CN115033649A

CN115033649A - Fault processing method, device, equipment and storage medium based on report development

Info

Publication number: CN115033649A
Application number: CN202210613123.7A
Authority: CN
Inventors: 张洪姣
Original assignee: Xinao Shuneng Technology Co Ltd
Current assignee: Xinao Shuneng Technology Co Ltd
Priority date: 2022-05-31
Filing date: 2022-05-31
Publication date: 2022-09-09

Abstract

The disclosure provides a fault processing method, a fault processing device, fault processing equipment and a storage medium based on report development. The method comprises the following steps: taking risk prevention and control information, online fault statistical information, fault completion statistical information, quality score statistical information and test fault statistical information as initialization information, and synchronizing the initialization information into a data warehouse; processing the initialization information into form data by using a data warehouse, cleaning and aggregating the form data, and summarizing the forms after cleaning and aggregating the data according to the business theme to obtain summarized form data; and querying summarized table data stored in a data warehouse by using a preset report platform, generating a fault processing report based on the queried target table data and pre-established report configuration, and monitoring the fault of the business domain of the target enterprise object based on the fault processing report. The method and the device reduce the time consumption of data processing, improve the data processing efficiency and can accurately acquire the fault related information.

Description

Fault processing method, device, equipment and storage medium based on report development

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a fault processing method, apparatus, device, and storage medium based on report development.

Background

The data warehouse of the traditional big data platform is generally divided into a source data layer, a data warehouse layer, a data mart layer and a data application layer, wherein the data warehouse layer is used for cleaning data of the source data layer and gathering fields with the same granularity so as to provide the data mart layer with better use. In practical applications, the data warehouse is mainly used for data storage, cleaning and processing of various data, so that data processing results based on the data warehouse provide needed data for users.

In the prior art, when a data warehouse is used for processing data, data is simply layered without strictly dividing data layering, so that data tables in the data warehouse are relatively disordered and the data warehouse is not easily expanded; in addition, when the relevant data of fault processing is generated, statistical analysis aiming at risk scores, risk types and the like of each business domain is not performed, statistics on the proposed condition and the completed condition of the on-line fault is not performed, the quality score of each team of an enterprise is not evaluated, so that the performance of the team and the individual cannot be evaluated, the condition of testing problems of testers is not subjected to statistics, and the like. Therefore, the existing data processing method of the data warehouse has the problems that the time consumption of data processing is long, the data processing result is inaccurate, the fault reason cannot be determined and the like.

Disclosure of Invention

In view of this, embodiments of the present disclosure provide a fault processing method, an apparatus, a device, and a storage medium based on report development to solve the problems in the prior art, such as long time consumed for data processing, inaccurate data processing result, and failure to specify a fault cause.

In a first aspect of the embodiments of the present disclosure, a fault handling method based on report development is provided, including: grouping preset risk types, determining service domains, and determining risk scores and risk types corresponding to the service domains of the target enterprise object based on the risk types and the service domains; acquiring risk prevention and control information corresponding to a business domain of a target enterprise object from a distributed task platform, counting online faults corresponding to the business domain to obtain online fault statistical information, and counting fault completion conditions according to the online faults to obtain fault completion condition statistical information; counting the total quality score, the research and development quality score, the online quality score and the fault quality score of the target enterprise object according to a pre-configured quality score counting mode, and determining the quality score counting information corresponding to the target enterprise object according to a counting result; acquiring test information generated by a tester during fault testing, counting the test information to obtain test fault statistical information, taking risk prevention and control information, on-line fault statistical information, fault completion condition statistical information, quality score statistical information and test fault statistical information as initialization information, and synchronizing the initialization information into a data warehouse; processing the initialization information into form data by using a data warehouse, cleaning and aggregating the form data, and summarizing the forms subjected to data cleaning and aggregation according to a preset service theme to obtain summarized form data; and querying summarized table data stored in a data warehouse by using a preset report platform, generating a fault processing report based on the queried target table data and pre-established report configuration, and monitoring the fault of the business domain of the target enterprise object based on the fault processing report.

In a second aspect of the embodiments of the present disclosure, a fault processing apparatus based on report development is provided, including: the determining module is configured to group preset risk types, determine business domains, and determine risk scores and risk types corresponding to all the business domains of the target enterprise object based on the risk types and the business domains; the acquisition module is configured to acquire risk prevention and control information corresponding to a business domain of a target enterprise object from the distributed task platform, count online faults corresponding to the business domain to obtain online fault statistical information, and count fault completion conditions according to the online faults to obtain fault completion condition statistical information; the statistical module is configured to count the total quality score, the research and development quality score, the online quality score and the fault quality score of the target enterprise object according to a pre-configured quality score statistical mode, and determine quality score statistical information corresponding to the target enterprise object according to a statistical result; the system comprises a synchronization module, a data warehouse and a data processing module, wherein the synchronization module is configured to acquire test information generated by a tester during fault testing, count the test information to obtain test fault statistical information, and synchronize the initialization information into the data warehouse by taking risk prevention and control information, on-line fault statistical information, fault completion statistical information, quality score statistical information and test fault statistical information as initialization information; the processing module is configured to process the initialization information into form data by using the data warehouse, perform data cleaning and aggregation on the form data, and summarize the forms subjected to data cleaning and aggregation according to a preset service theme to obtain summarized form data; and the monitoring module is configured to query the summary table data stored in the data warehouse by using a preset report platform, generate a fault processing report based on the queried target table data and the report configuration created in advance, and monitor the fault of the business domain of the target enterprise object based on the fault processing report.

The embodiment of the present disclosure adopts at least one technical scheme that can achieve the following beneficial effects:

synchronizing the initialization information into a data warehouse by taking risk prevention and control information, on-line fault statistical information, fault completion statistical information, quality score statistical information and test fault statistical information as initialization information; processing the initialization information into form data by using a data warehouse, cleaning and aggregating the form data, and summarizing the forms after cleaning and aggregating the data according to the business theme to obtain summarized form data; and querying summarized table data stored in a data warehouse by using a preset report platform, generating a fault processing report based on the queried target table data and pre-established report configuration, and monitoring the fault of the business domain of the target enterprise object based on the fault processing report. The method and the device reduce the time consumption of data processing, improve the data processing efficiency and can accurately acquire the fault related information.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.

Fig. 1 is a schematic flowchart of a fault handling method based on report development according to an embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of a fault handling apparatus based on report development according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.

Many businesses of enterprises are complex, the accessed third party systems are numerous, online failures often occur, and once the online failures occur, the recovery can be carried out for several hours. Each time a fault occurs, emergency repair is required, emergency online is required, and a problem is repaired, so that a new problem is often brought, and the problems in the emergency repair are that no fault emergency plan, core service data monitoring and availability monitoring, routing inspection mechanism and the like exist. The existing fault processing system does not count the proposed condition and the completion condition of the on-line fault; the quality scores of each team of the enterprise are not evaluated, so that the performance of the teams and individuals cannot be evaluated; no statistics are made of the conditions of the test questions of the tester, and so on.

In order to guarantee the stability and safety of the service, specifications and procedures need to be introduced. The enterprise can formulate reward punishment rule in order to ensure delivery and operation quality, and regular company can not directly wushu go to the judgement responsibility to the problem of finding on-line, and the range that the omission of problem was dragged is extremely wide, only after the problem analysis is clear, can decide which field is main responsibility, and which field is time then, just so can make closed loop improvement. According to the method and the system, the performance of the staff is comprehensively evaluated through the risk level and type of the fault, the completion condition of the on-line fault and the quality of the staff, the reward and punishment degree is achieved, the risk assessment and the plan before the on-line service can be carried out according to experience teaching, the monitoring of the system is realized, the fault level is set, and the fault loss stopping and recovery are carried out. The method and the system can improve the enthusiasm of staff in solving faults and problems and accumulate experience and training, so that the existing problems do not go wrong any more, the trust and the satisfaction of customers to enterprises are improved, and the method and the system are very beneficial to long-term development of companies.

In the following, a detailed description is given of risks that may exist in a business domain of an existing target enterprise object with reference to specific embodiments, and in actual work, the business domain may have many risks, which roughly include security risks, operational risks, and technical risks.

Firstly, the security risks are mainly as follows:

(1) information asset security risk: data leakage behavior: monitoring and alarming mechanism for data using behavior; data desensitization: effectiveness of data desensitization mechanism.

(2) Third party interaction risk: firstly, ciphertext transmission: third party interaction request information ciphertext transmission; controlling a white list: whether the third party interaction is white list limited.

(3) Network security risk: firstly, network attack: detecting and protecting network attack; the installation and the update of the anti-virus software detect and protect the network attack; secondly, network protection: the validity period of the certificate and the firewall; thirdly, network isolation: whether the production network is effectively isolated from other networks results in the online environment invoking the test environment.

Secondly, the operational risk is mainly as follows:

(1) risk of business operation function: the change and issue of the background core has no recheck risk: the background core change release has no rechecking risk: if the setting of the parameters lacks a proper approval authorization and rechecking mechanism, the wrong operation can be caused to bring business risks; validity of log management: the contents, desensitization, access and storage requirements of log records are determined, and if the log storage life is insufficient or no log record exists, the lack of log is caused to carry out responsibility tracing; risk of rationality of rights design/assignment: the system authority design authority granularity is too coarse and the authority distribution is unreasonable, which may cause the fraud/operation risk caused by too large authorization; fourthly, behavior audit: regular audit is established for key operation, and operation risks can be identified in time; whether to access an audit diary framework.

(2) Risk of release change: releasing authority control risks: the online release has no control flow, and the release authority is too large; and changing the domain name configuration: the changes of the main domain name, the secondary domain name and the jump link are not verified, and the drainage scheme causes the failure of accessing the old domain name by the user; third, configuring change risk: the configuration files are issued on line and frequently updated, so that configuration errors and omissions of the configuration files are caused;

(3) risk of database operation: managing DB authority: the database has no limiting function, for example, the database can be locally and directly connected with a production library; data export control: the database export data has no limitation and flows, such as sensitive data desensitization processing and data export approval flows.

Thirdly, the technical risks are mainly as follows:

(1) middleware and data storage risks: the master-slave mechanism: the core service database has no master-slave mechanism, and the whole service is unavailable due to database abnormality; processing the big data set: the data processed at one time is excessive, so that the resource is exhausted, the performance is low, the response is delayed, the overtime fails and even the downtime is caused; message backlog and fault tolerance mechanism: message backlog and message consumption failure in a core link cause abnormal business flow of a consumer; fourthly, index deletion: the database index orders the data for easy lookup. If a certain field is used as a filtering condition of SQL (for example, an order is searched according to a user id), an index missing will cause a full data traversal to be performed for each data query, resulting in low performance. This risk level is high when the data volume of the data table is large (may not be much at present, but increases over time), and the library search operation is frequent; buffer use strategy: the core service cache is avalanche and penetrated, and all flow is directly checked to cause the database to be abnormal and unavailable; sixthly, reading and writing separation: the reading and writing are separated, and the main library and the standby library are delayed for a long time, so that the query service data is inaccurate.

(2) Transaction risk: consistency of affairs: the transaction is inconsistent, for example, the order is successfully made, and the stock is over-sold due to failure of stock deduction; distributed transaction: data inconsistency and lack of distributed locks in distributed transactions lead to data inconsistency among multiple parallel transactions and abnormal business.

(3) Performance capacity risk: planning the capacity: core services have no capacity planning, resulting in the unavailability of large-flow incoming services; secondly, exporting limitation in batches: the batch export or import operation causes the service memory overflow service to be unavailable; data growth predictability: whether the data volume is reasonably estimated to increase, which causes the service to be unavailable (such as the user volume is increased); fourthly, dynamic planning of service: whether the core service has a dynamic capacity expansion scheme or not; flow limiting: core service meets the measures of whether the large flow has the flow limitation or not.

(4) Service dependent risk: interfaces are incompatible: the interface changes and the relying party must be notified to evaluate the impact and the joint debugging. Otherwise, the situation of incompatible interface and call-out will occur. When the interface is changed, the upstream and the client side joint debugging are required to be informed, if a third party is involved, the third party is also required to be informed of the joint debugging; the third party interface change also needs to inform the joint debugging of the party, and the party debugging does not pass through the third party interface, and an error level log needs to be printed, and a monitoring alarm is triggered immediately; service unavailable: when external dependencies are not available, how the service is to be handled, whether it is interrupted, rolled back, continued, or retried, is whether the service requirements are met. And monitoring alarm is carried out on the fault application and the upstream application. Whether a degradation scheme exists or not is applied upstream; monitoring alarm loss: the APM full link and the monitoring alarm platform are not accessed, so that the faults are difficult to find and position in time; fourthly, the timeout mechanism is unreasonable: the overtime is too short, so that the normal request can also fail; the overtime is too long, when the man-machine recognition service is unavailable or the response is slow, the number of application threads is full, and a user requests a large amount of overtime; the processing mode after overtime does not meet the product expectation, which causes abnormal service; how the downstream application responds to the timeout, the upstream application handles; whether to retry or not, and whether the service is considered to be successful, failed or unknown after the retry reaches the upper limit of times; and the man-machine identification and wind control protection service is used for retrying after overtime, and whether the retrying is the default release or the default interception after the retrying reaches the upper limit of times.

(5) Risk of architecture design: the coupling degree: the coupling degree is too high, for example, a plurality of core business processes are in the same service; invoking a link to be too long: the core links are too long, one of which is problematic, resulting in the unavailability of the entire core service.

(6) Disaster recovery risk: backup and recovery test: whether the important service system executes timely and effective backup and carries out data recovery test; and secondly, a disaster recovery drilling scheme: whether the core system and the node perform disaster recovery drilling or not; and thirdly, disaster recovery planning and planning: whether the important service system is brought into the disaster recovery plan or not and whether the important service system makes an emergency plan or not.

Fig. 1 is a schematic flowchart of a fault handling method based on report development according to an embodiment of the present disclosure. The report development-based fault handling method of fig. 1 may be performed by a server. As shown in fig. 1, the fault handling method based on report development may specifically include:

s101, grouping preset risk types, determining business domains, and determining risk scores and risk types corresponding to all the business domains of the target enterprise object based on the risk types and the business domains;

s102, acquiring risk prevention and control information corresponding to a business domain of a target enterprise object from a distributed task platform, counting online faults corresponding to the business domain to obtain online fault statistical information, and counting fault completion conditions according to the online faults to obtain fault completion condition statistical information;

s103, counting the total quality score, the research and development quality score, the online quality score and the fault quality score of the target enterprise object according to a pre-configured quality score counting mode, and determining quality score counting information corresponding to the target enterprise object according to a counting result;

s104, acquiring test information generated by a tester during fault testing, counting the test information to obtain test fault statistical information, taking risk prevention and control information, on-line fault statistical information, fault completion statistical information, quality score statistical information and test fault statistical information as initialization information, and synchronizing the initialization information into a data warehouse;

s105, processing the initialization information into form data by using a data warehouse, cleaning and aggregating the form data, and summarizing the forms subjected to data cleaning and aggregation according to a preset service theme to obtain summarized form data;

s106, querying summary table data stored in the data warehouse by using a preset report platform, generating a fault processing report based on the queried target table data and the pre-established report configuration, and monitoring the fault of the business domain of the target enterprise object based on the fault processing report.

Specifically, the Data Warehouse (DW) of the embodiment of the disclosure is obtained by performing system processing, summarizing and arrangement on the basis of extracting and cleaning original dispersed database Data. The purpose of data warehouse construction is to provide functional analysis and decision support for front-end query and analysis as a basis. The embodiment of the present disclosure uses a Hive data warehouse tool, which is mainly used to process structured data, and the Hive data warehouse is generally divided into 4 levels, i.e., an ODS layer, a DWD layer, a DWS layer, and an ADS layer, and each level is used to store different types of tables.

Further, the report platform may adopt a universal report platform, and in the following embodiments, the report platform in the embodiments of the present disclosure may also be replaced by the universal report platform. And in the universal report platform, the widget is the diagram content of the report. In the embodiment of the disclosure, a Presto query engine is used for query operation in the universal reporting platform, and Presto is used for querying data in the Hive data warehouse tool.

According to the technical scheme provided by the embodiment of the disclosure, risk prevention and control information, on-line fault statistical information, fault completion statistical information, quality score statistical information and test fault statistical information are used as initialization information, and the initialization information is synchronized into a data warehouse; processing the initialization information into form data by using a data warehouse, cleaning and aggregating the form data, and summarizing the forms after cleaning and aggregating the data according to the business theme to obtain summarized form data; and querying summarized table data stored in a data warehouse by using a preset report platform, generating a fault processing report based on the queried target table data and pre-established report configuration, and monitoring the fault of the business domain of the target enterprise object based on the fault processing report. The method and the device reduce the time consumption of data processing, improve the data processing efficiency and can accurately acquire the fault related information.

In some embodiments, determining the risk score and the risk type corresponding to each business domain of the target enterprise object based on the risk type and the business domain includes: and determining the risk score of each risk type corresponding to each business domain according to the business domain corresponding to the target enterprise object, and summing the risk scores of all the risk types in the business domains to obtain the risk score of each business domain of the target enterprise object.

Specifically, in daily work, in order to ensure safety, specifications and processes need to be introduced, service stability needs to be realized, but faults need to be reduced to the maximum extent as much as possible, information of faults on the line, such as the severity, the solution method and the severity level of the faults, leadership of fault departments and the like, is counted, and a basic situation of the faults can be roughly known based on the statistics of the fault information.

Further, after the fault occurs, the embodiment of the disclosure can perform statistical analysis on the completion condition of the fault and track the actual condition of subsequent fault resolution, so that on one hand, the enthusiasm of the staff for resolving the fault can be improved, and on the other hand, the trust and satisfaction of the client to the company are improved. In addition, in order to guarantee delivery and operation quality, an enterprise sets safety standards and makes fault punishment rules, so that the quality score of each employee is used for rating, the performance result is influenced, and the salary level is further influenced. The embodiment of the disclosure can count the quality scores of the employees so as to evaluate the performance. When a company tester tests the BUG, the embodiment of the disclosure realizes statistical analysis on the detailed test condition.

In some embodiments, acquiring risk prevention and control information corresponding to a business domain of a target enterprise object from a distributed task platform, and performing statistics on online faults corresponding to the business domain to obtain online fault statistical information includes: uploading online fault information generated by a service domain to a distributed task platform, and counting the online fault information of the service domain in the distributed task platform to obtain online fault statistical information; and synchronizing the risk prevention and control information and the online fault statistical information from the distributed task platform to a data warehouse by using a data scheduling platform, and mapping the structured information into a database table in an original data layer in the data warehouse, wherein the data warehouse adopts a Hive data warehouse.

Specifically, Hive is a data warehouse tool based on Hadoop, and can map structured data files into a database table and provide a query function similar to SQL. The UDF function adopted in Hive is a Hive self-defined function, the Hive self-contained function cannot completely meet the service requirement, the self-defined function is needed to be used, and different processing logics can be used for processing different requirements through the self-defined function.

Further, the embodiments of the present disclosure will count the following information:

first, relevant information of risk prevention and control: the method comprises the information of risk score and risk type of each business domain of the enterprise, the business domain, the service name, the service level, the risk type, the contained risk, the risk score, the primary risk, the secondary risk, the tertiary risk description and the like.

Secondly, relevant information of online fault statistics is as follows: the method comprises information such as BUG numbers, severity degrees, solving methods, states, BUG titles, BUG descriptions, BUG operation steps, organizations to which the BUG belongs, problem sources, creation dates, assignment/solving persons, expected solving times, assignment solving dates, creation-solving time periods, solution-closing time periods, creation-closing time periods, BUG links, requirement links, BUG types, entity leaders, severity levels 1, severity levels 2, severity levels 3, severity levels, requirement detail information provided after failures, requirement links, requirement processes, requirement classifications and requirement descriptions.

Thirdly, relevant information of fault completion condition statistics: the method comprises information such as requirement completion condition information, a problem response timeliness statistical report, a requirement response timeliness statistical report, a history delay unsolved BUG list, fault number statistics, fault number distribution, fault reasons, technical risk types, fault reply topic names, fault team personnel detailed lists and the like.

Fourth, the relevant information of the quality statistics analysis (assessed by quality scores): the method comprises the information of per-department per-capita quality score, total quality score, research and development quality score, online quality score, entity leader, P1, P2, P3, P4, P5, P1 hemostasis for more than 30 minutes, P2 hemostasis for more than 30 minutes, P5 unresolved for more than three days, quality score, team number, per-capita quality score, statistical period, fault duplication subject detail list (week), technical fault statistics (P5), fault behavior quality score (week), research and development quality score _ statistical item detail list (week), research and development quality score _ BUG statistical detail list, total quality score (week accumulation) and the like.

Fifthly, statistical information of testers during testing BUG is as follows: the method comprises the steps that when a company tester tests codes, the tester, the number of test cases, the number of effective BUGs, the number of BUGs, products, levels, the state of the BUGs, creators, creation dates, resolvers, solutions, solution dates, BUG links and other information.

Further, the embodiment of the present disclosure adopts a data warehouse ETL manner, by obtaining information related to a fault, such as information of risk score, risk type, primary risk, secondary risk, tertiary risk description, etc. of each business domain of an enterprise in risk prevention and control, detailed content of a BUG in online fault statistics and actual situation of demand solution, detailed information of fault completion statistics, problem response timeliness statistics report, statistics of number of faults and fault cause analysis, detailed statistical analysis information related to quality score, statistical information of testers during testing the BUG, etc., it is possible to determine a person responsible for the fault according to occurrence cause and completion situation of the fault, and to realize rapid fault location, for online fault, to do good-and-good-afterwork, avoid next rescission, from fundamental counterthinking, at the bottom of planning, the source of the problem is found. On the other hand, the evaluation of the quality score is also beneficial to the performance evaluation of the end-of-year performance, and the performance can be evaluated according to the fault occurrence condition of each department and each member in each department, so that the enthusiasm of the staff of the company for solving the fault problem can be improved, and the satisfaction degree of the company is improved.

In some embodiments, processing the initialization information into tabular data with a data warehouse comprises: synchronizing the initialization information from the distributed task platform to a data warehouse by using a data scheduling platform, partitioning the initialization information according to preset index dimensionality in an original data layer of the data warehouse, and mapping the partitioned initialization information into table data.

Specifically, the embodiment of the present disclosure synchronizes the acquired initialization information directly from the distributed task platform to the ODS layer in the Hive data warehouse. The ODS (operation Data store) of Hive is a source Data layer used for storing source Data, original logs and Data can be directly loaded from the MySQL database, and the Data is kept as an original appearance and is not processed. In other words, the ODS layer can directly obtain the initialization information in the data scheduling platform, and can modify the table name of the structured initialization information, or do nothing.

In some embodiments, the data cleaning and aggregation are performed on the table data, and the table after the data cleaning and aggregation is summarized according to a preset business theme to obtain summarized table data, including: the original data layer transmits the table data to a detail data layer of the data warehouse, in the detail data layer, data cleaning is carried out on the table data by using a preset data processing algorithm so as to remove null values, dirty data and redundant data, aggregation operation is carried out on the table data after data cleaning according to field information of the table data after data cleaning, and aggregation is carried out on the table data after aggregation according to a preset business theme so as to obtain summary table data.

Specifically, the HQL is used to perform ETL data cleaning on table data, such as NULL removal, filtering of meaningless data in the kernel field (e.g., system coding is NULL, etc.), desensitization of sensitive data, removal of NULL, dirty, and redundant data, etc. In practical application, the ODS layer table is transferred to a DWD layer (i.e. a detail data layer) of the Hive data Warehouse, and the DWD (data wave detail) layer in the Hive is used to perform data cleaning (such as removing null values and dirty data) and desensitization on the table data, so as to obtain the detail data table.

In some embodiments, querying summary table data stored in the data warehouse using a preset reporting platform includes: and querying summarized table data stored in a data warehouse by using a data query engine installed in the report platform and using a data query script and a preset calling rule to obtain target table data used for generating a fault processing report.

Specifically, summary form data obtained after ETL processing is stored in Hive, and the report development stage is completed in the universal report platform, that is, Hive is only used for storing summary form data and is not responsible for developing a final report, and therefore the universal report platform needs to be used for calling data in Hive to develop reports. The data set of the universal report platform comprises SQL scripts (data query scripts) for data query, the universal report platform queries summarized table data obtained after processing in the Hive data warehouse tool through a Presto query engine to obtain target table data for developing reports, and therefore the universal report platform can develop reports based on the target table data.

In some embodiments, generating a fault handling report based on the queried target table data and a pre-created report configuration, and monitoring a fault of a business domain of a target enterprise object based on the fault handling report includes: in the report platform, report configuration is created based on a predetermined report style, development operation is performed on a fault processing report by utilizing the report configuration and target table data so as to generate a fault processing report for representing fault processing statistics, the fault processing report comprises fault processing related information of different business domains corresponding to a target enterprise object, and fault monitoring is performed according to the fault processing related information.

Specifically, before report development, report configuration needs to be created in advance according to a report style required by a user, and the report configuration may include contents such as a data format, an arrangement manner, a data type, and a filling rule of a report. In practical application, after the Presto query engine is used to obtain the target table data, the report platform performs report development operation by using preset report configuration and the obtained target table data to generate a fault processing report, so as to monitor faults by using relevant information of fault processing.

The following describes in detail the contents of the fault handling report generated in the embodiment of the present disclosure with reference to a specific embodiment, and develops a report on a smart report platform, where 41 final statistical reports are obtained, where the method includes:

(1) risk prevention and control:

risk prevention and control test: including risk scores, risk types, and the like of various business domains of the enterprise (such as AI energy conservation, government platforms, data operation, energy utilization, comprehensive energy management, ecology, general products, and the like).

Application risk details: including information such as business domain, service name, service level, risk type, included risk, etc.

(2) Risk knowledge base:

the method comprises information such as first-level risk, second-level risk, third-level risk description and the like.

(3) On-line fault statistics

Statistics of faults on the line: the method comprises the information of BUG number, severity, solving method, state, BUG title, BUG description, BUG operation step, organization of the BUG, problem source, creation date, assignment/solving person, expected solving time, assignment solving date, creation-solving time, solving-closing time, creation-closing time, BUG link, requirement link, BUG type and the like.

Requirement list: including information such as creation time, product name, requirement ID, BUG presenter, creator, organization to which it belongs, requirement title, priority, assignment, status, phase, shutdown reason, latest update time, creation-shutdown duration, requirement description, requirement link, requirement process, requirement classification, etc.

(4) Operation-response aging statistical report

A problem response time efficiency statistical form: including information such as BUG number, severity, status, BUG description, organization to which it belongs, source of problem, date of creation, assignment/resolution to person, time desired to resolve, resolution, assignment/resolution date, length of unresponsive time (hours), BUG link, demand link, etc.

Secondly, a demand response aging statistical form: including information such as product name, demand ID, creator, organization to which it belongs, demand title, priority, assignment, status, phase, reason for shutdown, time of last update, length of time (hours) unresponsive, demand description, demand link, etc.

(5) Statistical analysis of quality

Quality score summary (week)

Total mass (weeks): the system comprises information such as per-department (such as power transaction, intelligent internet of things and products, data center station group-algorithm platform, data center station group-data warehouse, technology center station group-basic platform, technology center station group-ecological research and development, technology center station group-middleware, foreground technology group-front-end technology group and business sharing technology group) average quality score and total quality score.

Research and development quality is divided into (weeks): the system comprises information of per-capita quality score, research and development quality and the like of each department (such as electric power transaction, intelligent internet of things and products, data center station group-algorithm platform, data center station group-data warehouse, technology center station group-basic platform, technology center station group-ecological research and development, technology center station group-middleware, front station technology group-front end technology group and business sharing technology group).

On-line mass fractions (weeks): the system comprises information of per-department per-capita quality scores, on-line quality scores and the like (such as power transaction, intelligent internet of things and products, data center station group-algorithm platform, data center station group-data warehouse, technology center station group-basic platform, technology center station group-ecological research and development, technology center station group-middleware, foreground technology group-front-end technology group and business sharing technology group).

Secondly, the online quality is divided into detail lists (week):

online quality score _ statistics detail (week): the method comprises the information of entity leadership, P1, P2, P3, P4, P5, P1 hemostasis for more than 30 minutes, P2 hemostasis for more than 30 minutes, P5 hemostasis for more than three days, quality score, team number, per-person quality score, statistical period and the like.

Failure quality score _ failure duplicate issue detail (week) (details): the method comprises information such as fault numbers, fault names, fault handlers, emergency follow-up persons, hemostasis duration, main responsibility teams, main responsibility proportion, severity, reply time, reply reports, counting period and the like.

Technical failure statistics (P5): including entity leaders, product names, fault numbers, fault descriptions, creators, creation times, priorities, resolution/assignment, solutions, fault links, etc.

Failure action mass score (week) (details) (mass score is not taken into account for the moment): including task number, priority, status, creation time, completion time, expiration date, timeout, responsible person, responsibility team, statistical period, etc.

Research and development quality is divided into detail (week):

development quality score _ statistics item detail (week): the method comprises information such as entity leadership, high-level BUG, middle-level BUG, low-level BUG, solution time length more than or equal to 5 days (high level), solution time length more than or equal to 5 days (middle level), solution time length more than or equal to 5 days (low level), activation times, research and development quality scores, per-capita research and development quality scores, team number of people, statistical period and the like.

Researching and developing quality score _ BUG statistical detail list: the method comprises the information of entity leadership, product name, BUG number, BUG description, creator, person assigned to/solved, severity, level, state, solving method, creation date, solving date, BUG active time, activation times, overdue times, quality score, BUG link, statistical period and the like.

Fourthly, newly adding BUGList:

counting the number of newly added BUGs: including entity leader, severity level 1, severity level 2, severity level 3, severity level 4, etc.

Adding BUG detailed list: including entity leader, product name, BUG number, BUG, description, BUG level, creator, creation time, resolution/assignment, resolution, BUG link, etc.

Fifthly, historical delay does not solve BUGList:

including entity leader, BUG number, BUG description, BUG level, creator, creation date, resolution/assignment, resolution, BUG link, etc.

Fault statistics:

counting the number of faults: the method comprises information such as P1, P2, P3 and P4 fault numbers summarized in the last week, P1, P2, P3 and P4 fault numbers summarized up to now and the like.

Failure number statistical table: the method comprises information such as P1, P2, P3 and P4 fault numbers summarized in the last week, P1, P2, P3 and P4 fault numbers summarized up to now and the like.

Distribution of number of failures (up to now): the method comprises the information of P1, P2, P3, P4 fault numbers and the like gathered at present in various departments (such as electric power transaction, intelligent internet of things and products, data center station group-algorithm platform, data center station group-data warehouse, technology center station group-basic platform, technology center station group-ecological research and development, technology center station group-middleware, foreground technology group-front-end technology group and service sharing technology group).

Number of failures distribution (week) (histogram): the system comprises information such as P1, P2, P3 and P4 fault numbers summarized in the last week of each department (such as power transaction, intelligent internet of things and products, data center station group-algorithm platform, data center station group-data warehouse, technology center station group-basic platform, technology center station group-ecological research and development, technology center station group-middleware, foreground technology group-front-end technology group and service sharing technology group).

Failure _ cause distribution: the method comprises information such as percentage of fault reasons (including coding problems, operation and maintenance configuration, operation configuration, JOB execution, upstream and downstream dependence, product design, safety problems, performance capacity, architecture design, process communication and third party) classification and the like.

Type of technical risk: including information on the percentage of the respective technical risk types (including reliability-availability, reliability-fault tolerance, performance efficiency, usability, compatibility, functional characteristics, etc.).

Failure duplicate topic List: including information such as serial number, topic name, date of the re-disc session, failure level, responsibility team, link, etc.

Team staff detail: including information such as serial number, department name, team number, time, etc.

(6) Mass fraction plate (test)

The total mass is divided into (week accumulation): including information up to now such as total mass fraction (weekly accumulation).

② developing a quality score test (week) (bar chart): the system comprises information of average capability and total research and development quality of various departments (such as electric power transaction, intelligent internet of things and products, data center station group-algorithm platform, data center station group-data warehouse, technology center station group-basic platform, technology center station group-ecological research and development, technology center station group-middleware, front station technology group-front end technology group and business sharing technology group).

(7) Test self-drive

Testing BUG _ CASE statistics-testing BUG _ CASE statistics (histogram): the method comprises the information of the number of test cases, the number of effective BUGs and the like when a company tester tests codes.

Testing BUG _ CASE statistics: the method comprises the information of testers, the number of effective BUGs, the number of test cases and the like.

③ invalid BUG confirmation: including information such as the BUG number, the product to which the BUG belongs, the level, the BUG status, the creator, the creation date, the resolver, the solution date, the BUG link, etc.

(8) And (3) counting faults on the line:

including information such as BUG number, severity, status, BUG description, organization to which it belongs, source of problem, creation date, assignment/resolution person, expected resolution time, resolution method, assignment/resolution date, creation-resolution time, resolution-off time, creation-off time, BUG link, demand link, etc.

(9) Requirement list: including information such as product name, requirement ID, creator, organization to which it belongs, requirement title, priority, assignment, status, phase, reason for shutdown, time of creation, time of last update, time of creation-shutdown, requirement description, requirement link, etc.

(10) The method comprises the following steps: including information about the organization to which it belongs, creator, requirement ID, requirement title, status, phase, assignment, creation time, etc.

(11) And (3) data statistics: including information on organizational structure, number of requirements, resolved requirements, unresolved requirements, etc.

(12) The disadvantages are not solved

(ii) unresolved needs-statistics: including information on organization, number of requirements, resolved requirements, unresolved requirements, etc.

Unresolved demand-unresolved demand statistics: detail information of the unresolved demand is displayed.

(13)testDB

The demand state: including organization, creator, requirement ID, requirement title, status, phase, assignment, creation time, etc.

Data statistics: including information on organization, number of requirements, resolved requirements, unresolved requirements, etc.

(14) And (3) counting the detailed information of the demand: the method comprises the following steps of organizing name, total number of requirements, number of solved requirements, number of unresolved requirements, waiting requirements, planned requirements, established requirements, developing requirements, research and development completion requirements, requirements in test, test completion requirements, published requirements, verified requirements and the like.

4. And after the result is displayed according to the report, the report can be used for large-screen display, and is convenient for data statistical analysis or leader decision.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.

Fig. 2 is a schematic structural diagram of a fault processing apparatus based on report development according to an embodiment of the present disclosure. As shown in fig. 2, the fault handling apparatus based on report development includes:

the determining module 201 is configured to group preset risk types, determine business domains, and determine risk scores and risk types corresponding to the business domains of the target enterprise object based on the risk types and the business domains;

the acquisition module 202 is configured to acquire risk prevention and control information corresponding to a business domain of a target enterprise object from the distributed task platform, count online faults corresponding to the business domain to obtain online fault statistical information, and count fault completion conditions according to the online faults to obtain fault completion condition statistical information;

the statistical module 203 is configured to count the total quality score, the research and development quality score, the online quality score and the fault quality score of the target enterprise object according to a pre-configured quality score statistical mode, and determine quality score statistical information corresponding to the target enterprise object according to a statistical result;

the synchronization module 204 is configured to acquire test information generated by a tester during a test fault, count the test information to obtain test fault statistical information, and synchronize the initialization information into a data warehouse by using risk prevention and control information, online fault statistical information, fault completion statistical information, quality score statistical information and test fault statistical information as initialization information;

the processing module 205 is configured to process the initialization information into form data by using a data warehouse, perform data cleaning and aggregation on the form data, and summarize the forms subjected to data cleaning and aggregation according to a preset service theme to obtain summarized form data;

the generating module 206 is configured to query the summary table data stored in the data warehouse by using a preset report platform, generate a fault handling report based on the queried target table data and a pre-created report configuration, and monitor the fault of the business domain of the target enterprise object based on the fault handling report.

In some embodiments, the determining module 201 in fig. 2 determines the risk score of each risk type corresponding to each business domain according to the business domain corresponding to the target enterprise object, and sums the risk scores of all risk types in the business domain to obtain the risk score of each business domain of the target enterprise object.

In some embodiments, the obtaining module 202 in fig. 2 uploads the online fault information generated by the service domain to the distributed task platform, and performs statistics on the online fault information of the service domain in the distributed task platform to obtain online fault statistical information; and synchronizing the risk prevention and control information and the online fault statistical information from the distributed task platform to a data warehouse by using a data scheduling platform, and mapping the structured information into a database table in an original data layer in the data warehouse, wherein the data warehouse adopts a Hive data warehouse.

In some embodiments, the processing module 205 of fig. 2 synchronizes the initialization information from the distributed task platform to the data warehouse by using the data scheduling platform, partitions the initialization information according to a preset index dimension in the original data layer of the data warehouse, and maps the partitioned initialization information to table data.

In some embodiments, the processing module 205 of fig. 2 transfers the table data to a detail data layer of the data warehouse, in the detail data layer, performs data cleaning on the table data by using a predetermined data processing algorithm so as to remove null values, dirty data and redundant data, performs an aggregation operation on the table data after the data cleaning according to field information of the table data after the data cleaning, and summarizes the aggregated table data according to a preset business topic to obtain summary table data.

In some embodiments, the generating module 206 of fig. 2 queries summary table data stored in the data warehouse by using a data query script and a pre-configured calling rule by using a data query engine installed in the report platform to obtain target table data for generating the fault handling report.

In some embodiments, the generating module 206 in fig. 2 creates a report configuration based on a predetermined report style in the report platform, and performs a development operation on the fault handling report by using the report configuration and the target table data, so as to generate a fault handling report for representing fault handling statistics, where the fault handling report includes information related to fault handling of different business domains corresponding to the target enterprise object, and performs fault monitoring according to the information related to fault handling.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

Fig. 3 is a schematic structural diagram of an electronic device 3 provided in the embodiment of the present disclosure. As shown in fig. 3, the electronic apparatus 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and operable on the processor 301. The steps in the various method embodiments described above are implemented when the processor 301 executes the computer program 303. Alternatively, the processor 301 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 303.

Illustratively, the computer program 303 may be partitioned into one or more modules/units, which are stored in the memory 302 and executed by the processor 301 to accomplish the present disclosure. One or more of the modules/units may be a series of computer program instruction segments capable of performing certain functions, the instruction segments being used for describing the execution of the computer program 303 in the electronic device 3.

The electronic device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other electronic devices. The electronic device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that fig. 3 is merely an example of the electronic device 3, and does not constitute a limitation of the electronic device 3, and may include more or fewer components than those shown, or some of the components may be combined, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.

The Processor 301 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 302 may be an internal storage unit of the electronic device 3, for example, a hard disk or a memory of the electronic device 3. The memory 302 may also be an external storage device of the electronic device 3, for example, a plug-in hard disk provided on the electronic device 3, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 302 may also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 302 is used for storing computer programs and other programs and data required by the electronic device. The memory 302 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the above-described apparatus/computer device embodiments are merely illustrative, and for example, a division of modules or units, a division of logical functions only, an additional division may be made in actual implementation, multiple units or components may be combined or integrated with another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier signal, telecommunications signal, software distribution medium, etc. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.

The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.

Claims

1. A fault processing method based on report development is characterized by comprising the following steps:

grouping preset risk types, determining service domains, and determining risk scores and risk types corresponding to the service domains of the target enterprise object based on the risk types and the service domains;

acquiring risk prevention and control information corresponding to the business domain of the target enterprise object from a distributed task platform, counting online faults corresponding to the business domain to obtain online fault statistical information, and counting fault completion conditions according to the online faults to obtain fault completion condition statistical information;

counting the total quality score, the research and development quality score, the online quality score and the fault quality score of the target enterprise object according to a pre-configured quality score counting mode, and determining quality score counting information corresponding to the target enterprise object according to a counting result;

acquiring test information generated by a tester during a test fault, counting the test information to obtain test fault statistical information, taking the risk prevention and control information, the on-line fault statistical information, the fault completion statistical information, the quality score statistical information and the test fault statistical information as initialization information, and synchronizing the initialization information into a data warehouse;

processing the initialization information into form data by using the data warehouse, performing data cleaning and aggregation on the form data, and summarizing the forms subjected to data cleaning and aggregation according to a preset service theme to obtain summarized form data;

and querying the summary table data stored in the data warehouse by using a preset report platform, generating a fault processing report based on the queried target table data and pre-established report configuration, and monitoring the fault of the business domain of the target enterprise object based on the fault processing report.

2. The method of claim 1, wherein determining a risk score and a risk type for each business domain of a target enterprise object based on the risk types and the business domains comprises:

and determining a risk score of each risk type corresponding to each business domain according to the business domain corresponding to the target enterprise object, and summing all the risk scores of the risk types in the business domain to obtain the risk score of each business domain of the target enterprise object.

3. The method according to claim 1, wherein the obtaining risk prevention and control information corresponding to the business domain of the target enterprise object from the distributed task platform and performing statistics on online faults corresponding to the business domain to obtain online fault statistics information comprises:

uploading online fault information generated by the service domain to the distributed task platform, and counting the online fault information of the service domain in the distributed task platform to obtain online fault statistical information;

and synchronizing the risk prevention and control information and the online fault statistical information from the distributed task platform to the data warehouse by using a data scheduling platform, and mapping the structured information into a database table in an original data layer in the data warehouse, wherein the data warehouse adopts a Hive data warehouse.

4. The method of claim 3, wherein said processing said initialization information into table data using said data warehouse comprises:

synchronizing the initialization information from the distributed task platform to the data warehouse by using a data scheduling platform, partitioning the initialization information in an original data layer of the data warehouse according to a preset index dimension, and mapping the partitioned initialization information into table data.

5. The method according to claim 1, wherein the step of performing data cleansing and aggregation on the table data, and summarizing the table subjected to data cleansing and aggregation according to a preset business theme to obtain summarized table data comprises:

the original data layer transmits the table data to a detail data layer of the data warehouse, in the detail data layer, a preset data processing algorithm is used for carrying out data cleaning on the table data so as to remove null values, dirty data and redundant data, according to field information of the table data after the data cleaning, aggregation operation is carried out on the table data after the data cleaning, and the aggregated table data is summarized according to a preset service theme so as to obtain the summary table data.

6. The method according to claim 1, wherein the querying the summary table data stored in the data warehouse using a predefined reporting platform comprises:

and querying the summary table data stored in the data warehouse by using a data query engine installed in the report platform and using the data query script and a preset calling rule to obtain target table data used for generating the fault processing report.

7. The method of claim 1, wherein generating a fault handling report based on the queried target table data and a pre-created report configuration, and monitoring for a fault in a business domain of the target enterprise object based on the fault handling report comprises:

in the report platform, the report configuration is created based on a predetermined report style, development operation is performed on the fault processing report by using the report configuration and the target table data so as to generate the fault processing report for representing fault processing statistics, the fault processing report contains fault processing related information of different business domains corresponding to the target enterprise object, and fault monitoring is performed according to the fault processing related information.

8. A fault handling device based on report development is characterized by comprising:

the determining module is configured to group preset risk types, determine business domains, and determine risk scores and risk types corresponding to the business domains of the target enterprise object based on the risk types and the business domains;

the acquisition module is configured to acquire risk prevention and control information corresponding to the business domain of the target enterprise object from a distributed task platform, count online faults corresponding to the business domain to obtain online fault statistical information, and count fault completion conditions according to the online faults to obtain fault completion condition statistical information;

the statistical module is configured to count the total quality score, the research and development quality score, the online quality score and the fault quality score of the target enterprise object according to a pre-configured quality score statistical mode, and determine quality score statistical information corresponding to the target enterprise object according to a statistical result;

the system comprises a synchronization module, a data warehouse and a data processing module, wherein the synchronization module is configured to acquire test information generated by a tester during a test fault, count the test information to obtain test fault statistical information, and synchronize the initialization information into the data warehouse by taking the risk prevention and control information, the on-line fault statistical information, the fault completion condition statistical information, the quality score statistical information and the test fault statistical information as initialization information;

the processing module is configured to process the initialization information into form data by using the data warehouse, perform data cleaning and aggregation on the form data, and summarize the forms subjected to data cleaning and aggregation according to a preset service theme to obtain summarized form data;

the generating module is configured to query the summarized table data stored in the data warehouse by using a preset report platform, generate a fault processing report based on the queried target table data and a pre-established report configuration, and monitor the fault of the business domain of the target enterprise object based on the fault processing report.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.