An operation and maintenance control method based on an intelligent operation and maintenance method

Abstract

The application discloses an operation and maintenance control method based on an intelligent operation and maintenance method. When a user logs in an intelligent operation and maintenance system, the identity of the user is verified, and the result of the verification can be represented by a decision value. The decision value can represent the authority of the user, so that some function modules of the intelligent operation and maintenance system can be isolated from the user from the perspective of the user authority. In addition, the decision value can also limit the operation and maintenance operation of the user on some function modules from the perspective of whether the historical maintenance operation of the user will cause a failure of the intelligent operation and maintenance system, so as to guarantee the safety of the intelligent operation and maintenance system in the case that a criminal steals the identity of the user. The method in the application is suitable for a data processing method or method for administrative, commercial, financial, management, supervision or prediction purposes; and other special data processing methods or methods for administrative, commercial, financial, management, supervision or prediction purposes are not included.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of operation and maintenance control, and particularly relates to an operation and maintenance control method based on an intelligent operation and maintenance method. BACKGROUND

[0002] An operation and maintenance management system is a comprehensive operation and maintenance management platform integrating operation and maintenance resource monitoring, operation and maintenance work order processing, operation and maintenance report, operation and maintenance knowledge base and operation and maintenance display center based on an operation and maintenance management system and combined with actual business scenarios. With the increasing demand of users for operation and maintenance management capabilities, the use of operation and maintenance management systems has gradually become the mainstream of operation and maintenance management. However, the use scenarios of operation and maintenance management systems are complex, and a large number of personnel (users) are involved in operation and maintenance management, which makes it difficult to manage the personnel. The development of technology provides convenience for users and also enriches the means of attacks on operation and maintenance management systems by illegal persons. In the application scenarios of operation and maintenance management systems, it is inevitable that the operation and maintenance management system will be damaged by personnel due to operational errors, and it is also inevitable that illegal persons will disguise as users of the operation and maintenance management system and launch attacks on the operation and maintenance management system. SUMMARY

[0003] In order to solve the problems of the prior art, the application provides an operation and maintenance control method based on an intelligent operation and maintenance method. When a user logs in an intelligent operation and maintenance system, the identity of the user is verified, and the result of the verification can be represented by a decision value. The decision value can represent the authority of the user, so as to isolate the user from some function modules of the intelligent operation and maintenance system from the perspective of user authority. In addition, the decision value can also limit the operation and maintenance operation of the user on some function modules from the perspective of whether the historical maintenance operation of the user will cause a fault of the intelligent operation and maintenance system, so as to protect the security of the intelligent operation and maintenance system in the case of illegal person stealing the identity of the user. Further, in the process of determining the decision value, the behavior of the user is analyzed from a long-term time period and a short-term time period, which can effectively identify the identity risk of the user. The method in the application is applicable to data processing methods or methods for administrative, commercial, financial, management, supervision or prediction purposes; other categories do not include data processing methods or methods specially applicable to administrative, commercial, financial, management, supervision or prediction purposes.

[0004] The technical effects achieved by the application are realized through the following scheme:

[0005] In a first aspect, the present specification provides an operation and maintenance control method based on an intelligent operation and maintenance method. The method is based on an intelligent operation and maintenance system, and the system includes a decision module and an information acquisition module and a plurality of function modules electrically connected with the decision module.

[0006] The method is executed by the decision module, and the method includes:

[0007] detecting that a user logs in the intelligent operation and maintenance system;

[0008] comparing the login information with data in a preset first database to find a first characteristic value corresponding to the login information; the first database is used to manage the correspondence between the login information and the first characteristic value of each user, and the first characteristic value is positively correlated with the preset permission priority of the user;

[0009] comparing the login information with data in a preset second database to find a second characteristic value and a third characteristic value corresponding to the login information; the second database is used to manage the correspondence between the login information and the first failure frequency caused by the operation and maintenance operation performed by the user within a first specified time period after the user logs in the intelligent operation and maintenance system for a specified number of times in history closest to the current time, and the correspondence between the login information and the second failure frequency caused by the operation and maintenance operation performed by the user within a second specified time period after the user logs in the intelligent operation and maintenance system for a specified number of times in history closest to the current time; the duration of the second specified time period is greater than that of the first specified time period; the second characteristic value is positively correlated with the first failure frequency, and the third characteristic value is positively correlated with the second failure frequency;

[0010] inputting the first characteristic value, the second characteristic value and the third characteristic value into a pre-trained decision model to obtain a decision value output by the decision model; the decision value is positively correlated with the risk of the operation and maintenance operation of the user this time;

[0011] comparing the decision value with data in a preset third database to find a module identifier corresponding to the decision value; the third database is used to manage the correspondence between the decision value and the module identifier of the plurality of functional modules;

[0012] isolating the functional module to which the module identifier belongs to the operation and maintenance operation of the user this time;

[0013] if the decision value is greater than a decision threshold, the information acquisition module instructs the user to input verification information;

[0014] if the verification information passes the verification, the operation and maintenance operation of the user this time on the functional module not isolated is released.

[0015] In an optional embodiment of the present specification, the decision model is trained by the following steps:

[0016] For each of the plurality of function modules, a risk attribute value corresponding to the function module is determined according to a maximum risk that the function module can withstand; wherein the risk attribute value is negatively correlated with the maximum risk;

[0017] obtaining historical data; wherein the historical data indicates a priority of a permission of each user, a time at which each user performs an operation and maintenance operation, and a failure rate caused by the operation and maintenance operation performed by each user;

[0018] For each user, a first sample value, a second sample value and a third sample value corresponding to the user are constructed as sample input according to the historical data;

[0019] From the plurality of function modules, a function module with the maximum risk attribute value and a permission of the user to perform operation and maintenance is determined as a first module; and from the plurality of function modules, a function module with the minimum risk attribute value and no permission of the user to perform operation and maintenance is determined as a second module;

[0020] An average value of the risk attribute value of the first module and the risk attribute value of the second module is taken as a sample label corresponding to the sample input;

[0021] The sample input constructed based on the historical data of each user and the sample label corresponding to the sample input are used to train a decision model to be trained to obtain the preset decision model.

[0022] In an optional embodiment of the present specification, the length of the first specified time period is equal to the minimum interval length between any two adjacent operation and maintenance operations of the user in history.

[0023] In an optional embodiment of the present specification, the length of the second specified time period is positively correlated with the maximum interval length between any two adjacent operation and maintenance operations of the user in history, and is negatively correlated with the first specified time period.

[0024] In an optional embodiment of the present specification, the specified number of times is positively correlated with a failure frequency in history of the intelligent operation and maintenance system.

[0025] In an optional embodiment of the present specification, the method further comprises: if the decision value is not greater than a decision threshold, releasing the operation and maintenance operation of the user this time for a function module that is not isolated.

[0026] In an optional embodiment of the present specification, if the decision value is not greater than the decision threshold, and the first failure frequency is greater than the second failure frequency, the information acquisition module is instructed to input verification information by the user.

[0027] In an alternative embodiment of the present specification, the functional module comprises at least one of the following:

[0028] The communication module, the storage module, and the monitoring module.

[0029] In a second aspect, the present specification provides an operation and maintenance control device based on an intelligent operation and maintenance method, the device being applied to a decision module of an intelligent operation and maintenance system, and the device being used to implement the method of any one of the first aspect.

[0030] In a third aspect, the present application further provides an electronic device, comprising:

[0031] a processor; and

[0032] a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform the method in the first aspect.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium storing one or more programs, which, when executed by an electronic device comprising a plurality of applications, cause the electronic device to perform the method in the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the prior art solutions, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0035] Figure 1 FIG. 1 is a structural diagram of at least part of a structure of an intelligent operation and maintenance system according to an embodiment of the present application;

[0036] Figure 2 FIG. 2 is a flowchart of an operation and maintenance method based on an intelligent operation and maintenance system according to an embodiment of the present application;

[0037] Figure 3 FIG. 3 is a structural diagram of an operation and maintenance device based on an intelligent operation and maintenance method according to an embodiment of the present application;

[0038] Figure 4 FIG. 4 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in connection with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative work fall within the scope of protection of the present application.

[0040] The present application will be described in further detail below in connection with specific embodiments and corresponding drawings. In different embodiments, similar elements are denoted by associated similar element reference numbers. In the following embodiments, many details are described in order to make the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different cases, or can be replaced by other elements, materials or methods. In some cases, some operations related to the present application are not shown or described in the specification in order to avoid the core part of the present application being overwhelmed by too much description, and it is not necessary for those skilled in the art to describe these related operations in detail according to the description in the specification and general technical knowledge in the art.

[0041] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various embodiments. Meanwhile, the steps or actions in the method description can also be sequentially adjusted or adjusted in a manner that is obvious to those skilled in the art. Therefore, the order in the specification and the drawings is only for the purpose of clearly describing a certain embodiment, and does not mean that it is the necessary order, unless otherwise stated that a certain order must be followed.

[0042] The serial numbers of components in this paper, such as "first", "second", etc., are only used to distinguish the described objects, and do not have any order or technical meaning. The "connection" and "coupling" in the present application include direct and indirect connection (coupling) unless otherwise specified.

[0043] The technical solutions provided by the embodiments of the present application will be described in detail below in connection with the drawings.

[0044] Exemplarily, the architecture of the intelligent operation and maintenance system in the specification is as shown in Figure 1 The intelligent operation and maintenance system includes a decision module, and an information acquisition module and a plurality of function modules (such as function module 1 to function module i in Figure 1 ) electrically connected with the decision module respectively. Optionally, the function module includes at least one of the following: a communication module, a storage module, a monitoring module.

[0045] The method in the specification is executed by a decision model, and the operation and maintenance control method based on the intelligent operation and maintenance method in the specification includes the following steps:

[0046] S100: When detecting that a user logs in the intelligent operation and maintenance system, the information acquisition module is instructed to instruct the user to input login information.

[0047] In an optional embodiment of the specification, the intelligent operation and maintenance management system includes an information acquisition module (such as a display), which is used to display information to the user, so as to realize the interaction between the user and the intelligent operation and maintenance management system. In this embodiment, when the user calls the operation interface corresponding to the intelligent operation and maintenance management system through the information acquisition module by clicking or the like, the decision module detects that the user logs in the intelligent operation and maintenance system.

[0048] The user in the specification can be represented by a username, an account number, an employee number, and the like. The person holding the user identity may be a management personnel of the intelligent operation and maintenance system, or may be an illegal user who steals or forges the username of the management personnel.

[0049] Optionally, when detecting that the user logs in the intelligent operation and maintenance system, the decision module sends a login instruction to the information acquisition module, so that the information acquisition module instructs the user to input login information under the triggering of the login instruction.

[0050] The login information in the specification includes information for representing the user identity, such as a username, an account number, an employee number, and the like.

[0051] S102: Compare the login information with data in a preset first database to find a first characteristic value corresponding to the login information.

[0052] In an optional embodiment of the specification, before performing this step, the first database is established in advance based on historical data. The first database is used to manage the correspondence between the login information of each user and the first characteristic value, and the first characteristic value is positively correlated with the preset priority of the user. Optionally, when a user is created, the data in the first database is updated based on the login information of the created user. The priority of the user is set when the user is created, and the higher the priority of the user, the higher the management right of the user to the intelligent operation and maintenance system.

[0053] S104: Compare the login information with data in a preset second database to find a second characteristic value and a third characteristic value corresponding to the login information.

[0054] In an optional embodiment of the specification, before performing this step, the second database is established in advance based on historical data.

[0055] The second database is used to manage the correspondence between the frequency of first faults caused by the operation and maintenance operations performed by each user within a first specified time period after the most recent specified number of logins to the intelligent operation and maintenance system in history; and to manage the correspondence between the frequency of second faults caused by the operation and maintenance operations performed by each user within a second specified time period after the most recent specified number of logins to the intelligent operation and maintenance system in history; the duration of the second specified time period is greater than the duration of the first specified time period; the second feature value is positively correlated with the first fault frequency, and the third feature value is positively correlated with the second fault frequency.

[0056] The second eigenvalue is derived from the first failure rate, which represents user behaviors that can cause failures within a short timeframe defined by a first specified time period. Therefore, the second eigenvalue represents the risk posed by users' recent behavior. Correspondingly, the third eigenvalue represents the risk posed by users' long-term behavior. Short-term user behavior can indicate risks such as account theft, while long-term user behavior can indicate risks such as account forgery. When the intelligent operation and maintenance system has a large number of users, the difference between long-term and short-term user behavior can indicate organized attacks by malicious actors against the intelligent operation and maintenance system.

[0057] In an optional embodiment of this specification, the first specified time period and the second specified time period are recorded in a second database. The duration of the first specified time period is equal to the minimum interval between any two adjacent maintenance operations performed by the user in history. The time interval between two adjacent maintenance operations is considered a maintenance operation. Typically, when a maintenance operation ends, the user has achieved the purpose of this maintenance. The first failure rate can reflect the user's subjective intent during a single maintenance operation.

[0058] In a further optional embodiment of this specification, the duration of the second specified time period is positively correlated with the maximum interval between any two adjacent maintenance operations performed by the user in history. The duration of different maintenance operations varies, and it is possible that some maintenance operations are merely for checking the status of certain functional modules without performing effective maintenance. If a user performs a maintenance operation for a longer period, it indicates a higher probability that the operation was effective, making the second failure rate obtained based on the second specified time period reflect the user's subjective intent during effective maintenance. Furthermore, the second specified time period is also negatively correlated with the first specified time period. The shorter the first specified time period, the lower the authenticity of the first failure rate and the higher the risk of organized attacks; therefore, a longer second specified time period is needed to determine the user's intent based on the failure rate. Optionally, if the duration of the first specified time period is less than the duration threshold, the maximum interval duration is multiplied by the first coefficient to obtain the duration of the second specified time period; if the duration of the first specified time period is not less than the duration threshold (preset value), the maximum interval duration is multiplied by the second coefficient to obtain the duration of the second specified time period, wherein the first coefficient is greater than the second coefficient, and the second coefficient is greater than 1.

[0059] Optionally, the specified number of failures is positively correlated with the historical failure frequency of the intelligent operation and maintenance system. Since the failure frequency may also be affected by the intelligent operation and maintenance system itself in some cases, the failure frequency used when determining the specified number of failures is the failure rate of all failures in the history of the intelligent operation and maintenance system, rather than the failure rate corresponding to the user logging in this time.

[0060] S106: Input the first feature value, the second feature value, and the third feature value into the pre-trained decision model to obtain the decision value output by the decision model.

[0061] The decision values ​​in this specification are positively correlated with the risk of the user's current operation and maintenance. Since the decision values ​​in this specification are obtained from various feature values, they can comprehensively represent the user's identity and intent from multiple aspects.

[0062] All existing artificial intelligence models used for classification can be used as decision models in this specification.

[0063] S108: Compare the decision value with the data in the preset third database to find the module identifier corresponding to the decision value.

[0064] The third database in this specification is used to manage the correspondence between decision values ​​and module identifiers of the aforementioned functional modules.

[0065] It should be noted that the decision value output by the decision model can be a specific numerical value, such as 0.5. The decision value managed in the third database is a range, such as 0.46 to 0.52. If 0.5 falls within this range, then the module identifier corresponding to that range is the module identifier corresponding to the decision value output by the decision module.

[0066] S110: Isolate the functional module to which the module identifier belongs for the user's current operation and maintenance.

[0067] The goal of this step is to identify which functional modules pose a higher risk to this maintenance operation and isolate these high-risk modules from the scope of the operation. Optionally, information about the unisolated functional modules can be displayed through the information acquisition module.

[0068] S112: If the decision value is greater than the decision threshold, the information acquisition module instructs the user to input verification information.

[0069] The decision values ​​determined by the methods described in this specification can be used to determine which functional modules need to be isolated. Furthermore, when the decision value indicates a high risk to the user's identity (the decision value exceeds the decision threshold), it can also be used to verify the user's identity. In an optional embodiment of this specification, the intelligent operation and maintenance system includes a verification module. The verification module can be an application communicationally connected to the decision module. Each verification module corresponds one-to-one with an operation and maintenance personnel and runs on the personnel's terminal device. If the decision value exceeds the decision threshold, the decision module sends a verification command to the verification module corresponding to the operation and maintenance personnel whose login information belongs. After obtaining the verification information carried in the verification command through their corresponding verification module, the operation and maintenance personnel input the verification information into the information acquisition module.

[0070] S114: If the verification information passes the verification, the user's operation and maintenance of the non-isolated functional module is allowed.

[0071] An operation and maintenance control method based on intelligent operation and maintenance is adopted. When a user logs into the intelligent operation and maintenance system, user identity verification is triggered. The result of this verification can be represented by a decision value. The decision value represents the user's permissions, allowing for the isolation of certain functional modules of the intelligent operation and maintenance system from the user's perspective. Furthermore, the decision value can also restrict the user's operation and maintenance of certain functional modules based on whether the user's past maintenance operations might cause malfunctions in the intelligent operation and maintenance system, thus ensuring the security of the intelligent operation and maintenance system in the event of identity theft by malicious actors. Moreover, in the process of determining the decision value, user behavior is analyzed from both long-term and short-term time periods, effectively identifying user identity risks.

[0072] In an optional embodiment of this specification, if the user's identity risk is low (the decision value is not greater than the decision threshold), then the user's operation and maintenance of the non-isolated functional module is allowed.

[0073] In other optional embodiments of this specification, if the decision value is not greater than the decision threshold, the user's identity risk is low, and the first fault frequency is greater than the second fault frequency, indicating that the risk of user identity theft is the main risk, then the information acquisition module instructs the user to input verification information.

[0074] The following explains how to obtain the decision model.

[0075] In an optional embodiment of this specification, the training steps of the decision model are as follows:

[0076] S200: For each of the plurality of functional modules, determine the risk attribute value corresponding to that functional module based on the maximum risk that the functional module can withstand.

[0077] The risk attribute value is negatively correlated with the maximum risk. For example, if the log query module can withstand a higher maximum risk, its corresponding risk attribute value will be lower; if the log deletion module can withstand a lower maximum risk, its corresponding risk attribute value will be higher. The specific value of the risk attribute can be determined according to actual needs.

[0078] S202: Obtain historical data.

[0079] The historical data represents the permission priority of each user, the time when each user performed maintenance operations, and the failure rate caused by each user's maintenance operations. This historical data is used to generate training samples for the model. The historical data includes historical maintenance operations from different users, as well as different historical maintenance operations performed by the same user.

[0080] S204: For each user, construct a first sample value, a second sample value, and a third sample value corresponding to that user based on the historical data, and use them as sample input.

[0081] The first sample value corresponds to the first feature value, the second sample value corresponds to the second feature value, and the third sample value corresponds to the third feature value.

[0082] S206: From the plurality of functional modules, determine the functional module that the user has permission to perform operation and maintenance and has the highest risk attribute value, and designate it as the first module; from the plurality of functional modules, determine the functional module that the user does not have permission to perform operation and maintenance and has the lowest risk attribute value, and designate it as the second module.

[0083] S208: The average of the risk attribute values ​​of the first module and the risk attribute values ​​of the second module is used as the sample label corresponding to the sample input.

[0084] The sample label corresponds to the decision value.

[0085] S210: Using sample inputs constructed based on the historical data of each user and the sample labels corresponding to the sample inputs, the decision model to be trained is trained to obtain the preset decision model.

[0086] This manual describes a supervised training method for the decision-making model. The training objective is to minimize the difference between the model's output and the sample labels. The convergence condition can be determined based on actual business needs. For example, the number of iterations can be specified as the convergence condition.

[0087] Based on the same idea, the embodiments in this specification also provide corresponding... Figure 1 The process shown is part of a pesticide spraying device.

[0088] like Figure 3 As shown, the operation and maintenance control device based on the intelligent operation and maintenance method is applied to the decision-making module of the intelligent operation and maintenance system. The device may include one or more of the following units:

[0089] The detection unit 200 is configured to, when it detects that a user has logged into the intelligent operation and maintenance system, cause the information acquisition module to instruct the user to input login information.

[0090] The first feature value determination unit 202 is configured to: compare the login information with data in a preset first database to find the first feature value corresponding to the login information; wherein, the first database is used to manage the correspondence between the login information of each user and the first feature value, and the first feature value is positively correlated with the user's preset permission priority.

[0091] Other feature value determination unit 204 is configured as follows: the second database is used to manage the correspondence between the first fault frequency caused by the operation and maintenance operations performed by the user and the login information within a first specified time period after the most recent specified number of logins to the intelligent operation and maintenance system in history; and to manage the correspondence between the second fault frequency caused by the operation and maintenance operations performed by the user and the login information within a second specified time period after the most recent specified number of logins to the intelligent operation and maintenance system in history; the duration of the second specified time period is greater than the duration of the first specified time period; the second feature value is positively correlated with the first fault frequency, and the third feature value is positively correlated with the second fault frequency.

[0092] The decision value determination unit 206 is configured to input the first feature value, the second feature value, and the third feature value into a pre-trained decision model to obtain the decision value output by the decision model; wherein the decision value is positively correlated with the risk of the user's current operation and maintenance.

[0093] The module identifier determination unit 208 is configured to: compare the decision value with data in a preset third database to find the module identifier corresponding to the decision value; wherein, the third database is used to manage the correspondence between the decision value and the module identifiers of the plurality of functional modules.

[0094] The isolation unit 210 is configured to: isolate the functional module to which the module identifier belongs and isolate the user's current operation and maintenance.

[0095] The verification unit 212 is configured to: if the decision value is greater than the decision threshold, then cause the information acquisition module to instruct the user to input verification information.

[0096] The operation and maintenance unit 214 is configured to allow the user's operation and maintenance of the non-isolated functional module if the verification information passes the verification.

[0097] In an optional embodiment of this specification, the decision model is trained through the following steps:

[0098] For each of the plurality of functional modules, a risk attribute value is determined based on the maximum risk that the functional module can withstand; wherein, the risk attribute value is negatively correlated with the maximum risk;

[0099] Acquire historical data; wherein the historical data represents the permission priority of each user in history, the time when each user performed operation and maintenance operations, and the failure rate caused by each user's operation and maintenance operations.

[0100] For each user, a first sample value, a second sample value, and a third sample value are constructed based on the historical data, which are used as sample inputs;

[0101] From the aforementioned functional modules, the functional module with which the user has permission to perform maintenance and the highest risk attribute value is determined as the first module; from the aforementioned functional modules, the functional module with which the user does not have permission to perform maintenance and the lowest risk attribute value is determined as the second module.

[0102] The average of the risk attribute values ​​of the first module and the risk attribute values ​​of the second module is used as the sample label corresponding to the sample input.

[0103] The decision model to be trained is obtained by using sample inputs constructed based on the historical data of each user and the sample labels corresponding to the sample inputs.

[0104] In an optional embodiment of this specification, the duration of the first specified time period is equal to the minimum interval between any two adjacent maintenance operations performed by the user in history.

[0105] In an optional embodiment of this specification, the duration of the second specified time period is positively correlated with the maximum interval between any two adjacent maintenance operations performed by the user in history, and negatively correlated with the first specified time period.

[0106] In one optional embodiment of this specification, the specified number of times is positively correlated with the historical failure frequency of the intelligent operation and maintenance system.

[0107] In an optional embodiment of this specification, the verification unit is further configured to: allow the user's current maintenance operation on the unisolated functional module if the decision value is not greater than the decision threshold; and / or,

[0108] If the decision value is greater than the decision threshold, the information acquisition module instructs the user to input verification information, including: if the decision value is not greater than the decision threshold, and the first fault frequency is greater than the second fault frequency, the information acquisition module instructs the user to input verification information.

[0109] In an optional embodiment of this specification, the functional module includes at least one of the following:

[0110] Communication module, storage module, monitoring module.

[0111] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0112] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0113] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0114] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a maintenance control method based on intelligent maintenance at the logical level. The processor executes the program stored in memory and specifically performs any of the aforementioned maintenance control methods based on intelligent maintenance.

[0115] The above is as stated in this application. Figure 1The operation and maintenance control method based on intelligent operation and maintenance disclosed in the illustrated embodiment can be applied to a processor (i.e., the deletion control module in this specification) or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-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 can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0116] The electronic device can also perform Figure 1 An operation and maintenance control method based on intelligent operation and maintenance is proposed and implemented. Figure 1 The functions of the embodiments shown are not described in detail here.

[0117] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The embodiment shown is a method for executing an operation and maintenance control method based on an intelligent operation and maintenance method, and is specifically used to execute any of the aforementioned operation and maintenance control methods based on an intelligent operation and maintenance method.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, approaches, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (methods), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0123] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0124] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media 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 memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, methodologies, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An operation and maintenance control method based on intelligent operation and maintenance methods, characterized in that, The method is based on an intelligent operation and maintenance system, which includes: a decision-making module, an information acquisition module electrically connected to the decision-making module, and several functional modules. The method is executed by the decision module, and the method includes: When the system detects that a user has logged into the intelligent operation and maintenance system, the information acquisition module instructs the user to enter login information. The login information is compared with data in a preset first database to find the first feature value corresponding to the login information; wherein, the first database is used to manage the correspondence between the login information of each user and the first feature value, and the first feature value is positively correlated with the user's preset permission priority; The login information is compared with data in a preset second database to find the second and third feature values ​​corresponding to the login information. The second database is used to manage the correspondence between the frequency of first faults caused by the operation and maintenance operations performed by the user within a first specified time period after the most recent specified number of logins to the intelligent operation and maintenance system in history, and the login information; and to manage the correspondence between the frequency of second faults caused by the operation and maintenance operations performed by the user within a second specified time period after the most recent specified number of logins to the intelligent operation and maintenance system in history, and the login information. The duration of the second specified time period is longer than the duration of the first specified time period; the second feature value is positively correlated with the first fault frequency, and the third feature value is positively correlated with the second fault frequency. The first feature value, the second feature value, and the third feature value are input into a pre-trained decision model to obtain the decision value output by the decision model; wherein, the decision value is positively correlated with the risk of the user's current operation and maintenance. The decision value is compared with data in a preset third database to find the module identifier corresponding to the decision value; wherein, the third database is used to manage the correspondence between the decision value and the module identifiers of the plurality of functional modules; The module to which the module belongs is identified, thus isolating the user's current maintenance operation; If the decision value is greater than the decision threshold, the information acquisition module instructs the user to input verification information. If the verification information passes the verification, the user's maintenance operation on the non-isolated functional module will be allowed.

2. The method as described in claim 1, characterized in that, The decision model is trained through the following steps: For each of the plurality of functional modules, a risk attribute value is determined based on the maximum risk that the functional module can withstand; wherein, the risk attribute value is negatively correlated with the maximum risk. Acquire historical data; wherein the historical data represents the permission priority of each user in history, the time when each user performed operation and maintenance operations, and the failure rate caused by each user's operation and maintenance operations. For each user, a first sample value, a second sample value, and a third sample value are constructed based on the historical data, which are used as sample inputs; From the aforementioned functional modules, the functional module with which the user has permission to perform maintenance and the highest risk attribute value is determined as the first module; from the aforementioned functional modules, the functional module with which the user does not have permission to perform maintenance and the lowest risk attribute value is determined as the second module. The average of the risk attribute values ​​of the first module and the risk attribute values ​​of the second module is used as the sample label corresponding to the sample input. The decision model to be trained is obtained by using sample inputs constructed based on the historical data of each user and the sample labels corresponding to the sample inputs.

3. The method as described in claim 1, characterized in that, The duration of the first specified time period is equal to the minimum interval between any two adjacent maintenance operations performed by the user in history.

4. The method as described in claim 3, characterized in that, The duration of the second specified time period is positively correlated with the maximum interval between any two adjacent maintenance operations performed by the user in history, and negatively correlated with the first specified time period.

5. The method as described in claim 1, characterized in that, The specified number of times is positively correlated with the historical failure frequency of the intelligent operation and maintenance system.

6. The method as described in claim 1, characterized in that, The method further includes: if the decision value is not greater than the decision threshold, then allowing the user's current maintenance operation on the unisolated functional module; and / or, If the decision value is not greater than the decision threshold, and the first fault frequency is greater than the second fault frequency, then the information acquisition module instructs the user to input verification information.

7. The method as described in claim 1, characterized in that, The functional module includes at least one of the following: Communication module, storage module, monitoring module.

8. An operation and maintenance control device based on intelligent operation and maintenance methods, characterized in that, The device is applied to the decision-making module of an intelligent operation and maintenance system, and the device is used to implement the method of any one of claims 1 to 7.

9. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 7.

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