Industrial competitiveness determination method and device and computer equipment

Abstract

The invention relates to an industrial competitiveness determination method and device and computer equipment. The method comprises the following steps: obtaining standard operation information of a target industry, obtaining a plurality of evaluation indexes of industrial competitiveness of the target industry, carrying out bidirectional matching on the standard operation information and each evaluation index, determining index data corresponding to each evaluation index in the standard operation information, and carrying out correlation analysis based on each index data, and determining a competitiveness quantized value of the target industry. By adopting the method, the index data corresponding to the industrial competitiveness can be quickly obtained through the bidirectional matching process, so that the determination speed and efficiency of the industrial competitiveness can be integrally accelerated, and the timeliness of determining the industrial competitiveness is improved.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a method, device and computer equipment for determining industrial competitiveness. Background Art

[0002] With the continuous development of science and technology and the advancement of technology, the industrial competitiveness of various regions has been greatly affected. Industrial competitiveness represents the competitive ability of a region's industry relative to other regions in terms of production efficiency, meeting market demand, and sustainable profitability.

[0003] In the related art, when determining the industrial competitiveness of a region, it is mainly through loading the industrial competitiveness evaluation model and training the industrial competitiveness evaluation model, and then determining the industrial competitiveness of a region through the trained industrial competitiveness evaluation model.

[0004] However, the related art has the problem of slow determination when determining industrial competitiveness. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device and computer equipment for determining industrial competitiveness in response to the above technical problems, which can improve the speed of determining industrial competitiveness when determining industrial competitiveness.

[0006] In a first aspect, an embodiment of the present application provides a method for determining industrial competitiveness, the method comprising:

[0007] Obtain standard operating information of the target industry and obtain the competitiveness index system of the target industry; the competitiveness index system includes multiple evaluation indicators of industry competitiveness;

[0008] Perform bidirectional matching between the standard operation information and each evaluation indicator to determine the indicator data corresponding to each evaluation indicator in the standard operation information;

[0009] Conduct correlation analysis based on the data of each indicator to determine the quantitative value of the competitiveness of the target industry.

[0010] The technical solution in the embodiment of the present application obtains standard operating information of the target industry, and obtains multiple evaluation indicators of the industrial competitiveness of the target industry, performs bidirectional matching on the standard operating information and each evaluation indicator, determines the indicator data corresponding to each evaluation indicator in the standard operating information, and performs correlation analysis based on each indicator data to determine the quantitative value of the competitiveness of the target industry; the above method can quickly obtain each indicator data corresponding to the industrial competitiveness through the bidirectional matching process, thereby accelerating the speed and efficiency of determining the industrial competitiveness as a whole, and improving the timeliness of determining the industrial competitiveness; at the same time, the above method does not need to be processed by a neural network model, which can not only reduce the complexity of determining the industrial competitiveness and accelerate the speed of determining the industrial competitiveness, but also avoid the process of executing the model update and shorten the time required to determine the industrial competitiveness; in addition, the above method does not require human participation, nor does it require subjective configuration of the weight coefficient of the model, which can not only accelerate the speed of determining the industrial competitiveness, but also improve the correctness of the determined industrial competitiveness.

[0011] In one embodiment, the standard operation information includes a plurality of indicator description information and corresponding indicator data; bidirectional matching is performed on the standard operation information and each evaluation indicator to determine the indicator data corresponding to each evaluation indicator in the standard operation information, including:

[0012] For each evaluation indicator, obtain the similarity value between the description information of each indicator and the evaluation indicator;

[0013] Based on each similarity value, obtain target indicator description information in each indicator description information;

[0014] The indicator data associated with the target indicator description information in the standard operation information is determined as the indicator data corresponding to the evaluation indicator.

[0015] The technical solution in the embodiment of the present application obtains, for each evaluation indicator, a similarity value between each indicator description information and the evaluation indicator, obtains the target indicator description information in each indicator description information based on each similarity value, and determines the indicator data associated with the target indicator description information in the standard operation information as the indicator data corresponding to the evaluation indicator; the above method can quickly obtain the indicator data corresponding to each evaluation indicator from the standard operation information through the similarity value between each indicator description information in the standard operation information and the evaluation indicator, and the processing process does not require the participation of complex algorithms, thereby reducing the amount of data involved in the indicator data determination process and reducing the complexity of the indicator data determination process.

[0016] In one embodiment, obtaining the similarity value between each indicator description information and the evaluation indicator includes:

[0017] Performing word segmentation processing on each indicator description information to obtain first key information of each indicator description information; and performing word segmentation processing on the evaluation indicator to obtain second key information of the evaluation indicator;

[0018] The first key information of each evaluation description information and the second key information of the evaluation index are matched with each other to obtain each similarity value.

[0019] The technical solution in the embodiment of the present application can first perform word segmentation processing on each indicator description information and evaluation indicator, and then quickly realize similarity matching based on the word segmentation result. In addition, this process realizes similarity matching on the basis of word segmentation processing, which can also reduce the complexity of similarity matching. At the same time, the method does not require human participation, thereby reducing human participation errors and improving the accuracy of similarity matching results.

[0020] In one embodiment, based on each similarity value, obtaining target indicator description information in each indicator description information includes:

[0021] Obtain the maximum similarity value among the similarity values;

[0022] The indicator description information corresponding to the maximum similarity value is determined as the target indicator description information.

[0023] The technical solution in the embodiment of the present application obtains the maximum similarity value among the similarity values, and determines the indicator description information corresponding to the maximum similarity value as the target indicator description information; the above method does not require the participation of complex algorithms, and the processing process is relatively simple, thereby improving the speed of determining the target indicator description information, and on the basis of low complexity, it can also improve the correctness of the determined target indicator description information. In addition, on this basis, it can also prepare for the subsequent rapid acquisition of indicator data of each evaluation indicator from standard operation information.

[0024] In one embodiment, correlation analysis is performed based on the indicator data to determine the quantitative value of the competitiveness of the target industry, including:

[0025] According to the original operation information, standard operation information and various indicator data of the target industry, determine the correlation value between the original operation information, standard operation information and various indicator data;

[0026] According to each correlation value, reliable indicator data among each indicator data is determined;

[0027] Based on reliable indicator data, the industrial competitiveness of the target industry is evaluated to obtain the quantitative value of the competitiveness of the target industry.

[0028] The technical solution in the embodiment of the present application determines the correlation value between the original operation information and the standard operation information and each indicator data according to the original operation information, standard operation information and each indicator data of the target industry, determines the reliable indicator data in each indicator data according to each correlation value, and evaluates the industrial competitiveness of the target industry according to the reliable indicator data to obtain the quantitative value of the competitiveness of the target industry; the above method can screen out reliable indicator data that can realize reliable and effective evaluation of industrial competitiveness from all indicator data through the correlation value between the original operation information and the standard operation information and each indicator data, and the process does not require the participation of complex algorithms, and the processing process is relatively simple, thereby reducing the complexity of obtaining reliable indicator data, and then improving the speed of obtaining reliable indicator data; at the same time, the above method can evaluate the industrial competitiveness of the target industry based on the obtained reliable indicator data, thereby improving the evaluation result of industrial competitiveness.

[0029] In one embodiment, determining the correlation value between the original operation information, the standard operation information and each indicator data according to the original operation information, the standard operation information and each indicator data of the target industry includes:

[0030] For each indicator data, a first-order relationship value between the original operation information and the indicator data is obtained; and a second-order relationship value between the standard operation information and the indicator data is obtained;

[0031] According to the first-level correlation value and the second-level correlation value, the correlation value between the original operation information and the standard operation information and the indicator data is determined.

[0032] The technical solution in the embodiment of the present application obtains, for each indicator data, a first correlation value between the original operation information and the indicator data, and a second correlation value between the standard operation information and the indicator data, and determines the correlation value between the original operation information and the standard operation information and the indicator data according to the first correlation value and the second correlation value; the above method can determine the correlation value between the original operation information and the standard operation information and the indicator data through the correlation value between the original operation information and the indicator data, and the correlation value between the standard operation information and the indicator data. The processing process is relatively simple and does not require the participation of complex algorithms, thereby reducing the complexity of obtaining the correlation value between the original operation information and the standard operation information and the indicator data, accelerating the acquisition speed and efficiency, and the correlation value acquisition process can also reduce labor resources and reduce labor costs.

[0033] In one embodiment, according to each correlation value, determining reliable indicator data among each indicator data includes:

[0034] Obtain the maximum correlation value among the correlation values;

[0035] The indicator data corresponding to the maximum correlation value is determined as the reliable indicator data.

[0036] The technical solution in the embodiment of the present application obtains the maximum correlation value among the correlation values, and determines the indicator data corresponding to the maximum correlation value as the reliable indicator data; the above method does not require the participation of complex algorithms, and the processing process is relatively simple, thereby increasing the speed of determining reliable indicator data, and on the basis of low complexity, it can also improve the correctness of the determined reliable indicator data. In addition, on this basis, it can also provide reliable and effective reference information for the subsequent process of accurately evaluating industrial competitiveness.

[0037] In one embodiment, obtaining standard operation information of a target industry includes:

[0038] Obtain original operation information of the target industry;

[0039] The original operation information is preprocessed to obtain standard operation information; the preprocessing includes at least one of abnormal data compensation processing, standardization processing, normalization processing and timing balance processing.

[0040] The technical solution in the embodiment of the present application obtains the original operating information of the target industry, and pre-processes the original operating information to obtain standard operating information; the above method can obtain the standard operating information obtained after pre-processing, so as to subsequently obtain the correlation value between the original operating information and the standard operating information and each indicator data, and provide dependency information for obtaining more reliable reliable indicator data through each correlation value; at the same time, the above method pre-processes the original operating information, which can effectively reduce the dimension of the subsequent accurate acquisition of reliable indicator data and reduce the complexity of obtaining reliable indicator data.

[0041] In a second aspect, an embodiment of the present application provides a device for determining industrial competitiveness, the device comprising:

[0042] An acquisition module is used to acquire standard operation information of the target industry and acquire the competitiveness index system of the target industry; the competitiveness index system includes multiple evaluation indicators of industry competitiveness;

[0043] A matching module is used to perform bidirectional matching between the standard operation information and each evaluation indicator, and determine the indicator data corresponding to each evaluation indicator in the standard operation information;

[0044] The analysis module is used to conduct correlation analysis based on the data of each indicator and determine the quantitative value of the competitiveness of the target industry.

[0045] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any one of the embodiments of the first aspect when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method of any embodiment in the above-mentioned first aspect are implemented.

[0047] In a fifth aspect, an embodiment of the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method of any embodiment of the first aspect above.

[0048] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A diagram of an application environment of a method for determining industrial competitiveness in one embodiment;

[0050] Figure 2 A schematic diagram of a process of determining industrial competitiveness in one embodiment;

[0051] Figure 3 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0052] Figure 4 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0053] Figure 5 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0054] Figure 6 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0055] Figure 7 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0056] Figure 8 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0057] Fig. 9 A flowchart of a method for determining industrial competitiveness in another embodiment;

[0058] Fig.10 It is a structural block diagram of an industrial competitiveness determination device in another embodiment;

[0059] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] In the field of digital industry, in order to evaluate the industrial capabilities of multiple different regions, it is necessary to first determine the industrial competitiveness of each region. In the related art, when determining the industrial competitiveness of each region, it is mainly through loading the industrial competitiveness evaluation model, training the industrial competitiveness evaluation model to update the model parameters, and then evaluating the industrial competitiveness of each region through the trained industrial competitiveness evaluation model to determine the industrial competitiveness of each region. However, in the related art, when determining the industrial competitiveness, due to the slow model training speed and the complex model processing during the evaluation process, there is a problem of slow determination of industrial competitiveness. Based on this, the embodiment of the present application provides a method for determining industrial competitiveness, which can improve the speed of determining industrial competitiveness when determining industrial competitiveness.

[0062] The industrial competitiveness determination method provided in the embodiment of the present application can be applied to Figure 1 The industrial competitiveness determination system shown in the figure includes a computer device and an information management system. The information management system includes information management devices of all enterprises in the industrial chain in at least one region, wherein: Figure 1 In the description, an example is given in which an information management system includes three information management devices (information management device 1, information management device 2, and information management device 3). Optionally, a computer device and each information management system may be connected by communication, and the communication method may be Wi-Fi, mobile network, or Bluetooth connection, etc. Optionally, the computer device may be, but is not limited to, a personal computer, a desktop computer, a laptop computer, an iPad, or other electronic device with a data processing function, and each information management device may be, but is not limited to, a personal computer, a desktop computer, a laptop computer, an iPad, a database, or other electronic device with a data storage function. The following embodiment of the present application uses a computer device as the executor of the method for determining industrial competitiveness to introduce the specific process of the method for determining industrial competitiveness.

[0063] like Figure 2FIG. 1 is a flow chart of a method for determining industrial competitiveness provided in an embodiment of the present application. The method can be implemented by the following steps:

[0064] S100: Obtain standard operation information of the target industry and obtain the competitiveness index system of the target industry, wherein the competitiveness index system includes multiple evaluation indexes of industry competitiveness.

[0065] The target industry may be at least one industry in a region; the target industry may be, but is not limited to, agriculture, forestry, animal husbandry, fishery, mining, construction, transportation, postal and telecommunications, commerce, catering, material supply and marketing, warehousing, finance, insurance, geological survey, real estate and public utilities, residential services, tourism, consulting and information services, and various technical services. Optionally, the standard operation information of the target industry may include the target industry's R&D information, operation and maintenance information, resource information, production information, etc.

[0066] In practical applications, the competitiveness index system of the target industry and the competitiveness index system of different industries are the same. Optionally, the above competitiveness index system may include multiple of human resources, natural resources, capital resources, market size, market demand, consumer demand, the development level of upstream and downstream industries, enterprise management level, market competition level, R&D investment, application and promotion of new technologies, government incentives, infrastructure construction, industry profit margin, market share, etc.

[0067] In the embodiment of the present application, the above-mentioned competitiveness index system of the target industry may include explicit indicators, invisible indicators, indirect cause indicators, related industry factors and state factor indicators of the target industry; the above-mentioned explicit indicators, invisible indicators, indirect cause indicators, related industry factors and state factor indicators may all include at least one of the primary indicators, the secondary indicators under the primary indicators and the tertiary indicators under the secondary indicators. Optionally, the above-mentioned explicit indicators may include primary indicators (i.e., the market share of industrial products and the average asset profit rate of the industry); the above-mentioned invisible indicators may include primary indicators (i.e., direct cause indicators), secondary indicators (i.e., indicators related to productivity) and tertiary indicators (i.e., the enterprise scale, labor productivity, production cost and industrial price of all enterprises in the industrial chain of the target industry). Optionally, the above-mentioned indirect cause indicators may include primary indicators (i.e., production factor indicators) and secondary indicators (i.e., the number of patent application technology assessments, the proportion of research and development expenses to sales revenue and the proportion of technical invention engineering and technical personnel to professional talents). Optionally, the above-mentioned related industry factors may include primary indicators (i.e., the industrial output of industries and suppliers, the absolute amount generated by the industrial chain in economic activities during a certain period of time, and the proportion of the total industrial output of the corresponding scope of the industrial chain). Optionally, the above-mentioned state factor indicators may include primary indicators (i.e., the industry average salary and overall debt ratio of each enterprise in the industrial chain). The following Table 1 shows the indicator display table of the competitiveness indicator system of the target industry ("--" in the table means none).

[0068] Table 1

[0069]

[0070] In actual applications, the computer device can send a command to obtain the standard operation information of the target industry to the information management device of each enterprise in the area to be evaluated, and receive the standard operation information of the target industry sent by each information management device. In addition, the computer device can also receive the standard operation information of the target industry sent by the information management device of each enterprise in the area to be evaluated in real time. Among them, the above-mentioned area to be evaluated refers to any area where the competitiveness of the industry needs to be evaluated; the scope of the area is not limited in the embodiment of this application, and can be an area with a corresponding range of towns, counties, cities, provinces, etc.

[0071] At the same time, computer equipment can obtain pre-built competitiveness indicator systems of target industries from local, cloud, disk, hard disk and other storage locations.

[0072] S200: Perform bidirectional matching on the standard operation information and each evaluation indicator to determine the indicator data corresponding to each evaluation indicator in the standard operation information.

[0073] In practical applications, computer equipment can pre-train a two-way matching algorithm model, and then input the standard operation information and each evaluation index in the competitiveness index system into the two-way matching algorithm model to obtain the index data corresponding to each evaluation index in the standard operation information.

[0074] In addition, the computer device can also perform bidirectional matching on the standard operation information and each evaluation index according to the bidirectional matching strategy to obtain the index data corresponding to each evaluation index in the standard operation information. Optionally, the bidirectional matching strategy includes a process for implementing bidirectional matching.

[0075] Of course, the computer device can also match, perform arithmetic operations and / or compare the standard operation information and each evaluation index to achieve two-way matching to obtain the index data corresponding to each evaluation index in the standard operation information. Optionally, the arithmetic operation can be implemented by at least one of addition, subtraction, multiplication, division, logarithm, etc.

[0076] S300. Conduct correlation analysis based on the data of each indicator to determine the quantitative value of the competitiveness of the target industry.

[0077] Based on the indicator data obtained in the previous steps, correlation analysis can be performed based on the indicator data to determine the quantitative value of the competitiveness of the target industry.

[0078] In one implementation, the computer device may use a comparative analysis method to perform correlation analysis on the indicator data to determine the quantitative value of the competitiveness of the target industry.

[0079] In another implementation, multiple indicator data may be screened out from each indicator data, and then correlation calculation may be performed on the multiple indicator data to obtain a correlation value, and the quantitative value of the competitiveness of the target industry may be determined based on the correlation value.

[0080] The technical solution in the embodiment of the present application obtains standard operating information of the target industry, and obtains multiple evaluation indicators of the industrial competitiveness of the target industry, performs bidirectional matching on the standard operating information and each evaluation indicator, determines the indicator data corresponding to each evaluation indicator in the standard operating information, and performs correlation analysis based on each indicator data to determine the quantitative value of the competitiveness of the target industry; the above method can quickly obtain each indicator data corresponding to the industrial competitiveness through the bidirectional matching process, thereby accelerating the speed and efficiency of determining the industrial competitiveness as a whole, and improving the timeliness of determining the industrial competitiveness; at the same time, the above method does not need to be processed by a neural network model, which can not only reduce the complexity of determining the industrial competitiveness and accelerate the speed of determining the industrial competitiveness, but also avoid the process of executing the model update and shorten the time required to determine the industrial competitiveness; in addition, the above method does not require human participation, nor does it require subjective configuration of the weight coefficient of the model, which can not only accelerate the speed of determining the industrial competitiveness, but also improve the correctness of the determined industrial competitiveness.

[0081] The following is a description of the process of bidirectionally matching the standard operation information and each evaluation indicator to determine the indicator data corresponding to each evaluation indicator in the standard operation information. In one embodiment, the standard operation information includes multiple indicator description information and corresponding indicator data; Figure 3 As shown, the steps in S200 above can be implemented in the following manner:

[0082] S210: For each evaluation indicator, obtain a similarity value between each indicator description information and the evaluation indicator.

[0083] In one embodiment, the computer device may use a similarity calculation method to perform similarity calculation on each indicator description information and the evaluation indicator in the standard operation information to obtain a similarity value between each indicator description information and the evaluation indicator. Optionally, the similarity calculation method may be a cosine similarity method, a Hamming similarity method, a Pearson correlation coefficient method, etc.

[0084] In another embodiment, the computer device may send both the standard operation information and each evaluation index to the third-party device, instruct the third-party device to perform similarity calculation on each index description information and the evaluation index in the standard operation information, and send the similarity value between each index description information and the evaluation index to the computer device. Correspondingly, the computer device may receive the similarity value between each index description information and the evaluation index sent by the third-party device.

[0085] It should be noted here that the multiple indicator description information in the standard operation information may be indicator data corresponding to each evaluation indicator in the competitiveness indicator system.

[0086] In one embodiment, if Figure 4As shown, the step of obtaining the similarity value between each indicator description information and the evaluation indicator in the above S210 may include:

[0087] S211, performing word segmentation processing on each indicator description information to obtain first key information of each indicator description information; and performing word segmentation processing on the evaluation indicator to obtain second key information of the evaluation indicator.

[0088] In practical applications, the computer device can use a word segmentation algorithm to perform word segmentation processing on each indicator description information to obtain the first key information of each indicator description information; and use a word segmentation algorithm to perform word segmentation processing on the evaluation indicator to obtain the second key information of the evaluation indicator.

[0089] Optionally, the above-mentioned word segmentation algorithm can be a string matching algorithm, a shortest path word segmentation algorithm, a word segmentation algorithm based on n-gram grammar, a word segmentation algorithm based on hidden Markov model or a word segmentation algorithm based on conditional random field, etc., which is not limited in the embodiment of the present application. Optionally, the above-mentioned first key information and the second key information can both include information such as words or phrases.

[0090] S212: Perform similarity matching on the first key information of each evaluation description information and the second key information of the evaluation index to obtain each similarity value.

[0091] Based on the first key information of each evaluation description information obtained in the previous step, a similarity matching algorithm can be used to perform similarity matching on the first key information of each evaluation description information and the second key information of the evaluation index to obtain a similarity value between each first key information and the second key information. The similarity value can also represent the similarity between the corresponding evaluation description information and the evaluation index.

[0092] Optionally, the above similarity matching algorithm may include a text similarity matching algorithm, a cosine similarity algorithm, a word co-occurrence matrix algorithm, a keyword comparison method, etc.

[0093] In the embodiment of the present application, a highly usable edit distance algorithm can be used to perform similarity matching on the first key information of each evaluation description information and the second key information of the evaluation index to obtain the similarity value between each first key information and the second key information. Specifically, the frequency of occurrence of the second key information of the evaluation index in each first key information, the part of speech and / or semantics between the second key information of the evaluation index and each first key information, etc. can be analyzed to complete the similarity matching and obtain the similarity value between the second key information of the evaluation index and each first key information.

[0094] This embodiment can first perform word segmentation processing on each indicator description information and evaluation indicator, and then quickly realize similarity matching based on the word segmentation result. In addition, this process realizes similarity matching on the basis of word segmentation processing, which can also reduce the complexity of similarity matching. At the same time, this method does not require human participation, thereby reducing human participation errors and improving the accuracy of similarity matching results.

[0095] S220. Based on each similarity value, obtain target indicator description information in each indicator description information.

[0096] In practical applications, all similarity values ​​of the evaluation index can be compared or counted pairwise, and the minimum value, median value or mode value can be selected from all similarity values ​​of the evaluation index, and the index description information corresponding to the minimum value, median value or mode value can be determined as the target index description information.

[0097] S230: Determine the indicator data associated with the target indicator description information in the standard operation information as the indicator data corresponding to the evaluation indicator.

[0098] In an embodiment of the present application, the above-mentioned standard operation information may include different indicator description information and indicator data associated with each indicator description information.

[0099] Furthermore, indicator data associated with the target indicator description information may be screened out from the standard operation information and determined as the indicator data corresponding to the evaluation indicator.

[0100] The technical solution in the embodiment of the present application obtains, for each evaluation indicator, a similarity value between each indicator description information and the evaluation indicator, obtains the target indicator description information in each indicator description information based on each similarity value, and determines the indicator data associated with the target indicator description information in the standard operation information as the indicator data corresponding to the evaluation indicator; the above method can quickly obtain the indicator data corresponding to each evaluation indicator from the standard operation information through the similarity value between each indicator description information in the standard operation information and the evaluation indicator, and the processing process does not require the participation of complex algorithms, thereby reducing the amount of data involved in the indicator data determination process and reducing the complexity of the indicator data determination process.

[0101] The following is a description of the process of obtaining the target indicator description information in each indicator description information based on each similarity value. Figure 5 As shown, the steps in the above S220 can be implemented in the following manner:

[0102] S221. Obtain the maximum similarity value among the similarity values.

[0103] In one embodiment, for any evaluation indicator, the maximum similarity value between each indicator description information and the evaluation indicator may be calculated to obtain the maximum similarity value.

[0104] In another embodiment, for any evaluation indicator, the similarity values ​​between each indicator description information and the evaluation indicator may be compared with each other to select the maximum similarity value.

[0105] S222. Determine the indicator description information corresponding to the maximum similarity value as the target indicator description information.

[0106] Furthermore, the indicator description information corresponding to the maximum similarity value may be determined as the target indicator description information.

[0107] The technical solution in the embodiment of the present application obtains the maximum similarity value among the similarity values, and determines the indicator description information corresponding to the maximum similarity value as the target indicator description information; the above method does not require the participation of complex algorithms, and the processing process is relatively simple, thereby improving the speed of determining the target indicator description information, and on the basis of low complexity, it can also improve the correctness of the determined target indicator description information. In addition, on this basis, it can also prepare for the subsequent rapid acquisition of indicator data of each evaluation indicator from standard operation information.

[0108] The following is a description of how to evaluate the industrial competitiveness of the target industry based on the acquired indicator data. Figure 6 As shown, the step of performing correlation analysis based on each indicator data and determining the quantitative value of the competitiveness of the target industry in the above S300 can be implemented in the following manner:

[0109] S310. Determine the correlation value between the original operation information, the standard operation information and each indicator data according to the original operation information, the standard operation information and each indicator data of the target industry.

[0110] In practical applications, computer equipment can pre-train an algorithm model, and then for any indicator data, the original operating information, standard operating information and the indicator data of the target industry are input into the algorithm model, and the algorithm model outputs the correlation value between the original operating information and the standard operating information and the indicator data.

[0111] At the same time, for any indicator data, the computer equipment can also perform arithmetic operations on the original operation information, standard operation information and the indicator data of the target industry to obtain the correlation value between the original operation information, standard operation information and the indicator data.

[0112] In addition, for any indicator data, the computer equipment can also use the correlation analysis method to calculate the correlation between the original operation information, standard operation information and each indicator data of the target industry, and obtain the correlation value between the original operation information, standard operation information and each indicator data.

[0113] S320: Determine reliable indicator data among the indicator data according to the correlation degree values.

[0114] Specifically, the indicator data corresponding to any correlation value greater than a preset correlation threshold among all correlation values ​​may be obtained as reliable indicator data. Optionally, the preset correlation threshold may be user-defined or determined based on historical experience.

[0115] S330. Evaluate the industrial competitiveness of the target industry based on reliable indicator data to obtain a quantitative value of the competitiveness of the target industry.

[0116] Furthermore, the industrial competitiveness of the target industry can be evaluated based on the reliable indicator data by performing arithmetic operations, comparisons and / or analyses on the reliable indicator data to complete the evaluation of the industrial competitiveness of the target industry and obtain a quantitative value of the competitiveness of the target industry.

[0117] The technical solution in the embodiment of the present application determines the correlation value between the original operation information and the standard operation information and each indicator data according to the original operation information, standard operation information and each indicator data of the target industry, determines the reliable indicator data in each indicator data according to each correlation value, and evaluates the industrial competitiveness of the target industry according to the reliable indicator data to obtain the quantitative value of the competitiveness of the target industry; the above method can screen out reliable indicator data that can realize reliable and effective evaluation of industrial competitiveness from all indicator data through the correlation value between the original operation information and the standard operation information and each indicator data, and the process does not require the participation of complex algorithms, and the processing process is relatively simple, thereby reducing the complexity of obtaining reliable indicator data, and then improving the speed of obtaining reliable indicator data; at the same time, the above method can evaluate the industrial competitiveness of the target industry based on the obtained reliable indicator data, thereby improving the evaluation result of industrial competitiveness.

[0118] The following is a description of the process of determining the correlation values ​​between the original operation information, the standard operation information and each indicator data. Figure 7 As shown, the step of determining the correlation value between the original operation information, the standard operation information and each indicator data according to the original operation information, the standard operation information and each indicator data of the target industry in S310 can be implemented in the following manner:

[0119] S311. For each indicator data, obtain a first relation value between the original operation information and the indicator data; and obtain a second relation value between the standard operation information and the indicator data.

[0120] In an embodiment of the present application, the original operating information may be first dimensionally processed (such as averaging or initialization) to obtain dimensionless original information, and the standard operating information may be dimensionally processed to obtain dimensionless standard information.

[0121] Furthermore, for each indicator data, the grey correlation analysis method can be used to calculate the grey correlation coefficient value between the dimensionless original information and the indicator data, that is, the first correlation coefficient value; at the same time, the grey correlation analysis method can also be used to calculate the grey correlation coefficient value between the dimensionless standard information and the indicator data, that is, the second correlation coefficient value.

[0122] S312: Determine the correlation value between the original operation information, the standard operation information and the indicator data according to the first correlation value and the second correlation value.

[0123] In practical applications, the computer device can perform arithmetic operations on the first correlation value and the second correlation value to obtain the correlation value between the original operation information and the standard operation information and the indicator data. Optionally, the arithmetic operation can be at least one of addition, subtraction, multiplication, division, logarithm, exponential operation, etc.; in the embodiment of the present application, the arithmetic operation can be weighted calculation.

[0124] It should be noted here that the greater the correlation value between the original operation information and the standard operation information and the indicator data, the stronger the correlation between the original operation information and the standard operation information and the indicator data, which further indicates that the indicator data is closer to or is the target indicator data.

[0125] The technical solution in the embodiment of the present application obtains, for each indicator data, a first correlation value between the original operation information and the indicator data, and a second correlation value between the standard operation information and the indicator data, and determines the correlation value between the original operation information and the standard operation information and the indicator data according to the first correlation value and the second correlation value; the above method can determine the correlation value between the original operation information and the standard operation information and the indicator data through the correlation value between the original operation information and the indicator data, and the correlation value between the standard operation information and the indicator data. The processing process is relatively simple and does not require the participation of complex algorithms, thereby reducing the complexity of obtaining the correlation value between the original operation information and the standard operation information and the indicator data, accelerating the acquisition speed and efficiency, and the correlation value acquisition process can also reduce labor resources and reduce labor costs.

[0126] In one embodiment, if Figure 8 As shown, the step of determining reliable indicator data among the indicator data according to the correlation values ​​in the above S320 may include the following process:

[0127] S321. Obtain the maximum correlation value among the correlation values.

[0128] In one embodiment, for any evaluation index, the maximum value of the correlation values ​​corresponding to the evaluation indexes may be calculated to obtain the maximum correlation value.

[0129] In another embodiment, for any evaluation index, the correlation values ​​corresponding to the evaluation indexes may be compared with each other to select the maximum correlation value.

[0130] S322: Determine the indicator data corresponding to the maximum correlation value as reliable indicator data.

[0131] Furthermore, the indicator data corresponding to the maximum correlation value can be determined as reliable indicator data, and the reliable indicator data has the highest reliability when used for subsequent evaluation of industrial competitiveness.

[0132] The technical solution in the embodiment of the present application obtains the maximum correlation value among the correlation values, and determines the indicator data corresponding to the maximum correlation value as the reliable indicator data; the above method does not require the participation of complex algorithms, and the processing process is relatively simple, thereby increasing the speed of determining reliable indicator data, and on the basis of low complexity, it can also improve the correctness of the determined reliable indicator data. In addition, on this basis, it can also provide reliable and effective reference information for the subsequent process of accurately evaluating industrial competitiveness.

[0133] The following is an explanation of the process of obtaining the standard operation information of the target industry. Fig. 9 As shown, the step of obtaining the standard operation information of the target industry in the above S100 can be implemented in the following ways:

[0134] S110. Obtain original operation information of the target industry.

[0135] In actual applications, the computer device can send instructions for obtaining the original operation information of the target industry to the information management devices of each enterprise in the area to be evaluated, and receive the original operation information of the target industry sent by each information management device. In addition, the computer device can also receive the original operation information of the target industry sent by the information management devices of each enterprise in the area to be evaluated in real time. At the same time, the computer device can also obtain the original operation information of the target industry from storage locations such as local, cloud, disk, hard disk, database, etc.

[0136] In the embodiment of the present application, the original operation information of the target industry may include the industrial chain data of the industrial chain in the corresponding region and the enterprise data of each enterprise in the industrial chain. Optionally, the industrial chain may include the upstream industrial chain and the downstream industrial chain; the industrial chain data may include the risk data and leakage risk data of the industrial chain; the enterprise data may include the leakage risk data and credit information of the corresponding enterprise.

[0137] S120, preprocessing the original operation information to obtain standard operation information, wherein the preprocessing includes at least one of abnormal data compensation processing, standardization processing, normalization processing and time sequence balance processing.

[0138] In practical applications, the above abnormal data compensation processing may include abnormal data elimination processing, missing data filling processing (such as mean, median, mode and other data filling processing), duplicate data deletion processing, etc. It should be noted here that standardization and normalization of the original operation information is actually processing the indicator data in the original operation information, in order to make the data consistent at different scales and prevent inaccurate processing results.

[0139] In addition, the above-mentioned time series balancing processing can be understood as a processing for balancing data by under-sampling, over-sampling, and calculation for all indicator data with time series in the actual application process.

[0140] The technical solution in the embodiment of the present application obtains the original operating information of the target industry, and pre-processes the original operating information to obtain standard operating information; the above method can obtain the standard operating information obtained after pre-processing, so as to subsequently obtain the correlation value between the original operating information and the standard operating information and each indicator data, and provide dependency information for obtaining more reliable reliable indicator data through each correlation value; at the same time, the above method pre-processes the original operating information, which can effectively reduce the dimension of the subsequent accurate acquisition of reliable indicator data and reduce the complexity of obtaining reliable indicator data.

[0141] In one embodiment, the present application also provides a method for determining industrial competitiveness, which is applied to a computer device. The method includes the following process:

[0142] (1) Obtain the original operating information of the target industry;

[0143] (2) preprocessing the original operation information to obtain standard operation information; the preprocessing includes at least one of abnormal data compensation processing, standardization processing, normalization processing and time series balance processing, and the standard operation information includes a plurality of indicator description information and corresponding indicator data; and,

[0144] (3) Obtaining the competitiveness index system of the target industry; the competitiveness index system includes multiple evaluation indicators of industry competitiveness;

[0145] (4) For each evaluation indicator, segment the description information of each indicator to obtain the first key information of the description information of each indicator; and segment the evaluation indicator to obtain the second key information of the evaluation indicator;

[0146] (5) performing similarity matching on the first key information of each evaluation description information and the second key information of the evaluation index to obtain each similarity value;

[0147] (6) Obtaining the maximum similarity value among the similarity values;

[0148] (7) determining the indicator description information corresponding to the maximum similarity value as the target indicator description information;

[0149] (8) determining the indicator data associated with the target indicator description information in the standard operation information as the indicator data corresponding to the evaluation indicator;

[0150] (9) for each indicator data, obtaining a first-order relationship value between the original operation information and the indicator data; and obtaining a second-order relationship value between the standard operation information and the indicator data;

[0151] (10) determining the correlation value between the original operation information, the standard operation information and the indicator data according to the first correlation value and the second correlation value;

[0152] (11) Obtaining the maximum correlation value among the correlation values;

[0153] (12) determining the indicator data corresponding to the maximum correlation value as the reliable indicator data;

[0154] (13) Evaluate the industrial competitiveness of the target industry based on reliable indicator data to obtain a quantitative value of the competitiveness of the target industry.

[0155] The execution process of the above (1) to (13) can be specifically referred to the description of the above embodiment. The implementation principles and technical effects are similar and will not be repeated here.

[0156] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0157] Based on the same inventive concept, the embodiment of the present application also provides an industrial competitiveness determination device for implementing the industrial competitiveness determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more industrial competitiveness determination device embodiments provided below can refer to the limitations of the industrial competitiveness determination method above, and will not be repeated here.

[0158] In one embodiment, Fig.10 This is a schematic diagram of the structure of an industrial competitiveness determination device in one embodiment of the present application. The industrial competitiveness determination device provided in the embodiment of the present application can be applied to computer equipment. Fig.10 As shown, the industry competitiveness determination device of the embodiment of the present application may include: an acquisition module 11, a matching module 12 and an analysis module 13, wherein:

[0159] The acquisition module 11 is used to acquire standard operation information of the target industry and acquire the competitiveness index system of the target industry; the competitiveness index system includes multiple evaluation indicators of industry competitiveness;

[0160] A matching module 12 is used to perform bidirectional matching between the standard operation information and each evaluation index, and determine the index data corresponding to each evaluation index in the standard operation information;

[0161] The analysis module 13 is used to perform correlation analysis based on the index data to determine the quantitative value of the competitiveness of the target industry.

[0162] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0163] In one embodiment, the standard operation information includes a plurality of indicator description information and corresponding indicator data; the matching module 12 includes: a first acquisition unit, a second acquisition unit and a first determination unit, wherein:

[0164] A first acquisition unit is used to acquire, for each evaluation indicator, a similarity value between each indicator description information and the evaluation indicator;

[0165] A second acquisition unit is used to acquire target indicator description information in each indicator description information according to each similarity value;

[0166] The first determination unit is used to determine the indicator data associated with the target indicator description information in the standard operation information as the indicator data corresponding to the evaluation indicator.

[0167] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0168] In one embodiment, the first acquisition unit includes: a word segmentation processing subunit and a matching subunit, wherein:

[0169] The word segmentation processing subunit is used to perform word segmentation processing on each indicator description information to obtain the first key information of each indicator description information; and perform word segmentation processing on the evaluation indicator to obtain the second key information of the evaluation indicator;

[0170] The matching subunit is used to perform similarity matching on the first key information of each evaluation description information and the second key information of the evaluation index to obtain each similarity value.

[0171] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0172] In one embodiment, the second acquisition unit is specifically used for:

[0173] Obtain the maximum similarity value among the similarity values;

[0174] The indicator description information corresponding to the maximum similarity value is determined as the target indicator description information.

[0175] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0176] In one embodiment, the analysis module 13 includes: a second determination unit, a third determination unit and an evaluation unit, wherein:

[0177] A second determination unit is used to determine the correlation value between the original operation information, the standard operation information and each indicator data according to the original operation information, the standard operation information and each indicator data of the target industry;

[0178] A third determining unit, configured to determine reliable indicator data among the indicator data according to the respective correlation degree values;

[0179] The evaluation unit is used to evaluate the industrial competitiveness of the target industry based on reliable indicator data and obtain the quantitative value of the competitiveness of the target industry.

[0180] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0181] In one embodiment, the second determining unit is specifically configured to:

[0182] For each indicator data, a first-order relationship value between the original operation information and the indicator data is obtained; and a second-order relationship value between the standard operation information and the indicator data is obtained;

[0183] According to the first-level correlation value and the second-level correlation value, the correlation value between the original operation information and the standard operation information and the indicator data is determined.

[0184] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0185] In one embodiment, the third determining unit is specifically configured to:

[0186] Obtain the maximum correlation value among the correlation values;

[0187] The indicator data corresponding to the maximum correlation value is determined as the reliable indicator data.

[0188] The industrial competitiveness determination device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0189] In one embodiment, the industrial competitiveness determination device further includes: a pre-processing module, wherein:

[0190] The preprocessing module is specifically used for:

[0191] Obtain original operation information of the target industry;

[0192] The original operation information is preprocessed to obtain standard operation information; the preprocessing includes at least one of abnormal data compensation processing, standardization processing, normalization processing and timing balance processing.

[0193] For the specific definition of the industrial competitiveness determination device, please refer to the definition of the industrial competitiveness determination method above, which will not be repeated here. Each module in the above industrial competitiveness determination device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0194] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.11 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide processing power. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store backup data of the logical disk. The network interface of the computer device is used to communicate with an external endpoint through a network connection. When the computer program is executed by the processor, a method for determining industrial competitiveness is implemented.

[0195] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0196] In one embodiment, a computer device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the technical solution in the above-mentioned industrial competitiveness determination method embodiment of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0197] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the technical solution of the above-mentioned industrial competitiveness determination method of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0198] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the technical solution of the above-mentioned industrial competitiveness determination method of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0199] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0200] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0201] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for determining industrial competitiveness, characterized in that: The method comprises: Obtaining standard operation information of the target industry and obtaining a competitiveness index system of the target industry; the competitiveness index system includes multiple evaluation indicators of industry competitiveness; Performing bidirectional matching on the standard operation information and each of the evaluation indicators to determine the indicator data corresponding to each of the evaluation indicators in the standard operation information; A correlation analysis is performed based on the indicator data to determine the quantitative value of the competitiveness of the target industry.

2. The method according to claim 1, characterized in that The standard operation information includes a plurality of indicator description information and corresponding indicator data; the bidirectional matching of the standard operation information and each of the evaluation indicators to determine the indicator data corresponding to each of the evaluation indicators in the standard operation information includes: For each evaluation indicator, obtaining a similarity value between each indicator description information and the evaluation indicator; Based on each of the similarity values, obtaining target indicator description information in each of the indicator description information; The indicator data associated with the target indicator description information in the standard operation information is determined as the indicator data corresponding to the evaluation indicator.

3. The method according to claim 2, characterized in that The obtaining of the similarity value between each indicator description information and the evaluation indicator comprises: Performing word segmentation processing on each of the indicator description information to obtain first key information of each of the indicator description information; and performing word segmentation processing on the evaluation indicator to obtain second key information of the evaluation indicator; The first key information of each evaluation description information and the second key information of the evaluation index are matched for similarity to obtain each similarity value.

4. The method according to claim 2, characterized in that: The acquiring, based on each of the similarity values, target indicator description information in each of the indicator description information comprises: Obtaining the maximum similarity value among the similarity values; The indicator description information corresponding to the maximum similarity value is determined as the target indicator description information.

5. The method according to any one of claims 1 to 4, characterized in that The performing of correlation analysis based on each of the indicator data to determine the quantitative value of the competitiveness of the target industry includes: Determine, according to the original operation information of the target industry, the standard operation information and each of the indicator data, a correlation value between the original operation information and the standard operation information and each of the indicator data; Determining reliable indicator data among the indicator data according to the respective correlation degree values; The industrial competitiveness of the target industry is evaluated based on the reliable indicator data to obtain a quantitative value of the competitiveness of the target industry.

6. The method according to claim 5, characterized in that The determining, based on the original operation information of the target industry, the standard operation information and the indicator data, the correlation value between the original operation information, the standard operation information and the indicator data includes: For each indicator data, obtaining a first correlation value between the original operation information and the indicator data; and obtaining a second correlation value between the standard operation information and the indicator data; According to the first correlation value and the second correlation value, a correlation value between the original operation information and the standard operation information and the indicator data is determined.

7. The method according to claim 5, characterized in that Determining reliable indicator data among the indicator data according to the association degree values ​​includes: Obtaining the maximum correlation value among the correlation values; The indicator data corresponding to the maximum correlation value is determined as the reliable indicator data.

8. The method according to any one of claims 1 to 4, characterized in that The obtaining of standard operation information of the target industry includes: Obtaining original operation information of the target industry; The original operation information is preprocessed to obtain the standard operation information; the preprocessing includes at least one of abnormal data compensation processing, standardization processing, normalization processing and timing balance processing.

9. An industrial competitiveness determination device, characterized in that: The device comprises: An acquisition module, used to acquire standard operation information of a target industry and acquire a competitiveness index system of the target industry; the competitiveness index system includes a plurality of evaluation indexes of industry competitiveness; A matching module, used for performing bidirectional matching between the standard operation information and each of the evaluation indicators, and determining the indicator data corresponding to each of the evaluation indicators in the standard operation information; The analysis module is used to perform correlation analysis based on each of the indicator data to determine the quantitative value of the competitiveness of the target industry.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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