Method, device and equipment for sci-tech intelligence analysis based on generative artificial intelligence

Abstract

The application belongs to the technical field of data analysis, and discloses a scientific and technological information analysis method, device and equipment based on generative artificial intelligence. The method comprises the following steps: collecting an original scientific and technological information data set, and extracting a plurality of technical keywords from the original scientific and technological information data set; using a search engine to count the frequency distribution of each technical keyword, and constructing time series data corresponding to the plurality of technical keywords based on the frequency distribution; selecting a plurality of burst keywords from the plurality of technical keywords according to the time series data; performing hot technology analysis on the original scientific and technological information data set based on the plurality of burst keywords; performing technology clustering based on the hot technology analysis result, and predicting the technology maturity of various hot technologies. The above-mentioned method can quantitatively analyze the dynamic change of keywords, and combines clustering with maturity prediction, which not only can predict the maturity of the analyzed hot technologies, but also improves the timeliness of scientific and technological information analysis.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method, apparatus, and equipment for analyzing scientific and technological intelligence based on generative artificial intelligence. Background Technology

[0002] With the accelerating pace of global technological innovation and the significantly shortened technology iteration cycle, accurately identifying emerging hot technologies and predicting their maturity has become a core requirement for enterprises in formulating R&D strategies and for governments in planning industrial policies. Currently, science and technology intelligence analysis mainly relies on manual screening of keywords from literature, patents, and other data, combined with expert experience to judge technological trends. This process has the following limitations: it relies heavily on manual keyword extraction, which is inefficient and susceptible to subjective bias; it lacks quantitative analysis of the dynamic changes of keywords, making it difficult to capture rapidly growing keywords in the short term; and the technology clustering and maturity prediction stages are independent, failing to form an integrated analysis process from data collection to trend prediction. Ultimately, this results in the hot technologies analyzed from science and technology intelligence being outdated and lacking maturity prediction.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, and equipment for analyzing scientific and technological intelligence based on generative artificial intelligence, aiming to solve the technical problem that the analysis of hot technologies from scientific and technological intelligence is lagging behind and lacks maturity prediction.

[0005] To achieve the above objectives, this invention provides a method for analyzing scientific and technological intelligence based on generative artificial intelligence, which includes the following steps:

[0006] Collect raw science and technology intelligence datasets and extract several technical keywords from the raw science and technology intelligence datasets;

[0007] The frequency distribution of various technical keywords is statistically analyzed using search engines, and time series data corresponding to the aforementioned technical keywords is constructed based on the frequency distribution.

[0008] Based on the time series data, several breakout keywords are selected from the several technical keywords;

[0009] Hotspot technology analysis is performed on the original science and technology intelligence dataset based on the aforementioned several outbreak keywords;

[0010] Based on the analysis results of hot technologies, technology clustering is performed, and the technology maturity of each type of hot technology is predicted.

[0011] In some embodiments, the step of selecting a plurality of breakout keywords from the plurality of technical keywords based on the time series data includes:

[0012] Based on the time series data, determine the actual frequency of any technical keyword at each time point;

[0013] The arrival rate per unit time of any technical keyword is determined based on the actual frequency.

[0014] Based on the arrival rate per unit time, construct the arrival rate sequence corresponding to any technical keyword, and return to execute the step of determining the actual frequency of any technical keyword at each time point based on the time series data, until the arrival rate sequence corresponding to each technical keyword is obtained;

[0015] The burst state detection of the several technical keywords is performed based on the arrival rate sequence corresponding to each technical keyword.

[0016] Several outbreak keywords were selected based on the outbreak state detection results.

[0017] In some embodiments, the step of detecting the burst state of the plurality of technical keywords based on the arrival rate sequence corresponding to each technical keyword includes:

[0018] Identify the stable and abnormal intervals in the arrival rate sequence corresponding to each of the technical keywords;

[0019] Calculate the first arrival rate of the stable interval and the second arrival rate of the abnormal interval, respectively. The first arrival rate represents the average arrival rate within the stable interval, and the second arrival rate represents the average arrival rate within the abnormal interval.

[0020] Calculate the likelihood ratio based on the first arrival rate and the second arrival rate;

[0021] Determine the outbreak start point and outbreak end point corresponding to each technical keyword;

[0022] The outbreak interval is determined based on the time points corresponding to the outbreak start point and the outbreak end point, respectively.

[0023] The burst state of each technical keyword is detected based on the likelihood ratio and the duration corresponding to the burst interval.

[0024] In some embodiments, the filtering of several outbreak keywords based on the outbreak state detection results includes:

[0025] Based on the outbreak state detection results, several candidate outbreak keywords in the outbreak state were selected;

[0026] The outbreak interval is determined based on the outbreak state detection results;

[0027] Determine the burst intensity within the burst range;

[0028] Based on the burst intensity, several candidate burst keywords are selected from the several candidate burst keywords.

[0029] In some embodiments, the hotspot technology analysis of the original science and technology intelligence dataset based on the plurality of outbreak keywords includes:

[0030] Obtain the frequency distribution of each outbreak keyword;

[0031] The popularity of a technology is determined based on the frequency distribution.

[0032] The technological fields and outbreak ranges are determined based on each outbreak keyword, and the outbreak ranges are used to determine the technological development stages.

[0033] Based on the aforementioned technology popularity, the aforementioned technology field, and the aforementioned outbreak range, the hot technology analysis results are output.

[0034] In some embodiments, the technology clustering based on hotspot technology analysis results includes:

[0035] All hot technologies were identified based on the results of the hot technology analysis.

[0036] Identify the common breakout keywords in each hot technology, and construct an overlap matrix based on the common breakout keywords, wherein the overlap matrix is ​​a stacked matrix, and the i-th row and j-th column of the matrix represents the number of common breakout keywords of hot technology i and hot technology j;

[0037] Based on the set parameter k, all items in the overlapping matrix whose off-diagonal elements are less than k-1 and whose diagonal elements are less than k are set to 0, and the other elements are set to 1, to obtain the target matrix.

[0038] The target matrix is ​​transformed into an adjacency matrix of a graph, connected chains in the adjacency matrix of the graph are identified, and clusters for clustering are determined based on the connected chains.

[0039] Based on the clusters, all hot technologies are clustered.

[0040] In some embodiments, the step of predicting the technology maturity of various hot technologies includes:

[0041] A cumulative change curve is established based on the explosive keywords of various trending technologies. The formula for the change curve is as follows: ;

[0042] The velocity function is determined based on the formula for the cumulative change curve. The formula for the velocity function is as follows: , where k is the limit value of the cumulative curve change, a and b are the key parameters to be solved, and t is the time variable;

[0043] The key parameters are solved based on the velocity function, and three key time points are determined based on the solved key parameters. The three key time points are t1, t2, and t3, where t1 = t2= t3= ;

[0044] The current time point is compared with the three key time points to determine the technological maturity of various hot technologies.

[0045] Furthermore, to achieve the above objectives, the present invention also proposes a technology intelligence analysis device based on generative artificial intelligence, wherein the technology intelligence analysis device based on generative artificial intelligence includes:

[0046] The data acquisition module is used to collect raw scientific and technological intelligence datasets and extract several technical keywords from the raw scientific and technological intelligence datasets.

[0047] The module is used to utilize search engines to statistically analyze the frequency distribution of various technical keywords, and to construct time series data corresponding to the aforementioned technical keywords based on the frequency distribution.

[0048] A filtering module is used to filter out several outbreak keywords from the several technical keywords based on the time series data;

[0049] The analysis module is used to perform hot technology analysis on the original science and technology intelligence dataset based on the aforementioned outbreak keywords;

[0050] The analysis module is used to cluster technologies based on the analysis results and predict the technology maturity of various hot technologies.

[0051] Furthermore, to achieve the above objectives, the present invention also proposes a technology intelligence analysis device based on generative artificial intelligence. The technology intelligence analysis device based on generative artificial intelligence includes: a memory, a processor, and a technology intelligence analysis program based on generative artificial intelligence stored in the memory and executable on the processor. The technology intelligence analysis program based on generative artificial intelligence is configured to implement the steps of the technology intelligence analysis method based on generative artificial intelligence as described above.

[0052] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a technology intelligence analysis program based on generative artificial intelligence, wherein when the technology intelligence analysis program based on generative artificial intelligence is executed by a processor, it implements the steps of the technology intelligence analysis method based on generative artificial intelligence as described above.

[0053] This invention collects raw science and technology intelligence datasets and extracts several technical keywords from them. It then uses search engines to statistically analyze the frequency distribution of these keywords, constructing time-series data corresponding to each keyword. Based on this time-series data, it selects several emerging keywords from these emerging keywords. Finally, it performs hot technology analysis on the raw science and technology intelligence dataset based on these emerging keywords. Based on the results of this hot technology analysis, it performs technology clustering and predicts the maturity of various hot technologies. This approach enables quantitative analysis of the dynamic changes in keywords and combines clustering with maturity prediction, thus not only predicting the maturity of the analyzed hot technologies but also improving the timeliness of science and technology intelligence analysis. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the first embodiment of the technology intelligence analysis method based on generative artificial intelligence of the present invention.

[0055] Figure 2 This is a structural block diagram of the first embodiment of the technology intelligence analysis device based on generative artificial intelligence of the present invention.

[0056] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0058] This invention provides a method for analyzing scientific and technological intelligence based on generative artificial intelligence, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of a technology intelligence analysis method based on generative artificial intelligence according to the present invention.

[0059] In this embodiment, the technology intelligence analysis method based on generative artificial intelligence includes the following steps:

[0060] Step S10: Collect the original scientific and technological intelligence dataset and extract several technical keywords from the original scientific and technological intelligence dataset.

[0061] In this embodiment, the executing entity is a technology intelligence analysis device based on generative artificial intelligence. This technology intelligence analysis device based on generative artificial intelligence has functions such as data processing, data communication, and program execution. The technology intelligence analysis device based on generative artificial intelligence can be a computer terminal device or other network device, or other devices with similar functions. This embodiment does not limit the scope of such devices.

[0062] It should be noted that with the accelerating pace of global technological innovation and the significantly shortened technology iteration cycle, accurately identifying emerging hot technologies and predicting their maturity has become a core requirement for enterprises in formulating R&D strategies and for governments in planning industrial policies. Currently, science and technology intelligence analysis mainly relies on manual screening of keywords from literature, patents, and other data, combined with expert experience to judge technological trends. This process has the following limitations: it relies heavily on manual keyword extraction, which is inefficient and susceptible to subjective bias; it lacks quantitative analysis of the dynamic changes of keywords; it is difficult to capture rapidly growing keywords in the short term; and the technology clustering and maturity prediction stages are independent, failing to form an integrated analysis process from data collection to trend prediction. Ultimately, this results in the hot technologies analyzed from science and technology intelligence being outdated and lacking maturity prediction.

[0063] To address the aforementioned technical issues, this embodiment collects a raw science and technology intelligence dataset and extracts several technical keywords from it. It then uses a search engine to statistically analyze the frequency distribution of each technical keyword, constructing time-series data corresponding to these keywords. Based on the time-series data, it selects several emerging keywords from these keywords. Finally, it performs hot technology analysis on the raw science and technology intelligence dataset based on these emerging keywords. Based on the results of the hot technology analysis, it performs technology clustering and predicts the maturity of various hot technologies. This approach enables quantitative analysis of the dynamic changes in keywords and combines clustering with maturity prediction. It not only predicts the maturity of the analyzed hot technologies but also improves the timeliness of science and technology intelligence analysis. Specifically, it can be implemented as follows.

[0064] In this implementation, the original scientific and technological intelligence dataset needs to be collected first. After collecting the dataset, several technical keywords are extracted. These keywords can be pre-defined by the user or set according to requirements. For example, if the keyword is "deep learning," then the keyword "deep learning" is extracted from the original scientific and technological intelligence dataset. Once the keyword is set to "deep learning," the subsequent analysis will focus on hot technologies related to deep learning. In practical applications, the above technical keywords can be adaptively adjusted according to actual needs; this embodiment does not impose any restrictions on this.

[0065] Step S20: Use a search engine to statistically analyze the frequency distribution of each technical keyword, and construct time series data corresponding to the several technical keywords based on the frequency distribution.

[0066] In practice, after determining the keywords, you can first use a search engine to statistically analyze the frequency distribution of each technical keyword. The frequency distribution can be calculated by pre-defined time units (such as day, week, month, quarter), and then time series data can be generated. Time series data could look like "Keyword A: [5, 12, 30, ..., 89]", where each element represents the frequency of that keyword within the corresponding time window. This time window can be year, month, day, etc., and the specific time points can be set according to actual needs.

[0067] Step S30: Select several breakout keywords from the several technical keywords based on the time series data.

[0068] In specific implementation, the actual frequency of keywords can be obtained based on the aforementioned time series data. Taking any technical keyword as an example, the specific process is as follows: determine the actual frequency of any technical keyword at each time point based on the time series data; determine the unit time arrival rate of the technical keyword based on the actual frequency; construct the arrival rate sequence corresponding to the technical keyword based on the unit time arrival rate, and return to execute the step of determining the actual frequency of any technical keyword at each time point based on the time series data until the arrival rate sequence corresponding to each technical keyword is obtained; perform burst state detection on the several technical keywords based on the arrival rate sequence corresponding to each technical keyword; and filter out several burst keywords based on the burst state detection results.

[0069] It should be noted that after obtaining the actual frequency, the arrival rate per unit time can be further calculated using the formula λ = F / T, where F represents the actual frequency and T represents the unit time length, which can be year, month, day, etc. Based on the calculated arrival rate per unit time, an arrival rate sequence can be constructed, for example (λ1, λ2, ..., λ...). n ).

[0070] Furthermore, based on the aforementioned arrival rate sequence, outbreak state detection can be performed on the aforementioned technical keywords. This process specifically includes identifying stable intervals and abnormal intervals in the arrival rate sequence corresponding to each technical keyword; calculating a first arrival rate for the stable interval and a second arrival rate for the abnormal interval, where the first arrival rate represents the average arrival rate within the stable interval and the second arrival rate represents the average arrival rate within the abnormal interval; calculating a likelihood ratio based on the first arrival rate and the second arrival rate; determining the outbreak start point and outbreak end point corresponding to each technical keyword; determining the outbreak interval based on the time points corresponding to the outbreak start point and the outbreak end point; and performing outbreak state detection on each technical keyword based on the likelihood ratio and the duration corresponding to the outbreak interval.

[0071] It should be noted that the stable interval is the interval where the keyword frequency is relatively stable, while the abnormal interval is the interval where the keyword frequency occurs more frequently. The division of the stable and abnormal intervals is based on the corresponding time points in the time series and the actual frequency of the keyword at each time point. Assume the average arrival rate of the stable interval is λs, and the average arrival rate of the abnormal interval is λ... β For example, if the time series data is [1.2, 1.5, 1.3, 5.8, 6.2, 5.9, 2.1], then λ s = (1.2 + 1.5 + 1.3) / 3 = 1.33 (the stationary state of the first 3 time units). λ = (5.8+6.2+5.9) / 3 = 5.97, the likelihood ratio is λs / λ β Assume λ i Let λ be the reach rate of any technical keyword at each time point. i When the value is significantly higher than λs, this time point is taken as the outbreak initiation point, when the first λ i When the price falls back to or near λs, this time point is taken as the end point of the outbreak, and these two time points constitute the outbreak interval. The conditions required to detect whether each technical keyword is in an outbreak state are that the likelihood ratio exceeds a threshold and the duration exceeds a preset duration. For example, the likelihood ratio threshold can be set to 2; if the time unit is months, the preset duration can be set to 5 months; if the time unit is years, the preset duration can be set to 1 year, etc. In practical applications, these can be adjusted according to requirements; this embodiment does not impose any restrictions on this.

[0072] Furthermore, based on the above-mentioned outbreak state detection results, several candidate outbreak keywords in the outbreak state can be directly identified. The above-mentioned outbreak interval can not only determine the duration of the outbreak, but also calculate its outbreak intensity. The outbreak intensity can be the difference between the frequencies of the outbreak keywords at the start and end points of the outbreak. The larger the difference, the greater the outbreak intensity. Several candidate outbreak keywords with outbreak intensity exceeding the preset intensity are selected from several candidate outbreak keywords. The difference corresponding to the preset intensity can be set according to the actual situation. This embodiment does not impose any restrictions on this.

[0073] Step S40: Perform hotspot technology analysis on the original science and technology intelligence dataset based on the aforementioned several breakout keywords.

[0074] In this specific implementation, after identifying the trending keywords, this embodiment can analyze hot technologies based on these keywords. The specific process involves obtaining the frequency distribution of each trending keyword; determining the technology popularity based on the frequency distribution; determining the technology field and trending range based on each trending keyword, where the trending range is used to determine the technology development stage; and outputting the hot technology analysis results based on the technology popularity, the technology field, and the trending range.

[0075] It should be noted that the term frequency (TF) of the explosive keywords can be obtained based on the frequency distribution. TF represents the frequency of occurrence of a term (keyword) in the retrieved dataset. Considering that documents vary in length, the normalized TF is:

[0076]

[0077] Furthermore, the formula for calculating the inverse document frequency (IDF) is:

[0078]

[0079] The more common a word is, the larger the denominator, and the smaller and closer the inverse document frequency is to 0. The denominator is incremented by 1 to avoid a denominator of 0 (i.e., no document contains the word). `log` represents taking the logarithm of the resulting value.

[0080] Finally, the final technology popularity can be obtained based on the above parameters, as shown in the following formula:

[0081]

[0082] TF-IDF is directly proportional to the frequency of a word in a document and inversely proportional to the frequency of that word in the entire language. The TF-IDF value of each word in the document is calculated, and then the words are sorted in descending order, with the top few words selected. A breakout interval has a start and end point, each corresponding to a time point. The duration of the breakout can be determined based on these time points, and this duration represents the technological development stage of the trending technology.

[0083] Step S50: Based on the analysis results of hot technologies, perform technology clustering and predict the technology maturity of each type of hot technology.

[0084] In the specific implementation, based on the above hot technology analysis results, all hot technologies can be identified, and then clustering is performed on these hot technologies. The specific clustering process is as follows: determine the common burst keywords in each hot technology, and construct an overlap matrix based on the common burst keywords, wherein the overlap matrix is ​​a stacked matrix, and the i-th row and j-th column of the matrix represents the number of common burst keywords between hot technology i and hot technology j; based on a set parameter k, set all items in the overlap matrix whose off-diagonal elements are less than k-1 and whose diagonal elements are less than k to 0, and set the other elements to 1 to obtain the target matrix; transform the target matrix into an adjacency matrix of a graph, identify the connected chains in the adjacency matrix of the graph, and determine the clusters used for clustering based on the connected chains; perform technology clustering on all hot technologies based on the clusters.

[0085] It should be noted that, assuming the target matrix is ​​as follows: In the graph structure, node T1 T2 T3 T4 T5 forms a connected chain. All nodes belong to the same connected component, and therefore cluster into a single cluster {T1, T2, T3, T4, T5}.

[0086] Furthermore, after completing the clustering, based on the clustering results, cumulative change curves can be established using the breakout keywords of various hot technologies. The formula for the change curve is as follows: The velocity function is determined based on the formula of the cumulative change curve, and the formula for the velocity function is as follows: Where k is the limit value of the cumulative curve change, a and b are the key parameters to be solved, and t is the time variable; the key parameters are solved according to the velocity function, and three key time points are determined based on the solved key parameters, where the three key time points are t1, t2, and t3, and t1 = t2= t3= ; Compare the current time point with the three key times respectively to determine the technology maturity of various hot technologies.

[0087] It should be noted that assume the current time is t now , if t now < t1, it means that the hot technology is in the germination stage. If t1 ≤ t now < t2, it means that the hot technology is in the growth stage. If t2 ≤ t now < t3, it means that the hot technology is in the mature stage. If t now > t3, it means that the hot technology is in the stage of being mature but stagnant in development.

[0088] In this embodiment, an original scientific and technological intelligence dataset is collected, and several technical keywords are extracted from the original scientific and technological intelligence dataset; the frequency distribution of each technical keyword is statistically analyzed by using a search engine, and time series data corresponding to several technical keywords are constructed based on the frequency distribution; several burst keywords are screened out from several technical keywords according to the time series data; hot technology analysis is performed on the original scientific and technological intelligence dataset based on several burst keywords; technology clustering is performed based on the hot technology analysis results, and the technology maturity of various hot technologies is predicted. The above method can perform quantitative analysis on the dynamic changes of keywords, and combines clustering with maturity prediction, which can not only predict the maturity of the analyzed hot technologies, but also improve the timeliness of scientific and technological intelligence analysis.

[0089] In addition, an embodiment of the present invention also proposes a storage medium, on which a scientific and technological intelligence analysis program based on generative artificial intelligence is stored. When the scientific and technological intelligence analysis program based on generative artificial intelligence is executed by a processor, the steps of the scientific and technological intelligence analysis method based on generative artificial intelligence as described above are implemented.

[0090] Refer to Figure 2 , Figure 2 This is a structural block diagram of the first embodiment of the scientific and technological intelligence analysis device based on generative artificial intelligence of the present invention.

[0091] As Figure 2 shown, the scientific and technological intelligence analysis device based on generative artificial intelligence proposed by the embodiment of the present invention includes:

[0092] An acquisition module 10, configured to collect an original scientific and technological intelligence dataset, and extract several technical keywords from the original scientific and technological intelligence dataset;

[0093] A construction module 20, configured to statistically analyze the frequency distribution of each technical keyword by using a search engine, and construct time series data corresponding to several technical keywords based on the frequency distribution;

[0094] The filtering module 30 is used to filter out several outbreak keywords from the several technical keywords based on the time series data;

[0095] Analysis module 40 is used to perform hot technology analysis on the original science and technology intelligence dataset based on the aforementioned outbreak keywords;

[0096] The analysis module 40 is used to perform technology clustering based on the analysis results and to predict the technology maturity of various hot technologies.

[0097] This embodiment collects a raw science and technology intelligence dataset and extracts several technical keywords from it. It then uses a search engine to statistically analyze the frequency distribution of each technical keyword, constructing time-series data corresponding to these keywords. Based on the time-series data, it selects several emerging keywords from these emerging keywords. Finally, it performs hot technology analysis on the raw science and technology intelligence dataset based on these emerging keywords. Based on the results of the hot technology analysis, it performs technology clustering and predicts the maturity of various hot technologies. This approach enables quantitative analysis of the dynamic changes in keywords and combines clustering with maturity prediction, not only predicting the maturity of the analyzed hot technologies but also improving the timeliness of science and technology intelligence analysis.

[0098] This application embodiment also provides a technology intelligence analysis device based on generative artificial intelligence, including a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The memory is used to store technology intelligence analysis programs based on generative artificial intelligence. When the processor executes the program stored in the memory, it implements the above-mentioned technology intelligence analysis method based on generative artificial intelligence.

[0099] The communication bus mentioned in the aforementioned generative artificial intelligence-based technology intelligence analysis equipment can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0100] The communication interface is used for communication between the aforementioned generative artificial intelligence-based technology intelligence analysis device and other devices.

[0101] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0102] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0103] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0105] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0107] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0108] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0109] In addition, for technical details not described in detail in this embodiment, please refer to the technology intelligence analysis method based on generative artificial intelligence provided in any embodiment of the present invention, which will not be repeated here.

[0110] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0111] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0113] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

[0114] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

Claims

1. A method for analyzing scientific and technological intelligence based on generative artificial intelligence, characterized in that, The technology intelligence analysis method based on generative artificial intelligence includes: Collect raw science and technology intelligence datasets and extract several technical keywords from the raw science and technology intelligence datasets; The frequency distribution of various technical keywords is statistically analyzed using search engines, and time series data corresponding to the aforementioned technical keywords is constructed based on the frequency distribution. Based on the time series data, several breakout keywords are selected from the several technical keywords. The breakout keywords are technical keywords extracted from the original scientific and technological intelligence data and whose frequency of occurrence increases within a certain period of time. Hotspot technology analysis is performed on the original science and technology intelligence dataset based on the aforementioned several outbreak keywords; Based on the analysis results of hot technologies, technology clustering is performed, and the technology maturity of each type of hot technology is predicted. The step of selecting several outbreak keywords from the several technical keywords based on the time series data includes: Based on the time series data, determine the actual frequency of any technical keyword at each time point; The arrival rate per unit time of any technical keyword is determined based on the actual frequency. Based on the arrival rate per unit time, construct the arrival rate sequence corresponding to any technical keyword, and return to execute the step of determining the actual frequency of any technical keyword at each time point based on the time series data, until the arrival rate sequence corresponding to each technical keyword is obtained; The burst state detection of the several technical keywords is performed based on the arrival rate sequence corresponding to each technical keyword. Several outbreak keywords were selected based on the outbreak status detection results; The technology maturity prediction of various hot technologies includes: A cumulative change curve is established based on the explosive keywords of various trending technologies. The formula for the change curve is as follows: ; The velocity function is determined based on the formula for the cumulative change curve. The formula for the velocity function is as follows: , where k is the limit value of the cumulative curve change, a and b are the key parameters to be solved, and t is the time variable; The key parameters are solved based on the velocity function, and three key time points are determined based on the solved key parameters. The three key time points are t1, t2, and t3, where t1 = t2= t3= ; The current time point is compared with the three key time points to determine the technological maturity of various hot technologies.

2. The scientific and technological intelligence analysis method based on generative artificial intelligence as described in claim 1, characterized in that, The step of detecting the burst state of the plurality of technical keywords based on the arrival rate sequence corresponding to each technical keyword includes: Identify the stable and abnormal intervals in the arrival rate sequence corresponding to each of the technical keywords; Calculate the first arrival rate of the stable interval and the second arrival rate of the abnormal interval, respectively. The first arrival rate represents the average arrival rate within the stable interval, and the second arrival rate represents the average arrival rate within the abnormal interval. Calculate the likelihood ratio based on the first arrival rate and the second arrival rate; Determine the outbreak start point and outbreak end point corresponding to each technical keyword; The outbreak interval is determined based on the time points corresponding to the outbreak start point and the outbreak end point, respectively. The burst state of each technical keyword is detected based on the likelihood ratio and the duration corresponding to the burst interval.

3. The scientific and technological intelligence analysis method based on generative artificial intelligence as described in claim 2, characterized in that, The burst keywords selected based on the burst state detection results include: Based on the outbreak state detection results, several candidate outbreak keywords in the outbreak state were selected; The outbreak interval is determined based on the outbreak state detection results; Determine the burst intensity within the burst range; Candidate outbreak keywords are selected from the plurality of candidate outbreak keywords based on the outbreak intensity.

4. The method for analyzing scientific and technological intelligence based on generative artificial intelligence as described in claim 1, characterized in that, The analysis of hot technology in the original science and technology intelligence dataset based on the aforementioned several breakout keywords includes: Obtain the frequency distribution of each outbreak keyword; The popularity of a technology is determined based on the frequency distribution. The technological fields and outbreak ranges are determined based on each outbreak keyword, and the outbreak ranges are used to determine the technological development stages. Based on the aforementioned technology popularity, the aforementioned technology field, and the aforementioned outbreak range, the hot technology analysis results are output.

5. The method for analyzing scientific and technological intelligence based on generative artificial intelligence as described in claim 1, characterized in that, The technology clustering based on the hotspot technology analysis results includes: All hot technologies were identified based on the results of the hot technology analysis. Identify the common breakout keywords in each hot technology, and construct an overlap matrix based on the common breakout keywords, wherein the overlap matrix is ​​a stacked matrix, and the i-th row and j-th column of the matrix represents the number of common breakout keywords of hot technology i and hot technology j; Based on the set parameter k, all items in the overlapping matrix whose off-diagonal elements are less than k-1 and whose diagonal elements are less than k are set to 0, and the other elements are set to 1, to obtain the target matrix. The target matrix is ​​transformed into an adjacency matrix of a graph, connected chains in the adjacency matrix of the graph are identified, and clusters for clustering are determined based on the connected chains. Based on the clusters, all hot technologies are clustered.

6. A technology intelligence analysis device based on generative artificial intelligence, characterized in that, The technology intelligence analysis device based on generative artificial intelligence is applied to the technology intelligence analysis method based on generative artificial intelligence as described in any one of claims 1 to 5, wherein the device comprises: The data acquisition module is used to collect raw scientific and technological intelligence datasets and extract several technical keywords from the raw scientific and technological intelligence datasets. The module is used to utilize search engines to statistically analyze the frequency distribution of various technical keywords, and to construct time series data corresponding to the aforementioned technical keywords based on the frequency distribution. The filtering module is used to filter out several breakout keywords from the several technical keywords based on the time series data. The breakout keywords are technical keywords extracted from the original scientific and technological intelligence data and whose frequency of occurrence increases within a certain period of time. The analysis module is used to perform hot technology analysis on the original science and technology intelligence dataset based on the aforementioned outbreak keywords; The analysis module is used to cluster technologies based on the analysis results and predict the technology maturity of various hot technologies.

7. A technology intelligence analysis device based on generative artificial intelligence, characterized in that, The technology intelligence analysis device based on generative artificial intelligence includes: a memory, a processor, and a technology intelligence analysis program based on generative artificial intelligence stored on the memory and executable on the processor, wherein the technology intelligence analysis program based on generative artificial intelligence is configured to implement the steps of the technology intelligence analysis method based on generative artificial intelligence as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a technology intelligence analysis program based on generative artificial intelligence, which, when executed by a processor, implements the steps of the technology intelligence analysis method based on generative artificial intelligence as described in any one of claims 1 to 5.

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