Project processing method, device, equipment, computer medium and product

Abstract

The invention discloses a project processing method, device and equipment, a computer medium and a product, which are applied to the technical field of financial information, and the method comprises the following steps: receiving a project processing demand input by a user; the project processing demand comprises a target project, a plurality of data sources of the target project, a data processing demand and a preset urging strategy; determining a target data processing rule matched with the data processing demand; obtaining current source data from a plurality of data sources, and processing the current source data by adopting a target data processing rule to obtain current real-time state data; determining a current urging strategy corresponding to the target project in preset urging strategies according to the current real-time state data; adjusting the urging strategy by adopting a strategy optimization algorithm to obtain a target urging strategy; the strategy optimization algorithm is constructed according to a historical urging strategy and a historical urging execution result of the target project; and based on the target urging strategy, urging to do the target project. The project processing efficiency can be improved.

Description

Technical Field

[0001] This application belongs to the field of financial information technology, and in particular relates to a project processing method, apparatus, equipment, computer medium and product. Background Technology

[0002] Many large enterprises distribute critical business data across multiple independent software systems during their operations and management. For example, data from technology projects is often scattered across project management systems, code repositories, and operations and maintenance platforms. Therefore, when processing projects, it is necessary to obtain data from different systems, perform data extraction, cleaning, correlation, and fusion, and execute corresponding actions based on the processing results, such as risk warnings and task reminders.

[0003] Currently, automated scripts or robotic process automation (RPA) are often used to achieve the above requirements. These methods retrieve data from different systems, process the data, and then send reminders and follow-ups to relevant personnel based on the results. However, automated scripts and RPA rely on pre-written fixed scripts and rules by technical personnel, making them unsuitable for adapting to business changes. In actual project processing, if business rules or processing requirements frequently change, technical personnel must perform tedious manual modifications, testing, and redeployment, resulting in high maintenance costs and low processing efficiency. Furthermore, sending reminders and follow-ups to relevant personnel based on fixed rules is prone to ineffective reminders and follow-ups, impacting project processing efficiency.

[0004] In summary, existing project processing methods suffer from low project processing efficiency. Summary of the Invention

[0005] This application provides a project processing method, apparatus, device, and computer storage medium that can improve project processing efficiency.

[0006] In a first aspect, embodiments of this application provide a project processing method, the method comprising: Receive project processing requests input by the user; the project processing requests include the target project, as well as multiple data sources, data processing requirements, and preset reminder strategies for the target project; Based on multiple preset data processing rules in the preset rule base, determine the target data processing rule that matches the data processing requirements; The source data is obtained from the multiple data sources, and the source data is processed using the target data processing rules to obtain the real-time status data of the target project. Based on the current real-time status data, the current expediting strategy corresponding to the target project in the preset expediting strategy is determined; the current expediting strategy includes at least one current expediting parameter; The current follow-up strategy is input into a strategy optimization algorithm, which adjusts at least one current follow-up parameter of the current follow-up strategy to obtain a target follow-up strategy. The strategy optimization algorithm is constructed based on the historical follow-up strategies and historical follow-up execution results of the target project. Based on the aforementioned target expediting strategy, expedite the aforementioned target project.

[0007] In some possible implementations, the plurality of preset data processing rules include basic data processing rules; the step of determining the target data processing rule that matches the data processing requirements based on the plurality of preset data processing rules in the preset rule base includes: Extract the project fields and field processing requirements involved in the data processing requirements; Determine the matching status between each of the preset data processing rules and the project fields and the field processing requirements; the matching status includes complete match, partial match, and no match; If it is determined that there exists a preset data processing rule that is a perfect match, then the preset data processing rule that is a perfect match is determined as the target data processing rule. If it is determined that there is no preset data processing rule that is a complete match, but there is a preset data processing rule that is a partial match, then the target data processing rule is generated based on the preset data processing rule that is a partial match and the basic data processing rule. If it is determined that all the aforementioned matching conditions are non-matches, then the target data processing rule is generated based on the aforementioned basic data processing rule.

[0008] In some possible implementations, processing the current source data using the target data processing rules to obtain the current real-time status data of the target project includes: The source data is processed using the target data processing rules to obtain the intermediate data. Obtain the historical status data of the target project; Compare the current intermediate data with the historical state data to obtain the comparison result; Based on the comparison results, the current status level of the target project is determined; The intermediate data containing the current status level is determined as the current real-time status data.

[0009] In some possible implementations, determining the current reminder strategy for the target project within the preset reminder strategy based on the current real-time status data includes: Based on the current status level, the corresponding reminder level is determined from a preset reminder strategy mapping table; the reminder strategy mapping table includes the mapping relationship between different status levels and reminder levels, wherein the reminder level includes at least one of alarm, warning and reminder. Based on the aforementioned follow-up level, at least one follow-up parameter is determined; the follow-up parameter includes the recipient of the follow-up request, the channel through which the follow-up request is sent, and the timing of the follow-up request.

[0010] In some possible implementations, the historical follow-up strategy includes at least one historical follow-up parameter; the step of inputting the current follow-up strategy into a strategy optimization algorithm, and using the strategy optimization algorithm to adjust at least one current follow-up parameter of the current follow-up strategy to obtain the target follow-up strategy, includes: Based on the historical follow-up strategies and the historical follow-up execution results, calculate the historical contribution of the follow-up parameters to the follow-up execution. Based on the historical contribution, a prediction function is constructed that takes the follow-up parameters as input and the predicted follow-up execution utility value as output. Based on the historical contribution levels from largest to smallest, and with the goal of increasing the predicted value of the follow-up execution utility calculated by the prediction function, at least one of the current follow-up parameters is adjusted within the corresponding preset adjustable range to obtain the target follow-up strategy.

[0011] In some possible implementations, calculating the historical contribution of the reminder parameters to the reminder execution based on the historical reminder strategy and the historical reminder execution results includes: Each historical follow-up parameter in the historical follow-up strategy is quantized and mapped to obtain the quantized value of the historical follow-up parameter; The historical follow-up execution results are quantitatively mapped to obtain the quantitative values ​​of the historical follow-up execution results; Regression analysis is performed on the quantified values ​​of the historical follow-up parameters and the quantified values ​​of the historical follow-up execution results to obtain the historical contribution of the follow-up parameters to the follow-up execution.

[0012] In some possible implementations, after expediting the target project based on the target expediting strategy, the method further includes: The results of this follow-up execution for the target project are obtained according to a preset cycle. The source data, the current intermediate data, the current real-time status data, the current reminder strategy, the target reminder strategy, and the current reminder execution result are associated and archived and stored in a time-based manner.

[0013] Secondly, embodiments of this application provide a project processing apparatus, the apparatus comprising: The receiving module is used to receive project processing requirements input by the user; the project processing requirements include the target project, as well as multiple data sources, data processing requirements and preset reminder strategies for the target project; The first determining module is used to determine the target data processing rule that matches the data processing requirements based on multiple preset data processing rules in the preset rule base; The processing module is used to obtain the current source data from the multiple data sources and process the current source data using the target data processing rules to obtain the current real-time status data of the target project. The second determining module is used to determine the current urging strategy in the preset urging strategy corresponding to the target project based on the current real-time status data; the current urging strategy includes at least one current urging parameter; An optimization module is used to input the current follow-up strategy into a strategy optimization algorithm, and use the strategy optimization algorithm to adjust at least one current follow-up parameter of the current follow-up strategy to obtain a target follow-up strategy; the strategy optimization algorithm is constructed based on the historical follow-up strategies and historical follow-up execution results of the target project; The expediting module is used to expedite the target project based on the target expediting strategy.

[0014] Thirdly, embodiments of this application provide a project processing device, the device comprising: A processor and a memory storing computer program instructions; a project processing method that implements any of the above when the processor executes the computer program instructions.

[0015] Fourthly, embodiments of this application provide a computer storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, they implement the project processing method described above.

[0016] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform any of the above-mentioned item processing methods.

[0017] The project processing method, apparatus, device, computer storage medium, and computer program product of this application embodiment receive project processing requirements input by the user, determine a target data processing rule matching the data processing requirements according to multiple preset data processing rules in a preset rule base, and can automatically construct data processing logic. By obtaining current source data from the multiple data sources and processing the current source data using the target data processing rule, the current real-time status data of the target project is obtained. This enables automatic integration and processing of multi-source heterogeneous data. By determining the current reminder strategy corresponding to the target project in the preset reminder strategy based on the current real-time status data, the current reminder strategy is input into a strategy optimization algorithm. The strategy optimization algorithm is used to adjust at least one current reminder parameter of the current reminder strategy to obtain the target reminder strategy. Different current reminder strategies can be used for target projects in different states, and the reminder parameters are dynamically adjusted using a strategy optimization algorithm built based on historical data. Then, based on the target reminder strategy, the target project is reminded, making project processing intervention more precise and targeted, and improving project processing efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a project processing method provided in one embodiment of this application; Figure 2 This is a data processing flowchart provided in one embodiment of this application; Figure 3 This is a data processing flowchart provided in another embodiment of this application; Figure 4 This is a schematic diagram of the structure of a project processing device provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a project processing device provided in one embodiment of this application. Detailed Implementation

[0020] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0022] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0023] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0024] The background technology involved in this application is described below.

[0025] Many large enterprises distribute critical business data across multiple independent software systems during their operations and management. For example, data from technology projects is often scattered across project management systems, code repositories, and operations and maintenance platforms. Therefore, when processing projects, it is necessary to obtain data from different systems, perform data extraction, cleaning, correlation, and fusion, and execute corresponding actions based on the processing results, such as risk warnings and task reminders.

[0026] Currently, automated scripts or robotic process automation (RPA) are often used to achieve the above requirements. These methods retrieve data from different systems, process the data, and then send reminders and follow-ups to relevant personnel based on the results. However, automated scripts and RPA rely on pre-written fixed scripts and rules by technical personnel, making them unsuitable for adapting to business changes. In actual project processing, if business rules or processing requirements frequently change, technical personnel must perform tedious manual modifications, testing, and redeployment, resulting in high maintenance costs and low processing efficiency. Furthermore, sending reminders and follow-ups to relevant personnel based on fixed rules is prone to ineffective reminders and follow-ups, impacting project processing efficiency.

[0027] To address the aforementioned technical issues, while using workflow robots or automated scripts can improve project processing efficiency, these scripts are pre-written by technical personnel. If processing requirements, data source structures, or business rules change, the scripts must be manually modified, tested, and redeployed. Therefore, automatically selecting or generating appropriate data processing rules based on business or processing rules can reduce the maintenance of these rules and improve project processing efficiency. Furthermore, reminders based on fixed rules are prone to ineffective reminders and follow-ups. Dynamically adjusting reminder strategies based on historical follow-up effectiveness, such as differences in response rates between different responsible parties for emails and SMS messages, can avoid ineffective reminders and improve project processing efficiency. Therefore, the inventors have proposed a project processing method, apparatus, device, computer medium, and product according to this application. The project processing method includes: receiving a project processing request input by a user; the project processing request includes a target project, multiple data sources for the target project, data processing requirements, and a preset follow-up strategy; determining a target data processing rule matching the data processing requirements based on multiple preset data processing rules in a preset rule base; obtaining current source data from multiple data sources and processing the current source data using the target data processing rule to obtain current real-time status data of the target project; determining the current follow-up strategy corresponding to the target project in the preset follow-up strategy based on the current real-time status data; the current follow-up strategy includes at least one current follow-up parameter; inputting the current follow-up strategy into a strategy optimization algorithm, and adjusting at least one current follow-up parameter of the current follow-up strategy using the strategy optimization algorithm to obtain a target follow-up strategy; the strategy optimization algorithm is constructed based on the target project's historical follow-up strategies and historical follow-up execution results; and following up on the target project based on the target follow-up strategy. This system receives user-inputted project processing requests and determines target data processing rules matching the requests based on multiple preset data processing rules in a pre-defined rule base. It automatically constructs data processing logic, obtains current source data from multiple data sources, processes it using the target data processing rules, and acquires the current real-time status data of the target project. This enables automatic integration and processing of multi-source heterogeneous data. Based on the current real-time status data, it determines the current urging strategy corresponding to the target project within the preset urging strategy. This urging strategy is then input into a strategy optimization algorithm, which adjusts at least one urging parameter to obtain the target urging strategy. Different urging strategies can be applied to target projects in different states. The algorithm dynamically adjusts urging parameters using a strategy optimization algorithm built on historical data, and then urges the target project based on the target urging strategy. This makes project urging intervention more precise and targeted, improving project processing efficiency.

[0028] This application provides a project processing method, apparatus, device, computer storage medium, and computer program product.

[0029] The project processing method provided in the embodiments of this application will be introduced first below.

[0030] Figure 1 A flowchart illustrating a project processing method provided in one embodiment of this application is shown. Figure 1 As shown, the subject executing the project processing method can be a project processing device, and the method can include steps 101 to 106.

[0031] Step 101: Receive project processing requirements input by the user; project processing requirements include the target project, as well as multiple data sources, data processing requirements, and preset reminder strategies for the target project.

[0032] In some embodiments, a target project refers to a project instance that requires data tracking, status monitoring, and execution follow-up. A target project may be a project belonging to an implementing agency.

[0033] A data source refers to multiple independent software systems, platforms, or databases that store raw data related to a target project. In the project operation and processing of many large enterprises, raw data related to the target project is stored in different platforms. For example, for a technology project, raw data related to project implementation is stored on the implementation plan submission platform, raw data related to project version is stored on the version task processing platform, and raw data related to project review and approval is stored on the approval and verification processing platform.

[0034] Data processing requirements are the logical rules and computational goals described by users from a business perspective for processing raw data from data sources. Data processing requirements include, but are not limited to, operations such as data filtering, cross-table joins, indicator calculation, and status judgment.

[0035] The preset reminder strategy includes multiple preset reminder parameters, as well as reminder parameters mapped to different task statuses. For example, the preset reminder parameters include multiple reminder levels, and each reminder level corresponds to a different reminder frequency, reminder information recipient, and reminder information sending channel.

[0036] Step 102: Based on multiple preset data processing rules in the preset rule base, determine the target data processing rule that matches the data processing requirements.

[0037] In some embodiments, multiple preset data processing rules include basic data processing rules, which include field filtering, field merging, field matching mapping, and table joining.

[0038] For example, column a of table A contains the organization number, column b contains the organization name, column a of table B contains the organization number, and column c contains the organization affiliation. Field filtering can be done by filtering columns a and b from table A, for example, filtering out the organization number and organization name from table A. Field merging can be done by merging columns a and b from table A into a new column, for example, concatenating the organization number from column a of table A with the organization name from column b of table A to form a new column. Field matching mapping can be done by matching columns a and b of table A with columns a and c of table B, for example, establishing a mapping relationship between columns b of table A and columns c of table B through matching columns a of table A and table a. Table joining can be done by joining columns a, b, and c of table A to form a new table, for example, joining the organization number, organization name, and organization affiliation to form a new table.

[0039] Multiple preset data processing rules also include composite data processing rules composed of basic rules.

[0040] Once the data processing requirements are obtained, natural language processing can be performed on them to extract the fields and operational requirements involved. For example, fields could be project name or project affiliation, and operational requirements could be filtering, association, or calculation. After extracting these fields and operational requirements, matching rules can be searched from a pre-defined rule base. If a matching pre-defined data processing rule exists in the rule base, it can be designated as the target data processing rule. If only partially matching rules exist in the base, for example, the data processing requirement is to filter columns a and b in table A by organization, and filter column c in table B that is associated with column a in table A, and then connect column a, column b, and column a in table B, and rule 1 (filtering by organization) and rule 2 (associating tables A and B) are found, but no rule fully meets the current data processing requirements, a new target data processing rule specifically for this data processing requirement can be generated based on rules 1 and 2, and by invoking basic rules. This target data processing rule can then be added to the pre-defined rule base. The target data processing rule can be a Python script or an SQL query statement.

[0041] Step 103: Obtain source data from multiple data sources and process the source data using the target data processing rules to obtain the real-time status data of the target project.

[0042] In some embodiments, the current real-time status data refers to structured information reflecting the current business status of the target project after processing the current source data using target data processing rules. The project processing device can directly access multiple data sources to obtain the current source data, such as implementation plan forms from the implementation plan platform, version task forms from the version task processing platform, and approval result forms from the approval and verification processing platform. After obtaining the current metadata, the target data processing rules can be used to process the current source data to obtain the current real-time status data of the target project.

[0043] Figure 2 This is a data processing flowchart provided in one embodiment of this application, such as... Figure 2 As shown, in one embodiment, multiple data sources include an implementation planning platform 21, a version task processing platform 22, and an approval verification processing platform 23. Based on the target project, an implementation plan form A is obtained from the implementation planning platform 21, a version task form B is obtained from the version task processing platform 22, and an approval result form C is obtained from the approval verification processing platform 23.

[0044] According to the target data processing rules, filter the implementation plans for the target project from Implementation Plan Form A, including those that are overdue, submitted within the stipulated time, or submitted but do not meet the requirements (Form A'). Implementation Plan Form A' contains information such as the requirement item, task number, issuance time, responsible organization, and whether the implementation plan has been submitted.

[0045] Based on the target data processing rules, the version task form B' for the main implementation and auxiliary implementation requirements of the target project is filtered from version task form B. Version task form B' contains information such as task number, task type, and the unit to which the task belongs.

[0046] According to the target data processing rules, filter the approval result form C′ of the implementation plan for the target project's requirements from the approval result form C. Approval result form C′ contains the approval time, approval status, and reasons for disapproval.

[0047] The implementation plan form A′ and the version task form B′ are associated and matched to obtain the first intermediate table E′. The data table A′ and the approval result form C′ are associated and matched to obtain the second intermediate table F′. The first intermediate table E′ and the second intermediate table F′ are associated and matched to obtain the implementation plan information form D′. The implementation plan information form D′ ​​is the real-time status data for this implementation.

[0048] Step 104: Determine the current reminder strategy in the preset reminder strategy for the target project based on the current real-time status data; the current reminder strategy includes at least one current reminder parameter.

[0049] In some embodiments, the real-time status data includes the status of the target project, such as expired, about to expire, rejected, or normal.

[0050] The preset follow-up strategy includes follow-up levels corresponding to different states. These levels quantify the urgency and can be categorized into various discrete levels such as alarm, warning, reminder, or high, medium, and low. The strategy also includes follow-up parameters corresponding to each level, including the recipient, channel, and frequency. The recipient refers to the information receiver, such as the person in charge, manager, or supervisor. The channel refers to the method through which the follow-up message is sent, such as email, SMS, push notifications within the enterprise application, or internal communication software messages. The frequency refers to the number of times a follow-up action is performed within a given time period, such as once per day, three times per day, or once every two hours.

[0051] For example, the follow-up level of the target project in the preset follow-up strategy can be determined based on the status of the target project, and the follow-up parameters of that follow-up level in the preset follow-up strategy can be determined as the follow-up parameters for this follow-up, thus obtaining the follow-up strategy for this time. For example, if the status of the target project is overdue, the follow-up level is an alarm; if it is rejected, the follow-up level is a warning; if it is about to be overdue, the follow-up level is a reminder. If the follow-up level is an alarm, the follow-up targets are the person in charge, the person in charge, and the department head, and the follow-up channels are email, SMS, and push notifications within the enterprise application, with a follow-up frequency of 3 times per day. If the follow-up level is a warning, the follow-up targets are the person in charge and the person in charge, and the follow-up channels are email and push notifications within the enterprise application, with a follow-up frequency of 2 times per day. If the follow-up level is a reminder, the follow-up target is the person in charge, the follow-up channel is push notifications from the app, and the follow-up frequency is 1 time per day.

[0052] Step 105: Input the current follow-up strategy into the strategy optimization algorithm, and use the strategy optimization algorithm to adjust at least one follow-up parameter of the current follow-up strategy to obtain the target follow-up strategy; the strategy optimization algorithm is constructed based on the historical follow-up strategies and historical follow-up execution results of the target project.

[0053] In some embodiments, the current follow-up strategy is input into a strategy optimization algorithm. The algorithm fine-tunes the follow-up parameters based on the target project's historical follow-up strategies and execution results, such as the correlation between follow-up channels, timing, and final completion time for similar tasks and the same responsible person. For example, if sending an email for follow-up results in an average response time of 4 hours, while sending an app push notification results in an average response time of only 1 hour, the strategy optimization algorithm can adjust the notification channel for the technical manager from email to instant messaging app push notifications to obtain the target follow-up strategy and achieve a faster response time.

[0054] Step 106: Based on the target follow-up strategy, follow up on the target project.

[0055] In some embodiments, reminder messages can be sent in batches and automatically according to the channels, frequencies and recipient lists required in the target reminder strategy to expedite target projects.

[0056] The project processing method provided in this application receives project processing requirements input by the user, determines a target data processing rule matching the data processing requirements based on multiple preset data processing rules in a preset rule base, and can automatically construct data processing logic. By obtaining current source data from multiple data sources and processing the current source data using the target data processing rule, the current real-time status data of the target project is obtained. This enables automatic integration and processing of multi-source heterogeneous data. By determining the current reminder strategy corresponding to the target project in the preset reminder strategy based on the current real-time status data, the current reminder strategy is input into a strategy optimization algorithm. The strategy optimization algorithm is used to adjust at least one current reminder parameter of the current reminder strategy to obtain the target reminder strategy. Different current reminder strategies can be applied to target projects in different states. The reminder parameters are dynamically adjusted using a strategy optimization algorithm built based on historical data. Then, based on the target reminder strategy, the target project is reminded, making the processing intervention more precise and targeted, and improving project processing efficiency.

[0057] In some embodiments, in order to further improve project processing efficiency, multiple preset data processing rules in the preset rule base are utilized as much as possible. The multiple preset data processing rules include basic data processing rules, and step 102 is further refined to include steps 201 to 205.

[0058] Step 201: Extract the project fields and field processing requirements involved in the data processing requirements.

[0059] In some embodiments, project processing requirements can be described in a high-level language. Natural language processing is used to parse the data processing requirements to determine which project fields need to be processed, such as planned submission date, task status, approval result, etc., and to determine which processing needs to be performed on the project fields, such as filtering out overdue tasks from the task status and associating the approval results of submitted plans.

[0060] Step 202: Determine the matching status of each preset data processing rule with the project fields and field processing requirements; the matching status includes complete matching, partial matching, and no matching.

[0061] In some embodiments, the extracted project fields and field processing requirements are compared with the functional descriptions of all rules in the preset rule base. The comparison results are divided into three cases: complete match, partial match, and no match.

[0062] Step 203: If it is determined that there is a preset data processing rule with a perfect match, then the preset data processing rule with a perfect match is determined as the target data processing rule.

[0063] In some embodiments, a perfect match means that the required fields and operations of the preset data processing rule are completely consistent with the current data processing requirements, in which case the preset data processing rule is determined as the target data processing rule.

[0064] Step 204: If it is determined that there is no preset data processing rule with a complete match, but there is a preset data processing rule with a partial match, then the target data processing rule is generated based on the preset data processing rule with a partial match and the basic data processing rule.

[0065] In some embodiments, partial matching means that the required fields or operations that can be performed by the preset data processing rule partially overlap with the current requirement. In this case, based on the preset data processing rule, the missing or different parts are identified, and the basic data processing rule is called to supplement, replace or combine them. For example, a filter condition is added or an associated field is modified to generate the target data processing rule, thereby obtaining the target data processing rule that meets the current data processing requirements.

[0066] Step 205: If it is determined that all matching cases are non-matches, then generate target data processing rules based on the basic data processing rules.

[0067] In some embodiments, a mismatch means that all the preset data processing rules in the preset rule base are not related to the current data processing requirements. In this case, the basic data processing rules can be called to generate the target data processing rules, thereby obtaining the target data processing rules that meet the current data processing requirements.

[0068] In some embodiments, after generating the target data processing rules, the target data processing rules can also be added to a preset rule base so that they can be reused directly next time.

[0069] The project processing method provided in this application determines the matching status of each preset data processing rule with the project fields and field processing requirements. The preset data processing rule that matches completely is determined as the target data processing rule. The target data processing rule is generated based on the preset data processing rule that matches partially, or the target data processing rule is generated directly according to the basic data processing rule. By making full use of the preset database and basic data processing rules, the method can generate new target data processing rules as business rules or processing requirements change, continuously improve the preset rule library, and does not require manual modification by technical personnel. Therefore, it can improve project processing efficiency.

[0070] In some embodiments, in order to more accurately determine the current real-time status data of the target project, step 103 is further refined to include steps 301 to 304.

[0071] Step 301: Process the source data using the target data processing rules to obtain the intermediate data.

[0072] Step 302: Obtain historical status data of the target project.

[0073] Step 303: Compare the current intermediate data with the historical status data to obtain the comparison results.

[0074] Step 304: Determine the current status level of the target project based on the comparison results.

[0075] Step 305: The intermediate data containing the current status level is determined as the current real-time status data.

[0076] In some embodiments, the source data is cleaned, correlated, and calculated to obtain intermediate data. This intermediate data can reflect the current status of the target project, but it cannot reflect the historical status and historical follow-up execution results of the target project. Therefore, the intermediate data can be processed again to integrate the results of the previous one or several times and the tracking implementation status.

[0077] Figure 3 This is a data processing flowchart provided in another embodiment of this application, such as... Figure 3 As shown, in another embodiment, multiple data sources include an implementation planning platform 21, a version task processing platform 22, and an approval verification processing platform 23. Based on the target project, an implementation plan form A is obtained from the implementation planning platform 21, a version task form B is obtained from the version task processing platform 22, and an approval result form C is obtained from the approval verification processing platform 23. This is done using a method similar to... Figure 2 The same method as in the corresponding embodiment is used to generate the implementation plan form A′, version task form B′, approval result form C′, first intermediate table E′, and second intermediate table F′. The first intermediate table E′ and the second intermediate table F′ are processed by association and matching to obtain the current intermediate table G. At least one historical status data table G- for the target project is obtained. The historical status data table G- includes historical reminder execution results. By comparing the current intermediate data and the historical status data, a comparison result is obtained. Based on the comparison result, the current intermediate table G is classified and categorized to form the current real-time status data table G′. The comparison result can be used to check the timeliness and accuracy of implementation. The comparison result can also indicate anomalies in the current intermediate data, such as overdue tasks, and whether they already exist in the historical status data. If an anomaly has persisted for a period of time, it indicates that all reminders have been ineffective.

[0078] In some embodiments, if the comparison result indicates that the anomaly in the current intermediate data does not exist in the historical status data, the current status level of the target project can be determined as an alert level. If the comparison result indicates that the anomaly in the current intermediate data already exists in the historical status data, but its status has not deteriorated—for example, the duration of the anomaly is within a preset time range, or the progress shows an improving trend—the current status level of the target project is determined as a warning level. If the comparison result indicates that the anomaly in the current intermediate data persists in the historical status data and the duration of the anomaly exceeds a preset time range, the current status level of the target project is determined as an alarm level.

[0079] In some embodiments, the current real-time status data table can also be displayed. The display format can be based on the rules and requirements configured in this instance. If the configuration rules for the previous display are the same as those for this instance, only the displayed data is updated. If the configuration rules for the previous information display are different from those for this instance, the data results are displayed hierarchically according to the current configuration rules, meaning the data display format and the data itself need to be updated. This example allows for hierarchical output and display of result data and detailed data on both the enterprise web page and the enterprise application interface.

[0080] The project processing method provided in this application compares the current intermediate data with historical status data containing historical follow-up results, and determines the current status level of the target project based on the comparison results. This allows for a more accurate determination of the current status of the target project, and subsequently, a more accurate follow-up strategy can be determined.

[0081] In some embodiments, in order to accurately determine the current follow-up strategy, step 104 is further refined to include steps 401 to 402.

[0082] Step 401: Determine the corresponding reminder level from the preset reminder strategy mapping table according to the current status level; the reminder strategy mapping table includes the mapping relationship between different status levels and reminder levels, wherein the reminder level includes at least one of alarm, warning and reminder. Step 402: Determine at least one reminder parameter based on the reminder level; the reminder parameter includes the recipient of the reminder, the reminder sending channel, and the reminder sending timing.

[0083] In some embodiments, the current status level includes a reminder level, a warning level, and an alarm level. A preset reminder strategy mapping table includes different reminder parameters corresponding to different status levels. For example, the reminder recipients for the alarm level can be the project handler, manager, and department head; the reminder recipients for the warning level can be the project handler and manager; and the reminder recipients can be the project handler. The reminder sending timing refers to the time and frequency at which it is sent. For example, the reminder sending timing for the alarm level can be immediate and sent three times a day; the reminder sending timing for the warning level can be during working hours of the day, sent once a day. In some embodiments, the status level is also described as a reminder level. Different reminder strategies corresponding to different status levels can be found in the descriptions in the above embodiments, and will not be repeated here.

[0084] The project processing method provided in this application uses a pre-set reminder strategy mapping table to map status levels to reminder levels and bind specific reminder parameters to different reminder levels. This can accurately determine different reminder strategies for projects in different statuses, reduce invalid reminders, and improve project processing efficiency.

[0085] In some embodiments, in order to further improve project processing efficiency, the historical reminder strategy includes at least one historical reminder parameter; step 105 is further refined to include steps 501 to 503.

[0086] Step 501: Calculate the historical contribution of the reminder parameters to the reminder execution based on the historical reminder strategies and historical reminder execution results.

[0087] In some embodiments, the historical contribution of reminder parameters to reminder execution can be calculated through regression analysis of historical reminder strategies and historical reminder execution results.

[0088] Step 502: Based on historical contribution, construct a prediction function that takes the follow-up parameters as input and the predicted follow-up execution utility value as output.

[0089] In some embodiments, a prediction function can be constructed based on the regression analysis in step 501, taking the reminder parameters as input and the predicted value of the reminder execution utility as output.

[0090] Step 503: According to the order of historical contribution from largest to smallest, with the goal of increasing the predicted value of the follow-up execution utility calculated by the prediction function, adjust at least one follow-up parameter within the corresponding preset adjustable range to obtain the target follow-up strategy.

[0091] In some embodiments, the preset adjustable range corresponds to the status level or reminder level of the target project. For example, for a target project with an alarm status level, the reminder recipient can be selected from at least one of the handler, the person in charge, and the supervisor. For a target project with a preset status level, the reminder recipient can be selected from at least one of the handler and the person in charge.

[0092] In some embodiments, only the parameter that contributes the most to the predicted value of the follow-up execution utility in this follow-up parameter can be adjusted, or multiple parameters in this follow-up parameter can be adjusted.

[0093] The project processing method provided in this application provides a prediction function that takes reminder parameters as input and utility prediction values ​​as output. This function can pre-evaluate and compare the potential effects of different reminder strategies before actual execution. Then, with the goal of increasing the predicted utility value of reminder execution, at least one comparison parameter is adjusted in order of contribution. This allows the adjusted target reminder strategy to have a larger predicted utility value of reminder execution, thereby improving the reminder effect and increasing project processing efficiency.

[0094] In some embodiments, in order to more accurately determine the contribution of the reminder parameters to the reminder execution, step 501 is further refined to include steps 601 to 603.

[0095] Step 601: Quantize and map each historical follow-up parameter in the historical follow-up strategy to obtain the quantized value of the historical follow-up parameter.

[0096] Step 602: Quantify and map the historical follow-up execution results to obtain the quantitative values ​​of the historical follow-up execution results.

[0097] In some embodiments, each historical reminder parameter in the historical reminder strategy is quantitatively mapped. For example, for reminder channels, email is mapped to 1, SMS to 2, enterprise application push to 3, email and SMS to 4, and email, SMS, and enterprise application push to 5. Simultaneously, historical reminder execution results are quantitatively mapped, including whether the reminder was executed, and if executed, the execution time. Based on whether it was executed and different execution times, historical reminder execution results can be quantified into a comparable historical reminder execution utility value. For example, the execution time can be the reciprocal of the time from reminder start to execution. The reciprocal of the time from reminder start to execution can be normalized to obtain the quantitative mapping result of historical reminder execution results, where unexecuted results can be quantified as 0. Alternatively, execution times within different preset ranges can be quantified into different utility values. For example, resolving within half an hour is quantified as 100, resolving within the same day as 80, resolving the next day as 60, and unexecuted results as 0.

[0098] Step 603: Perform regression analysis on the quantitative values ​​of historical follow-up parameters and the quantitative values ​​of historical follow-up execution results to obtain the historical contribution of follow-up parameters to follow-up execution.

[0099] In some embodiments, regression analysis is performed by using the quantified values ​​of all historical follow-up parameters as independent variables and the quantified values ​​of all historical follow-up execution results as dependent variables. The historical contribution of each follow-up parameter to the final follow-up effect can be determined based on the coefficients or feature importance scores in the regression model, thereby accurately identifying the impact of different follow-up claims on the follow-up effect.

[0100] The project processing method provided in this application embodiment quantifies the historical contribution of the follow-up parameters through regression analysis, which can accurately identify the key factors that have the greatest impact on the follow-up effect, so as to accurately adjust the current follow-up strategy to obtain the target follow-up strategy.

[0101] In some embodiments, in order to record the results of the follow-up execution more accurately, steps 701 to 702 are included after step 106.

[0102] Step 701: Obtain the execution result of this follow-up for the target project according to the preset cycle.

[0103] Step 702: Associate the source data, current intermediate data, current real-time status data, current reminder strategy, target reminder strategy, and current reminder execution result, and archive and store them in a time-based manner.

[0104] In some embodiments, after a reminder message is sent, the actual effect of the reminder is checked and collected at a preset period, such as 30 minutes, 2 hours, or 8:00 AM the next day. For example, the latest status of the target project is queried from the data source, and the latest status is compared with the real-time status data at the time of the reminder to determine whether the task status has been updated, such as from never started to in progress, or whether the task has been completed. Alternatively, it can be confirmed whether the recipient has responded and how long the time interval is from the issuance of the reminder to the generation of a response.

[0105] In some embodiments, all data or tables related to the current reminder and its execution result can be extracted, associated with a common identifier, such as task ID + timestamp, and stored and archived according to the time dimension. The archived content may include: source data, current intermediate data, current real-time status data, current reminder strategy, target reminder strategy, and current reminder execution results for different feedback periods. The archived data can be used as historical reminder strategies and historical reminder execution results.

[0106] In some embodiments, for Figure 3The data processing flowchart in the illustrated embodiment allows for the archiving and storage of the following documents according to their generation time: Implementation Plan Form A, Implementation Plan Form A′, Version Task Form B, Version Task Form B′, Review and Approval Result Form C, Approval Result Form C′, First Intermediate Table E′, Second Intermediate Table F′, Current Intermediate Table G, Historical Status Data Table G-, and Current Real-time Status Data Table G′.

[0107] The project processing method provided in this application embodiment can obtain the current reminder execution result according to a preset cycle, thereby providing real and reliable feedback data for subsequent strategy optimization. By associating the source data, current intermediate data, current real-time status data, current reminder strategy, target reminder strategy, and current reminder execution result, and archiving and storing them in a time dimension, it is easy to quickly and accurately find each reminder strategy and reminder result in the future, and it can provide data support for strategy optimization.

[0108] Based on the project processing method provided in the above embodiments, this application also provides specific implementations of the project processing apparatus. Please refer to the following embodiments.

[0109] Figure 4 This is a schematic diagram of the structure of a project processing device provided in one embodiment of this application. Figure 4 As shown, the project processing apparatus 40 provided in this embodiment includes: The receiving module 41 is used to receive project processing requirements input by the user; the project processing requirements include the target project, as well as multiple data sources of the target project, data processing requirements and preset reminder strategies; The first determining module 42 is used to determine the target data processing rule that matches the data processing requirements based on multiple preset data processing rules in the preset rule base; Processing module 43 is used to obtain the source data from multiple data sources and process the source data using the target data processing rules to obtain the real-time status data of the target project. The second determining module 44 is used to determine the current urging strategy in the preset urging strategy corresponding to the target project based on the current real-time status data; the current urging strategy includes at least one current urging parameter; The optimization module 45 is used to input the current follow-up strategy into the strategy optimization algorithm, and use the strategy optimization algorithm to adjust at least one follow-up parameter of the current follow-up strategy to obtain the target follow-up strategy; the strategy optimization algorithm is constructed based on the historical follow-up strategies and historical follow-up execution results of the target project. The expediting module 46 is used to expedite target projects based on target expediting strategies.

[0110] In some possible implementations, multiple preset data processing rules include basic data processing rules; the first determining module 42 is specifically used for: Extract the project fields and field processing requirements involved in the data processing needs; Determine the matching status between each preset data processing rule and the project fields and field processing requirements; the matching status includes complete match, partial match, and no match; If it is determined that there is a preset data processing rule that is a perfect match, then the preset data processing rule that is a perfect match will be determined as the target data processing rule. If it is determined that there is no preset data processing rule with a complete match, but there is a preset data processing rule with a partial match, then the target data processing rule is generated based on the preset data processing rule with a partial match and the basic data processing rule. If it is determined that all matching cases are non-matches, then the target data processing rules are generated based on the basic data processing rules.

[0111] In some possible implementations, processing module 43 is specifically used for: The source data is processed using the target data processing rules to obtain the intermediate data. Obtain historical status data for the target project; Compare the current intermediate data with historical status data to obtain the comparison results; Based on the comparison results, determine the current status level of the target project; The intermediate data containing the current status level will be determined as the current real-time status data.

[0112] In some possible implementations, the second determining module 44 is specifically used for: Based on the current status level, the corresponding reminder level is determined from the preset reminder strategy mapping table; the reminder strategy mapping table includes the mapping relationship between different status levels and reminder levels, wherein the reminder level includes at least one of alarm, warning and reminder. Based on the urgency level, at least one urgency parameter is determined; the urgency parameter includes the recipient of the urgency request, the channel through which the urgency is sent, and the timing of the urgency request.

[0113] In some possible implementations, the historical reminder strategy includes at least one historical reminder parameter; the optimization module 45 is specifically used for: Based on historical follow-up strategies and historical follow-up execution results, calculate the historical contribution of follow-up parameters to follow-up execution. Based on historical contribution, a prediction function is constructed that takes the follow-up parameters as input and the predicted value of follow-up execution effectiveness as output. Based on the order of historical contribution from largest to smallest, and with the goal of increasing the predicted value of the follow-up execution utility calculated by the prediction function, at least one follow-up parameter for this follow-up is adjusted within the corresponding preset adjustable range to obtain the target follow-up strategy.

[0114] In some possible implementations, the optimization module 45 is specifically used for: Quantify and map each historical follow-up parameter in the historical follow-up strategy to obtain the quantified value of the historical follow-up parameter; Quantitatively map the historical follow-up execution results to obtain quantitative values ​​for the historical follow-up execution results; Regression analysis was performed on the quantitative values ​​of historical follow-up parameters and the quantitative values ​​of historical follow-up execution results to obtain the historical contribution of follow-up parameters to follow-up execution.

[0115] In some possible implementations, the project processing device also includes a storage module, which is used for: Obtain the results of this follow-up action for the target project according to the preset cycle; The source data, current intermediate data, current real-time status data, current reminder strategy, target reminder strategy, and current reminder execution results are associated and archived and stored in a time-based manner.

[0116] The various modules of the project processing device provided in this application embodiment can achieve Figure 1 It provides functions for each step of the project processing method and can achieve the corresponding technical effects. For the sake of brevity, it will not be described in detail here.

[0117] Figure 5 This is a schematic diagram of the structure of a project processing device provided in one embodiment of this application, as shown below. Figure 5 As shown, the project processing method in the above embodiments can be further described in this application embodiment as a project processing device 50, which includes a processor 51 and a memory 52 storing computer program instructions; when the processor 51 executes the computer program instructions, it implements any of the project processing methods in the above embodiments.

[0118] The project processing methods described in the above embodiments can be implemented using a computer storage medium provided in this application. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the project processing methods described in the above embodiments.

[0119] This application also provides a computer program product, including a computer program, which, when executed, implements any of the project processing methods described in the above embodiments.

[0120] It should be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0121] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0122] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A project processing method, characterized in that, The method includes: Receive project processing requests input by the user; the project processing requests include the target project, as well as multiple data sources, data processing requirements, and preset reminder strategies for the target project; Based on multiple preset data processing rules in the preset rule base, determine the target data processing rule that matches the data processing requirements; The source data is obtained from the multiple data sources, and the source data is processed using the target data processing rules to obtain the real-time status data of the target project. Based on the current real-time status data, the current expediting strategy corresponding to the target project in the preset expediting strategy is determined; the current expediting strategy includes at least one current expediting parameter; The current follow-up strategy is input into a strategy optimization algorithm, which adjusts at least one current follow-up parameter of the current follow-up strategy to obtain a target follow-up strategy. The strategy optimization algorithm is constructed based on the historical follow-up strategies and historical follow-up execution results of the target project. Based on the aforementioned target expediting strategy, expedite the aforementioned target project.

2. The method according to claim 1, characterized in that, The plurality of preset data processing rules include basic data processing rules; the step of determining the target data processing rule that matches the data processing requirements based on the plurality of preset data processing rules in the preset rule base includes: Extract the project fields and field processing requirements involved in the data processing requirements; Determine the matching status between each of the preset data processing rules and the project fields and the field processing requirements; the matching status includes complete match, partial match, and no match; If it is determined that there exists a preset data processing rule that is a perfect match, then the preset data processing rule that is a perfect match is determined as the target data processing rule. If it is determined that there is no preset data processing rule that is a complete match, but there is a preset data processing rule that is a partial match, then the target data processing rule is generated based on the preset data processing rule that is a partial match and the basic data processing rule. If it is determined that all the aforementioned matching conditions are non-matches, then the target data processing rule is generated based on the aforementioned basic data processing rule.

3. The method according to claim 1, characterized in that, The process of processing the current source data using the target data processing rules to obtain the current real-time status data of the target project includes: The source data is processed using the target data processing rules to obtain the intermediate data. Obtain the historical status data of the target project; Compare the current intermediate data with the historical state data to obtain the comparison result; Based on the comparison results, the current status level of the target project is determined; The intermediate data containing the current status level is determined as the current real-time status data.

4. The method according to claim 3, characterized in that, The step of determining the current reminder strategy for the target project within the preset reminder strategy based on the current real-time status data includes: Based on the current status level, the corresponding reminder level is determined from a preset reminder strategy mapping table; the reminder strategy mapping table includes the mapping relationship between different status levels and reminder levels, wherein the reminder level includes at least one of alarm, warning and reminder. Based on the aforementioned follow-up level, at least one follow-up parameter is determined; the follow-up parameter includes the recipient of the follow-up request, the channel through which the follow-up request is sent, and the timing of the follow-up request.

5. The project processing method according to claim 1, characterized in that, The historical follow-up strategy includes at least one historical follow-up parameter; the step of inputting the current follow-up strategy into a strategy optimization algorithm, and using the strategy optimization algorithm to adjust at least one current follow-up parameter of the current follow-up strategy to obtain the target follow-up strategy, includes: Based on the historical follow-up strategies and the historical follow-up execution results, calculate the historical contribution of the follow-up parameters to the follow-up execution. Based on the historical contribution, a prediction function is constructed that takes the follow-up parameters as input and the predicted follow-up execution utility value as output. Based on the historical contribution levels from largest to smallest, and with the goal of increasing the predicted value of the follow-up execution utility calculated by the prediction function, at least one of the current follow-up parameters is adjusted within the corresponding preset adjustable range to obtain the target follow-up strategy.

6. The method according to claim 5, characterized in that, The step of calculating the historical contribution of the reminder parameters to the reminder execution based on the historical reminder strategy and the historical reminder execution results includes: Each historical follow-up parameter in the historical follow-up strategy is quantized and mapped to obtain the quantized value of the historical follow-up parameter; The historical follow-up execution results are quantitatively mapped to obtain the quantitative values ​​of the historical follow-up execution results; Regression analysis is performed on the quantified values ​​of the historical follow-up parameters and the quantified values ​​of the historical follow-up execution results to obtain the historical contribution of the follow-up parameters to the follow-up execution.

7. The method according to any one of claims 1-6, characterized in that, After expediting the target project based on the target expediting strategy, the process further includes: The results of this follow-up execution for the target project are obtained according to a preset cycle. The source data, the current intermediate data, the current real-time status data, the current reminder strategy, the target reminder strategy, and the current reminder execution result are associated and archived and stored in a time-based manner.

8. A project processing device, characterized in that, The device includes: The receiving module is used to receive project processing requirements input by the user; the project processing requirements include the target project, as well as multiple data sources, data processing requirements and preset reminder strategies for the target project; The first determining module is used to determine the target data processing rule that matches the data processing requirements based on multiple preset data processing rules in the preset rule base; The processing module is used to obtain the current source data from the multiple data sources and process the current source data using the target data processing rules to obtain the current real-time status data of the target project. The second determining module is used to determine the current urging strategy in the preset urging strategy corresponding to the target project based on the current real-time status data; the current urging strategy includes at least one current urging parameter; An optimization module is used to input the current follow-up strategy into a strategy optimization algorithm, and use the strategy optimization algorithm to adjust at least one current follow-up parameter of the current follow-up strategy to obtain a target follow-up strategy; the strategy optimization algorithm is constructed based on the historical follow-up strategies and historical follow-up execution results of the target project; The expediting module is used to expedite the target project based on the target expediting strategy.

9. A project processing device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the project processing method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the project processing method as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device is able to perform the project processing method as described in any one of claims 1-7.

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