CN118171953A - Target enterprise screening method, device, computer equipment and storage medium - Google Patents

Target enterprise screening method, device, computer equipment and storage medium Download PDF

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CN118171953A
CN118171953A CN202410304177.4A CN202410304177A CN118171953A CN 118171953 A CN118171953 A CN 118171953A CN 202410304177 A CN202410304177 A CN 202410304177A CN 118171953 A CN118171953 A CN 118171953A
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张馨遇
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The application relates to a target enterprise screening method, a target enterprise screening device, computer equipment and a storage medium. The method comprises the following steps: acquiring index data of enterprise evaluation index information of different enterprises in different index types, and performing index grading treatment on the enterprise evaluation index information of each type according to each index type to obtain index grading information of each index type; carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information; respectively calculating gray correlation degree between each data matrix and the extremum solution set, and calculating the grading value of each enterprise through an evaluation result algorithm based on each gray correlation degree, the extremum solution set and each data matrix; and screening enterprises larger than the scoring threshold value as target enterprises. By adopting the method, the accuracy of a small target enterprise positioned can be improved.

Description

Target enterprise screening method, device, computer equipment and storage medium
Technical Field
The present application relates to the field of big data technologies, and in particular, to a target enterprise screening method, apparatus, computer device, and storage medium.
Background
The small micro enterprises are numerous and widely distributed, and usually show clustered development, and most of the required financial products are single, so that the large-scale marketing of the small micro enterprises is easy to carry out due to the characteristics, and the large-scale marketing has large marketing value and development potential. Compared with the traditional large and medium-sized enterprises, the small micro-enterprises are weak in scale, technology and the like, and the traditional household marketing model can discover partial small micro-enterprises to a certain extent, and most of the small micro-enterprises are ignored in the household marketing model due to scale, funds, technology and the like. For the salesman, when the salesman needs to develop the households of numerous small and micro enterprises, the problems that the households are difficult to find, the households are not clear in scope and the like exist. It is therefore a current focus of research how to locate target small micro-businesses.
The traditional technology constructs the behavior preference of the enterprise by acquiring the behavior information of the enterprise so as to identify whether the enterprise is a target enterprise needing to be expanded, but the mode is more one-sided, so that the deviation degree of the acquired target enterprise is larger, and the accuracy of the small positioned target enterprise is poorer.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a target enterprise screening method, apparatus, computer device, computer readable storage medium, and computer program product.
In a first aspect, the present application provides a method for screening a target enterprise. The method comprises the following steps:
Acquiring index data of enterprise evaluation index information of different enterprises in different index types, and performing index grading treatment on the enterprise evaluation index information of each index type to obtain index grading information of the index type;
Carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information;
Respectively calculating gray correlation degree between each data matrix and the extremum solution set, and calculating the grading value of each enterprise through an evaluation result algorithm based on each gray correlation degree, the extremum solution set and each data matrix;
and screening enterprises larger than the scoring threshold value as target enterprises.
Optionally, the performing index classification processing on the type of enterprise evaluation index information to obtain index classification information of the index type includes:
based on the index grading strategy of each type, identifying the index grade corresponding to the index information of each enterprise evaluation of the type, and collecting the forward processing strategy of each index grade;
Based on a forward processing strategy of each index level, respectively carrying out forward processing on index data of enterprise evaluation index information corresponding to each index level to obtain sub-index classification information corresponding to each enterprise evaluation index information;
And taking sub-index grading information corresponding to all enterprise evaluation index information as index grading information of the index type.
Optionally, the normalizing the index data of the enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information includes:
identifying a standardized processing algorithm corresponding to each index level, and respectively carrying out standardized processing on the sub-index grading information corresponding to each index level based on the standardized processing algorithm corresponding to each index level to obtain standard index data corresponding to the sub-index grading information of each index level;
And constructing an index matrix corresponding to each index level based on the standard index data of each index level.
Optionally, the identifying the extremum solution set of all enterprise evaluation index information includes:
identifying a minimum value of each enterprise evaluation index information and a maximum value of each enterprise evaluation index information;
And constructing an extremum solution set of all enterprise evaluation index information based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
Optionally, the calculating gray correlation between each data matrix and the extremum solution set includes:
acquiring a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set;
For each data matrix, calculating a first gray correlation between the data matrix and the minimum value set through the first correlation algorithm, and calculating a second gray correlation between the data matrix and the maximum value set through the second correlation algorithm;
And taking the first gray correlation degree and the second gray correlation degree as gray correlation degrees between the data matrix and the extremum solution set.
Optionally, the calculating, by an evaluation result algorithm, a scoring value of each enterprise based on each gray association degree, the extremum solution set, and each data matrix includes:
for each enterprise, inputting a first gray correlation degree between each data matrix of the enterprise and the minimum value set and a second gray correlation degree between each data matrix and the maximum value set into the evaluation result algorithm, and calculating the evaluation value of the enterprise.
In a second aspect, the application further provides a target enterprise screening device. The device comprises:
The system comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring index data of enterprise evaluation index information of different enterprises in different index types, and performing index classification processing on the enterprise evaluation index information of each index type to obtain index classification information of the index type;
The identification module is used for carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information;
the computing module is used for respectively computing gray correlation degree between each data matrix and the extremum solution set, and computing the grading value of each enterprise through an evaluation result algorithm based on each gray correlation degree, each extremum solution set and each data matrix;
and the screening module is used for screening enterprises larger than the scoring threshold value to serve as target enterprises.
Optionally, the acquiring module is specifically configured to:
based on the index grading strategy of each type, identifying the index grade corresponding to the index information of each enterprise evaluation of the type, and collecting the forward processing strategy of each index grade;
Based on a forward processing strategy of each index level, respectively carrying out forward processing on index data of enterprise evaluation index information corresponding to each index level to obtain sub-index classification information corresponding to each enterprise evaluation index information;
And taking sub-index grading information corresponding to all enterprise evaluation index information as index grading information of the index type.
Optionally, the identification module is specifically configured to:
identifying a standardized processing algorithm corresponding to each index level, and respectively carrying out standardized processing on the sub-index grading information corresponding to each index level based on the standardized processing algorithm corresponding to each index level to obtain standard index data corresponding to the sub-index grading information of each index level;
And constructing an index matrix corresponding to each index level based on the standard index data of each index level.
Optionally, the identification module is specifically configured to:
identifying a minimum value of each enterprise evaluation index information and a maximum value of each enterprise evaluation index information;
And constructing an extremum solution set of all enterprise evaluation index information based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
Optionally, the computing module is specifically configured to:
acquiring a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set;
For each data matrix, calculating a first gray correlation between the data matrix and the minimum value set through the first correlation algorithm, and calculating a second gray correlation between the data matrix and the maximum value set through the second correlation algorithm;
And taking the first gray correlation degree and the second gray correlation degree as gray correlation degrees between the data matrix and the extremum solution set.
Optionally, the computing module is specifically configured to:
for each enterprise, inputting a first gray correlation degree between each data matrix of the enterprise and the minimum value set and a second gray correlation degree between each data matrix and the maximum value set into the evaluation result algorithm, and calculating the evaluation value of the enterprise.
In a third aspect, the present application provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of any of the first aspects when the processor executes the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium. On which a computer program is stored which, when being executed by a processor, implements the steps of the method of any of the first aspects.
In a fifth aspect, the present application provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects.
According to the target enterprise screening method, the target enterprise screening device, the computer equipment and the storage medium, index grading treatment is carried out on the enterprise evaluation index information of different types of index by acquiring the index data of the enterprise evaluation index information of different types of index, and the index grading information of the type of index is obtained for each index type; carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information; respectively calculating gray correlation degree between each data matrix and the extremum solution set, and calculating the grading value of each enterprise through an evaluation result algorithm based on each gray correlation degree, the extremum solution set and each data matrix; and screening enterprises larger than the scoring threshold value as target enterprises. According to the scheme, a calculation strategy of the micro enterprise topology score is constructed through gray correlation and Topsis method, the topology value of the micro enterprise is subjected to grading processing from evaluation index information of different index types, so that the topology value of the micro enterprise is comprehensively analyzed, the reality and the accuracy of the analyzed topology value are improved, the topology value of each enterprise is converted into a score value which can be visually evaluated, the comprehensive topology value of each enterprise is conveniently and intuitively known, and the intuitiveness of analyzing each micro enterprise is improved. Finally, through grey correlation and Topsis method, the inefficiency problem of analyzing the topology value of enterprises by complex models is avoided, and the efficiency of analyzing the topology value of micro enterprises is improved. Finally, the method comprehensively analyzes the micro enterprises through the index information of a plurality of index types, different index levels, standardization and forward direction, so that the method comprehensively improves the accuracy of the positioned target micro enterprises.
Drawings
FIG. 1 is a flow chart of a target enterprise screening method in one embodiment;
FIG. 2 is a flow diagram of an example of target enterprise screening in one embodiment;
FIG. 3 is a block diagram of a target enterprise screening apparatus in one embodiment;
Fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The target enterprise screening method provided by the embodiment of the application can be applied to an application environment shown in figure 1. The method can be applied to the terminal, the server and a system comprising the terminal and the server, and is realized through interaction of the terminal and the server. The terminal may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like. The terminal builds a calculation strategy of the micro enterprise topology score through gray correlation and Topsis method, and carries out grading treatment on the topology value of the micro enterprise from the evaluation index information of different index types, so that the topology value of the micro enterprise is comprehensively analyzed, the reality and the accuracy of the analyzed topology value are improved, the topology value of each enterprise is converted into a score value which can be visually evaluated, and therefore the comprehensive topology value of each enterprise is conveniently and intuitively known, and the intuitiveness of analyzing each micro enterprise is improved. Finally, through grey correlation and Topsis method, the inefficiency problem of analyzing the topology value of enterprises by complex models is avoided, and the efficiency of analyzing the topology value of micro enterprises is improved. Finally, the method comprehensively analyzes the micro enterprises through the index information of a plurality of index types, different index levels, standardization and forward direction, so that the method comprehensively improves the accuracy of the positioned target micro enterprises.
In one embodiment, as shown in fig. 1, a target enterprise screening method is provided, and the method is applied to a terminal for illustration, and includes the following steps:
step S101, index data of enterprise evaluation index information of different enterprises in different index types are obtained, and index classification processing is carried out on the enterprise evaluation index information of the type aiming at each index type, so that index classification information of the index type is obtained.
In this embodiment, the terminal obtains the enterprise evaluation index information of different index types, and then obtains the index data of the enterprise evaluation index information of different index types by the big data crawling program and the enterprise data reporting mode. Wherein the index type includes, but is not limited to, a development scale index type, and a risk assessment index type. The terminal selects the development scale and risk assessment of the small micro-enterprises as the primary index of the small micro-enterprises, and the small micro-enterprises are divided into secondary indexes under the primary index. The secondary index of the development scale is subjected to index screening from enterprise development potential, enterprise development environment, enterprise scale and the like, and the screened indexes are shown in table 1; the secondary index of risk assessment is selected from credit information of main operators and compliance information of enterprise behaviors, and the selected index is shown in table 2. The evaluation index information of each enterprise corresponding to the development scale index type is shown in table 1, and the evaluation index information of each enterprise corresponding to the risk evaluation index type is shown in table 2.
TABLE 1 development Scale index
TABLE 2 Risk assessment index
And then, aiming at each index type, carrying out index grading treatment on the enterprise evaluation index information of the type to obtain index grading information of the index type. The specific grading process will be described in detail later, wherein the index grading information includes index grading information for different index grades, which are very small index, very large index, and intermediate index. As shown in table 3, after the index levels are classified, the corresponding enterprise evaluation index information is registered for each index.
TABLE 3 index Classification
Step S102, carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information.
In this embodiment, the terminal performs standardization processing on the index data of each enterprise evaluation index information corresponding to each index grading information, so as to obtain an index matrix corresponding to each index grading information, and identifies extremum solutions of all enterprise evaluation index information. The extremum solution set comprises a minimum value set of all enterprise evaluation index information and a maximum value set of all enterprise evaluation index information. The specific identification process will be described in detail later.
Step S103, gray correlation degree between each data matrix and the extremum solution set is calculated respectively, and the grading value of each enterprise is calculated through an evaluation result algorithm based on each gray correlation degree, the extremum solution set and each data matrix.
In this embodiment, the terminal calculates the gray correlation between each data matrix and the extremum solution set, and calculates the score value of each enterprise through the evaluation result algorithm based on each gray correlation, the extremum solution set, and each data matrix. Wherein each data matrix corresponds to an index level. Each index grading information is index grading information corresponding to different index grades.
Step S104, screening enterprises larger than the scoring threshold as target enterprises.
In this embodiment, the terminal screens the enterprises larger than the scoring threshold as the target enterprises.
Based on the scheme, a calculation strategy of the micro enterprise topology score is constructed through the gray correlation degree and the Topsis method, and the topology value of the micro enterprise is subjected to grading processing on the evaluation index information of different index types, so that the topology value of the micro enterprise is comprehensively analyzed, the reality and the accuracy of the analyzed topology value are improved, and the topology value of each enterprise is converted into a scoring value which can be visually evaluated, so that the comprehensive topology value of each enterprise is conveniently and intuitively known, and the intuitiveness of analyzing each micro enterprise is improved. Finally, through grey correlation and Topsis method, the inefficiency problem of analyzing the topology value of enterprises by complex models is avoided, and the efficiency of analyzing the topology value of micro enterprises is improved. Finally, the method comprehensively analyzes the micro enterprises through the index information of a plurality of index types, different index levels, standardization and forward direction, so that the method comprehensively improves the accuracy of the positioned target micro enterprises.
Optionally, performing index classification processing on the type of enterprise evaluation index information to obtain index classification information of the index type, including: based on the index grading strategy of each type, identifying index grades corresponding to the index information of each enterprise evaluation of the type, and collecting a forward processing strategy of each index grade; based on a forward processing strategy of each index level, respectively carrying out forward processing on index data of enterprise evaluation index information corresponding to each index level to obtain sub-index classification information corresponding to each enterprise evaluation index information; and taking the sub-index grading information corresponding to all enterprise evaluation index information as index grading information of the index type.
In this embodiment, the terminal identifies an index level corresponding to each enterprise evaluation index information of a type based on an index classification policy of each type, and collects a forward processing policy of each index level. And then, the terminal respectively carries out forward processing on the index data of the enterprise evaluation index information corresponding to each index level based on the forward processing strategy of each index level to obtain sub-index classification information corresponding to each enterprise evaluation index information. And then, the terminal takes sub-index grading information corresponding to all enterprise evaluation index information as index grading information of the index type. Wherein each piece of sub-index grading information is index data after forward processing of each piece of index data.
Specifically, the forward treatment formula of the minimal index is as follows:
x i is a very small index sequence.
The intermediate index forward processing formula is as follows:
M＝max{|xi-xbest|}
Conversion value calculation:
Where x best is the optimal value for the index and x i is the intermediate index sequence.
Based on the scheme, the forward processing is carried out on the index data of the enterprise evaluation index information with different index levels, so that the analysis efficiency of comprehensively analyzing the index data of the enterprise evaluation index information with different index levels is improved.
Optionally, performing standardization processing on the index data of the enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, where the method includes: identifying a standardized processing algorithm corresponding to each index level, and respectively carrying out standardized processing on the sub-index grading information corresponding to each index level based on the standardized processing algorithm corresponding to each index level to obtain standard index data corresponding to the sub-index grading information of each index level; and constructing an index matrix corresponding to each index level based on the standard index data of each index level.
In this embodiment, the terminal identifies a normalization processing algorithm corresponding to each index level, and performs normalization processing on each piece of sub-index classification information corresponding to each index level based on the normalization processing algorithm corresponding to each index level, so as to obtain standard index data corresponding to each piece of sub-index classification information of each index level. Then, the terminal constructs an index matrix corresponding to each index level based on the standard index data of each index level.
Specifically, the terminal forms a data matrix x= (X ij)m×n) from a plurality of sequences (enterprises to be evaluated) and indexes, wherein m is m sequences, and n is n evaluation indexes.
Performing standardized calculation on the data matrix:
If x j is the minimum and intermediate index:
if x j is a very large index:
Wherein, the elements in the x' ij data matrix are the elements after forward processing.
In this embodiment, the terminal performs standardized processing on the sub-index classification information of different index levels, so as to improve analysis efficiency of comprehensively analyzing the self-index classification information of different index levels.
Optionally, identifying extremum solutions of all enterprise evaluation index information includes: identifying a minimum value of each enterprise evaluation index information and a maximum value of each enterprise evaluation index information; and constructing an extremum solution set of all enterprise evaluation index information based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
In this embodiment, the terminal identifies the minimum value of each enterprise evaluation index information and the maximum value of each enterprise evaluation index information. Then, the terminal constructs an extremum solution set of all enterprise evaluation index information based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
The minimum value set is composed of the minimum values of the enterprise evaluation indexes:
Vmin＝{min(x1j),min(x2j),…,min(xij)}
The maximum value set is composed of the maximum values of the enterprise evaluation indexes:
Vmax＝{max(x1j),max(x2j),…,max(xij)}
based on the scheme, the minimum value and the maximum value of each index are identified to construct an extremum solution set of enterprise evaluation index information, so that the analysis accuracy of analyzing the association degree of each data matrix with the maximum value set and the minimum value set is improved.
Optionally, calculating the gray correlation degree between each data matrix and the extremum solution set respectively includes: acquiring a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set; for each data matrix, calculating a first gray correlation between the data matrix and the minimum value set through a first correlation algorithm, and calculating a second gray correlation between the data matrix and the maximum value set through a second correlation algorithm; and taking the first gray correlation degree and the second gray correlation degree as gray correlation degrees between the data matrix and the extremum solution set.
In this embodiment, the terminal obtains a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set. Then, the terminal calculates a first gray correlation between the data matrix and the minimum value set through a first correlation algorithm and calculates a second gray correlation between the data matrix and the maximum value set through a second correlation algorithm for each data matrix. And then, the terminal takes the first gray correlation degree and the second gray correlation degree as the gray correlation degree between the data matrix and the extremum solution set.
Specifically, the terminal uses the minimum value set and the maximum value set as reference vectors x 1、x2, respectively, and sets the data matrix to x 3. Generating an absolute value matrix a 1、A2:
A1＝[x1-x3],A2＝[x2-x3]
The terminal calculates a gray correlation matrix B 1、B2 of a 1、A2:
Wherein d 1min、d2min is the minimum value of the absolute value matrix a 1、A2, and d 1max、d1max is the maximum value of the absolute value matrix a 1、A2.
The terminal calculates gray correlation degree according to the gray correlation matrix B 1、B2:
Based on the scheme, the gray correlation degree between each data matrix and the maximum value set and the minimum value set is calculated respectively, so that the coefficients between each data matrix and the maximum value set and between each data matrix and the minimum value set are analyzed, the expansion value of each enterprise is analyzed, and the accuracy and the efficiency of analyzing the expansion value of each enterprise are improved.
Optionally, calculating, by an evaluation result algorithm, a score value of each enterprise based on each gray association degree, the extremum solution set, and each data matrix includes: and inputting a first gray correlation degree between each data matrix and the minimum value set and a second gray correlation degree between each data matrix and the maximum value set of each enterprise into an evaluation result algorithm for each enterprise, and calculating the grading value of the enterprise.
In this embodiment, the terminal inputs, for each enterprise, a first gray correlation between each data matrix and a minimum value set of the enterprise and a second gray correlation between each data matrix and a maximum value set of the enterprise into an evaluation result algorithm, and calculates a score value of the enterprise.
Wherein, the evaluation result algorithm is as follows:
the terminal can Score the corporation according to the Score j calculated by each corporation, and the greater the Score j, the more worth the corporation is.
Based on the scheme, the enterprise evaluation index information of the evaluation index types is comprehensively compared, so that the comprehensive score of each enterprise is obtained, the target enterprise is screened, and the accuracy of the screened target enterprise is improved.
The application also provides a target enterprise screening example, as shown in fig. 2, the specific processing procedure comprises the following steps:
Step S201, obtaining index data of enterprise evaluation index information of different enterprises in different index types.
Step S202, based on the index grading strategy of each type, identifying the index grade corresponding to the index information of each enterprise evaluation of the type, and collecting the forward processing strategy of each index grade.
Step S203, based on the forward processing policy of each index level, forward processing is performed on the index data of the enterprise evaluation index information corresponding to each index level, so as to obtain sub-index classification information corresponding to each enterprise evaluation index information.
Step S204, sub-index classification information corresponding to all enterprise evaluation index information is used as index classification information of the index type.
Step S205, a standardized processing algorithm corresponding to each index level is identified, and standardized processing is performed on the sub-index classification information corresponding to each index level based on the standardized processing algorithm corresponding to each index level, so as to obtain standard index data corresponding to the sub-index classification information of each index level.
Step S206, constructing an index matrix corresponding to each index level based on the standard index data of each index level.
Step S207, a minimum value of each enterprise evaluation index information and a maximum value of each enterprise evaluation index information are identified.
Step S208, an extremum solution set of all enterprise evaluation index information is constructed based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
Step S209, a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set are obtained.
Step S210, for each data matrix, calculating a first gray correlation between the data matrix and the minimum value set through a first correlation algorithm, and calculating a second gray correlation between the data matrix and the maximum value set through a second correlation algorithm.
Step S211, the first gray correlation degree and the second gray correlation degree are used as gray correlation degrees between the data matrix and the extremum solution set.
Step S212, inputting a first gray correlation degree between each data matrix and the minimum value set and a second gray correlation degree between each data matrix and the maximum value set of each enterprise into an evaluation result algorithm for each enterprise, and calculating the grading value of the enterprise.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a target enterprise screening device for realizing the above related target enterprise screening method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of one or more target enterprise screening devices provided below may be referred to the limitation of the target enterprise screening method hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 3, there is provided a target enterprise screening apparatus, comprising: an acquisition module 310, an identification module 320, a calculation module 330, and a screening module 340, wherein:
An obtaining module 310, configured to obtain index data of enterprise evaluation index information of different index types of different enterprises, and perform index classification processing on the enterprise evaluation index information of each index type to obtain index classification information of the index type;
the identifying module 320 is configured to normalize the index data of the enterprise evaluation index information corresponding to each index grading information, obtain an index matrix corresponding to each index grading information, and identify extremum solutions of all enterprise evaluation index information;
The calculating module 330 is configured to calculate a gray correlation between each data matrix and the extremum solution set, and calculate a scoring value of each enterprise through an evaluation result algorithm based on each gray correlation, the extremum solution set, and each data matrix;
and a screening module 340, configured to screen enterprises that are greater than the scoring threshold as target enterprises.
Optionally, the acquiring module 310 is specifically configured to:
based on the index grading strategy of each type, identifying the index grade corresponding to the index information of each enterprise evaluation of the type, and collecting the forward processing strategy of each index grade;
Based on a forward processing strategy of each index level, respectively carrying out forward processing on index data of enterprise evaluation index information corresponding to each index level to obtain sub-index classification information corresponding to each enterprise evaluation index information;
And taking sub-index grading information corresponding to all enterprise evaluation index information as index grading information of the index type.
Optionally, the identifying module 320 is specifically configured to:
identifying a standardized processing algorithm corresponding to each index level, and respectively carrying out standardized processing on the sub-index grading information corresponding to each index level based on the standardized processing algorithm corresponding to each index level to obtain standard index data corresponding to the sub-index grading information of each index level;
And constructing an index matrix corresponding to each index level based on the standard index data of each index level.
Optionally, the identifying module 320 is specifically configured to:
identifying a minimum value of each enterprise evaluation index information and a maximum value of each enterprise evaluation index information;
And constructing an extremum solution set of all enterprise evaluation index information based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
Optionally, the computing module 330 is specifically configured to:
acquiring a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set;
For each data matrix, calculating a first gray correlation between the data matrix and the minimum value set through the first correlation algorithm, and calculating a second gray correlation between the data matrix and the maximum value set through the second correlation algorithm;
And taking the first gray correlation degree and the second gray correlation degree as gray correlation degrees between the data matrix and the extremum solution set.
Optionally, the computing module 330 is specifically configured to:
for each enterprise, inputting a first gray correlation degree between each data matrix of the enterprise and the minimum value set and a second gray correlation degree between each data matrix and the maximum value set into the evaluation result algorithm, and calculating the evaluation value of the enterprise.
The various modules in the above-described target enterprise screening apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor implements a target enterprise screening method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method of any of the first aspects when the computer program is executed.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method of any of the first aspects.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims (10)

1. A method for screening a target enterprise, the method comprising:
Acquiring index data of enterprise evaluation index information of different enterprises in different index types, and performing index grading treatment on the enterprise evaluation index information of each index type to obtain index grading information of the index type;
Carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information;
Respectively calculating gray correlation degree between each data matrix and the extremum solution set, and calculating the grading value of each enterprise through an evaluation result algorithm based on each gray correlation degree, the extremum solution set and each data matrix;
and screening enterprises larger than the scoring threshold value as target enterprises.
2. The method according to claim 1, wherein the performing the index classification processing on the enterprise evaluation index information of the type to obtain the index classification information of the index type includes:
based on the index grading strategy of each type, identifying the index grade corresponding to the index information of each enterprise evaluation of the type, and collecting the forward processing strategy of each index grade;
Based on a forward processing strategy of each index level, respectively carrying out forward processing on index data of enterprise evaluation index information corresponding to each index level to obtain sub-index classification information corresponding to each enterprise evaluation index information;
And taking sub-index grading information corresponding to all enterprise evaluation index information as index grading information of the index type.
3. The method according to claim 2, wherein the normalizing the index data of the enterprise evaluation index information corresponding to each index classification information to obtain the index matrix corresponding to each index classification information includes:
identifying a standardized processing algorithm corresponding to each index level, and respectively carrying out standardized processing on the sub-index grading information corresponding to each index level based on the standardized processing algorithm corresponding to each index level to obtain standard index data corresponding to the sub-index grading information of each index level;
And constructing an index matrix corresponding to each index level based on the standard index data of each index level.
4. The method of claim 1, wherein identifying extremum solutions for all enterprise valuation metrics comprises:
identifying a minimum value of each enterprise evaluation index information and a maximum value of each enterprise evaluation index information;
And constructing an extremum solution set of all enterprise evaluation index information based on the minimum value set of all enterprise evaluation index information and the maximum value set of all enterprise evaluation index information.
5. The method of claim 1, wherein the separately computing gray associations between each data matrix and the extremum solution sets comprises:
acquiring a first association algorithm corresponding to the minimum value set and a second association algorithm corresponding to the maximum value set;
For each data matrix, calculating a first gray correlation between the data matrix and the minimum value set through the first correlation algorithm, and calculating a second gray correlation between the data matrix and the maximum value set through the second correlation algorithm;
And taking the first gray correlation degree and the second gray correlation degree as gray correlation degrees between the data matrix and the extremum solution set.
6. The method of claim 5, wherein calculating a scoring value for each business by an evaluation result algorithm based on each gray relevance, the extremum solution set, and each data matrix, comprises:
for each enterprise, inputting a first gray correlation degree between each data matrix of the enterprise and the minimum value set and a second gray correlation degree between each data matrix and the maximum value set into the evaluation result algorithm, and calculating the evaluation value of the enterprise.
7. A target enterprise screening apparatus, the apparatus comprising:
The system comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring index data of enterprise evaluation index information of different enterprises in different index types, and performing index classification processing on the enterprise evaluation index information of each index type to obtain index classification information of the index type;
The identification module is used for carrying out standardization processing on index data of each enterprise evaluation index information corresponding to each index grading information to obtain an index matrix corresponding to each index grading information, and identifying extremum solution sets of all enterprise evaluation index information;
the computing module is used for respectively computing gray correlation degree between each data matrix and the extremum solution set, and computing the grading value of each enterprise through an evaluation result algorithm based on each gray correlation degree, each extremum solution set and each data matrix;
and the screening module is used for screening enterprises larger than the scoring threshold value to serve as target enterprises.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
CN202410304177.4A 2024-03-18 2024-03-18 Target enterprise screening method, device, computer equipment and storage medium Pending CN118171953A (en)

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