Data processing and statistical method and system based on user-defined evaluation model

Abstract

The invention provides a data processing and statistical method and system based on a user-defined evaluation model, and relates to the technical field of enterprise management software, and the method comprises the following steps: setting a data attribute value weight of subject sample data in normalization processing and a data attribute value screening condition of a subject sample; performing normalization processing on the data of the main body sample; setting quantization configuration and main body configuration of the normalization result in quantization processing; a to-be-evaluated subject is associated with a subject sample; performing quantification processing on the normalization processing result of the to-be-evaluated subject, and performing ranking statistics on the quantification processing through a ranking function; setting evaluation configuration; and performing evaluation statistics on the quantification result and the ranking statistics of the to-be-evaluated subject to obtain the evaluation grade and the ranking of the subject. The method has good integration and intelligence, the user-defined normalized quantification result is used for user-defined evaluation, and the closed-loop requirement in enterprise management is met.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise management software. Specifically, it particularly relates to a data processing and statistical method and system based on a custom evaluation model. Background Art

[0002] In the era of enterprise digital transformation, all industries are developing digital technologies and support capabilities. Under this digital business model, enterprises redefine all aspects of organizational activities, processes, business models, and employee capabilities, and at the same time generate a large amount of data during operation. The data in each industry has different classifications, and each classification includes multiple types of data. Each type of data consists of a subject and multiple attributes. The subjects of the data generally include people, objects, organizations, etc., and each business evaluation system for the subject is usually not unique and is generally formulated according to the data classification or type of the subject. Managers not only need to make relevant evaluations based on a certain evaluation system of the subject, but also need to make a comprehensive evaluation of the subject by combining multiple evaluation systems.

[0003] Currently, most domestic and foreign enterprise management uses OKR and KPI assessment methods to define the assessment algorithms of relevant data according to the assessment systems of specific businesses. There are many existing patents that define relevant models according to specific business assessment methods, as well as patents that obtain relevant models and parameter configurations based on data distribution, eigenvalue, and regression algorithms in statistics. However, when faced with multiple types of business data with different classifications and multiple attributes for a certain subject, these methods seem to be relatively single and rigid. Under a certain business assessment model, managers are difficult to define the processing and statistical methods for multiple types of business data with different classifications and cannot obtain a comprehensive evaluation result of the subject that meets the custom requirements. Therefore, the existing technical methods are not convenient for managers to use in comprehensive evaluations.

[0004] In response to the problems in the related art, no effective solution has been proposed yet. Summary of the Invention

[0005] In view of this, the present invention provides a data processing and statistical method and system based on a custom evaluation model to solve the above-mentioned problems.

[0006] To solve the above problems, the specific technical solutions adopted by the present invention are as follows: According to one aspect of the present invention, there is provided a data processing and statistical method based on a custom evaluation model, the method comprising the following steps: S1. Construct a visual operation interface, and based on the visual operation interface, set the weight of the data attribute value in the normalization process and the data attribute value screening conditions for the pre-selected subject sample data; S2. Normalize the data of the subject sample based on the weight of the data attribute value and the screening condition of the data attribute value of the subject sample in the normalization process to obtain the normalization result; S3. Use the visual operation interface to set the quantization configuration and subject configuration of the normalization result in the quantization process; S4. According to the subject configuration, screen and determine the subject to be evaluated, and obtain the normalization result of the subject to be evaluated by associating the subject to be evaluated with the subject sample; S5. According to the quantization configuration of the normalization result in the quantization process, perform quantization processing on the normalization result of the subject to be evaluated to obtain the quantization result, and perform ranking statistics on the quantization processing through the ranking function; S6. Set the evaluation configuration according to the quantization result of the subject to be evaluated and the ranking statistics of the quantization processing; S7. According to the quantization level of the subject and the range of the subject ranking, perform evaluation statistics on the quantization result and ranking statistics of the subject to be evaluated to obtain the evaluation level and ranking of the subject.

[0007] Preferably, construct a visual operation interface, and set the weight of the data attribute value and the screening condition of the data attribute value of the subject sample in the normalization process based on the visual operation interface for the pre-selected subject sample data, including the following steps: S11. Use the report designer to construct a visual operation interface for customizing the normalization configuration; S12. Based on the constructed visual operation interface, set the weight of the data attribute value of the subject sample in the normalization process; S13. Based on the constructed visual operation interface, set the screening condition of the data attribute value of the subject sample in the normalization process.

[0008] Preferably, the value range of the weight of the data attribute value is [0, 1], and the sum of the weight values of each attribute of the subject sample data is equal to 1; The weight of the ranking attribute in the subject sample data is set by the 80 / 20 principle or the reverse proportion method.

[0009] Preferably, normalizing the data of the subject sample based on the weight of the data attribute value and the screening condition of the data attribute value of the subject sample in the normalization process to obtain the normalization result includes the following steps: S21. According to the screening condition of the data attribute value of the subject sample, use the report designer to screen the attribute values of the subject sample data; S22. Associate the screened subject sample data with the weight value matching the data attribute value of the subject sample; S23. Use a preset summation function to sum the product of the weight values of each data attribute value of a single subject sample to obtain a normalized value.

[0010] Preferably, use a visual operation interface to set the quantization configuration and subject configuration of the normalization result in the quantization process, including the following steps: S31. Use a visual operation interface with customizable normalization configuration to set the quantization level, dimension and dimension weight, dimension data and dimension data weight of the normalization result in the quantization process; S32. Set the data quantization level threshold according to the normalization result; S33. Use a report designer to establish the attribute screening range conditions of the subject.

[0011] Preferably, according to the subject configuration, screen and determine the subject to be evaluated, and obtain the normalization result of the subject to be evaluated by associating the subject to be evaluated with the subject sample, including the following steps: S41. According to the attribute screening range conditions of the subject, use a report designer to screen the attributes of the subject to obtain the subject to be evaluated; S42. Associate the subject to be evaluated with the subject sample, and determine and generate the normalization result of the subject to be evaluated according to the normalization result of the subject sample; S43. Use a visual operation interface to visually display the normalization result of the evaluation subject.

[0012] Preferably, according to the quantization configuration of the normalization result in the quantization process, perform quantization processing on the normalization result of the subject to be evaluated to obtain a quantization result, and perform ranking statistics on the quantization processing through a ranking function, including the following steps: S51. Associate the data normalization result of the subject to be evaluated with the set quantization level threshold, and generate a comprehensive quantization level of relevant data and dimension sets to obtain the quantization result of the subject to be evaluated; S52. Generate a quantization level ranking for the quantization result of the subject to be evaluated through a ranking function, and display the quantization level ranking through a visual picture.

[0013] Preferably, according to the quantization result of the subject to be evaluated and the ranking statistics of the quantization processing, set the evaluation configuration, including the following steps: S61. Use a report designer to query the data quantization result and ranking of the subject to be evaluated; S62. Set the range of the data, dimension, comprehensive quantization level and its ranking of the subject according to the data quantization result and ranking of the subject to be evaluated.

[0014] Preferably, according to the quantization level of the subject and the range of the subject ranking, the evaluation statistics of the quantization result and ranking statistics of the subject to be evaluated are carried out, and obtaining the evaluation level and ranking of the subject includes the following steps: S71. Screen the quantization results and ranking statistics of the subject to be evaluated for the quantization level and its ranking, and obtain the data, dimension, and comprehensive quantization level of the subject after screening; S72. Sum the quantization levels of the data, dimension, and comprehensive quantization level of the subject after screening according to the quantization level of the subject and the range of the subject ranking, and obtain the sum of the quantization levels; S73. Rank the sum of the quantization levels in descending order to obtain the evaluation level ranking.

[0015] According to another aspect of the present invention, a data processing and statistics system based on a custom evaluation model is provided, and the system includes: a normalization configuration module, a normalization processing module, a quantization configuration module, a subject association module, a quantization processing module, an evaluation configuration module, and a subject evaluation module; The normalization configuration module is used to construct a visual operation interface, and based on the visual operation interface, set the data attribute value weights and data attribute value screening conditions of the subject sample data in the normalization process for the pre-selected subject sample data; The normalization processing module is used to perform normalization processing on the data of the subject sample based on the set data attribute value weights and data attribute value screening conditions of the subject sample data in the normalization process, and obtain the normalization processing result; The quantization configuration module is used to use the visual operation interface to set the quantization configuration and subject configuration of the normalization result in the quantization process; The subject association module is used to screen and determine the subject to be evaluated according to the subject configuration, and obtain the normalization processing result of the subject to be evaluated by associating the subject to be evaluated with the subject sample; The quantization processing module is used to perform quantization processing on the normalization processing result of the subject to be evaluated according to the quantization configuration of the normalization result in the quantization process, obtain the quantization result, and perform ranking statistics on the quantization processing through a ranking function; The evaluation configuration module is used to set the evaluation configuration according to the quantization result of the subject to be evaluated and the ranking statistics of the quantization processing; The subject evaluation module is used to perform evaluation statistics on the quantization result and ranking statistics of the subject to be evaluated according to the quantization level of the subject and the range of the subject ranking, and obtain the evaluation level and ranking of the subject.

[0016] Preferably, a visual operation interface is constructed, and for the pre-selected main sample data based on the visual operation interface, the weight of the data attribute value in the normalization process and the screening conditions for the data attribute value of the main sample data are set, including: constructing a visual operation interface for customizing normalization configuration using a report designer; setting the weight of the data attribute value of the main sample data in the normalization process based on the constructed visual operation interface; setting the screening conditions for the data attribute value of the main sample data in the normalization process based on the constructed visual operation interface.

[0017] Preferably, based on the set weight of the data attribute value of the main sample data in the normalization process and the screening conditions for the data attribute value of the main sample data, the data of the main sample is normalized to obtain a normalization result, including: screening the attribute values of the main sample data using a report designer according to the screening conditions for the data attribute value of the main sample; associating the screened main sample data with the weight value matching the data attribute value of the main sample; using a pre-set summation function to sum the product of the weight values of each data attribute value of a single main sample to obtain a normalized value.

[0018] Preferably, using the visual operation interface for the normalization result, the quantization configuration and the main body configuration in the quantization process are set, including: setting the quantization level, dimension and dimension weight, dimension data and dimension data weight of the normalization result in the quantization process using a visual operation interface that can be customized for normalization configuration; setting the data quantization level threshold according to the normalization result; establishing the attribute screening range condition of the main body using a report designer.

[0019] Preferably, according to the main body configuration, the main body to be evaluated is screened and determined, and by associating the main body to be evaluated with the main sample, the normalization result of the main body to be evaluated is obtained, including: screening the attributes of the main body using a report designer according to the attribute screening range condition of the main body to obtain the main body to be evaluated; associating the main body to be evaluated with the main sample, and determining and generating the normalization result of the main body to be evaluated according to the normalization result of the main sample; visually displaying the normalization result of the evaluated main body using the visual operation interface.

[0020] Preferably, according to the quantization configuration of the normalization result in the quantization process, the normalization result of the main body to be evaluated is quantized to obtain a quantization result, and the quantization process is ranked and statistically analyzed using a ranking function, including: associating the data normalization result of the main body to be evaluated with the set quantization level threshold, and generating a comprehensive quantization level of relevant data and dimension sets to obtain the quantization result of the main body to be evaluated; generating a quantization level ranking for the quantization result of the main body to be evaluated using a ranking function, and displaying the quantization level ranking through a visual picture.

[0021] Preferably, according to the quantification result of the subject to be evaluated and the ranking statistics of the quantification process, the evaluation configuration is set as follows: using a report designer, query the data quantification result and ranking of the subject to be evaluated; according to the data quantification result and ranking of the subject to be evaluated, set the range of the subject's data, dimensions, comprehensive quantification level and its ranking.

[0022] Preferably, according to the quantification level of the subject and the range of the subject's ranking, evaluate and statistically analyze the quantification result and ranking statistics of the subject to be evaluated, and obtain the evaluation level and ranking of the subject, including: screening the quantification result and ranking statistics of the subject to be evaluated for the quantification level and its ranking, and obtaining the data, dimensions, and comprehensive quantification level of the subject after screening; summing up the quantification levels according to the quantification level of the subject and the range of the subject's ranking for the data, dimensions, and comprehensive quantification level of the subject after screening, and obtaining the sum of the quantification levels; ranking the sum of the quantification levels in descending order to obtain the evaluation level ranking.

[0023] The beneficial effects of the present invention are as follows: 1. When the present invention faces different classifications of a certain subject and multiple business data containing multiple attributes, the manager first customizes the configuration of the evaluation model according to relevant business systems, then screens, normalizes, quantifies and statistically ranks the data of the subject sample, and then customizes the statistical method of evaluation according to the defined dimensions in the evaluation model and the quantification results of the data they contain. Finally, the evaluation level and ranking of the subject are obtained, which is convenient for the manager to obtain the comprehensive evaluation result of the subject that meets the customized requirements.

[0024] 2. The present invention has good integration and intelligence, and uses the customized normalized quantification result for customized evaluation, meeting the closed-loop requirements in enterprise management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 is one of the flowcharts of a data processing and statistical method based on a customized evaluation model according to an embodiment of the present invention; Figure 2 is the second flowchart of a data processing and statistical method based on a customized evaluation model according to an embodiment of the present invention; Figure 3 is a design screenshot of the normalization weight configuration in a data processing and statistical method based on a customized evaluation model according to an embodiment of the present invention; Figure 4It is a screenshot of the use of normalized weight configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 5 It is a design screenshot of normalized screening configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 6 It is a screenshot of the use of normalized screening configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 7 It is a design screenshot of normalization processing in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 8 It is a screenshot of the use of normalization processing in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 9 It is a design screenshot of quantization configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 10 It is a screenshot of the use of quantization configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 11 It is a design screenshot of subject configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 12 It is a screenshot of the use of subject configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 13 It is a design screenshot of subject association in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 14 It is a screenshot of the use of subject association in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 15 It is a design screenshot of quantization processing in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 16 It is a screenshot of the use of quantization processing in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 17 It is a design screenshot of quantization result statistics in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 18It is a screenshot of the use of the quantification result statistics in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 19 It is a design screenshot of the evaluation configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 20 It is a screenshot of the use of the evaluation configuration in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 21 It is a design screenshot of the price statistics in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 22 It is a screenshot of the use of the evaluation statistics in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 23 It is a schematic diagram of data normalization in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 24 It is a schematic diagram of the association of the subject to be evaluated in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 25 It is a schematic diagram of the subject quantification in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 26 It is a schematic diagram of the quantification of the subject to be evaluated in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 27 It is a schematic diagram of the evaluation of the subject to be evaluated in a data processing and statistics method based on a custom evaluation model according to an embodiment of the present invention; Figure 28 It is a principle block diagram of a data processing and statistics system based on a custom evaluation model according to an embodiment of the present invention; Figure 29 It is a schematic diagram of the custom configuration in a data processing and statistics system based on a custom evaluation model according to an embodiment of the present invention.

[0026] In the figure: 1. Normalization configuration module; 2. Normalization processing module; 3. Quantification configuration module; 4. Subject association module; 5. Quantification processing module; 6. Evaluation configuration module; 7. Subject evaluation module. Detailed implementation manners

[0027] To enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0028] According to an embodiment of the present invention, a data processing and statistical method and system based on a custom evaluation model are provided.

[0029] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. As Figure 1-2 shown, according to an embodiment of the present invention, a data processing and statistical method based on a custom evaluation model is provided. The method includes the following steps: S1. Construct a visual operation interface, and based on the visual operation interface, set the data attribute value weights and the data attribute value screening conditions of the pre-selected main sample data in the normalization process. As a preferred embodiment, constructing a visual operation interface and setting the data attribute value weights and the data attribute value screening conditions of the pre-selected main sample data in the normalization process based on the visual operation interface include the following steps: S11. Use a report designer to construct a visual operation interface for custom normalization configuration. It should be noted that custom normalization configuration means customizing the data attribute value weights of the data of the main sample in the normalization process and the screening of the data attribute values of the main sample. The data attribute value weight ∈ [0, 1], and the sum of the attribute value weights of each attribute of the data is equal to 1.

[0030] S12. Based on the constructed visual operation interface, set the data attribute value weights of the main sample data in the normalization process. S13. Based on the constructed visual operation interface, set the data attribute value screening conditions of the main sample data in the normalization process.

[0031] For example, the data attributes of the main sample include a, b, c, etc.; the weights of attribute value 1 and attribute value 2 of attribute a can be defined as 0.7 and 0.3 respectively, and the weights of attribute value 3 and attribute value 4 of attribute b can be defined as 0.9 and 0.1 respectively for data normalization; the range of attribute c values can be defined for the screening of the required data.

[0032] Specifically, a visual operation interface for implementing the custom normalization configuration function is realized using a report designer. Customize the data attribute value weights of the main body sample in the normalization process, as well as the data attribute value filtering of the main body sample. The data attribute value weight ∈ [0, 1], and the sum of the attribute value weights of each attribute of the data is equal to 1. Since the range of the ranking attribute values of the data is relatively large and it is difficult to define the weights of all its attribute values, two formulas, namely the "80 / 20 principle" and the "reverse order proportion", are defined to calculate the weights of the ranking type attribute values. The 80 / 20 principle means that the weights of the main body rankings in the top 20% (rounded up) of the overall rankings are all 0.8, and the weights of the other rankings are all 0.2. The reverse order proportion means that the weight = the reverse order ranking of the main body / Σ(rankings).

[0033] For example, when using the 80 / 20 principle formula, if the ranking attribute value is 1(4), that is, ranked 1st among 4 main bodies, then the rankings in the top 20% (rounded up) of the 4 main bodies are 0.8 and take 1. Therefore, the weights of the main body rankings ≤ 1 are all 0.8, and the weights of the main body rankings > 1 are all 0.2. When using the reverse order proportion formula, if the ranking attribute value is 1(4), that is, ranked 1st among 4 main bodies, then the reverse order ranking is 4(4), and Σ rankings = 1 + 2 + 3 + 4 = 10. Therefore, the weights of the main body rankings from 1 to 4 are 0.4, 0.3, 0.2, and 0.1 respectively.

[0034] As Figure 3 shown, if the main body sample includes the patent inventors of a certain scientific research unit, then the data of this main body sample is patent data. Use the report designer to develop the custom functions of the inventor rankings, first right holders, types, and status value weights of the patent data.

[0035] As Figure 4 shown, when customizing the attribute value weights of the inventor rankings on the visual page, the reverse order proportion formula is used for calculation. The weights of the first right holders being yes and no are 0.9 and 0.1 respectively, the weights of the types being invention, utility model, and design are 0.7, 0.2, and 0.1 respectively, and the weights of the status being accepted, authorized, and others are 0.9, 0.1, and 0 respectively, for data normalization processing.

[0036] As Figure 5 shown, if the main body sample includes the patent inventors of a certain scientific research unit, then the data of this main body sample is patent data. Use the report designer to develop the custom functions of the status date and professional value of the patent data. As Figure 6 shown, on the visual page, customize that the status date belongs to 2022 and the major belongs to electrical automation for screening the required patent data.

[0037] S2. Based on the set data attribute value weights of the main body sample in the normalization process and the data attribute value screening conditions of the main body sample, perform normalization processing on the data of the main body sample to obtain the normalization processing result; As a preferred embodiment, based on the weight of the data attribute value and the screening condition of the data attribute value of the main sample in the normalization process for the set main sample data, the data of the main sample is normalized, and the normalization result includes the following steps: S21. According to the screening condition of the data attribute value of the main sample, use the report designer to screen the attribute values of the main sample data; S22. Associate the weight value matching the data attribute value of the screened main sample data; S23. Use a preset summation function to sum the product of the weight values of each data attribute value of a single main sample to obtain a normalized value.

[0038] Specifically, as Figure 23 shown, use the report designer to implement a visual operation interface for the normalization function of the data of the main sample. According to the definition of step S1, the data of the main sample is screened by attribute value and then normalized, that is, the weight multiplication of the relevant attribute values of the data of the main sample. Regardless of the type and range of the data value, the normalization result of a single data ∈ [0,1], so as to realize the comparability after normalization of different types of data. Therefore, the normalized value of a certain data of a certain subject is equal to the product of the weight of the relevant attribute value defined for the data, and the normalized value of the data of a certain subject is equal to the sum of the products of the weights of the attribute values defined for each data of the subject.

[0039] For example, based on the example in step S1 above, subject 1 has two data. For the first data, the a attribute value = attribute value 1, and the b attribute value = attribute value 3. Then the normalized value of the first data of subject 1 = weight of a attribute value 1 * weight of b attribute value 3 = 0.7 * 0.9 = 0.63; the a and b attribute values of the second data of subject 1 are the same as those of the first data. Then the normalized value of the data of subject 1 = 0.63 + 0.63 = 1.26.

[0040] As Figure 7 shown, in the report designer, use SQL instructions. First, screen the patent data of the main sample for the "status date, specialty" attribute values through the where statement, and then use the left join statement to associate the weight values or weight calculation formulas matching the "inventor ranking, first right holder, type, status" attribute values. Finally, use the sum() function (summation function) to sum the product of the weight values of each patent attribute value of a single subject to obtain a normalized value, and realize the normalization function of the patent data of the main sample.

[0041] As Figure 8As shown, Shuming-li (the person numbered 1) is the main body, and the relevant attribute values, weights, normalized values, and their summation results of each patent data are presented on the visualization page. The custom data value unit is 1000, which means multiplying the normalized result by 1000, so that the number of decimal places of the normalized value is less, facilitating quantification processing and not affecting the ranking of the data results of the sample main body.

[0042] S3. Use the visualization operation interface to set the quantization configuration and main body configuration of the normalized result in the quantification process; As a preferred implementation, using the visualization operation interface to set the quantization configuration and main body configuration of the normalized result in the quantification process includes the following steps: S31. Use the visualization operation interface with customizable normalization configuration to set the quantization level, dimension and dimension weight, dimension data and dimension data weight of the normalized result in the quantification process; S32. Set the data quantization level threshold according to the normalized result; S33. Use the report designer to establish the attribute screening range conditions of the main body.

[0043] Specifically, as Figure 24 shown, use the report designer to implement the visualization operation interface for custom quantization and main body configuration functions. Set the quantization configuration of the normalized result in the quantification process, that is, customize the quantization level, dimension and its weight, dimension data and its weight of the above normalized result in the quantification process, and define the data quantization level threshold according to the data normalization result. The quantization level is a positive integer, set according to the evaluation requirements, and the larger the level, the smaller the level gap between adjacent levels; the quantization level threshold ∈ the data normalization result, and is set according to the distribution law of the data normalization result to ensure that the quantization processing result, that is, the quantization level, is evenly distributed; the data quantization processing result is an integer level less than the quantization level, each weight ∈ [0,1], the sum of the dimension weights is equal to 1, and the sum of the weights of each dimension data is equal to 1.

[0044] For example, it can be defined that the quantization level is equal to 5, and the data quantization processing result is an integer level ∈ [0,5]; it can be defined that the dimensions are X and Y, and their dimension weights are 0.4 and 0.6 respectively; it can be defined that the X dimension includes data a and b, and their data weights are 0.2 and 0.8 respectively; it can be defined that the Y dimension includes data c and d, and their data weights are 0.7 and 0.3 respectively; the normalized result of data a ∈ [0,50], and the thresholds of data a quantization levels 1 to 5 can be defined as 0, 10, 20, 35, and 45 respectively.

[0045] Set the main body configuration of the normalized result in the quantification process, that is, customize the screening of the attribute values of the evaluation main body. For example, the range of attribute value a and the range of attribute value b of the main body can be defined, thereby determining the screening of the main body to be evaluated. AsFigure 9 As shown in the figure, use JavaScript scripts in the report designer to implement the call of the data normalization results of the main body sample, and develop custom functions for quantization levels, dimensions and their weights, dimension data and their weights, and data quantization level thresholds.

[0046] As Figure 10 shown in the figure, if the custom quantization level on the visualization page is equal to 5, then the level of the quantization processing result ∈ [0, 5]. When the data of the main body sample includes data such as patents, software copyrights, standards, and papers, the dimension can be defined as intellectual property, etc. Define the weight of the intellectual property dimension as 0.2, and the sum of the weights of other dimensions as 0.8; define that the data of the intellectual property dimension includes patents, software copyrights, standards, and papers, and their data weights are 0.7, 0.1, 0.1, and 0.1 respectively. The definition of other dimension data and weights will not be elaborated here. According to the normalization results of patent data, define the thresholds for patent data quantization levels 1 to 5 as 0, 110, 220, 330, and 440 respectively. As Figure 11 shown in the figure, when the main body is a R & D personnel, use the report designer to develop a custom function for the screening range of the attribute values of the main body's gender, department, major, age, and working years. As Figure 12 shown in the figure, customize the full selection of all attribute values on the visualization page, that is, all main bodies to be evaluated.

[0047] S4. According to the main body configuration, screen and determine the main bodies to be evaluated, and obtain the normalization processing results of the main bodies to be evaluated by associating the main bodies to be evaluated with the main body sample; As a preferred implementation method, according to the main body configuration, screen and determine the main bodies to be evaluated, and the steps for obtaining the normalization processing results of the main bodies to be evaluated by associating the main bodies to be evaluated with the main body sample include the following: S41. According to the attribute screening range conditions of the main body, use the report designer to screen the attributes of the main body to obtain the main bodies to be evaluated; S42. Associate the main bodies to be evaluated with the main body sample, and determine and generate the normalization results of the main bodies to be evaluated according to the normalization results of the main body sample; S43. Use the visualization operation interface to visually display the normalization results of the evaluation main bodies.

[0048] Specifically, use the report designer to implement the visualization operation interface of the main body association function. According to the definition in step S3 and the definition of the main body in the evaluation, first screen the attribute values of the main body to determine the main bodies to be evaluated, and then associate them with the main body sample to obtain the data normalization results of the main bodies to be evaluated.

[0049] For example, evaluate the domestic employment situation of female graduates majoring in engineering at a certain school. After gender screening, the student numbers of female graduates majoring in engineering at this school are from 1 to 20, and the student numbers of students majoring in engineering at this school who are employed domestically are from 2 to 99. Then the associated subject sample result of the subject to be evaluated is the normalized value of the domestic employment situation data of female students majoring in engineering at this school with student numbers from 2 to 20.

[0050] As Figure 13 shown, in the report designer, use SQL instructions. First, determine the subject to be evaluated after screening the subject through the where statement of the custom attribute value, then use the left join statement to associate its associated subject sample, and finally generate the normalized result of the subject to be evaluated. As Figure 14 shown, display the normalized result of the subject to be evaluated on the visualization page.

[0051] S5. According to the quantization configuration in the quantization process of the normalized result, perform quantization processing on the normalized processing result of the subject to be evaluated to obtain a quantization result, and perform ranking statistics on the quantization processing through a ranking function; As a preferred implementation method, according to the quantization configuration in the quantization process of the normalized result, performing quantization processing on the normalized processing result of the subject to be evaluated to obtain a quantization result, and performing ranking statistics on the quantization processing through a ranking function includes the following steps: S51. Associate the normalized result of the subject to be evaluated with the set quantization level threshold, and generate a comprehensive quantization level of relevant data and dimension sets to obtain the quantization result of the subject to be evaluated; S52. Generate a quantization level ranking for the quantization result of the subject to be evaluated through a ranking function, and display the quantization level ranking through a visualization picture.

[0052] Specifically, as Figure 25 shown, use the report designer to implement the visualization operation interface for data quantization processing and its result ranking statistics function. According to the definition in step S3, in the data quantization result of the subject to be evaluated, the quantization level of a certain data of a certain subject is equal to the quantization level threshold that the data normalized value matches, that is, the maximum quantization level among the quantization level thresholds that the data normalized value is greater than, and the quantization level is 0 if the normalized value is 0; the quantization level of a certain dimension of this subject is equal to the sum of the products of the quantization levels of each data in this dimension and their data weights; the comprehensive quantization level of this subject is equal to the sum of the products of the quantization levels of each dimension and their dimension weights. The data, dimension, and comprehensive quantization result rankings of the subject to be evaluated are generated in descending order according to their corresponding quantization levels, that is, the subject with the highest quantization level ranks first.

[0053] For example, based on the examples in the above steps S3 and S4, the quantization level thresholds of data a, b, c, and d are the same. The normalized values of data a, b, c, and d of subject 1 are 0, 10, 21, and 46 respectively. Then the data quantization levels are 0, 1, 3, and 5 respectively, and the rankings are 20, 15, 10, and 1 respectively. The quantization level of the X dimension = 0 * 0.2 + 1 * 0.8 = 0.8, and the ranking is 15; the quantization level of the Y dimension = 3 * 0.7 + 5 * 0.3 = 3.6, and the ranking is 5; the comprehensive quantization level = 0.4 * 0.8 + 0.6 * 3.6 = 2.48, and the ranking is 10.

[0054] As Figure 15 shown, in the report designer, use SQL instructions to associate the normalized result of the data of the subject to be evaluated with the custom quantization level threshold using the left join statement, and use the case when statement to generate the relevant data, dimension, and comprehensive quantization level, so as to implement the data quantization processing function of the subject to be evaluated. As Figure 16 shown, shuming-li (the person with serial number 12) is used as the subject, and the quantization processing results of each type of data, each dimension, and the comprehensive quantization of the subject are presented on the visualization page. As Figure 17 shown, in the report designer, use SQL instructions to generate the relevant quantization level rankings for the data quantization results of the subject to be evaluated using the rank() function (ranking function), and use radar charts, bar charts, and strip charts to implement the quantization result ranking statistics function of the subject to be evaluated. As Figure 18 shown, shuming-li (the second person from the left in the bar chart) is used as the subject, and the quantization processing results of each type of data, each dimension, and the comprehensive quantization of the subject are presented on the visualization page.

[0055] S6. Set the evaluation configuration according to the quantization results of the subject to be evaluated and the ranking statistics of the quantization processing; As a preferred implementation method, setting the evaluation configuration according to the quantization results of the subject to be evaluated and the ranking statistics of the quantization processing includes the following steps: S61. Use the report designer to query the data quantization results and rankings of the subject to be evaluated; S62. According to the data quantization results and rankings of the subject to be evaluated, set the ranges of the subject's data, dimension, comprehensive quantization level, and their rankings.

[0056] Specifically, use the report designer to implement the visual operation interface of the custom evaluation configuration function. According to the data quantization results and rankings of the subject to be evaluated, customize the ranges of the subject's data, dimension, comprehensive quantization level, and their rankings. The rankings include the ranking and the percentage of the top rankings, which are used to screen the statistical results of the subjects that meet the evaluation.

[0057] For example, the quantization level ranges of c and d data can be defined as [3, 5] and [4, 5] respectively, and the quantization level ranking range of the X dimension is [1, 15]; the range of the top percentage of the comprehensive quantization level ranking can be defined as ≤ 50%.

[0058] As Figure 19 shown, use SQL instructions in the report designer to query the data quantization results and rankings of the subjects to be evaluated, for customizing the ranges of data, dimensions, comprehensive quantization levels and their rankings of the subjects. The rankings include the ranking and the top percentage, to achieve the function of screening the statistical results of the subjects that meet the evaluation. As Figure 20 shown, customize the ranges of data, dimensions, comprehensive quantization levels and their rankings on the visualization page. The rankings include the ranking and the top percentage.

[0059] S7. According to the quantization level of the subject and the range of the subject ranking, conduct an evaluation and statistics on the quantization results and ranking statistics of the subject to be evaluated, to obtain the evaluation level and ranking of the subject.

[0060] As a preferred implementation, according to the quantization level of the subject and the range of the subject ranking, conduct an evaluation and statistics on the quantization results and ranking statistics of the subject to be evaluated, to obtain the evaluation level and ranking of the subject, including the following steps: S71. Screen the quantization results and ranking statistics of the subject to be evaluated for the quantization level and its ranking, to obtain the data, dimensions, and comprehensive quantization levels of the subject after screening; S72. Sum up the quantization levels of the data, dimensions, and comprehensive quantization levels of the subject after screening according to the quantization level of the subject and the range of the subject ranking, to obtain the sum of the quantization levels; S73. Rank the sum of the quantization levels in descending order to obtain the evaluation level ranking.

[0061] Specifically, as Figure 26-27 shown, use the report designer to implement the visualization operation interface of the evaluation and statistics function. According to the definition in step S6, screen the data quantization results and their rankings of the subject to be evaluated for the relevant quantization level and its ranking, then sum up the data, dimensions, and comprehensive quantization levels of the subject after screening according to the relevant quantization level defined in step 6, and rank the sum of the relevant quantization levels in descending order, that is, the subject with the largest sum of the relevant quantization levels ranks first.

[0062] For example, based on the examples in steps S5 and S6 above, the data, dimensions, comprehensive quantization levels and rankings of subject 1 meet the definition in step S6; The sum of the relevant quantization levels of Subject 1 = c data quantization level + d data quantization level + X - dimension quantization level + comprehensive quantization level = 3 + 5 + 0.8 + 2.48 = 11.28. If there are still Subjects 8 and 15 in the screening results of the relevant quantization levels and their rankings of the subject to be evaluated, and the sums of their relevant quantization levels are 10.1 and 8.87 respectively, then the evaluation statistical results are: the evaluation level of Subject 1 is 11.28 (first place), the evaluation level of Subject 8 is 10.1 (second place), and the evaluation level of Subject 15 is 8.87 (third place).

[0063] As Figure 21 shown, in the report designer, using SQL instructions, first filter the quantization results and rankings of the subject to be evaluated through the where statement of the custom evaluation configuration to determine the subjects that meet the custom evaluation; then use the sum() function to generate the sum of the relevant quantization levels, that is, the evaluation level; finally use the rank() function to generate the ranking of the evaluation level. As Figure 22 shown, display the evaluation levels and rankings of the subjects that meet the custom evaluation on the visualization page.

[0064] As Figure 28 shown, according to another embodiment of the present invention, a data processing and statistical system based on a custom evaluation model is provided. The system includes: a normalization configuration module 1, a normalization processing module 2, a quantization configuration module 3, a subject association module 4, a quantization processing module 5, an evaluation configuration module 6, and a subject evaluation module 7; The normalization configuration module 1 is used to construct a visualization operation interface and set the data attribute value weights and data attribute value screening conditions of the subject sample data in the normalization processing based on the visualization operation interface for the pre - selected subject sample data; The normalization processing module 2 is used to perform normalization processing on the data of the subject sample based on the set data attribute value weights and data attribute value screening conditions of the subject sample data in the normalization processing to obtain a normalization processing result; The quantization configuration module 3 is used to set the quantization configuration and subject configuration in the quantization processing for the normalization result using the visualization operation interface; The subject association module 4 is used to screen and determine the subject to be evaluated according to the subject configuration, and obtain the normalization processing result of the subject to be evaluated by associating the subject to be evaluated with the subject sample; The quantization processing module 5 is used to perform quantization processing on the normalization processing result of the subject to be evaluated according to the quantization configuration in the quantization processing of the normalization result to obtain a quantization result, and perform ranking statistics on the quantization processing through a ranking function; The evaluation configuration module 6 is used to set the evaluation configuration according to the quantization result of the subject to be evaluated and the ranking statistics of the quantization processing; The main body evaluation module 7 is used to evaluate and statistically analyze the quantification result and ranking statistics of the main body to be evaluated according to the quantification level of the main body and the range of the main body ranking, so as to obtain the evaluation level and ranking of the main body.

[0065] Specifically, as Figure 29 shown, the custom configuration in the present invention includes: custom normalization, quantization, main body, and evaluation module configurations. The custom normalization configuration is to customize the data attribute value weights and the data attribute value screening of the main body sample data in the normalization process. The data attribute value weight ∈ [0, 1], and the sum of the attribute value weights of each attribute of the data is equal to 1. Since the range of the ranking attribute values of the data is relatively large and it is difficult to define all the attribute value weights, two formulas, namely the "80 / 20 principle" and the "reverse proportion", are defined to calculate the ranking type attribute value weights. The 80 / 20 principle means that the weights of the main body rankings that are rounded up to the top 20% of the overall rankings are all 0.8, and the weights of other rankings are all 0.2. The reverse proportion means that the weight = the reverse ranking of the main body / Σ (the rankings of the main body).

[0066] The custom quantization configuration is to customize the quantization levels, dimensions and their weights, dimension data and their weights in the quantization process of the above normalization results, and define the data quantization level threshold according to the data normalization results; the quantization level is a positive integer, the data quantization processing result is an integer level less than the quantization level, each weight ∈ [0, 1], the sum of the dimension weights is equal to 1, and the sum of the dimension data weights of each dimension is equal to 1. The custom main body configuration is to customize the attribute value screening of the evaluation main body. The custom evaluation configuration is to customize the data, dimensions, comprehensive quantization level and the range of their rankings of the main body. The ranking includes the ranking and the percentage before the ranking, and is used to screen the main body statistical results that meet the evaluation.

[0067] As a preferred implementation manner, a visual operation interface is constructed, and based on the visual operation interface, the data attribute value weights and the data attribute value screening conditions of the main body sample data in the normalization process are set for the pre-selected main body sample data, including: constructing a visual operation interface for custom normalization configuration by using a report designer; setting the data attribute value weights of the main body sample data in the normalization process based on the constructed visual operation interface; setting the data attribute value screening conditions of the main body sample data in the normalization process based on the constructed visual operation interface.

[0068] As a preferred embodiment, based on the weight of the data attribute value in the normalization process and the data attribute value screening conditions of the main sample data set, the data of the main sample is normalized, and the normalization result includes: screening the attribute values of the main sample data using a report designer according to the data attribute value screening conditions of the main sample; associating the screened main sample data with the weight values matching the data attribute values of the main sample; using a preset summation function to sum the product of the weight values of each data attribute value of a single main sample to obtain a normalization value.

[0069] As a preferred embodiment, a visualization operation interface is used to set the quantization configuration and main body configuration of the normalization result in the quantization process, including: using a visualization operation interface with customizable normalization configuration to set the quantization level, dimension and dimension weight, dimension data and dimension data weight of the normalization result in the quantization process; setting the data quantization level threshold according to the normalization result; using a report designer to establish the attribute screening range condition of the main body.

[0070] As a preferred embodiment, according to the main body configuration, the main body to be evaluated is screened and determined, and by associating the main body to be evaluated with the main sample, the normalization result of the main body to be evaluated is obtained, including: screening the attributes of the main body using a report designer according to the attribute screening range condition of the main body to obtain the main body to be evaluated; associating the main body to be evaluated with the main sample, and determining and generating the normalization result of the main body to be evaluated according to the normalization result of the main sample; using a visualization operation interface to visually display the normalization result of the evaluation main body.

[0071] As a preferred embodiment, according to the quantization configuration of the normalization result in the quantization process, the normalization result of the main body to be evaluated is quantized to obtain a quantization result, and ranking statistics of the quantization process are performed through a ranking function, including: associating the data normalization result of the main body to be evaluated with the set quantization level threshold, and generating a comprehensive quantization level of relevant data and dimension sets to obtain the quantization result of the main body to be evaluated; generating a quantization level ranking for the quantization result of the main body to be evaluated through a ranking function, and visually displaying the quantization level ranking through a visualization picture.

[0072] As a preferred embodiment, according to the quantization result of the main body to be evaluated and the ranking statistics of the quantization process, an evaluation configuration is set, including: using a report designer to query the data quantization result and ranking of the main body to be evaluated; setting the range of the data, dimension, comprehensive quantization level and its ranking of the main body according to the data quantization result and ranking of the main body to be evaluated.

[0073] As a preferred embodiment, according to the quantization level of the subject and the range of the subject ranking, the evaluation statistics are carried out on the quantization result and ranking statistics of the subject to be evaluated, and the obtained evaluation level and ranking of the subject include: screening the quantization result and ranking statistics of the subject to be evaluated by quantization level and its ranking to obtain the data, dimension, and comprehensive quantization level of the subject after screening; summing up the quantization levels according to the quantization level of the subject and the range of the subject ranking for the data, dimension, and comprehensive quantization level of the subject after screening to obtain the sum of quantization levels; ranking the sum of quantization levels in descending order to obtain the evaluation level ranking.

[0074] In summary, by means of the above technical solution of the present invention, when the present invention faces different classifications of a certain subject and multiple service data including multiple attributes, the manager first customizes the configuration of the evaluation model according to relevant business systems, then screens, normalizes, quantizes, and ranks the statistics of the data of the subject sample, and then customizes the statistical method of evaluation according to the quantization results of the defined dimensions and the data included in the evaluation model, and finally obtains the evaluation level and ranking of the subject, which is convenient for the manager to obtain the comprehensive evaluation result of the subject that meets the customized requirements. The present invention has good integration and intelligence, and uses the customized normalization quantization result for customized evaluation, meeting the closed-loop requirements in enterprise management.

[0075] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0076] The above specific embodiments further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processing and statistical method based on a custom evaluation model, characterized in that: The method comprises the following steps: S1. Construct a visual operation interface, and set the data attribute value weights of the subject sample data in the normalization process and the data attribute value screening conditions of the subject sample based on the visual operation interface for the pre-selected subject sample data; S2. Based on the set data attribute value weights of the subject sample data in the normalization process and the data attribute value screening conditions of the subject sample, the data of the subject sample is normalized to obtain a normalization result; S3, using a visual operation interface to set the quantization configuration and main configuration of the normalized result in the quantization processing for the normalized result; S4. Screen and determine the subject to be evaluated according to the subject configuration, and obtain a normalized processing result of the subject to be evaluated by associating the subject to be evaluated with the subject sample; S5. According to the quantitative configuration of the normalized result in the quantitative processing, the normalized processing result of the subject to be evaluated is quantified to obtain a quantitative result, and the quantitative processing is ranked and counted by a ranking function; S6. Setting evaluation configuration according to the quantitative results of the subject to be evaluated and the ranking statistics of the quantitative processing; S7. According to the quantitative grade of the subject and the range of the subject's ranking, the quantitative results and ranking statistics of the subject to be evaluated are evaluated and statistically analyzed to obtain the evaluation grade and ranking of the subject.

2. A data processing and statistical method based on a custom evaluation model according to claim 1, characterized in that: The construction of the visual operation interface and setting the data attribute value weights of the subject sample data in the normalization process and the data attribute value screening conditions of the subject sample based on the pre-selected subject sample data in the visual operation interface include the following steps: S11. Use the report designer to build a visual operation interface for customizing the normalized configuration; S12. Based on the constructed visual operation interface, the data attribute value weights of the subject sample data in the normalization process are set; S13. Based on the constructed visual operation interface, set the data attribute value screening conditions in the normalization processing of the main sample data.

3. The data processing and statistical method based on a custom evaluation model according to claim 2, characterized in that: The value range of the data attribute value weight is [0,1], and the sum of the attribute value weights of each attribute of the main sample data is equal to 1; The weights of the ranking attributes in the subject sample data are set using the 80 / 20 principle or in reverse order.

4. The data processing and statistical method based on a custom evaluation model according to claim 1, characterized in that: The method of performing normalization processing on the data of the subject sample based on the set data attribute value weights of the subject sample data in the normalization processing and the data attribute value screening conditions of the subject sample to obtain the normalization processing result includes the following steps: S21, according to the data attribute value screening condition of the main sample, use the report designer to screen the attribute value of the main sample data; S22, associating the filtered subject sample data with the weight value matching the subject sample data attribute value; S23. Using a preset summation function, sum the products of the weight values ​​of each attribute value of each data of a single subject sample to obtain a normalized value.

5. The data processing and statistical method based on a custom evaluation model according to claim 1, characterized in that: The method of using the visual operation interface to normalize the processing result and setting the quantization configuration and main configuration of the normalization result in the quantization processing includes the following steps: S31, using a visual operation interface that can customize normalization configuration, setting the quantization level, dimension and dimension weight, dimension data and dimension data weight of the normalization result in the quantization process; S32, setting a data quantization level threshold according to the normalization processing result; S33. Use the report designer to establish the attribute filter range conditions of the subject.

6. A data processing and statistical method based on a custom evaluation model according to claim 5, characterized in that: The method of screening and determining the subject to be evaluated according to the subject configuration, and obtaining the normalized processing result of the subject to be evaluated by associating the subject to be evaluated with the subject sample includes the following steps: S41. According to the attribute screening range condition of the subject, the attribute of the subject is screened by using the report designer to obtain the subject to be evaluated; S42, associating the subject to be evaluated with the subject sample, and determining and generating the normalized result of the subject to be evaluated according to the normalized result of the subject sample; S43. Use a visual operation interface to visualize the normalized results of the evaluation subject.

7. The data processing and statistical method based on a custom evaluation model according to claim 1, characterized in that: According to the quantitative configuration of the normalized result in the quantitative processing, the normalized processing result of the subject to be evaluated is quantized to obtain the quantitative result, and the quantitative processing is ranked by the ranking function, which includes the following steps: S51, associating the data normalization result of the subject to be evaluated with the set quantization level threshold, and generating a comprehensive quantization level of the relevant data and dimension set to obtain a quantization result of the subject to be evaluated; S52. The quantitative results of the subject to be evaluated are used to generate a quantitative grade ranking through a ranking function, and the quantitative grade ranking is displayed through a visual image.

8. The data processing and statistical method based on a custom evaluation model according to claim 1, characterized in that: The setting of the evaluation configuration according to the quantitative results of the subject to be evaluated and the ranking statistics of the quantitative processing includes the following steps: S61. Use the report designer to query the data quantification results and rankings of the subject to be evaluated; S62. According to the data quantification results and ranking of the subject to be evaluated, set the scope of the subject's data, dimensions, comprehensive quantitative level and ranking.

9. The data processing and statistical method based on a custom evaluation model according to claim 1, characterized in that: According to the quantitative grade of the subject and the range of the subject ranking, the quantitative results and ranking statistics of the subject to be evaluated are evaluated and statistically analyzed to obtain the evaluation grade and ranking of the subject, which includes the following steps: S71. Filter the quantitative results and ranking statistics of the subject to be evaluated by quantitative level and ranking, and obtain the data, dimensions, and comprehensive quantitative level of the subject after the screening; S72, summing up the data, dimensions, and comprehensive quantitative grades of the screened subjects according to the quantitative grades of the subjects and the range of the subject rankings to obtain the sum of the quantitative grades; S73. Rank the sum of the quantitative levels in descending order to obtain an evaluation level ranking.

10. A data processing and statistical system based on a custom evaluation model, characterized in that: Used to implement the data processing and statistical method based on the custom evaluation model described in any one of claims 1 to 9, the system includes: a normalization configuration module, a normalization processing module, a quantization configuration module, a subject association module, a quantization processing module, an evaluation configuration module and a subject evaluation module; The normalization configuration module is used to construct a visual operation interface, and to set the data attribute value weights of the subject sample data in the normalization process and the data attribute value screening conditions of the subject sample based on the pre-selected subject sample data in the visual operation interface; The normalization processing module is used to perform normalization processing on the data of the main sample based on the set data attribute value weight of the main sample data in the normalization processing and the data attribute value screening condition of the main sample to obtain the normalization processing result; The quantization configuration module is used to set the quantization configuration and main configuration of the normalization result in the quantization processing for the normalization processing result by using the visual operation interface; The subject association module is used to screen and determine the subject to be evaluated according to the subject configuration, and obtain the normalized processing result of the subject to be evaluated by associating the subject to be evaluated with the subject sample; The quantization processing module is used to quantize the normalization processing result of the subject to be evaluated according to the quantization configuration of the normalization result in the quantization processing, obtain the quantization result, and rank the quantization processing through the ranking function; The evaluation configuration module is used to set the evaluation configuration according to the quantification results of the subject to be evaluated and the ranking statistics of the quantification processing; The subject evaluation module is used to evaluate and count the quantitative results and ranking statistics of the subject to be evaluated according to the quantitative level of the subject and the range of the subject ranking, so as to obtain the evaluation level and ranking of the subject; The setting of the evaluation configuration according to the quantitative results of the subject to be evaluated and the ranking statistics of the quantitative processing includes: Use the report designer to query the data quantification results and rankings of the subject to be evaluated; According to the data quantification results and ranking of the subject to be evaluated, set the scope of the subject's data, dimensions, comprehensive quantitative level and its ranking; According to the quantitative level of the subject and the range of the subject ranking, the quantitative results and ranking statistics of the subject to be evaluated are evaluated and statistically analyzed to obtain the evaluation level and ranking of the subject, including: The quantitative results and ranking statistics of the subject to be evaluated are used to screen the quantitative level and ranking, and the data, dimensions and comprehensive quantitative level of the screened subject are obtained; The data, dimensions, and comprehensive quantitative grades of the screened subjects are summed up according to the quantitative grades of the subjects and the range of the subject rankings to obtain the sum of the quantitative grades; The sum of the quantitative levels is ranked in descending order to obtain the evaluation level ranking.

11. A data processing and statistical system based on a custom evaluation model according to claim 10, characterized in that: The construction of the visual operation interface and setting the data attribute value weights of the subject sample data in the normalization process and the data attribute value screening conditions of the subject sample based on the pre-selected subject sample data in the visual operation interface include: Use the report designer to build a visual operation interface for customizing normalized configuration; Based on the constructed visual operation interface, the data attribute value weights of the main sample data in the normalization process are set; Based on the constructed visual operation interface, set the data attribute value screening conditions for the main sample data in the normalization processing.

12. A data processing and statistical system based on a user-defined evaluation model according to claim 10, characterized in that: The normalization process is performed on the data of the subject sample based on the set data attribute value weights of the subject sample data in the normalization process and the data attribute value screening conditions of the subject sample, and the normalization process result is obtained, including: According to the data attribute value screening conditions of the main sample, the attribute values ​​of the main sample data are screened using the report designer; Associating the filtered subject sample data with the weight value matching the subject sample data attribute value; Using a preset summation function, the product of the weighted values ​​of each attribute value of each data of a single subject sample is summed to obtain a normalized value.

13. The data processing and statistical system based on the user-defined evaluation model according to claim 10, characterized in that: The use of a visual operation interface to normalize the processing result, setting the quantization configuration and main configuration of the normalization result in the quantization processing includes: Use the visual operation interface with customizable normalization configuration to set the quantization level, dimension and dimension weight, dimension data and dimension data weight of the normalization result in the quantization process; According to the normalization processing result, the data quantization level threshold is set; Use the report designer to create the attribute filter range conditions for the subject.

14. A data processing and statistical system based on a user-defined evaluation model according to claim 13, characterized in that: The method of screening and determining the subject to be evaluated according to the subject configuration, and obtaining the normalized processing result of the subject to be evaluated by associating the subject to be evaluated with the subject sample includes: According to the subject's attribute selection range conditions, use the report designer to select the subject's attributes to obtain the subject to be evaluated; Associating the subject to be evaluated with the subject sample, and determining and generating the normalized result of the subject to be evaluated based on the normalized result of the subject sample; The normalized results of the evaluation subject are visualized using the visual operation interface.

15. The data processing and statistical system based on the user-defined evaluation model according to claim 10, characterized in that: According to the quantitative configuration of the normalized result in the quantitative processing, the normalized processing result of the subject to be evaluated is quantized to obtain the quantitative result, and the quantitative processing is ranked and counted by the ranking function, including: The data normalization result of the subject to be evaluated is associated with the set quantitative level threshold, and the comprehensive quantitative level of the relevant data and dimension set is generated to obtain the quantitative result of the subject to be evaluated; The quantitative results of the subject to be evaluated are used to generate a quantitative grade ranking through a ranking function, and the quantitative grade ranking is displayed through a visual picture.

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