Method and device for determining investment attracting enterprises in industrial park, equipment and medium

Abstract

The invention discloses a method, a device, equipment and a medium for determining an investment attracting enterprise in an industrial park, and the method comprises the steps: collecting original enterprise data related to the enterprise from a data source, and building an enterprise feature information library according to the original enterprise data; constructing an industrial chain atlas based on an industrial classification standard and the enterprise feature information base, and obtaining park enterprise data corresponding to a target industrial park; and determining a target enterprise corresponding to the target industrial park according to the park enterprise data, the industrial chain spectrum and the enterprise feature information base. Based on the technical scheme, the enterprise data of the plurality of data sources are acquired, the enterprise feature database is established, and the target enterprise is determined according to the enterprise data of the industrial park, so that the accuracy of determining the investment attracting enterprises is improved, and the efficiency of determining the investment attracting enterprises is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and medium for determining investment promotion enterprises in an industrial park. Background Art

[0002] As an important way to promote the rapid development of the industrial park, attracting investment in the industrial park is an effective way to attract external funds and technology and achieve local economic catch-up. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for determining investment attraction enterprises in an industrial park, which improves the accuracy of determining investment attraction enterprises and the efficiency of determining investment attraction enterprises by acquiring enterprise data from multiple data sources and establishing an enterprise feature database, and determining target enterprises based on the enterprise data in the industrial park.

[0005] According to one aspect of the present invention, a method for determining an industrial park investment attraction enterprise is provided, comprising:

[0006] Collecting original enterprise data related to the enterprise from the data source, and establishing an enterprise feature information database based on the original enterprise data;

[0007] Building an industrial chain map based on the industrial classification standard and the enterprise characteristic information database, and obtaining park enterprise data corresponding to the target industrial park;

[0008] The target enterprise corresponding to the target industrial park is determined according to the park enterprise data, the industrial chain spectrum and the enterprise feature information database.

[0009] According to another aspect of the present invention, a device for determining an industrial park investment attraction enterprise is provided, which includes:

[0010] An information base establishment module, used to collect original enterprise data related to the enterprise from the data source, and establish an enterprise feature information base based on the original enterprise data;

[0011] A graph construction module is used to construct an industrial chain graph based on the industry classification standard and the enterprise characteristic information database, and obtain park enterprise data corresponding to the target industrial park;

[0012] The target enterprise determination module is used to determine the target enterprise corresponding to the target industrial park based on the park enterprise data, the industrial chain spectrum and the enterprise feature information database.

[0013] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining an industrial park investment attraction enterprise described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining an industrial park investment attraction enterprise described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention collects original enterprise data related to the enterprise from the data source, establishes an enterprise feature information database based on the original enterprise data, builds an industrial chain map based on the industrial classification standard and the enterprise feature information database, and obtains the park enterprise data corresponding to the target industrial park, and then determines the target enterprise corresponding to the target industrial park based on the park enterprise data, the industrial chain map and the enterprise feature information database. Based on the above technical solution, by obtaining enterprise data from multiple data sources and establishing an enterprise feature database, and determining the target enterprise based on the enterprise data of the industrial park, the accuracy of determining the investment attraction enterprise is improved, and the efficiency of determining the investment attraction enterprise is improved.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a method for determining an industrial park investment attraction enterprise provided according to an embodiment of the present invention;

[0022] Figure 2 is a flow chart of establishing an enterprise feature information database according to an embodiment of the present invention;

[0023] Figure 3 is a flowchart of establishing an industrial chain map provided according to an embodiment of the present invention;

[0024] Figure 4 is a flow chart for determining a target enterprise according to an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a device for determining an investment attraction enterprise in an industrial park provided according to an embodiment of the present invention;

[0026] Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flowchart of a method for determining an industrial park investment attraction enterprise provided by an embodiment of the present invention. This embodiment can be applied to establish an enterprise feature information database based on original enterprise data, and then determine the target enterprise corresponding to the target industrial park based on the enterprise feature information database and the industrial chain map. The method can be executed by a device for determining an industrial park investment attraction enterprise. The device for determining an industrial park investment attraction enterprise can be implemented in the form of hardware and / or software, and the device for determining an industrial park investment attraction enterprise can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110, collecting original enterprise data related to the enterprise from a data source, and establishing an enterprise feature information database based on the original enterprise data.

[0032] The data source may be a pre-selected interface for collecting raw enterprise data. The user may select a data source according to the needs, and the data source may be one or more. Raw enterprise data may be understood as unprocessed enterprise text data collected from the data source. The enterprise feature information library may be a database for storing feature data of each enterprise.

[0033] Specifically, multiple data sources can be determined, including internal data sources and external data sources. Internal data sources can include internal enterprise databases, ERP systems, CRM systems, etc. External data sources: such as public data resource libraries, social networking platforms, e-commerce websites, industry associations, etc. Then, according to different data sources, you can choose database docking, API interface calls, web crawlers, file imports, manual entry and other methods to collect original enterprise data, and extract feature data associated with the enterprise from the original enterprise data to establish an enterprise feature information database.

[0034] Based on the above technical solution, the collecting of original enterprise data related to the enterprise from the data source includes: collecting enterprise structured data from the data interface corresponding to each data source based on a data collection tool, and using the enterprise structured data as the original enterprise data.

[0035] Among them, the data collection tool can be a tool for realizing automatic data collection.

[0036] Specifically, data collection tools are used to collect enterprise structured data from the data interfaces corresponding to the data sources, including but not limited to basic enterprise information (such as name, legal person, registered address), financial data (such as operating income, net profit, total assets), product information, industry category, etc. Exemplarily, in the process of data acquisition, web crawler technology (such as Scrapy, BeautifulSoup, etc.) can be used for automated data collection, and APIs such as open APIs of government and industry data, company official websites, and securities regulatory agency websites can be used to acquire structured data, and information on related enterprises and industries can be collected from different data sources such as enterprise databases, market research reports, news information, intellectual property platforms, etc. as raw data.

[0037] On the basis of the above technical scheme, the enterprise characteristic information database is established according to the original enterprise data, including: performing data clearing on the original enterprise data to determine the enterprise data to be processed, and performing word segmentation processing on the enterprise data to be processed based on a word segmentation tool; performing feature extraction on the enterprise data to be processed after the word segmentation processing according to preset recognition fields to determine enterprise key characteristic words; and establishing the enterprise characteristic information database based on the enterprise key characteristic words.

[0038] The word segmentation tool may be a tool for performing word segmentation processing on a text. The preset recognition field may be understood as a preset field for performing named entity recognition, which may include enterprise name, legal person, product, etc. The enterprise feature keyword may be a keyword for identifying enterprise features.

[0039] Specifically, Figure 2 As shown, the collected data is cleaned, segmented, POS tagged, named entity recognized, keyword extracted and relation extracted by natural language processing technology to build a structured information base containing key features of the enterprise. For example, data cleaning is performed by cleaning the collected text to remove redundant spaces, special characters, punctuation marks, etc., to remove stop words, special characters and irrelevant words, to remove irrelevant parts (such as page numbers, directories, and chart descriptions), to retain useful text content, and to improve the accuracy and effectiveness of subsequent data analysis and modeling. Segmentation and POS tagging are performed by using Chinese segmentation tools (such as jieba, THULAC, etc.) to segment each text and decompose the text into individual words or phrases, such as: enterprise name, legal person, product, operating income, net profit, total assets, industry category, etc.

[0040] It should be noted that Jieba is an open source Chinese word segmentation tool, written in Python, mainly used for Chinese text processing. It can segment Chinese text into independent words, providing basic support for subsequent text processing, analysis, mining and other tasks. THULAC is a Chinese lexical analysis tool developed by Tsinghua University, integrating two major functions: word segmentation and part-of-speech tagging. The F1 value of word segmentation on the standard data set can reach 97.3%, and the F1 value of part-of-speech tagging is 92.9%. At the same time, it maintains a high processing speed, which is suitable for large-scale Chinese data processing.

[0041] Named Entity Recognition (NER) extracts specific words from the text in the document according to preset fields (company name, legal person, product, etc.). For example, key feature words such as company name, legal person, product, etc. can be extracted from the company's annual report. Then spaCy and BERT deep learning models are used to implement the NER task. spaCy is an open source NLP library that supports multiple languages ​​and provides various aspects of text processing, including word segmentation, part-of-speech tagging, named entity recognition (NER), dependency syntax analysis, etc. BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model proposed by Google and is a type of Transformer model.

[0042] On the basis of the above technical scheme, the enterprise characteristic information database is established based on the enterprise key characteristic words, including: processing the enterprise key characteristic words based on a keyword extraction algorithm to determine the enterprise characteristic information corresponding to the enterprise key characteristic words; generating a dependency tree for the enterprise data to be processed based on a dependency syntax analysis tool, and determining enterprise association relationships based on the dependency tree; and establishing the enterprise characteristic information database based on the enterprise association relationships and the enterprise characteristic information.

[0043] Specifically, advanced keyword extraction algorithms (such as TF-IDF, TextRank, RAKE, etc.) are used to further process the identified key feature words of the enterprise to evaluate the importance and relevance of each feature word, and then the most representative enterprise feature information is screened out according to the output of the algorithm. Dependency syntactic analysis tools (such as Stanford Parser, LTP, etc.) are used to conduct in-depth analysis on the cleaned and segmented enterprise data to be processed, and generate a dependency tree describing the sentence structure. It should be noted that the dependency tree shows the dependency relationship between words, such as subject-predicate relationship, verb-object relationship, etc., which helps to understand the complex semantics and related information in enterprise data.

[0044] For example, Figure 2 As shown in the figure, keyword extraction extracts keywords or phrases by using algorithms such as TF-IDF and TextRank to identify specific information related to the enterprise. When a word appears frequently in a document (high TF) and is uncommon in the entire document collection (high IDF), its weight should be higher because it is more informative for distinguishing the content of the document. TF-IDF is used in natural language processing tasks such as information retrieval, text classification, topic modeling, and text summarization to improve the quality of text features. TF term frequency refers to the frequency of a specific word appearing in a document. It is usually expressed as a ratio, and the calculation formula is as follows:

[0045]

[0046] Where t represents a specific word and d represents a specific document. IDF inverse document frequency is a measure of the importance of a word, reflecting the rarity of a word in the entire document set. Its calculation formula is: Where N is the total number of documents, and |{d∈D:t∈d}| is the number of documents containing t. TF-IDF calculation: The final value of TF-IDF is calculated by multiplying TF and IDF: TF-IDF(t,d,D)=TF(t,d)×IDF(t,D). It should be noted that the higher the final value, the more important the word t is in document d.

[0047] Relationship extraction analyzes the sentence structure in the text and uses the dependency syntax analysis tool spaCy to analyze the sentences, generate dependency trees, and extract the relationships between entities. For example, extract entity relationships in the word segmentation results, such as "merger", "subsidiary" and other modifying relationship words. According to the results of dependency syntax analysis, filter out relationships that meet these patterns. Construct an enterprise feature library by using a unique ID identifier as a unique index for an enterprise, organize the extracted information into a structured format, and establish an enterprise feature information library.

[0048] It should be noted that a data-driven model is built for this enterprise feature library for training, a test set is used to evaluate the model performance, the trained model is applied to new enterprise data, and the enterprise feature library is updated in real time.

[0049] S120. Construct an industrial chain map based on the industrial classification standard and the enterprise characteristic information database, and obtain park enterprise data corresponding to the target industrial park.

[0050] The industry classification standard may be pre-set standard information for industry classification, for example, the Classification of Strategic Emerging Industries (2018) may be used as the classification standard. The target industrial park may be understood as an industrial park that needs to attract business investment. The park enterprise data may be relevant data of enterprises in the current park corresponding to the target industrial park.

[0051] Specifically, relying on the data of the enterprise characteristic information database and in accordance with national standards, we will build an intelligent industrial chain map that meets the needs of industrial analysis. It should be noted that the industrial chain is a path for one or several resources to continuously transfer to downstream industries through several industrial levels and directly reach consumers. The construction of the industrial chain map is composed of industrial chain nodes and industrial chain relationships. The nodes are the products and services involved in the industrial chain. The industrial chain relationship is composed of the upper and lower industrial relations and the upstream and downstream industrial chain relations. It is the basic structure that connects the industrial chain nodes and constitutes the industrial chain map. The upstream and downstream relationship nodes of the industrial chain include the following: Raw materials for production: refers to the most basic materials required for the production of products or the provision of services. These raw materials constitute the main components of the products. They are the first link in the industrial chain and provide basic materials for subsequent production activities. Production accessories: including self-made parts and purchased parts. Self-made parts mainly refer to non-standard parts with high technical content and high added value, such as gears and shaft parts, which are usually produced by manufacturing enterprises themselves. Purchased parts include outsourced purchased parts and standard parts, such as bearings, bolts, etc. These accessories are usually provided by external suppliers. Auxiliary materials: refers to supporting materials for the main application, such as refrigerants for air conditioners, inks for ink cartridges, lubricants, etc. These materials are used to change the main materials so that the products have specific characteristics, or other non-production raw materials used before the product is completed, such as packaging materials. Production equipment: including processing equipment, assembly equipment, transportation equipment, packaging equipment, etc., used in various stages of the production process. These equipment are necessary for the production of products and directly affect the production efficiency and quality of the products. Processing technology: refers to the specific operation process and steps in the processing process, such as mechanical processing technology, rubber processing technology, etc. The processing technology determines the shape, size and performance of the product and is the key link in the industrial chain to transform raw materials into finished products. Technical services: technical services provided to the subject, including processing, logistics, installation, testing, maintenance, consulting, design, engineering, integration, etc. Technical services are the part of the industrial chain that provides professional knowledge and skills support to help enterprises improve production efficiency and product quality. Product business: refers to the subject as an intermediate product that can be directly integrated and applied by downstream products or services. For example, lithium-ion batteries, as intermediate products, can be integrated and applied by automotive battery systems. The product business relationship reflects how the products in the industrial chain are further processed or assembled to become part of the final product.

[0052] On the basis of the above technical solution, the construction of the industrial chain map based on the industrial classification standard and the enterprise characteristic information database includes: processing the enterprise data in the enterprise characteristic information database based on the industrial classification standard, and constructing an industrial chain map including industrial chain nodes and industrial chain relationships.

[0053] Among them, the industrial chain nodes represent the products and services in the industrial chain, and the industrial chain relationships include upper and lower industrial relationships and upstream and downstream industrial chain relationships.

[0054] Specifically, the construction process is as follows Figure 3 As shown in the figure, it includes knowledge modeling: defining industry connection points, taking the new energy industry chain as an example: first determine the key products and components in the new energy industry, such as solar panels, wind turbines, battery cells, electric vehicles, etc. Then define the attributes of each node, including technical specifications, production capacity, supplier information, etc. Knowledge acquisition: follow the data collection and construction of enterprise feature information base processing flow. Knowledge fusion: The goal of knowledge fusion is to integrate various entities (such as raw materials, components, equipment, services, etc.) and their relationships (such as supplier relationships, technology dependencies, etc.) into a comprehensive graph. Weighted average, voting mechanism, Bayesian and other algorithms are used to solve data conflicts and inconsistencies. Graph generation: Generate an industry chain graph using the results of knowledge fusion to display nodes and relationships. Use Gephi, Cytoscape, etc. to convert graph data into intuitive graphical representations. Data verification: Data verification is performed using a manual + automated method, in which the automated method uses an NLP model to automatically identify entities in the text (such as company names, product names) and match them with nodes in the graph. Use the model to identify the relationships between entities and compare them with the relationships in the graph to find missing or incorrect links. Dynamic update: The above process is automated through code, and machine learning combined with data verification mechanism is used to achieve iterative upgrades of nodes and relationships in the industrial chain map.

[0055] S130. Determine a target enterprise corresponding to the target industrial park according to the park enterprise data, the industrial chain spectrum and the enterprise feature information database.

[0056] Specifically, the industrial chain map can be used to sort out the industrial chain of the industry in which the target industrial park is located, clarify the upstream, midstream and downstream links of the industrial chain, as well as the relationship and interaction between the links, and then identify the key links and core enterprises in the industrial chain. According to the positioning and development direction of the target industrial park, the key features for enterprise screening can be set. For example, for a science and technology innovation industrial park, more attention may be paid to the R&D capabilities, number of patents, technological innovation capabilities, etc. of the enterprise. The key features can be used to match the enterprise feature information database and select enterprises that meet the needs of the target industrial park.

[0057] On the basis of the above technical solution, the target enterprise corresponding to the target industrial park is determined according to the park enterprise data, the industrial chain spectrum and the enterprise characteristic information database, including: obtaining a park evaluation system corresponding to the target industrial park, and determining an evaluation score corresponding to the target industrial park according to the park evaluation system; determining the target enterprise based on the evaluation score, the industrial chain spectrum and the enterprise characteristic information database.

[0058] Among them, the park evaluation system can be understood as a pre-established evaluation system corresponding to the target park. For example, a corresponding evaluation system can be established through preset evaluation dimensions, including health status indicators, such as output value growth rate, return on investment, innovation capability and other industrial chain evaluation indicators.

[0059] Specifically, evaluation indicators are selected according to the positioning, development goals and industry characteristics of the target industrial park, which may include economic benefits: such as total enterprise output value, tax contribution, employment promotion, etc., technological innovation: such as R&D investment, number of patents, proportion of high-tech enterprises, etc., industrial chain collaboration: such as industrial chain integrity, upstream and downstream enterprise supporting conditions and other indicators, and then according to the importance of each indicator, the corresponding weight is assigned, and the evaluation score of each enterprise is calculated based on the set indicators and weights.

[0060] On the basis of the above technical solution, the target enterprise is determined based on the evaluation score, the industrial chain spectrum and the enterprise characteristic information database, including: determining the industrial weak link corresponding to the target industrial park based on the evaluation score; and determining the target enterprise from the industrial chain spectrum and the enterprise characteristic information database according to the industrial weak link.

[0061] Among them, the weak links in the industry can be understood as the industrial links within the industrial chain of the target industrial park whose evaluation scores are lower than the preset score threshold.

[0062] Specifically, the industrial chain spectrum is used to analyze the industrial chain structure of the target industrial park in detail, and each node in the industrial chain is marked, especially the nodes directly related to the identified weak links, and the enterprise feature information database is accessed to search for those enterprises related to the weak links. For example, Figure 4 As shown in the figure, for the data of park enterprises, a park enterprise characteristic information database is established in the form of an enterprise characteristic information database according to the plan. In combination with local policies and industrial conditions of the park, a multi-dimensional indicator system for park evaluation is established, such as indicators of health status, such as output value growth rate, return on investment, innovation ability and other industrial chain evaluation indicator systems. Relying on the multi-dimensional feature vectors in the park enterprise characteristic information database, the leading industries in the region are split into links, and the value creation capabilities of each link in the park industrial chain are analyzed. Combined with SWOT analysis, value chain analysis and difference analysis methods, the weak links are analyzed by comparing the indicators in the park evaluation system. The results of the analysis are used as input factors to find target enterprises in the industrial chain map.

[0063] The technical solution of the embodiment of the present invention collects original enterprise data related to the enterprise from the data source, establishes an enterprise feature information database based on the original enterprise data, builds an industrial chain map based on the industrial classification standard and the enterprise feature information database, and obtains the park enterprise data corresponding to the target industrial park, and then determines the target enterprise corresponding to the target industrial park based on the park enterprise data, the industrial chain map and the enterprise feature information database. Based on the above technical solution, by obtaining enterprise data from multiple data sources and establishing an enterprise feature database, and determining the target enterprise based on the enterprise data of the industrial park, the accuracy of determining the investment attraction enterprise is improved, and the efficiency of determining the investment attraction enterprise is improved.

[0064] Embodiment 2

[0065] Figure 5 A schematic diagram of a device for determining an industrial park investment attraction enterprise provided by an embodiment of the present invention. Figure 5 As shown, the device includes: an information base building module 510, a graph building module 520, and a target enterprise determination module 530; wherein,

[0066] An information base building module 510 is used to collect original enterprise data related to the enterprise from a data source and build an enterprise feature information base based on the original enterprise data;

[0067] A graph construction module 520 is used to construct an industrial chain graph based on the industry classification standard and the enterprise characteristic information database, and obtain park enterprise data corresponding to the target industrial park;

[0068] The target enterprise determination module 530 is used to determine the target enterprise corresponding to the target industrial park according to the park enterprise data, the industrial chain spectrum and the enterprise feature information database.

[0069] On the basis of the above technical solution, the information database establishment module is used to perform data clearing on the original enterprise data to determine the enterprise data to be processed, and perform word segmentation on the enterprise data to be processed based on a word segmentation tool; perform feature extraction on the enterprise data to be processed after word segmentation according to preset recognition fields to determine enterprise key feature words; and establish the enterprise feature information database based on the enterprise key feature words.

[0070] On the basis of the above technical solution, the information database establishment module is used to process the enterprise key feature words based on the keyword extraction algorithm to determine the enterprise feature information corresponding to the enterprise key feature words; generate a dependency tree for the enterprise data to be processed based on the dependency syntax analysis tool, and determine the enterprise association relationship based on the dependency tree; establish the enterprise feature information database based on the enterprise association relationship and the enterprise feature information.

[0071] Based on the above technical solution, the graph construction module is used to process the enterprise data in the enterprise feature information database based on the industry classification standard, and construct an industrial chain graph including industrial chain nodes and industrial chain relationships; wherein the industrial chain nodes represent products and services in the industrial chain, and the industrial chain relationships include hierarchical relationships between industries and upstream and downstream relationships between industries.

[0072] Based on the above technical solution, the target enterprise determination module is used to obtain the park evaluation system corresponding to the target industrial park, and determine the evaluation score corresponding to the target industrial park according to the park evaluation system; and determine the target enterprise based on the evaluation score, the industrial chain spectrum and the enterprise feature information database.

[0073] On the basis of the above technical solution, the target enterprise determination module is used to determine the industrial weak links corresponding to the target industrial park based on the evaluation scores; and determine the target enterprise from the industrial chain spectrum and the enterprise feature information database according to the industrial weak links.

[0074] On the basis of the above technical solution, the information base establishment module is used to collect enterprise structured data from the data interfaces corresponding to the various data sources based on a data collection tool, and use the enterprise structured data as the original enterprise data.

[0075] The technical solution of the embodiment of the present invention collects original enterprise data related to the enterprise from the data source, establishes an enterprise feature information database based on the original enterprise data, builds an industrial chain map based on the industrial classification standard and the enterprise feature information database, and obtains the park enterprise data corresponding to the target industrial park, and then determines the target enterprise corresponding to the target industrial park based on the park enterprise data, the industrial chain map and the enterprise feature information database. Based on the above technical solution, by obtaining enterprise data from multiple data sources and establishing an enterprise feature database, and determining the target enterprise based on the enterprise data of the industrial park, the accuracy of determining the investment attraction enterprise is improved, and the efficiency of determining the investment attraction enterprise is improved.

[0076] The device for determining an industrial park investment attraction enterprise provided in the embodiment of the present invention can execute the method for determining an industrial park investment attraction enterprise provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0077] Embodiment 3

[0078] Figure 6A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0079] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0080] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0081] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for determining an industrial park investment attraction enterprise.

[0082] In some embodiments, the method for determining an industrial park investment attraction enterprise may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining an industrial park investment attraction enterprise described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining an industrial park investment attraction enterprise in any other appropriate manner (e.g., by means of firmware).

[0083] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0085] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0087] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0088] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0089] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0090] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining enterprises for investment attraction in an industrial park, characterized in that: include: Collecting original enterprise data related to the enterprise from the data source, and establishing an enterprise feature information database based on the original enterprise data; Building an industrial chain map based on the industrial classification standard and the enterprise characteristic information database, and obtaining park enterprise data corresponding to the target industrial park; The target enterprise corresponding to the target industrial park is determined according to the park enterprise data, the industrial chain spectrum and the enterprise feature information database.

2. The method according to claim 1, characterized in that The step of establishing an enterprise feature information database according to the original enterprise data includes: Performing data clearing on the original enterprise data to determine the enterprise data to be processed, and performing word segmentation processing on the enterprise data to be processed based on a word segmentation tool; Extract features from the enterprise data to be processed after word segmentation according to the preset recognition fields to determine the key feature words of the enterprise; The enterprise feature information database is established based on the enterprise key feature words.

3. The method according to claim 2, characterized in that The step of establishing the enterprise feature information database based on the enterprise key feature words includes: Processing the enterprise key feature words based on a keyword extraction algorithm to determine enterprise feature information corresponding to the enterprise key feature words; Generate a dependency tree for the enterprise data to be processed based on a dependency syntax analysis tool, and determine enterprise association relationships according to the dependency tree; The enterprise characteristic information database is established based on the enterprise association relationship and the enterprise characteristic information.

4. The method according to claim 1, characterized in that The construction of the industrial chain map based on the industrial classification standard and the enterprise characteristic information database includes: Processing the enterprise data in the enterprise characteristic information database based on the industry classification standard to construct an industrial chain map including industrial chain nodes and industrial chain relationships; Among them, the industrial chain nodes represent the products and services in the industrial chain, and the industrial chain relationships include upper and lower industrial relationships and upstream and downstream industrial chain relationships.

5. The method according to claim 1, characterized in that The determining of the target enterprise corresponding to the target industrial park according to the park enterprise data, the industrial chain spectrum and the enterprise feature information database includes: Acquire a park evaluation system corresponding to the target industrial park, and determine an evaluation score corresponding to the target industrial park according to the park evaluation system; The target enterprise is determined based on the evaluation score, the industrial chain spectrum and the enterprise feature information database.

6. The method according to claim 5, characterized in that The determining the target enterprise based on the evaluation score, the industrial chain spectrum and the enterprise feature information database includes: Determine the weak links of the industry corresponding to the target industrial park based on the evaluation score; The target enterprise is determined from the industrial chain spectrum and the enterprise feature information database according to the weak links of the industry.

7. The method according to claim 1, characterized in that The collecting of original enterprise data related to the enterprise from the data source includes: Based on the data collection tool, the enterprise structured data is collected from the data interfaces corresponding to the data sources, and the enterprise structured data is used as the original enterprise data.

8. A device for determining enterprises for investment attraction in an industrial park, characterized in that: include: An information base establishment module, used to collect original enterprise data related to the enterprise from the data source, and establish an enterprise feature information base based on the original enterprise data; A graph construction module is used to construct an industrial chain graph based on the industry classification standard and the enterprise characteristic information database, and obtain park enterprise data corresponding to the target industrial park; The target enterprise determination module is used to determine the target enterprise corresponding to the target industrial park based on the park enterprise data, the industrial chain spectrum and the enterprise feature information database.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining an industrial park investment attraction enterprise according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining an industrial park investment attraction enterprise according to any one of claims 1 to 7 when executed.

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