Business data evaluation method and system and readable storage medium

Abstract

The invention provides a business data evaluation method and system, and a readable storage medium. The business data evaluation method comprises the following steps: acquiring business data; an evaluation model is established, the evaluation model is configured according to evaluation rules, a configuration result is obtained, and the evaluation rules comprise one or a combination of a filtering rule, a scoring rule, a triggering rule, a grading rule and scoring result configuration; periodically evaluating the service data through an evaluation model according to the configuration result to obtain an evaluation result; and generating an evaluation result graph according to the evaluation result, and displaying the evaluation result and the evaluation result graph to the user. According to the technical scheme, the evaluation model can be self-defined, periodic automatic scoring is carried out through the evaluation model, the data scoring real-time performance is improved, and the multi-dimensional scoring requirement of a user is met.

Description

Technical Field

[0001] The present application relates to the technical field of data evaluation, and in particular to a business data evaluation method and system, and a readable storage medium. Background Art

[0002] In order to improve the efficiency of sales staff and corporate revenue during the sales process, companies often need to calculate the scores of potential customers, clients, business opportunities, leads, etc. according to different dimensions based on business data and behavioral actions. These scores can help sales staff prioritize potential customers, clients, etc., improve work efficiency, and thus improve sales performance. In the past, business data was scattered in various documents, and the value of the data itself could not be fully utilized. The purpose of the evaluation model is to provide effective assistance to user decision-making and sales based on flexible business rule configuration. The current shortcomings of the evaluation model are as follows: The data scoring of indicator statistics cannot achieve real-time performance, and the indicator statistics capability of the data center (T + 1 mode) is currently used. Indicator data needs to rely on the capabilities of the data center. Summary of the invention

[0003] This application aims to solve or improve the above technical problems.

[0004] To this end, the first purpose of this application is to provide a business data evaluation method.

[0005] The second objective of the present application is to provide a business data evaluation system.

[0006] The third objective of this application is to provide a business data evaluation system.

[0007] The fourth object of the present application is to provide a readable storage medium.

[0008] To achieve the first purpose of the present application, the technical solution of the first aspect of the present application provides a business data evaluation method, including: obtaining business data; establishing an evaluation model, and configuring the evaluation model according to evaluation rules to obtain configuration results, the evaluation rules including one of the following or a combination thereof: filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration; performing periodic evaluation of business data through the evaluation model according to the configuration results to obtain evaluation results; generating an evaluation result graph according to the evaluation results, and displaying the evaluation results and the evaluation result graph to the user.

[0009] According to the business data evaluation method provided by the present application, business data is first obtained. Then an evaluation model is established, and the evaluation model is configured according to the evaluation rules to obtain the configuration results. The evaluation rules include filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration. Then, the business data is periodically evaluated through the evaluation model according to the configuration results to obtain the evaluation results. Finally, an evaluation result graph is generated according to the evaluation results, and the evaluation results and the evaluation result graph are displayed to the user. It can be understood that filtering rules, scoring rules, triggering rules, grading rules, scoring result configuration, etc. are created according to business needs, and a good evaluation model is defined, and then the evaluation model is periodically calculated, and the scoring results and grading results are stored. In the details of documents such as customers and business opportunities, the evaluation results can be configured according to the scoring results, and the evaluation results can be graphically displayed to the salesperson, which can improve the real-time performance of data scoring and meet the needs of users for multi-dimensional scoring and different display needs of users.

[0010] In addition, the technical solution provided by this application may also have the following additional technical features: In some technical solutions, optionally, business data is periodically evaluated through an evaluation model according to the configuration results to obtain evaluation results, including: setting the scheduling task time of the evaluation model to determine the evaluation cycle; filtering the business data through the evaluation model according to the evaluation cycle to obtain filtered business data; scoring the filtered business data through the evaluation model according to the evaluation cycle to obtain scoring results; scoring and grading the scoring results through the evaluation model according to the evaluation cycle to obtain grading results, and the scoring and grading methods include one of the following or a combination thereof: score grading, quantity grading and percentage grading.

[0011] In this technical solution, the business data is periodically evaluated through the evaluation model according to the configuration results to obtain the evaluation results. Specifically, the scheduling task time of the evaluation model is first set to determine the evaluation cycle. The scheduling task time of the configuration evaluation is connected with the platform scheduling task, and the evaluation scheduling task can be flexibly created. Then, the business data is filtered through the evaluation model according to the evaluation cycle to obtain filtered business data. Then, the filtered business data is scored through the evaluation model according to the evaluation cycle to obtain the scoring results. Finally, the scoring results are scored and graded through the evaluation model according to the evaluation cycle to obtain the grading results. Among them, the scoring and grading methods include score grading, quantity grading and percentage grading. Specifically, score grading is simple and efficient, and the level can be directly determined based on the score range. However, the quantity and percentage need to be graded according to the order of the scores after all scores are calculated. Here, in order to improve efficiency, the calculated scores are stored in the redis zset (Remote Dictionary Server sorted set). When grading the quantity and percentage, the sorted data is directly taken out from the redis zset queue and then graded according to the configuration. After the X-axis and Y-axis grading are completed, the matrix comprehensive grading can be performed according to the configuration.

[0012] In some technical solutions, optionally, business data is filtered through an evaluation model to obtain filtered business data, including: dynamically splicing query statements according to configuration results to obtain filtered business data, and the query method of the statement includes a scrolling paging method; and / or performing memory calculation filtering according to the configuration results to obtain filtered business data.

[0013] In this technical solution, the business data is filtered through the evaluation model to obtain filtered business data. Specifically, query statements are dynamically spliced ​​according to the configuration results to obtain filtered business data, and the query method of the statement includes a scrolling paging method. Or according to the configuration results, memory calculation filtering is performed to obtain filtered business data. Specifically, the filtering rules mainly control the data range of the evaluation, and can be flexibly configured according to the preset fields. The backend dynamically splices query statements according to the configuration, and the query uses a scrolling paging method to improve the query efficiency. Here, the filtering component and the backend encapsulate and implement two methods. One is to dynamically splice query statements according to the configuration, and the other is memory calculation filtering. Two methods are considered when designing the storage of the filtering conditions, one is form row storage, and the other is stored as a json (JavaScript ObjectNotation, JavaScript object representation) string. The evaluation model is stored as a json string to avoid data conversion during front-end display and back-end calculation.

[0014] In some technical solutions, optionally, the filtered business data is scored through an evaluation model according to an evaluation cycle to obtain a scoring result, including: performing in-memory calculations on the filtered business data according to the configuration results to obtain a scoring result; and / or obtaining indicator data from the marketing data middle station based on composite indicators, and calculating the filtered business data according to the configuration results to obtain a scoring result.

[0015] In this technical solution, the filtered business data is scored through the evaluation model according to the evaluation cycle to obtain the scoring result, specifically, the filtered business data is calculated in memory according to the configuration result to obtain the scoring result. Or according to the composite indicator, the indicator data is obtained from the marketing data center, and the filtered business data is calculated according to the configuration result to obtain the scoring result. Specifically, the configuration scoring details support field scoring and indicator scoring, and support multi-dimensional scoring. The field scoring is based on the configuration conditions and business data, and the memory calculation score is performed. The indicator scoring needs to query the indicator data from the marketing data center according to the composite indicator, and then calculate the score according to the configuration conditions. In the business implementation, the field scoring calculation logic and the indicator scoring logic are separated so that the indicator data can be queried from the data center in batches. At the same time, in order to improve the scoring efficiency, multi-threaded processing is adopted.

[0016] In some technical schemes, optionally, the scoring results are scored and graded through the evaluation model according to the evaluation cycle to obtain a grading result, including: grading the first business data according to the scoring result of the first business data to obtain a first grading result; grading the second business data according to the scoring result of the second business data to obtain a second grading result; performing matrix comprehensive grading based on the configuration results, the first grading result, and the second grading result to obtain a comprehensive grading result.

[0017] In this technical solution, the scoring results are scored and graded according to the evaluation model according to the evaluation cycle to obtain the grading results. Specifically, the first business data is graded according to the scoring results of the first business data to obtain the first grading result. Then the second business data is graded according to the scoring results of the second business data to obtain the second grading result. Finally, matrix comprehensive grading is performed according to the configuration results, the first grading results, and the second grading results to obtain a comprehensive grading result. The score grading can directly determine the level according to the score interval, while the quantity and percentage need to be graded according to the order of the scores after all the scores are calculated. Specifically, the calculated scores are stored in the redis zset. When the quantity and percentage are graded, the sorted data is directly taken out from the redis zset queue and then graded according to the configuration. After the X-axis and Y-axis grading are completed, the matrix comprehensive grading can be performed according to the configuration.

[0018] In some technical schemes, optionally, an evaluation result graph is generated based on the evaluation results, including: dynamically configuring the display content of the evaluation result graph to obtain a second configuration result, the display content including one of the following or a combination thereof: an evaluation score trend graph, addition and subtraction items, the number of addition and subtraction item display items, and a trend data display time range; an evaluation result graph is generated based on the evaluation results and the second configuration result.

[0019] In this technical solution, an evaluation result graph is generated according to the evaluation results, specifically, the display content of the evaluation result graph is dynamically configured to obtain a second configuration result. Among them, the display content includes an evaluation score trend graph, addition and subtraction items, the number of addition and subtraction item display items, and the trend data display time range. Then, an evaluation result graph is generated according to the evaluation results and the second configuration result. By graphically displaying the scoring results and dynamically configuring the evaluation score trend graph, addition and subtraction items, the number of addition and subtraction item display items, and the trend data display time range, the different display needs of users can be met. In addition, the trend data display time range can be configured, and historical data that exceeds the configured time range can be deleted in time through a scheduled task, thereby clearing invalid data in time.

[0020] In some technical solutions, optionally, the scoring includes one of the following or a combination thereof: field scoring, indicator scoring, and multi-dimensional scoring, and the scoring method includes a multi-threaded method.

[0021] In this technical solution, scoring includes field scoring, indicator scoring and multi-dimensional scoring, and the scoring method includes a multi-threaded method.

[0022] To achieve the second purpose of the present application, the technical solution of the second aspect of the present application provides a business data evaluation system, including: an acquisition module, used to acquire business data; a configuration module, used to establish an evaluation model, and configure the evaluation model according to the evaluation rules to obtain the configuration results, the evaluation rules include one of the following or a combination thereof: filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration; an evaluation module, used to periodically evaluate the business data through the evaluation model according to the configuration results to obtain the evaluation results; a display module, used to generate an evaluation result graph according to the evaluation results, and display the evaluation results and the evaluation result graph to the user.

[0023] According to the business data evaluation system provided by the present application, it includes an acquisition module, a configuration module, an evaluation module and a display module. Among them, the acquisition module is used to obtain business data. The configuration module is used to establish an evaluation model, and configure the evaluation model according to the evaluation rules to obtain the configuration results. The evaluation rules include one of the following or a combination thereof: filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration. The evaluation module is used to periodically evaluate the business data through the evaluation model according to the configuration results to obtain the evaluation results. The display module is used to generate an evaluation result graph according to the evaluation results, and display the evaluation results and the evaluation result graph to the user. It can be understood that according to business needs, filtering rules, scoring rules, triggering rules, grading rules, scoring result configuration, etc. are created to define a good evaluation model, and then the evaluation model is periodically calculated, and the scoring results and grading results are stored. In the details of documents such as customers and business opportunities, the evaluation results can be configured according to the scoring results, and the evaluation results can be graphically displayed to the salesperson, which can improve the real-time performance of data scoring and meet the user's multi-dimensional scoring needs and different display needs of users.

[0024] In order to achieve the third purpose of the present application, the technical solution of the third aspect of the present application provides a business data evaluation system, including: a memory and a processor, wherein the memory stores programs or instructions that can be run on the processor, and when the processor executes the program or instruction, it implements the business data evaluation method of any one of the technical solutions of the first aspect, and thus has the technical effect of any one of the technical solutions of the first aspect mentioned above, which will not be repeated here.

[0025] In order to achieve the fourth purpose of the present application, the technical solution of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the business data evaluation method of any one of the technical solutions of the first aspect are implemented, and thus it has the technical effect of any one of the technical solutions of the first aspect mentioned above, which will not be repeated here.

[0026] Additional aspects and advantages of the present application will become apparent in the following description or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 A schematic diagram of the steps of a business data evaluation method according to an embodiment of the present application; Figure 2 A schematic diagram of the steps of a business data evaluation method according to an embodiment of the present application; Figure 3 A schematic diagram of the steps of a business data evaluation method according to an embodiment of the present application; Figure 4A schematic diagram of the steps of a business data evaluation method according to an embodiment of the present application; Figure 5 A schematic diagram of the steps of a business data evaluation method according to an embodiment of the present application; Figure 6 A schematic diagram of the steps of a business data evaluation method according to an embodiment of the present application; Figure 7 This is a schematic block diagram of the structure of a business data evaluation system according to an embodiment of the present application; Figure 8 This is a schematic block diagram of the structure of a business data evaluation system according to an embodiment of the present application; Fig. 9 A technical schematic diagram of a business data evaluation method according to an embodiment of the present application; Fig.10 A schematic diagram of filtering rule configuration of a business data evaluation method according to an embodiment of the present application; Fig.11 A schematic diagram of a scoring rule configuration of a business data evaluation method according to an embodiment of the present application; Fig.12 A schematic diagram of trigger rule configuration of a business data evaluation method according to an embodiment of the present application; Fig.13 A schematic diagram of a scoring and grading configuration of a business data evaluation method according to an embodiment of the present application; Fig.14 This is a schematic diagram showing the evaluation results of a business data evaluation method according to an embodiment of the present application.

[0028] in, Figure 7 and Figure 8 The corresponding relationship between the reference numerals and component names in the figure is: 10: business data evaluation system; 110: acquisition module; 120: configuration module; 130: evaluation module; 140: display module; 20: business data evaluation system; 300: memory; 400: processor. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0031] Refer to the following Figures 1 to 14 Describe the business data evaluation method and system and readable storage medium of some embodiments of the present application.

[0032] like Figure 1 As shown, the embodiment of the first aspect of the present application provides a business data evaluation method, comprising the following steps: Step S102: Acquire business data; Step S104: establishing an evaluation model, and configuring the evaluation model according to evaluation rules to obtain a configuration result, wherein the evaluation rules include one or a combination of the following: filtering rules, scoring rules, triggering rules, grading rules, and scoring result configuration; Step S106: periodically evaluate the service data through the evaluation model according to the configuration result to obtain the evaluation result; Step S108: Generate an evaluation result graph according to the evaluation result, and display the evaluation result and the evaluation result graph to the user.

[0033] According to the business data evaluation method provided in this embodiment, business data is first obtained. Then an evaluation model is established, and the evaluation model is configured according to the evaluation rules to obtain configuration results. The evaluation rules include filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration. Then, the business data is periodically evaluated through the evaluation model according to the configuration results to obtain evaluation results. Finally, an evaluation result graph is generated according to the evaluation results, and the evaluation results and the evaluation result graph are displayed to the user. It can be understood that filtering rules, scoring rules, triggering rules, grading rules, scoring result configuration, etc. are created according to business needs, and a good evaluation model is defined. Then the evaluation model is periodically calculated, and the scoring results and grading results are stored. In the details of documents such as customers and business opportunities, the evaluation results can be graphically displayed to the salesperson according to the scoring result configuration, which can improve the real-time performance of data scoring and meet the needs of users for multi-dimensional scoring and different display needs of users.

[0034] like Figure 2 As shown, according to a business data evaluation method of an embodiment proposed in the present application, the business data is periodically evaluated through an evaluation model according to the configuration result to obtain an evaluation result, which specifically includes the following steps: Step S202: Setting the scheduling task time of the evaluation model and determining the evaluation cycle; Step S204: filtering the business data through the evaluation model according to the evaluation cycle to obtain filtered business data; Step S206: Score the filtered business data using the evaluation model according to the evaluation cycle to obtain a scoring result; Step S208: The scoring results are scored and graded using the evaluation model according to the evaluation cycle to obtain a graded result. The scoring and grading methods include one of the following or a combination thereof: score grading, quantity grading and percentage grading.

[0035] In this embodiment, the business data is periodically evaluated through the evaluation model according to the configuration results to obtain the evaluation results. Specifically, the scheduling task time of the evaluation model is first set to determine the evaluation cycle. The scheduling task time of the configuration evaluation is connected with the platform scheduling task, and the evaluation scheduling task can be flexibly created. Then, the business data is filtered through the evaluation model according to the evaluation cycle to obtain filtered business data. Then, the filtered business data is scored through the evaluation model according to the evaluation cycle to obtain the scoring results. Finally, the scoring results are scored and graded through the evaluation model according to the evaluation cycle to obtain the grading results. Among them, the scoring and grading methods include score grading, quantity grading and percentage grading. Specifically, score grading is simple and efficient, and the level can be directly determined based on the score range. However, the quantity and percentage need to be graded according to the order of the scores after all scores are calculated. Here, in order to improve efficiency, the calculated scores are stored in the redis zset (Remote Dictionary Server sorted set). When grading the quantity and percentage, the sorted data is directly taken out from the redis zset queue and then graded according to the configuration. After the X-axis and Y-axis grading are completed, the matrix comprehensive grading can be performed according to the configuration.

[0036] like Figure 3 As shown, according to a business data evaluation method of an embodiment proposed in the present application, the business data is filtered through the evaluation model to obtain filtered business data, which specifically includes the following steps: Step S302: dynamically concatenate query statements according to the configuration results to obtain filtered business data, wherein the query method of the statements includes a scrolling paging method; and / or perform memory calculation filtering according to the configuration results to obtain filtered business data.

[0037] In this embodiment, the business data is filtered by the evaluation model to obtain filtered business data, specifically, query statements are dynamically spliced ​​according to the configuration results to obtain filtered business data, and the query method of the statement includes a scrolling paging method. Or according to the configuration results, memory calculation filtering is performed to obtain filtered business data. Specifically, the filtering rules mainly control the data range of the evaluation, and can be flexibly configured according to the preset fields. The backend dynamically splices query statements according to the configuration, and the query uses a scrolling paging method to improve the query efficiency. Here, the filtering component and the backend encapsulate and implement two methods, one is to dynamically splice query statements according to the configuration, and the other is memory calculation filtering. Two methods are considered when designing the storage of the filtering conditions, one is form row storage, and the other is stored as a json (JavaScript Object Notation) string. The evaluation model is stored as a json string to avoid data conversion during front-end display and back-end calculation.

[0038] like Figure 4 As shown, according to a business data evaluation method of an embodiment proposed in the present application, the filtered business data is scored by the evaluation model according to the evaluation cycle to obtain a scoring result, which specifically includes the following steps: Step S402: Perform in-memory calculations on the filtered business data according to the configuration results to obtain scoring results; and / or obtain indicator data from the marketing data center according to composite indicators, and calculate the filtered business data according to the configuration results to obtain scoring results.

[0039] In this embodiment, the filtered business data is scored through the evaluation model according to the evaluation cycle to obtain the scoring result, specifically, the filtered business data is calculated in memory according to the configuration result to obtain the scoring result. Or according to the composite indicator, the indicator data is obtained from the marketing data center, and the filtered business data is calculated according to the configuration result to obtain the scoring result. Specifically, the scoring details are configured to support field scoring and indicator scoring, and multi-dimensional scoring is supported, wherein the field scoring performs the score calculated in memory according to the configuration conditions and business data, and the indicator scoring requires querying the indicator data from the marketing data center according to the composite indicator, and then calculating the score according to the configuration conditions. In the business implementation, the field scoring calculation logic and the indicator scoring logic are separated so that the indicator data can be queried from the data center in batches. At the same time, in order to improve the scoring efficiency, multi-threaded processing is adopted.

[0040] like Figure 5 As shown, according to a business data evaluation method of an embodiment proposed in the present application, the evaluation results are scored and graded by the evaluation model according to the evaluation cycle to obtain the graded results, which specifically includes the following steps: Step S502: grading the first business data according to the scoring result of the first business data to obtain a first grading result; Step S504: grading the second business data according to the scoring result of the second business data to obtain a second grading result; Step S506: Perform matrix comprehensive grading according to the configuration result, the first grading result, and the second grading result to obtain a comprehensive grading result.

[0041] In this embodiment, the scoring results are scored and graded by the evaluation model according to the evaluation cycle to obtain the grading results. Specifically, the first business data is graded according to the scoring results of the first business data to obtain the first grading result. Then, the second business data is graded according to the scoring results of the second business data to obtain the second grading result. Finally, matrix comprehensive grading is performed according to the configuration results, the first grading results, and the second grading results to obtain the comprehensive grading result. The score grading can directly determine the level according to the score interval, while the quantity and percentage need to be graded according to the order of the scores after all the scores are calculated. Specifically, the calculated scores are stored in the redis zset. When the quantity and percentage are graded, the sorted data is directly taken out from the redis zset queue and then graded according to the configuration. After the X-axis and Y-axis grading are completed, the matrix comprehensive grading can be performed according to the configuration.

[0042] like Figure 6 As shown, according to a business data evaluation method of an embodiment proposed in the present application, an evaluation result graph is generated according to the evaluation result, specifically including the following steps: Step S602: dynamically configuring the display content of the evaluation result graph to obtain a second configuration result, wherein the display content includes one or a combination of the following: an evaluation score trend graph, addition and subtraction items, the number of addition and subtraction item display items, and a trend data display time range; Step S604: Generate an evaluation result graph according to the evaluation result and the second configuration result.

[0043] In this embodiment, an evaluation result graph is generated according to the evaluation results, specifically, the display content of the evaluation result graph is dynamically configured to obtain a second configuration result. Among them, the display content includes an evaluation score trend graph, addition and subtraction items, the number of addition and subtraction item display items, and the trend data display time range. Then, an evaluation result graph is generated according to the evaluation results and the second configuration result. By graphically displaying the scoring results and dynamically configuring the evaluation score trend graph, addition and subtraction items, the number of addition and subtraction item display items, and the trend data display time range, the different display needs of users can be met. In addition, the trend data display time range can also be configured, and the historical data that exceeds the configured time range can be deleted in time through the scheduled task, thereby clearing invalid data in time.

[0044] In some embodiments, optionally, the scoring includes one of the following or a combination thereof: field scoring, indicator scoring, and multi-dimensional scoring, and the scoring method includes a multi-threaded method.

[0045] like Figure 7 As shown, an embodiment of the second aspect of the present application provides a business data evaluation system 10, including: an acquisition module 110, used to acquire business data; a configuration module 120, used to establish an evaluation model, and configure the evaluation model according to evaluation rules to obtain configuration results, the evaluation rules include one of the following or a combination thereof: filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration; an evaluation module 130, used to periodically evaluate the business data through the evaluation model according to the configuration results to obtain evaluation results; a display module 140, used to generate an evaluation result graph according to the evaluation results, and display the evaluation results and the evaluation result graph to the user.

[0046] The business data evaluation system 10 provided according to the present embodiment includes an acquisition module 110, a configuration module 120, an evaluation module 130 and a display module 140. Among them, the acquisition module 110 is used to acquire business data. The configuration module 120 is used to establish an evaluation model, and configure the evaluation model according to the evaluation rules to obtain the configuration results. The evaluation rules include one of the following or a combination thereof: filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration. The evaluation module 130 is used to periodically evaluate the business data through the evaluation model according to the configuration results to obtain the evaluation results. The display module 140 is used to generate an evaluation result graph according to the evaluation results, and display the evaluation results and the evaluation result graph to the user. It can be understood that according to business needs, filtering rules, scoring rules, triggering rules, grading rules, scoring result configuration, etc. are created to define a good evaluation model, and then the evaluation model is calculated periodically, and the scoring and grading results are stored. In the details of documents such as customers and business opportunities, the evaluation results can be graphically displayed to the salesperson based on the scoring result configuration, which can improve the real-time performance of data scoring and meet users' multi-dimensional scoring needs and different display needs.

[0047] like Figure 8 As shown, an embodiment of the third aspect of the present application provides a business data evaluation system 20, including: a memory 300 and a processor 400, wherein the memory 300 stores programs or instructions that can be run on the processor 400, and when the processor 400 executes the program or instruction, the steps of the business data evaluation method of any one of the embodiments of the first aspect are implemented, and thus the technical effect of any one of the embodiments of the first aspect mentioned above is not repeated here.

[0048] The embodiment of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the business data evaluation method of any one of the embodiments of the first aspect are implemented, and thus it has the technical effect of any one of the embodiments of the first aspect mentioned above, which will not be repeated here.

[0049] like Figures 9 to 14 As shown, according to a specific embodiment of the business data evaluation method provided by this application, as shown in Fig. 9 As shown, the salesperson creates filtering rules, scoring rules, triggering rules, grading rules, scoring result configuration, etc. according to business needs, defines the evaluation model, and the scheduling task will periodically calculate the evaluation model according to the time configured by the timing rules, and store the scoring results and grading results. In the details of the customer, business opportunity and other documents, the evaluation results can be graphically displayed to the salesperson according to the scoring result configuration. Specifically, 1. The business personnel configure the scoring rules; 2. Select the scoring indicators and call the data middle platform interface to pass the rule parameters; 3. Calculate the indicator score; 4. The platform returns the indicator score after calculation; 5. Scoring calculation, rating calculation; 6. Back up the customer scoring history records to the data middle platform; 7. Read the customer scoring records from the customer middle platform in real time.

[0050] Filter rules: Fig.10 As shown, it mainly controls the data range of the evaluation, and can be flexibly configured according to the preset fields. The backend dynamically splices query statements according to the configuration, and the query uses scrolling paging to improve query efficiency. Here, the filtering component is encapsulated and implemented in two ways by the backend. One is to dynamically splice query statements according to the configuration, and the other is memory calculation filtering. Two methods are considered when designing the storage of filtering conditions, one is form row storage, and the other is stored as a json string. The evaluation model is stored as a json string to avoid data conversion during front-end display and back-end calculation.

[0051] Scoring rules: Fig.11 As shown, the configuration scoring details support field scoring and indicator scoring, and also support multi-dimensional scoring. Field scoring is based on the configuration conditions and business data, and the score is calculated in memory. Indicator scoring requires querying indicator data from the marketing data platform based on composite indicators, and then calculating the score based on the configuration conditions. In business implementation, the field scoring calculation logic and indicator scoring logic are separated so that indicator data can be queried from the data platform in batches. At the same time, in order to improve the scoring efficiency, multi-threading is used for processing.

[0052] Trigger rules: Fig.12 As shown in the figure, the scheduling task time of the evaluation is configured and connected with the platform scheduling task, so that the evaluation scheduling task can be flexibly created.

[0053] Rating: Fig.13 As shown, it supports grading by score, quantity and percentage. Score grading is simple and efficient, and the level can be directly determined according to the score range. However, for quantity and percentage, grading is performed according to the sorting of scores after all scores are calculated. In order to improve efficiency, the calculated scores are stored in redis zset. When grading by quantity and percentage, the sorted data is directly taken out from the redis zset queue and graded according to the configuration. After the X-axis and Y-axis grading is completed, matrix comprehensive grading can be performed according to the configuration.

[0054] Evaluation results show: Fig.14 As shown, in the business assistant on the right, the scores and grades, the evaluation score trend chart and the addition and subtraction items are displayed. The trend chart, addition and subtraction items and the number of addition and subtraction items displayed can be dynamically configured, and the trend data display time range can also be configured. Historical data that exceeds the configured time range will be deleted in time through scheduled tasks.

[0055] In summary, the beneficial effects of the embodiments of the present application are: 1. Support customized evaluation models and automatic scoring.

[0056] 2. Support matrix evaluation to meet users' needs for multi-dimensional ratings.

[0057] 3. The scoring results are displayed graphically and are configurable to meet the different display needs of users.

[0058] In this application, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise expressly defined. Terms such as "installed", "connected", "connected", and "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0059] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or module referred to must have a specific direction, be constructed and operated in a specific direction, and therefore, should not be understood as a limitation on the present application.

[0060] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0061] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A business data evaluation method, characterized in that: include: Get business data; Establishing an evaluation model, and configuring the evaluation model according to evaluation rules to obtain a configuration result, wherein the evaluation rules include one or a combination of the following: filtering rules, scoring rules, triggering rules, grading rules, and scoring result configuration; Periodically evaluating the service data using the evaluation model according to the configuration result to obtain an evaluation result; Generate an evaluation result graph according to the evaluation result, and display the evaluation result and the evaluation result graph to the user.

2. The business data evaluation method according to claim 1, characterized in that: The step of periodically evaluating the service data through the evaluation model according to the configuration result to obtain the evaluation result includes: Set the scheduling task time of the evaluation model and determine the evaluation cycle; Filtering the business data through the evaluation model according to the evaluation period to obtain filtered business data; Scoring the filtered business data using the evaluation model according to the evaluation period to obtain a scoring result; The scoring results are scored and graded using the evaluation model according to the evaluation period to obtain a grading result, wherein the scoring and grading methods include one of the following or a combination thereof: score grading, quantity grading and percentage grading.

3. The business data evaluation method according to claim 2, characterized in that: The filtering of the business data by the evaluation model to obtain filtered business data includes: According to the configuration result, dynamically splice query statements to obtain filtered business data, wherein the query method of the statement includes a scrolling paging method; and / or According to the configuration result, memory calculation filtering is performed to obtain filtered business data.

4. The business data evaluation method according to claim 2, characterized in that: Scoring the filtered business data by using the evaluation model according to the evaluation period to obtain a scoring result includes: Perform memory calculation on the filtered business data according to the configuration result to obtain a scoring result; and / or According to the composite index, the index data is obtained from the marketing data center, and the filtered business data is calculated according to the configuration result to obtain the scoring result.

5. The business data evaluation method according to claim 2, characterized in that: The step of scoring and grading the scoring result by using the evaluation model according to the evaluation period to obtain the grading result includes: Classify the first business data according to the scoring result of the first business data to obtain a first classification result; grading the second business data according to the scoring result of the second business data to obtain a second grading result; Perform matrix comprehensive grading according to the configuration result, the first grading result, and the second grading result to obtain a comprehensive grading result.

6. The business data evaluation method according to any one of claims 1 to 5, characterized in that: The step of generating an evaluation result graph according to the evaluation result comprises: Dynamically configure the display content of the evaluation result graph to obtain a second configuration result, wherein the display content includes one or a combination of the following: an evaluation score trend graph, plus or minus score items, the number of plus or minus score items displayed, and a trend data display time range; The evaluation result graph is generated according to the evaluation result and the second configuration result.

7. The business data evaluation method according to claim 2, characterized in that: The scoring includes one of the following or a combination thereof: field scoring, indicator scoring and multi-dimensional scoring, and the scoring method includes a multi-threaded method.

8. A business data evaluation system, characterized in that: include: An acquisition module (110), used to acquire business data; A configuration module (120) is used to establish an evaluation model and configure the evaluation model according to evaluation rules to obtain a configuration result, wherein the evaluation rules include one of the following or a combination thereof: filtering rules, scoring rules, triggering rules, grading rules and scoring result configuration; An evaluation module (130), configured to periodically evaluate the business data using the evaluation model according to the configuration result to obtain an evaluation result; A display module (140) is used to generate an evaluation result graph according to the evaluation result, and to display the evaluation result and the evaluation result graph to a user.

9. A business data evaluation system, characterized in that: include: A memory (300) and a processor (400), wherein the memory (300) stores a program or instruction that can be run on the processor (400), and when the processor (400) executes the program or the instruction, the steps of the business data evaluation method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or the instruction is executed by a processor, the steps of the business data evaluation method according to any one of claims 1 to 7 are implemented.

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