CN115904897A - Credit assessment monitoring method and system based on big data - Google Patents
Credit assessment monitoring method and system based on big data Download PDFInfo
- Publication number
- CN115904897A CN115904897A CN202211306683.4A CN202211306683A CN115904897A CN 115904897 A CN115904897 A CN 115904897A CN 202211306683 A CN202211306683 A CN 202211306683A CN 115904897 A CN115904897 A CN 115904897A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- index
- report
- information
- indexes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a credit assessment monitoring method and a credit assessment monitoring system based on big data, which belong to the technical field of big data and public credit information, and aim to solve the technical problems that a credit monitoring platform cannot meet the application requirements under various environments, the parameter configuration is difficult to multiplex, the adjustment is difficult, and the feedback processing environment is lost, and adopt the technical scheme that: the method comprises the following specific steps: inquiring, adding, modifying and deleting the data source through the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source; maintaining the parameter information: the method comprises the steps of inquiring, adding, modifying and deleting parameters; index management: performing query, addition, modification and deletion on indexes in the monitoring task; managing an omnibearing monitoring model; monitoring feedback; generating a monitoring report; monitoring and early warning and problem tracking.
Description
Technical Field
The invention relates to the technical field of big data and public credit information, in particular to a credit assessment monitoring method and system based on big data.
Background
The credit information is information reflecting the macroscopic credit status of credit subjects in corresponding areas, and is mainly characterized by complexity and diversity. Therefore, the credit information needs to be subjected to customized analysis according with local characteristics according to the actual conditions of each region, so that the user can timely and comprehensively master the credit condition of the user, a decision basis is further provided for credit supervision, and the application of the credit information in social management, public service, operator environment optimization and other aspects is promoted.
The method requirement realization period of the specific program is long, the requirement change cannot be dealt with in real time, and the analyzable scene is single and narrow; although the conventional credit monitoring platform shortens the requirement realization period, the common credit monitoring platform is a uniform credit monitoring platform, cannot realize the monitoring of characteristic indexes in some places, and does not provide technical support for subsequent monitoring, early warning and feedback processing.
Therefore, how to adjust the application requirements of the credit monitoring platform on the targets that change continuously, and simultaneously realize the multiplexing of parameter configuration and flexible adjustment, and can provide feedback processing and the like is a technical problem to be solved urgently at present.
Disclosure of Invention
The technical task of the invention is to provide a credit assessment monitoring method and system based on big data, so as to solve the problems that a credit monitoring platform cannot meet the application requirements under various environments, parameter configuration is difficult to reuse and adjust, and a feedback processing environment is lacked.
The technical task of the invention is realized in the following way, a credit assessment monitoring method based on big data, the method realizes the configuration of an index by linking data sources, uploading set parameters and carrying out self-defined operation on corresponding parameters; then, a plurality of indexes are used for achieving the maintenance of the same target dimension, and the establishment of an all-dimensional monitoring model is realized by the plurality of dimensions; the method comprises the following specific steps:
inquiring, adding, modifying and deleting the data source through the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source;
maintaining the parameter information: the method comprises the steps of inquiring, adding, modifying and deleting parameters;
index management: performing query, addition, modification and deletion on indexes in the monitoring task;
management of an omnibearing monitoring model: the method comprises the steps of querying, adding, modifying, deleting, querying detail information, enabling and disabling the model, configuring the dimension, associating indexes and configuring result display of the model;
monitoring feedback: inquiring the state and detailed information of the monitoring result through the name, type and period of the monitoring task and the screening condition of the result state;
and (3) generating a monitoring report: previewing, downloading, returning and regenerating the monitoring report;
monitoring early warning and problem tracking: and the user performs rule configuration on the index name, the index score and the rule description information of the early warning index according to the user requirement.
Preferably, the maintenance parameter information is a parameter information newly added parameter through a parameter name, a parameter code, an acquisition mode, a parameter type, a parameter description and a parameter default value;
wherein, the 'parameter name' is the Chinese name of the parameter and is unique; the 'parameter coding' is the English name of the parameter, and only allows the expression of a capital English letter and underline combined form; the acquisition mode comprises three modes of unit reporting (data is from a database corresponding to the reporting unit storage), system operation (common fixed parameters built in the system) and sql operation (data is from information managed and maintained by a data source); the "parameter type" includes both a numerical type and a boolean type.
Preferably, the index management is specifically as follows:
maintaining all indexes in the monitoring task, and combining the monitoring indexes in a quantized form through parameters by using an index value calculation formula;
the basic information of the index is also supported to be maintained, and the change of the use state of the index in the monitoring task is realized through starting and stopping operations;
when a maintenance index is added in detail, a user inputs the maintenance index according to an index name, an index code, an index description and the category of an index calculation formula;
in the aspect of parameter selection, stored parameters are inquired for a user to select, two preset placeholder records, one index value record and one index score record are generated, an index value placeholder reference method is assigned to be $ { quota (XXX) }, an index score placeholder reference method is assigned to be $ { quota (XXX) }, and XXX represents index codes.
Preferably, the dimension configuration provides a function of maintaining the dimensions in the omnibearing monitoring model, a tree structure is used for displaying and maintaining the form of the basic dimension information, and the basic dimension information comprises a dimension name, a final stage and dimension description information;
"index association" is the processing of the dimension and index complex association relation involved in the omnibearing monitoring model, thus realizing the operations of adding, modifying, deleting, starting and stopping the association index under the dimension of the model; on the technical side, one tree dimension is divided into a plurality of atomic index dimensions according to the levels, and the divided index dimensions are flattened, so that various logic operations of dynamic flexible configuration and self-definition of tree dimension nodes are supported, the established model dimensions and level information are displayed through a tree structure, and index information related to the dimensions is displayed in a list; the index association comprises four score calculation modes of calculation formula, rule judgment, system operation and formula plus system operation:
(1) calculating the formula: inputting a calculation formula of the index score, wherein the index VALUE in the calculation formula is represented by $ { QUOTA _ VALUE };
(2) and (4) judging the rule: maintaining index scores corresponding to index values in different ranges, wherein the index values are numerical values, and the index scores are numerical values or maintenance calculation formulas;
(3) and (3) system operation: storing a formula of the common indexes and directly calling;
(4) formula addition systematic operation: a custom index is further added on the basis of using a common formula for operation;
the result display configuration provides a display configuration function for the operation result of the omnibearing monitoring model, modifies the displayed header name and the display sequence of the header, and adds or deletes the header of the result display; the header display data are not limited to indexes, but also comprise added parameters, dimensions, index values and model values.
Preferably, the monitoring result states are divided into 'unpublished' and 'published', and the user can switch between 'unpublished' and 'published'; when the state is switched to 'issued', a user generates a monitoring report or sends a monitoring result to other units through in-station information, and technical support is provided for advertising, timely research, delivery and deployment, active learning, discussion and comprehension; the core of the technology lies in BSP organization mechanism query service, which stores data information of each organization mechanism and is convenient to query and call at any time;
when a user generates wrong scores due to unreasonable monitoring results caused by some special conditions, a superior user modifies the monitoring scores of the user through a monitoring problem processing function and stores processing modification opinions; the monitoring problem processing function provides convenience for processing special conditions generated in the actual use process of a user;
the monitoring problem processing function displays detailed information of the monitoring indexes, wherein the detailed information of the monitoring indexes comprises monitored task names, monitoring periods, monitoring objects, index names, problem indexes, index scores, monitoring rule description, monitoring problem description and responsibility unit information;
the 'processing state' parameter of the monitoring problem processing function is used for counting and controlling the feedback condition of each monitoring task, and the processing state is divided into 'unprocessed state' and 'processed state'. When the problem is in an unprocessed state, the operation column displays processing operation, and the operation column drills down to a monitoring problem processing page, meanwhile, a monitoring problem processing method is stored, and the problem processing state is modified into a processed state; when the processing state is detected, the operation column displays the checking operation and goes down to a detailed monitoring problem page;
the stored public organization structure tree is called at any time through the BSP organization structure inquiry service, and a monitoring problem processing unit (the same level of the current processing unit or the lower level unit thereof) for forwarding the monitoring problem is selected.
Preferably, the generation of the monitoring report is specifically as follows:
monitoring template of monitoring report: summarizing and summarizing the report; the monitoring template is configured according to the template name, the template type, the template description, the watermark state and the watermark mode, and the configured monitoring template is directly applied in a starting state; wherein, the watermark mode is divided into characters and pictures; when selecting the character, displaying the watermark content, hiding the watermark picture, and inputting the watermark content; when the picture is selected, the watermark content is hidden, the watermark picture is displayed, and an uploading watermark picture is selected and adopts a resource file management mode;
and (3) generating report text content: the rich text editor control is used for generation, a user-defined button function of 'inserting placeholders' is designed, and after the placeholders are selected, elements of the placeholders are backfilled to the cursor position of the text content, so that the problem of mistaken modification of the placeholder text which possibly occurs can be avoided, and the placeholders are guaranteed to be effectively available; all available placeholder information comprises automatically generated parameter value reference information, index value reference information and placeholders needing to develop corresponding programs; the placeholders comprise picture type placeholders, character type placeholders and character + picture type placeholders; the placeholder type indicates how to replace the corresponding placeholder in the generated monitoring report;
logging of report generation: the system management personnel can check the generation state, the existence of the abnormal condition and the abnormal information of each report;
monitoring report preview and download: in the previewing process, fuzzy query is carried out according to the report name, and after the report period type is selected, accurate query is carried out according to the report period; the undeleted report records are inquired, so that each unit can conveniently check the content of the monitoring report, find weak links in the credit work of the unit in time and make an improvement plan in advance;
report back: after the downloaded monitoring report is finely adjusted and modified, the report which better meets the actual requirement is supported to be transmitted back for each monitoring object to download and view; after the report is returned, the records in the current report table are modified by executing sql statements, the report state field is modified to be deleted, a new log record is generated in the report log table, and the report return is marked in the remark.
Preferably, the monitoring and early warning and problem tracking are as follows:
screening and sorting the monitoring results, distinguishing the monitoring data results of different degrees, and providing early warning rules and query of early warning indexes for all units in the same level or lower level units; the early warning indexes show all the early warning index result information of the monitored object; the data screens the early warning index result through the monitoring task name (fuzzy query), the monitoring type, the monitoring period (the last monitoring period is defaulted according to the linkage of the monitoring type) and the query condition of the monitoring object, and skips to a specific early warning index rule page, and checks the rule configuration of the early warning index;
sending a message to inform monitoring objects of any type of early warning levels: acquiring a lower-level unit user through a BSP organization inquiry service, and calling and sending a message to the lower-level unit user through an interface so as to display the lower-level unit user on a platform of the user in real time;
carrying out problem tracking on the problem indexes: providing an overview of each monitoring problem for managers, wherein the overview comprises the frequency of problem occurrence, the frequency of problem processing, the frequency of continuous problem occurrence and detailed information of each monitoring result of tracking problem indexes; simultaneously displaying the starting and ending time of the monitoring period, the monitored object and the monitored index information, and displaying the problem of each monitoring period of the monitoring index according to the descending order of the monitoring period in the tracking details, wherein the problem of the monitoring period comprises the information of each processing unit and each processing scheme;
when a user selects to track any detection index, marking the corresponding field, and accordingly displaying the page classified according to the index as tracked in a targeted manner; aiming at a tracking field design data overview page, the importance of indexes such as a user is reminded according to a monitoring type, a monitoring period starting time, a monitoring period ending time, a monitoring index (fuzzy query) and a query condition of a monitored object, the design idea is that the index type with problems is displayed, the user can conveniently and clearly position commonalities among various problems, and the efficiency of a problem analysis stage is greatly improved.
A credit assessment monitoring system based on big data comprises,
the data source management module is used for inquiring, adding, modifying and deleting the data source according to the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source;
the parameter management module is used for maintaining parameter information, and specifically comprises parameter inquiry, addition, modification and deletion;
the index management module is used for inquiring, adding, modifying and deleting indexes in the monitoring task;
the all-dimensional monitoring model management module is used for inquiring, newly adding, modifying, deleting, inquiring detail information, starting and stopping the model, configuring dimensionality, associating indexes, configuring timed tasks and displaying results of the all-dimensional monitoring model;
the data management module is used for realizing data submission, initial review of data submission and final review of data submission;
the monitoring operation module is used for realizing timing task operation, means operation and operation detail checking;
the monitoring result generation module is used for managing monitoring results and monitoring problems, and performing problem early warning and problem tracking;
the monitoring report generating module is used for generating a monitoring report template, generating a monitoring report according to the monitoring report template, managing the monitoring report, and generating a monitoring report log, downloading the monitoring report and returning the monitoring report according to the monitoring report;
and the statistical analysis module is used for carrying out comprehensive unified analysis, unit monitoring analysis and unit problem analysis.
An electronic device, comprising: a memory and at least one processor;
wherein the memory has stored thereon a computer program;
the at least one processor executes the computer program stored by the memory such that the at least one processor performs the big-data based credit assessment monitoring method as described above.
A computer-readable storage medium having stored thereon a computer program executable by a processor to implement a big-data based credit assessment monitoring method as described above.
The credit assessment monitoring method and system based on big data have the following advantages:
the method has the advantages that a stable analysis model is formed after multi-dimensional modeling, and the method has strong adaptability to continuously changing analysis requirements and analysis scenes, so that the implementation period of the analysis requirements is greatly shortened; it is worth mentioning that dimension and index extraction is performed in a distributed batch mode by using a big data technology, larger-scale data can be processed, and more information which cannot be extracted by a conventional means can be extracted by using a natural language processing technology;
the invention supports credit monitoring of administrative divisions at all levels, creates a service closed loop of monitoring, early warning, feedback and processing, and also provides the display functions of credit reports and various visual charts;
the invention applies the idea of dimension modeling to the information analysis in the field of public credit, establishes a monitoring parameter system, a monitoring index system, a main body dimension system and a monitoring dimension system, and provides a set of distributed batch-executable index dimension assignment method, a custom analysis and analysis engine and a visual display suite;
through the inquiry function of an organization mechanism, a dragon service of monitoring, early warning, feedback and processing is created for the actual application logic scene of a target user, the forwarding, transmission and deployment of superior and subordinate users are facilitated to be rapidly promoted, meanwhile, the display functions of credit reports and various visual charts are provided, and the user can master the overall situation of credit monitoring;
fifthly, calculating dimensionality is provided for the model by adding parameters and setting indexes; meanwhile, a single model supports the analysis and calculation of associated multiple dimensions, so that the flexibility of the model is greatly enhanced, and the model is convenient to adjust in time to meet the application requirement of the standard which changes continuously; the method is simultaneously suitable for the characteristics of quick change of the analysis requirement and multiple analysis scenes of the monitoring information in the field of public credit, and solves the problems of difficult multiplexing and difficult adjustment of the conventional system model parameter configuration;
the invention not only supports the monitoring and feedback mechanism of the whole process, but also carries out early warning on the monitoring problem, thereby being convenient for finding the problem and improving the problem in time and being capable of actually improving the actual score of the monitored object; meanwhile, the poor problems in the monitoring process can be tracked and fed back, the problem that only monitoring is carried out but not processed is fundamentally solved, a full-flow closed loop of monitoring, early warning, feedback and processing is created, and the problem that the conventional platform lacks feedback processing is solved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic flow diagram of a credit assessment monitoring method based on big data;
FIG. 2 is a block diagram of a credit assessment monitoring system based on big data.
Detailed Description
The credit assessment monitoring method and system based on big data of the invention are explained in detail with reference to the drawings and the specific embodiments of the specification.
Example 1:
as shown in fig. 1, the embodiment provides a credit assessment monitoring method based on big data, and the method realizes configuration of an index by linking a data source, uploading set parameters, and performing custom operation on corresponding parameters; then, a plurality of indexes are used for achieving the maintenance of the same target dimension, and the establishment of an all-dimensional monitoring model is realized by the plurality of dimensions; the method comprises the following specific steps:
s1, inquiring, adding, modifying and deleting a data source through the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source;
the data source is used for recording connection information of all databases used by the monitoring system. For example: the data used in the monitoring system may originate from multiple databases, and the different databases may be queried for parameters using connection information of the data sources.
S2, maintaining parameter information: the method comprises the steps of inquiring, adding, modifying and deleting parameters;
the parameter is the minimum data item used in the monitoring operation, and is a data item which is decomposed from the monitoring index and is used for quantifying the index. For example: the monitoring index is 'double-display reported data accuracy', and the calculation formula is the ratio of 'correct data volume of double-display system' to 'reported data volume'. In this case, the index may be divided into two parameters, one is "correct data volume of the dual presentation system" and the other is "delivery data volume of the dual presentation system". The values of the parameters can be obtained through data reporting, SQL operation and system operation, basic information of the parameters is maintained (including adding, modifying, starting, stopping and deleting) by a user, and each parameter has a unique parameter code and a unique parameter name. And for the enabled parameters, the subsequent monitoring operation can be participated in, and the monitoring task is completed.
S3, index management: performing query, addition, modification and deletion on indexes in the monitoring task;
wherein, the indexes are as follows: the monitoring work is decomposed into a plurality of quantifiable monitoring items which are indexes. The index is formed by combining four arithmetic operations such as addition, subtraction, multiplication and division with other operation signs through a calculation formula form by parameters. Meanwhile, the indexes correspond to the scores, the proportion of the indexes in the monitoring task can be reflected through the scores, and the final state of the monitoring result is displayed in a quantitative mode. For example: the credit status of the institution is used as a monitoring index, and the score is 4. The user can maintain the addition, modification, activation, deactivation and deletion of the indexes.
S4, managing an omnibearing monitoring model: the method comprises the steps of querying, adding, modifying, deleting, querying detail information, enabling and disabling the model, configuring the dimension, associating indexes and configuring result display of the model;
wherein, the dimension: monitoring indexes of the same type are classified into one type, and the type of the indexes is called monitoring dimension. For example: the credit condition of the institution, the integrity file of the officer and the information sharing condition of the enterprise-involved credit, which reflect the construction condition of the integrity credit of the government affairs, can determine the monitoring dimension as the integrity construction of the government affairs. In one monitoring model, multiple dimensions can be established and the configuration of rapid modification, activation and deactivation and the like can be supported. Therefore, the method and the device realize the multi-dimensional monitoring of the monitoring object by the user, establish the many-to-many relationship between the monitoring object and the monitoring model, realize the credit monitoring from all angles, various aspects and more levels and achieve the final aim of monitoring work.
The omnibearing monitoring model is obtained by scientifically and reasonably configuring monitoring indexes, and the flexibility of the model is greatly enhanced due to the configuration and association of multiple layers. The system based on the multi-dimensional modeling idea thoroughly solves the problems that model index configuration is difficult to reuse and adjustment is difficult.
The system supports information such as basic information of a user management and maintenance model, index information related to the model, dimension information of the model, display result configuration of a model result and the like, and can also configure a timing task of the model operation in a user-defined manner, realize generation of a data management task and remind the user of reporting data in time.
Secondly, the system preferentially obtains the most atomic information for business processing in the selection of the constructed model, so that the analysis requirement of the user can be quickly completed, and the response performance of large-scale complex query is better. Finally, a flexible and quick star model is used on the multidimensional analysis and development Dimension model, namely the star model consists of a Fact Table (Fact Table) and a set of Dimension tables (Dimension Table), the Fact Table is taken as the center, and all Dimension tables are connected to the Fact Table. Meanwhile, each dimension table has one dimension as a main key, the main keys of all the dimensions are combined into the main key of the fact table, the fact table is used as a core, and the dimension tables are distributed around the core in a star shape.
Compared with a wide table, the fact table and the dimension table are disassembled in the star mode, the data structure is more flexible, and the whole data structure cannot be influenced if the dimension table data changes (the external keys are unchanged); the dimension table is only associated with the fact table, query and analysis of mass data are optimized, and the data structure is easier to understand and maintain and has stronger readability.
In the design of the granularity, a minimum granularity principle is adopted. According to the knowledge of the data requirements of the user and the size of the space of the data warehouse occupied by the information, in the process of customizing the dimension of the user, the low granularity does not cause excessive influence on the performance and the storage space of the data warehouse, and therefore the granularity is designed according to the lowest parameter unit. At the same time, it is also possible in real business scenarios to add more dimensions to the base granularity of the fact table, and these additional dimensions will naturally take a unique value in terms of each combined value of the base dimensions. From the lowest level of granularity, a fact table row corresponds to a metric event, and vice versa.
S5, monitoring and feeding back: querying the state and detailed information of a monitoring result through the name, type and period of the monitoring task and the screening condition of the result state;
s6, generating a monitoring report: previewing, downloading, returning and regenerating the monitoring report;
s7, monitoring and early warning and problem tracking: and the user performs rule configuration on the index name, the index score and the rule description information of the early warning index according to the user requirement.
In this embodiment, the maintenance parameter information in step S2 is a parameter information addition parameter obtained by a parameter name, a parameter code, an acquisition mode, a parameter type, a parameter description, and a parameter default value;
wherein, the 'parameter name' is the Chinese name of the parameter and is unique; the 'parameter coding' is the English name of the parameter, and only allows the expression of a capital English letter and underlining combination form; the 'acquisition mode' comprises three modes of unit reporting (data comes from a database corresponding to the reporting unit storage), system operation (common fixed parameters built in the system) and sql operation (data comes from information of data source management and maintenance); the "parameter type" includes both a numerical type and a boolean type.
The index management in step S3 of this embodiment is specifically as follows:
s301, maintaining all indexes in the monitoring task, and combining the monitoring indexes in a quantized form through parameters by using an index value calculation formula;
s302, supporting maintenance of basic information of the indexes, and realizing change of the use states of the indexes in a monitoring task through starting and stopping operations;
when a maintenance index is added in detail, a user inputs the maintenance index according to an index name, an index code, an index description and the category of an index calculation formula;
in the aspect of parameter selection, stored parameters are inquired for a user to select, two preset placeholder records, one index value record and one index score record are generated, an index value placeholder reference method is assigned to be $ { quota (XXX) }, an index score placeholder reference method is assigned to be $ { quota (XXX) }, and XXX represents index codes.
The dimension configuration in step S4 of this embodiment provides a function of maintaining the dimensions in the omni-directional monitoring model, and a tree structure is used to display and maintain the form of the basic dimension information, where the basic dimension information includes a dimension name, whether the last level is the last level, and dimension description information;
the index association is the processing of the dimension and index complex association relation involved in the omnibearing monitoring model, so as to realize the operations of adding, modifying, deleting, starting and stopping the associated index under the dimension of the model; on the technical side, one tree dimension is divided into a plurality of atomic index dimensions according to the levels, and the divided index dimensions are flattened, so that various logic operations of dynamic flexible configuration and self-definition of tree dimension nodes are supported, the established model dimensions and level information are displayed through a tree structure, and index information related to the dimensions is displayed in a list; the index association comprises four score calculation modes of calculation formula, rule judgment, system operation and formula plus system operation:
(1) calculating the formula: inputting a calculation formula of the index score, wherein the index VALUE in the calculation formula is represented by $ { QUOTA _ VALUE };
(2) and (4) judging the rule: maintaining index scores corresponding to index values in different ranges, wherein the index values are numerical values, and the index scores are numerical values or maintenance calculation formulas;
(3) and (3) system operation: storing a formula of the common indexes and directly calling;
(4) formula addition systematic operation: a custom index is further added on the basis of using a common formula for operation;
the result display configuration provides a display configuration function for the operation result of the omnibearing monitoring model, modifies the displayed header name and the display sequence of the header, and adds or deletes the header of the result display; the header display data are not limited to indexes, but also comprise added parameters, dimensions, index values and model values.
After the monitoring operation is completed, a monitoring result is generated (the monitoring result refers to the credit score ranking condition of each monitored object in the monitoring period). The system provides various operational functions on the monitoring results. Through multiple management functions, the monitoring result can be rapidly issued, the monitoring report can be generated, all monitoring data generated in the monitoring operation process can be checked, a user can conveniently perform subsequent data analysis and perform ranking display and custom report display on the monitoring object, or the monitoring object or other lower-level units are informed in a sending station through a message notification mode.
In this embodiment, the monitoring result status in step S5 is divided into "not published" and "published", and the user switches between "not published" and "published"; when the state is switched to 'issued', a user generates a monitoring report or sends a monitoring result to other units through in-station information, and technical support is provided for advertising, timely research, delivery and deployment, active learning, discussion and comprehension; the core of the technology lies in BSP organization mechanism query service, which stores data information of each organization mechanism and is convenient to query and call at any time;
for example: the user 1 establishes a set of monitoring models for the number of the docking departments of each unit in the monitoring area. And rules are specified for the number of gates to exceed 10 to 2 points, 5-10 to 1 points, and less than 5 to 0 points. The final monitoring result is that the user 1.1 (the subordinate department of the user 1) gets 0 point, and the user 1 can send the situation to the peer unit through the forwarding button to transmit a report and send the report to the subordinate unit to be led to a ring.
When a user generates an error score due to unreasonable monitoring results caused by some special conditions, a superior user modifies the monitoring score of the user through a monitoring problem processing function and stores and processes modification opinions; the monitoring problem processing function provides convenience for processing special conditions generated in the actual use process of a user;
the monitoring problem processing function displays detailed information of the monitoring indexes, wherein the detailed information of the monitoring indexes comprises monitored task names, monitoring periods, monitoring objects, index names, problem indexes, index scores, monitoring rule description, monitoring problem description and responsibility unit information;
the 'processing state' parameter of the monitoring problem processing function is used for counting and controlling the feedback condition of each monitoring task, and the processing state is divided into 'unprocessed state' and 'processed state'. When the problem is in an unprocessed state, the operation column displays processing operation, and the operation column drills down to a monitoring problem processing page, meanwhile, a monitoring problem processing method is stored, and the problem processing state is modified into a processed state; when the processing state is detected, the operation column displays the checking operation and goes down to a detailed monitoring problem page;
and calling the stored common organization structure tree at any time through the BSP organization structure inquiry service, and selecting a monitoring problem processing unit (the same level of the current processing unit or the lower level unit thereof) for forwarding the monitoring problem.
In the above example, the scores were erroneous due to statistical data being missed. "user 1.1" explains the situation to "user 1", and after receiving the upper level approval, "user 1" decides to modify the score, and does not notify any more, that is, performs processing by using the "processing" function.
In an actual service scene, the content of the monitoring report changes according to different service requirements. In order to facilitate timely adjustment of the monitoring report and realize configurability of the monitoring report, the system provides a monitoring report template configuration function, the text of the monitoring report is realized by configuring a rich text editor control and a placeholder, and an incidence relation between the monitoring model and the monitoring report template is established.
Because there are many types of monitoring reports, different reports need to be provided for different roles and different monitoring periods. Based on the requirement, the system designs a monitoring report template management function. The key point of the design logic is to monitor different types of report templates, and whether the types of the templates are integrated reports or independent reports needs to be set. The integrated report is a single report generated, and the independent reports are corresponding reports generated according to different units. By flexible configuration, the diversity of monitoring reports is achieved. Monitoring reports are no longer implemented using a single system code, but instead are flexibly custom configured.
The generation of the monitoring report in step S6 in this embodiment is specifically as follows:
s601, monitoring a monitoring template of a monitoring report: summarizing and summarizing the report; the monitoring template is configured according to the template name, the template type, the template description, the watermark state and the watermark mode, and the configured monitoring template is directly applied in a starting state; wherein, the watermark mode is divided into characters and pictures; when selecting the character, displaying the watermark content, hiding the watermark picture, and inputting the watermark content; when the picture is selected, the watermark content is hidden, the watermark picture is displayed, and the uploading watermark picture is selected and adopts a resource file management mode;
s602, generating report text content: the method comprises the steps that a rich text editor control is used for generation, a user-defined button function of inserting placeholders is designed, and after the placeholders are selected, elements of the placeholders are backfilled to the cursor positions of text contents, so that the problem of error modification of the placeholder texts possibly occurring can be avoided, and the placeholders are guaranteed to be effective and usable; all available placeholder information comprises automatically generated parameter value reference information, index value reference information and placeholders needing to develop corresponding programs; the placeholders comprise picture type placeholders, character type placeholders and character + picture type placeholders; the placeholder type indicates how to replace the corresponding placeholder in the generated monitoring report;
s603, recording a report generation log: the system management personnel can check the generation state, the existence of the abnormality and the abnormal information of each report;
s604, monitoring report preview and downloading: in the previewing process, fuzzy query is carried out according to the report name, and after the report period type is selected, accurate query is carried out according to the report period; the undeleted report records are inquired, so that each unit can conveniently check the content of the monitoring report, find weak links in the credit work of the unit in time and make an improvement plan in advance;
s605, report back: after the downloaded monitoring report is finely adjusted and modified, the report which is more in line with the actual requirement is supported to be returned for each monitoring object to download and view; after returning the report, executing sql statement to modify the record in the current report table, modifying the report status field to be deleted, generating a new log record in the report log table, and marking the report returning in the remark.
The monitoring, early warning and problem tracking in step S7 of this embodiment are specifically as follows:
s701, screening and sorting the monitoring results, distinguishing the monitoring data results of different degrees, and providing early warning rules and query of early warning indexes for all units in the same level or lower level units; the early warning indexes show all the early warning index result information of the monitored object; the data is subjected to early warning index screening results through the monitoring task name (fuzzy query), the monitoring type, the monitoring period (the last monitoring period is defaulted according to the linkage of the monitoring type) and the query condition of a monitoring object, and the early warning index screening results are jumped to a specific early warning index rule page, and the rule configuration of the early warning index is checked;
s702, sending a message to inform monitoring objects of any early warning level: acquiring a lower-level unit user through a BSP organization inquiry service, and calling and sending a message to the lower-level unit user through an interface so as to display the lower-level unit user on a platform of the user in real time;
the description is still made according to the above example: "user 1" is defined as "medium risk" according to the score of 1, and the score of less than 1 is defined as "high risk", and the "message notification" function is used in the monitoring and early warning function, and the system can send medium risk warning to the corresponding "user 1.1" to remind the user to process as soon as possible.
S703, problem tracking is carried out on the problem indexes: providing an overview of each monitoring problem for managers, wherein the overview comprises the frequency of problem occurrence, the frequency of problem processing, the frequency of continuous problem occurrence and detailed information of each monitoring result of tracking problem indexes; simultaneously displaying the starting and ending time of the monitoring period, the monitored object and the monitored index information, and displaying the problem of each monitoring period of the monitoring index according to the descending order of the monitoring period in the tracking details, wherein the problem of the monitoring period comprises the information of each processing unit and each processing scheme;
s704, when the user selects to track any detection index, marking the corresponding field, and accordingly displaying the page classified according to the index as tracked in a targeted manner; aiming at a tracking field design data overview page, the importance of indexes such as a user is reminded according to a monitoring type, a monitoring period starting time, a monitoring period ending time, a monitoring index (fuzzy query) and a query condition of a monitored object, the design idea is that the index type with problems is displayed, the user can conveniently and clearly position commonalities among various problems, and the efficiency of a problem analysis stage is greatly improved.
Example 2:
as shown in fig. 2, the present embodiment provides a credit assessment monitoring system based on big data, which comprises,
the data source management module is used for inquiring, adding, modifying and deleting the data source according to the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source;
the parameter management module is used for maintaining parameter information, and specifically comprises the steps of inquiring, adding, modifying and deleting parameters;
the index management module is used for inquiring, adding, modifying and deleting indexes in the monitoring task;
the all-dimensional monitoring model management module is used for inquiring, newly adding, modifying, deleting, inquiring detail information, starting and stopping the model, configuring dimensionality, associating indexes, configuring timed tasks and displaying results of the all-dimensional monitoring model;
the data management module is used for realizing data submission, initial review of data submission and final review of data submission;
the monitoring operation module is used for realizing timing task operation, means operation and operation detail checking;
the monitoring result generating module is used for managing the monitoring results and the monitoring problems and carrying out problem early warning and problem tracking;
the monitoring report generating module is used for generating a monitoring report template, generating a monitoring report according to the monitoring report template, managing the monitoring report, and generating a monitoring report log, downloading the monitoring report and returning the monitoring report according to the monitoring report;
and the statistical analysis module is used for carrying out comprehensive unified analysis, unit monitoring analysis and unit problem analysis.
Example 3:
the present embodiment also provides an electronic device, including: a memory and at least one processor;
wherein the memory stores computer-executable instructions;
the at least one processor executes the computer-executable instructions stored by the memory to cause the at least one processor to perform a big-data based credit assessment monitoring method in any embodiment of the invention.
Example 4:
the embodiment of the invention also provides a computer-readable storage medium, wherein a plurality of instructions are stored, and the instructions are loaded by the processor, so that the processor executes the credit assessment monitoring method based on the big data in any embodiment of the invention. Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion unit is caused to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the embodiments described above.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A credit assessment monitoring method based on big data is characterized in that the method realizes the configuration of an index by linking data sources, uploading set parameters and carrying out self-defined operation on corresponding parameters; then, a plurality of indexes are used for achieving the maintenance of the same target dimension, and the establishment of an all-dimensional monitoring model is realized by the plurality of dimensions; the method comprises the following specific steps:
inquiring, adding, modifying and deleting the data source through the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source;
maintaining the parameter information: the method comprises the steps of inquiring, adding, modifying and deleting parameters;
index management: performing query, addition, modification and deletion on indexes in the monitoring task;
management of an all-dimensional monitoring model: the method comprises the steps of querying, adding, modifying, deleting, querying detail information, enabling and disabling the model, configuring the dimension, associating indexes and configuring result display of the model;
monitoring feedback: querying the state and detailed information of a monitoring result through the name, type and period of the monitoring task and the screening condition of the result state;
and (3) generating a monitoring report: previewing, downloading, returning and regenerating the monitoring report;
monitoring early warning and problem tracking: and the user performs rule configuration on the index name, the index score and the rule description information of the early warning index according to the user requirement.
2. The credit assessment monitoring method based on big data as claimed in claim 1, wherein the maintenance parameter information is a parameter information added parameter through parameter name, parameter coding, obtaining mode, parameter type, parameter description and parameter default value;
wherein, the 'parameter name' is the Chinese name of the parameter and is unique; the 'parameter coding' is the English name of the parameter, and only allows the expression of a capital English letter and underline combined form; the 'acquisition mode' comprises three modes of unit reporting, system operation and sql operation; the "parameter type" includes both a numerical type and a non-numerical type.
3. The credit assessment monitoring method based on big data as claimed in claim 1, wherein index management is as follows:
maintaining all indexes in the monitoring task, and combining the monitoring indexes in a quantized form through parameters by using an index value calculation formula;
the basic information of the index is also supported to be maintained, and the change of the use state of the index in the monitoring task is realized through starting and stopping operations;
when a maintenance index is added in detail, a user inputs the maintenance index according to an index name, an index code, an index description and the category of an index calculation formula;
in the aspect of parameter selection, stored parameters are inquired for a user to select, two preset placeholder records, one index value record and one index score record are generated, an index value placeholder reference method is assigned to be $ { quota (XXX) }, an index score placeholder reference method is assigned to be $ { quota (XXX) }, and XXX represents index codes.
4. The credit assessment monitoring method based on big data as claimed in claim 1, wherein the dimension configuration provides a maintenance function for the dimension in the omnibearing monitoring model, the tree structure is used for displaying and maintaining the form of the basic information of the dimension, the basic information of the dimension includes the name of the dimension, whether the last level is and the description information of the dimension;
the index association is the processing of the dimension and index complex association relation involved in the omnibearing monitoring model, so as to realize the operations of adding, modifying, deleting, starting and stopping the associated index under the dimension of the model; on the technical side, one tree dimension is divided into a plurality of atomic index dimensions according to the levels, and the divided index dimensions are flattened, so that various logic operations of dynamic flexible configuration and self-definition of tree dimension nodes are supported, the established model dimensions and level information are displayed through a tree structure, and index information related to the dimensions is displayed in a list; the index association comprises four score calculation modes of calculation formula, rule judgment, system operation and formula plus system operation:
(1) calculating the formula: inputting a calculation formula of the index score, wherein the index VALUE in the calculation formula is represented by $ { QUOTA _ VALUE };
(2) and (4) judging the rule: maintaining index scores corresponding to index values in different ranges, wherein the index values are numerical values, and the index scores are numerical values or maintenance calculation formulas;
(3) and (3) system operation: storing a formula of the common indexes and directly calling;
(4) formula addition systematic operation: a custom index is further added on the basis of using a common formula for operation;
the result display configuration provides a display configuration function for the operation result of the omnibearing monitoring model, modifies the displayed header name and the display sequence of the header, and adds or deletes the header of the result display; the header display data are not limited to indexes, but also comprise additional parameters, dimensions, index values and model values.
5. The credit assessment monitoring method based on big data as claimed in claim 1, wherein the monitoring result status is divided into "not published" and "published", and the user switches between "not published" and "published" at will; when the state is switched to 'released', a user generates a monitoring report or sends a monitoring result to other units through station internal information;
when the monitoring result is unreasonable, an error score is generated, and a superior user modifies the monitoring score of the user through a monitoring problem processing function and stores and processes modification opinions;
the monitoring problem processing function displays detailed information of the monitoring indexes, wherein the detailed information of the monitoring indexes comprises monitored task names, monitoring periods, monitoring objects, index names, problem indexes, index scores, monitoring rule description, monitoring problem description and responsibility unit information;
the 'processing state' parameter of the monitoring problem processing function is used for counting and controlling the feedback condition of each monitoring task, and the processing state is divided into 'unprocessed state' and 'processed state'. When the problem is in an unprocessed state, the operation column displays processing operation, and the operation column drills down to a monitoring problem processing page, meanwhile, a monitoring problem processing method is stored, and the problem processing state is modified into a processed state; when the state is processed, displaying and checking operation in an operation column, and drilling down to a detailed page of the monitoring problem;
and calling the stored public organization mechanism tree at any time through the BSP organization mechanism inquiry service, and selecting a monitoring problem processing unit for forwarding the monitoring problems.
6. The big-data-based credit assessment monitoring method according to claim 1, wherein the generation of the monitoring report is specifically as follows:
monitoring template of monitoring report: summarizing and summarizing the report; the monitoring template is configured according to the template name, the template type, the template description, the watermark state and the watermark mode, and the configured monitoring template is directly applied in a starting state; wherein, the watermark mode is divided into characters and pictures; when selecting the character, displaying the watermark content, hiding the watermark picture, and inputting the watermark content; when the picture is selected, the watermark content is hidden, the watermark picture is displayed, and an uploading watermark picture is selected and adopts a resource file management mode;
and (3) generating report text content: generating by using a rich text editor control, designing a user-defined button function of 'inserting placeholders', and backfilling elements of the placeholders to the cursor position of text content after selecting the placeholders; all available placeholder information comprises automatically generated parameter value reference information, index value reference information and placeholders needing to develop corresponding programs; the placeholders comprise picture type placeholders, character type placeholders and character + picture type placeholders; the placeholder type indicates how to replace the corresponding placeholder in the generated monitoring report;
logging of report generation: the system management personnel can check the generation state, the existence of the abnormal condition and the abnormal information of each report;
monitoring report preview and download: in the previewing process, fuzzy query is carried out according to the report name, and after the report period type is selected, accurate query is carried out according to the report period; the undeleted report records are inquired, so that each unit can conveniently check the content of the monitoring report, find weak links in the credit work of the unit in time and make an improvement plan in advance;
report back: after the downloaded monitoring report is finely adjusted and modified, the report which is more in line with the actual requirement is supported to be returned for each monitoring object to download and view; after the report is returned, the records in the current report table are modified by executing sql statements, the report state field is modified to be deleted, a new log record is generated in the report log table, and the report return is marked in the remark.
7. The credit assessment monitoring method based on big data as claimed in any one of claims 1-6, wherein the monitoring early warning and problem tracking are as follows:
screening and sequencing the monitoring results, distinguishing the monitoring data results of different degrees, and providing early warning rules and query of early warning indexes for all units in the same level or lower level units; the early warning indexes show all the early warning index result information of the monitored object; the data is screened by monitoring task name, monitoring type, monitoring period and query condition of the monitored object, and the early warning index result is jumped to a specific early warning index rule page, and the rule configuration of the early warning index is checked;
sending a message to inform monitoring objects of any type of early warning levels: acquiring a lower-level unit user through a BSP organization inquiry service, transmitting a message to the lower-level unit user through interface calling, and displaying the message in real time;
carrying out problem tracking on the problem indexes: providing an overview of each monitoring problem for managers, wherein the overview comprises the occurrence frequency of the problems, the problem processing frequency, the continuous occurrence frequency of the problems and detailed information of each monitoring result of tracking problem indexes; simultaneously displaying the starting and ending time of the monitoring period, the monitored object and the monitored index information, and displaying the problem of each monitoring period of the monitoring index according to the descending order of the monitoring period in the tracking details, wherein the problem of the monitoring period comprises the information of each processing unit and each processing scheme;
when any detection index is tracked, marking the corresponding field, and accordingly displaying the page classified according to the index as tracked in a targeted manner; and designing a data overview page aiming at the tracking field, and reminding the user of the importance of the indexes according to the monitoring type, the starting of the monitoring period, the ending of the monitoring period, the monitoring indexes and the query conditions of the monitored object.
8. A credit assessment monitoring system based on big data is characterized in that the system comprises,
the data source management module is used for inquiring, adding, modifying and deleting the data source according to the name of the data source, the type of the database and the state transition of the database, inquiring and maintaining the information of the data source, and further realizing the skip link of a detail page of an inquiry result list and the detection of the connectivity of the data source;
the parameter management module is used for maintaining parameter information, and specifically comprises the steps of inquiring, adding, modifying and deleting parameters;
the index management module is used for inquiring, adding, modifying and deleting indexes in the monitoring task;
the all-dimensional monitoring model management module is used for inquiring, newly adding, modifying, deleting, inquiring detail information, starting and stopping the model, configuring dimensionality, associating indexes, configuring timed tasks and displaying results of the all-dimensional monitoring model;
the data management module is used for realizing data submission, initial review of data submission and final review of data submission;
the monitoring operation module is used for realizing timing task operation, means operation and operation detail checking;
the monitoring result generation module is used for managing monitoring results and monitoring problems, and performing problem early warning and problem tracking;
the monitoring report generating module is used for generating a monitoring report template, generating a monitoring report according to the monitoring report template, managing the monitoring report, and generating a monitoring report log, downloading the monitoring report and returning the monitoring report according to the monitoring report;
and the statistical analysis module is used for carrying out comprehensive unified analysis, unit monitoring analysis and unit problem analysis.
9. An electronic device, comprising: a memory and at least one processor;
wherein the memory has stored thereon a computer program;
the at least one processor executing the computer program stored by the memory causes the at least one processor to perform the big-data based credit assessment monitoring method of any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, the computer program being executable by a processor to implement the big-data based credit assessment monitoring method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211306683.4A CN115904897A (en) | 2022-10-25 | 2022-10-25 | Credit assessment monitoring method and system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211306683.4A CN115904897A (en) | 2022-10-25 | 2022-10-25 | Credit assessment monitoring method and system based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115904897A true CN115904897A (en) | 2023-04-04 |
Family
ID=86470060
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211306683.4A Pending CN115904897A (en) | 2022-10-25 | 2022-10-25 | Credit assessment monitoring method and system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115904897A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116911642A (en) * | 2023-09-12 | 2023-10-20 | 中国长江电力股份有限公司 | Multi-dimensional multi-state oriented hydroelectric generating set equipment index calculation system and method |
-
2022
- 2022-10-25 CN CN202211306683.4A patent/CN115904897A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116911642A (en) * | 2023-09-12 | 2023-10-20 | 中国长江电力股份有限公司 | Multi-dimensional multi-state oriented hydroelectric generating set equipment index calculation system and method |
| CN116911642B (en) * | 2023-09-12 | 2023-12-26 | 中国长江电力股份有限公司 | Multi-dimensional multi-state oriented hydroelectric generating set equipment index calculation system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11715069B2 (en) | Chat and email messaging integration | |
| US11238033B1 (en) | Interactive location queries for raw machine data | |
| US20200167350A1 (en) | Loading queries using search points | |
| US7464060B2 (en) | Time series pattern extraction apparatus and method | |
| US6647390B2 (en) | System and methods for standardizing data for design review comparisons | |
| US11172065B1 (en) | Monitoring framework | |
| CN106445536B (en) | Automatic business design management system | |
| CN105378721A (en) | Knowledge capture and discovery system | |
| US20100042745A1 (en) | Workflow diagram generation program, apparatus and method | |
| CN105723335A (en) | Data flow exploration | |
| US20030172082A1 (en) | Method and system for accessing action item information | |
| CN115904897A (en) | Credit assessment monitoring method and system based on big data | |
| CN112861494A (en) | Road network data-based visual report generation method, equipment and storage medium | |
| CN116468010A (en) | Report generation method, device, terminal and storage medium | |
| CN112148260A (en) | Decision engine implementation method, device, equipment and storage medium | |
| CN115170048A (en) | Workflow implementation method, system, and medium based on model and rule | |
| CN109408494B (en) | Model production record generation method based on form self-defining function | |
| JP2010015458A (en) | Program correction support system, program correction support method, and program correction support program | |
| CN103870252A (en) | Resource managing method and device | |
| CN117494690A (en) | Intelligent data processing marking customizing platform | |
| CN115564405A (en) | Research and development project management method and system with FMEA as drive and storage medium | |
| CN115796758A (en) | Factory rule management platform | |
| US20090049015A1 (en) | Data management device and terminal device | |
| Bakaev et al. | Component-based engineering of web user interface designs for evolutionary optimization | |
| JP6750838B1 (en) | Procedure definition device for business automation processing and procedure definition system for business automation processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |