Cross-enterprise product data standardization classification method and management software system

Abstract

The present invention provides a cross-enterprise product data standardization classification method and its management software system, belonging to the fields of product data management, standardization management, artificial intelligence and natural language processing technology. The classification method includes: designing a product database and a front-end interface; inputting product data and verifying and checking the product data; determining a training data set and constructing a long short-term memory network text classification model; classifying product data based on the trained long short-term memory network text classification model; the management software system includes a project information module, a contract information module, a data reporting module and a standard download module. Taking into account the problem that the classified texts processed by traditional classification algorithms are mostly discrete and poorly correlated with each other, the present invention uses a long short-term memory network architecture to supplement the global memory results of each layer, and combines random reorganization to expand the training data set, so as to provide a highly versatile product data management service and ensure product data quality.

Description

Technical Field

[0001] The present invention belongs to the technical fields of product data management, standardization management, artificial intelligence and natural language processing, and in particular to a cross-enterprise product data standardization classification method and a management software system thereof. Background Art

[0002] Product Data Management (PDM) is a key enterprise management strategy and technology. It focuses on the comprehensive, systematic, and centralized management of all product-related information, data, and processes within an enterprise. Through an integrated information system, it comprehensively manages various product-related data (such as part information, configurations, documents, CAD files, structures, and permissions) and processes (such as design, manufacturing, and sales). By effectively managing this product data, a PDM system ensures its accuracy, consistency, and security, improves collaborative product development efficiency, and enables companies to quickly respond to market changes.

[0003] Standardized product data management is an integral part of modern enterprise management. First, standardization helps ensure data consistency and accuracy, reducing misunderstandings and errors. Second, unified data formats and definitions can improve collaboration efficiency across departments and teams. Third, standardized data management can reduce maintenance costs while ensuring data security. Fourth, good data standardization management helps companies better comply with industry standards and regulatory requirements, avoiding potential legal risks. However, existing product data management software is developed based on corporate standards and even business needs, resulting in a low degree of standardization, hindering team collaboration and secondary product development. Furthermore, PDM software offers poor reusability when faced with new business scenarios. Furthermore, because PDM software focuses too heavily on internal information management, data processing standards for product data, such as presentation, storage methods, and verification rules, vary across different companies, reducing product liquidity and lowering the efficiency of tasks like price comparison, procurement, acceptance, and archiving. Natural Language Processing (NLP) is an interdisciplinary field in computer science, artificial intelligence, and linguistics. It is a technology that converts natural language (such as the language used by humans in everyday life) into a computer-processable form. Its purpose is to enable computers to understand, interpret, and generate human language, thereby performing various tasks such as text classification, sentiment analysis, and machine translation. This provides a new direction for product data standardization. However, the classification rules of existing text classification tools conflict and differ from those used in this business scenario, resulting in existing classification algorithms being unable to effectively complete the classification task and suffering from high error rates. Furthermore, due to business scenario limitations and small amounts of training sample data, training using traditional text classification models yields poor results, with problems such as underfitting and unsuccessful feature extraction. Therefore, to address the small sample size and high error rates of traditional text classification models, it is imperative to develop a standardized classification method and management software system for cross-enterprise product data to achieve versatile product data management, ensure product data quality, and improve work efficiency. Summary of the Invention

[0004] In response to the above-mentioned defects in the existing technology, the present invention proposes a cross-enterprise product data standardization classification method. The method includes designing a product database and a front-end interface; inputting product data and verifying the product data; determining a training data set and constructing a long short-term memory network text classification model; classifying product data based on the trained long short-term memory network text classification model, and based on this, providing a management software system, which includes a project information module, a contract information module, a data reporting module and a standard download module. Taking into account the problem that the classified texts processed by traditional classification algorithms are mostly discrete and have poor correlation with each other, the present invention uses the long short-term memory network architecture to supplement the global memory results of each layer, and combines random reorganization to expand the training data set, so as to provide a highly versatile product data management service and ensure the quality of product data.

[0005] The present invention provides a cross-enterprise product data standardization classification method, which includes the following steps:

[0006] S1. Design product database and front-end interface;

[0007] S11. Design product database: Extract the reporting requirements for each product attribute from the standard, design the database field attributes and field lengths; extract the relationship between projects, contracts, products, and between products from the standard, and design the database table structure;

[0008] S12. Design a front-end interface: Extract the reporting requirements for each product attribute from the standard and display the standard requirements in the form of input box prompts and help boxes; the front-end interface has required product data items;

[0009] S2. Input product data and perform verification check on the product data;

[0010] S21. Determine product data based on the reporting requirements of the front-end interface;

[0011] S22. Perform a general verification check on the determined product data;

[0012] S23. Perform digital verification check on the determined product data;

[0013] S24. Perform a character verification check on the determined product data;

[0014] S3. Determine the training dataset and build a long short-term memory network text classification model;

[0015] S31. Determine a training dataset based on the product name S1, product summary S2, product keywords S3, and product application scenarios and description S4.

[0016] S32. Expand the training dataset: Randomly reorganize the product name S1, product summary S2, product keyword S3, product application scenario and description S4 text sequences of different products in the same category to generate a new input sequence;

[0017] S33. Based on the long short-term memory network architecture, the global memory results of each layer are recorded and stored for a long time to build a long short-term memory network text classification model;

[0018] S4. Classify product data based on the trained long short-term memory network text classification model;

[0019] S41. Initialize contract information and perform a classification operation to obtain contract information processing results. Where m represents the mth layer of the long short-term memory network text classification model; last represents the last time step of the text sequence:

[0020] S42. Based on the trained parameters, perform n classification operations, calculate the probability that the product data belongs to the preset classification, and return the product classification result;

[0021] S421, during the nth classification operation, the input sequence X of the long short-term memory network text classification model n Expressed as:

[0022] X n =S 1n +S 2n +S 3n +S 4n , n=1,2,…… (1)

[0023] Among them, S 1n 、S 2n 、S 3n and S 4n Respectively represent the product name, product summary, product keywords, product application scenarios and description entered during the nth classification operation;

[0024] S422, during the nth classification operation, the initial input of the mth layer of the long short-term memory network text classification model Expressed as:

[0025]

[0026] in, represents the global memory result of the mth layer of the long short-term memory network text classification model during the n-1th classification operation; μ represents the first influencing factor; λ represents the second influencing factor.

[0027] Furthermore, step S22 specifically includes the following steps:

[0028] S221. Check whether the product data is complete and whether any necessary items are missing. If the content is incomplete or any necessary items are missing, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0029] S222. Check whether the length of each field item of the product data meets the requirements and whether there is an overflow risk. If it does not meet the requirements or there is an overflow, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0030] Step S23 specifically includes the following steps:

[0031] S231. Check whether the value in the product data is reasonable and within a reasonable range. If the value is unreasonable or outside a reasonable range, the test fails and step S21 needs to be executed to re-determine the product data.

[0032] S232, check whether the digital format in the product data is valid; if not, the inspection fails and step S21 needs to be executed to re-determine the product data;

[0033] Step S24 specifically includes the following steps:

[0034] S241. Check whether the field items in the product data are safe to input; if the field items have risks, the inspection fails and step S21 needs to be executed to re-determine the product data;

[0035] S242. Based on natural language processing technology, check whether the product data is valid input; if invalid, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0036] Preferably, in step S41, the contract information in the product data is preprocessed and encoded to obtain the initialization input digital sequence X0 of the long short-term memory network text classification model, perform a classification operation on the initialization input digital sequence X0, and record the global memory result of the mth layer of the long short-term memory network text classification model. and will As a result of contract information processing.

[0037] Preferably, the amount of data in the same category in the training data set in step S32 is expanded to 4 q times, where q represents the amount of original data under the same category.

[0038] Preferably, the initial input of the mth layer of the long short-term memory network text classification model in step S42 is Set to null value 0.

[0039] Preferably, the training data set in step S31 includes data set product data, software product data, algorithm product data, knowledge product data, and model and hardware product data.

[0040] Another aspect of the present invention provides a management software system that utilizes the aforementioned cross-enterprise product data standardization classification method, which includes a project information module, a contract information module, a data reporting module and a standard download module. The project information module is used to manage project information, project documents, contract information and product data, and provides a project-level product data interface; the contract information module is used to manage contract information, contract documents and product data, and provides a contract-level product data interface; the data reporting module is used to classify and manage product data, perform standardized product data reporting work and automatic verification of product data; the standard download module is used to provide download of standard files; the data reporting module includes a front-end interface, a product database, a back-end verification function and a product classification intelligent auxiliary tool; the back-end verification function includes a general verification module, a digital verification module and a character verification module.

[0041] Preferably, the front-end interface is designed with prompt words and guide words according to standard requirements; the product database is designed with database field items and library table structure according to standard requirements; the back-end verification function is designed with detection rules according to the input content type and standard requirements; the product classification intelligent auxiliary tool uses artificial intelligence and natural language processing technology to make product classification recommendations based on the basic product information filled in by the user.

[0042] Preferably, the general verification module includes verifying the integrity of product data, detecting whether the mandatory items of the reported product data are filled in completely, verifying the standardization of product data, and detecting whether the length of the reported product data complies with the standard specifications; the digital verification module includes verifying the rationality of numerical product data, detecting whether the reported product data is within the valid range, verifying the standardization of digital product data, and detecting whether the reported product data complies with the standard specifications and format specifications; the character verification module includes verifying the security of character product data, detecting whether there is an injection risk in the reported product data, verifying the validity of character product data, and detecting whether the reported product data is meaningless garbled code.

[0043] Compared with the prior art, the technical effects of the present invention are:

[0044] 1. The present invention designs a cross-enterprise standardized classification method for product data. Taking into account the problem that the classification texts that traditional classification algorithms need to process are mostly discrete and poorly correlated with each other, while considering product information as time-series input, it also regards different product information under the same contract as time-series input, adopts the long short-term memory network (LSTM) architecture to design the text classification model, and uses the correlation between products to improve the accuracy of the classification results.

[0045] 2. The present invention designs a cross-enterprise product data standardization classification method. In order to solve the problem of small training data scale, the product names, product summaries, product keywords, product application scenarios and description text sequences of different products under the same category are randomly reorganized, which greatly enriches the number of samples. At the same time, in order to avoid feature confusion that may be caused by sample expansion, secondary classification is introduced as the basis for sample expansion to refine the combination requirements of text data.

[0046] 3. The present invention designs a cross-enterprise product data standardization management software system, which uses project information, contract information, data filling and standard download to provide highly versatile product data management services for small and medium-sized enterprises or multi-enterprise joint projects, ensure product data quality, facilitate communication and cooperation between enterprises, and improve the efficiency of communication, exchange, acceptance, etc. between enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0048] Figure 1 is a flow chart of the cross-enterprise product data standardization classification method of the present invention;

[0049] Figure 2 It is a schematic diagram of the process of verifying and checking product data of the present invention;

[0050] Figure 3 Schematic diagram of the training data set expansion of the present invention;

[0051] Figure 4 is a schematic diagram of the secondary classification of the training data set of the present invention;

[0052] Figure 5 It is a schematic diagram of the structure of the long short-term memory network text classification model of the present invention;

[0053] Figure 6 This is a schematic diagram of the process of using the product classification intelligent auxiliary tool of the present invention;

[0054] Figure 7 It is a schematic diagram of the composition of the management software system of the present invention. DETAILED DESCRIPTION

[0055] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0056] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] Figure 1 The cross-enterprise product data standardization classification method of the present invention is shown, and the method includes the following steps:

[0058] S1. Design product database and front-end interface;

[0059] S11. Design product database: Extract the reporting requirements for each product attribute from the standard, and design the database field attributes and field lengths; extract the relationship between projects, contracts, products, and between products from the standard, design the database table structure, implement product association functions, and facilitate user retrieval.

[0060] S12. Design the front-end interface: Extract the reporting requirements for each product attribute from the standard, and display the standard requirements in the form of input box prompts and help boxes; set up the required product data items in the front-end interface.

[0061] S2. Input product data and perform verification check on the product data, such as Figure 2 shown.

[0062] S21. Determine product data based on the reporting requirements of the front-end interface.

[0063] S22. Perform a general verification check on the determined product data.

[0064] S221. Check whether the product data content is complete and whether there are any missing necessary items; if the content is incomplete or there are any missing necessary items, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0065] S222. Check whether the length of each field item of the product data meets the requirements and whether there is an overflow risk; if it does not meet the requirements or there is an overflow, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0066] In a specific embodiment, the general verification check is specifically performed as follows:

[0067] After the user completes the project information form, the required fields (project number, project name, project confidentiality level, project research unit, project leader, and project start and end dates) are checked for completeness. Incomplete required fields are highlighted in red, and a pop-up window prompts the user to complete the project information. All field entries are checked, and fields exceeding the length limit are highlighted in red, with a pop-up window prompting the user to modify the field content.

[0068] After the user completes the contract information form, the system checks the required fields (contract number, contract name, contract confidentiality level, contract price, contract party A unit, contract party B unit, contract party B person in charge, contract party B contact number, and contract start and end time) to ensure they are complete. Incomplete required fields are highlighted in red, and a pop-up window prompts the user to complete the contract information. All input fields are checked, and fields exceeding the length limit are highlighted in red, with a pop-up window prompting the user to modify the field content.

[0069] After the user completes the model product information form, the required items, including model number, model name, model confidentiality level, data product level, model completion time, model keywords, model summary, model application scenario and description, model format, associated data product number and relationship description, model operating environment (software environment), model operating environment (hardware environment), development unit, development unit contact number, model type, model version number, model application and verification status, are checked to see if they are complete. Required items that are not filled in are marked in red, and a pop-up window is used to remind the user to complete the model product information. All field input information is checked, and fields that exceed the length limit are marked in red, and a pop-up window is used to remind the user to modify the field content.

[0070] After the user completes the dataset product information form, the system checks whether the required fields (dataset number, dataset name, dataset confidentiality level, data product level, dataset completion time, dataset keywords, dataset summary, dataset application scenario and description, dataset format, associated data product number and relationship description, dataset operating environment (software environment), dataset operating environment (hardware environment), development unit, development unit contact number, dataset type, dataset version number, potential application scenarios and value, data quality self-assessment, data reuse self-recommendation, and total dataset quantity) are complete. Incomplete required fields will be highlighted in red, and a pop-up window will be displayed to remind the user to complete the dataset product information. All field input information will be checked, and fields exceeding the length limit will be highlighted in red, and a pop-up window will be displayed to remind the user to modify the field content.

[0071] After the user completes the hardware product information form, the system checks whether the required items, including hardware number, hardware name, hardware confidentiality level, data product level, hardware completion time, hardware keywords, hardware summary, hardware application scenario and description, hardware format, related data product number and relationship description, hardware operating environment (software environment), hardware operating environment (hardware environment), development unit, development unit contact number, hardware type, main functional indicators, main performance indicators, hardware version number, hardware location, hardware storage conditions, and hardware maintenance requirements, are complete. Required items that are not filled in are marked in red, and a pop-up window is used to remind the user to complete the hardware product information. All field input information is checked, and fields that exceed the length limit are marked in red, and a pop-up window is used to remind the user to modify the field content.

[0072] After the user completes the knowledge product information form, check whether the required items, such as knowledge number, knowledge name, knowledge confidentiality, data product level, knowledge completion time, knowledge keywords, knowledge summary, knowledge application scenario and description, knowledge format, related data product number and relationship description, knowledge operating environment (software environment), knowledge operating environment (hardware environment), development unit, development unit contact number, knowledge type, knowledge version number, key technology / innovation point, number of knowledge items, assessment and verification status, current application status, potential application scenarios and value, data quality self-assessment, and data reuse self-recommendation, are complete. Mark the required items that are not filled in in red, and prompt the user to complete the knowledge product information through a pop-up window. Check the input information of all fields, mark the fields that exceed the length limit in red, and prompt the user to modify the field content through a pop-up window.

[0073] After the user completes the software product information form, the required items, including software number, software name, software confidentiality level, data product level, software completion time, software keywords, software summary, software application scenario and description, software format, associated data product number and relationship description, software operating environment (software environment), software operating environment (hardware environment), development unit, development unit contact number, software type, software function description, software performance description, software version number, software dependency, software development environment (hardware configuration), software development environment (software configuration), software application and verification status, are checked to see if they are complete. Required items that are not filled in are marked in red, and a pop-up window is used to remind the user to complete the software product information. All field input information is checked, and fields that exceed the length limit are marked in red, and a pop-up window is used to remind the user to modify the field content.

[0074] After the user completes the algorithm product information form, the required fields (algorithm number, algorithm name, algorithm confidentiality level, data product level, algorithm completion time, algorithm keywords, algorithm summary, algorithm application scenario and description, algorithm format, associated data product number and relationship description) are checked for completeness. The form then highlights unfilled required fields in red, and prompts the user to complete the algorithm product information via a pop-up window. All field input information is checked, and fields exceeding the length limit are highlighted in red, with a pop-up window prompting the user to modify the field content.

[0075] S23. Perform digital verification check on the determined product data.

[0076] S231. Check whether the value in the product data is a reasonable value and whether it is within a reasonable range; if the value is unreasonable or not within a reasonable range, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0077] S232, check whether the digital format in the product data is valid; if not, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0078] In a specific embodiment, the digital verification check is specifically performed as follows:

[0079] For field items that require numerical values ​​such as "quantity", "total amount", "number of items", and "size", check whether the values ​​meet the integer requirements (if necessary) and whether the values ​​are within a reasonable range.

[0080] For fields containing "Contact Phone Number," check whether the number format meets the length and format requirements for a valid mobile or landline number. Fields with incorrect formatting will be highlighted in red, and a pop-up window will prompt the user to modify the format.

[0081] S24. Perform a character verification check on the determined product data.

[0082] S241. Check whether the field items in the product data are safe to input; if there is a risk in the field items, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0083] S242. Based on natural language processing technology, check whether the product data is valid input; if invalid, the inspection fails and step S21 needs to be executed to re-determine the product data.

[0084] In a specific embodiment, the character verification check is specifically performed as follows:

[0085] Check the input content to determine whether it contains SQL statements, regular expressions, and other potentially risky content. Mark risky fields in red and prompt the user to modify them through a pop-up window.

[0086] Check the input content, using NLP (natural language processing) technology to automatically segment the input content. Based on the segmentation results, check the validity of the sentence, such as whether the sentence is smooth. Fields with potentially invalid input content are marked in red and a pop-up window prompts the user to check.

[0087] S3. Determine the training dataset and build a long short-term memory network text classification model.

[0088] S31. Determine a training dataset based on the product name S1, product summary S2, product keywords S3, and product application scenarios and description S4. The training dataset includes dataset product data, software product data, algorithm product data, knowledge product data, and model and hardware product data.

[0089] S32. Expand the training dataset: Randomly reorganize the product name S1, product summary S2, product keyword S3, product application scenario and description S4 text sequences of different products in the same category to generate a new input sequence, such as Figure 3 As shown. The amount of data in the same category in the training dataset is expanded to 4 q times, where q represents the amount of original data under the same category.

[0090] The quality of the training dataset largely determines the effectiveness of the model, but in real-world scenarios, there is often insufficient and diverse data, which significantly impacts the accuracy of the model. This invention is limited to a specific business domain, and the number of training data samples is relatively limited. This problem is particularly severe when it comes to product categories in less frequent secondary categories, as these products account for a relatively small proportion of the dataset.

[0091] This paper proposes a new solution for this application scenario. By further strengthening the existing random exchange method and expanding the scope of exchangeable content, it aims to reduce the risk of model overfitting. At the same time, by optimizing the data set splitting method and enhancement strategy, it strengthens the model's feature extraction effect and improves the model's ability to recognize low-frequency product categories.

[0092] Considering that data samples under the same secondary classification have more uniform text features, to ensure the validity of the expansion results, this paper performs a more detailed split of the dataset based on the secondary classification system specified in the standard. When performing random combination, the text is reorganized based on the secondary classification results rather than the primary classification.

[0093] In a specific embodiment, the secondary classification diagram of the training data set is as follows: Figure 4 As shown in the figure, software data products are used as an example. Software products account for approximately 21.17% of all samples, but this category is primarily concentrated under the secondary classification "Application Software." Data samples under the secondary classification "System Software" account for only 0.93%. To ensure the model's ability to identify "System Software" products and avoid misclassifying "System Software" as algorithmic data products, the "System Software" samples need to be expanded. The algorithm randomly selects product A from the data sample labeled "Software Data Products - System Software" and records the product name (S1). It then repeats this operation and records the product summary (S2) of product B, the product keywords (S3), and the product application scenarios and description (S4) of product C. The above data is then merged and labeled as "Software Data Products - System Software" and placed into a new dataset.

[0094] S33. Based on the long short-term memory network architecture, the global memory results of each layer are recorded and stored for a long time to build a long short-term memory network text classification model.

[0095] As a special type of recurrent neural network (RNN), the Long Short-Term Memory (LSTM) effectively addresses the vanishing or exploding gradient problems faced by traditional RNNs when processing long sequences of data by introducing a "gate" mechanism (forget gate, input gate, and output gate). This enables LSTM to capture and memorize long-term dependencies in the data, resulting in excellent performance when processing temporal and correlated data. It has applications in scenarios such as text classification, time series forecasting, anomaly detection, and pattern recognition.

[0096] The LSTM network consists of multiple layers, each composed of multiple LSTM units. Each unit uses forward propagation, receiving the output of the previous layer as its input and its output as the input of the next layer. Except for the last layer, the output of all intermediate layers is passed to the following layers, establishing a chronological process for processing input data. Each LSTM unit in each layer consists of four parts: an input gate, a forget gate, an output gate, and candidate memory cells. The input gate controls which information enters the LSTM network, the forget gate controls which memories are forgotten, and the output gate controls which information is output to the hidden state. Candidate memory cells are used to store long-term information.

[0097] Considering that within the same project or contract, product R&D, application, and other scenarios are often identical or complementary, adjacent classification data is more correlated than in traditional classification scenarios. Based on actual business scenarios, we consider both product information and information on different products under the same contract as sequential inputs. We design a text classification model using a long short-term memory (LSTM) network architecture, leveraging the relationships between products to improve the accuracy of classification results.

[0098] In order to extract and process the correlation between text sequences, based on the long short-term memory network architecture, the global memory results of each layer are recorded and stored for a long time, and a long short-term memory network text classification model is constructed, such as Figure 5 shown.

[0099] LSTM networks are typically used to extract dependencies within sequences, rather than between them. Since contract information and product information are closely related, using contract information can help more accurately classify product information. Therefore, the model parameters need to be adjusted to enable it to identify and extract dependencies between sequences.

[0100] S4. Classify product data based on the trained long short-term memory network text classification model.

[0101] S41. Initialize contract information and perform a classification operation to obtain contract information processing results.

[0102] The contract information in the product data is preprocessed and encoded to obtain the initialization input digital sequence X0 of the long short-term memory network text classification model. The initialization input digital sequence X0 is classified once, and the global memory result of the mth layer of the long short-term memory network text classification model is recorded. and will As the result of contract information processing, where last represents the last time step of the text sequence.

[0103] S42. Based on the trained parameters, perform n classification operations, calculate the probability that the product data belongs to the preset classification, and return the product classification result.

[0104] Initial input of the mth layer of the LSTM text classification model Set to null value 0.

[0105] S421, during the nth classification operation, the input sequence X of the long short-term memory network text classification model n Expressed as:

[0106] X n =S 1n +S 2n +S 3n+S 4n , n=1,2,…… (1)

[0107] Among them, S 1n 、S 2n 、S 3n and S 4n They respectively represent the product name, product summary, product keywords, product application scenarios and description entered during the nth classification operation.

[0108] S422, during the nth classification operation, the initial input of the mth layer of the long short-term memory network text classification model Expressed as:

[0109]

[0110] in, It represents the global memory result of the mth layer of the short-term memory network text classification model during the n-1th classification operation; μ represents the first influencing factor; λ represents the second influencing factor.

[0111] Another aspect of the present invention provides a management software system using the aforementioned cross-enterprise product data standardization classification method, which includes a project information module, a contract information module, a data reporting module and a standard download module. Figure 7 shown.

[0112] The project information module is used to manage project information, project documents, contract information and product data, and provides a project-level product data interface; the contract information module is used to manage contract information, contract documents and product data, and provides a contract-level product data interface; the data reporting module is used to classify and manage product data, perform standardized product data reporting work and automatic verification of product data; the standard download module is used to provide downloads of standard files.

[0113] The data reporting module includes a front-end interface, a product database, back-end validation functionality, and intelligent product classification tools. The front-end interface features prompts and guide words designed according to standard requirements; the product database features fields and table structures designed according to standard requirements; and the back-end validation functionality features detection rules designed based on the input content type and standard requirements.

[0114] In the front-end interface design, the product data collection form was designed according to all submission requirements stipulated in the "Data Product Submission Requirements" series of standards, with mandatory fields marked with an asterisk (*). HTML5 and CSS technologies were used to beautify the form interface, improving the system's usability and aesthetics. The relevant requirements and descriptions of product attributes in the standards were displayed as input requirements in the input box and "Help," saving users time in consulting the standards.

[0115] During database design, we designed database field lengths and reserved security redundancy to accommodate user input in accordance with the product attribute requirements of the "Data Product Submission Requirements" and "Data Product Core Metadata" standards. We also designed the database table structure to establish relationships between projects, contracts, products, and between products, making it easier for users to access and manage products.

[0116] The back-end verification function includes a general verification module, a digital verification module, and a character verification module. The general verification module includes verifying the integrity of product data, checking whether the required items of the reported product data are filled in completely, verifying the standardization of product data, and checking whether the length of the reported product data complies with the standard specifications; the digital verification module includes verifying the rationality of numerical product data, checking whether the reported product data is within the valid range, verifying the standardization of numerical product data, and checking whether the reported product data complies with the standard specifications and format specifications; the character verification module includes verifying the security of character product data, checking whether there is an injection risk in the reported product data, verifying the validity of character product data, and checking whether the reported product data is meaningless garbled code, to ensure the quality of product data. The back-end verification function is designed to verify the product information entered by the user, including the length and content. Field items that fail the verification are marked in red to remind the user to modify them, to ensure data security and standards, and to improve data quality.

[0117] The intelligent product classification assistance tool uses artificial intelligence and natural language processing technology to make product classification recommendations based on all or part of the basic product information filled in by the user.

[0118] Product classification intelligent auxiliary tool usage flow chart Figure 6 As shown, after entering the data submission module, users open the auxiliary tool and enter at least one of the following: product name, product summary, product keywords, product application scenario, and description. The more complete and comprehensive the product information, the more accurate the classification results. The intelligent product classification auxiliary tool uses artificial intelligence and natural language processing technologies to implement a classification algorithm, providing classification suggestions based on the input content. Users can choose whether to accept the classification suggestion. The system will then proceed based on the user's selection, including opening a new page for the corresponding product category, reclassifying the product, and resetting the input content.

[0119] In a specific embodiment, after the user enters at least one of the product name, product summary, product keywords, product application scenarios and description, he clicks the "Start Classification" button. The intelligent auxiliary tool for product classification automatically copies part of the input content to complete the input dimension, implements classification recommendation through the trained long short-term memory network text classification model, and returns the classification results to the user, who makes a decision. If the user chooses "Accept", the system will automatically open a new page for this type of product based on the classification results; if the user chooses "Reclassify", the system will replace the automatically filled input content (if there are unentered fields) and re-classify the recommendation; if the user chooses "Reset", the system will clear all input content and wait for user input.

[0120] In a specific embodiment, take Project A, "XXX Digital Construction Project," Project A's Subsidiary Contract A, "XXX Platform Management Software R&D Contract," and Project A's Subsidiary Contract B, "XXX Platform Digital Capacity Enhancement R&D Contract," as examples. Contract A includes Data Product A, "XXX Platform Management Software," and Data Product B, "XXX Platform Product Recommendation Algorithm."

[0121] After entering the data filling module through Contract A, the product classification intelligent auxiliary tool is opened. The product classification intelligent auxiliary tool automatically reads the contract information of Contract A and possible project information, completes the initialization operation, records the final output results of each layer of the model, and waits for user input.

[0122] After entering at least one basic piece of information for Data Product A, click "Confirm." The program automatically completes the form and, combined with the recorded contract information, performs a classification calculation on the entered text. It then returns the classification result with the most consistent features and temporarily stores the final results of each model layer. In this case, the returned result is "Software Data Product."

[0123] After entering at least one basic piece of information for Data Product B, click "Confirm." The intelligent product classification tool will calculate the classification for Data Product B based on the user input, the contract information processing results, and the processing results for Data Product A. The result returned here is "Algorithm Data Product."

[0124] After completing the data entry for Contract A and switching to Contract B, the intelligent product classification assistance tool clears all previous records, automatically reads the contract information of Contract B and the project information of Project A, automatically completes a new round of initialization operations, and waits for user input.

[0125] The present invention designs a cross-enterprise product data standardization classification method and its management software system. Taking into account the problem that the classification texts that traditional classification algorithms need to process are mostly discrete and poorly correlated with each other, while considering product information as time-series input, it also regards different product information under the same contract as time-series input, adopts a long short-term memory network (LSTM) architecture to design a text classification model, and uses the correlation between products to improve the accuracy of the classification results; to solve the problem of small training data scale, the product names, product summaries, product keywords, product application scenarios and description text sequences of different products under the same category are randomly reorganized, greatly enriching the number of samples; at the same time, to avoid feature confusion that may be caused by sample expansion, secondary classification is introduced as the basis for sample expansion, and the combination requirements of text data are refined; with the help of project information, contract information, data reporting and standard downloading, a highly versatile product data management service is provided for small and medium-sized enterprises or multi-enterprise joint projects, ensuring product data quality, facilitating communication and cooperation between enterprises, and improving the work efficiency of communication, exchange, acceptance, etc. between enterprises.

[0126] Finally, it should be noted that the above embodiments are only intended to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A cross-enterprise product data standardization classification method, characterized by: It includes the following steps: S1. Design product database and front-end interface; S11. Design product database: Extract the reporting requirements for each product attribute from the standard, design the database field attributes and field lengths; extract the relationship between projects, contracts, products, and between products from the standard, and design the database table structure; S12. Design a front-end interface: Extract the reporting requirements for each product attribute from the standard and display the standard requirements in the form of input box prompts and help boxes; the front-end interface has required product data items; S2. Input product data and perform verification check on the product data; S21. Determine product data based on the reporting requirements of the front-end interface; S22. Perform a general verification check on the determined product data; S23. Perform digital verification check on the determined product data; S24. Perform a character verification check on the determined product data; S3. Determine the training dataset and build a long short-term memory network text classification model; S31. Determine a training dataset based on the product name S1, product summary S2, product keywords S3, and product application scenarios and description S4. S32. Expand the training dataset: Randomly reorganize the product name S1, product summary S2, product keyword S3, product application scenario and description S4 text sequences of different products in the same category to generate a new input sequence; S33. Based on the long short-term memory network architecture, the global memory results of each layer are recorded and stored for a long time to build a long short-term memory network text classification model; S4. Classify product data based on the trained long short-term memory network text classification model; S41. Initialize contract information and perform a classification operation to obtain contract information processing results. Where m represents the mth layer of the long short-term memory network text classification model; last represents the last time step of the text sequence: S42. Based on the trained parameters, perform n classification operations, calculate the probability that the product data belongs to the preset classification, and return the product classification result; S421, during the nth classification operation, the input sequence X of the long short-term memory network text classification model n Expressed as: X n =S 1n +S 2n +S 3n +S 4n ,n=1,2,…… (1) Among them, S 1n 、S 2n 、S 3n and S 4n Respectively represent the product name, product summary, product keywords, product application scenarios and description entered during the nth classification operation; S422, during the nth classification operation, the initial input of the mth layer of the long short-term memory network text classification model Expressed as: in, represents the global memory result of the mth layer of the long short-term memory network text classification model during the n-1th classification operation; μ represents the first influencing factor; λ represents the second influencing factor.

2. The cross-enterprise product data standardization classification method according to claim 1, characterized in that: Step S22 specifically includes the following steps: S221. Check whether the product data is complete and whether any necessary items are missing. If the content is incomplete or any necessary items are missing, the inspection fails and step S21 needs to be executed to re-determine the product data. S222. Check whether the length of each field item of the product data meets the requirements and whether there is an overflow risk. If it does not meet the requirements or there is an overflow, the inspection fails and step S21 needs to be executed to re-determine the product data. Step S23 specifically includes the following steps: S231. Check whether the value in the product data is reasonable and within a reasonable range. If the value is unreasonable or outside a reasonable range, the test fails and step S21 needs to be executed to re-determine the product data. S232, check whether the digital format in the product data is valid; if not, the test fails and step S21 needs to be executed to re-determine the product data; Step S24 specifically includes the following steps: S241. Check whether the field items in the product data are safe to input; if the field items have risks, the inspection fails and step S21 needs to be executed to re-determine the product data; S242. Based on natural language processing technology, check whether the product data is valid input; if invalid, the inspection fails and step S21 needs to be executed to re-determine the product data.

3. The cross-enterprise product data standardization classification method according to claim 1, characterized in that: In step S41, the contract information in the product data is preprocessed and encoded to obtain the initialization input digital sequence X0 of the long short-term memory network text classification model, and a classification operation is performed on the initialization input digital sequence X0, and the global memory result of the mth layer of the long short-term memory network text classification model is recorded. and will As a result of contract information processing.

4. The cross-enterprise product data standardization classification method according to claim 1, characterized in that: The amount of data in the same category in the training data set in step S32 is expanded to 4 q times, where q represents the amount of original data under the same category.

5. The cross-enterprise product data standardization classification method according to claim 1, characterized in that: The initial input of the mth layer of the long short-term memory network text classification model in step S42 Set to null value 0.

6. The cross-enterprise product data standardization classification method according to claim 1, characterized in that: The training data set in step S31 includes data set product data, software product data, algorithm product data, knowledge product data, and model and hardware product data.

7. A management software system for the cross-enterprise product data standardization classification method according to any one of claims 1 to 6, characterized in that: It includes a project information module, a contract information module, a data reporting module and a standard download module. The project information module is used to manage project information, project documents, contract information and product data, and provide a project-level product data interface; the contract information module is used to manage contract information, contract documents and product data, and provide a contract-level product data interface; the data reporting module is used to classify and manage product data, perform standardized product data reporting and automatic verification of product data; the standard download module is used to provide downloads of standard files; the data reporting module includes a front-end interface, a product database, a back-end verification function and a product classification intelligent auxiliary tool; the back-end verification function includes a general verification module, a digital verification module and a character verification module.

8. The management software system for cross-enterprise product data standardization classification method according to claim 7, characterized in that: The front-end interface is designed with prompt words and guide words according to standard requirements; The product database designs database field items and library table structure according to standard requirements; the back-end verification function designs detection rules according to the input content type and standard requirements; the product classification intelligent auxiliary tool uses artificial intelligence and natural language processing technology to recommend product categories based on the basic product information filled in by the user.

9. The management software system for cross-enterprise product data standardization classification method according to claim 7, characterized in that: The general verification module includes verifying the integrity of product data, checking whether the required items of the reported product data are filled in completely, verifying the standardization of product data, and checking whether the length of the reported product data complies with the standard specifications; the digital verification module includes verifying the rationality of numerical product data, checking whether the reported product data is within the valid range, verifying the standardization of digital product data, and checking whether the reported product data complies with the standard specifications and format specifications; the character verification module includes verifying the security of character product data, checking whether there is an injection risk in the reported product data, verifying the validity of character product data, and checking whether the reported product data is meaningless garbled code.

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