the specific data simulation generation method is exactly the same. Based on the above data control parameters, the saturation simulation operation and maintenance index data can be simulated, such as Figure 4 What is shown is an example of saturation simulation operation and maintenance indicator data. Of course, this embodiment is only an example for illustration and does not limit the specific generation method of the error number simulation operation and maintenance indicator data.

[0053] Among them, when the indicator is classified as stable indicator data according to the basic information of the indicator, the simulation method corresponding to the stable indicator data will be called, and the stable simulation operation and maintenance indicator data will be generated based on the control parameters through the called simulation method. In this embodiment, the simulation of stable normal indicator data is taken as an example for explanation. The normal data control parameters include the oscillation interval: [20 40], the noise ratio 0.001, the noise interval 100, and the total number of simulation points: 10080. For example, at a certain moment, a random number is generated in the oscillation interval, and noise is inserted into the generated random number based on the noise ratio and the noise interval to generate stable simulation operation and maintenance indicator data, such as Figure 5 What is shown is an example of stable simulation operation and maintenance indicator data. Of course, this embodiment is only an example for illustration and does not limit the specific generation method of the stable simulation operation and maintenance indicator data.

[0054] Optionally, the fault data control parameters include an indicator name and a sudden increase ratio of a sudden increase type, or an indicator name and a sudden decrease ratio of a sudden decrease type.

[0055] It should be noted that the above examples are only illustrative of the simulation of normal data, but in actual applications, fault data can also be simulated, and fault data can be simulated for the above-mentioned periodic, saturation, number of errors and stable indicators. Whether to simulate normal data or fault data is determined according to the control parameters. Generally, in the fault data control parameters, relative to the normal data control parameters, additional indicator names and sudden increase ratios of the sudden increase type, or indicator names and sudden decrease ratios of the sudden decrease type will be added. For example, indicators such as average time consumption, number of errors and number of call timeouts may be included for sudden increase faults; indicators such as success rate and number of successful calls of the business may be included for sudden decrease faults. Of course, this embodiment is only an example, and does not limit the specific fault mode corresponding to each indicator data. When simulating fault data, the basic parameters of the fault simulation are read, the indicator time series data is generated normally according to the normal procedure of the operation and maintenance indicator data simulation, the data is classified according to the indicator name, and the data of different categories are enlarged, reduced or special values ​​are set according to a certain proportion according to the pre-set parameters to determine whether the data of the fault simulation meets the required minimum number of points. If so, the fault simulation is terminated, otherwise it is executed in a loop until the number of data points is sufficient.

[0056] Step S104, pushing the simulated operation and maintenance indicator data with reference to the push object information.

[0057] Specifically, the push objects in this embodiment include the target address and the topic, so the simulated operation and maintenance indicator data will be pushed to the topic in the target address. By pushing by address, accurate push can be achieved, which is convenient for subsequent users to extract the simulated operation and maintenance indicator data from the target address for model training or deep learning, etc.

[0058] It should be noted that users can pre-establish a correspondence between the target address and the indicator name, so that when the user needs to use a specified type of simulated operation and maintenance indicator data, they can query the simulated data from the specified target address according to the pre-established correspondence, and perform deep learning and other related operations based on the simulated data that has been pushed to the target address.

[0059] In the implementation mode of the present application, an indicator list is obtained through the user's configuration, and various operation and maintenance indicator sample data are simulated according to the indicator list, and pushed according to the push object information read from the configuration, thereby solving the problem that various machine learning and deep learning algorithms require a long time to accumulate data due to insufficient data samples.

[0060] Embodiment 2

[0061] Figure 6The second embodiment of the present invention provides a flow chart of a method for simulating operation and maintenance indicator data. This embodiment is based on the above embodiment. Before pushing the simulated operation and maintenance indicator data with reference to the push object information, it also includes: testing the simulated operation and maintenance indicator data to generate a test report; and determining that the simulated operation and maintenance indicator data has passed the test according to the test report. Figure 6 As shown, the method includes:

[0062] Step S201, read push object information and indicator list from the global configuration file.

[0063] Optionally, the push object information and indicator list are read from the global configuration file, including: obtaining a pre-set global configuration file; reading the push object information from a first storage location in the global configuration file, wherein the push object includes a target address and a subject; reading the indicator list from a second storage location in the global configuration file, wherein the indicator basic information includes the indicator name, monitoring object name, time interval and service level.

[0064] Step 202: Determine the indicator classification based on the indicator basic information.

[0065] Optionally, determining the indicator classification according to the indicator basic information includes: extracting the indicator name in the indicator basic information; querying the classification list according to the indicator name, and obtaining the indicator classification corresponding to the indicator name, wherein the classification list includes the correspondence between the indicator name and the indicator classification.

[0066] Step S203, calling a simulation method according to the indicator classification, and generating simulated operation and maintenance indicator data based on the control parameters through the simulation method.

[0067] Optionally, simulated operation and maintenance indicator data are generated based on the control parameters through a simulation method, including: when the control parameter is a fault data control parameter, fault simulation operation and maintenance indicator data are generated based on the fault data control parameter through a simulation method; when the control parameter is a normal data control parameter, normal simulation operation and maintenance indicator data are generated based on the normal data control parameter through a simulation method.

[0068] Optionally, the fault data control parameters include an indicator name and a sudden increase ratio of a sudden increase type, or an indicator name and a sudden decrease ratio of a sudden decrease type.

[0069] Step S204, test the simulated operation and maintenance indicator data to generate a test report; determine whether the simulated operation and maintenance indicator data has passed the test according to the test report.

[0070] Specifically, in this implementation, before the simulated operation and maintenance indicator data is pushed, the simulated operation and maintenance indicator data will be tested first. Since different types of indicator data have specific attributes, after obtaining the simulated operation and maintenance indicator data, the standard attribute value corresponding to the indicator data will be obtained, and the actual attribute value of the simulated operation and maintenance indicator data will be extracted. By comparing the two, it can be determined whether the simulated indicator data is correct. For example, the standard benchmark value corresponding to the number of errors is 0, but the actual benchmark value contained in the simulated indicator data is 100, which is obviously wrong. Of course, this implementation is only an example, and does not limit the attribute type corresponding to each indicator data.

[0071] It should be noted that in this embodiment, after the detection of each attribute of the simulated operation and maintenance indicator data is completed, a test report will be generated, and the test report includes the specific attribute type detected, the test result of each attribute, and the final test result of the simulated operation and maintenance data. Among them, the attributes may include basic and additional items, among which, only when all the basic items are passed, can the final test result be determined to be a test pass, and when any basic item fails, it can be determined that the final test result is a failure; when an additional item fails, the user's configuration information for the additional item can be viewed. If the configuration information is a required item, the final test result is determined to be a test failure. Of course, this embodiment is only an example, and does not limit the specific test process of the simulated operation and maintenance indicator data. And only when it is determined that the simulated operation and maintenance indicator data has passed according to the test report, will the simulated operation and maintenance indicator data be pushed.

[0072] Step S205: Push the simulated operation and maintenance indicator data with reference to the push object information.

[0073] In the implementation mode of the present application, an indicator list is obtained through the user's configuration, and various operation and maintenance indicator sample data are simulated according to the indicator list, and pushed according to the push object information read from the configuration, thereby solving the problem that various machine learning and deep learning algorithms require a long time to accumulate data due to insufficient data samples.

[0074] Embodiment 3

[0075] Figure 7 A schematic diagram of the structure of an operation and maintenance indicator data simulation device provided in Embodiment 3 of the present invention. Figure 7 As shown, the device includes: a global configuration file reading module 310, an indicator classification determination module 320, a simulation operation and maintenance indicator data generation module 330 and a simulation operation and maintenance indicator data push module 340.

[0076] The global configuration file reading module 310 is used to read the push object information and the indicator list from the global configuration file, wherein the indicator list includes the indicator basic information and the control parameters;

[0077] An indicator classification determination module 320 is used to determine the indicator classification according to the indicator basic information, wherein the indicator classification includes stable type, periodic type, saturation and number of errors;

[0078] The simulation operation and maintenance indicator data generating module 330 is used to call the simulation method according to the indicator classification, and generate the simulation operation and maintenance indicator data based on the control parameters through the simulation method;

[0079] The simulated operation and maintenance indicator data pushing module 340 is used to push the simulated operation and maintenance indicator data with reference to the push object information.

[0080] Optionally, a global configuration file reading module is used to obtain a preset global configuration file;

[0081] Reading push object information from a first storage location in a global configuration file, wherein the push object includes a target address and a subject;

[0082] The indicator list is read from the second storage location in the global configuration file, wherein the indicator basic information includes the indicator name, the monitoring object name, the time interval and the service level.

[0083] Optionally, an indicator classification determination module is used to extract the indicator name from the indicator basic information;

[0084] The classification list is queried according to the indicator name, and the indicator classification corresponding to the indicator name is obtained, wherein the classification list includes the corresponding relationship between the indicator name and the indicator classification.

[0085] Optionally, a simulation operation and maintenance indicator data generation module is used to generate fault simulation operation and maintenance indicator data based on the fault data control parameter by simulation when the control parameter is a fault data control parameter;

[0086] When the control parameter is a normal data control parameter, normal simulation operation and maintenance indicator data is generated based on the normal data control parameter in a simulation manner.

[0087] Optionally, the fault data control parameters include an indicator name and a sudden increase ratio of a sudden increase type, or an indicator name and a sudden decrease ratio of a sudden decrease type.

[0088] Optionally, the device further includes a detection module, which is used to detect the simulated operation and maintenance indicator data and generate a detection report;

[0089] According to the test report, it is confirmed that the simulated operation and maintenance indicator data test has passed.

[0090] Optionally, a simulated operation and maintenance indicator data push module is used to push the simulated operation and maintenance indicator data to the topic in the target address.

[0091] The operation and maintenance indicator data simulation device provided in the embodiment of the present invention can execute the operation and maintenance indicator data simulation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0092] Embodiment 4

[0093] Figure 8 The present invention is a block diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0094] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can 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 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] A number of 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, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0096] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the operation and maintenance indicator data simulation method.

[0097] In some embodiments, the operation and maintenance indicator data simulation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the operation and maintenance indicator data simulation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the operation and maintenance indicator data simulation method in any other appropriate manner (e.g., by means of firmware).

[0098] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0100] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein may 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 the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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, voice input, or tactile input).

[0102] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0103] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0105] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for simulating operation and maintenance indicator data, characterized in that: include: Read push object information and indicator list from the global configuration file, wherein the indicator list includes indicator basic information and control parameters; Determining an indicator classification according to the indicator basic information, wherein the indicator classification includes periodicity, saturation, number of errors and stability; Calling a simulation method according to the indicator classification, and generating simulated operation and maintenance indicator data based on the control parameters through the simulation method; The simulated operation and maintenance indicator data is pushed with reference to the push object information.

2. The method according to claim 1, characterized in that The push object information and indicator list are read from the global configuration file, including: Get the pre-set global configuration file; Reading the push object information from a first storage location in the global configuration file, wherein the push object includes a target address and a subject; The indicator list is read from a second storage location in the global configuration file, wherein the indicator basic information includes an indicator name, a monitoring object name, a time interval, and a service level.

3. The method according to claim 2, characterized in that Determining the indicator classification according to the indicator basic information includes: Extracting the indicator name from the indicator basic information; A classification list is queried according to the indicator name, and an indicator classification corresponding to the indicator name is obtained, wherein the classification list includes a corresponding relationship between the indicator name and the indicator classification.

4. The method according to claim 1, characterized in that: The generating simulated operation and maintenance indicator data based on the control parameters by the simulation method includes: When the control parameter is a fault data control parameter, fault simulation operation and maintenance indicator data is generated based on the fault data control parameter by the simulation method; When the control parameter is a normal data control parameter, normal simulation operation and maintenance indicator data is generated based on the normal data control parameter through the simulation method.

5. The method according to claim 4, characterized in that The fault data control parameters include an indicator name and a sudden increase ratio of a sudden increase type, or an indicator name and a sudden decrease ratio of a sudden decrease type.

6. The method according to claim 4, characterized in that Before pushing the simulated operation and maintenance indicator data with reference to the push object information, the method further includes: Testing the simulated operation and maintenance indicator data to generate a test report; It is determined that the simulated operation and maintenance indicator data test has passed according to the test report.

7. The method according to claim 2, characterized in that The pushing of the simulated operation and maintenance indicator data with reference to the push object information includes: The simulated operation and maintenance indicator data is pushed to the topic in the target address.

8. An operation and maintenance indicator data simulation device, characterized in that: include: A global configuration file reading module, used to read push object information and an indicator list from a global configuration file, wherein the indicator list includes basic indicator information and control parameters; An indicator classification determination module, used to determine the indicator classification according to the indicator basic information, wherein the indicator classification includes stable type, periodic type, saturation and number of errors; A simulation operation and maintenance indicator data generation module, used to call a simulation method according to the indicator classification, and generate simulation operation and maintenance indicator data based on the control parameters through the simulation method; The simulated operation and maintenance indicator data pushing module is used to push the simulated operation and maintenance indicator data with reference to the push object information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method according to any one of claims 1 to 7 when executed.

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