Prediction method and device for intelligent development rule of measurement and control equipment, medium and product

Abstract

The invention discloses a prediction method and device for an intelligent development rule of measurement and control equipment, a medium and a product, and relates to the technical field of intelligent level evaluation, and the method comprises the steps: obtaining a document data set in an open-source document library, dividing the document data set into domestic document data and foreign document data, and carrying out the age grouping of the domestic document data and the foreign document data, and obtaining the age grouping data; performing sentence segmentation and word segmentation processing on the age grouping data by using a word segmentation tool, extracting feature words related to the intelligent measurement and control equipment to obtain a feature word set, and screening to obtain high-frequency feature words; counting the high-frequency feature words of each age stage, and classifying the high-frequency feature words of each age stage from four dimensions to obtain high-frequency classification data; and predicting the intelligent development law of the measurement and control equipment in each age stage according to the high-frequency classification data. According to the invention, a large amount of abundant and comprehensive data can be automatically obtained, and comprehensive and accurate prediction of the intelligent development rule of the measurement and control equipment can be carried out.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent level evaluation, in particular to a method, device, medium and product for predicting the intelligent development law of measurement and control equipment. BACKGROUND

[0002] Measurement and control equipment is equipment with measurement, control and information transmission functions, is the core carrier for realizing data acquisition and analysis and control execution, is the key material basis for developing digital economy and promoting industrial digital transformation, and its digitalization and intelligent level determines the level and ability of intelligent manufacturing. Enterprises in various countries are vigorously promoting the research on intelligent level evaluation technology of measurement and control equipment, but the overall research is still in the initial stage. How to characterize and evaluate the intelligent level of products is both a hot spot and a difficulty.

[0003] Usually, a technology engineer or researcher obtains the history, current situation and future development trend of intelligent technology of measurement and control equipment by manually browsing and reading related materials in a library or a data room. This requires the engineer or researcher to complete the following work: find the necessary data archive room; according to the literature classification of the archive room, according to the classification of disciplines or other classification methods, accurately find the corresponding articles or reports in a large number of technical literature; spend a lot of time reading the articles, understand the problems and technical solutions in the articles; and find out the development history, current situation and future development trend of intelligentization by comparing the technical routes of various measurement and control equipment. The traditional method for analyzing the development law of intelligent measurement and control equipment may have the following problems or deficiencies: the appropriate and required technical literature cannot be found; the technical data cannot be understood; a lot of time is spent on translating and reading the technical solutions in the literature; a lot of human and financial resources are consumed; only partial and local development law is grasped, and the overall and in-depth development law cannot be grasped. This brings potential risks to the product technology route of an enterprise and the regional industrial strategic planning. SUMMARY

[0004] The purpose of the present application is to provide a method, device, medium and product for predicting the intelligent development law of measurement and control equipment, which can automatically obtain a large amount of rich and comprehensive data and make a comprehensive and accurate prediction of the intelligent development law of measurement and control equipment.

[0005] To achieve the above purpose, the present application provides the following solutions. In a first aspect, the present application provides a method for predicting the intelligent development law of measurement and control equipment, comprising: obtaining a document data set in an open source literature library; the content in the document data set includes: literature name, keyword, author, publication time and abstract data; The document dataset is divided into domestic literature data and foreign literature data according to literature sources, and is respectively grouped by years to obtain year-grouped data; The year-grouped data is subjected to sentence segmentation and word segmentation processing by using a word segmentation tool to obtain a word segmentation dataset; The TF-IDF method is used to extract feature words related to intelligent measurement and control equipment in the word segmentation dataset to obtain a feature word set; Words with an occurrence frequency greater than a preset threshold in the feature word set are screened to obtain high-frequency feature words; The high-frequency feature words and corresponding years are used to count high-frequency feature words in each year stage; The high-frequency feature words in each year stage are classified from four dimensions of scientific principles of measurement and control equipment, physical and chemical index data acquisition, functional application and instruments and meters to obtain high-frequency classification data; The high-frequency classification data are used to predict intelligent core features, key technologies, typical applications and development breakthroughs of measurement and control equipment in each year stage to obtain intelligent development rules of measurement and control equipment.

[0006] Optionally, the sentence segmentation and word segmentation processing of the year-grouped data by using a word segmentation tool to obtain a word segmentation dataset specifically includes: According to the language types and professional fields of the year-grouped data, data cleaning is performed to remove redundant spaces, network symbols, redundant line breaks, irrelevant numbers and stop words to obtain preprocessed data; The preprocessed data is subjected to sentence segmentation and word segmentation processing by using a word segmentation tool to obtain a word segmentation dataset.

[0007] Optionally, the stop words include Chinese stop words and English stop words; The Chinese stop words include: non-technical idioms, afterwords and conjunctions; The English stop words include: words with no value for intelligent feature analysis.

[0008] Optionally, the open source literature library includes: Zhiwu, Baidu Scholar, IEEE database and Springer database; The document dataset includes: patent data, journal literature data and technical report data corresponding to literature-related information.

[0009] Optionally, the document dataset is divided into domestic literature data and foreign literature data according to literature sources, and is respectively grouped by years to obtain year-grouped data, specifically including: The document dataset is divided into domestic literature data and foreign literature data according to literature sources; The domestic literature data is divided into seven stages: before 1990, [1990, 2000), [2000, 2008), [2008, 2015), [2015, 2020), [2020, 2022), [2022, 2025]. The foreign literature data is divided into 9 stages: before 1900, [1900, 1935), [1935, 1950), [1950, 1980), [1980, 2000), [2000, 2015), [2015, 2020), [2020, 2022), [2022, 2025].

[0010] Optionally, the word segmentation tool includes an NLTK module and a jieba module; the step of using the word segmentation tool to perform sentence segmentation and word segmentation processing on the age-grouped data to obtain a word segmentation dataset specifically includes: The English literature data in the chronological grouping data is processed using the NLTK module, and the Chinese literature data in the chronological grouping data is processed using the jieba module to obtain the word segmentation dataset.

[0011] Optionally, based on the high-frequency classification data, the core intelligent features, key technologies, typical applications, and development breakthroughs of measurement and control equipment in different eras can be predicted to obtain the development law of intelligent measurement and control equipment, specifically including: Based on the high-frequency classification data, combined with the technological background and industry development of each era, the core intelligent features, key technologies, typical applications and development breakthroughs of measurement and control equipment in each era are predicted, thus obtaining the development law of intelligent measurement and control equipment.

[0012] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the intelligent development pattern of measurement and control equipment as described in any one of the above.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting the intelligent development pattern of measurement and control equipment as described above.

[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method for predicting the intelligent development pattern of measurement and control equipment as described above.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, device, medium, and product for predicting the intelligent development pattern of measurement and control equipment. The method includes: acquiring a document dataset from an open-source literature database; the document dataset includes: document name, keywords, author, publication time, and abstract data; dividing the document dataset into domestic and foreign literature data according to the source of the documents, and grouping them by year to obtain year-grouped data; using a word segmentation tool to perform sentence segmentation and word segmentation on the year-grouped data to obtain a word segmentation dataset; using the TF-IDF method to extract feature words related to intelligent measurement and control equipment from the word segmentation dataset to obtain a feature word set; filtering words in the feature word set whose frequency of occurrence is greater than a preset threshold to obtain high-frequency feature words; statistically analyzing the high-frequency feature words for each year stage based on the high-frequency feature words and their corresponding years; classifying the high-frequency feature words for each year stage from four dimensions: scientific principles, physicochemical index data collection, functional applications, and instruments of the measurement and control equipment to obtain high-frequency classification data; and predicting the core intelligent features, key technologies, typical applications, and development breakthroughs of the measurement and control equipment in each year stage based on the high-frequency classification data to obtain the intelligent development pattern of measurement and control equipment. This application provides a prerequisite for comprehensive and accurate prediction by acquiring a large amount of rich and comprehensive data from open source literature databases. Furthermore, after grouping the literature into domestic and foreign categories, feature word extraction and high-frequency word extraction operations are performed. Finally, development pattern prediction is carried out by year, thereby making a comprehensive and accurate prediction of the intelligent development pattern of measurement and control equipment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an application environment diagram of a method for predicting the intelligent development trend of measurement and control equipment according to an embodiment of this application.

[0018] Figure 2 This is a flowchart illustrating a method for predicting the intelligent development trend of measurement and control equipment, provided as an embodiment of this application.

[0019] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Predicting the intelligent characteristics and technological routes of measurement and control equipment can provide overall guidance for enterprises' product strategic planning, design, and development of measurement and control equipment, influencing the overall strategic layout of enterprise product development. Domestic and international research has been conducted on testing and evaluation technologies for the level of intelligence or specific intelligent functions in different fields of measurement and control equipment. The National Institute of Standards and Technology (NIST) in the United States believes that the degree of control an intelligent system has over its behavior is the core indicator for evaluating intelligent equipment. To this end, it has proposed an evaluation model and indicators for artificial intelligence autonomy, aiming to establish a standard framework for autonomy in the scientific community through measurement methods and exploring the ethics and impacts of different degrees of autonomy. Researchers at Brigham Young University believe that the evaluation of robot intelligence lies in the efficiency of human-machine collaborative work. They evaluate human-machine interaction efficiency from the tolerance for neglect and interaction efficiency of human-machine interaction in test tasks, thereby obtaining a measure of the robot's intelligence level. In 2022, ISO released the ISO 23704 series of standards, "General Requirements for Cyber-Physical Intelligent CNC Machine Tool Systems," which provides evaluation standards for machine tool manufacturing technology and functional intelligent machine tools, and introduces relevant examples, providing a reference for subsequent evaluation of machine tool intelligence levels.

[0022] The purpose of this application is to provide a method, equipment, medium, and product for predicting the intelligent development trend of measurement and control equipment, which can automatically acquire a large amount of rich and comprehensive data and make comprehensive and accurate predictions of the intelligent development trend of measurement and control equipment.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] The prediction method for the intelligent development pattern of measurement and control equipment provided in this application embodiment can be applied to, for example, Figure 1 The application environment shown is illustrated. Terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server.

[0025] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0026] In one exemplary embodiment, such as Figure 2 As shown, a method for predicting the intelligent development trend of measurement and control equipment is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S1 to S8. Wherein: S1. Obtain the document dataset from the open-source document repository; the content of the document dataset includes: document name, keywords, author, publication time, and abstract data.

[0027] The open-source literature repositories include: CNKI, Baidu Scholar, IEEE database, and Springer database.

[0028] The document dataset includes: patent data, journal article data, and literature-related information corresponding to technical report data.

[0029] S2. Divide the document dataset into domestic document data and foreign document data according to the source of the document, and then group them by year to obtain year-grouped data.

[0030] In this embodiment, the document dataset is divided into domestic literature data and foreign literature data according to the source of the literature.

[0031] The domestic literature data is divided into 7 stages: before 1990, [1990, 2000), [2000, 2008), [2008, 2015), [2015, 2020), [2020, 2022), [2022, 2025].

[0032] The foreign literature data is divided into 9 stages: before 1900, [1900, 1935), [1935, 1950), [1950, 1980), [1980, 2000), [2000, 2015), [2015, 2020), [2020, 2022), [2022, 2025].

[0033] S3. Use a word segmentation tool to perform sentence segmentation and word segmentation on the age-grouped data to obtain a word segmentation dataset.

[0034] In this embodiment, data cleaning is performed based on the language and professional field of the age-grouped data to remove redundant spaces, network symbols, redundant line breaks, irrelevant numbers, and stop words, resulting in preprocessed data. The stop words include both Chinese and English stop words. Chinese stop words include: colloquialisms, proverbs, and conjunctions without technical meaning; specifically, "better to be early than late," "as the saying goes," "why not," "without reservation," "passed," "otherwise," and "from now on." English stop words include: words that are of no value to intelligent feature analysis; specifically, "wasn't," "thousand," "successfully," and "significantly."

[0035] The preprocessed data is segmented into sentences and words using a word segmentation tool to obtain a word segmentation dataset.

[0036] The word segmentation tool includes an NLTK module and a jieba module. The NLTK module is used to process the English literature data in the chronological grouping data, and the jieba module is used to process the Chinese literature data in the chronological grouping data to obtain a word segmentation dataset.

[0037] S4. Using the TF-IDF method, extract the feature words related to intelligent measurement and control equipment from the word segmentation dataset to obtain the feature word set.

[0038] S5. Filter words in the feature word set whose frequency of occurrence is greater than a preset threshold to obtain high-frequency feature words.

[0039] S6. Based on the high-frequency feature words and their corresponding eras, count the high-frequency feature words for each era.

[0040] S7. From the four dimensions of scientific principles, physicochemical index data acquisition, functional application and instrumentation of measurement and control equipment, the high-frequency characteristic words of each era are classified to obtain high-frequency classification data.

[0041] S8. Based on the high-frequency classification data, predict the core intelligent features, key technologies, typical applications and development breakthroughs of measurement and control equipment in each era, and obtain the intelligent development law of measurement and control equipment.

[0042] In this embodiment, based on the high-frequency classification data and combined with the technological background and industry development of each era, the core intelligent features, key technologies, typical applications and development breakthroughs of measurement and control equipment in each era are predicted, thereby obtaining the intelligent development law of measurement and control equipment.

[0043] Specifically, this embodiment can be implemented using the following steps: Step 1, Data Acquisition: Document data is generated by acquiring information such as document titles, keywords, authors, publication dates, and abstracts from publicly available open-source literature repositories (e.g., patent data, journal article data, technical report data) through purchase, batch download, or other methods.

[0044] Step 2, Data Grouping and Preprocessing: Because the amount of data collected in domestic and foreign document databases differs, the collected domestic and foreign document / document data are grouped by year.

[0045] For example, Chinese literature data is divided into seven stages: before 1990, [1990, 2000), [2000, 2008), [2008, 2015), [2015, 2020), [2020, 2022), and [2022, 2025).

[0046] Foreign language literature data is divided into nine stages: before 1900, [1900, 1935), [1935, 1950), [1950, 1980), [1980, 2000), [2000, 2015), [2015, 2020), [2020, 2022), [2022, 2025).

[0047] Then, based on the language and professional field of the collected literature data, the collected data is cleaned, including removing extra spaces, web symbols and extra line breaks, numbers and stop words.

[0048] The removal of stop words is closely related to language, era, professional field, and application scenario. For example, the following words appear frequently in the literature data and are colloquialisms, proverbs, and conjunctions in this patent, and should be removed as stop words: "better to be early than late", "as the saying goes", "why not", "without reservation", "passed", "otherwise", "from now on", etc.

[0049] Stop words in English, such as "wasnt," "thousand," "successfully," and "significantly," are removed. These words are not included in a general stop word database, but they have no value for the analysis of intelligent features and for technical prediction.

[0050] Then, by calling the NLTK and jieba modules, the document data is segmented into sentences and words.

[0051] In simple terms, the above process can be summarized as follows: data acquisition (extracting document summaries, etc.) - document grouping (for the extracted documents / documents (summaries)) - sentence segmentation (the document is divided into several sentences) - word segmentation (the sentence is divided into verbs, nouns, etc.).

[0052] Step 3, Feature Extraction: The TFIDF method was used to extract feature words from literature corpora related to intelligent measurement and control equipment. Feature words were extracted using the TF-IDF method. TF-IDF (Term Frequency-Inverse Document Frequency) is a commonly used weighting technique for information retrieval. TF-IDF is a statistical method used to evaluate the importance of a feature word to a document in a corpus. The importance of a feature word increases proportionally to the number of times it appears in the document, but decreases inversely proportionally to its frequency of appearance in the corpus. The more times a word appears in an article, and the fewer times it appears in all documents, the more representative it is of that article. The TF-IDF method was used to extract feature words from a corpus of literature related to intelligent measurement and control equipment. The feature word patterns extracted in this step are shown below.

[0053] Step 4, word frequency analysis: By using TF / IDF technology, or simply by counting the frequency of word occurrences, we can analyze the technical characteristics of high-frequency words.

[0054] Step 5: Keyword Classification: (1) High-frequency words are classified and grouped according to the period, and the top 10 / 50 characteristic words appearing in each period are counted.

[0055] (2) Group the keywords based on the scientific principles, physical and chemical index data acquisition (object / parameter), functions and industry applications of the measurement and control equipment, as well as the instruments / products / equipment / components.

[0056] Instruments / equipment. Characteristic terms related to the components, instruments, parts, and structural elements of measurement and control equipment. Examples include: positioner, transmitter, actuator, thermometer, etc.

[0057] Scientific principles. The technical principles, technical characteristics, implementation information, operational characteristics, and behavioral characteristics of measurement and control equipment that realizes measurement and control functions. Scientific principles are key characteristic terms reflecting the era's features of measurement and control equipment. Based on the inherent technical characteristics of these characteristic terms, through analysis, the technical features are further summarized as follows: measurement and control technology, automation, integration, standardization, electronization, digitalization, informatization, systematization, networking, and intelligence.

[0058] Physicochemical data acquisition. Measurement and control equipment, as an means of observing the physical world, collects technical parameters and physical quantities, such as temperature, humidity, speed, and length. With the development of technology, measurement and control equipment, as well as the objects and data being measured and controlled, are constantly evolving.

[0059] Functions and Applications. Frequency analysis of the literature reveals a large number of characteristic words related to application scenarios, representing the era-specific characteristics of the development and evolution of measurement and control equipment. Examples include: fertilizer plant, thermal power plant, nuclear power plant, and Shenzhou spacecraft.

[0060] Step 6: Analyze and extract intelligent features.

[0061] To demonstrate the usability of this embodiment, a specific experiment was also conducted, and the results are as follows: Key features extracted from each stage: (1) Before 1935: the nascent stage of mechanical automation.

[0062] Core feature: Simple closed-loop control based on mechanical principles.

[0063] Typical vocabulary: duty cycle, load average, mechanical operation.

[0064] Technical features: Steam / hydraulic drive unit, gear transmission system, limit switch control.

[0065] Limitations: Lacks feedback adjustment mechanism and relies on manual calibration.

[0066] (2) 1935-1980: The period of electronic transformation.

[0067] Breakthrough technologies: transistor applications and the development of analog circuits.

[0068] Intelligent features: pneumatic pressure control, analog signal processing.

[0069] Representative systems: pneumatic actuators, mechatronic instruments.

[0070] Signs of progress: Achieving continuous monitoring of parameters such as pressure and flow rate.

[0071] (3) 1980-2000: The period of computer integration.

[0072] Key technologies: widespread adoption of microprocessors and standardization of PLCs.

[0073] Intelligent features: PID algorithm, expert system (ES).

[0074] Typical applications: automotive production line control systems, power plant SCADA systems.

[0075] Milestone event: The IEC 61131-3 PLC programming standard was established in 1987.

[0076] (4) 2000-2008: The period of networked intelligence.

[0077] Core technology: Fieldbus technology (Profibus, CANopen).

[0078] Intelligent features: distributed control architecture, predictive maintenance.

[0079] Typical system: Smart grid feeder automation.

[0080] Significant progress: The IEC 61850 substation communication standard was released in 2003.

[0081] (5) 2008-2015: The early stage of the Internet of Things.

[0082] Key technologies: RFID, ZigBee, WSN.

[0083] Intelligent features: device self-diagnosis, edge computing nodes.

[0084] Typical application: Industry 4.0 demonstration factory.

[0085] Breakthrough: In 2012, the MQTT protocol became the communication standard for the Internet of Things.

[0086] (6) 2015-2020: Deep learning integration period.

[0087] Core technologies: CNN, LSTM neural networks.

[0088] Intelligent features: visual inspection system, abnormal pattern recognition.

[0089] Typical application: Intelligent traffic monitoring system.

[0090] Significant progress: In 2017, NVIDIA launched the Jetson edge AI computing platform.

[0091] (7) 2020-2023: The period of autonomous decision-making evolution.

[0092] Key technologies: reinforcement learning, digital twins.

[0093] Intelligent features: self-optimizing control strategy, virtual debugging.

[0094] Typical application: Unmanned aerial vehicle (UAV) swarm collaborative control system.

[0095] Breakthrough: In 2021, Google DeepMind released the AlphaFold protein prediction system.

[0096] (8) 2023-2025: Quantum Intelligence Foresight Period.

[0097] Key technologies: quantum sensors, photonic computing.

[0098] Intelligent features: Ultra-precision measurement and enhanced anti-interference capability.

[0099] Typical application: Real-time monitoring system for nuclear fusion devices.

[0100] Theoretical Breakthrough: Application of Quantum Annealing Algorithm in Combinatorial Optimization.

[0101] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting the intelligent development patterns of measurement and control equipment.

[0102] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0103] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0104] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0105] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0108] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the intelligent development law of measurement and control equipment, characterized in that, The method comprises the following steps: obtaining a document dataset in an open-source literature database; the content in the document dataset includes: literature name, keyword, author, publication time and abstract data; dividing the document dataset into domestic literature data and foreign literature data according to the literature source, and respectively grouping the data by years to obtain year-grouped data; using a word segmentation tool to perform sentence segmentation and word segmentation processing on the year-grouped data to obtain a word segmentation dataset; using the TF-IDF method, extracting feature words related to intelligent measurement and control equipment in the word segmentation dataset to obtain a feature word set; screening words with a frequency greater than a preset threshold in the feature word set to obtain high-frequency feature words; statistically analyzing the high-frequency feature words in each year stage according to the high-frequency feature words and corresponding years; classifying the high-frequency feature words in each year stage from four dimensions of scientific principle of measurement and control equipment, physical and chemical index data acquisition, function application and instrument and meter to obtain high-frequency classification data; predicting the intelligent core features, key technologies, typical applications and development breakthroughs of measurement and control equipment in each year stage according to the high-frequency classification data to obtain the intelligent development law of measurement and control equipment.

2. The method of claim 1, wherein, The method for obtaining the word segmentation dataset by using the word segmentation tool to perform sentence segmentation and word segmentation processing on the year-grouped data comprises the following steps: performing data cleaning according to the language types and professional fields of the year-grouped data to remove redundant spaces, network symbols, redundant line breaks, irrelevant numbers and stop words to obtain preprocessed data; using a word segmentation tool to perform sentence segmentation and word segmentation processing on the preprocessed data to obtain a word segmentation dataset.

3. The method of claim 2, wherein, The stop words include Chinese stop words and English stop words; The Chinese stop words include: non-technical idioms, afterthoughts and conjunctions; The English stop words include: words with no value for intelligent feature analysis.

4. The method of claim 1, wherein, The open-source literature database includes: Zhiwu, Baidu Scholar, IEEE database and Springer database; The document dataset includes: patent data, journal literature data and technical report data corresponding to literature related information.

5. The method of claim 1, wherein, The method for dividing the document dataset into domestic literature data and foreign literature data according to the literature source, and respectively grouping the data by years to obtain year-grouped data comprises the following steps: dividing the document dataset into domestic literature data and foreign literature data according to the literature source; dividing the domestic literature data into several stages, including: before 1990, [1990, 2000), [2000, 2008), [2008, 2015), [2015, 2020), [2020, 2022), [2022, 2025); dividing the foreign literature data into several stages, including: before 1900, [1900, 1935), [1935, 1950), [1950, 1980), [1980, 2000), [2000, 2015), [2015, 2020), [2020, 2022), [2022, 2025).

6. The method of claim 1, wherein, The word segmentation tool includes an NLTK module and a jieba module; the sentence segmentation and word segmentation processing of the chronological grouping data by using the word segmentation tool to obtain a word segmentation data set, specifically including: The English literature data in the chronological grouping data is processed by using the NLTK module, and the Chinese literature data in the chronological grouping data is processed by using the jieba module to obtain a word segmentation data set.

7. The method of claim 1, wherein the method further comprises: According to the high-frequency classification data, the intelligent core features, key technologies, typical applications and development breakthroughs of the measurement and control equipment in each era stage are predicted to obtain the intelligent development law of the measurement and control equipment, specifically including: According to the high-frequency classification data, the intelligent core features, key technologies, typical applications and development breakthroughs of the measurement and control equipment in each era stage are predicted to obtain the intelligent development law of the measurement and control equipment.

8. A computer device comprising: The memory, the processor and the computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to realize the prediction method of the intelligent development law of the measurement and control equipment according to any one of claims 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the prediction method of the intelligent development law of the measurement and control equipment according to any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the prediction method of the intelligent development law of the measurement and control equipment according to any one of claims 1-7.

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