CN117541406A - Service early warning method, device, computer equipment and storage medium - Google Patents

Abstract

The application relates to a business early warning method, a business early warning device, computer equipment and a storage medium. Obtaining business data in a preset statistical period based on each unit of business data of business in each unit statistical period when the business progress statistical condition is met, determining business progress information of the business based on the preset statistical period, the business data and a corresponding business data target value, generating business early warning information based on the business progress information and the target business progress information when the business progress information does not meet the preset business progress condition, and sending the business early warning information to an executing mechanism corresponding to the business. Compared with the traditional mode of checking and early warning under the manual line, the method and the device have the advantages that the service progress information of the service is determined based on the statistical period, the service data and the corresponding service data target value, and when the service progress information does not meet the service progress condition, the corresponding service early warning information is sent to the corresponding actuating mechanism of the service, so that the accuracy of service early warning is improved.

Description

Business early warning method, device, computer equipment and storage medium

Technical field

This application relates to the technical field of financial insurance business, and in particular to a business early warning method, device, computer equipment, storage medium and computer program product.

Background technique

During the processing of financial and insurance business, each business personnel will generate a large amount of business data. In order to ensure the normal operation of the financial and insurance business, it is necessary to set corresponding business goals for the financial and insurance business to encourage relevant business personnel to actively implement the corresponding financial and insurance policies. business. The current way to provide reminders and early warnings on the business progress of financial and insurance businesses is usually to publicly display the business progress data, and then relevant business personnel will compare the progress offline to achieve business early warnings. However, early warning through offline comparison can easily lead to missing indicator observations, resulting in low accuracy of business early warning.

Therefore, the current business early warning methods for financial and insurance businesses have the disadvantage of low accuracy.

Contents of the invention

Based on this, it is necessary to provide a business early warning method, device, computer equipment, computer readable storage medium and computer program product that can improve the accuracy of early warning in response to the above technical problems.

In the first aspect, this application provides a business early warning method, which includes:

When it is detected that the business progress statistics conditions are met, the business to be counted and its corresponding unit statistics cycle are determined;

Obtain business data within a preset statistical period based on at least one unit business data of the business in at least one unit statistical period;

Obtain the preset business data target value of the business within the preset statistical period, and determine the business progress information of the business according to the preset statistical period, the business data and the business data target value;

If the business progress information does not meet the preset business progress conditions, business early warning information is generated according to the business progress information and the target business progress information, the executing agency corresponding to the business is obtained, and the business early warning information is sent to the executing agency.

In one embodiment, the method further includes:

Obtain the total target value of business data within a preset time period preset for the execution agency of the business; the total target value of business data is determined based on the historical business data of the execution agency;

At least one preset statistical period is determined according to the preset time period, and the business data target value corresponding to each preset statistical period is stored according to the total target value of business data.

In one embodiment, determining the services to be counted and their corresponding unit statistical periods includes:

Determine the data type of the business;

According to the data type, the unit statistical period corresponding to the service is determined.

In one embodiment, the data type includes at least one of business resource data and business personnel data; and determining the unit statistical period corresponding to the business according to the data type includes:

If the data type is business resource data, determine the unit statistical period as the first unit statistical period;

If the data type is business personnel data, determine the unit statistical period as the second unit statistical period;

Wherein, the first unit statistical period is smaller than the second unit statistical period.

In one embodiment, determining the business progress information of the business based on the preset statistical period, the business data, and the business data target value includes:

According to the preset statistical period, determine the period start time and period end time, and obtain the recording time corresponding to the business data;

According to the recording time, the cycle start time and the cycle end time, the sequential progress information corresponding to the service is obtained;

According to the business data and the business data target value, obtain the task progress information corresponding to the business;

According to the sequence progress information and the task progress information, the service progress information of the service is obtained.

After the business progress information of the business is determined, the method further includes:

If it is detected that the value of the task progress is less than the value of the sequential progress, it is determined that the business progress information does not meet the preset business progress conditions.

In one embodiment, determining the business progress information of the business based on the preset statistical period, the business data, and the business data target value includes:

Obtain year-on-year business data corresponding to the preset statistical period corresponding to the preset statistical period;

According to the ratio of the business data and the year-on-year business data, the business progress information of the business is determined.

After the business progress information of the business is determined, the method further includes:

If the ratio of the business data to the year-on-year business data is less than one, it is determined that the business progress information does not meet the preset business progress conditions.

In one of the embodiments, the execution agency is stored in a preset agency relationship table, and the agency relationship table includes affiliation relationships between multiple execution agencies;

After the business progress information of the business is determined, the method further includes:

Obtain business progress information at each level of each execution agency at the same level corresponding to the execution agency;

The business progress information and the business progress information of each peer are sorted. If it is detected that the ranking of the business progress information is within a preset ranking range, it is determined that the business progress information does not meet the preset business progress conditions.

In a second aspect, this application provides a business early warning device, which includes:

The detection module is used to determine the business to be counted and its corresponding unit statistical period when it detects that the business progress statistics conditions are met;

An acquisition module, configured to obtain business data within a preset statistical period based on at least one unit business data of the business in at least one unit statistical period;

Determining module, configured to obtain the preset business data target value of the business within the preset statistical period, and determine the business data target value according to the preset statistical period, the business data and the business data target value. Business progress information;

An early warning module, used to generate business early warning information based on the business progress information and the target business progress information if the business progress information does not meet the preset business progress conditions, obtain the execution mechanism corresponding to the business, and transfer the business progress information to the target business progress information. Business warning information is sent to the execution agency.

In a third aspect, the present application provides a computer device, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the above method are implemented.

In a fourth aspect, the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

In a fifth aspect, this application also provides a computer program product. The computer program product includes a computer program that implements the steps of the above method when executed by a processor.

The above-mentioned business early warning methods, devices, computer equipment, storage media and computer program products obtain business data within a preset statistical period based on at least one unit business data of the business in at least one unit statistical period when the business progress statistical conditions are met. , based on the preset statistical period, business data and corresponding business data target value, the business progress information of the business is determined. When the above business progress information does not meet the preset business progress conditions, the business is generated based on the business progress information and the target business progress information. Early warning information and send business early warning information to the corresponding execution agency of the business. Compared with the traditional method of manual offline verification and early warning, this solution counts the business data of the business according to the preset statistical cycle, and determines the business data based on the cycle, business data and corresponding business data target values. Progress information, when the business progress information does not meet the business progress conditions, corresponding business warning information is sent to the execution agency corresponding to the business, thereby improving the accuracy of the business warning.

Description of drawings

Figure 1 is an application environment diagram of the business early warning method in one embodiment;

Figure 2 is a schematic flow chart of a business early warning method in one embodiment;

Figure 3 is a schematic interface diagram of a business warning in an embodiment;

Figure 4 is a schematic interface diagram of early warning settings in an embodiment;

Figure 5 is a schematic structural diagram of early warning information in an embodiment;

Figure 6 is a schematic flow chart of early warning detection in one embodiment;

Figure 7 is a schematic flowchart of early warning push in one embodiment;

Figure 8 is a schematic interface diagram of business warning information in an embodiment;

Figure 9 is a schematic structural diagram of business data in an embodiment;

Figure 10 is a schematic structural diagram of indicator data in an embodiment;

Figure 11 is a schematic structural diagram of a business target in an embodiment;

Figure 12 is a schematic diagram of the business progress judgment steps in one embodiment;

Figure 13 is a sequence diagram of a business early warning method in an embodiment;

Figure 14 is a structural block diagram of a business early warning device in one embodiment;

Figure 15 is an internal structure diagram of a computer device in one embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

The business early warning method provided by the embodiment of this application can be applied in the application environment as shown in Figure 1. Among them, the terminal communicates with the server through the network. Data storage systems can store the data that servers need to process. The data storage system can be integrated on the server, or placed on the cloud or other network servers. The data storage system includes databases and ODS (Operational Data Store, operational data storage) data warehouses, etc. The terminal is also called the data dashboard front-end, which can be used to display corresponding business data and provide users with corresponding business setting pages and warning information display pages. Users can set corresponding business progress statistical conditions in the terminal and send them to the server. In addition, the terminal can also collect and send the business data generated by the business to the server, and the server is used to store the corresponding business data. The server is also called the data dashboard access terminal. When it detects that the business progress statistical conditions are met, it can obtain the corresponding business data from the data storage system and determine whether the business progress conditions are met. When the business progress conditions are not met, it sends a corresponding response to the terminal. business warning information, so that the terminal can display the corresponding business warning information. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster composed of multiple servers.

In one embodiment, as shown in Figure 2, a business early warning method is provided. This method is explained by taking the method applied to the server in Figure 1 as an example, and includes the following steps:

Step S202: When it is detected that the business progress statistics conditions are met, determine the business to be counted and its corresponding unit statistics period.

Among them, the above-mentioned business can be a business in the financial field. Before the business is executed, the goals that the business needs to achieve within a period of time can be pre-set to realize the phased operation plan of the business. Among them, the goal that the business needs to achieve may be that the business data of the above-mentioned business within a period of time needs to reach a preset value. During the execution of the business, the server can continuously detect the completion of the business. For example, the server can continuously detect whether the business progress statistical conditions are currently met, and when it is detected that the business progress statistical conditions are met, the server can continuously detect the business progress statistical conditions based on the currently met business progress statistical conditions. , determine the business to be counted. Among them, the above business progress statistics conditions can have multiple conditions, and each condition can correspond to different businesses.

After the server determines the business to be counted based on the above business progress statistics conditions, it can also determine the unit statistical period of the business to be counted. Among them, the unit statistical period represents the minimum statistical period of the business, and multiple unit statistical periods can constitute a complete statistical period.

Among them, the above-mentioned business can be executed by the corresponding execution agency. Each execution agency has its own corresponding business execution preference and operation rhythm, then the business data target value corresponding to the above-mentioned business to be counted can correspond to the execution agency. The server can pre-store business data target values set for business execution preferences and operating rhythms of different execution agencies. Among them, the business data target value represents the target that the execution agency needs to achieve to execute the above-mentioned business value within a preset time period. The business data target value is also called a phased operation target. Specifically, the user can upload the set staged business data target values to the server in the form of a table based on the current operating rhythm of the executing agency. The server parses the table file line by line into the business data target values of each executing agency and stores it. in the database.

Step S204: Obtain business data within a preset statistical period based on at least one unit business data of the business in at least one unit statistical period.

When the above-mentioned services are executed, corresponding business data can be generated, such as resource values used by users to handle business transactions, etc. When the server counts the above-mentioned business to be counted, it can determine the unit statistical period of the business and obtain at least one unit business data of at least one unit statistical period within the preset statistical period, so that the server can obtain predetermined data based on at least one unit business data. Assume the business data within the statistical period.

Among them, the data types of the above-mentioned services can be of various types. For different types of services, the server can collect based on different unit statistical periods. Specifically, the terminal can upload the business data of each of the above businesses to the ODS data warehouse in the server for storage. The server can regularly start the progress calculation task and obtain the statistics period of each unit corresponding to each business from the unified ODS data warehouse. For business data, the server can synchronize the business data of each unit mentioned above to the database of the system and perform task progress calculation tasks.

Step S206: Obtain the preset business data target value of the business within the preset statistical period, and determine the business progress information of the business based on the preset statistical period, business data and business data target value.

Among them, after the server obtains the business data within the above-mentioned preset statistical period, it can calculate the business progress information. Wherein, the server can pre-obtain the preset business data target value of the above-mentioned service within the preset statistical period, and the business data target value can be the target value that the business data of the above-mentioned service needs to reach within the above-mentioned preset statistical period. The server can determine the business progress information of the above-mentioned business based on the above-mentioned preset statistical period, business data and business data target value. Among them, the server can determine the business progress information of the business through a variety of algorithms based on the above-mentioned preset statistical period, business data and business data target values, including but not limited to calculating sequential progress, year-on-year progress and progress ranking algorithms. Determine business progress information.

Step S208: If the business progress information does not meet the preset business progress conditions, business warning information is generated based on the business progress information and target business progress information, the execution agency corresponding to the business is obtained, and the business warning information is sent to the execution agency.

Wherein, after the server determines the business progress information of the above-mentioned business, it can compare the business progress information with preset business progress conditions. The business progress conditions may include a variety of forms, such as but not limited to numerical thresholds, percentage thresholds, ranking range thresholds, etc. If the business progress information does not meet the corresponding above-mentioned business progress conditions, the server can generate business warning information based on the above-mentioned business progress information and target business progress information, obtain the execution agency corresponding to the above-mentioned business, and send the business warning information to the corresponding execution agency. , so that the corresponding user can see the displayed business warning information in the terminal of the execution agency, and take corresponding measures to process the business warning information.

Specifically, the above business warning information can be displayed on the interface as shown in Figure 3. Figure 3 is a schematic diagram of the business warning interface in one embodiment. The page shown in Figure 3 may be the business management page corresponding to the above-mentioned execution agency. In some embodiments, the above-mentioned services include resource services and personnel services, where the personnel services represent personnel information owned by the execution agency, which is also called team services. Then the server can display team data, team data statistics, business data, business data statistics, early warning data and other information on the page shown in Figure 3. Among them, the team data includes the execution of the personnel business of the execution agency. The business data can display the business progress information of each business. The server can also display the team data statistics chart showing the personnel changes and the business data of the business data changes in the form of charts. summary graph. Personnel changes include addition of manpower and termination of manpower. Moreover, the server can also display business warning information that does not meet the preset business progress conditions.

Among them, the server can regularly start early warning detection and early warning push tasks. The early warning detection task can determine the execution agency that triggers the business warning information and the corresponding business data target value through the above steps. The server sends the business warning information to the execution agency through the early warning push task. IM (Instant Messaging, real-time communication system) system implements notifications to users. After receiving the notification, users can view detailed business progress information through the visual analysis system or.

Among them, the server can pre-define early warning rules, that is, the push rules for the above-mentioned business early warning information. As shown in Figure 4, Figure 4 is a schematic interface diagram of early warning settings in an embodiment. Users can set early warning rules on the page shown in Figure 4. For example, decision-makers can customize data warning rules based on the business operation rhythm of the executing agency. The business data target value of the executing agency of each business can correspond to the operational preferences and habits of the executing agency to achieve personalized assessment. Specifically, the rules that can be configured on the above-mentioned early warning setting page include but are not limited to early warning type, early warning indicator, early warning time, early warning organization, early warning rules, early warning frequency, whether to push through IM, push target configuration items, etc. The early warning indicators can be set to any data indicators of the visual analysis system, such as corresponding types of business data, etc.; the calculation method supports year-on-year, sequential progress, ranking and other definition methods. Among them, year-on-year means determining whether an early warning is needed by calculating the year-on-year data of the business, sequential progress means determining whether an early warning is needed by comparing the time progress with the progress of business data, and ranking means determining whether an early warning is needed by calculating the ranking of business progress in each executing agency. Early warning.

The relevant information of the above-mentioned business warning can be stored in the database of the server, including rule information, specific forms of business warning information, etc. Specifically, as shown in Figure 5, Figure 5 is a schematic structural diagram of early warning information in an embodiment. Early warning related information can be stored in an ER diagram, including entities such as early warning indicators, early warning rules, early warning results, and early warning messages. Among them, the early warning indicator defines the database table name, indicator name and indicator code that require early warning indicators. It is used to provide users with an optional indicator list and is used for the program to locate the corresponding system indicator value. Among them, different indicators represent different types of businesses.

Early warning rules define the agency code, start and end time, push target, warning type, calculation rules, etc. for which the warning is effective. Among them, the above-mentioned execution agencies can correspond to corresponding subordinate execution agencies. The agency code indicates that the current agency and all its subordinate agencies need to perform early warning detection according to this rule; the calculation rules are stored in json format, recording the calculation method and corresponding thresholds, for example The calculation method is ranking. The lower bound of the threshold is 5 and the upper bound is 10. The calculation rule is to trigger the early warning operation ranked 5-10 among all subordinate institutions of the same parent institution; the push target field is multi-option, and institution management can be selected. positions such as senior management, parent agency management, and provincial operators. Each warning rule is associated with an warning indicator.

When the server needs to push business warning information, it can generate a push message for each user who needs to be pushed at a specified time point based on the push rules and push targets in the warning rules. The early warning message defines the push time, the work number of the person being pushed, and the push status. Among them, each early warning message will be associated with the corresponding early warning result, and the different types of businesses mentioned above can be stored in different data tables. The server can obtain the corresponding early warning information of the executing agency by associating two data tables of different business types. All businesses, so that the push program will push the warning information to the target users based on the correlation results of the two tables.

In some embodiments, the server can regularly start the service progress detection task, as shown in Figure 6. Figure 6 is a schematic flow chart of early warning detection in one embodiment. Through the scheduling platform, tasks are started regularly every day. The server can receive the called information through the application program interface and trigger early warning detection. After the server receives the request, it can query all early warning rules in effect in the current time period from the database, that is, detect which businesses are still in effect. Within the validity period, and which services are configured with early warning rules that are still valid in the current time period. The server can loop through all early warning rules, and traverse the indicator data of the corresponding execution agency in the database based on the indicator code configured for each early warning rule. The indicator code represents the type of business, and the indicator data represents the data of the business. Based on the above calculation rules, the server determines whether the indicators of the current execution agency meet the conditions for triggering the early warning, that is, whether the above business progress conditions are met. If not, it records all the execution agencies that meet the conditions for triggering the early warning and their corresponding business data, that is, the indicator value, and Write the warning result table to form business warning information.

In addition, the server can also push early warning information according to set rules. As shown in Figure 7, Figure 7 is a schematic flow chart of early warning push in one embodiment. The dispatching platform can be connected to the server. The dispatching platform starts tasks regularly every day, and the start time of the push task can be set so that after the early warning detection task, the server receives the request from the dispatching platform and starts the early warning information push task. Among them, the server can query all message records to be pushed on the day from the database. These message records can be the execution agencies that need to be pushed. The server can traverse all message records and associate the corresponding warning results to generate warning message messages, that is, business warning information. . Among them, the above-mentioned early warning message message includes early warning agency, early warning indicator, indicator definition, early warning time range, early warning threshold, and current indicator value. Among them, the indicator represents the business, and the indicator value represents the value of the business data.

Moreover, in order to optimize push efficiency and avoid pushing a large number of messages to each user, the server can also summarize the messages of the same user into a data table. For example, the business warning information of the same user can be clustered to form a table picture, as shown in the figure. As shown in 8, FIG. 8 is a schematic interface diagram of business warning information in an embodiment. When the server determines that a user has multiple business warning information, it can integrate the corresponding multiple business warning information into a table image for push. Among them, the above business warning information can represent the warning information of a user in the executing agency. The server can push the business warning information to the IM system through the IM system interface of the corresponding executing agency. After receiving the business warning information, the IM system can The user identification in the business warning information, such as the user's work number, sends the business warning information to the corresponding user to achieve targeted business warning notifications.

In the above business early warning method, when the business progress statistical conditions are met, the business data within the preset statistical period is obtained based on at least one unit business data of the business in at least one unit statistical period. Based on the preset statistical period, business data and corresponding The business data target value determines the business progress information of the business. When the above business progress information does not meet the preset business progress conditions, the business early warning information is generated based on the business progress information and the target business progress information, and the business early warning information is sent to the business corresponding executing agency. Compared with the traditional method of manual offline verification and early warning, this solution counts the business data of the business according to the preset statistical cycle, and determines the business data based on the cycle, business data and corresponding business data target values. Progress information, when the business progress information does not meet the business progress conditions, corresponding business warning information is sent to the execution agency corresponding to the business, thereby improving the accuracy of the business warning.

In one embodiment, the method further includes: obtaining a total target value of business data within a preset time period preset by the execution agency of the business; determining the total target value of business data based on historical business data of the execution agency; based on the preset time period Determine at least one preset statistical period, and store the business data target value corresponding to each preset statistical period according to the total target value of business data.

In this embodiment, the business data target value of the above-mentioned preset statistical period can be obtained by segmentation. The above-mentioned preset time period may be a time period greater than the time span of the preset statistical period, and the server may obtain the total target value of business data within the preset time period preset for the execution mechanism of the above-mentioned business. Among them, the total target value of the above business data can be determined based on the historical business data of the executing agency. For example, the difference between the value of the total target of business data in the later time period and the value of historical business data may be less than a preset value threshold.

The server can determine at least one preset statistical period according to the above-mentioned preset time period, and store the business data target value corresponding to each preset statistical period according to the total target value of business data. For example, when the number of preset statistical periods is two, the business data target value of each preset statistical period may be the above total target value of business data divided by two. Specifically, assuming that the preset time period is two months, the server can determine that the preset statistical period is one month through segmentation, and then the server determines the business within the preset time period by combining the business data of the two preset statistical periods. Progress information. Among them, since different types of services correspond to different unit statistical periods, the number of unit statistical periods of the business data included in the above-mentioned preset statistical period may be different.

Through this embodiment, the server can determine the business data target value of each preset statistical period by dividing the preset time period into at least one preset statistical period, and determine the preset business data target value by collecting the business data of at least one preset statistical period. The business data progress within the time period improves the accuracy of business progress warnings.

In one embodiment, determining the business to be counted and its corresponding unit statistical period includes: determining the data type of the business; and determining the unit statistical period corresponding to the business based on the data type.

In this embodiment, services of different data types may correspond to different unit statistical periods. When the server detects business progress, it can determine the data type of the above-mentioned business, and determine the unit statistical period corresponding to the business based on the data type. The above data type may include at least one of business resource data and business personnel data. The business resource data represents the data of virtual resources generated by executing the business, and the business personnel data represents the change data of the business personnel in the execution organization. In one embodiment, if the server detects that the data type is business resource data, the server may determine that the above unit statistical period is the first unit statistical period. If the server detects that the data type is business personnel data, the server can determine that the unit statistical period is the second unit statistical period. Among them, the first unit statistical period is smaller than the second unit statistical period.

Specifically, for business resource data, the unit statistical period can be hours, that is, the server can count the progress of business resource data every hour; for business personnel data, the unit statistical period can be days, that is, the server can count business personnel data every day. The data of the above-mentioned unit statistical period can be summarized to form the business data of the preset statistical period and the preset time period. The above unit statistical period can be stored in the database in the form of rules. Among them, the database contains a multi-layer structure. As shown in Figure 9, Figure 9 is a schematic structural diagram of storing business data in one embodiment. The structure and data flow of business warning information consists of the original data layer, summary data layer and assessment data layer.

Among them, the early warning rule management module in the assessment early warning layer can be used to define early warning rules; the early warning push module can open up the instant messaging system of the executive agency. When an agency triggers an early warning, the server will automatically send a message to the corresponding agent through the instant messaging system. The management of the organization can proactively report the situation to help the management gain timely insight into business operation problems. The early warning display module can display the early warning data of the current user's executive agency and subordinate organizations in the visual analysis system. Users can click on the early warning entry to view detailed early warning information, including indicator values, business data target values, budget completion rates, and balance gaps. , year-on-year data, warning push time, push times, etc.

The business resource summary of the summary data layer can include the business resource value table of each executing agency, including total business resource value, first-year regular payment, first-year standard guarantee for long-term business, ten years and above, standard guarantee for ten years and above, Business resource values for three-year period, short-term business, healthy business, comprehensive sustainability rate and other categories. The server can store two years of data in days, and the server will update the stored business resource data every hour based on incremental business resource data to ensure effectiveness. The team summary can include the team data table of each executive agency. The team data table includes relevant information of business personnel, etc., for example, it can include certified manpower, comprehensive manpower, effective manpower, star manpower, additional manpower, terminated manpower, etc. Data used to measure team development. The server can store two years of data in days and update it daily.

The data summary and processing module can summarize through multiple processing links based on the effectiveness of data updates and the type of data. For example, for business data with a unit statistical period of one day and it is updated at the day level, the server can start the data processing task regularly every early morning to calculate the accumulated business resources and team data as of 0:00 of the previous day; for the unit statistical period: Hourly business data is updated at an hourly level. The server can start data processing tasks every hour, calculate the incremental business resource data as of the previous hour, and update it cumulatively to the business resource data of the day.

The policy data module of the original data layer can store all business resource data tables and is used for the summary and processing of business resources and partial business personnel data, such as comprehensive manpower and effective manpower. The organization information module stores an organization information table, which records all provinces, cities, counties, outlets, and workplace organizations at all levels and their hierarchical relationships. The server's summary program can combine the business data of organizations at all levels based on the data in this table. Statistics are aggregated layer by layer. The business personnel information module can store the business personnel information table, which records the organizational affiliation, jurisdictional relationship, recommendation relationship, entry time, employment status and other information of the business personnel, and is used for the summary and processing of some team indicators.

The above business data can be stored in the server in table form, as shown in Figure 10. Figure 10 is a schematic structural diagram of the indicator data in one embodiment. Includes business resource data table and team data table. Among them, the business resource data table is used to store various business resource data of each organization, and in order to support the early warning function of flexibly defining the time range, two fields, time dimension and business date, are also designed. Among them, the business date indicates which day the business data of this data is counted. In order to support year-on-year calculations, the database will store daily data within two years. The time dimension represents the time span of this data statistics and supports days, weeks, months, quarters, years and custom periods. Among them, if the time period is monthly, then this data statistics is the summary business resource data within one month before the business date. The rest are the same. Through the above design, the user can flexibly set which areas to pay attention to when configuring the early warning function. Data within a period of time can be flexibly adapted to business needs. The agency code indicates the executing agency for which this data takes effect. During the calculation process, the indicator data will be summarized layer by layer based on the organizational structure tree of the agency relationship, thereby counting the indicator data at the workplace, outlet, district, city, and provincial levels.

The team data table can be used to store the team data of each executive agency, that is, the business personnel data of each executive agency. Its design concept is the same as the business resource data table. The above-mentioned business data target value may be a joint result including the above-mentioned business resource data table and the team data table, and may be stored in a table form. Specifically, as shown in Figure 11, Figure 11 is a schematic structural diagram of a business target in an embodiment. The business data target table can store the phased business data target values customized by each execution agency. The time dimension corresponds to the time dimension of the above-mentioned business resource data table and team data table, and represents the current preset statistical period. The start time and end time define the validity period of the currently preset service. In order to support the flexible decomposition of business data target values into multi-stage sub-goals according to the experience, preferences and habits of the execution agency, the server can introduce superior target coding fields to record the master-slave relationship of the business data target values; each target field is the corresponding business Specific target values for the data. The system will associate the target table with the business resource data table and team data table every day to calculate the business data target achievement status of each executing agency.

Through the above embodiments, the server can use different unit statistical periods to conduct business data statistics according to different data types, and then the server obtains business progress information and provides early warning by summarizing the business data of different unit statistical periods, which improves the effectiveness of business early warning. Accuracy.

In one embodiment, determining the business progress information of the service based on the preset statistical period, business data, and business data target values includes: determining the cycle start time and cycle end time based on the preset statistical period, and obtaining the business data corresponding to The recording time; according to the recording time, cycle start time and cycle end time, the sequential progress information corresponding to the business is obtained; according to the business data and business data target value, the task progress information corresponding to the business is obtained; according to the sequential progress information and task Progress information, get the business progress information of the business.

In this embodiment, the server can determine the service progress information in multiple ways. For example, the server can determine the period start time and period end time based on the above-mentioned preset statistical period, and obtain the corresponding recording time of the business data. The server obtains the sequential progress information corresponding to the business based on the recording time, cycle start time, and cycle end time, and obtains the task progress information corresponding to the business based on the business data and business data target values, so that the server can obtain the sequential progress information and Task progress information, obtain the business progress information of the business. That is, the server can determine the business progress information by comparing the sequential progress. In one embodiment, when the server detects that the value of the task progress is smaller than the value of the sequential progress, the server may determine that the business progress information does not meet the preset business progress conditions.

Specifically, this embodiment provides a method of determining business progress based on sequential progress. The server can calculate the sequence progress information of the above services at the current point in time. Among them, the sequential progress information can be specifically expressed as: sequential progress information = (current time - business target start time) / (business target end time - business target start time); where, the business target start time represents the current forecast time Assume the starting time of the statistical period, and the business target end time represents the end time of the current preset statistical period. The server can also calculate the current task progress information, which is specifically expressed as: task progress = indicator value/target value. Among them, the indicator value represents the numerical value of the business data, and the target value represents the numerical value of the target value of the business data. If the server detects that the above-mentioned task progress information is less than the sequential progress information, the server determines that the above-mentioned business does not meet the business progress conditions.

Specifically, as shown in Figure 12, Figure 12 is a schematic diagram of the service progress judgment steps in one embodiment. Each executive agency will formulate assessment targets for a period of time based on the business data targets assigned by superiors throughout the year and its own operating rhythm. For example, February and March are used as preset time periods. The server determines the total target value of business data corresponding to the preset time period and splits it into assessment targets for multiple time periods. At the current point in time, when the server detects the above two When the progress of the business data in the February and March time periods is less than the sequential progress in the overall time period of February and March, it can be determined that a business early warning is needed, and the management decision-making layer of the corresponding execution agency is promptly notified to remind them to follow up on business execution. situation. In addition, in addition to sequential progress, the server can also set up year-on-year negative growth and completion rate rankings for the above-mentioned time periods to make early warning judgments.

Through the above embodiments, the server can determine whether a business warning is needed by detecting the sequence progress and task progress, thereby improving the accuracy of the business warning.

In one embodiment, determining the business progress information of the business based on the preset statistical period, business data and business data target value includes: obtaining the year-on-year business data corresponding to the preset statistical period; based on the business data The ratio with year-on-year business data determines the business progress information of the business.

In this embodiment, the server can also determine whether the business progress condition is met by calculating the year-on-year growth. For example, the server can obtain the year-on-year business data corresponding to the preset statistical period corresponding to the above-mentioned preset statistical period. Among them, the year-on-year preset statistical period represents the preset statistical period of the same time and time span in the previous year. Year-on-year business data represents business data for the same time period in the previous year. The server can determine the business progress information of the business based on the ratio of business data to year-on-year business data. Specifically, it can be expressed as: year-on-year = indicator value of the day/indicator value of the same period last year * 100%. In one embodiment, if the server detects that the ratio of the above-mentioned business data to the year-on-year business data is less than one, the server may determine that the above-mentioned business progress information does not meet the preset business progress conditions and needs to issue a business early warning.

Through the above embodiment, the server can determine whether a business early warning is needed by calculating year-on-year parameters, thereby improving the accuracy of the business early warning.

In one embodiment, after determining the business progress information of the business, it also includes: obtaining the business progress information of each peer execution agency corresponding to the execution agency; sorting the business progress information and the business progress information of each peer, if It is detected that the ranking of the business progress information is within the preset ranking range, and it is determined that the business progress information does not meet the preset business progress conditions.

In this embodiment, the above-mentioned executing agencies may be stored in a preset agency relationship table, and the agency relationship table may include affiliation relationships between multiple executing agencies. Then the server can determine the execution agencies that do not meet the business progress conditions based on the business data ranking of each execution agency. For example, the server can obtain the business progress information of each peer level execution agency corresponding to the execution agency, and sort the business progress information and the business progress information of each peer level. Among them, the execution agency at the same level means the execution agency that belongs to the same superior execution agency as the above-mentioned execution agency. If the server detects that the ranking of the business progress information is within the preset ranking range, it is determined that the business progress information does not meet the preset business progress conditions.

Specifically, the server can select a certain business data and obtain the business progress information of the business data in each execution agency. The server ranks the business progress information of each execution agency in reverse order. If the ranking is greater than the configured lower threshold and less than the upper threshold, , it is considered that the conditions for achievement rate ranking warning are met. For example, if the lower bound of the server setting threshold is 5 and the upper bound is 10, the calculation rule is that among all subordinate institutions of the same parent institution, those ranked 5-10 do not meet the preset business progress conditions. In addition, the above-mentioned agency information and early warning rules are also set with push target fields for push targets of business alert information, including agency management, superior agency management, provincial operators, etc. Among them, the organization management means pushing to all management employees of the current organization; the upper-level organization management means the employees who found the current organization's parent-organization administrator based on the organizational structure tree; the provincial operator means the data of the provincial organization to which the current organization belongs. Kanban operators. The server can generate an early warning message for each user and write it into the database, so that the server pushes the business early warning information based on the preset warning information push rules.

Through this embodiment, the server can determine the execution agency that needs to perform business warning by calculating the completion rate ranking of the execution agency, thereby improving the accuracy of the business warning.

In one embodiment, as shown in Figure 13, Figure 13 is a sequence diagram of a service early warning method in an embodiment. In this embodiment, steps such as inputting business data target values, business data calculation, and early warning execution are performed. When entering the business data target value, the user can upload the staged business data target value to the server in the form of a table based on the current operating rhythm of the executing agency. The server will parse the table line by line into the business goals of each organization and store it in in the database.

When calculating business data, the server can start the calculation task regularly. The business resource data indicator calculation task is started once an hour, and the team indicator calculation task is started once a day. The server obtains all target data within the statistical period from the unified ODS data warehouse and synchronizes them to the database of this system. Then it first performs the indicator calculation task. Among them, when the business resource data calculation task is run for the first time that day, the server will first generate a data record for each organization in each time dimension with the business time for today, and then every subsequent hour it will calculate the data for today based on the latest business resource data. The data records are updated to ensure the timeliness of the data. The overall running logic of the team indicator calculation task is the same as the first running logic of the business resource data of the day. The server can also calculate the target completion status. For example, the server can traverse the business data target values that are effective at the current time point of each execution agency, associate the business data target values with two indicator tables based on the agency code and time dimension, and calculate the target completion rate. . Finally, all results are stored in the database.

When the early warning is executed, the server can regularly start the early warning detection and early warning push tasks. The early warning detection task will find all the institutions and their business data that triggered the early warning, and then send the early warning message to the IM system through the early warning push task, thereby notifying the user. After receiving the notification, users can view detailed business conditions through the visual analysis system.

Through the above embodiments, the server can count the business data of the business according to the preset statistical cycle, and determine the business progress information of the business based on the cycle, business data and its corresponding business data target value. When the business progress information does not meet the requirements of the business When the progress conditions are met, corresponding business warning information is sent to the execution agency corresponding to the business, thereby improving the accuracy of business warning. In addition, this plan is aimed at the marketing-oriented industry attributes and unique operating rhythm of the financial and insurance business. By building an assessment and early warning function in the financial and insurance business data visual analysis system, it effectively realizes the analysis of three types of data: business data, business objectives and assessment rules. Opening up and integrating enables the management and decision-making layers of the executive agency to flexibly customize performance management goals and assessment and warning rules according to the organization's unique operating rhythm, so as to understand the completion status of important indicators related to business assessment in a timely and intuitive manner. Provide the data visualization analysis system with the ability to proactively discover business operation problems, help improve operating efficiency, and realize a data-driven operation and management system.

It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

Based on the same inventive concept, embodiments of the present application also provide a service warning device for implementing the above-mentioned service warning method. The solution to the problem provided by this device is similar to the solution recorded in the above method. Therefore, for the specific limitations in one or more embodiments of the business early warning device provided below, please refer to the limitations on the business early warning method mentioned above. I won’t go into details here.

In one embodiment, as shown in Figure 14, a service early warning device is provided, including: a detection module 500, an acquisition module 502, a determination module 504 and an early warning module 506, wherein:

The detection module 500 is used to determine the business to be counted and its corresponding unit statistical period when it is detected that the business progress statistics conditions are met.

The acquisition module 502 is configured to obtain business data within a preset statistical period based on at least one unit business data of the business within at least one unit statistical period.

The determination module 504 is used to obtain the preset business data target value of the business within the preset statistical period, and determine the business progress information of the business based on the preset statistical period, business data and business data target value.

The early warning module 506 is used to generate business early warning information based on the business progress information and target business progress information if the business progress information does not meet the preset business progress conditions, obtain the execution agency corresponding to the business, and send the business early warning information to the execution agency.

In one embodiment, the above device further includes: a segmentation module for obtaining the total target value of business data within a preset time period preset for the execution agency of the business; the total target value of business data is based on the historical business data of the execution agency Determine; determine at least one preset statistical period according to the preset time period, and store the business data target value corresponding to each preset statistical period according to the total target value of business data.

In one embodiment, the above-mentioned detection module 500 is used to determine the data type of the service; according to the data type, determine the unit statistical period corresponding to the service.

In one embodiment, the above-mentioned detection module 500 is used to determine the unit statistical period to be the first unit statistical period if the data type is business resource data; to determine the unit statistical period to be the second unit statistical period if the data type is business personnel data. ; Among them, the first unit statistical period is smaller than the second unit statistical period.

In one embodiment, the above-mentioned determination module 504 is used to determine the cycle start time and cycle end time according to the preset statistical period, and obtain the recording time corresponding to the business data; according to the recording time, cycle start time and cycle end time , obtain the sequential progress information corresponding to the business; obtain the task progress information corresponding to the business according to the business data and business data target value; obtain the business progress information of the business according to the sequential progress information and task progress information.

In one embodiment, the above device further includes: a first judgment module, configured to determine that the business progress information does not meet the preset business progress conditions if it is detected that the value of the task progress is less than the value of the sequential progress.

In one embodiment, the above-mentioned determination module 504 is used to obtain the year-on-year business data corresponding to the preset statistical period corresponding to the preset statistical period; and determine the business progress information of the business based on the ratio of the business data and the year-on-year business data.

In one embodiment, the above device further includes: a second judgment module, configured to determine that the business progress information does not meet the preset business progress conditions if the ratio of the business data to the year-on-year business data is less than one.

In one embodiment, the above-mentioned device also includes: a third judgment module, used to obtain the business progress information of each same-level execution agency of each same-level execution agency corresponding to the execution agency; sort the business progress information and the business progress information of each same-level, If it is detected that the ranking of the business progress information is within the preset ranking range, it is determined that the business progress information does not meet the preset business progress conditions.

Each module in the above-mentioned business early warning device can be realized in whole or in part by software, hardware and their combination. Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in Figure 15. The computer device includes a processor, memory, and network interfaces connected through a system bus. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems, computer programs and databases. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The computer device's database is used to store business data. The network interface of the computer device is used to communicate with external terminals through a network connection. The computer program implements a business early warning method when executed by the processor.

Those skilled in the art can understand that the structure shown in Figure 15 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Specific computer equipment can May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.

In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the above business warning method.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned business warning method is implemented.

In one embodiment, a computer program product is provided, including a computer program that implements the above business warning method when executed by a processor.

It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all It is information and data authorized by the user or fully authorized by all parties.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage. In the media, when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random) Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration and not a limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to this.

The technical features of the above embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, all possible combinations should be used. It is considered to be within the scope of this manual.

The above-described embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims (10)

1. A business pre-warning method, characterized in that the method comprises:

when the condition of satisfying the business progress statistics is detected, determining the business to be counted and the corresponding unit statistics period;

obtaining service data in a preset statistic period according to at least one unit service data of the service in at least one unit statistic period;

acquiring a business data target value preset by the business in the preset statistical period, and determining business progress information of the business according to the preset statistical period, the business data and the business data target value;

And if the business progress information does not meet the preset business progress condition, generating business early warning information according to the business progress information and the target business progress information, acquiring an executing mechanism corresponding to the business, and sending the business early warning information to the executing mechanism.

2. The method according to claim 1, wherein the method further comprises:

acquiring a total target value of service data in a preset time period preset by an execution mechanism of the service; the service data total target value is determined based on historical service data of the executing mechanism;

and determining at least one preset statistical period according to the preset time period, and storing service data target values corresponding to the preset statistical periods according to the service data total target value.

3. The method of claim 1, wherein the determining the traffic to be counted and the corresponding unit counting period thereof comprises:

determining the data type of the service;

and determining a unit statistical period corresponding to the service according to the data type.

4. A method according to claim 3, wherein the data type comprises at least one of business resource data and business personnel data; the determining the unit statistical period corresponding to the service according to the data type comprises the following steps:

If the data type is business resource data, determining the unit statistical period as a first unit statistical period;

if the data type is business personnel data, determining the unit statistical period as a second unit statistical period;

wherein the first unit statistical period is smaller than the second unit statistical period.

5. The method of claim 1, wherein the determining the business progress information of the business according to the preset statistical period, the business data and the business data target value comprises:

determining cycle starting time and cycle ending time according to the preset statistical period, and acquiring recording time corresponding to the service data;

obtaining sequence time progress information corresponding to the service according to the recording time, the cycle starting time and the cycle ending time;

obtaining task progress information corresponding to the service according to the service data and the service data target value;

obtaining business progress information of the business according to the time sequence progress information and the task progress information;

after the service progress information of the service is determined, the method further comprises the following steps:

And if the numerical value of the task progress is detected to be smaller than the numerical value of the time sequence progress, determining that the business progress information does not meet the preset business progress condition.

6. The method of claim 1, wherein the determining the business progress information of the business according to the preset statistical period, the business data and the business data target value comprises:

acquiring the same-ratio business data corresponding to the same-ratio preset statistical period corresponding to the preset statistical period;

determining service progress information of the service according to the ratio of the service data to the same-ratio service data;

after the service progress information of the service is determined, the method further comprises the following steps:

if the ratio of the service data to the same-ratio service data is smaller than one, determining that the service progress information does not meet a preset service progress condition.

7. The method of any one of claims 1 to 6, wherein the actuators are stored in a preset mechanism relationship table, the mechanism relationship table comprising dependencies between a plurality of actuators;

after the service progress information of the service is determined, the method further comprises the following steps:

acquiring the business progress information of each peer of executing mechanism corresponding to the executing mechanism;

And sequencing the business progress information and the business progress information of each peer, and if the ranking of the business progress information is detected to be in a preset ranking range, determining that the business progress information does not meet a preset business progress condition.

8. A business pre-warning device, characterized in that the device comprises:

the detection module is used for determining the business to be counted and the corresponding unit counting period when the business progress counting condition is detected to be met;

the acquisition module is used for acquiring service data in a preset statistic period according to at least one unit service data of the service in at least one unit statistic period;

the determining module is used for obtaining a service data target value preset by the service in the preset statistical period and determining service progress information of the service according to the preset statistical period, the service data and the service data target value;

and the early warning module is used for generating service early warning information according to the service progress information and the target service progress information if the service progress information does not meet the preset service progress condition, acquiring an executing mechanism corresponding to the service, and sending the service early warning information to the executing mechanism.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.