Work order index mining method and device, equipment, storage medium and program product

Abstract

The invention relates to a work order index mining method and device, equipment, a storage medium and a program product. The method comprises the steps of performing crawling processing on a work order of a financial system to obtain multiple pieces of target work order data; performing data fragmentation processing on the target work order data to obtain a plurality of work order data blocks; performing statistical processing on the plurality of work order data blocks to obtain work order indexes of multiple dimensions; wherein the work order indexes of the multiple dimensions comprise work order time consumption, whether the version is issued or not, work order types and the like. By adopting the method, the work order index mining efficiency can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a work order indicator mining method, device, equipment, storage medium and program product. Background Art

[0002] With the widespread use of computers and the widespread popularization of computer networks, the software industry has developed rapidly. Banks and financial systems have a large number of work orders that need to be processed every day. These work orders involve supervision, information query, function resolution, etc. Development and operation and maintenance personnel need to adjust the functions of the bank's internal systems or query the information in the system according to the content of the work order. The processing of a large number of work orders will increase the workload of development and operation and maintenance personnel. At the same time, the circulation and resolution of work orders takes a certain amount of time, and the timeliness is low, which will affect the customer experience. In order to reduce the number of work orders and increase efficiency, work order indicators are analyzed and mined, the real needs of work orders are located, a large number of work orders are classified according to rules, and optimization solutions are sought. For example, if the number of information query work orders is too large, a report display page can be added to the corresponding financial system of the bank, and there is no need to issue information query tasks through work orders.

[0003] In traditional technology, the mining of work order indicators mainly relies on manual work.

[0004] However, when analyzing and mining work order indicators in this way, it is difficult to conduct a comprehensive and in-depth analysis and mining of work order indicators due to the large number of work orders and single statistical indicators, that is, the mining efficiency of work order indicators is low. Summary of the invention

[0005] Based on this, it is necessary to provide a work order indicator mining method, device, equipment, storage medium and program product that can improve the efficiency of work order indicator mining in response to the above technical problems.

[0006] In a first aspect, the present application provides a work order indicator mining method, comprising:

[0007] The work orders of the financial system are crawled to obtain multiple target work order data; each target work order data is sharded to obtain multiple work order data blocks; multiple work order data blocks are statistically processed to obtain work order indicators in multiple dimensions; among them, the work order indicators in multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc.

[0008] In one embodiment, the work orders of the financial system are crawled to obtain multiple target work order data, including:

[0009] The work orders of the financial system are crawled to obtain multiple initial work order data; missing value processing is performed on each initial work order data, and each initial work order data is reduced to obtain multiple target work order data.

[0010] In one embodiment, the missing value processing is performed on each initial work order data, including:

[0011] For each initial work order data, check whether the missing fields in the initial work order data are key fields. If the missing fields in the initial work order data are not key fields, fill in the missing fields in the initial work order data with default values.

[0012] In one embodiment, the method further comprises:

[0013] If the missing field in the initial work order data is a key field, the initial work order data will be deleted.

[0014] In one embodiment, each initial work order data is subjected to a reduction process, including:

[0015] For each initial work order data, the PCA technology is used to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0016] In one embodiment, multiple work order data blocks are statistically processed to obtain work order indicators in multiple dimensions, including:

[0017] A Map task is created for each work order data block through the big data analysis system; the first data output after each Map task is processed is sorted to obtain second data, and the second data is passed to the Reduce task; the second data is processed through multiple custom methods to obtain third data with statistics in multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as the dimension, counting the work order data of each consumption period; taking whether the version is issued as the dimension, counting the work order data of the issued version and the non-issued version; taking the category of the work order as the dimension, counting the work order data of business demand changes, data foundation, application troubleshooting and statistical query; the third data with statistics in multiple dimensions are summarized and output to obtain work order indicators in multiple dimensions.

[0018] In a second aspect, the present application also provides a work order indicator mining device, comprising:

[0019] The acquisition module is used to crawl the work orders of the financial system to obtain multiple target work order data;

[0020] A sharding module is used to perform data sharding processing on each target work order data to obtain multiple work order data blocks;

[0021] The processing module is used to perform statistical processing on multiple work order data blocks to obtain work order indicators in multiple dimensions; among which, the work order indicators in multiple dimensions include: work order time consumption, whether a version is issued, and work order type, etc.

[0022] In one of the embodiments, the acquisition module is specifically used to: crawl the work orders of the financial system to obtain multiple initial work order data; process missing values ​​of each initial work order data, and perform reduction processing on each initial work order data to obtain multiple target work order data.

[0023] In one of the embodiments, the acquisition module is specifically used to: for each initial work order data, check whether the missing field in the initial work order data is a key field; if the missing field in the initial work order data is not a key field, fill the missing field in the initial work order data with a default value.

[0024] In one of the embodiments, the acquisition module is specifically used to: if the missing field in the initial work order data is a key field, delete the initial work order data.

[0025] In one embodiment, the acquisition module is specifically used to: for each initial work order data, use the PCA technology to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0026] In one of the embodiments, the processing module is specifically used to: create a Map task for each work order data block through a big data analysis system; organize the first data output after each Map task is processed to obtain second data, and pass the second data to the Reduce task; process the second data through multiple custom methods to obtain third data with statistics in multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as a dimension, counting the work order data of each consumption period, taking whether the version is issued as a dimension, counting the work order data of the issued version and the non-issued version, taking the category of the work order as a dimension, counting the work order data of business demand changes, data foundation, application troubleshooting and statistical query; summarizing and outputting the third data with statistics in multiple dimensions to obtain work order indicators in multiple dimensions.

[0027] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any of the methods described in the first aspect is implemented.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0029] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0030] The above-mentioned work order indicator mining method, device, equipment, storage medium and program product first crawl the work orders of the financial system to obtain multiple target work order data, then perform data sharding on each target work order data to obtain multiple work order data blocks, and finally perform statistical processing on the multiple work order data blocks to obtain work order indicators of multiple dimensions, wherein the work order indicators of multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc. This method is based on a large amount of existing work order data, extracts and merges similar data, analyzes and calculates various indicator data, and the statistically obtained indicators are in-depth and rich, which can effectively improve the efficiency of work order indicator mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A schematic diagram of a flow chart of a work order indicator mining method in one embodiment;

[0033] Figure 2 A schematic diagram of a process for crawling and processing work orders of a financial system in one embodiment;

[0034] Figure 3 It is a flowchart of the steps of performing statistical processing on multiple work order data blocks in one embodiment;

[0035] Figure 4 A schematic diagram of a flow chart of a work order indicator mining method in another embodiment;

[0036] Figure 5 is a structural block diagram of a work order indicator mining device in an embodiment;

[0037] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0039] With the widespread use of computers and the widespread popularization of computer networks, the software industry has developed rapidly. There are a large number of work orders that need to be processed every day in the banking and financial systems. These work orders involve supervision, information query, function solution, etc. Development and operation and maintenance personnel need to adjust the functions of the banking and financial systems or query the information in the system according to the content of the work orders. The processing of a large number of work orders will increase the workload of development and operation and maintenance personnel. At the same time, the circulation and resolution of work orders takes a certain amount of time, and the timeliness is low, which will affect the customer experience. In order to achieve the reduction of work orders and increase efficiency, the work order indicators are analyzed and mined, the real needs of the work orders are located, a large number of work orders are classified according to the rules, and optimization solutions are sought. For example, if the number of information query work orders is too large, a report display page can be added to the corresponding financial system of the bank, and there is no need to issue information query tasks through work orders.

[0040] In traditional technology, the mining of work order indicators mainly relies on manual work.

[0041] However, when analyzing and mining work order indicators in this way, it is difficult to conduct a comprehensive and in-depth analysis and mining of work order indicators due to the large number of work orders and single statistical indicators, that is, the mining efficiency of work order indicators is low.

[0042] In view of this, the present application provides a work order indicator mining method that can effectively improve the efficiency of work order indicator mining. The work order indicator mining method provided in the embodiment of the present application, its execution subject can be a work order indicator mining device, and the work order indicator mining device can be implemented by software, hardware, or a combination of software and hardware. It can be embedded in or independent of the processor in the computer device in the form of hardware, or it can be stored in the memory of the computer device in the form of software. The computer device can be a server. In the following method embodiments, the execution subject is taken as an example to illustrate.

[0043] In an exemplary embodiment, Figure 1 As shown, a work order indicator mining method is provided, including the following steps 101 to 103. Among them:

[0044] S101, the server crawls and processes the work orders of the financial system to obtain multiple target work order data.

[0045] Optionally, the work order can be a task order issued to development and operation and maintenance personnel. Development and operation and maintenance personnel adjust the functions of the banking and financial system or query and output reports on the content in the system according to the content of the work order. The content of the work order involves supervision, information query, and function resolution, etc.

[0046] Optionally, crawling the work orders of the financial system can be implemented by Python crawler tools, Java language, or C / C++ language. It should be noted that the language or tool used when crawling the work orders of the financial system can be set according to the actual application, and the embodiments of the present application are not limited thereto.

[0047] Optionally, data is crawled on the work order page in the financial system in the browser to obtain all work order data for a period of time from the work order page, and the initial work order data obtained is stored, and the initial work order data is cleaned and integrated into regular data that is suitable for the lower-level input, that is, multiple target work order data are obtained.

[0048] In one possible implementation, the initial work order data may be cleaned by data deduplication, for example, by comparing unique identifiers or key fields in the initial target work order data records to remove duplicate records in the data set.

[0049] In one possible implementation, the initial work order data can be cleaned by processing missing values.

[0050] In another possible implementation, the initial work order data may be cleaned by data standardization and data conversion, that is, the data format is standardized into a consistent format for easy processing and analysis. For example, a date in text format may be converted into a date format.

[0051] S102, the server performs data slicing processing on each target work order data to obtain multiple work order data blocks.

[0052] Optionally, data sharding processing for each target work order data may refer to logical sharding, that is, separating the target work order data according to byte indexes. For example, the 0-1MB position is defined as the first shard to obtain a work order data block, and 1-2MB is defined as the second shard to obtain another work order data block, and so on.

[0053] The target work order data is processed by data sharding to obtain multiple work order data blocks. Multiple work order data blocks can be processed in parallel, and each work order data block is relatively small, which can improve the processing speed of work order indicator mining.

[0054] S103, the server performs statistical processing on multiple work order data blocks to obtain work order indicators in multiple dimensions.

[0055] Among them, the work order indicators in multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc.

[0056] Optionally, use big data analysis methods such as MapReduce and Spark to analyze and process the work order data blocks, deeply explore various work order indicators, and obtain work order indicators in multiple dimensions to form work order operation data.

[0057] Optionally, after obtaining the work order indicators in multiple dimensions, the statistical processing results can be evaluated, spot checks can be performed to verify the correctness of the processing results, the statistical processing logic can be calibrated, and the work order indicator mining method can be further optimized.

[0058] In the above work order indicator mining method, firstly, the work orders of the financial system are crawled to obtain multiple target work order data, then each target work order data is sliced ​​to obtain multiple work order data blocks, and finally multiple work order data blocks are statistically processed to obtain work order indicators of multiple dimensions, where the work order indicators of multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc. This method is based on a large amount of existing work order data, extracts and merges similar data, analyzes and calculates various indicator data, and the statistically obtained indicators are in-depth and rich, which can effectively improve the efficiency of work order indicator mining.

[0059] In an exemplary embodiment, Figure 2 As shown, based on the above embodiment, optionally, the work orders of the financial system are crawled to obtain multiple target work order data, including steps 201 to 202. Among them:

[0060] S201, the server crawls and processes the work orders of the financial system to obtain multiple initial work order data.

[0061] Optionally, crawl the work order page of the financial system to obtain work order data in TXT format. Each work order corresponds to one work order data, including: work order number, product, processor, work order arrival time, work order closing time, processing time, work order category, whether a version is issued, and other work order indicator data.

[0062] In one possible implementation, crawling and processing of work orders in the financial system can be achieved through the Python crawler tool Grab, which is a Python web crawling framework.

[0063] In one possible implementation, crawling and processing of work orders of financial systems can be achieved through the Python crawler tool Scrapy, which is used to crawl websites and extract structured data from their pages.

[0064] In another possible implementation method, crawling and processing of work orders in the financial system can be achieved through the Python crawler tool Cola. Cola is a distributed crawler framework. You only need to write a few specific functions without paying attention to the details of distributed operation. Tasks will be automatically distributed to multiple machines, and the whole process is transparent.

[0065] S202, the server performs missing value processing on each initial work order data, and performs reduction processing on each initial work order data to obtain multiple target work order data.

[0066] Optionally, during the data collection process, some initial work order data information is often incomplete, and it is necessary to check the missing values ​​of the initial work order data, identify invalid and missing initial work order data, and process them, ultimately improving the uniqueness, completeness, validity and accuracy of the initial work order data quality.

[0067] Optionally, the initial work order data is reduced to reduce the size of the data set required for the work order indicator mining. That is, mining on the reduced data set will be more effective and will still produce the same or similar analysis results.

[0068] In one possible implementation, the reduction processing of the initial work order data can be achieved through dimensionality reduction, which is to reduce the number of random conveniences or attributes considered. The methods used include attribute subset selection, wavelet transform and principal component analysis.

[0069] In another possible implementation, the reduction processing of the initial work order data can be achieved through quantity reduction, which is to replace the original data with an alternative, smaller data representation, including parameter methods and non-parameter methods. The parameter method uses a model to evaluate the data and only needs to store parameters without storing actual data, while the non-parameter method needs to store actual data.

[0070] The above crawling process is performed on the work orders of the financial system to obtain multiple initial work order data, and each initial work order data is processed for missing values, and each initial work order data is processed for specification to obtain multiple target work order data. This can improve the quality of the initial work order data and reduce the amount of the initial work order data, that is, reduce the amount of input data for work order indicator mining on the basis of ensuring the completeness of the data rows, which can effectively improve the efficiency of work order indicator mining.

[0071] In an exemplary embodiment, based on the above embodiment, optionally, each initial work order data is processed for missing values, including:

[0072] For each initial work order data, the server checks whether the missing fields in the initial work order data are key fields. If the missing fields in the initial work order data are not key fields, the missing fields in the initial work order data are filled with default values.

[0073] If the missing field in the initial work order data is a key field, the initial work order data will be deleted.

[0074] Optionally, for example, the isnull() and notnull() functions of Pandas can be used to check missing values. The method for checking missing values ​​of the initial work order data can be set according to actual applications and is not limited in the embodiments of the present application.

[0075] Optionally, the key fields in the initial work order data may be fields such as "work order category" and "work order processing time", while fields such as "work order assignee" may be non-key fields. It is determined whether the relevant fields are key fields based on the preset field information.

[0076] The above method of filling the missing fields in the initial work order data with default values ​​if the missing fields in the initial work order data are not key fields, and deleting the initial work order data if the missing fields in the initial work order data are key fields can reduce the impact of some data records on the overall data, thereby improving the accuracy of the initial work order data. At the same time, the initial work order data is deleted only when the missing fields in the initial work order data are key fields, which can avoid the situation where data features are reduced when too much initial work order data is deleted.

[0077] In an exemplary embodiment, based on the above embodiment, optionally, each initial work order data is subjected to a specification process, including:

[0078] For each initial work order data, the server uses PCA technology to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0079] Optionally, PCA technology, namely principal component analysis (Principal Component Analysis), is a widely used data dimensionality reduction method. Its main idea is to map n-dimensional features to k-dimensional features. This k-dimensional feature is a new orthogonal feature also called principal component, which is a k-dimensional feature reconstructed on the basis of the original n-dimensional feature.

[0080] Principal component analysis is performed on each initial work order data to eliminate irrelevant or redundant features in the initial work order data, and the original n-dimensional data features are reduced in dimension and mapped to k dimensions, which speeds up the algorithm processing speed and improves the execution performance, and can improve the efficiency of work order indicator mining.

[0081] In an exemplary embodiment, Figure 3 As shown, based on the above embodiment, optionally, multiple work order data blocks are statistically processed to obtain work order indicators of multiple dimensions, including steps 301 to 304, wherein:

[0082] S301, the server creates a Map task for each work order data block through the big data analysis system.

[0083] Optionally, format each work order data block as a key-value pair<Key,value> The data is in the form of, where Key is the offset index and value is the corresponding work order data.

[0084] Optionally, a Map task is constructed for each work order data block, that is, a relevant custom map() function is customized, and the Map task runs the customized map() function to process each record in each work order data block.

[0085] S302, the server organizes the first data output after processing each Map task to obtain second data, and passes the second data to the Reduce task.

[0086] Optionally, the first data output after each Map task is processed is also a key-value pair.<Key,value> The data is in the form of, where Key is a field in the target work order data, and value is the corresponding target work order data. The specific field corresponding to the Key is determined by the Map task. The first data output after each Map task is processed is sorted and merged according to the specified rules to obtain the second data with certain rules. The format of the second data is<Key,{value list}> In this format, Key is a field in the target work order data, and value list is the corresponding target work order data list.

[0087] S303, the server processes the second data through multiple custom methods to obtain third data with statistics in multiple dimensions.

[0088] Among them, multiple custom methods include: taking the work order consumption time period as the dimension, counting the work order data of each consumption time period; taking whether the version is issued as the dimension, counting the work order data of issued versions and non-issued versions; taking the work order category as the dimension, counting the work order data of business demand changes, data foundation, application troubleshooting and statistical query types.

[0089] Optionally, use multiple custom Reduce tasks to perform logical processing on the second data, and separately count data in multiple dimensions such as work order time consumption, whether the version is issued, and work order type to obtain key-value pairs.<Key,value> The third data in the form of, where Key is a field in the target work order data and value is the corresponding target work order data.

[0090] S304, the server aggregates and outputs the third data statistically obtained in multiple dimensions to obtain work order indicators in multiple dimensions.

[0091] Optionally, the data finally counted from multiple dimensions are summarized and output, including the number of work orders counted by work order duration, the number of work orders counted by whether the work orders involve versions, the number of work orders counted by work order types, and so on.

[0092] In the above, a Map task is first created for each work order database through the big data analysis system, and then the first data output after each Map task is processed is sorted to obtain the second data, and then the second data is passed to the Reduce task, and the second data is processed through multiple custom methods to obtain the third data of multiple dimensional statistics, and finally the third data of multiple dimensional statistics are summarized and output to obtain the work order indicators of multiple dimensions. Through program automated intelligent analysis and processing, instead of manually completing the task of statistical solidification, the efficiency of work order indicator mining can be effectively improved.

[0093] As an optional implementation, Figure 4 As shown, the work order indicator mining method provided in the embodiment of the present application may include the following specific steps:

[0094] S401, the server crawls and processes the work orders of the financial system to obtain multiple initial work order data.

[0095] S402, the server checks whether the missing fields in each initial work order data are key fields. If the missing fields in the initial work order data are not key fields, the missing fields in the initial work order data are filled with default values.

[0096] S403: If the missing field in the initial work order data is a key field, the server deletes the initial work order data.

[0097] S404, the server uses PCA technology to eliminate irrelevant features or redundant features in each initial work order data to reduce the feature dimension of the initial work order data to obtain multiple target work order data.

[0098] S405, the server performs data slicing processing on each target work order data to obtain multiple work order data blocks.

[0099] S406, the server creates a Map task for each work order data block through the big data analysis system.

[0100] S407, the server organizes the first data output after processing each Map task to obtain second data, and passes the second data to the Reduce task.

[0101] S408, the server processes the second data through multiple custom methods to obtain third data with statistics in multiple dimensions.

[0102] Among them, multiple custom methods include: taking the work order consumption time period as the dimension, counting the work order data of each consumption time period; taking whether the version is issued as the dimension, counting the work order data of issued versions and non-issued versions; taking the work order category as the dimension, counting the work order data of business demand changes, data foundation, application troubleshooting and statistical query types.

[0103] S409, the server aggregates and outputs the third data statistically obtained in multiple dimensions to obtain work order indicators in multiple dimensions.

[0104] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0105] Based on the same inventive concept, the embodiment of the present application also provides a work order indicator mining device for implementing the work order indicator mining method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more work order indicator mining device embodiments provided below can refer to the limitations of the work order indicator mining method above, and will not be repeated here.

[0106] In an exemplary embodiment, Figure 5 As shown, a work order indicator mining device 500 is provided, comprising: an acquisition module 501, a sharding module 502 and a processing module 503, wherein:

[0107] The acquisition module 501 is used to crawl the work orders of the financial system to obtain multiple target work order data.

[0108] The slicing module 502 is used to perform data slicing processing on each target work order data to obtain multiple work order data blocks.

[0109] The processing module 503 is used to perform statistical processing on multiple work order data blocks to obtain work order indicators of multiple dimensions; wherein the work order indicators of multiple dimensions include: work order time consumption, whether a version is issued, and work order type, etc.

[0110] In an optional embodiment of the present application, the acquisition module 501 is specifically used to: crawl the work orders of the financial system to obtain multiple initial work order data; process missing values ​​of each initial work order data, and perform reduction processing on each initial work order data to obtain multiple target work order data.

[0111] In an optional embodiment of the present application, the acquisition module 501 is specifically used to: for each initial work order data, check whether the missing field in the initial work order data is a key field; if the missing field in the initial work order data is not a key field, fill the missing field in the initial work order data with the default value.

[0112] In an optional embodiment of the present application, the acquisition module 501 is specifically used to: if the missing field in the initial work order data is a key field, delete the initial work order data.

[0113] In an optional embodiment of the present application, the acquisition module 501 is specifically used to: for each initial work order data, use the PCA technology to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0114] In an optional embodiment of the present application, the processing module 503 is specifically used to: create a Map task for each work order data block through a big data analysis system; organize the first data output after each Map task is processed to obtain second data, and pass the second data to the Reduce task; process the second data through multiple custom methods to obtain third data with statistics in multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as a dimension, counting the work order data of each consumption period, taking whether the version is issued as a dimension, counting the work order data of the issued version and the non-issued version, taking the category to which the work order belongs as a dimension, counting the work order data of business demand changes, data foundation, application troubleshooting and statistical query types; summarizing and outputting the third data with statistics in multiple dimensions to obtain work order indicators in multiple dimensions.

[0115] Each module in the above work order indicator mining device can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0116] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a work order indicator mining method is implemented.

[0117] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0119] The work orders of the financial system are crawled to obtain multiple target work order data; each target work order data is sharded to obtain multiple work order data blocks; multiple work order data blocks are statistically processed to obtain work order indicators in multiple dimensions; among them, the work order indicators in multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc.

[0120] In one embodiment, the work orders of the financial system are crawled to obtain multiple target work order data. When the processor executes the computer program, the following steps are implemented: crawling the work orders of the financial system to obtain multiple initial work order data; performing missing value processing on each initial work order data, and performing reduction processing on each initial work order data to obtain multiple target work order data.

[0121] In one embodiment, missing value processing is performed on each initial work order data, and the following steps are implemented when the processor executes the computer program: for each initial work order data, check whether the missing field in the initial work order data is a key field; if the missing field in the initial work order data is not a key field, fill the missing field in the initial work order data with the default value.

[0122] In one embodiment, when the processor executes the computer program, the following steps are further implemented: if the missing field in the initial work order data is a key field, the initial work order data is deleted.

[0123] In one embodiment, each initial work order data is reduced, and the processor implements the following steps when executing the computer program: for each initial work order data, the PCA technology is used to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0124] In one embodiment, multiple work order data blocks are statistically processed to obtain work order indicators of multiple dimensions, and the processor implements the following steps when executing the computer program: a Map task is created for each work order data block through a big data analysis system; the first data output after each Map task is processed is sorted to obtain second data, and the second data is passed to the Reduce task; the second data is processed through multiple custom methods to obtain third data with statistics of multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as a dimension, counting the work order data of each consumption period; taking whether the version is issued as a dimension, counting the work order data of the issued version and the non-issued version; taking the category to which the work order belongs as a dimension, counting the work order data of business demand changes, data preparation, application troubleshooting and statistical query; summarizing and outputting the third data with statistics of multiple dimensions to obtain work order indicators of multiple dimensions.

[0125] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0126] The work orders of the financial system are crawled to obtain multiple target work order data; each target work order data is sharded to obtain multiple work order data blocks; multiple work order data blocks are statistically processed to obtain work order indicators in multiple dimensions; among them, the work order indicators in multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc.

[0127] In one embodiment, the work orders of the financial system are crawled to obtain multiple target work order data. When the computer program is executed by the processor, the following steps are implemented: crawling the work orders of the financial system to obtain multiple initial work order data; performing missing value processing on each initial work order data, and performing reduction processing on each initial work order data to obtain multiple target work order data.

[0128] In one embodiment, missing value processing is performed on each initial work order data, and the following steps are implemented when the computer program is executed by the processor: for each initial work order data, check whether the missing field in the initial work order data is a key field; if the missing field in the initial work order data is not a key field, fill the missing field in the initial work order data with the default value.

[0129] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: if the missing field in the initial work order data is a key field, the initial work order data is deleted.

[0130] In one embodiment, each initial work order data is reduced, and the following steps are implemented when the computer program is executed by the processor: for each initial work order data, the PCA technology is used to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0131] In one embodiment, multiple work order data blocks are statistically processed to obtain work order indicators of multiple dimensions, and the following steps are implemented when the computer program is executed by the processor: a Map task is created for each work order data block through the big data analysis system; the first data output after each Map task is processed is sorted to obtain second data, and the second data is passed to the Reduce task; the second data is processed through multiple custom methods to obtain third data with statistics of multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as the dimension, counting the work order data of each consumption period; taking whether the version is issued as the dimension, counting the work order data of the issued version and the non-issued version; taking the category to which the work order belongs as the dimension, counting the work order data of business demand changes, data preparation, application troubleshooting and statistical query; summarizing and outputting the third data with statistics of multiple dimensions to obtain work order indicators of multiple dimensions.

[0132] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0133] The work orders of the financial system are crawled to obtain multiple target work order data; each target work order data is sharded to obtain multiple work order data blocks; multiple work order data blocks are statistically processed to obtain work order indicators in multiple dimensions; among them, the work order indicators in multiple dimensions include: work order time consumption, whether the version is issued, and work order type, etc.

[0134] In one embodiment, a crawling process is performed on a work order of a financial system to obtain a plurality of target work order data. When a computer program is executed by a processor, the following steps are implemented:

[0135] Crawl the work orders of the financial system to obtain multiple initial work order data;

[0136] Missing value processing is performed on each initial work order data, and each initial work order data is reduced to obtain multiple target work order data.

[0137] In one embodiment, missing value processing is performed on each initial work order data, and when the computer program is executed by the processor, the following steps are implemented:

[0138] For each initial work order data, check whether the missing fields in the initial work order data are key fields. If the missing fields in the initial work order data are not key fields, fill in the missing fields in the initial work order data with default values.

[0139] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: if the missing field in the initial work order data is a key field, the initial work order data is deleted.

[0140] In one embodiment, each initial work order data is reduced, and the following steps are implemented when the computer program is executed by the processor: for each initial work order data, the PCA technology is used to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

[0141] In one embodiment, multiple work order data blocks are statistically processed to obtain work order indicators of multiple dimensions, and the computer program also implements the following steps when executed by the processor: a Map task is created for each work order data block through a big data analysis system; the first data output after each Map task is processed is sorted to obtain second data, and the second data is passed to a Reduce task; the second data is processed through multiple custom methods to obtain third data with statistics of multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as a dimension, counting the work order data of each consumption period; taking whether the version is issued as a dimension, counting the work order data of the issued version and the non-issued version; taking the category to which the work order belongs as a dimension, counting the work order data of business demand changes, data preparation, application troubleshooting and statistical query; summarizing and outputting the third data with statistics of multiple dimensions to obtain work order indicators of multiple dimensions.

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0143] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A work order indicator mining method, characterized in that: The method comprises: Crawl the work orders of the financial system to obtain multiple target work order data; Performing data slicing processing on each of the target work order data to obtain multiple work order data blocks; Performing statistical processing on the multiple work order data blocks to obtain work order indicators in multiple dimensions; The work order indicators of the multiple dimensions include: work order time consumption, whether a version is issued, and work order type.

2. The method according to claim 1, characterized in that The work orders of the financial system are crawled to obtain multiple target work order data, including: Crawl the work orders of the financial system to obtain multiple pieces of initial work order data; Missing value processing is performed on each of the initial work order data, and reduction processing is performed on each of the initial work order data to obtain the multiple target work order data.

3. The method according to claim 2, characterized in that The performing missing value processing on each of the initial work order data includes: For each of the initial work order data, check whether the missing fields in the initial work order data are key fields. If the missing fields in the initial work order data are not key fields, fill in the missing fields in the initial work order data with default values.

4. The method according to claim 3, characterized in that The method further comprises: If the missing field in the initial work order data is a key field, the initial work order data is deleted.

5. The method according to claim 2, characterized in that: The performing of regulation processing on each of the initial work order data includes: For each of the initial work order data, the PCA technology is used to eliminate irrelevant features or redundant features in the initial work order data to reduce the feature dimension of the initial work order data.

6. The method according to claim 1, characterized in that The statistical processing of the multiple work order data blocks to obtain work order indicators in multiple dimensions includes: Create a Map task for each of the work order data blocks through the big data analysis system; Arrange the first data output after processing by each Map task to obtain second data, and pass the second data to the Reduce task; The second data is processed by multiple custom methods to obtain third data with statistics in multiple dimensions, wherein the multiple custom methods include: taking the work order consumption period as a dimension, counting the work order data of each consumption period, taking whether the version is issued as a dimension, counting the work order data of the issued version and the unissued version, and taking the category of the work order as a dimension, counting the work order data of business demand changes, data foundation, application troubleshooting and statistical query types; The third data statistically obtained in the multiple dimensions are aggregated and output to obtain work order indicators in the multiple dimensions.

7. A work order indicator mining device, characterized in that: The device comprises: The acquisition module is used to crawl the work orders of the financial system to obtain multiple target work order data; A sharding module, used to perform data sharding processing on each of the target work order data to obtain multiple work order data blocks; The processing module is used to perform statistical processing on the multiple work order data blocks to obtain work order indicators in multiple dimensions; wherein the work order indicators in multiple dimensions include: work order time consumption, whether a version is issued, and work order type, etc.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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