CN112598228B - Enterprise competitiveness analysis method, device, equipment and storage medium - Google Patents
Enterprise competitiveness analysis method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides an enterprise competitiveness analysis method, device, equipment and storage medium, wherein the method comprises the following steps: acquiring a first parameter value corresponding to an index to be analyzed of a target enterprise; combining sample data in a preset sample library and the first parameter value, carrying out statistical analysis on the index to be analyzed based on the parameter value, and obtaining competitive state analysis data corresponding to the index to be analyzed; and performing evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report. The method can analyze the competitive position of the enterprise in the same industry or the same type of enterprise, analyze the current competitive position of the enterprise, intuitively know the competitive situation of the enterprise in the same industry or the same type of enterprise, and facilitate the subsequent development planning for the enterprise.
Description
Technical Field
The application belongs to the technical field of data processing and data analysis, and particularly relates to an enterprise competitiveness analysis method, device, equipment and storage medium.
Background
The enterprise competitiveness is that under the competitive market condition, the enterprise realizes the comprehensive capability of self value on the basis of creating value for customers by cultivating self resources and capability and acquiring external available resources and comprehensively utilizing the resources. However, in analyzing the business competitiveness of a business, it is often necessary to analyze from multiple indicators of multiple dimensions of the business, such as a business income situation, a net profit situation, a business core talent situation, and so on. The existing enterprise competitiveness analysis methods can only be used for comparing the enterprise competitiveness between one enterprise and another enterprise, cannot analyze the competitive positions of the enterprises in the same industry or the same type of enterprises, and cannot acquire the current competitive length of the enterprises, so that the existing enterprise competitiveness analysis methods cannot be used for making an enterprise development plan.
Disclosure of Invention
In view of the above, the embodiments of the present application provide an analysis method, apparatus, device and storage medium for enterprise competitiveness, which can intuitively understand the competitiveness of an enterprise in the same industry or the same type of enterprise, and facilitate the subsequent development planning for the enterprise.
A first aspect of an embodiment of the present application provides an enterprise competitiveness analysis method, including:
acquiring a first parameter value corresponding to an index to be analyzed of a target enterprise;
combining sample data in a preset sample library and the first parameter value, carrying out statistical analysis on the index to be analyzed based on the parameter value, and obtaining competitive state analysis data corresponding to the index to be analyzed;
and performing evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the step of performing statistical analysis based on a parameter value on the index to be analyzed by combining sample data in a preset sample library and the first parameter value to obtain competitive state analysis data corresponding to the index to be analyzed includes:
acquiring a sample parameter value corresponding to a sample in the sample data;
and comparing the first parameter value with a sample parameter value corresponding to a sample in the sample data, and generating an exceeding duty ratio of the first parameter value corresponding to the sample data according to a comparison result.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the step of comparing the first parameter value with a sample parameter value corresponding to a sample in the sample data, and generating, according to a comparison result, an excess duty ratio of the first parameter value corresponding to the sample data includes:
Counting the number of samples with a first parameter value greater than a sample parameter value in the sample data;
and calculating the ratio of the number of samples with the first parameter value larger than the sample parameter value to the total number of samples of the sample data, and obtaining the exceeding duty ratio of the first parameter value corresponding to the sample data.
With reference to the first possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the step of comparing the first parameter value with a sample parameter value corresponding to a sample in the sample data, and generating, according to a comparison result, an excess duty ratio of the first parameter value corresponding to the sample data includes:
judging whether the first parameter value is larger than the largest sample parameter value in the sample data or not and judging whether the first parameter value is smaller than the smallest sample parameter value in the sample data or not;
and if the first parameter value is larger than the minimum sample parameter value in the sample data and smaller than the maximum sample parameter value in the sample data, calculating an excess duty ratio of the first parameter value in the sample data according to a preset probability algorithm.
With reference to the first aspect and any one of the first to third possible implementation manners of the first aspect, in a fourth possible implementation manner of the first aspect, the step of performing statistical analysis based on a parameter value on the index to be analyzed by combining sample data in a preset sample library and the first parameter value, to obtain competitive state analysis data corresponding to the index to be analyzed includes:
Calculating an average value of sample parameter values corresponding to samples in the sample data to obtain the average parameter values of the sample data; or/and (b)
And sorting the magnitude of the sample parameter values corresponding to the samples in the sample data, and acquiring the median parameter values of the sample data according to the sorting relation.
With reference to the first aspect, in a fifth possible implementation manner of the first aspect, before the step of performing statistical analysis based on the parameter value on the index to be analyzed by combining sample data in a preset sample library and the first parameter value to obtain competitive state analysis data corresponding to the index to be analyzed, the method further includes:
sample data are configured for the index to be analyzed of the target enterprise according to user requirements or according to preset configuration rules, wherein the sample data comprise enterprise samples for carrying out statistical analysis on the index to be analyzed and parameter values of the sample enterprises corresponding to the index to be analyzed.
With reference to the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, the configuring sample data for the to-be-analyzed index of the target enterprise according to a user requirement or according to a preset configuration rule, where the sample data includes an enterprise sample for performing statistical analysis on the to-be-analyzed index and a parameter value of the sample enterprise corresponding to the to-be-analyzed index, includes:
Acquiring industry/type information corresponding to the target enterprise;
performing traversal screening in the preset sample library according to the industry/type information corresponding to the target enterprise to obtain enterprise sample data of the same industry/type as the target enterprise;
and according to the index to be analyzed of the target enterprise, acquiring a parameter value corresponding to the index to be analyzed from the enterprise sample data, mapping and correlating the enterprise sample and the parameter value corresponding to the index to be analyzed, and recording the mapping and correlated enterprise sample and the parameter value in a sample data table.
A second aspect of an embodiment of the present application provides an enterprise competitiveness analysis device, including:
the acquisition module is used for acquiring a first parameter value corresponding to the target enterprise index to be analyzed;
the analysis module is used for carrying out statistic analysis based on parameter values on the index to be analyzed by combining sample data in a preset sample library and the first parameter values to obtain competitive state analysis data corresponding to the index to be analyzed;
and the generation module is used for carrying out evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report.
A third aspect of an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the enterprise competitiveness analysis method according to any one of the first aspects when the computer program is executed.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for analyzing enterprise competitiveness according to any one of the first aspects.
Compared with the prior art, the embodiment of the application has the beneficial effects that:
the method comprises the steps of obtaining a first parameter value corresponding to an index to be analyzed of a target enterprise; combining sample data in a preset sample library and the first parameter value, carrying out statistical analysis on the index to be analyzed based on the parameter value, and obtaining competitive state analysis data corresponding to the index to be analyzed; and performing evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report. The method can analyze the competitive position of the enterprise in the same industry or the same type of enterprise, analyze the current competitive position of the enterprise, intuitively know the competitive situation of the enterprise in the same industry or the same type of enterprise, and facilitate the subsequent development planning for the enterprise.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a basic method for analyzing enterprise competitiveness according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for performing parameter-based statistical analysis on an index to be analyzed in the enterprise competitiveness analysis method according to the embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for calculating an excess duty ratio of a first parameter value in sample data according to an exemplary embodiment of the present application;
FIG. 4 is a flowchart illustrating another method for calculating the excess duty ratio of the first parameter value in the sample data according to the method for analyzing enterprise competitiveness according to the embodiment of the present application;
FIG. 5 is a schematic flow chart of a method for configuring sample data in an enterprise competitiveness analysis method according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of an enterprise competitiveness analysis device according to an embodiment of the present application;
fig. 7 is a schematic diagram of an electronic device for implementing an analysis method for enterprise competitiveness according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
In order to illustrate the technical scheme of the application, the following description is made by specific examples.
In some embodiments of the present application, referring to fig. 1, fig. 1 is a schematic flow chart of a basic method for analyzing enterprise competitiveness according to an embodiment of the present application. The details are as follows:
in step S101, a first parameter value corresponding to an index to be analyzed of a target enterprise is obtained.
In this embodiment, when the enterprise competitiveness analysis is performed, the enterprise competitiveness of the enterprise mapped by each relevant index of the enterprise is intuitively displayed by analyzing one or more indexes of relevant indexes such as enterprise net profit, enterprise income, enterprise gross interest, enterprise net interest, enterprise core technicians, enterprise research and development technicians, enterprise patent number, enterprise research and development investment occupation ratio, and the like, and specifically determining the ranking of the enterprise's competitiveness in the same industry or the same type of enterprise by analyzing the parameter value corresponding to the index, so that the enterprise competitiveness of the enterprise mapped by each relevant index of the enterprise can be formulated for the enterprise according to the enterprise competitiveness of the enterprise mapped by the relevant index. In this embodiment, the analysis method for enterprise competitiveness is implemented based on a pre-configured statistical analysis model, and a user only needs to input parameter value data of an index to be analyzed of a target enterprise in the model, where the parameter value data of the index to be analyzed of the target enterprise is a first parameter value.
In step S102, combining the sample data in the preset sample library and the first parameter value, performing statistical analysis based on the parameter value on the index to be analyzed, and obtaining competitive state analysis data corresponding to the index to be analyzed.
In this embodiment, when performing enterprise competitiveness analysis, the statistical analysis model may obtain sample data from a preset sample library, combine the sample data, compare parameter value data (i.e., sample parameter values) corresponding to each sample in the sample data with the first parameter value, and perform statistical analysis based on the parameter values on the index to be analyzed according to the comparison result, so as to obtain the competitiveness state analysis data corresponding to the index to be analyzed. For example, the sorting position of the first parameter value in the sample data is determined by comparing the magnitude relation between the first parameter value and the sample parameter value corresponding to each sample in the sample data, and the current competitive state of the target enterprise can be reflected according to the sorting position. And counting the number of samples with the first parameter value larger than the sample parameter value in the sample data through the sequencing position, and obtaining that the first parameter value corresponds to the exceeding duty ratio in the sample data. The exceeding duty ratio can be used as competitive state analysis data corresponding to the index to be analyzed and used for reflecting the current competitive state of the target enterprise, wherein the larger the exceeding duty ratio is, the stronger the enterprise competitiveness of the target enterprise is indicated.
In step S103, an enterprise competition status of the target enterprise is evaluated and analyzed according to the competitive status analysis data, and an evaluation report is generated.
In this embodiment, the statistical analysis model evaluates the enterprise competitiveness of the enterprise according to the competitiveness state analysis data obtained by the analysis thereof, and generates an evaluation report. In the statistical analysis model, the enterprise competitiveness state of the enterprise may be evaluated by classifying the enterprise according to the strength of the enterprise competitiveness state in advance, and the classified classification is supplied to the statistical analysis model. In this embodiment, after the competitive state analysis data is obtained, the competitive state analysis data is compared with the data requirements corresponding to the pre-divided levels, and the level of the competitive state analysis data meeting the requirements is determined, where the level meeting the requirements is the enterprise competitive state of the target enterprise obtained by evaluating the target enterprise based on the index to be analyzed, and the index to be analyzed, the competitive state analysis data, and the information such as the level determined according to the competitive state analysis data are generated and output.
According to the enterprise competitiveness analysis method provided by the embodiment, the sample data in the preset sample library and the first parameter value corresponding to the target enterprise obtained based on the index to be analyzed are combined, statistical analysis based on the parameter value is carried out on the index to be analyzed, the competitiveness state analysis data corresponding to the index to be analyzed is obtained, then the enterprise competitiveness state of the target enterprise is evaluated according to the competitiveness state analysis data, and an evaluation report is generated. In the evaluation report, based on the competition state analysis data, the competition condition of the target enterprise in the same industry or the same type of enterprises and the advantages and the disadvantages of the current enterprise competition of the target enterprise can be intuitively known, so that development planning for the enterprise can be conveniently formulated later.
In some embodiments of the present application, referring to fig. 2, fig. 2 is a flow chart of a method for performing statistical analysis based on parameter values on an index to be analyzed in the method for analyzing enterprise competitiveness according to the embodiment of the present application. The details are as follows:
step S21: acquiring a sample parameter value corresponding to a sample in the sample data;
step S22: and comparing the first parameter value with a sample parameter value corresponding to a sample in the sample data, and generating an exceeding duty ratio of the first parameter value corresponding to the sample data according to a comparison result.
In this embodiment, the sample data is a data set generated by the statistical analysis model based on the configuration of the index to be analyzed, and the data set includes a plurality of samples and parameter value data related to the index to be analyzed, i.e. sample parameter values. The sample is characterized as an enterprise of the same industry or the same type as the target enterprise, and the sample parameter value is the parameter value data of the enterprise characterized by the sample on the aspect of the index to be analyzed. For example, if the index to be analyzed is a revenue index, then the sample parameter value included in the data set is revenue parameter value data of the same industry or type of business as the target business, which is characterized by the sample. The excess duty cycle is used to measure the level of enterprise competitiveness of the target enterprise, wherein a higher excess duty cycle of the first parameter value in the sample data indicates a higher enterprise competitiveness of the target enterprise within the enterprise range encompassed by the sample data, and a higher ranking position of the first parameter value indicates a higher excess duty cycle of the first parameter value in the sample data. In this embodiment, sample parameter values corresponding to respective samples in the sample data are obtained by analyzing the data set. After obtaining the sample parameter values corresponding to each sample in the sample data, comparing the first parameter value with the sample parameter values corresponding to each sample in the sample data, and sequencing all parameter values comprising the first parameter value and all sample parameter values in the sample data from large to small to obtain a comparison result, wherein the comparison result is embodied as a sequencing sequence comprising all the parameter values. Thereby, based on the comparison result, the ordering position of the first parameter value in the sample data can be obtained from the ordering sequence.
Referring to fig. 3 together, fig. 3 is a flow chart illustrating a method for calculating an excess duty ratio of a first parameter value in sample data according to an embodiment of the application. The details are as follows:
step S31: counting the number of excess samples of the sample data, wherein the first parameter value is larger than the sample parameter value;
step S32: and calculating the ratio of the excess sample number to the total sample number of the sample data, and obtaining the excess duty ratio of the first parameter value corresponding to the sample data.
In this embodiment, the more front the sorting position of the first parameter value is, the stronger the enterprise competitiveness of the target enterprise in terms of the index to be analyzed is indicated, so, according to the sorting position of the first parameter value in the sample data in the comparison result, the number of samples exceeding the first parameter value by more than the sample parameter value in the sample data can be counted, and according to the number of samples exceeding the sample value and the total number of samples in the sample data, the ratio of the first parameter value to the total number of samples in the sample data is calculated, so as to obtain the exceeding duty ratio of the first parameter value in the sample data, where the exceeding duty ratio is represented as the ratio of the number of samples, where the first parameter value is greater than the sample parameter value, to the total number of samples in the sample data. Based on the excess duty cycle, the excess duty cycle may be used to analyze enterprise competitive status of the target enterprise across the enterprise encompassed by the sample data. According to the embodiment, the competitive position of the target enterprise in the same industry or the same type of enterprises can be analyzed according to the sample quantity ratio, and the competitive strength of the target enterprise in the aspect of indexes to be analyzed is analyzed.
In some embodiments of the present application, referring to fig. 4, fig. 4 is a flowchart illustrating another method for calculating an excess duty ratio of a first parameter value in sample data according to the method for analyzing enterprise competitiveness provided by the embodiment of the present application. The details are as follows:
step S41: judging whether the first parameter value is larger than the largest sample parameter value in the sample data or not and judging whether the first parameter value is smaller than the smallest sample parameter value in the sample data or not;
step S42: and if the first parameter value is larger than the largest sample parameter value in the sample data and smaller than the smallest sample parameter value in the sample data, calculating the exceeding duty ratio of the first parameter value in the sample data according to a preset probability algorithm.
In this embodiment, when calculating the excess duty ratio of the first parameter value in the sample data, the calculation may be further performed according to a probability algorithm, and specifically, based on the probability algorithm, the sorting positions of the first parameter value in the sample data may be classified into the following three types: the first is that the first parameter value is larger than the largest sample parameter value in the sample data, i.e. larger than any sample parameter value in the sample data; the second is that the first parameter value is smaller than the smallest sample parameter value in the sample data, i.e. smaller than any sample parameter value in the sample data; the third is that the first parameter value is larger than the smallest sample parameter value in the sample data and smaller than the largest sample parameter value in the sample data, i.e. between the smallest sample parameter value and the largest sample parameter value. Thus, in this embodiment, the excess duty ratio of the first parameter value in the sample data may be calculated according to the probability algorithm when the first parameter value is greater than the minimum sample parameter value in the sample data and less than the maximum sample parameter value in the sample data by determining whether the first parameter value is greater than the maximum sample parameter value in the sample data and determining whether the first parameter value is less than the minimum sample parameter value in the sample data. In this embodiment, the probability algorithm is a standard normal distribution algorithm. Specifically, the average parameter value μ and the sample standard deviation σ of the sample data are calculated based on all the sample parameter values in the sample data.
The calculation formula of the average parameter value mu is as follows:
the calculation formula of the sample standard deviation sigma is as follows:
wherein x is 1 、x 2 、...、x n Sample parameter values corresponding to all samples in the sample data; n is the number of samples in the sample data.
Based on a standard normal distribution algorithm, a normal distribution probability value corresponding to a first parameter value can be obtained by calculating a normal distribution critical value corresponding to the first parameter value and then inquiring a preset normal distribution critical value table through the normal distribution critical value, wherein the probability value is the excess duty ratio of the first parameter value corresponding to sample data. The calculation formula of the normal distribution critical value Z corresponding to the first parameter value is as follows:
wherein X is a first parameter value; n is the number of samples in the sample data; μ is the average parameter value of the sample data; sigma is the sample standard deviation of the sample data.
In this embodiment, further comprising: when the first parameter value is larger than the largest sample parameter value in the sample data, directly configuring the excess duty ratio of the first parameter value in the sample data to be 100%; and when the first parameter value is smaller than the minimum sample parameter value in the sample data, directly configuring the first parameter value to be corresponding to the excess duty ratio of 0% in the sample data.
In some embodiments of the present application, the average parameter value and the median parameter value of the sample data may also be used as competitive status analysis data corresponding to the index to be analyzed. The average parameter value of the sample data can be obtained by calculating the average value of the sample parameter values corresponding to the samples in the sample data. The median parameter value of the sample data can be obtained by sorting the magnitudes of the sample parameter values corresponding to the samples in the sample data and then according to the sorting relation. In this embodiment, if the average parameter value and the median parameter value of the sample data are used as the competitive status analysis data corresponding to the index to be analyzed to perform the enterprise competitive analysis on the target enterprise, the specific analysis process includes, but is not limited to: and comparing the first parameter value with the average parameter value and the median parameter value respectively, wherein if the comparison result is that the first parameter value is larger than the average parameter value and the median parameter value, it can be judged that the target enterprise exceeds the industry average level in terms of the index to be analyzed, and the ranking in the same industry is in a middle-to-top state and belongs to a better development state. If the comparison result is that the first parameter value is larger than the average parameter value but smaller than the median parameter value, it can be judged that the target enterprise exceeds the industry average level in terms of the index to be analyzed, but the rank in the same industry is under the middle bias, the overall development in the industry is better but the competition is larger, and the enterprise development planning can be formulated by taking the mode of improving the business capability of the company into consideration, so that the method has an advantage in the industry competition. If the comparison result is that the first parameter value is smaller than the average parameter value but larger than the median parameter value, the target enterprise can be judged to be lower than the industry average level in terms of the index to be analyzed, and although the ranking in the same industry is on the middle bias, the gap between the target enterprise and the center enterprise in the industry is larger, and the enterprise development planning can be formulated by reducing the gap between the target enterprise and the center enterprise. If the comparison result is that the first parameter value is smaller than the average parameter value and the median parameter value, the target enterprise can be judged to be lower than the industry average level in the aspect of the index to be analyzed, and the ranking in the same industry is in a moderate bias state in the industry.
In some embodiments of the present application, the sample data for performing statistical analysis may be flexibly configured according to a user requirement or a configuration rule preset based on an actual scenario, so as to obtain the sample data for configuring the index to be analyzed of the target enterprise. In this embodiment, the sample data may be configured in a preset sample library, or may be accessed from a third party through an open interface configured in the sample library. The sample data is customized by accessing from a third party, so that the statistical analysis model can adapt to other application scenes, such as analysis product qualification rate, analysis achievement qualification rate and the like besides the related indexes stored in the preset sample library. The sample data comprises sample enterprises for carrying out statistical analysis on the index to be analyzed and parameter value data, namely sample parameter values, of the sample enterprises corresponding to the index to be analyzed. Sample enterprises in the sample data are enterprises of the same industry or type as the target enterprises. For example, the index to be analyzed of the target enterprise is a business income index, and then the configured sample data is the parameter value data of the business income index of some enterprises in the same industry or the same type as the target enterprise. The number of sample data is determined according to the user's requirements or pre-fixed requirements in the configuration rules. For example, based on the requirement of the user or a preset configuration rule, the method can be set according to the region position, if the user requirement is analyzed only by the enterprise competitiveness of the target enterprise in the city where the user is located, the sample data is the enterprise of the same industry or the same type as the target enterprise contained in the city, and the number of the sample data is the number obtained by counting the enterprise of the same industry or the same type as the target enterprise contained in the city; if the user needs to analyze only the enterprise competitiveness of the target enterprise in the country where the user needs to analyze, the number of sample data obtained by counting the enterprises which are included in the country and are in the same industry or the same type with the target enterprise. Therefore, the statistical analysis model can flexibly configure sample data according to actual scenes, and flexibly regulate and control the accuracy of the enterprise competitive state analysis.
In some embodiments of the present application, referring to fig. 5, fig. 5 is a flowchart of a method for configuring sample data in an enterprise competitiveness analysis method according to an embodiment of the present application. The details are as follows:
step S51: acquiring industry/type information corresponding to the target enterprise;
step S52: performing traversal screening in the preset sample library according to the industry/type information corresponding to the target enterprise to obtain enterprise sample data of the same industry/type as the target enterprise;
step S53: and according to the index to be analyzed of the target enterprise, acquiring a parameter value corresponding to the index to be analyzed from the enterprise sample data, mapping and correlating the enterprise sample and the parameter value corresponding to the index to be analyzed, and recording the mapping and correlated enterprise sample and the parameter value in a sample data table.
In this embodiment, the preset sample library stores sample data including, but not limited to, eight analysis indexes including enterprise net profit, enterprise income, enterprise gross interest rate, enterprise net interest rate, enterprise core technician, enterprise research and development technician, enterprise patent number, enterprise research and development investment ratio, etc., which can be obtained by collecting financial and business information disclosed by enterprise disclosure. Furthermore, the preset sample library can also set timed data update, so that the validity of sample data stored in the sample library is ensured. In this embodiment, when performing enterprise competition analysis on a target enterprise, the statistical analysis model needs to first acquire an index to be analyzed based on industry information to which the target enterprise belongs, so as to determine an analysis range. Therefore, in this embodiment, after determining the index to be analyzed, the industry/type information corresponding to the target enterprise may be obtained, and then traversal screening is performed in the preset sample library according to the industry/type information corresponding to the target enterprise, so as to obtain enterprise sample data of the same industry/type as the target enterprise; and then according to the determined index to be analyzed, acquiring a parameter value corresponding to the index to be analyzed from enterprise sample data of a preset sample library, and then mapping and correlating the enterprise sample and the parameter value corresponding to the index to be analyzed in a sample data table. And obtaining a corresponding relation table of enterprise samples and corresponding parameter values of the indexes to be analyzed, wherein the corresponding relation table is sample data configured for the indexes to be analyzed of the target enterprise.
It should be understood that, the sequence number of each step in the foregoing embodiment does not mean the execution sequence, and the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In some embodiments of the present application, referring to fig. 6, fig. 6 is a schematic structural diagram of an enterprise competitiveness analysis device according to an embodiment of the present application, which is described in detail below:
in this embodiment, the enterprise competitiveness analysis device includes: an acquisition module 601, an analysis module 602 and a generation module 603. The obtaining module 601 is configured to obtain a first parameter value corresponding to an index to be analyzed of a target enterprise. The analysis module 602 is configured to perform statistical analysis on the index to be analyzed based on a parameter value by combining sample data in a preset sample library and the first parameter value, and obtain competitive state analysis data corresponding to the index to be analyzed. The generating module 603 is configured to perform evaluation analysis on the enterprise competition status of the target enterprise according to the competitive status analysis data, and generate an evaluation report.
The enterprise competitiveness analysis device corresponds to the enterprise competitiveness analysis method one by one, and is not described herein.
In some embodiments of the present application, please refer to fig. 7, fig. 7 is a schematic diagram of an electronic device for implementing an analysis method of enterprise competitiveness according to an embodiment of the present application. As shown in fig. 7, the electronic device 7 of this embodiment includes: a processor 71, a memory 72 and a computer program 73, such as an enterprise competitiveness analysis program, stored in said memory 72 and executable on said processor 71. The processor 71, when executing the computer program 72, implements the steps of the various enterprise competitiveness analysis method embodiments described above. Alternatively, the processor 71 may perform the functions of the modules/units of the above-described device embodiments when executing the computer program 73.
Illustratively, the computer program 73 may be partitioned into one or more modules/units that are stored in the memory 72 and executed by the processor 71 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions for describing the execution of the computer program 73 in the electronic device 7. For example, the computer program 73 may be split into:
The acquisition module is used for acquiring a first parameter value corresponding to the target enterprise index to be analyzed;
the analysis module is used for carrying out statistic analysis based on parameter values on the index to be analyzed by combining sample data in a preset sample library and the first parameter values to obtain competitive state analysis data corresponding to the index to be analyzed;
and the generation module is used for carrying out evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report.
The electronic device may include, but is not limited to, a processor 71, a memory 72. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the electronic device 7 and is not meant to be limiting as the electronic device 7 may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The processor 71 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 72 may be an internal storage unit of the electronic device 7, such as a hard disk or a memory of the electronic device 7. The memory 72 may be an external storage device of the electronic device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 7. Further, the memory 72 may also include both an internal storage unit and an external storage device of the electronic device 7. The memory 72 is used to store the computer program as well as other programs and data required by the electronic device. The memory 72 may also be used to temporarily store data that has been output or is to be output.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the various method embodiments described above.
Embodiments of the present application provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (7)
1. A method for analyzing competitiveness of an enterprise, comprising:
acquiring a first parameter value corresponding to an index to be analyzed of a target enterprise and configuring sample data for the index to be analyzed of the target enterprise according to user requirements or a preset configuration rule, wherein the sample data comprises enterprise samples for carrying out statistical analysis on the index to be analyzed and parameter values of the sample enterprises corresponding to the index to be analyzed;
The sample parameter values corresponding to the samples in the sample data are obtained, the first parameter values are compared with the sample parameter values corresponding to the samples in the sample data in size, an excess duty ratio of the first parameter values corresponding to the samples in the sample data is generated according to comparison results, the excess duty ratio of the first parameter values corresponding to the samples in the sample data is obtained as competitive state analysis data corresponding to the index to be analyzed, wherein the sample parameter values corresponding to the samples in the sample data comprise average parameter values or/and median parameter values, the average parameter values are obtained by carrying out average value calculation on the sample parameter values corresponding to the samples in the sample data, and the median parameter values are obtained by carrying out size sorting on the sample parameter values corresponding to the samples in the sample data;
and performing evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report.
2. The method for analyzing the competitiveness of an enterprise according to claim 1, wherein the step of comparing the first parameter value with a sample parameter value corresponding to a sample in the sample data, and generating an excess duty ratio of the first parameter value corresponding to the sample data according to the comparison result includes:
Counting the number of samples with a first parameter value greater than a sample parameter value in the sample data;
and calculating the ratio of the number of samples with the first parameter value larger than the sample parameter value to the total number of samples of the sample data, and obtaining the exceeding duty ratio of the first parameter value corresponding to the sample data.
3. The method for analyzing the competitiveness of an enterprise according to claim 1, wherein the step of comparing the first parameter value with a sample parameter value corresponding to a sample in the sample data, and generating an excess duty ratio of the first parameter value corresponding to the sample data according to the comparison result includes:
judging whether the first parameter value is larger than the largest sample parameter value in the sample data or not and judging whether the first parameter value is smaller than the smallest sample parameter value in the sample data or not;
and if the first parameter value is larger than the minimum sample parameter value in the sample data and smaller than the maximum sample parameter value in the sample data, calculating an excess duty ratio of the first parameter value in the sample data according to a preset probability algorithm.
4. The method for analyzing the competitiveness of an enterprise according to claim 1, wherein the step of configuring sample data for the target enterprise to be analyzed according to user requirements or according to a preset configuration rule, wherein the sample data includes an enterprise sample for performing statistical analysis on the target enterprise to be analyzed and a parameter value of the sample enterprise corresponding to the target enterprise to be analyzed, includes:
Acquiring industry/type information corresponding to the target enterprise;
performing traversal screening in the preset sample library according to the industry/type information corresponding to the target enterprise to obtain enterprise sample data of the same industry/type as the target enterprise;
and according to the index to be analyzed of the target enterprise, acquiring a parameter value corresponding to the index to be analyzed from the enterprise sample data, mapping and correlating the enterprise sample and the parameter value corresponding to the index to be analyzed, and recording the mapping and correlated enterprise sample and the parameter value in a sample data table.
5. An enterprise competitiveness analysis device for performing the method according to any one of claims 1-4, comprising:
the system comprises an acquisition module, a configuration module and a storage module, wherein the acquisition module is used for acquiring a first parameter value corresponding to a target enterprise to-be-analyzed index and configuring sample data for the target enterprise to-be-analyzed index according to user requirements or a preset configuration rule, wherein the sample data comprises enterprise samples for carrying out statistical analysis on the to-be-analyzed index and parameter values of the sample enterprises corresponding to the to-be-analyzed index;
the analysis module is used for combining the sample data and the first parameter value, obtaining a sample parameter value corresponding to a sample in the sample data, comparing the first parameter value with a sample parameter value corresponding to the sample in the sample data, generating an excess duty ratio of the first parameter value corresponding to the sample data according to a comparison result, and obtaining the excess duty ratio of the first parameter value corresponding to the sample data as competitive state analysis data corresponding to the index to be analyzed, wherein the sample parameter value corresponding to the sample in the sample data comprises an average parameter value or/and a median parameter value, the average parameter value is obtained by carrying out average value calculation on the sample parameter value corresponding to the sample in the sample data, and the median parameter value is obtained by carrying out size sorting on the sample parameter value corresponding to the sample in the sample data;
And the generation module is used for carrying out evaluation analysis on the enterprise competition state of the target enterprise according to the competition state analysis data to generate an evaluation report.
6. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the enterprise competitiveness analysis method according to any one of claims 1 to 4 when the computer program is executed.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the enterprise competitiveness analysis method according to any one of claims 1 to 4.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002015108A (en) * | 2000-06-30 | 2002-01-18 | Nomura Holding Inc | Device and method for analyzing enterprise value |
| JPWO2005050511A1 (en) * | 2003-12-19 | 2007-08-23 | 株式会社アイ・ピー・ビー | Company evaluation device and company evaluation program |
| KR20120075537A (en) * | 2010-11-23 | 2012-07-09 | 한국과학기술정보연구원 | System and method for diagnosis of business competitiveness of company |
| CN105574680A (en) * | 2015-12-27 | 2016-05-11 | 乌鲁木齐三力元生产力促进中心有限公司 | Diagnosis method and system of enterprise competitiveness |
| CN107944760A (en) * | 2017-12-15 | 2018-04-20 | 广州市齐明软件科技有限公司 | A kind of enterprise's bidding competition power analysis method and system |
| KR101864509B1 (en) * | 2016-11-30 | 2018-06-04 | 영남대학교 산학협력단 | Method and system for analyzing bigdata |
| CN108198092A (en) * | 2018-01-15 | 2018-06-22 | 国网上海市电力公司 | A kind of Power Generation competitiveness integrated evaluating method |
| CN108985600A (en) * | 2018-07-03 | 2018-12-11 | 龙马智芯（珠海横琴）科技有限公司 | Analysis method, computer readable storage medium and the analytical equipment of enterprise competitiveness |
| CN109493118A (en) * | 2018-10-17 | 2019-03-19 | 国家电网有限公司 | A kind of power grid enterprises' market competitiveness Visual evaluation model building method and system |
| CN109523116A (en) * | 2018-10-11 | 2019-03-26 | 平安科技（深圳）有限公司 | Business risk analysis method, device, computer equipment and storage medium |
| CN110428087A (en) * | 2019-06-25 | 2019-11-08 | 万翼科技有限公司 | Business stability prediction technique, device, computer equipment and storage medium |
| CN110991936A (en) * | 2019-12-23 | 2020-04-10 | 业如商业保理(重庆)有限公司 | Enterprise grading and rating method, device, equipment and medium |
| CN111260402A (en) * | 2020-01-16 | 2020-06-09 | 精硕科技（北京）股份有限公司 | Brand competitiveness analysis method and device |
| CN111680859A (en) * | 2020-04-16 | 2020-09-18 | 北京信息科技大学 | Platform patent value evaluation method based on information ecological theory and RF-GA-BP neural network |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6956716B2 (en) * | 2003-07-30 | 2005-10-18 | Hitachi Global Storage Technologies Netherlands, B.V. | Magnetic head having multilayer heater for thermally assisted write head and method of fabrication thereof |
| US20150154613A1 (en) * | 2013-02-27 | 2015-06-04 | Google Inc. | Competitor analytics |
| CN110969330A (en) * | 2018-09-30 | 2020-04-07 | 曹西军 | Enterprise competitive barrier assessment method and system |
-
2020
- 2020-12-07 CN CN202011418246.2A patent/CN112598228B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002015108A (en) * | 2000-06-30 | 2002-01-18 | Nomura Holding Inc | Device and method for analyzing enterprise value |
| JPWO2005050511A1 (en) * | 2003-12-19 | 2007-08-23 | 株式会社アイ・ピー・ビー | Company evaluation device and company evaluation program |
| KR20120075537A (en) * | 2010-11-23 | 2012-07-09 | 한국과학기술정보연구원 | System and method for diagnosis of business competitiveness of company |
| CN105574680A (en) * | 2015-12-27 | 2016-05-11 | 乌鲁木齐三力元生产力促进中心有限公司 | Diagnosis method and system of enterprise competitiveness |
| KR101864509B1 (en) * | 2016-11-30 | 2018-06-04 | 영남대학교 산학협력단 | Method and system for analyzing bigdata |
| CN107944760A (en) * | 2017-12-15 | 2018-04-20 | 广州市齐明软件科技有限公司 | A kind of enterprise's bidding competition power analysis method and system |
| CN108198092A (en) * | 2018-01-15 | 2018-06-22 | 国网上海市电力公司 | A kind of Power Generation competitiveness integrated evaluating method |
| CN108985600A (en) * | 2018-07-03 | 2018-12-11 | 龙马智芯（珠海横琴）科技有限公司 | Analysis method, computer readable storage medium and the analytical equipment of enterprise competitiveness |
| CN109523116A (en) * | 2018-10-11 | 2019-03-26 | 平安科技（深圳）有限公司 | Business risk analysis method, device, computer equipment and storage medium |
| CN109493118A (en) * | 2018-10-17 | 2019-03-19 | 国家电网有限公司 | A kind of power grid enterprises' market competitiveness Visual evaluation model building method and system |
| CN110428087A (en) * | 2019-06-25 | 2019-11-08 | 万翼科技有限公司 | Business stability prediction technique, device, computer equipment and storage medium |
| CN110991936A (en) * | 2019-12-23 | 2020-04-10 | 业如商业保理(重庆)有限公司 | Enterprise grading and rating method, device, equipment and medium |
| CN111260402A (en) * | 2020-01-16 | 2020-06-09 | 精硕科技（北京）股份有限公司 | Brand competitiveness analysis method and device |
| CN111680859A (en) * | 2020-04-16 | 2020-09-18 | 北京信息科技大学 | Platform patent value evaluation method based on information ecological theory and RF-GA-BP neural network |
Non-Patent Citations (5)
| Title |
|---|
| "三大航"企业竞争力评价;江逢生;;企业家天地(第04期);第117-120页 * |
| 企业竞争力的定量评价方法;张晓文, 于武, 胡运权;管理评论(第01期);第32-37页 * |
| 企业竞争力评价指标体系构建与应用;李文绚;姚长青;刘志辉;;中国科技资源导刊(第04期);第79-86页 * |
| 竞争情报研究中企业竞争能力的雷达图分析法;何晓兵;;图书情报工作(第04期);第86-90页 * |
| 陕西企业核心竞争力的模糊综合评价;张颖;安立仁;;统计与决策(第03期);第49-50页 * |
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