CN112990703A - International engineering market matching degree evaluation method, electronic device and storage medium - Google Patents
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Abstract
The invention provides an international engineering market matching degree evaluation method, electronic equipment and a storage medium, wherein index data of engineering enterprises in a plurality of national markets in an international regional market are obtained, the index data are quantized, and the quantized index data are subjected to matching degree evaluation by using a plurality of matching methods to obtain a corresponding matching degree evaluation value; and the matching degree interval between the engineering enterprise and the international regional market is determined based on the obtained matching degree evaluation values, so that the matching degree between the enterprise business and the international market environment is effectively and accurately evaluated, a plurality of quantitative evaluation models are integrated in the matching process to provide the concept of the matching degree interval, the subjectivity of the matching model provided by the predecessor can be eliminated, and the fault tolerance of the matching model is improved.
Description
Technical Field
The invention relates to the technical field of market economy evaluation, in particular to an international engineering market matching degree evaluation method, electronic equipment and a storage medium.
Background
At present, few evaluation models which quantitatively describe the matching degree of engineering company business and international market are used, and the qualitative evaluation matching is generally carried out by subjective experience.
For example: bainian et al, from the perspective of business competition among engineering companies, divide the business-market matching into business, technical and economic standards, and propose a method for calculating matching degree based on arrays to evaluate competitive matching among engineering companies, but neglect the influence of market environment on business expansion. In the morning, the process matching, the technical matching and the rule cognitive matching in the engineering construction process are researched, and a matching model based on the construction process is provided. In addition, various business matching such as talent position matching, information matching, resource matching and the like for constructing the market are based on the enterprise, but the business environment different from the domestic business environment in the international engineering project construction market is usually the first one to face by international engineering companies, and an effective matching method is not proposed for the matching between the enterprise business and the international market environment. In terms of calculation methods, evaluation methods such as data envelope analysis, analytic hierarchy process, fuzzy comprehensive evaluation and the like also become evaluation methods commonly used in the field of matching evaluation, but in the methods, subjective parameters have large influence on results and fault tolerance intervals are small, so that the evaluation results are greatly dependent on the experience of a parameter selector, and the subjectivity is high.
Disclosure of Invention
The invention aims to provide an international engineering market matching degree evaluation method, electronic equipment and a storage medium, which can effectively and accurately evaluate the matching degree between enterprise business and an international market environment, integrate a plurality of quantitative evaluation models in the matching process and provide a concept of a matching degree interval, and not only eliminate the subjectivity of a matching model provided by a person before matching, but also improve the fault tolerance of the matching model.
The invention provides an international engineering market matching degree evaluation method, which comprises the following steps:
acquiring index data of a plurality of national markets of an engineering enterprise in an international regional market, and quantifying the index data; the index data is used for evaluating the market matching degree of the engineering enterprise, and the index data at least comprises the following components: at least two index data of technical standard, qualification certification, export mode, after-sale service, market concentration and product concentration;
and evaluating the matching degree of the quantized index data by at least two matching methods of the following matching methods to obtain corresponding evaluation values of the matching degree: an affinity value method, a rank and ratio method, a quality solution distance method, an entropy value method and an efficacy coefficient method;
and determining a matching degree interval between the engineering enterprise and the international regional market based on the obtained matching degree evaluation values.
The present invention also provides an electronic device comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the international project market matching degree evaluation method as described above.
The present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the international project market matching degree evaluation method as described above.
According to the technical scheme, index data of engineering enterprises in multiple national markets in international regional markets are obtained, the index data are quantized, and matching degree evaluation is performed on the quantized index data by using multiple matching methods to obtain corresponding matching degree evaluation values; and the matching degree interval between the engineering enterprise and the international regional market is determined based on the obtained matching degree evaluation values, so that the matching degree between the enterprise business and the international market environment is effectively and accurately evaluated, a plurality of quantitative evaluation models are integrated in the matching process to provide the concept of the matching degree interval, the subjectivity of the matching model provided by the predecessor can be eliminated, and the fault tolerance of the matching model is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a detailed flowchart of an international project market matching degree evaluation method according to a first embodiment of the present invention;
fig. 2 is a detailed flowchart of a matching degree evaluation method using an affinity method according to a second embodiment of the present invention;
fig. 3 is a detailed flowchart of a matching degree evaluation method using a rank and ratio method according to a second embodiment of the present invention;
fig. 4 is a detailed flowchart of a matching degree evaluation method using a good-bad solution distance method according to a second embodiment of the present invention;
fig. 5 is a detailed flowchart of a matching degree evaluation method using an entropy method according to a second embodiment of the present invention;
fig. 6 is a detailed flowchart of a matching degree evaluation method using an efficacy coefficient method according to a second embodiment of the present invention;
fig. 7 is a schematic structural view of an electron according to a third embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment of the invention relates to an international engineering market matching degree evaluation method, which is characterized in that index data of a plurality of national markets of an engineering enterprise in an international regional market are obtained, and the index data are quantized; the index data is used for evaluating the market matching degree of the engineering enterprise, and the index data at least comprises the following components: at least two index data of technical standard, qualification certification, export mode, after-sale service, market concentration and product concentration; and evaluating the matching degree of the quantized index data by at least two matching methods of the following matching methods to obtain corresponding evaluation values of the matching degree: an affinity value method, a rank and ratio method, a quality solution distance method, an entropy value method and an efficacy coefficient method; and determining a matching degree interval between the engineering enterprise and the international regional market based on the obtained matching degree evaluation values, thereby effectively and accurately evaluating the matching degree between the enterprise business and the international market environment, integrating a plurality of quantitative evaluation models in the matching process and providing the concept of the matching degree interval, not only eliminating the subjectivity of the matching model provided by the person before matching, but also improving the fault tolerance of the matching model.
As shown in fig. 1, the method for evaluating the matching degree of the international engineering market includes:
step 101: acquiring index data of a plurality of national markets of an engineering enterprise in an international regional market, and quantifying the index data; the index data is used for evaluating the market matching degree of the engineering enterprise, and the index data at least comprises the following components: at least two index data of technical standard, qualification certification, export mode, after-sale service, market concentration and product concentration.
Market matching is the practical application of matching theory in the product-market matching scenario, and in short, is the process of matching commodities with customer needs in the market. According to the intensive research and summary of the selection index system of suppliers by many international scholars, the embodiment introduces at least two index data of technical standard, qualification certification, export mode, after-sales mode, market concentration and product concentration for evaluating the market matching degree of engineering enterprises.
After the index data is obtained, the index data may be quantified to achieve an assessment of the degree of match:
aiming at technical standards, quantifying the proportion of the quantity of projects which adopt standards made by engineering enterprises in key projects entering national markets;
aiming at qualification certification, quantifying the condition of qualification certification standards currently met by engineering enterprises in the national market, wherein the condition comprises the definition of standards and quantity met by different types of electric power equipment and technologies;
quantifying the main export mode of the engineering enterprise in the national market aiming at the export mode;
quantifying whether the engineering enterprise has a special after-sale service team in the national market or not aiming at the after-sale service;
quantifying the occupation condition of the turnover of the engineering enterprises in the national market according to the market concentration;
and quantifying the proportion of products and technologies produced by engineering enterprises in the market segments in the national market according to the product concentration.
For example, taking the example that chinese enterprises enter the european market, the quantified index data are shown in table 1:
TABLE 1 evaluation index description of a certain enterprise in China in each national market
Step 102: and evaluating the matching degree of the quantized index data by at least two matching methods of the following matching methods to obtain corresponding evaluation values of the matching degree: an affinity value method, a rank and ratio method, a good and bad solution distance method, an entropy value method and an efficacy coefficient method.
Specifically, a national market corresponding to quantized index data is used as an evaluation object, the index data is used as an evaluation index, and an index matrix (data table) is constructed; and then processing the index data in the index matrix by using at least two listed matching methods to obtain the matching degree evaluation value of each evaluation object on each evaluation index. The matching degree evaluation value reflects the matching degree of the engineering enterprise on a corresponding evaluation object (national market).
Step 103: and determining a matching degree interval between the engineering enterprise and the international regional market based on the obtained matching degree evaluation values.
The matching degree evaluation value for evaluating the matching degree of the engineering enterprise and the corresponding national market can be obtained by each matching method, and the range of the matching degree of the engineering enterprise and the corresponding national market, namely the matching degree interval, can be roughly determined based on the matching degree evaluation values obtained by at least two matching methods.
Compared with the prior art, the method has the advantages that index data of the engineering enterprises in multiple national markets in the international regional market are obtained, the index data are quantized, and the quantized index data are subjected to matching degree evaluation by using multiple matching methods to obtain corresponding matching degree evaluation values; and the matching degree interval between the engineering enterprise and the international regional market is determined based on the obtained matching degree evaluation values, so that the matching degree between the enterprise business and the international market environment is effectively and accurately evaluated, a plurality of quantitative evaluation models are integrated in the matching process to provide the concept of the matching degree interval, the subjectivity of the matching model provided by the predecessor can be eliminated, and the fault tolerance of the matching model is improved.
The second embodiment of the present invention relates to an international project market matching degree evaluation method. The second embodiment is an improvement of the first embodiment, and is improved in that the above-described various matching methods are explained in detail.
As shown in fig. 2, the evaluation of the matching degree of the quantized index data by using the affinity method specifically includes:
step 201: and taking the national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an index matrix.
For n evaluation objects, each evaluation object has m matching models of evaluation indexes, and an index matrix is constructed:
wherein, aijThe j (j) th evaluation index is a value of 1, 2, …, m) th evaluation index of the i (i) th evaluation object (i is 1, 2, …, n).
Step 202: and establishing a homodromous index matrix aiming at the index matrix.
When the evaluation index is a positive index, the numerical value takes a positive value; when the evaluation index is a negative index, the numerical value takes a negative value to obtain an equidirectional index matrix:
step 203: and standardizing elements in the equidirectional index matrix to form a standardized matrix.
Establishing a standardized matrix:
wherein the content of the first and second substances,
step 204: and calculating the optimal point and the worst point of each evaluation index in the standardized matrix.
Calculate the "best Point" and the "worst Point"
Wherein A is+And A-And sequentially obtaining an optimal point set and a worst point set.
Step 205: and calculating the distance between each evaluation object and the optimal point and the worst point.
Wherein the content of the first and second substances,
and
the rich degree is the distance from the i (i ═ 1, 2, …, n) th evaluation object to the optimal point and the worst point.
Step 206: based on the distances from the evaluation objects to the optimal point and the worst point, the closeness value of each evaluation object is calculated.
Wherein D isiThe value is the closeness value of the i (i ═ 1, 2, …, n) th evaluation target.
When the affinity value DiThe closer to the "optimum point" and the farther from the "worst point", i.e., the higher the quality, the smaller D is, the best the quality is, i.e., the "optimum point".
Step 207: and normalizing the affinity value of each evaluation object to obtain a matching degree evaluation value corresponding to the affinity value method.
Wherein, C1iThe value is the normalized closeness value of the i (i ═ 1, 2, …, n) th evaluation object. C1iCloser to 1 indicates a higher degree of matching, and closer to 0 a lower degree of matching.
As shown in fig. 3, the evaluation of the matching degree of the quantized index data by using a Rank-sum ratio (RSR) method specifically includes:
step 301: and taking the national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, constructing an original data table and coding the rank to form a rank matrix.
The m evaluation indexes of the n evaluation objects are arranged into an original data table with n rows and m columns. And compiling the rank of each evaluation object under each evaluation index to obtain a rank matrix, and recording the rank matrix as: r ═ Rij)n*mThe high-quality indexes are ranked from small to large, and the low-quality indexes are ranked from large to small.
Step 302: and calculating the rank sum ratio of each evaluation object in the rank matrix.
Calculating a rank sum ratio according to the formula:
wherein R isijIs the ith row and the jth column elementThe rank of the element.
When the weights of the evaluation indexes are different, a Weighted Rank Sum Ratio (WRSR) is calculatedi) Calculating according to a formula:
wherein, wjIn order to evaluate the weight of the index,
step 303: and determining the distribution of the rank sum ratio and calculating a probability unit.
The distribution of RSR refers to a value-specific cumulative frequency expressed in probability units Probit. Compiling a RSR (or WRSR) frequency distribution table, listing frequency factors f of each group, calculating accumulated frequency factors sigma f of each group, and determining the rank range R and the average rank R of each group of RSR; calculating the accumulated frequency (R/n) x 100%, marking as P, and correcting according to P (1-1/4 n); and inquiring a percentage and probability unit comparison table to obtain a probability unit Probit value corresponding to the P.
Step 304: and calculating a linear regression equation based on the probability unit and the rank matrix corresponding to each evaluation object.
Probability unit Probit corresponding to accumulated frequencyiAs independent variable, with RSRiThe values being dependent variables, calculating a linear regression equation, i.e.
RSR＝a+b*Probit
Step 305: and calculating the rank and ratio estimation value of each evaluation object according to a linear regression equation.
Step 306: and normalizing the rank and ratio estimation value of each evaluation object to obtain a matching degree evaluation value corresponding to the rank and ratio method.
And mapping the estimated value of the rank sum ratio of each evaluation object into a numerical value between 0 and 1 by adopting a max-min normalization method.
Wherein, C2iThe normalized rank sum ratio is estimated for the i (i ═ 1, 2, …, n) th evaluation object. C2iCloser to 1 indicates a higher degree of matching, and closer to 0 a lower degree of matching.
As shown in fig. 4, the evaluation of the matching degree of the quantized index data by using the top-bottom solution distance (TOPSIS) method specifically includes:
step 401: and taking the national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an original data matrix.
N evaluation objects and m evaluation indexes are set to form an original data matrix A ═ xij)n*m：
Step 402: and normalizing the original data matrix to establish a normalized matrix.
Normalizing the original data matrix to obtain a normalized vector, and establishing a normalized matrix related to the normalized vector:
wherein r isijIs the element in the normalized matrix.
Step 403: and determining the weight of each evaluation index by using a coefficient of variation method.
For an evaluation model defined by n evaluation objects and m evaluation indexes, the weight of the j (j ═ 1, 2, … …, m) th evaluation index is determined as follows:
Vj-coefficient of variation, also called standard deviation coefficient, of the j-th evaluation index;
σj-standard deviation of the j-th evaluation index;
weight W of jth evaluation indexjIs coefficient of variation VjThe proportion of the sum of all the variation coefficients is represented as follows:
step 404: and processing the normalization matrix based on the weight to obtain the normalization matrix with the weight.
After the weight is determined, establishing a normalized matrix with the weight:
vij＝wjrij，i＝1，2，…，n，j＝1，2，…，m
wherein v isiiAre elements in the weighted normalized matrix.
Step 405: and calculating the optimal point and the worst point of each evaluation index in the weighted normalization matrix.
Determining the optimum point of each evaluation index
And worst point
Step 406: and calculating the distance from each evaluation object to the optimal point and the worst point respectively.
Calculating the distance from each evaluation object to the optimal point
And distance to the worst point
The distance scale may be calculated by n-dimensional euclidean distances.
Step 407: and calculating the closeness of each evaluation object to the optimal point according to the distance from each evaluation object to the optimal point and the distance from each evaluation object to the worst point.
Calculating the closeness of each evaluation object to the optimal point:
step 408: and normalizing the closeness of each evaluation object to obtain a matching degree evaluation value corresponding to the good-bad solution distance method.
And mapping the closeness of each evaluation object into a numerical value between 0 and 1 by adopting a max-min normalization method.
Wherein, C3iThe value is the normalized closeness of the i (i ═ 1, 2, …, n) th evaluation object. C3iCloser to 1 indicates a higher degree of matching, and closer to 0 a lower degree of matching.
As shown in fig. 5, the evaluation of the matching degree of the quantized index data by using the entropy method specifically includes:
step 501: and taking the national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an original data matrix.
N evaluation objects and m evaluation indexes are set to form an original data matrix A ═ xij)n*mFor a certain evaluation index, the larger the difference between the index values is, the larger the role of the evaluation index in the comprehensive evaluation is; if the index values under a certain evaluation index are all equal, the evaluation index does not play a role in the comprehensive evaluation.
Original data matrix:
step 502: and carrying out non-negative processing on the original data matrix to obtain a non-negative matrix.
The entropy method adopts the ratio of a certain index of each scheme to the sum of the same index value, so that the influence of dimension does not exist, standardization processing is needed, and nonnegative processing is needed to be carried out on the data if the data has negative numbers. There are two common nonnegative treatments, and one of them may be selected depending on the actual situation.
The forward direction index is as follows:
negative direction index:
for convenience, the nonnegatively processed data is still denoted as xij。
Step 503: and calculating the proportion of each evaluation object in the corresponding evaluation index under each evaluation index in the non-negative matrix.
Calculating the proportion of the ith evaluation object in the jth evaluation index:
step 504: and calculating entropy values of various evaluation indexes and information entropy redundancy based on the specific gravity.
Calculating the entropy e of the j-th evaluation indexj：
Calculating the information entropy redundancy d of the jth evaluation indexj：
dj＝1-ej
Step 505: and calculating the weight of each evaluation index according to the information entropy redundancy, and calculating the comprehensive score of each evaluation object based on the weight.
Calculating the weight w of the jth evaluation indexj：
Calculating the comprehensive score s of the j-th evaluation indexi：
Step 506: and normalizing the comprehensive scores of the evaluation objects to obtain a matching degree evaluation value corresponding to the entropy method.
And mapping the comprehensive score of each evaluation object into a numerical value between 0 and 1 by adopting a max-min normalization method.
Wherein, C4iThe score is a normalized composite score of the i (i ═ 1, 2, …, n) -th evaluation object. C4iCloser to 1 indicates a higher degree of matching, and closer to 0 a lower degree of matching.
As shown in fig. 6, the evaluation of the matching degree of the quantized index data by using the power coefficient method specifically includes:
step 601: and taking the national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an original data matrix.
Constructing an original data matrix:
step 602: and determining the allowable range of each evaluation index.
Determining the permissible range, i.e. satisfaction, of the j-th evaluation index
And not allowed value
The satisfactory value is an optimum value that can be achieved under the current conditions, the disallowed value is a minimum value at which the evaluation index should not appear, and the reference system of the allowable range is a difference between the satisfactory value and the disallowed value.
The forward direction index is as follows:
negative direction index:
step 603: and calculating the efficacy coefficient of each evaluation index according to the allowable range of each evaluation index.
Calculating the efficacy coefficient x of the jth evaluation indexij′：
Step 604: and determining the weight of each evaluation index by using a coefficient of variation method.
For an evaluation model defined by n evaluation objects and m evaluation indexes, the weight of the j (j ═ 1, 2, ….., m) evaluation indexes is determined as follows:
Vj-coefficient of variation, also called standard deviation coefficient, of the j-th evaluation index;
σj-standard deviation of the j-th evaluation index;
weight W of jth evaluation indexjIs coefficient of variation VjThe proportion of the sum of all the variation coefficients is represented as follows:
step 605: and calculating the total efficiency coefficient of each evaluation object according to the efficiency coefficient and the weight of each evaluation index.
The specific calculation method may be determined according to actual conditions, and there are generally (weighted) arithmetic average, geometric average and the like. In this embodiment, an arithmetic mean method is adopted. Total efficiency coefficient V of i-th evaluation objecti：
Step 606: and normalizing the total efficiency coefficient of each evaluation object to obtain the matching degree evaluation value corresponding to the efficiency coefficient method.
And (3) adopting a max-min normalization method to emit the total efficiency coefficient of each evaluation object into a numerical value between 0 and 1.
Wherein, C5iThe normalized total efficiency coefficient is the i (i ═ 1, 2, …, n) th evaluation object. C5iCloser to 1 indicates a higher degree of matching, and closer to 0 a lower degree of matching.
In addition, when step 103 is executed to determine the matching degree interval between the engineering enterprise and the international regional market based on the obtained matching degree evaluation values, the following steps may be specifically implemented:
and normalizing each obtained matching degree evaluation value according to an evaluation object, forming a numerical interval by taking the maximum value and the minimum value in the normalized matching degree evaluation values as boundary values, and determining the numerical interval corresponding to each evaluation object as the matching degree interval between the engineering enterprise and the international regional market.
For example, for the ith evaluation target, 5 matching degree evaluation values are obtained by the above 5 matching methods: c1i、C2i、C3i、C4i，C5iThe maximum value and the minimum value of these evaluation values are extracted as boundary values to form a numerical range:
the numerical range (C)min，Cmax) Namely the matching degree interval between the engineering enterprise and the national market corresponding to the ith evaluation object in the international regional market.
Compared with the prior art, the matching process of various quantitative evaluation models (methods) is explained in detail in the embodiment, and the concept of the matching degree interval is introduced, so that the subjectivity of the matching model proposed before matching is eliminated, and the fault tolerance of the matching model is improved.
A third embodiment of the invention relates to an electronic device, as shown in FIG. 7, comprising at least one processor 702; and a memory communicatively coupled to the at least one processor 702; the memory 701 stores instructions executable by the at least one processor 702, and the instructions are executable by the at least one processor 702 to enable the at least one processor 702 to perform any of the above method embodiments.
The memory 701 and the processor 702 are coupled by a bus, which may comprise any number of interconnecting buses and bridges that couple one or more of the various circuits of the processor 702 and the memory 701 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. Data processed by the processor 702 may be transmitted over a wireless medium through an antenna, which may receive the data and transmit the data to the processor 702.
The processor 702 is responsible for managing the bus and general processing, and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 701 may be used for storing data used by processor 702 in performing operations.
A fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program realizes any of the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An international engineering market matching degree evaluation method is characterized by comprising the following steps:
acquiring index data of a plurality of national markets of an engineering enterprise in an international regional market, and quantifying the index data; the index data is used for evaluating the market matching degree of the engineering enterprise, and the index data at least comprises the following components: at least two index data of technical standard, qualification certification, export mode, after-sale service, market concentration and product concentration;
and evaluating the matching degree of the quantized index data by at least two matching methods of the following matching methods to obtain corresponding evaluation values of the matching degree: an affinity value method, a rank and ratio method, a quality solution distance method, an entropy value method and an efficacy coefficient method;
and determining a matching degree interval between the engineering enterprise and the international regional market based on the obtained matching degree evaluation values.
2. The method of claim 1, wherein the quantifying the metric data comprises:
quantifying the proportion condition of the quantity of the project adopting the standard formulated by the engineering enterprise in the key projects entering the national market aiming at the technical standard;
quantifying the qualification certification standard conditions currently met by the engineering enterprises in the national market aiming at the qualification certification, wherein the qualification certification comprises determining the standards and the quantity met by different types of electric power equipment and technologies;
quantifying the main export mode of the engineering enterprise in the national market according to the export mode;
quantifying whether the engineering enterprise has a special after-sales service team in the national market for the after-sales service;
quantifying the share of the turnover of the engineering enterprise in the national market according to the market concentration;
and quantifying the proportion of the products and technologies produced by the engineering enterprises in the market segments in the national market according to the product concentration.
3. The method according to claim 1, wherein the evaluation of the degree of matching of the quantified indicator data by the affinity method includes:
taking a national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an index matrix;
establishing a homodromous index matrix aiming at the index matrix;
standardizing elements in the equidirectional index matrix to form a standardized matrix;
calculating the optimal point and the worst point of each evaluation index in the standardized matrix;
calculating the distance from each evaluation object to the optimal point and the worst point;
calculating an affinity value of each evaluation object based on the distance from each evaluation object to the optimal point and the worst point;
and normalizing the affinity values of the evaluation objects to obtain the matching degree evaluation value corresponding to the affinity value method.
4. The method of claim 1, wherein the matching degree evaluation of the quantified index data by using a rank and ratio method comprises:
taking a national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, constructing an original data table and forming a rank matrix by arranging the original data table;
calculating the rank sum ratio of each evaluation object in the rank matrix;
determining the distribution of rank sum ratio and calculating probability units;
calculating a linear regression equation based on the probability unit and the rank matrix corresponding to each evaluation object;
calculating the rank and ratio estimation value of each evaluation object according to the linear regression equation;
and normalizing the rank and ratio estimation value of each evaluation object to obtain the matching degree evaluation value corresponding to the rank and ratio method.
5. The method of claim 1, wherein the evaluating the matching degree of the quantized index data by using a good-bad solution distance method comprises:
taking a national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an original data matrix;
normalizing the original data matrix to establish a normalized matrix;
determining the weight of each evaluation index by adopting a variation coefficient method;
processing the normalization matrix based on the weight to obtain a normalization matrix with the weight;
calculating the optimal point and the worst point of each evaluation index in the weighted normalization matrix;
calculating the distance from each evaluation object to the optimal point and the worst point respectively;
calculating the closeness of each evaluation object to the optimal point according to the distance from each evaluation object to the optimal point and the distance from each evaluation object to the worst point;
and normalizing the closeness of each evaluation object to obtain the matching degree evaluation value corresponding to the good-bad solution distance method.
6. The method of claim 1, wherein the matching degree evaluation of the quantized index data by using an entropy method comprises:
taking a national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an original data matrix;
carrying out non-negative processing on the original data matrix to obtain a non-negative matrix;
calculating the proportion of each evaluation object in the corresponding evaluation index under each evaluation index in the non-negative matrix;
calculating entropy values of all evaluation indexes and information entropy redundancy based on the specific gravity;
calculating the weight of each evaluation index according to the information entropy redundancy, and calculating the comprehensive score of each evaluation object based on the weight;
and normalizing the comprehensive scores of the evaluation objects to obtain the matching degree evaluation value corresponding to the entropy method.
7. The method of claim 1, wherein the evaluation of the degree of matching of the quantified indicator data by using an efficacy coefficient method comprises:
taking a national market corresponding to the quantized index data as an evaluation object, taking the index data as an evaluation index, and constructing an original data matrix;
determining the allowable range of each evaluation index;
calculating the efficiency coefficient of each evaluation index according to the allowable range of each evaluation index;
determining the weight of each evaluation index by adopting a variation coefficient method;
calculating the total efficiency coefficient of each evaluation object according to the efficiency coefficient and the weight of each evaluation index;
and normalizing the total efficiency coefficient of each evaluation object to obtain the matching degree evaluation value corresponding to the efficiency coefficient method.
8. The method according to any one of claims 1 to 7, wherein the determining a matching degree interval between the engineering enterprise and the international regional market based on the obtained matching degree evaluation values comprises:
and normalizing each obtained matching degree evaluation value according to an evaluation object, forming a numerical interval by taking the maximum value and the minimum value in the normalized matching degree evaluation values as boundary values, and determining the numerical interval corresponding to each evaluation object as the matching degree interval between the engineering enterprise and the international regional market.
9. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the international project market matching degree evaluation method of any one of claims 1 to 8.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the international project market matching degree evaluation method according to any one of claims 1 to 8.
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