Key technical field evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a key technical field evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of initial analysis files, analyzing statistical granularity information and evaluation dimension information, carrying out the technical field classification of initial to-be-analyzed files, obtaining a technical field file set, and carrying out the evaluation of the technical field file set; performing data statistics on the file set of each technical field according to the analysis and statistics granularity information to obtain granularity statistics information of each technical field, performing cooking degree prediction fitting to obtain a cooking degree fitting data set of each technical field, determining evaluation dimension index parameters in the cooking degree fitting data set according to the evaluation dimension information, and determining evaluation dimension index parameters in the cooking degree fitting data set according to the evaluation dimension information; scoring the technical fields based on the evaluation dimension index parameters and determining key technical fields according to evaluation scores; according to the method, the technical maturity of technology fitting is predicted through statistical data support and data analysis provided by the granularity statistical information, the development stage and potential of the technology are reflected more accurately, the generalization ability of an evaluation system is improved, and the accuracy and effectiveness of decision making are improved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to an evaluation method, device, electronic device and storage medium in a key technical field. Background Art

[0002] In today's rapidly developing technological era, scientific and technological breakthroughs, academic research and the promotion of industrialization have become important driving forces for promoting social progress. Whether it is government agencies, universities or corporate units, in the face of an increasingly fierce international competitive environment, they must conduct in-depth evaluations and decisions on key technology fields to determine which technologies are worth investing resources in for in-depth research and development, which research directions have long-term development potential, and which scientific and technological achievements have the possibility of being transformed into actual productivity, and then consider whether to carry out large-scale industrial promotion.

[0003] However, how to scientifically evaluate key technology areas and make accurate and reasonable decisions is not only a research focus within the industry, but also a difficult problem facing all innovation entities. Traditional evaluation and decision-making in key technology areas is usually based on the experience of industry experts. Key technologies in various industries are sorted out, and then scored according to established rules. Finally, key technology areas are selected. Although this method can reflect the current status and development trends of the industry to a certain extent, the differences in experts' knowledge and experience can easily lead to a lack of comprehensiveness and objectivity in the evaluation results. In addition, the scoring rules are often based on historical data and existing successful cases, which makes it difficult to cover new situations and new problems that may arise in the future, and cannot effectively guarantee the generalization ability of the decision-making system. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a key technical field evaluation method, device, electronic device and storage medium to solve the above technical problems.

[0005] The present invention provides a key technology field evaluation method, which includes: obtaining multiple initial analysis files, analysis statistical granularity information and evaluation dimension information; classifying the initial files to be analyzed into technical fields according to preset technical field category information, and aggregating the initial files to be analyzed in the same technical field to obtain multiple technical field file sets; performing data statistics on each technical field file set according to the analysis statistical granularity information to obtain granularity statistical information of each technical field, and performing maturity prediction fitting based on the granularity statistical information of each technical field to obtain a maturity fitting data set of each technical field; determining the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, and scoring each technical field according to the evaluation dimension indicator parameters respectively to obtain an evaluation score of each technical field, so as to determine the key technology field according to the evaluation score of each technical field.

[0006] In one embodiment of the present invention, the technical field classification of the initial files to be analyzed according to preset technical field category information includes: text type classification of the initial files to be analyzed according to the pre-examination text type information, determining the text type corresponding to each initial file to be analyzed, and marking the initial files to be analyzed based on the text type corresponding to each initial file to be analyzed to obtain multiple first-classified files to be analyzed; each first-classified file to be analyzed is matched with a corresponding preset text classification model according to the marked text type, and each first-classified file to be analyzed is input into the corresponding preset text classification model to classify the technical field according to the preset technical field category information, and determine the technical field to which each of the first-classified files to be analyzed belongs.

[0007] In one embodiment of the present invention, data statistics are performed on each technical field file set according to the analysis statistical granularity information, including: in each technical field file set, data statistics are performed on the classified files to be analyzed of the same text type according to the analysis statistical granularity information, to obtain multiple text type statistical information, and the multiple text type statistical information is determined as granular statistical information.

[0008] In one embodiment of the present invention, maturity prediction fitting based on granular statistical information of each technical field includes: obtaining target growth curve model information corresponding to different text types; in each technical field, performing growth curve fitting on the statistical information of each text type according to the corresponding target growth curve model information to obtain a fitted growth curve corresponding to each text type, and determining a growth fitting peak value and a fitting growth parameter set according to the fitted growth curve corresponding to each text type; determining a current maturity parameter in each text type according to the growth fitting peak value; and determining the current maturity parameters and the fitting growth parameter set corresponding to all text types in any technical field as a maturity fitting data set for the technical field.

[0009] In one embodiment of the present invention, scoring is performed according to the evaluation dimension indicator parameters of each technical field, including: performing evaluation calculations based on the evaluation dimension indicator parameters of each technical field based on the TOPSIS algorithm to obtain the optimal solution for each technical field, and determining the optimal solution for each technical field as the evaluation score for each technical field.

[0010] In one embodiment of the present invention, determining the key technical field according to the evaluation scores of each technical field includes: sorting the technical fields according to the evaluation scores of each technical field; and determining a preset number of technical fields with the highest evaluation scores as the key technical fields.

[0011] In one embodiment of the present invention, the evaluation dimension information includes the growth rate of the number of each text type in the technical field within a preset time interval, the total number of each text type in the technical field within the preset time interval, and the current maturity parameters of each text type in the technical field.

[0012] An embodiment of the present invention also provides a key technology field evaluation device, which includes: an evaluation information acquisition module, used to acquire multiple initial analysis files, analysis statistical granularity information and evaluation dimension information; an evaluation data processing module, used to classify the initial files to be analyzed into technical fields according to preset technical field category information, and aggregate the initial files to be analyzed in the same technical field to obtain multiple technical field file sets; perform data statistics on each technical field file set according to the analysis statistical granularity information to obtain granularity statistical information of each technical field, and perform maturity prediction fitting based on the granularity statistical information of each technical field to obtain a maturity fitting data set of each technical field; a field scoring determination module, used to determine the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, and score each technical field according to the evaluation dimension indicator parameters respectively to obtain an evaluation score of each technical field, so as to determine the key technology field according to the evaluation score of each technical field.

[0013] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the key technical field evaluation method as described in any of the above embodiments.

[0014] An embodiment of the present invention further provides a computer-readable storage medium having computer-readable instructions stored thereon. When the computer-readable instructions are executed by a processor of a computer, the computer is enabled to execute a key technical field evaluation method as described in any one of the above embodiments.

[0015] The present invention provides a key technology field evaluation method, device, electronic device and storage medium, which obtains multiple initial analysis files, analyzes statistical granularity information and evaluation dimension information, classifies the initial files to be analyzed into technical fields, obtains multiple technical field file sets, performs data statistics on each technical field file set according to the analysis statistical granularity information, obtains granularity statistical information of each technical field and performs maturity prediction fitting to obtain a maturity fitting data set of each technical field, determines the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, scores each technical field respectively, and determines the key technology field according to the evaluation score; the present invention uses big data analysis and machine learning technology to automatically process a large number of ... It starts by analyzing documents and extracting valuable information from them, which improves the objectivity and comprehensiveness of the evaluation results. By performing data statistics and maturity prediction fitting on the collection of documents in the technical field, it can dynamically adjust the evaluation criteria to make them more in line with the current technological development trends, and improve the generalization ability of the evaluation system, so that it can quickly adapt to and provide accurate evaluation results when facing new technologies and new market changes. Through automated data processing and intelligent analysis, it can quickly respond to changes in the technical field. Through the statistical data support provided by granular statistical information, maturity prediction fitting predicts the maturity of technology through data analysis, which can more accurately reflect the development stage and potential of the technology, provide a scientific basis for the rational allocation and priority sorting of resources, and improve the accuracy and effectiveness of decision-making.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0018] Figure 1 is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application;

[0019] Figure 2 is a flowchart of a key technical field evaluation method shown in an exemplary embodiment of the present application;

[0020] Figure 3 It is a specific technical field classification diagram shown by an exemplary embodiment of the present application;

[0021] Figure 4is a growth curve fitting schematic diagram shown in an exemplary embodiment of the present application;

[0022] Figure 5 is a schematic diagram of a key technical field evaluation device shown in an exemplary embodiment of the present application;

[0023] Figure 6 It is a structural diagram of a computer system of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will describe the embodiments of the present invention with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0025] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0026] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0027] The "and / or" mentioned in this application describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0028] Figure 1 It is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application.

[0029] Reference Figure 1As shown, the system architecture may include a database 110 and a computer device 120. The computer device 120 obtains multiple initial analysis files, analysis statistical granularity information, and evaluation dimension information through the database 110; the initial files to be analyzed are classified into technical fields according to the preset technical field category information, and the initial files to be analyzed in the same technical field are aggregated to obtain multiple technical field file sets; data statistics are performed on each technical field file set according to the analysis statistical granularity information to obtain the granularity statistical information of each technical field, and the maturity prediction fitting is performed based on the granularity statistical information of each technical field to obtain the maturity fitting data set of each technical field; the evaluation dimension indicator parameters of each technical field are determined in the maturity fitting data set of each technical field according to the evaluation dimension information, and the evaluation dimension indicator parameters of each technical field are scored respectively to obtain the evaluation score of each technical field, so as to determine the key technical field according to the evaluation score of each technical field. The above-mentioned computer device 120 may be at least one of a microcomputer, an embedded computer, a network computer, an industrial computer, etc.

[0030] Illustratively, the computer device 120 obtains multiple initial analysis files, analysis statistical granularity information and evaluation dimension information through the database 110, classifies the initial files to be analyzed into technical fields, obtains multiple technical field file sets, performs data statistics on each technical field file set according to the analysis statistical granularity information, obtains granularity statistical information of each technical field and performs maturity prediction fitting to obtain a maturity fitting data set of each technical field, determines the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, scores each technical field respectively, and determines the key technical field according to the evaluation score; the present application automatically processes a large number of initial analysis files through big data analysis and machine learning technology, from It can extract valuable information from the data, improve the objectivity and comprehensiveness of the evaluation results, and dynamically adjust the evaluation criteria to make them more in line with the current technology development trend by performing data statistics and maturity prediction fitting on the collection of documents in the technology field. It can also improve the generalization ability of the evaluation system, so that it can quickly adapt and provide accurate evaluation results when facing new technologies and new market changes. It can quickly respond to changes in the technology field through automated data processing and intelligent analysis, and provide statistical data support through granular statistical information. Maturity prediction fitting predicts the maturity of technology through data analysis, which can more accurately reflect the development stage and potential of the technology, provide a scientific basis for the rational allocation and priority sorting of resources, and improve the accuracy and effectiveness of decision-making.

[0031] Figure 2 is a flowchart of a key technical field evaluation method shown in an exemplary embodiment of the present application. The key technical field evaluation method can be Figure 1The implementation environment may be implemented in other implementation environments, and the above implementation environments are not specifically limited here. Figure 2 As shown, the flowchart of the key technology field evaluation method includes at least steps S210 to S240, which are described in detail as follows:

[0032] In step S210, a plurality of initial analysis files, analysis statistical granularity information and evaluation dimension information are obtained.

[0033] In one embodiment of the present application, the above-mentioned initial analysis file contains technical description files of multiple text types in multiple technical fields, wherein the text types include but are not limited to paper texts, patent texts, etc.

[0034] In one embodiment of the present application, the above-mentioned initial analysis file is called and captured through the Internet, local area network, data interface, etc., and stored in a local database to facilitate calling of the data.

[0035] In the implementable environment of the present application, the above-mentioned analysis statistical granularity information includes the selection granularity information of the data statistical process, which includes but is not limited to various statistical granularities such as time slices and statistical slices.

[0036] In the implementable environment of the present application, the above-mentioned evaluation dimension information includes the growth rate of the number of each text type in the technical field within a preset time interval, the total number of each text type in the technical field within a preset time interval, and the current maturity parameters of each text type in the technical field. Specifically, assuming that the text types include papers and patents, a specific setting method of the evaluation dimension information includes the growth rate of papers in the belonging technical field in the past year, the growth rate of patents in the belonging technical field in the past year, the total number of papers in the belonging technical field in the past year, the total number of patents in the belonging technical field in the past year, the predicted value of technical maturity in the belonging technical field, and the predicted value of industrialization maturity in the belonging technical field. It should be noted that the setting method of the above-mentioned evaluation dimension information is only an illustrative example, and can also be specifically set according to the data requirements, evaluation objectives, and evaluation accuracy requirements. The setting method of the above-mentioned evaluation dimension information is not limited here.

[0037] In step S220, the initial files to be analyzed are classified into technical field categories according to preset technical field category information, and the initial files to be analyzed in the same technical field are aggregated to obtain multiple technical field file sets.

[0038] In one embodiment of the present application, classifying the initial files to be analyzed in the technical field according to preset technical field category information includes classifying the initial files to be analyzed in the text type according to the preset text type information, determining the text type corresponding to each initial file to be analyzed, and marking the initial files to be analyzed based on the text type corresponding to each initial file to be analyzed, so as to obtain a plurality of first-classified files to be analyzed, each first-classified file to be analyzed is matched with a corresponding preset text classification model according to the marked text type, and each first-classified file to be analyzed is input into the corresponding preset text classification model to classify the technical field according to the preset technical field category information, and determine the technical field to which each first-classified file to be analyzed belongs.

[0039] Reference Figure 3 As shown, Figure 3 This is a specific technical field classification diagram shown in an exemplary embodiment of the present application. Figure 3 As shown, assuming that the industry can be divided into technical field 1, technical field 2, technical field 3...technical field n, data such as papers and patents are classified and attached to the technical field classification through a text classification model, where the text classification model is not limited to: FastText, TextCNN, Transfomer and other semantic recognition deep learning models.

[0040] In step S230, data statistics are performed on the document sets of each technical field according to the analysis and statistical granularity information to obtain the granularity statistical information of each technical field, and maturity prediction fitting is performed based on the granularity statistical information of each technical field to obtain the maturity fitting data set of each technical field.

[0041] In one embodiment of the present application, data statistics are performed on each technical field file set based on the analysis statistical granularity information, including in each technical field file set, data statistics are performed on the classified files to be analyzed of the same text type respectively based on the analysis statistical granularity information to obtain multiple text type statistical information, and the multiple text type statistical information is determined as granular statistical information.

[0042] In one embodiment of the present application, maturity prediction fitting based on granular statistical information of each technical field includes obtaining target growth curve model information corresponding to different text types; in each technical field, growth curve fitting is performed on the statistical information of each text type according to the corresponding target growth curve model information to obtain a fitted growth curve corresponding to each text type, and a growth fitting peak value and a fitting growth parameter set are determined according to the fitted growth curve corresponding to each text type; the current maturity parameter is determined in each text type according to the growth fitting peak value; the current maturity parameters and the fitting growth parameter set corresponding to all text types in any technical field are determined as the maturity fitting data set for the technical field.

[0043] In one embodiment of the present application, the process of technological development and industrial development is regarded as a growth process. Things go through three stages: occurrence, development, and maturity, and the development speed of each stage is different. Usually, in the occurrence stage, the rate of change is relatively slow; in the development stage, the rate of change is faster; and in the maturity stage, the rate of change slows down again. The change and development curve of things obtained according to the development laws of these three stages is usually called a growth curve or growth curve, also known as a logical growth curve. Since this type of curve often resembles an S-shape, it is also called an S curve. Growth curve function models include but are not limited to Logistic, Fisher-pry and other growth curve models. For example, the input data is the number of patents in a certain technical field over the years, which are 1, 4, 31, 92, 242, 624, 949, and 966. Through the growth curve model, the growth process is fitted to obtain the peak value K=1248, the current industrial maturity=966 / K=0.774, and the fitting curve refers to Figure 4 As shown, Figure 4 FIG. 1 is a growth curve fitting schematic diagram shown in an exemplary embodiment of the present application. Figure 4 As shown, it can be seen that the growth trend of the number of patents in this technical field over the years presents an S-shaped curve.

[0044] In step S240, the evaluation dimension indicator parameters of each technical field are determined in the maturity fitting data set of each technical field according to the evaluation dimension information, and scores are performed according to the evaluation dimension indicator parameters of each technical field to obtain the evaluation score of each technical field, so as to determine the key technical field according to the evaluation score of each technical field.

[0045] In one embodiment of the present application, scoring is performed according to the evaluation dimension indicator parameters of each technical field, including performing evaluation calculations based on the evaluation dimension indicator parameters of each technical field based on the TOPSIS algorithm to obtain the optimal solution for each technical field, and determining the optimal solution for each technical field as the evaluation score for each technical field.

[0046] In one embodiment of the present application, the process of performing evaluation calculation by the TOPSIS algorithm includes the following steps:

[0047] Step 1: Determine the evaluation dimension indicator parameters of the technical field and the weight of each indicator W = (w1, w2, ..., w m ), the evaluation dimension indicator parameters of the technical field include n rows and m evaluation indicators, and form the following technical field data matrix X:

[0048]

[0049] Step 2: Perform isotropic and normalized processing on the indicator attributes of the evaluation dimension indicator parameters in the technical field data matrix to obtain the processed technical field data matrix Z:

[0050]

[0051] Step 3: Loop through each column of the processed technical field data matrix Z to find the optimal solution and the worst solution, where the optimal solution is The worst solution is

[0052] Step 4: Calculate the degree of proximity between each evaluation object and the optimal solution:

[0053]

[0054] And the closeness to the worst solution:

[0055]

[0056] Step 5: Calculate the final score C i , where C i The closer to 1, the better.

[0057]

[0058] Step 6: According to C i The final evaluation results can be obtained by sorting.

[0059] In one embodiment of the present application, determining the key technology fields according to the evaluation scores of each technology field includes sorting the technology fields according to the evaluation scores of each technology field; and determining a preset number of technology fields with the highest evaluation scores as the key technology fields.

[0060] The present invention provides a key technology field evaluation method, device, electronic device and storage medium, which obtains multiple initial analysis files, analyzes statistical granularity information and evaluation dimension information, classifies the initial files to be analyzed into technical fields, obtains multiple technical field file sets, performs data statistics on each technical field file set according to the analysis statistical granularity information, obtains granularity statistical information of each technical field and performs maturity prediction fitting to obtain a maturity fitting data set of each technical field, determines the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, scores each technical field respectively, and determines the key technology field according to the evaluation score; the present invention uses big data analysis and machine learning technology to automatically process a large number of ... It starts by analyzing documents and extracting valuable information from them, which improves the objectivity and comprehensiveness of the evaluation results. By performing data statistics and maturity prediction fitting on the collection of documents in the technical field, it can dynamically adjust the evaluation criteria to make them more in line with the current technological development trends, and improve the generalization ability of the evaluation system, so that it can quickly adapt to and provide accurate evaluation results when facing new technologies and new market changes. Through automated data processing and intelligent analysis, it can quickly respond to changes in the technical field. Through the statistical data support provided by granular statistical information, maturity prediction fitting predicts the maturity of technology through data analysis, which can more accurately reflect the development stage and potential of the technology, provide a scientific basis for the rational allocation and priority sorting of resources, and improve the accuracy and effectiveness of decision-making.

[0061] The following describes an apparatus embodiment of the present application, which can be used to perform the key technical field evaluation method in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the embodiment of the key technical field evaluation method in the above-mentioned embodiment of the present application.

[0062] Figure 5 This is a schematic diagram of a key technical field evaluation device shown in an exemplary embodiment of the present application. The device can be applied to Figure 2 The method implementation process shown in the figure can be based on Figure 1 The present invention is executed in the implementation environment shown in the figure, and may also be applicable to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the apparatus is applicable.

[0063] like Figure 5 As shown, the exemplary key technology field evaluation device includes: an evaluation information acquisition module 501, an evaluation data processing module 502, and a field score determination module 503.

[0064] Among them, the evaluation information acquisition module 501 is used to obtain multiple initial analysis files, analysis statistical granularity information and evaluation dimension information; the evaluation data processing module 502 is used to classify the initial files to be analyzed into technical fields according to the preset technical field category information, and aggregate the initial files to be analyzed in the same technical field to obtain multiple technical field file sets; perform data statistics on each technical field file set according to the analysis statistical granularity information to obtain the granularity statistical information of each technical field, and perform maturity prediction fitting based on the granularity statistical information of each technical field to obtain the maturity fitting data set of each technical field; the field score determination module 503 is used to determine the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, and score each technical field according to the evaluation dimension indicator parameters respectively to obtain the evaluation score of each technical field, so as to determine the key technical field according to the evaluation score of each technical field.

[0065] An embodiment of the present application also provides an electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the electronic device implements the key technical field evaluation method provided in the above-mentioned embodiments.

[0066] Figure 6 is a schematic diagram of the structure of a computer system of an electronic device according to an exemplary embodiment of the present application. It should be noted that: Figure 6 The computer system 600 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0067] like Figure 6 As shown, computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in a read-only memory (ROM) 602 or the program loaded from the storage part to a random access memory (RAM) 603, such as the method in the above embodiment. In RAM 603, various programs and data required for system operation are also stored. CPU 601, ROM 602 and RAM 603 are connected to each other via a bus. Input / output (I / O) interface 605 is also connected to bus 604.

[0068] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

[0069] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 609, and / or installed from a removable medium 611. When the computer program is executed by a central processing unit (CPU) 601, various functions defined in the system of the present application are executed.

[0070] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0072] In the corresponding drawings of the above embodiments, connecting lines may indicate the connection relationship between the various components to indicate more constituent signal paths (constituent_signalpath) and / or one or more ends of some lines may have arrows to indicate the main information flow direction. Connecting lines, as a mark, are not a limitation to the scheme itself, but the use of these lines in combination with one or more exemplary embodiments helps to connect circuits or logic units more easily. Any represented signal (determined by design requirements or preferences) may actually include one or more signals that can be transmitted in either direction and implemented with any appropriate type of signal scheme.

[0073] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0074] Another aspect 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 method as described above is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0075] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0076] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0077] It should be noted that the present application can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0078] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0079] It should be understood that the above content of the present application is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. Ordinary technicians in this field can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application should be the scope of protection required by the claims.

Claims

1. A method for evaluating key technology fields, characterized in that: The evaluation methods for key technical fields include: Obtain multiple initial analysis files, analysis statistical granularity information, and evaluation dimension information; Classify the initial files to be analyzed in the technical field according to the preset technical field category information, and aggregate the initial files to be analyzed in the same technical field to obtain multiple technical field file sets; Performing data statistics on the document collections in each technical field according to the analysis and statistical granularity information to obtain the granularity statistical information of each technical field, and performing maturity prediction fitting based on the granularity statistical information of each technical field to obtain the maturity fitting data set of each technical field; According to the evaluation dimension information, the evaluation dimension indicator parameters of each technical field are determined in the maturity fitting data set of each technical field, and scores are given according to the evaluation dimension indicator parameters of each technical field to obtain the evaluation score of each technical field, so as to determine the key technical field according to the evaluation score of each technical field.

2. The key technology field evaluation method according to claim 1 is characterized in that: Classifying the initial file to be analyzed in the technical field according to the preset technical field category information includes: Classifying the text types of the initial files to be analyzed according to the pre-examination text type information, determining the text type corresponding to each initial file to be analyzed, and marking the initial files to be analyzed based on the text type corresponding to each initial file to be analyzed, to obtain a plurality of first-classified files to be analyzed; Each first-classified file to be analyzed is matched with a corresponding preset text classification model according to the marked text type, and each first-classified file to be analyzed is input into the corresponding preset text classification model to classify the technical field according to the preset technical field category information to determine the technical field to which each of the first-classified files to be analyzed belongs.

3. The key technology field evaluation method according to claim 2 is characterized in that: Data statistics of the document collection in each technical field based on the analysis and statistical granularity information include: In each technical field document set, data statistics are performed on the classified documents to be analyzed of the same text type according to the analysis statistical granularity information to obtain multiple text type statistical information, and the multiple text type statistical information is determined as granular statistical information.

4. The key technology field evaluation method according to claim 3 is characterized in that: The maturity prediction fitting based on the granular statistical information of each technical field includes: Obtain target growth curve model information corresponding to different text types; In each technical field, growth curve fitting is performed on statistical information of each text type according to corresponding target growth curve model information to obtain a fitting growth curve corresponding to each text type, and a growth fitting peak value and a fitting growth parameter set are determined according to the fitting growth curve corresponding to each text type; Determining a current maturity parameter in each text type according to the growth fitting peak value; The current maturity parameters and fitting growth parameter sets corresponding to all text types in any technical field are determined as the maturity fitting data set of the technical field.

5. The key technology field evaluation method according to claim 1 is characterized in that: Scoring is based on the evaluation dimension indicator parameters of each technical field, including: An evaluation calculation is performed based on the TOPSIS algorithm according to the evaluation dimension indicator parameters of each technical field to obtain the optimal solution of each technical field, and the optimal solution of each technical field is determined as the evaluation score of each technical field.

6. The key technology field evaluation method according to claim 5 is characterized in that: The key technical fields determined based on the evaluation scores of each technical field include: Rank the technical fields according to the evaluation scores of each technical field; A preset number of technical fields with the highest evaluation scores are identified as key technical fields.

7. The key technology field evaluation method according to any one of claims 1 to 6, characterized in that: The evaluation dimension information includes the growth rate of the number of each text type in the technical field within a preset time interval, the total number of each text type in the technical field within the preset time interval, and the current maturity parameter of each text type in the technical field.

8. A key technology field evaluation device, characterized in that: The key technical field evaluation devices include: An evaluation information acquisition module is used to obtain multiple initial analysis files, analysis statistical granularity information and evaluation dimension information; An evaluation data processing module is used to classify the initial files to be analyzed in technical fields according to preset technical field category information, and to aggregate the information of the initial files to be analyzed in the same technical field to obtain multiple technical field file sets; to perform data statistics on the file sets in each technical field according to the analysis statistical granularity information to obtain granular statistical information in each technical field, and to perform maturity prediction fitting based on the granular statistical information in each technical field to obtain a maturity fitting data set in each technical field; The domain scoring determination module is used to determine the evaluation dimension indicator parameters of each technical field in the maturity fitting data set of each technical field according to the evaluation dimension information, and to score each technical field according to the evaluation dimension indicator parameters to obtain the evaluation score of each technical field, so as to determine the key technical field according to the evaluation score of each technical field.

9. An electronic device, characterized in that: It comprises a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute the computer program stored in the memory to implement the key technology field evaluation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to enable a computer to execute the key technical field evaluation method as described in any one of claims 1-7.

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