CN110991936A - Enterprise grading and rating method, device, equipment and medium - Google Patents
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Abstract
The invention provides a method, a device, equipment and a medium for grading enterprise scores, wherein the method comprises the following steps: acquiring original data of an enterprise, and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise; the initialization model leads various analysis indexes and an industry mean value into the model; and respectively calculating corresponding one-dimensional parameters and two-dimensional parameters according to the incidence relations among various analysis indexes in the model, calculating the grade of the enterprise by combining the two-dimensional parameters and the corresponding alarm deduction conditions, and grading the enterprise according to the grade. Abnormal data in the data are filtered by a preprocessing mode to obtain various analysis indexes and industry mean values of enterprises, so that the authenticity of original data is ensured; meanwhile, various analysis indexes are introduced for combined analysis, and the scores of enterprises of the enterprises are calculated in combination with a plurality of angles, so that the enterprises are quantitatively graded, the true level of the enterprises in the belonging industry is reflected, and the accuracy of risk early warning and grading is improved.
Description
Technical Field
The invention relates to the technical field of financial assessment, in particular to a method, a device, equipment and a medium for enterprise grading.
Background
Enterprises are generally provided with quantitative scoring systems, mainly aiming at improving market competitiveness of the enterprises, promoting social credit and reducing transaction cost, and generally evaluating from the aspects of management and operation quality, financial conditions, growth capacity, income growth rate of main business and operation and the like so as to form the quantitative scoring systems.
However, when the existing enterprise scoring system scores and grades, on one hand, the source data is not comprehensive and accurate enough, so that the obtained original enterprise data is not real enough; on the other hand, the obtained original enterprise data and the corresponding field cannot be subjected to correlation reference, so that the scoring rating cannot truly reflect the condition of the enterprise in the corresponding field.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention provides a method, an apparatus, a device and a medium for rating an enterprise by rating, which are used to solve the problem in the prior art that the level of the enterprise in the field cannot be truly and accurately reflected by rating.
To achieve the above and other related objects, in a first aspect of the present application, the present invention provides an enterprise rating method, including:
acquiring original data of an enterprise, and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise;
initializing a model and importing various analysis indexes and industry mean values;
and respectively calculating corresponding one-dimensional parameters and two-dimensional parameters according to the incidence relation among various analysis indexes in the model, combining the two-dimensional parameters with the corresponding alarm deduction conditions, calculating the grade of the enterprise, and grading the enterprise according to the grade.
In a second aspect of the present application, there is provided an enterprise rating device, comprising:
the data preprocessing module is used for acquiring original data of an enterprise and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise;
the data import module is used for initializing a model and importing various analysis indexes and industry mean values;
and the grading module is used for respectively calculating corresponding one-dimensional parameters and two-dimensional parameters according to the incidence relation between various analysis indexes in the model, calculating the grade of the enterprise by combining the two-dimensional parameters and the corresponding alarm deduction condition, and grading the enterprise according to the grade.
In a third aspect of the present application, there is provided an electronic device for scoring and rating various enterprises, comprising:
one or more processors;
a memory storing program instructions; and
the one or more processors execute program instructions stored in the memory to cause the electronic device to execute an electronic device to perform the enterprise rating method.
In a fourth aspect of the present application, there is provided a business scoring and rating medium comprising:
the storage medium stores a program, wherein the program implements the enterprise rating method when invoked for execution.
As described above, the enterprise rating method, device, equipment and medium of the present invention have the following advantages:
the method comprises the steps of obtaining original data of an enterprise, filtering abnormal data in the original data in a preprocessing mode to obtain various analysis indexes and industry mean values of the enterprise, and ensuring the authenticity of the original data; meanwhile, a plurality of analysis indexes are introduced for combined analysis, and the scores of enterprises of the enterprises are calculated in all directions from multiple angles, so that the enterprises are quantitatively graded, the true level of the enterprises in the belonging industry is reflected, and the accuracy of risk early warning and grading is improved.
Drawings
FIG. 1 is a flow chart of a method for rating enterprise scores according to the present invention;
fig. 2 is a flowchart illustrating a step S1 in an enterprise rating method according to the present invention;
fig. 3 is a flowchart illustrating a step S3 in an enterprise rating method according to the present invention;
FIG. 4 is a block diagram illustrating an enterprise rating device according to the present invention;
FIG. 5 is a block diagram illustrating a first embodiment of an enterprise rating device according to the present invention;
FIG. 6 is a block diagram illustrating a second embodiment of an enterprise rating device according to the present invention;
FIG. 7 is a schematic structural diagram of an electronic device according to the present invention;
fig. 8 a-f show statistical graphs of an enterprise rating parameter provided by the present invention.
Description of the element reference numerals
1 data preprocessing module
2 data import module
3 grading module
4 processor
5 memory
11 data acquisition unit
12 pretreatment unit
31 parameter calculation unit
32 rating unit
S1-S3 Steps 1 to 3
Detailed Description
The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.
In the following description, reference is made to the accompanying drawings that describe several embodiments of the application. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "above," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first preset threshold may be referred to as a second preset threshold, and similarly, the second preset threshold may be referred to as a first preset threshold, without departing from the scope of the various described embodiments. The first preset threshold and the preset threshold are both described as one threshold, but they are not the same preset threshold unless the context clearly indicates otherwise. Similar situations also include a first volume and a second volume.
Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise, it should be further understood that the terms "comprises" and "comprising" indicate the presence of the stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, items, species, and/or groups. A; b; c; a and B; a and C; b and C; A. b and C "are only exceptions to this definition should be done when combinations of elements, functions, steps or operations are inherently mutually exclusive in some manner.
Referring to fig. 1, the present invention provides a flowchart of an enterprise rating method, including:
step S1, acquiring original data of an enterprise, and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise;
step S2, initializing a model and importing various analysis indexes and industry mean values;
step S3, calculating corresponding one-dimensional parameters and two-dimensional parameters according to the incidence relation between various analysis indexes in the model, calculating the grade of the enterprise by combining the two-dimensional parameters and the corresponding alarm deduction conditions, and grading the enterprise according to the grade.
In the embodiment, various analysis indexes and industry mean values of the enterprise are obtained by acquiring the original data of the enterprise and filtering abnormal data in the original data in a preprocessing mode, so that the authenticity of the original data is ensured; meanwhile, various analysis indexes are introduced for combined analysis, and the scores of enterprises of the enterprises are calculated in combination with a plurality of angles, so that the enterprises are quantitatively graded, the true level of the enterprises in the belonging industry is reflected, and the accuracy of risk early warning and grading is improved.
Referring to fig. 2, a flowchart of step S1 in the enterprise rating method according to the present invention includes:
step S101, acquiring enterprise public information by adopting crawler information, and screening the enterprise public information into original data;
step S102, calculating the difference Dij of each analysis index of each enterprise, namely (Xij-Ej)/Aj, in the original data according to the standard deviation Aj and the simple mean value Ej of M analysis indexes in the same industry, wherein Xij is the jth analysis index of the ith enterprise; after the abnormality analysis indicators are removed, the difference degree Dij is used as a weight value to calculate a weighted average value E2j ═ sum (Xij) Dij)/sum (Dij).
Specifically, the enterprise public information acquired from the internet comprises financial data and non-financial data, and the non-financial data comprises information such as industrial and commercial information, court announcements, intellectual property, news information, microblog information, WeChat public numbers and products. On one hand, the collected enterprise public information is screened, and enterprise information needing to be evaluated is selected, so that the accuracy of data is guaranteed; on the other hand, preset parameter names are screened from the original data, such as: the method comprises the steps of increasing income, consuming expenses, selling gross profit, selling net profit, having interest, selling remittance rate in a generalized way, giving a net profit after deduction and changing net profit and business value, and selecting parameter names according to the settings of corresponding two-dimensional parameters, large names and the like to remove abnormal analysis indexes in the enterprise; finally, calculating a weighted average value by taking the difference Dij of each analysis index of each enterprise as a weight value, for example, further comprising: calculating the median of each type of analysis index, for example, taking 10-90% quantiles on the basis of the original initial value; industry mean value: that is, the whole industry is regarded as an enterprise, and all the analysis indexes are calculated after the industries are summed up, such as: the rate of assets and liabilities is the sum of assets and liabilities of the whole industry/the sum of liabilities of the whole industry.
In the above embodiment, the model includes one-dimensional indexes formed by various types of the analysis indexes and associated parameters for calculating the one-dimensional parameters, wherein the one-dimensional indexes reflect the financial data of the operation capacity, the growth performance, the equity structure and the fund flow of the enterprise, such as income increase, consumption cost, gross profit sale, net profit sale, interest gained, generalized remittance rate of sale, net profit after deduction and net profit and business change of fair value, and the like; the related parameters comprise two-dimensional parameters corresponding to the one-dimensional indexes, large class names, alarm values, optimal values, minimum boundary values, maximum boundary values, absolute alarm values-small directions, absolute alarm values-large directions, one-way coefficients, single total scores, positive and negative relations, instruction types, single weight scores and alarm coefficients.
Specifically, the alarm values (the first alarm value, the second alarm value) and the optimal values (the first optimal value, the second optimal value) are derived from the maximum boundary value and the minimum boundary value of the industry quantile, wherein each type of analysis index is substantially the same and is set according to a preset value.
The minimum boundary value, the maximum boundary value, the absolute alarm value-small direction and the absolute alarm value-large direction are set according to past experience in historical data;
the single coefficient is a coefficient determined according to importance, is set according to experience, ranges from 0 to 2, and is 10 points in total, wherein the single weight score is the single coefficient and is a one-way total score, whether the alarm item is a coefficient for alarm deduction or not is preset, and the value range is 0 to 2; the positive and negative relations are also preset, and the larger the corresponding parameter in each analysis index is, the larger the parameter is, the value is marked as 1, and the opposite value is, the value is marked as-1; the analysis index types of each type of analysis index are classified into 5 types according to the calculation method, and the analysis index types are represented by numerals 1 to 5.
The two-dimensional parameters comprise income cost index, sale cost index, gross profit index, net profit index, EBIT index, refund index, non-net profit deduction and the like, and the general names comprise: growth, consistency check, profitability, debt paying ability and profitability structure; the major class name: growth, consistency check, profitability, compensation capability, growth and profit structure, etc., and longitudinal parameters (analysis indexes) including revenue growth rate, sales cost growth rate, sales gross interest rate variation, sales net interest rate variation, interest gained multiple, generalized sales reimbursement rate, profit/net profit after deduction, etc., are shown in fig. 8 (a-f).
The solution is performed for each parameter of each analysis index, thereby calculating a score to each analysis index.
Referring to fig. 3, a flowchart of step S3 in the enterprise rating method according to the present invention includes:
step S301, calculating corresponding scores of one-dimensional parameters by adopting an interpolation method, and carrying out same-dimensional combination on the one-dimensional parameters according to weights to obtain two-dimensional parameters;
and S302, logically verifying each analysis index in the two-dimensional parameters, accumulating each analysis index according to the weight of the analysis index to obtain a primary score of an enterprise, calculating the score of the enterprise according to the alarm number of the enterprise on the basis of the primary score, and grading the score of the enterprise according to a preset threshold value.
Wherein, five types of analysis indexes are respectively calculated by adopting an interpolation method, wherein the analysis indexes are divided into 5 types of calculation methods according to the property difference.
the first step is as follows: the corresponding steps for calculating the first type of analysis index are as follows:
TXS _ ij ═ (Xij-alarm value 1 j)/(optimum value 1 j-alarm value 1j) — (1)
TXL _ ij ═ (Xij-alarm value 2 j)/(optimum value 2 j-alarm value 2j) — individual total score; (2)
t1_ ij ═ Min (Max (0, Min (TXS _ ij, TXL _ ij) × individual total points), individual total points); (3)
in the formula, TXS _ ij represents a score calculated by the optimum value 1 and the alarm value 1, TXL _ ij represents a score calculated by the optimum value 2 and the alarm value 2, and T1_ ij represents that the two are between 0 and the single total score, and the small values of TXS _ ij and TXL _ ij are taken.
The second step is that: determining the state of the positive and negative relation of the section where the analysis index Xij is located, if the positive and negative relation corresponding to the analysis index Xij is in the opposite direction, calculating T1_ ij according to a preset discount coefficient corresponding to the industry to which the analysis index Xij belongs, and subtracting the single total score and multiplying the single total score by the preset coefficient to calculate a score; if the positive and negative relations are positive, adding 1 point to the initial value of T1_ ij to calculate the score;
the third step: according to whether an absolute alarm item of the analysis index Xij in the model is zero or not, if the absolute alarm item is zero, the absolute alarm item is not processed; and if the absolute alarm item is non-zero, calculating the corresponding alarm value. If the analysis index value exceeds the absolute alarm value, recording one time of alarm and alarm content, and recalculating T1_ ij as A _ j single item total score, wherein A _ j is an alarm discount coefficient and takes a value of-0.8. Simultaneously recording an alarm deduction A _ ij (alarm coefficient) and an alarm deduction score as well as an alarm positive and negative item discount, wherein the alarm positive and negative item discount is set in advance, the homodromous value is 0.9, and the non-homodromous value is 1; the alarm score is also preset, for example, -2, so that the calculated score corresponding to each analysis index is calculated.
the first step is as follows: the same as the first step in the first type of calculation method;
the second step is that: the second step is the same as the first calculation method, but the preset discount coefficient and the preset coefficient are reset;
the third step: the third step of the calculation method is the same as that of the first type, but the alarm coefficient, the alarm deduction item and the alarm positive and negative discount amount are different.
the first step is as follows: before calculation, checking the positive and negative relation of the correlation analysis index value, if the correlation analysis index value is a negative number and the analysis index is a negative number, directly assigning a higher value, and not entering the second step; if the correlation analysis index value is a negative number and the correlation analysis index value is a positive number, directly entering the third step of the first type of calculation method; if the correlation analysis index is positive, the second step is carried out by the same calculation method as the first step;
the second step is that: if the correlation analysis index value is a positive number, the second step is the same as the first type of calculation method;
the third step: if the correlation analysis index value is positive, the third step is the same as the first calculation method.
the first step is as follows: before calculation, checking the positive and negative relation of the correlation analysis index value, if the correlation analysis index value is a negative number, directly assigning the correlation analysis index value, and not entering the second step; if the correlation analysis index value is a positive number, the second step is carried out by the same calculation method as the first step;
the second step is that: if the correlation analysis index value is a positive number, the second step is the same as the first type of calculation method;
the third step: if the correlation analysis index value is positive, the third step is the same as the first calculation method.
the first step is as follows: before calculation, the analysis index is divided by the correlation analysis index, and the divided value is used as Xij to participate in subsequent operation.
The second step is that: the second step is the same as the first type of calculation method;
the third step: the third step is the same as the first calculation method.
Combining the calculated one-dimensional parameter scores in the same category to generate a two-dimensional parameter score, which is detailed as follows:
the score of each two-dimensional parameter is defined as T2_ in (i is the ith enterprise, n is the nth two-dimensional analysis index), the nth two-dimensional analysis index may correspond to 1 or two-dimensional analysis indexes, respectively, T1_ ij (i is the ith enterprise, j is the jth one-dimensional analysis index) and T1_ ik (i is the ith enterprise, j is the kth one-dimensional analysis index), and the weights corresponding to T1_ ij and T1_ ik are Q _ j and Q _ k, respectively.
Then T2_ in ═ (T1_ ij × Q _ j + T1_ ik × Q _ k)/(Q _ j + Q _ k)
The total score is Tallj (Sum (Q _ j) Dij)/Sum (Q _ j) 10+ Sum (A _ ij)
And calculating the grade (final grade) of the enterprise according to the alarm quantity of the enterprise on the basis of the initial grade, and grading the grade of the enterprise according to a preset threshold value.
Compared with other enterprise grading and rating modes, the embodiment adopts a multi-dimensional and multi-index mode to calculate various financial data of the enterprise to be graded, can comprehensively reflect the operation, development and industry positions of the enterprise, and can objectively and fairly evaluate the enterprise aiming at a third party, so that the enterprise is quantitatively graded, the real level of the enterprise in the industry is favorably reflected, and the accuracy of risk early warning and grading is improved.
Referring to fig. 4, a block diagram of an enterprise rating device according to the present invention includes:
the data preprocessing module 1 is used for acquiring original data of an enterprise and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise;
the data import module 2 is used for initializing a model and importing various analysis indexes and industry mean values;
the model comprises a one-dimensional index formed by various analysis indexes and associated parameters for calculating the one-dimensional parameters, wherein the analysis indexes comprise financial data reflecting the operation capacity, the growth performance, the equity structure and the capital flow of an enterprise, such as income increase, consumption cost, gross sale profit, net sale profit, interest gained, generalized sale remittance rate, net profit after deduction and net profit and business change of fair value; the related parameters comprise two-dimensional parameters corresponding to the one-dimensional indexes, large class names, alarm values, optimal values, minimum boundary values, maximum boundary values, absolute alarm values-small directions, absolute alarm values-large directions, one-way coefficients, single total scores, positive and negative relations, instruction types, single weight scores and alarm coefficients.
And the grading module 3 is used for respectively calculating corresponding one-dimensional parameters and two-dimensional parameters according to the incidence relations among the various analysis indexes in the model, calculating the grade of the enterprise by combining the two-dimensional parameters and the corresponding alarm deduction conditions, and grading the enterprise according to the grade.
On the basis of the above embodiments, please refer to fig. 5, which is a block diagram illustrating a first embodiment of an enterprise rating device according to the present invention, including:
the data acquisition unit 11 is used for acquiring enterprise public information by adopting crawler information and screening the enterprise public information into original data;
the preprocessing unit 12 is configured to calculate, in the original data, a difference Dij of each analysis index of each enterprise, which is (Xij-Ej)/Aj according to a standard deviation Aj and a simple mean Ej of M analysis indexes in the same industry, where Xij is a jth analysis index of an ith enterprise; after the abnormality analysis indicators are removed, the difference degree Dij is used as a weight value to calculate a weighted average value E2j ═ sum (Xij) Dij)/sum (Dij).
Referring to fig. 6, a block diagram of a second embodiment of an enterprise rating device according to the present invention is shown;
the parameter calculating unit 31 calculates the corresponding scores of the one-dimensional parameters by an interpolation method, and performs the same-dimensional combination on the one-dimensional parameters according to the weights to obtain two-dimensional parameters;
and the grading unit 32 is used for logically verifying each analysis index in the two-dimensional parameters, accumulating each analysis index according to the weight of the analysis index to obtain a preliminary score of an enterprise, calculating the grade of the enterprise according to the alarm quantity of the enterprise on the basis of the preliminary score, and grading the grade of the enterprise according to a preset threshold value.
Specifically, the corresponding score of the one-dimensional parameter is calculated by interpolation as follows:
TXS _ ij ═ (Xij-alarm value 1 j)/(optimum value 1 j-alarm value 1j) — (1)
TXL _ ij ═ (Xij-alarm value 2 j)/(optimum value 2 j-alarm value 2j) — individual total score; (2)
t1_ ij ═ Min (Max (0, Min (TXS _ ij, TXL _ ij) × individual total points), individual total points); (3)
determining the state of the positive and negative relation of the section where the analysis index Xij is located, if the positive and negative relation corresponding to the analysis index Xij is negative, calculating T1_ ij according to a preset discount coefficient corresponding to the industry to which the analysis index Xij belongs, and subtracting the single total score and multiplying the single total score by a preset error coefficient to calculate a score; if the corresponding positive-negative relation is positive, adding 1 point to the initial value of T1_ ij to calculate the score;
according to whether an absolute alarm item of the analysis index Xij in the model is zero or not, if the absolute alarm item is zero, the absolute alarm item is not processed; and if the absolute alarm item is non-zero, calculating the corresponding alarm value.
Since the apparatus and the method are in a one-to-one correspondence relationship, the technical details and technical effects thereof are described with reference to the above embodiments, which are not repeated herein.
Referring to fig. 7, an electronic device for enterprise rating and scoring according to the present invention includes:
one or more processors 4;
a memory 5 storing program instructions; and
the one or more processors 4 executing program instructions stored in memory 5 cause the electronic device to execute an electronic device to perform the enterprise rating method described above.
The processor 4 is operatively coupled to memory and/or non-volatile storage. More specifically, the processor 4 may execute instructions stored in the memory and/or non-volatile storage device to perform operations in the computing device, such as generating image data and/or transmitting image data to an electronic display. As such, the processor may include one or more general purpose microprocessors, one or more application specific processors (ASICs), one or more field programmable logic arrays (FPGAs), or any combination thereof.
The application provides a storage medium for enterprise rating, wherein the storage medium stores a program, and when the program runs, the device where the storage medium is located is controlled to execute the enterprise rating method.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. With this understanding in mind, the technical solutions of the present application, or portions thereof, that essentially contribute to the prior art may be embodied in the form of a software product stored on a storage medium that includes instructions for causing a computer device (which may be a personal computer, a server, or in the embodiments provided herein, a computer-readable and/or writable storage medium that may include Read-only memory (ROM), Random Access Memory (RAM), EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, a USB flash disk, a removable hard disk, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer, any connection is properly termed a computer-readable medium. For example, if the instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium.
In conclusion, the method and the device have the advantages that the original data of the enterprise are obtained, abnormal data in the original data are filtered out in a preprocessing mode to obtain various analysis indexes and industry mean values of the enterprise, and authenticity of the original data is guaranteed; meanwhile, various analysis indexes are introduced for combined analysis, and the scores of enterprises of the enterprises are calculated in combination with a plurality of angles, so that the enterprises are quantitatively graded, the true level of the enterprises in the belonging industry is reflected, and the accuracy of risk early warning and grading is improved. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (12)
1. A method for rating a business score, the method comprising:
acquiring original data of an enterprise, and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise;
initializing a model and importing various analysis indexes and industry mean values;
and respectively calculating corresponding one-dimensional parameters and two-dimensional parameters according to the incidence relations among various analysis indexes in the model, calculating the grade of the enterprise by combining the two-dimensional parameters and the corresponding alarm deduction conditions, and grading the enterprise according to the grade.
2. The enterprise rating method according to claim 1, wherein the step of obtaining the original data of the enterprise and eliminating the abnormal data in the original data to obtain various analysis indexes and industry averages corresponding to the enterprise comprises:
acquiring enterprise public information by adopting crawler information, and screening the enterprise public information into original data; calculating the difference Dij (Xij-Ej)/Aj of each analysis index of each enterprise according to the standard deviation Aj and the simple mean value Ej of M analysis indexes in the same industry in the original data, wherein Xij is the jth analysis index of the ith enterprise; after the abnormality analysis indexes are removed, the difference degree Di is used as a weight value to calculate a weighted average value E2j ═ sum (Xij) Dij)/sum (Dij).
3. The method of claim 1, wherein the model comprises a one-dimensional index composed of various types of analysis indexes and associated parameters for calculating the one-dimensional parameters, wherein the one-dimensional index reflects financial data of operation capacity, growth, equity structure and fund flow of the enterprise; the related parameters comprise two-dimensional parameters corresponding to the one-dimensional indexes, large class names, alarm values, optimal values, minimum boundary values, maximum boundary values, absolute alarm values-small directions, absolute alarm values-large directions, one-way coefficients, single total scores, positive and negative relations, instruction types, single weight scores and alarm coefficients.
4. The method according to claim 1, wherein the step of calculating the corresponding one-dimensional parameter and two-dimensional parameter according to the correlation between each type of analysis index in the model, calculating the score of the enterprise according to the two-dimensional parameter and the corresponding alarm deduction condition, and rating the enterprise risk according to the score comprises:
calculating corresponding scores of the one-dimensional parameters by adopting an interpolation method, and carrying out same-dimensional combination on the one-dimensional parameters according to weights to obtain two-dimensional parameters;
logically verifying each analysis index in the two-dimensional parameters, accumulating each analysis index according to the weight of the analysis index to obtain a preliminary score of an enterprise, calculating the score of the enterprise according to the alarm quantity of the enterprise on the basis of the preliminary score, and grading the score of the enterprise according to a preset threshold value.
5. The method of claim 4, wherein the step of calculating the respective scores of the one-dimensional parameters by interpolation comprises:
TXS _ ij ═ (Xij-alarm value 1 j)/(optimum value 1 j-alarm value 1j) — (1)
TXL _ ij ═ (Xij-alarm value 2 j)/(optimum value 2 j-alarm value 2j) — individual total score; (2)
t1_ ij ═ Min (Max (0, Min (TXS _ ij, TXL _ ij) × individual total points), individual total points); (3)
determining the state of the positive and negative relation of the section where the analysis index Xij is located, if the positive and negative relation corresponding to the analysis index Xij is negative, calculating T1_ ij according to a preset discount coefficient corresponding to the industry to which the analysis index Xij belongs, and subtracting the single total score and multiplying the single total score by a preset error coefficient to calculate a score; if the corresponding positive-negative relation is positive, adding 1 point to the initial value of T1_ ij to calculate the score;
according to whether an absolute alarm item of the analysis index Xij in the model is zero or not, if the absolute alarm item is zero, the absolute alarm item is not processed; and if the absolute alarm item is non-zero, calculating the corresponding alarm value.
6. An enterprise rating apparatus, comprising:
the data preprocessing module is used for acquiring original data of an enterprise and removing abnormal data in the original data to obtain various analysis indexes and industry mean values corresponding to the enterprise;
the data import module is used for initializing a model and importing various analysis indexes and industry mean values;
and the grading module is used for calculating corresponding one-dimensional parameters and two-dimensional parameters by using the correlation among various analysis indexes through the model, calculating the score of the enterprise according to the two-dimensional parameters and the corresponding alarm deduction condition of the two-dimensional parameters, and grading the enterprise according to the score.
7. The enterprise rating apparatus of claim 6, wherein the data preprocessing module comprises:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit acquires enterprise public information by adopting crawler information and screens the enterprise public information into original data;
the preprocessing unit is used for calculating the difference Dij ═ of (Xij-Ej)/Aj of each analysis index of each enterprise according to the standard deviation Aj and the simple mean value Ej of the M analysis indexes in the same industry in the original data, wherein Xij is the jth analysis index of the ith enterprise; after the abnormality analysis indicators are removed, the difference degree Dij is used as a weight value to calculate a weighted average value E2j ═ sum (Xij) Dij)/sum (Dij).
8. The apparatus according to claim 6, wherein the model comprises a one-dimensional index composed of various types of the analysis indexes and associated parameters for calculating the one-dimensional parameters, wherein the one-dimensional index reflects financial data of operation ability, growth, equity structure and fund flow of the enterprise; the related parameters comprise two-dimensional parameters corresponding to the one-dimensional indexes, large class names, alarm values, optimal values, minimum boundary values, maximum boundary values, absolute alarm values-small directions, absolute alarm values-large directions, one-way coefficients, single total scores, positive and negative relations, instruction types, single weight scores and alarm coefficients.
9. The enterprise rating system of claim 6, wherein the rating module comprises:
the parameter calculation unit calculates the corresponding scores of the one-dimensional parameters by adopting an interpolation method, and performs the same-dimensional combination on the one-dimensional parameters according to the weights to obtain two-dimensional parameters;
and the grading unit is used for logically verifying each analysis index in the two-dimensional parameters, accumulating each analysis index according to the weight of the analysis index to obtain a preliminary score of an enterprise, calculating the grade of the enterprise according to the alarm quantity of the enterprise on the basis of the preliminary score, and grading the grade of the enterprise according to a preset threshold value.
10. The enterprise rating apparatus of claim 9, wherein the parameter calculating unit comprises:
TXS _ ij ═ (Xij-alarm value 1 j)/(optimum value 1 j-alarm value 1j) — (1)
TXL _ ij ═ (Xij-alarm value 2 j)/(optimum value 2 j-alarm value 2j) — individual total score; (2)
t1_ ij ═ Min (Max (0, Min (TXS _ ij, TXL _ ij) × individual total points), individual total points); (3)
determining the state of the positive and negative relation of the section where the analysis index Xij is located, if the positive and negative relation corresponding to the analysis index Xij is negative, calculating T1_ ij according to a preset discount coefficient corresponding to the industry to which the analysis index Xij belongs, and subtracting the single total score and multiplying the single total score by a preset error coefficient to calculate a score; if the corresponding positive-negative relation is positive, adding 1 point to the initial value of T1_ ij to calculate the score;
according to whether an absolute alarm item of the analysis index Xij in the model is zero or not, if the absolute alarm item is zero, the absolute alarm item is not processed; and if the absolute alarm item is non-zero, calculating the corresponding alarm value.
11. An electronic device, comprising:
one or more processors;
a memory storing program instructions; and
the one or more processors execute program instructions stored in the memory to cause the electronic device to execute the electronic device to perform the enterprise rating method of any of claims 1-5.
12. A storage medium storing a program, wherein the program when invoked for execution implements the enterprise rating method of any of claims 1-5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112418652A (en) * | 2020-11-19 | 2021-02-26 | 税友软件集团股份有限公司 | Risk identification method and related device |
| CN112598228A (en) * | 2020-12-07 | 2021-04-02 | 深圳价值在线信息科技股份有限公司 | Enterprise competitiveness analysis method, device, equipment and storage medium |
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| CN113537725A (en) * | 2021-06-24 | 2021-10-22 | 浙江乾冠信息安全研究院有限公司 | Unit comprehensive scoring method and electronic device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112418652A (en) * | 2020-11-19 | 2021-02-26 | 税友软件集团股份有限公司 | Risk identification method and related device |
| CN112418652B (en) * | 2020-11-19 | 2024-01-30 | 税友软件集团股份有限公司 | Risk identification method and related device |
| CN112598228A (en) * | 2020-12-07 | 2021-04-02 | 深圳价值在线信息科技股份有限公司 | Enterprise competitiveness analysis method, device, equipment and storage medium |
| CN112598228B (en) * | 2020-12-07 | 2023-09-22 | 深圳价值在线信息科技股份有限公司 | Enterprise competitiveness analysis method, device, equipment and storage medium |
| CN112926833A (en) * | 2021-01-28 | 2021-06-08 | 北京安九信息技术有限公司 | Detection method and detection system for health condition of enterprise |
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