A platform fault prediction method, system, device, and readable storage medium

Abstract

This invention relates to the fields of data processing and digital healthcare, and provides a platform fault prediction method, system, device, and readable storage medium. The method includes: acquiring a platform fault prediction request sent by a user device; the platform fault prediction request carrying a target demand; acquiring demand data and fault data according to the platform fault prediction request, and calculating the fault frequency based on the demand data and fault data; classifying faults according to the fault frequency and demand data, and establishing a platform fault prediction model based on the classified fault types; and predicting the fault data of the target demand based on the fault prediction model. By analyzing historical platform data, classifying faults, and predicting them using the model, platform fault prediction can be performed more efficiently and accurately.

Description

Technical Field

[0001] This invention relates to the fields of data processing and digital healthcare technology, specifically to a platform fault prediction method, system, device, and readable storage medium. Background Technology

[0002] Today, internet platforms have become an integral part of everyone's life, changing the way people live and entertain themselves. With the continuous iteration of internet products, it's inevitable that some system flaws will be introduced into the production environment, causing problems for users and potentially leading to financial losses for businesses.

[0003] Current platform failure prediction mainly relies on the experience and judgment of platform developers, and on developers taking turns on duty to stop losses in time after a failure occurs. This approach is inefficient and costly.

[0004] Therefore, predicting and analyzing potential faults in the target platform or system to prevent them from occurring is an urgent problem to be solved. Summary of the Invention

[0005] Based on this, embodiments of this application provide a platform fault prediction method, system, device, and readable storage medium, which analyzes historical platform data, classifies faults, and performs model prediction, thereby enabling more efficient and accurate platform fault prediction.

[0006] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0007] According to a first aspect of the embodiments of this application, a platform fault prediction method is provided, the method comprising:

[0008] Obtain a platform fault prediction request sent by the user equipment; the platform fault prediction request carries the target requirement;

[0009] The platform obtains demand data and fault data according to the fault prediction request, and calculates the fault frequency based on the demand data and fault data.

[0010] Based on the fault frequency and demand data, faults are classified, and a platform fault prediction model is established based on the classified fault types.

[0011] The fault prediction model is used to predict the fault data of the target requirement.

[0012] Optionally, the platform fault prediction request also carries a set time range;

[0013] Based on the platform's fault prediction request, demand data and fault data are obtained, including:

[0014] Based on the set time range and target requirements, demand data and fault data are retrieved from the database; the demand data includes the total number of demand releases; the fault data includes the total number of faults and the fault standard value; the total number of demand releases includes the total number of demand releases on the platform and the total number of demand releases by the companies to which the platform belongs; the total number of faults includes the total number of faults on the platform and the total number of faults by the companies to which the platform belongs; the fault standard value includes the fault standard value on the platform and the fault standard value by the companies to which the platform belongs.

[0015] Optionally, the calculation of the fault frequency based on demand data and fault data includes:

[0016] The enterprise failure frequency is calculated based on the platform's enterprise failure standard value and the total number of requests published by the platform's enterprises.

[0017] The platform's failure frequency is calculated based on the platform's failure standard value and the total number of platform requests.

[0018] Optionally, the fault classification includes a fault-free type and a fault type;

[0019] The fault classification based on fault frequency and demand data includes:

[0020] Obtain the median release demand for platforms that have not experienced failures within a specified time frame;

[0021] The fault-free type is determined based on the median of the published demands and the demand data;

[0022] The fault type is determined based on the fault frequency and the set frequency threshold.

[0023] Optionally, before establishing the platform fault prediction model based on the classified fault types, the method further includes:

[0024] Calculate the platform's demand percentage based on the aforementioned demand data;

[0025] Platform faults are sorted according to the platform demand ratio and fault classification.

[0026] Optionally, a platform fault prediction model is established based on the classified fault types, including:

[0027] Extract the corresponding fault features based on the classified fault types;

[0028] The platform fault prediction model is trained based on the classified fault characteristics to obtain the trained platform fault prediction model.

[0029] Optionally, the method further includes:

[0030] The front-end rendering is based on the total number of platform faults and the total number of faults of the companies to which the platform belongs, the fault type, the platform faults, and the fault data of the target requirement.

[0031] According to a second aspect of the embodiments of this application, a platform fault prediction system is provided, the system comprising:

[0032] The message receiving module is used to acquire platform fault prediction requests sent by user equipment; the platform fault prediction requests carry target requirements.

[0033] The fault data calculation module is used to obtain demand data and fault data according to the platform fault prediction request, and to calculate the fault frequency based on the demand data and fault data.

[0034] The fault prediction module is used to classify faults based on the fault frequency and demand data, and to establish a platform fault prediction model based on the classified fault types.

[0035] The fault data determination module is used to predict the fault data of the target requirement based on the fault prediction model.

[0036] According to a third aspect of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0037] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method described in the first aspect above.

[0038] In summary, this application provides a platform fault prediction method, system, device, and readable storage medium. The method involves acquiring a platform fault prediction request sent by a user device; the platform fault prediction request carries a target requirement; acquiring requirement data and fault data based on the platform fault prediction request, and calculating the fault frequency based on the requirement data and fault data; classifying faults based on the fault frequency and requirement data; establishing a platform fault prediction model based on the classified fault types; and predicting fault data for the target requirement based on the fault prediction model. By analyzing historical platform data, classifying faults, and predicting them using the model, platform fault prediction can be performed more efficiently and accurately. Attached Figure Description

[0039] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0040] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0041] Figure 1 This is a schematic flowchart of a platform fault prediction method provided in an embodiment of this application;

[0042] Figure 2 This is a schematic diagram of another platform fault calculation method provided in an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the platform fault calculation process provided in an embodiment of this application;

[0044] Figure 4 This is a schematic diagram of another platform fault prediction process provided in an embodiment of this application;

[0045] Figure 5 A block diagram of a platform fault prediction system provided in this application embodiment;

[0046] Figure 6 This illustration shows a structural schematic diagram of an electronic device provided in an embodiment of this application;

[0047] Figure 7 A schematic diagram of a computer-readable storage medium provided in an embodiment of this application is shown. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another.

[0050] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Figure 1 This application illustrates a platform fault prediction method provided in an embodiment, the method comprising:

[0052] Step 101: Obtain the platform fault prediction request sent by the user equipment; the platform fault prediction request carries the target requirement;

[0053] Step 102: Obtain demand data and fault data according to the platform fault prediction request, and calculate the fault frequency based on the demand data and fault data;

[0054] Step 103: Classify faults based on the fault frequency and demand data, and establish a platform fault prediction model based on the classified fault types;

[0055] Step 104: Predict the fault data of the target requirement based on the fault prediction model.

[0056] In one possible implementation, the platform fault prediction request also carries a set time range; in step 102, obtaining demand data and fault data according to the platform fault prediction request includes:

[0057] Based on the set time range and target requirements, demand data and fault data are retrieved from the database; the demand data includes the total number of demand releases; the fault data includes the total number of faults and the fault standard value; the total number of demand releases includes the total number of demand releases on the platform and the total number of demand releases by the companies to which the platform belongs; the total number of faults includes the total number of faults on the platform and the total number of faults by the companies to which the platform belongs; the fault standard value includes the fault standard value on the platform and the fault standard value by the companies to which the platform belongs.

[0058] In one possible implementation, the calculation of the fault frequency based on the demand data and fault data in step 102 includes:

[0059] The enterprise failure frequency is calculated based on the enterprise failure standard value and the total number of demand releases by the enterprise on the platform; the platform failure frequency is calculated based on the platform failure standard value and the total number of demand releases by the platform.

[0060] In one possible implementation, in step 103, the fault classification includes fault-free types and fault types; fault classification based on fault frequency and demand data includes:

[0061] Obtain the median of release requests from platforms that have not experienced failures within a set time range; determine the failure-free type based on the median release requests and the request data; determine the failure type based on the failure frequency.

[0062] In one possible implementation, before establishing the platform fault prediction model based on the classified fault types, the method further includes:

[0063] Calculate the platform demand percentage based on the demand data; sort platform faults based on the platform demand percentage and fault classification.

[0064] In one possible implementation, a platform fault prediction model is established based on the classified fault types, including:

[0065] Extract the corresponding fault features based on the classified fault types; train the platform fault prediction model based on the classified fault features to obtain the trained platform fault prediction model.

[0066] In one possible implementation, the method further includes: front-end rendering and displaying based on the total number of platform faults and the total number of faults of the enterprise to which the platform belongs, the fault type, the platform faults and the fault data of the target requirement.

[0067] Existing technologies lack a unified assessment method for platform or system failures, and horizontal comparisons of overall system failure situations across different development teams lack objective evidence. Furthermore, there is a lack of intuitive visualization, failing to provide a warning effect. Therefore, the platform failure prediction method provided in this application analyzes historical platform data, classifies failures, and uses models for prediction, thereby enabling more efficient and accurate platform failure prediction.

[0068] The embodiments of this application can also be applied to fault prediction in APP. Figure 2 This paper presents a schematic diagram of another APP fault prediction method provided in an embodiment of the present application:

[0069] Step 201: Obtain the APP fault prediction request sent by the user device; the APP fault prediction request carries the target requirement and the set time range;

[0070] Step 202: Calculate the enterprise failure frequency based on the failure standard value of the enterprise to which the APP belongs and the total number of requests released by the enterprise to which the APP belongs; calculate the APP failure frequency based on the APP failure standard value and the total number of APP requests released;

[0071] Step 203: Calculate the failure frequency based on the demand data and failure data;

[0072] Step 204: Obtain the median of release requests for apps that have not experienced failures within a set time range; determine the failure-free type based on the median release requests and the request data; determine the failure type based on the failure frequency;

[0073] Step 205: Calculate the APP demand percentage based on the demand data; sort APP faults according to the APP demand percentage and fault classification;

[0074] Step 206: Establish an APP fault prediction model based on the classified fault types;

[0075] Step 207: Predict the fault data of the target requirement based on the fault prediction model.

[0076] Step 208: Perform front-end rendering and display based on the total number of APP failures and the total number of failures of the company to which the APP belongs, the failure type, the APP failures and the failure data of the target requirement.

[0077] In one possible implementation, obtaining demand data and fault data based on the APP fault prediction request includes:

[0078] Based on the set time range and target requirements, demand data and fault data are retrieved from the database; the demand data includes the total number of demand releases; the fault data includes the total number of faults and the fault standard value; the total number of demand releases includes the total number of APP demand releases and the total number of demand releases of the APP's parent company; the total number of faults includes the total number of APP faults and the total number of faults of the APP's parent company; the fault standard value includes the APP fault standard value and the APP's parent company fault standard value.

[0079] In one possible implementation, an APP fault prediction model is established based on the classified fault types, including:

[0080] Extract the corresponding fault features based on the classified fault types; train the APP fault prediction model based on the classified fault features to obtain the trained APP fault prediction model.

[0081] Figure 3 This diagram illustrates a flowchart of the method provided in this application for fault prediction when applied to an enterprise platform. It calculates fault scores based on fault classification, fault rating, number of faults, and number of published requirements. Furthermore, the data is visualized to facilitate internal understanding of the overall fault situation and provide a helpful warning.

[0082] Step 1: Enter the page. The user can select the target time and object to be queried through the page. The target time includes year and month. The object includes platform and individual.

[0083] Step 2: The system obtains the total number of product requirement releases, the total number of failures, and the total failure points for the target time through the interface. Among them, the total number of product requirement releases PRTotle0 and PRTotle1 for the enterprise and the platform, the total number of failures FailureTotle0 and FailureTotle1 for the enterprise and the platform, and the total failure points FailurePoint0 and FailurePoint1 for the enterprise and the platform.

[0084] Step 3: Calculate the enterprise requirement failure ratio = total enterprise failure points / total enterprise requirement releases; Rate0 = FailurePoint0 / PRTotle0.

[0085] Calculate the platform requirement failure ratio = total platform failure points / total platform requirement releases; Rate1 = FailurePoint1 / PRTotle1.

[0086] Step 4: Calculate the median PRMedian of the release requirements of the platforms without failures.

[0087] Step 5: Conduct type classification based on the enterprise requirement failure ratio, the platform requirement failure ratio, and the total number of requirement releases.

[0088] Case 1: No failures occurred on the platform this month

[0089] A: The total number of platform requirement releases PRTotle1 > PRMedian, and the type is determined as the first type.

[0090] B: The total number of platform requirement releases PRTotle1 < PRMedian, and the type is determined as the second type.

[0091] Case 2: Failures occurred on the platform this month

[0092] C: The platform requirement failure ratio Rate1 > Rate0, and the type is determined as the third type.

[0093] D: The platform requirement failure ratio Rate1 < Rate0, and the type is determined as the fourth type.

[0094] Step 6: Calculate the platform requirement ratio = total number of platform requirement releases / total number of enterprise requirement releases; Ratio = PRTotle1 / PRTotle0.

[0095] Step 7: Rank the platforms based on their type and the number of posted demands; for platforms of the same type, rank them by comparing their demand ratio, with the higher the ratio, the higher the ranking.

[0096] Step 8: Render the platform's failure count (FailureTotle1), failure category (FailurePoint1), type, and ranking on the front end.

[0097] In this embodiment, it is necessary to calculate the median (PRMedian) of release requests for platforms that have not experienced failures. The median is the middle value in a sequence of numbers arranged in ascending order. As defined, half of the data studied is less than the median, and half is greater than the median. The median serves a similar purpose to the arithmetic mean, acting as a representative value for the data. In an arithmetic sequence or a normally distributed sequence, the median is equal to the arithmetic mean. The median can be used in conjunction with other methods in the processing and analysis of statistical data.

[0098] If a medical platform malfunctions, it will cause the entire workflow of the medical platform to stop, which may cause great inconvenience to patients and doctors. Figure 4 This application illustrates a medical platform fault prediction process provided by an embodiment of the present application, which can assist in predicting medical platform faults, including the following steps:

[0099] Step 401: Obtain the medical platform fault prediction request sent by the user equipment; the medical platform fault prediction request carries the target requirement and the set time range;

[0100] Step 402: Obtain demand data and fault data according to the fault prediction request of the medical platform, and calculate the fault frequency based on the demand data and fault data;

[0101] Step 403: Classify faults based on fault frequency and demand data;

[0102] Step 404: Calculate the demand ratio of the medical platform based on the demand data; sort the medical platform faults according to the demand ratio and fault classification.

[0103] Step 405: Establish a medical platform fault prediction model based on the classified fault types;

[0104] Step 406: Predict the fault data of the target requirement based on the fault prediction model.

[0105] Step 407: Perform front-end rendering and display based on the total number of medical platform failures and the total number of failures of the enterprise to which the medical platform belongs, the failure type, the medical platform failures and the failure data of the target requirement.

[0106] In one possible implementation, obtaining demand data and fault data based on the medical platform fault prediction request includes:

[0107] Based on the set time range and target requirements, demand data and fault data are retrieved from the database; the demand data includes the total number of demand releases; the fault data includes the total number of faults and the fault standard value; the total number of demand releases includes the total number of demand releases on the platform and the total number of demand releases by the companies to which the platform belongs; the total number of faults includes the total number of faults on the platform and the total number of faults by the companies to which the platform belongs; the fault standard value includes the fault standard value on the platform and the fault standard value by the companies to which the platform belongs.

[0108] In one possible implementation, calculating the fault frequency based on demand data and fault data includes:

[0109] The enterprise failure frequency is calculated based on the enterprise failure standard value and the total number of demand releases of the enterprise to which the medical platform belongs; the platform failure frequency is calculated based on the platform failure standard value and the total number of demand releases of the platform.

[0110] In one possible implementation, fault classification is performed based on fault frequency and demand data, including:

[0111] Obtain the median of release requests from platforms that have not experienced failures within a set time range; determine the failure-free type based on the median release requests and the request data; determine the failure type based on the failure frequency.

[0112] In one possible implementation, a medical platform fault prediction model is established based on the classified fault types, including:

[0113] Extract the corresponding medical fault features based on the classified medical fault types; train the platform fault prediction model based on the classified medical fault features to obtain the trained medical platform fault prediction model.

[0114] In summary, this application provides a platform fault prediction method. This method involves acquiring a platform fault prediction request sent by a user equipment; the request carries a target requirement; acquiring requirement data and fault data based on the request, and calculating the fault frequency based on the requirement data and fault data; classifying faults based on the fault frequency and requirement data; establishing a platform fault prediction model based on the classified fault types; and predicting the fault data of the target requirement based on the fault prediction model. By analyzing historical platform data, classifying faults, and predicting them using the model, platform fault prediction can be performed more efficiently and accurately.

[0115] Based on the same technical concept, embodiments of this application also provide a platform fault prediction system, such as... Figure 5 As shown, the system includes:

[0116] The message receiving module 501 is used to obtain a platform fault prediction request sent by the user equipment; the platform fault prediction request carries the target requirement.

[0117] The fault data calculation module 502 is used to obtain demand data and fault data according to the platform fault prediction request, and to calculate the fault frequency based on the demand data and fault data.

[0118] The fault prediction module 503 is used to classify faults based on fault frequency and demand data, and to establish a platform fault prediction model based on the classified fault types.

[0119] The fault data determination module 504 is used to predict the fault data of the target requirement based on the fault prediction model.

[0120] Optionally, the platform fault prediction request also carries a set time range;

[0121] The fault data calculation module is specifically used for:

[0122] Based on the set time range and target requirements, demand data and fault data are retrieved from the database; the demand data includes the total number of demand releases; the fault data includes the total number of faults and the fault standard value; the total number of demand releases includes the total number of demand releases on the platform and the total number of demand releases by the companies to which the platform belongs; the total number of faults includes the total number of faults on the platform and the total number of faults by the companies to which the platform belongs; the fault standard value includes the fault standard value on the platform and the fault standard value by the companies to which the platform belongs.

[0123] In one possible implementation, the fault data calculation module is specifically used for:

[0124] The enterprise failure frequency is calculated based on the platform's enterprise failure standard value and the total number of requests published by the platform's enterprises.

[0125] The platform failure frequency is calculated based on the platform failure standard value and the total number of platform requirements released.

[0126] In one possible implementation, the fault prediction module is configured to:

[0127] Obtain the median release demand for platforms that have not experienced failures within a specified time frame;

[0128] The fault-free type is determined based on the median of the published demands and the demand data;

[0129] The fault type is determined based on the fault frequency.

[0130] In one possible implementation, before establishing the platform fault prediction model based on the classified fault types, the system is further configured to:

[0131] Calculate the platform's demand percentage based on the aforementioned demand data;

[0132] Platform faults are sorted according to the platform demand ratio and fault classification.

[0133] In one possible implementation, the fault prediction module is configured to:

[0134] Extract the corresponding fault features based on the classified fault types;

[0135] The platform fault prediction model is trained based on the classified fault characteristics to obtain the trained platform fault prediction model.

[0136] In one possible implementation, the system further includes:

[0137] The rendering module is used to perform front-end rendering and display based on the total number of platform faults and the total number of faults of the companies to which the platform belongs, the fault type, the platform faults, and the fault data of the target requirement.

[0138] This application also provides an electronic device corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 6 The diagram illustrates an electronic device provided by some embodiments of this application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203, wherein the processor 200, the communication interface 203, and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can run on the processor 200, and when the processor 200 runs the computer program, it executes the method provided by any of the foregoing embodiments of this application.

[0139] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one physical port 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0140] Bus 202 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. After receiving an execution instruction, the processor 200 executes the program. The method disclosed in any of the foregoing embodiments of this application can be applied to the processor 200, or implemented by the processor 200.

[0141] The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 200 or by instructions in software form. The processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0142] The electronic devices and methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0143] This application also provides a computer-readable storage medium corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 7The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored, which, when run by a processor, executes the methods provided in any of the foregoing embodiments.

[0144] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0145] The computer-readable storage medium provided in the above embodiments of this application and the method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0146] It should be noted that:

[0147] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0148] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0149] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0150] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0151] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0152] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation apparatus according to embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0153] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0154] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims. Those skilled in the art can understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM).

[0155] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method of platform failure prediction, the method comprising: The method includes: Obtain a platform fault prediction request sent by the user equipment; the platform fault prediction request carries the target requirement and a set time range; The platform obtains demand data and fault data according to the fault prediction request, and calculates the fault frequency based on the demand data and fault data. Based on the fault frequency and demand data, faults are classified, and a platform fault prediction model is established based on the classified fault types. Based on the fault prediction model, predict the fault data of the target requirement; The process of obtaining demand data and fault data based on the platform fault prediction request includes: retrieving demand data and fault data from the database based on the set time range and target demand; the demand data includes the total number of demand releases; the fault data includes the total number of faults and fault standard values; the total number of demand releases includes the total number of demand releases on the platform and the total number of demand releases by the companies to which the platform belongs; the total number of faults includes the total number of faults on the platform and the total number of faults by the companies to which the platform belongs; the fault standard values ​​include the platform fault standard values ​​and the fault standard values ​​by the companies to which the platform belongs. The failure frequency is calculated based on demand data and failure data, including: calculating the enterprise failure frequency based on the failure standard value of the enterprise to which the platform belongs and the total number of demand releases of the enterprise to which the platform belongs; and calculating the platform failure frequency based on the platform failure standard value and the total number of demand releases of the platform. Fault classification is performed based on fault frequency and demand data, including: obtaining the median of release demands for platforms that have not experienced faults within a set time range; determining the fault-free type based on the median of release demands and demand data; and determining the fault type based on the fault frequency and a set frequency threshold; the fault type includes the fault-free type and the fault type.

2. The method of claim 1, wherein, Before establishing the platform fault prediction model based on the classified fault types, the method further includes: Calculate the platform's demand percentage based on the aforementioned demand data; Platform faults are sorted according to the platform demand ratio and fault classification.

3. The method of claim 1, wherein, Based on the classified fault types, a platform fault prediction model is established, including: Extract the corresponding fault features based on the classified fault types; The platform fault prediction model is trained based on the classified fault characteristics to obtain the trained platform fault prediction model.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: The front-end rendering is based on the total number of platform faults and the total number of faults of the companies to which the platform belongs, the fault type, the platform faults, and the fault data of the target requirement.

5. A platform failure prediction system, characterized by, The system includes: The message receiving module is used to acquire platform fault prediction requests sent by user equipment; the platform fault prediction request carries the target requirement and the set time range; The fault data calculation module is used to obtain demand data and fault data according to the platform fault prediction request, and to calculate the fault frequency based on the demand data and fault data. Obtaining demand data and fault data according to the platform fault prediction request includes: obtaining demand data and fault data from a database based on the set time range and target demand; the demand data includes the total number of demand releases; the fault data includes the total number of faults and a fault standard value; the total number of demand releases includes the total number of demand releases on the platform and the total number of demand releases by the companies to which the platform belongs; the total number of faults includes the total number of platform faults and the total number of faults by the companies to which the platform belongs; the fault standard value includes the platform fault standard value and the fault standard value by the companies to which the platform belongs; calculating the fault frequency based on the demand data and fault data includes: calculating the company fault frequency based on the fault standard value by the companies to which the platform belongs and the total number of demand releases by the companies to which the platform belongs; and calculating the platform fault frequency based on the platform fault standard value and the total number of demand releases by the platforms. The fault prediction module is used to classify faults based on the fault frequency and demand data, and to establish a platform fault prediction model based on the classified fault types. Fault classification based on fault frequency and demand data includes: obtaining the median of release demands for platforms that have not experienced faults within a set time range; determining the fault-free type based on the median release demands and demand data; and determining the fault type based on the fault frequency and a set frequency threshold. The fault type includes the fault-free type and the fault type. The fault data determination module is used to predict the fault data of the target requirement based on the fault prediction model.

6. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method as claimed in any one of claims 1-4.

7. A computer readable storage medium characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the method as described in any one of claims 1-4.

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