An evaluation method, apparatus, electronic device, and storage medium in a key technology field.

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for evaluating key technology fields. By acquiring multiple initial analysis files, statistical granularity information, and evaluation dimension information, the initial files to be analyzed are categorized by technology field to obtain a set of technology field files. Data statistics are then performed on each technology field set based on the statistical granularity information to obtain granular statistical information for each technology field. Maturity prediction fitting is then performed to obtain maturity fitting datasets for each technology field. Evaluation dimension index parameters are determined from the maturity fitting datasets based on the evaluation dimension index parameters. Each technology field is scored based on the evaluation dimension index parameters, and key technology fields are determined based on the evaluation scores. This application, through statistical data support provided by granular statistical information and data analysis prediction technology fitting of technology maturity, more accurately reflects the development stage and potential of technologies, improves the generalization ability of the evaluation system, and enhances the accuracy and effectiveness of decision-making.

Description

Technical Field

[0001] This application relates to the field of computers, and more particularly to an evaluation method, apparatus, electronic device, and storage medium in a key technology field. Background Technology

[0002] In today's rapidly developing technological era, technological breakthroughs, academic research, and industrialization have become important driving forces for social progress. Whether it is government agencies, universities, or enterprises, in the face of increasingly fierce international competition, they must conduct in-depth assessments and decisions on key technology areas 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 potential to be transformed into actual productivity, and then consider whether to carry out large-scale industrialization and promotion.

[0003] However, how to scientifically assess 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 key technology area assessment and decision-making typically rely on the experience of industry experts. This involves identifying key technologies across various industries, scoring them according to predetermined rules, and ultimately selecting the key technology areas. While this method can reflect the current state and development trends of the industry to some extent, differences in expert knowledge and experience can easily lead to a lack of comprehensiveness and objectivity in the assessment results. Furthermore, scoring rules are often based on historical data and existing successful cases, making it difficult to cover new situations and problems that may arise in the future, and thus failing to effectively guarantee the generalization ability of the decision-making system. Summary of the Invention

[0004] The purpose of this invention is to provide an evaluation method, apparatus, electronic device, and storage medium in the field of key technologies to solve the above-mentioned technical problems.

[0005] This invention provides a method for evaluating key technology areas. The method includes: acquiring multiple initial analysis files, analytical statistical granularity information, and evaluation dimension information; classifying the initial files to be analyzed into technology areas according to preset technology area category information, and aggregating the information of initial files to be analyzed within the same technology area to obtain multiple technology area file sets; performing data statistics on each technology area file set according to the analytical statistical granularity information to obtain granular statistical information for each technology area, and performing maturity prediction fitting based on the granular statistical information for each technology area to obtain maturity fitting datasets for each technology area; determining the evaluation dimension index parameters for each technology area in the maturity fitting datasets for each technology area according to the evaluation dimension information, and scoring each technology area according to the evaluation dimension index parameters to obtain an evaluation score for each technology area, thereby determining the key technology areas based on the evaluation scores for each technology area.

[0006] In one embodiment of the present invention, classifying the initial files to be analyzed according to preset technical field category information includes: classifying the initial files to be analyzed according to the text type information of the pre-examined text type, determining the text type corresponding to each initial file to be analyzed, and labeling the initial files to be analyzed based on the text type corresponding to each initial file to be analyzed, thereby obtaining multiple primary-classified files to be analyzed; each primary-classified file to be analyzed is matched with a corresponding preset text classification model according to the labeled text type, and each primary-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, thereby determining the technical field to which each primary-classified file to be analyzed belongs.

[0007] In one embodiment of the present invention, data statistics on file sets of various technical fields based on analytical statistical granularity information includes: in each file set of technical fields, data statistics are performed on files to be analyzed that are classified into the same text type according to analytical 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 various technical fields 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 the fitted growth curve corresponding to each text type, and determining the growth fitting peak and the fitted growth parameter set according to the fitted growth curve corresponding to each text type; determining the current maturity parameter in each text type according to the growth fitting peak; and determining the current maturity parameter and the fitted growth parameter set corresponding to all text types in any technical field as the maturity fitting dataset of the technical field.

[0009] In one embodiment of the present invention, scoring according to the evaluation dimension index parameters of each technical field includes: performing evaluation calculation based on the TOPSIS algorithm according to the evaluation dimension index parameters of each technical field to obtain the optimal solution of each technical field, and determining the optimal solution of each technical field as the evaluation score of each technical field.

[0010] In one embodiment of the present invention, determining the key technical fields based on the evaluation scores of each technical field includes: ranking 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 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 parameter of each text type in the technical field.

[0012] This invention also provides a key technology field evaluation device, comprising: an evaluation information acquisition module for acquiring multiple initial analysis files, analysis statistical granularity information, and evaluation dimension information; an evaluation data processing module for classifying the initial files to be analyzed into technical fields according to preset technical field category information, and aggregating the information of 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 granular statistical information for each technical field, and performing maturity prediction fitting based on the granular statistical information for each technical field to obtain a maturity fitting dataset for each technical field; and a field scoring determination module for determining the evaluation dimension index parameters of each technical field in the maturity fitting dataset for each technical field according to the evaluation dimension information, and scoring each technical field according to the evaluation dimension index parameters to obtain an evaluation score for each technical field, thereby determining the key technology field based on the evaluation scores of each technical field.

[0013] This invention also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the key technology evaluation method as described in any of the above embodiments.

[0014] This invention also provides a computer-readable storage medium storing computer-readable instructions that, when executed by a computer's processor, cause the computer to perform a key technical field evaluation method as described in any of the above embodiments.

[0015] This invention provides a method, apparatus, electronic device, and storage medium for evaluating key technical fields. It acquires multiple initial analysis files, analyzes statistical granularity information, and evaluates dimensional information. The initial files are categorized into technical fields to obtain multiple sets of files for each technical field. Data statistics are performed on each set of files based on the statistical granularity information to obtain granular statistical information for each technical field, and maturity prediction fitting is performed to obtain maturity fitting datasets for each technical field. Evaluation dimensionality index parameters for each technical field are determined from the maturity fitting datasets based on the evaluation dimensional information. Each technical field is scored, and key technical fields are determined based on the evaluation scores. This application automatically processes a large number of initial files using big data analysis and machine learning techniques. By analyzing documents and extracting valuable information, the objectivity and comprehensiveness of the evaluation results are improved. Through data statistics and maturity prediction fitting of the technical field document set, the evaluation criteria can be dynamically adjusted to better align with current technological development trends, enhancing the generalization ability of the evaluation system. This allows it to quickly adapt to and provide accurate evaluation results in the face of new technologies and market changes. Through automated data processing and intelligent analysis, it can quickly respond to changes in the technical field. Statistical data support provided by granular statistical information and maturity prediction fitting through data analysis predict the maturity of technologies, more accurately reflecting the development stage and potential of technologies. This provides a scientific basis for the rational allocation and prioritization of resources, improving the accuracy and effectiveness of decision-making.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0018] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application;

[0019] Figure 2 This is a flowchart illustrating an evaluation method in a key technology field, as shown in an exemplary embodiment of this application;

[0020] Figure 3 This is a schematic diagram illustrating a specific technical field classification in an exemplary embodiment of this application;

[0021] Figure 4This is a schematic diagram illustrating a growth curve fitting method, as shown in an exemplary embodiment of this application.

[0022] Figure 5 This is a schematic diagram illustrating an evaluation device in a key technical field, as shown in an exemplary embodiment of this application;

[0023] Figure 6 This is a schematic diagram of the structure of a computer system for an electronic device, as illustrated in an exemplary embodiment of this application. Detailed Implementation

[0024] The embodiments of the present invention will be described below with reference to the accompanying drawings and specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 and 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 schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0026] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0027] The term "and / or" used in this application describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship.

[0028] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this 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, analytical statistical granularity information, and evaluation dimension information from the database 110. It categorizes the initial files to be analyzed into technical fields according to preset technical field category information, and aggregates the information of initial files to be analyzed within the same technical field to obtain multiple technical field file sets. It performs data statistics on each technical field file set according to the analytical statistical granularity information to obtain granular statistical information for each technical field, and performs maturity prediction fitting based on the granular statistical information for each technical field to obtain maturity fitting datasets for each technical field. Based on the evaluation dimension information, it determines the evaluation dimension index parameters for each technical field from the maturity fitting datasets for each technical field, and scores each technical field according to the evaluation dimension index parameters to obtain an evaluation score for each technical field. The key technical fields are then determined based on the evaluation scores for each technical field. The computer device 120 may be at least one of a microcomputer, embedded computer, network computer, industrial computer, etc.

[0030] In a schematic manner, computer device 120 acquires multiple initial analysis files, analytical statistical granularity information, and evaluation dimension information from database 110. It categorizes the initial files to be analyzed into technical fields, resulting in multiple sets of files within each technical field. Data statistics are then performed on each technical field set based on the analytical statistical granularity information to obtain granular statistical information for each technical field. Maturity prediction fitting is then performed to obtain maturity fitting datasets for each technical field. Based on the evaluation dimension information, evaluation dimension index parameters for each technical field are determined from the maturity fitting datasets for each technical field. Each technical field is then scored, and key technical fields are determined based on the evaluation scores. This application automatically processes a large number of initial analysis files using big data analysis and machine learning techniques. This system extracts valuable information, improving the objectivity and comprehensiveness of the evaluation results. By performing data statistics and maturity prediction fitting on the technical field document set, the evaluation criteria can be dynamically adjusted to better align with current technological development trends. This enhances the generalization ability of the evaluation system, enabling it to quickly adapt to and provide accurate evaluation results in the face of new technologies and market changes. Through automated data processing and intelligent analysis, it can quickly respond to changes in the technical field. With statistical data support provided by granular statistical information, and maturity prediction fitting using data analysis to predict the maturity of technologies, it can more accurately reflect the development stage and potential of technologies, providing a scientific basis for the rational allocation and prioritization of resources, and improving the accuracy and effectiveness of decision-making.

[0031] Figure 2 This is a flowchart illustrating an exemplary embodiment of the present application of a method for evaluating a key technical field, which can be used in... Figure 1It can be executed in the implementation environment described above, but it can also be implemented in other implementation environments. No specific limitations are imposed on the aforementioned implementation environments here. (See also...) Figure 2 As shown, the flowchart of the evaluation method in this key technology field includes at least steps S210 to S240, which are described in detail below:

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

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

[0034] In one embodiment of this application, the aforementioned initial analysis file is accessed and retrieved via the Internet, local area network, data interface, etc., and stored in a local database for data retrieval.

[0035] In the feasible environment of this application, the above-mentioned analytical 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 feasible environment of this application, the aforementioned 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. Specifically, assuming that the text types include papers and patents, a specific way to set the evaluation dimension information includes the growth rate of papers belonging to the technical field in the past year, the growth rate of patents belonging to the technical field in the past year, the total number of papers belonging to the technical field in the past year, the total number of patents belonging to the technical field in the past year, the predicted value of the technology maturity of the technical field, and the predicted value of the industrialization maturity of the technical field. It should be noted that the above-mentioned setting method of evaluation dimension information is only an exemplary example, and specific settings can be made according to data requirements, evaluation objectives, and evaluation accuracy requirements. The above-mentioned setting method of evaluation dimension information is not limited here.

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

[0038] In one embodiment of this application, classifying the initial files to be analyzed according to preset technical field category information includes classifying the initial files to be analyzed by text type according to preset text type information, determining the text type corresponding to each initial file to be analyzed, and labeling the initial files to be analyzed based on the text type corresponding to each initial file to be analyzed, resulting in multiple primary-classified files to be analyzed. Each primary-classified file to be analyzed is matched with a corresponding preset text classification model according to the labeled text type, and each primary-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 primary-classified file to be analyzed belongs.

[0039] Reference Figure 3 As shown, Figure 3 This is a schematic diagram illustrating a specific technical field classification as shown in an exemplary embodiment of this application. For example... Figure 3 As shown, assuming 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 linked to the technical field category through a text classification model. The text classification model includes, but is not limited to, semantic recognition deep learning models such as FastText, TextCNN, and Transformer.

[0040] In step S230, data statistics are performed on the file 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 dataset of each technical field.

[0041] In one embodiment of this application, data statistics on document sets of various technical fields based on analytical statistical granularity information includes performing data statistics on the first-classified documents of the same text type in each document set of technical fields based on analytical statistical granularity information to obtain multiple text type statistical information, and determining the multiple text type statistical information as granular statistical information.

[0042] In one embodiment of this application, maturity prediction fitting based on granular statistical information of various technical fields 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 the fitted growth curve corresponding to each text type, and the growth fitting peak and fitted growth parameter set are determined according to the fitted growth curve corresponding to each text type; the current maturity parameter is determined according to the growth fitting peak in each text type; and the current maturity parameter and fitted growth parameter set corresponding to all text types in any technical field are determined as the maturity fitting dataset of the technical field.

[0043] In one embodiment of this application, the technological development and industrialization process is considered as a growth process. Things go through three stages: occurrence, development, and maturity, each with a different development speed. Typically, the rate of change is relatively slow in the occurrence stage; it accelerates in the development stage; and it slows down again in the maturity stage. The development curve obtained according to these three stages is usually called a growth curve or logical growth curve. Because such curves often resemble an S-shape, they are also called S-curves. Growth curve function models include, but are not limited to, Logistic and Fisher-Pry growth curve models. For example, if the input data is the number of patents in a certain technical field over the years as 1, 4, 31, 92, 242, 624, 949, and 966, by fitting the growth curve model, the peak value K = 1248 is obtained, and the current industrialization maturity = 966 / K = 0.774. The fitted curve is referenced... Figure 4 As shown, Figure 4 This is a schematic diagram illustrating a growth curve fitting method, as shown in an exemplary embodiment of this application. Figure 4 As shown, the growth trend of the number of patents in this technology field over the years follows an S-shaped curve.

[0044] In step S240, the evaluation dimension index parameters of each technical field are determined in the maturity fitting dataset of each technical field based on the evaluation dimension information, and scores are given according to the evaluation dimension index parameters of each technical field to obtain the evaluation score of each technical field, so as to determine the key technical fields based on the evaluation scores of each technical field.

[0045] In one embodiment of this application, scoring is performed based on the evaluation dimension index parameters of each technical field, including evaluation calculation based on the TOPSIS algorithm according to the evaluation dimension index parameters of each technical field, obtaining the optimal solution of each technical field, and determining the optimal solution of each technical field as the evaluation score of each technical field.

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

[0047] Step 1: Determine the evaluation dimensions and parameters for the technical field, as well as the weights of each indicator, W = (w1, w2, ..., w...). m The evaluation dimensions and parameters for the technical field include n rows and m evaluation indicators, forming the following technical field data matrix X:

[0048]

[0049] Step 2: Perform attribute normalization and homogenization processing on the evaluation dimension index parameters in the technical field data matrix to obtain the processed technical field data matrix Z:

[0050]

[0051] Step 3: Iteratively solve for the optimal and worst solutions for each column of the processed technical data matrix Z, where the optimal solution is... The worst solution is

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

[0053]

[0054] And how close the worst-case scenario is:

[0055]

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

[0057]

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

[0059] In one embodiment of this application, determining the key technical fields based on the evaluation scores of each technical field includes ranking the technical fields according to their evaluation scores and determining a preset number of technical fields with the highest evaluation scores as the key technical fields.

[0060] This invention provides a method, apparatus, electronic device, and storage medium for evaluating key technical fields. It acquires multiple initial analysis files, analyzes statistical granularity information, and evaluates dimensional information. The initial files are categorized into technical fields to obtain multiple sets of files for each technical field. Data statistics are performed on each set of files based on the statistical granularity information to obtain granular statistical information for each technical field, and maturity prediction fitting is performed to obtain maturity fitting datasets for each technical field. Evaluation dimensionality index parameters for each technical field are determined from the maturity fitting datasets based on the evaluation dimensional information. Each technical field is scored, and key technical fields are determined based on the evaluation scores. This application automatically processes a large number of initial files using big data analysis and machine learning techniques. By analyzing documents and extracting valuable information, the objectivity and comprehensiveness of the evaluation results are improved. Through data statistics and maturity prediction fitting of the technical field document set, the evaluation criteria can be dynamically adjusted to better align with current technological development trends, enhancing the generalization ability of the evaluation system. This allows it to quickly adapt to and provide accurate evaluation results in the face of new technologies and market changes. Through automated data processing and intelligent analysis, it can quickly respond to changes in the technical field. Statistical data support provided by granular statistical information and maturity prediction fitting through data analysis predict the maturity of technologies, more accurately reflecting the development stage and potential of technologies. This provides a scientific basis for the rational allocation and prioritization of resources, improving the accuracy and effectiveness of decision-making.

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

[0062] Figure 5 This is a schematic diagram illustrating an evaluation apparatus in a key technical field, as shown in an exemplary embodiment of this application. The apparatus can be applied to… Figure 2 The method implementation process shown can be based on the device Figure 1 The implementation environment shown can be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device 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] The evaluation information acquisition module 501 is used to acquire multiple initial analysis files, analytical 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 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. The file sets in each technical field are statistically analyzed according to the analytical statistical granularity information to obtain the granular statistical information of each technical field, and maturity prediction fitting is performed based on the granular statistical information of each technical field to obtain the maturity fitting dataset of each technical field. The domain scoring determination module 503 is used to determine the evaluation dimension index parameters of each technical field in the maturity fitting dataset of each technical field according to the evaluation dimension information, and to score each technical field according to the evaluation dimension index parameters to obtain the evaluation score of each technical field, so as to determine the key technical fields based on the evaluation scores of each technical field.

[0065] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the key technical field evaluation methods provided in the above embodiments.

[0066] Figure 6 This is a schematic diagram illustrating the structure of a computer system for an electronic device, as shown in an exemplary embodiment of this application. It should be noted that... Figure 6 The computer system 600 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0067] like Figure 6 As shown, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on a program stored in Read-Only Memory (ROM) 602 or a program loaded from storage into Random Access Memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus. An Input / Output (I / O) interface 605 is also connected to the bus 604.

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

[0069] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs various functions defined in the system of this application.

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

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0072] In the corresponding figures of the above embodiments, connecting lines can represent the connection relationship between various components, indicating more constitutive signal paths and / or one or more ends of some lines having arrows to indicate the main information flow direction. Connecting lines serve as an identifier and are not a limitation on the scheme itself, but rather, using these lines in conjunction with one or more exemplary embodiments helps to more easily connect circuits or logic units. Any signal represented (determined by design requirements or preferences) can actually include one or more signals that can be transmitted in any direction and can be implemented in any suitable type of signal scheme.

[0073] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0074] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0075] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0076] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0077] It should be noted that this application can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0078] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0079] It should be understood that the above content of this application is only a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be the scope of protection claimed in the claims.

Claims

1. A method for evaluating key technologies, characterized in that, The evaluation methods in the key technology areas include: Acquire multiple initial analysis files, analyze statistical granularity information, and evaluate dimensional information; The initial files to be analyzed are classified into technical fields according to preset technical field category information, and the information of initial files to be analyzed in the same technical field is aggregated to obtain multiple sets of files in different technical fields. Data statistics are performed on the document collections of each technical field based on the granularity of the analysis, to obtain the granularity statistics of each technical field. Based on the granularity statistics of each technical field, maturity prediction fitting is performed to obtain the maturity fitting dataset of each technical field. Based on the evaluation dimension information, the evaluation dimension index parameters of each technical field are determined in the maturity fitting dataset of each technical field, and scores are obtained according to the evaluation dimension index parameters of each technical field to obtain the evaluation score of each technical field. The key technical fields are determined based on the evaluation scores of each technical field.

2. The key technology evaluation method according to claim 1, characterized in that, The initial files to be analyzed are categorized into technical fields based on preset technical field category information, including: The initial files to be analyzed are classified according to the pre-examined text type information to determine the text type corresponding to each initial file to be analyzed. The initial files to be analyzed are then labeled based on the text type corresponding to each initial file to be analyzed, resulting in multiple files to be analyzed in a single classification. Each primary classification file is matched with a corresponding preset text classification model based on its labeled text type, and each primary classification file is input into the corresponding preset text classification model to classify it according to preset technical field category information, thereby determining the technical field to which each primary classification file belongs.

3. The key technology evaluation method according to claim 2, characterized in that, Data statistics were performed on the document collections for each technical field based on the analytical granularity information, including: In each technical field document set, the documents to be analyzed in the first classification of the same text type are statistically analyzed according to the granularity of the analysis, resulting in multiple text type statistical information, which are then identified as granular statistical information.

4. The key technology evaluation method according to claim 3, characterized in that, Maturity prediction fitting based on granularity statistics from various technical fields includes: Obtain target growth curve model information corresponding to different text types; In each technical field, the statistical information of each text type is fitted with the growth curve according to the corresponding target growth curve model information to obtain the fitted growth curve corresponding to each text type, and the growth fitting peak and the fitted growth parameter set are determined according to the fitted growth curve corresponding to each text type. The current maturity parameter is determined based on the growth fitting peak value in each text type; The set of current maturity parameters and fitted growth parameters corresponding to all text types in any technical field is determined as the maturity fitting dataset of the technical field.

5. The key technology evaluation method according to claim 1, characterized in that, Scoring is conducted based on the evaluation dimensions and parameters for each technical field, including: The TOPSIS algorithm is used to evaluate and calculate the optimal solution for each technical field based on the evaluation dimension index parameters of each technical field, and the optimal solution for each technical field is determined as the evaluation score of each technical field.

6. The key technology evaluation method according to claim 5, characterized in that, The key technology areas identified based on the evaluation scores of each technology area include: The technical fields are ranked according to their evaluation scores. The number of technical fields with the highest evaluation scores will be identified as key technical fields.

7. The key technology evaluation method according to any one of claims 1-6, 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. An evaluation device in a key technology field, characterized in that, The evaluation device in the key technology field includes: The evaluation information acquisition module is used to acquire multiple initial analysis files, analysis statistical granularity information, and evaluation dimension information; The evaluation data processing module is used to classify the initial files to be analyzed into 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 each technical field file set according to the analysis statistical granularity information to obtain the granular statistical information of each technical field, and to perform maturity prediction fitting based on the granular statistical information of each technical field to obtain the maturity fitting dataset of each technical field. The domain scoring determination module is used to determine the evaluation dimension index parameters of each technology field based on the maturity fitting dataset of each technology field according to the evaluation dimension information, and to score each technology field according to the evaluation dimension index parameters to obtain the evaluation score of each technology field, so as to determine the key technology fields based on the evaluation scores of each technology field.

9. An electronic device, characterized in that, It includes 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 a computer program stored in the memory to implement the key technology evaluation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that enables the computer to perform the critical technology evaluation method as described in any one of claims 1-7.

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