Water affair product recommendation method and device based on large model

Abstract

The invention provides a water affair product recommendation method and device based on a large model. The method comprises the following steps: acquiring water affair industry request information; performing feature extraction on the water affair industry request information to obtain target water affair key features; obtaining water affair feedback information according to the target water affair key features and a preset water affair knowledge base; obtaining water affair product recommendation information according to the target water affair key features and a preset water affair product library; and obtaining a feedback report according to the water affair industry request information, the water affair feedback information and the water affair product recommendation information. According to the method, professional solutions can be provided for users in the water affair industry, water affair industry related products with high matching degree can be recommended for the users according to the user intentions, the user satisfaction is improved, and the sales cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of water technology, and also to a method and device for recommending water products based on a large model. Background Art

[0002] The water industry refers to the industrial chain consisting of raw water, water supply, water conservation, drainage, sewage treatment and water resource recycling. When faced with problems in the professional field of the water industry, existing question-answering robots or large models still have limitations. They need the assistance of professionals in the water field to understand the user's intentions and provide answers. In addition, for problems in the water industry, large models cannot provide users with matching products based on the intentions in the questions, resulting in poor user experience and not conducive to the promotion of related products in the water industry. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for recommending water products based on a large model, so as to improve the user experience of the water industry and reduce sales costs.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] A first aspect of the present invention provides a water product recommendation method based on a large model, comprising:

[0006] Obtain information requested by the water industry;

[0007] Extracting features of the water industry request information to obtain key features of the target water industry;

[0008] Obtaining water affairs feedback information according to the target water affairs key features and a preset water affairs knowledge base;

[0009] Obtain water service product recommendation information based on the target water service key features and the preset water service product library;

[0010] A feedback report is obtained according to the water industry request information, the water feedback information and the water product recommendation information.

[0011] Optionally, obtain water industry request information, including:

[0012] Get the original request information;

[0013] The original request information is screened according to preset screening conditions to obtain water industry request information.

[0014] Optionally, feature extraction is performed on the water industry request information to obtain target water industry key features, including:

[0015] Obtain preset water affairs key feature database;

[0016] Extracting features from the water industry request information to obtain original key features;

[0017] The target water affairs key feature is determined according to the preset water affairs key feature library and the original key feature.

[0018] Optionally, water service feedback information is obtained according to the target water service key features and a preset water service knowledge base, including:

[0019] Access to preset water affairs knowledge base;

[0020] Obtaining water affairs information according to the target water affairs key features and the preset water affairs knowledge base;

[0021] The water affairs information is adjusted to obtain water affairs feedback information.

[0022] Optionally, according to the target water service key features and a preset water service product library, water service product recommendation information is obtained, including:

[0023] Get a library of preset water products;

[0024] Obtaining water service product matching information according to the target water service key features and the preset water service product library;

[0025] The water product recommendation information is determined based on the preset recommendation value and the water product matching information.

[0026] Optionally, obtain a library of pre-set water products, including:

[0027] Obtain information on water products on sale;

[0028] Dividing the water service product information on sale according to preset grouping conditions to obtain the divided water service product information on sale;

[0029] A preset water products library is determined based on the divided water products for sale information.

[0030] Optionally, a feedback report is obtained according to the water industry request information, the water feedback information and the water product recommendation information, including:

[0031] Get the feedback report template;

[0032] The feedback report template is filled in according to the water industry request information, the water feedback information and the water product recommendation information to obtain a feedback report.

[0033] A second aspect of the present invention provides a water product recommendation device based on a large model, comprising:

[0034] The acquisition module is used to obtain water industry request information;

[0035] The first processing module is used to extract features from the water industry request information to obtain key features of the target water industry;

[0036] A second processing module is used to obtain water affairs feedback information according to the target water affairs key features and a preset water affairs knowledge base;

[0037] A third processing module is used to obtain water service product recommendation information according to the target water service key features and a preset water service product library;

[0038] The fourth processing module is used to obtain a feedback report according to the water industry request information, the water feedback information and the water product recommendation information.

[0039] According to a third aspect of the present invention, a computing device is provided, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to the first aspect is executed.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the method described in the first aspect.

[0041] The above solution of the present invention includes at least the following beneficial effects:

[0042] The above scheme of the present invention obtains the target water service key features based on the acquired water service industry request information, and then obtains water service feedback information and water service product recommendation information based on the preset water service knowledge base and the preset water service product library. Finally, a feedback report is obtained based on the water service industry request information, water service feedback information and water service product recommendation information. It can not only provide professional answers to users in the water service industry, but also recommend water service industry-related products with a higher matching degree to users according to user intentions, which is conducive to improving user satisfaction and reducing sales costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a water product recommendation method based on a large model in an embodiment of the present invention;

[0044] Figure 2 It is a structural schematic diagram of a water product recommendation device based on a large model in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0046] like Figure 1 As shown, an embodiment of the present invention proposes a water product recommendation method based on a large model, comprising the following steps:

[0047] Step 101, obtaining water industry request information;

[0048] Step 102, extracting features of the water industry request information to obtain target water industry key features;

[0049] Step 103, obtaining water service feedback information according to the target water service key features and a preset water service knowledge base;

[0050] Step 104, obtaining water service product recommendation information according to the target water service key features and the preset water service product library;

[0051] Step 105: obtaining a feedback report according to the water industry request information, the water industry feedback information and the water industry product recommendation information.

[0052] The large-model-based water product recommendation method proposed in the embodiment of the present invention obtains the target water key features according to the acquired water industry request information, and then obtains water feedback information and water product recommendation information according to the preset water knowledge base and the preset water product library. Finally, a feedback report is obtained according to the water industry request information, water feedback information and water product recommendation information. It can not only provide professional answers to users in the water industry, but also recommend water industry-related products with a high matching degree to users according to user intentions, which is conducive to improving user satisfaction and reducing sales costs.

[0053] In an optional embodiment of the present invention, step 101 includes:

[0054] Step 1011, obtaining original request information;

[0055] Specifically, the original request information is the request information that is input by the user and needs to be answered. The original request information may include content in various formats such as text and numbers. For example, one original request information is "What is the maximum head of a certain water pump?", and another original request information is "What is the weather like today?". The original request information is subsequently processed.

[0056] Step 1012: filter the original request information according to preset filtering conditions to obtain water industry request information.

[0057] Specifically, since the method of this embodiment is mainly used to answer the request information of the water industry, it is necessary to screen the original request information. The preset screening condition can be: whether the original request information contains at least one key feature (or keyword) in the preset water key feature library. If so, the original request information is the water industry request information, and the subsequent steps are processed. For example, the original request information mentioned above is "What is the highest head of a certain water pump?", and the "water pump" can be found in the preset water key feature library. Therefore, the original request information is the water industry request information; if not, such as the original request information mentioned above is "How is the weather today?", all the key features "today's", "weather" and "how" cannot be found in the preset water key feature library. Therefore, the original request information is not the water industry request information, and "How is the weather today" is input into the existing large model for answering. The information that the large model may feedback is "Today's weather is cloudy", and the output content of the large model "Today's weather is cloudy" is fed back to the user.

[0058] In an optional embodiment of the present invention, step 102 includes:

[0059] Step 1021, obtaining a preset water affairs key feature library;

[0060] Specifically, the preset water affairs key feature library contains a large number of water affairs key features (or keywords) and their corresponding synonyms, misspelled words, pinyin expansion words, etc. Since the water industry request information may contain misspelled words, pinyin, letters, etc., the preset water affairs key feature library not only needs to contain a large number of water affairs key features, but also needs to include synonyms, words with similar fonts, misspelled words, pinyin expansion words, etc. corresponding to the water affairs key features to improve the applicability of the method and user experience.

[0061] Step 1022, extracting features from the water industry request information to obtain original key features;

[0062] Specifically, since the water industry request information is filtered information containing key features of the water industry, at least one original key feature can be extracted from the water industry request information, and the original key feature may include typos, similar words, and the like.

[0063] Step 1023: Determine target water affairs key features according to the preset water affairs key features library and the original key features.

[0064] Specifically, the accurate key features corresponding to the original key features can be determined according to the preset water affairs key feature library as target water affairs key features for subsequent processing and product recommendation.

[0065] In an optional embodiment of the present invention, step 103 includes:

[0066] Step 1031, obtaining a preset water affairs knowledge base;

[0067] Specifically, the preset water affairs knowledge base contains text materials in various directions within the water affairs industry, such as question banks, instruction manuals, operation manuals, user help, system introductions and other relevant documents and materials in users' daily production activities, and the formats are not limited to word, pdf, excel, ppt, photos, web pages, etc. Here, these text materials can also be extracted and classified, such as classifying the above text materials according to different directions such as water plants, sewage plants, secondary water supply, and pipe networks. That is, the preset water affairs knowledge base can contain knowledge bases in different directions such as water plant knowledge base, sewage plant knowledge base, secondary water supply knowledge base, and pipe network knowledge base. After classification, the text materials in each knowledge base are vectorized to achieve the purpose of converting text into vectors and facilitating retrieval and matching.

[0068] Step 1032, obtaining water service information according to the target water service key features and the preset water service knowledge base;

[0069] Specifically, named entity recognition (NER) is performed on the knowledge bases in different directions in the preset water affairs knowledge base to obtain the preset water affairs entity base. Entities with specific meanings in each knowledge base are identified, mainly including names of people, places, institutions, proper nouns, etc. For example, for the water plant knowledge base, the identified entities may include: equipment name, software system name, merchant name, contact number, contact address, equipment parameters, etc. This is mainly used to match the target water affairs key features with these entities, and then use the entities to find the corresponding water affairs information in the preset water affairs knowledge base.

[0070] Step 1033: adjust the water service information to obtain water service feedback information.

[0071] Specifically, since the at least one water affairs information obtained may be scattered, it needs to be adjusted to conform to the normal word order. Here, the at least one water affairs information obtained can be input into the existing large model for word order adjustment and arrangement to obtain water affairs feedback information that conforms to the normal word order.

[0072] In an optional embodiment of the present invention, step 104 includes:

[0073] Step 1041, obtaining a preset water product library;

[0074] Specifically, the preset water products library includes all product information related to the water industry on sale, such as product introductions, product manuals, instructions for use, function introductions, web links, etc. Since a large amount of these product information is obtained, they can be classified in different directions, such as dividing the above product information into water plant product library, sewage plant product library, secondary water supply product library and pipe network product library, etc. All these product libraries constitute the preset water products library. Taking the water plant product library as an example, it can include product introductions, product manuals, instructions for use, function introductions, web links and other product information of equipment or software related to the water plant, providing a basis for providing product recommendations to users in the future. It should be noted that it is also necessary to establish a corresponding relationship between the preset water product library and the preset water entity library to ensure that the product information in the preset water product library can be obtained through entity searches in the preset water entity library.

[0075] Step 1042, obtaining water service product matching information according to the target water service key features and the preset water service product library;

[0076] Specifically, the corresponding product information can be obtained by searching in the preset water product library according to the target water service key characteristics. There may be multiple corresponding product information. Here, multiple product information with high correlation with the target water service key characteristics can be output as water service product matching information for subsequent selection.

[0077] Step 1043, determining water product recommendation information according to the preset recommendation value and the water product matching information.

[0078] Specifically, the water product matching information may contain multiple product information that are highly correlated with the target water key characteristics. Therefore, the product information in the water product matching information can be output according to the preset recommendation value. For example, if the preset recommendation value is 5, the top 5 product information in the water product matching information will be output as water product recommendation information for user selection.

[0079] In an optional embodiment of the present invention, step 1041 includes:

[0080] Step 10411, obtaining information on water products on sale;

[0081] Specifically, all information on products for sale in the water industry, such as product introductions, product manuals, instructions for use, function introductions, web links, etc., can be obtained from manufacturers and websites of water industry-related equipment or software.

[0082] Step 10412, dividing the water service product information on sale according to a preset grouping condition to obtain the divided water service product information on sale;

[0083] Specifically, the preset grouping conditions are different directions of the water industry. For example, the preset grouping conditions may include: water plant, sewage plant, secondary water supply and pipeline network, etc. The water product information on sale is divided into the water plant product library and / or sewage plant product library and / or secondary water supply product library and / or pipeline network product library according to the direction to which it belongs. One water product information on sale is classified into at least one product library.

[0084] Step 10413, determining a preset water product library based on the divided water product information on sale.

[0085] Specifically, after the information on water products for sale is divided, the information on water products for sale in each direction constitutes the product library of this direction, such as the water plant product library, sewage plant product library, secondary water supply product library and pipeline product library, etc. These product libraries constitute the preset water product library.

[0086] In an optional embodiment of the present invention, step 105 includes:

[0087] Step 1051, obtaining a feedback report template;

[0088] Specifically, the feedback report template can be pre-set or obtained as needed to facilitate subsequent filling, quickly generate feedback reports, and improve processing efficiency. The feedback report template may include water industry request information input by the user, water industry feedback information that answers the water industry request information, and water industry product recommendation information that recommends products based on the water industry request information and water industry feedback information.

[0089] Step 1052: fill in the feedback report template according to the water industry request information, the water feedback information and the water product recommendation information to obtain a feedback report.

[0090] Specifically, the information required in the feedback report template is extracted from the water industry request information, water feedback information and water product recommendation information, and the feedback report template is filled, so that the feedback report can be generated quickly and effectively.

[0091] A specific embodiment of the water product recommendation method based on a large model proposed in an embodiment of the present invention is:

[0092] The preset water affairs knowledge base in this embodiment may include knowledge bases of different directions in the water affairs industry, such as a water plant knowledge base, a sewage plant knowledge base, a secondary water supply knowledge base, and a pipe network knowledge base, etc.; the preset water affairs entity base may include entity bases of different directions in the water affairs industry, such as a water plant entity base, a sewage plant entity base, a secondary water supply entity base, and a pipe network entity base, etc.; the preset water affairs product base may include product bases of different directions in the water affairs industry, such as a water plant product base, a sewage plant product base, a secondary water supply product base, and a pipe network product base, etc.; for the sake of convenience of explanation, this embodiment takes the water plant knowledge base, the water plant entity base, and the water plant product base as examples for explanation.

[0093] Step 111, obtaining user request information.

[0094] The method of this embodiment is mainly used to process the request information of the water industry. Therefore, after obtaining the user request information, it is necessary to screen the user request information to determine whether it is the request information of the water industry. If so, then perform subsequent processing. Otherwise, input the user request information into the existing question-and-answer robot, and feed back the content output by the question-and-answer robot to the user, which can improve the user experience and the applicability of this method. Specifically, if the user request information contains at least one key feature in the preset water key feature library, it is determined to be the request information of the water industry.

[0095] Step 112: extract key features of target water affairs.

[0096] First, it is necessary to determine a preset water service knowledge base. Obtain text materials in different directions within the water service field from channels such as the Internet (considering large scenarios, different directions include water supply plants, sewage treatment plants, secondary water supply, etc.; considering small scenarios, different directions include all equipment in water plants, inspection and maintenance services in water plants, etc.). These text materials include but are not limited to relevant document materials in users' daily production activities such as question banks, user manuals, operation manuals, usage guides, system introductions, etc. The format is not limited to word, pdf, excel, ppt, photos, web pages, etc. Preprocess these text materials, and the preprocessing includes the following steps: 1. Remove stop words: Remove common words such as "of", "is" that are not helpful for classification. 2. Stemming / lemmatization: Simplify words to their root forms to reduce lexical diversity. 3. Remove punctuation marks and special characters: Clean non-alphabetic characters in the text. 4. Lowercase: Convert all text to lowercase to avoid classification errors caused by case differences. 5. Word segmentation: For Chinese text, word segmentation processing is required to split sentences into words. After preprocessing, classify these text materials, that is, divide the text materials into at least one knowledge base such as a water supply plant knowledge base, a sewage treatment plant knowledge base, or a secondary water supply knowledge base according to different directions. Then, use an encoder to vectorize the text in each knowledge base for convenient subsequent retrieval and matching. Finally, extract NER entities (named entity recognition such as equipment names, software system names, merchant names, contact numbers, contact addresses, equipment parameters, etc.) for the text content under different knowledge bases.

[0097] After that, determine the corresponding entity library according to the entities identified from each knowledge base, that is, one knowledge base corresponds to one entity library. Taking the water plant knowledge base and the water plant entity library as an example, the entities identified from the water plant knowledge base include: water pipes, valves... etc. These entities such as water pipes, valves... etc. constitute the water plant entity library. Entity libraries in different directions or classifications constitute the preset water service entity library. In addition, it is also necessary to perform synonym, typo, and pinyin expansion on the content of the entity library. For example, the expanded synonyms of secondary water supply include community water supply (business synonyms), erci gongshui (typo), and secondary gongshui (pinyin expansion). Therefore, the preset water service entity library not only contains the entities identified from the preset water service knowledge base but also includes the synonyms, typos, pinyin expansions, etc. corresponding to each entity.

[0098] In this embodiment, the entity library can be used as a key feature library. That is, the preset water service entity library is also the preset water service key feature library, which is used to perform matching calculations on the original key features extracted from the water service industry request information to obtain the target water service key features.

[0099] The water industry request information is vectorized and key features are extracted. The preset water industry key feature library is then calculated based on the original key features. If an entity greater than the preset threshold can be found after calculation (the calculation can be similar or identical), the entity will be used as the target water industry key feature for subsequent processing.

[0100] Step 113, determining water service feedback information.

[0101] First, it is necessary to determine a preset water affairs knowledge base. The steps for determining the preset water affairs knowledge base are the same as the steps for determining the preset water affairs knowledge base in step 112, and will not be repeated here.

[0102] Then, the preset water affairs knowledge base is calculated according to the entity determined in step 112. If at least one text content (i.e., water affairs information) greater than the preset matching value can be found after the calculation (the calculation can be similar or identical), the found water affairs information is input into the existing question-and-answer robot for text sorting, and the sorted text output by the question-and-answer robot can be fed back to the user as water affairs feedback information.

[0103] Step 114, determining water product recommendation information.

[0104] Product information for different directions in the water industry (i.e., information on water products on sale) is obtained through web pages, manufacturers of water industry products, etc., including but not limited to product introductions, product manuals, instructions for use, function introductions, web links, etc., and the types are not limited to hardware products, software products, value-added services, etc. The product information is divided into the water plant product library and / or sewage plant product library and / or secondary water supply product library and / or pipe network product library according to the direction to which it belongs. One water product on sale information is divided into at least one product library. After the water product on sale information is divided, the water product on sale information in each direction constitutes the product library of this direction, such as the water plant product library, sewage plant product library, secondary water supply product library and pipe network product library, etc. These product libraries constitute the preset water product library.

[0105] Calculation is performed based on the target water service key features and the preset water service product library to obtain corresponding product information. There may be multiple corresponding product information. Multiple product information with high correlation with the target water service key features can be output as water service product matching information, and then the product information in the water service product matching information can be output according to the preset recommended value. For example, if the preset recommended value is 5, the top 5 product information in the water service product matching information will be output as water service product recommendation information for user selection.

[0106] In order to improve the effectiveness of product recommendations, the water industry request information input by the user can also be input into the trained intent recognition model to determine whether the user has the intention to purchase related products. If so, the water product recommendation information is output. The intention recognition model can be obtained by training the preset network model based on the collected user behavior data, wherein the collected user behavior data includes the user's behavior data on the website or application, such as browsing history, search keywords, click records, and stay time. The data with known purchase intentions is extracted from it as a training set, and which users ultimately purchased the goods and which did not are marked as preset labels. The preset network model is trained using the training set and preset labels to obtain a trained intent recognition model. Feature extraction is performed on the water industry request information input by the user to obtain input features, and the input features are input into the intent recognition model to obtain a prediction result of whether the user has a purchase intention. If the prediction result is that there is a purchase intention, the water product recommendation information is output to the user.

[0107] Step 115, determining the feedback report.

[0108] In order to improve the efficiency of generating feedback reports, a feedback report template can be set in advance. The feedback report template may include water industry request information input by the user, water industry feedback information that answers the water industry request information, and water product recommendation information that recommends products based on the water industry request information and water industry feedback information, etc. By extracting the information required in the feedback report template from the water industry request information, water industry feedback information and water product recommendation information, and filling in the feedback report template, the feedback report can be generated quickly and effectively.

[0109] Alternatively, the water industry request information input by the user, the water industry feedback information that answers the water industry request information, and the water industry product recommendation information based on the water industry request information and water industry feedback information are organized in the form of text and input into the existing Q&A robot or large model to obtain a summarized feedback report and generate a sharing link. Other users can quickly understand the dialogue conclusion, dialogue content and related product information by accessing the link. The dialogue conclusion includes the water industry request information input by the user in the Q&A, the water industry feedback information that answers the water industry request information, and the water industry product recommendation information based on the water industry request information and water industry feedback information.

[0110] The specific implementation of the water product recommendation method based on a large model in the water industry water plant direction is as follows:

[0111] Water plant equipment maintenance is crucial to ensuring water quality safety, ensuring water supply stability, improving operational efficiency, and reducing operating costs. Equipment maintenance mainly includes work such as filter replacement, membrane assembly replacement, pump component replacement, and bearing lubrication. Generally, equipment is managed through regular maintenance or daily inspections. During the entire equipment maintenance process, a variety of different situations may occur that cannot be resolved by on-site inspectors or engineers. It is necessary to contact the manufacturer or more professional personnel to resolve them. In the process of resolving the issues, time cost, communication cost, and personnel cost may arise, leading to low maintenance efficiency and high costs. Taking a water plant as an example, if the water industry request information contains keywords for water hardware products, the processing flow is as follows:

[0112] First, by establishing a knowledge base, entity library, and product library related to water plant equipment, we use a large model Q&A method to solve professional knowledge questions in the equipment maintenance process, including maintenance methods, problem solutions, and troubleshooting, to quickly assist in solving related business problems. At the same time, during multiple rounds of Q&A, we recommend products for the equipment, parts, or consumables that appear. In this embodiment, text materials related to water plant equipment are used as knowledge base construction data, including equipment manuals, repair manuals, maintenance records, maintenance methods, operating methods, and other related materials.

[0113] Then, in this embodiment, an entity library is established through NER entity recognition, and the entity library contains synonyms corresponding to each entity, etc., wherein the entity library includes entity id, category name, entity name, entity category, model, purpose and other related information, and synonyms include synonym id, associated category name, entity name, synonym type and other related information.

[0114] Afterwards, in this embodiment, the product information related to the equipment, accessories, consumables, etc. in the water treatment plant field is used as the product library building data, including product name, supplier information, product characteristics, accessories list, product parameters and other related information. A relationship is established between product C in the product library and entity E in the entity library, and the degree of correlation is represented by coefficient β, where b is an offset vector, so as to facilitate finding product C in the product library through entity E.

[0115] Afterwards, in this example, when a user asks a question about general knowledge (i.e., not related to the water industry), the existing large model capability is directly called to respond, for example: "Introduce Newton"; when a user asks about water equipment-related content, the knowledge base content is matched with the degree of association, for example: "What is the highest head of a certain water pump?" If there is no relevant information in the knowledge base, the question is directly input into the large model for answering. The knowledge base matching text information is represented by the correlation coefficient α, and the number of texts retrieved from the knowledge base n can be set in advance as needed. Finally, all the obtained text content is input into the existing large model for summary, such as I{i1,i2…i n}=W{α1i1,α2i2…α m i m}|Rank(α) topn . Among them, I{i1,i2…i n} is the text set returned by the large model (i.e., the summarized text), n is the preset number of texts; W{α1i1, α2i2…α m i m} is all the relevant texts obtained from the knowledge base, the number is m, and they are sorted according to the size of α, α1>α2>α m , when the coefficients are the same, random selection is performed. When m is less than n, the number returned is m, Rank(α) topn It means taking the top n texts according to the size of α.

[0116] In the question-answering process, from user questions or large model answers, including large model general ability answers, the degree of correlation between synonyms and entities is represented by the correlation coefficient λ. When the synonym S is identified by the NER algorithm, the final entity E{set} is determined by the coefficient λ as follows:

[0117] E{e1,e2…e j}=S{λ1e1,λ2e2…λ k e k}|Rank(λ) topn

[0118] Among them, E{e1,e2…e j} is the final entity set, j is the number of entities with the largest correlation coefficients among the first j preset entities S{λ1e1,λ2e2…λ k e k} are all entities corresponding to the identified synonyms, the number is k, and they are sorted according to the size of λ, λ1>λ2>λ k , make a random selection when the coefficients are the same. When k is less than j, the number returned is k, Rank(λ) topn It means taking the top j texts according to the size of λ.

[0119] In this embodiment, intent recognition is performed through a trained intent recognition model to determine whether the user has a purchase intention. When the prediction result output by the intent recognition model is that there is no purchase intention, no product recommendation is made. When there is a purchase intention, product information is returned based on the synonym S-entity E-product C association.

[0120] The entities associated with the synonyms are:

[0121] E{e1,e2…e j}=S{λ1e1,λ2e2…λ k e k}|Rank(λ) topn

[0122] Append the entities associated with the synonyms to the formula below to uniformly sort the relevance. If it is entity recognition, the associated products are:

[0123] C{c1,c2…c p}=E{β1e1,β2e2…β q e q}|Rank(β) topn

[0124] Among them, C{c1,c2…c p} is the final product set, p is the number of products with the largest correlation coefficients, E{β1e1,β2e2…β q e q} is the entity associated with the identified synonym, the number is q, and it is sorted according to the size of β, β1>β2>β q , make a random selection when the coefficients are the same. When q is less than p, the number returned is q, Rank(β) topn It means taking the top p entities according to the size of β.

[0125] This embodiment inputs the contents of multiple rounds of conversations into a large model to perform text summarization tasks, and finally generates a web link (the link contains a feedback report), which includes product links matched during the conversation process. The person being shared can learn about related content and products through the link.

[0126] The water product recommendation method based on a big model proposed in an embodiment of the present invention performs field recognition on the original information input by the user based on at least one water industry request information of the user, so that the answer result (feedback information) is more accurate; purchase intention is recognized through question and answer, and then product recommendation is performed, so that product recommendation and target users obtained are more accurate, which is conducive to improving the product order conversion rate; the question and answer based on the water industry knowledge base has more professional information and stronger ability to solve professional problems compared with the existing general big model, and also has the ability to answer general questions; NER recognition based on the synonym library improves the product library hit rate and product library product exposure rate, and at the same time improves the input fault tolerance and user experience; by using the product web page link method, the target user obtains more product information, sales communication, order purchase and other subsequent processes more conveniently, reducing the supplier's customer acquisition cost; by sharing the summary, outputting the dialogue overview in the form of needs, problems and solutions can help users make decisions faster and improve the product order conversion rate.

[0127] like Figure 2 As shown, the embodiment of the present invention proposes a water product recommendation device 200 based on a large model, comprising:

[0128] The acquisition module 201 is used to acquire the water industry request information;

[0129] The first processing module 202 is used to extract features from the water industry request information to obtain target water industry key features;

[0130] The second processing module 203 is used to obtain water service feedback information according to the target water service key features and a preset water service knowledge base;

[0131] The third processing module 204 is used to obtain water service product recommendation information according to the target water service key characteristics and the preset water service product library;

[0132] The fourth processing module 205 is used to obtain a feedback report according to the water industry request information, the water feedback information and the water product recommendation information.

[0133] Optionally, obtain water industry request information, including:

[0134] Get the original request information;

[0135] The original request information is screened according to preset screening conditions to obtain water industry request information.

[0136] Optionally, feature extraction is performed on the water industry request information to obtain target water industry key features, including:

[0137] Obtain preset water affairs key feature database;

[0138] Extracting features from the water industry request information to obtain original key features;

[0139] The target water affairs key feature is determined according to the preset water affairs key feature library and the original key feature.

[0140] Optionally, water service feedback information is obtained according to the target water service key features and a preset water service knowledge base, including:

[0141] Get access to preset water affairs knowledge base;

[0142] Obtaining water affairs information according to the target water affairs key features and the preset water affairs knowledge base;

[0143] The water affairs information is adjusted to obtain water affairs feedback information.

[0144] Optionally, according to the target water service key features and a preset water service product library, water service product recommendation information is obtained, including:

[0145] Get a library of preset water products;

[0146] Obtaining water service product matching information according to the target water service key features and the preset water service product library;

[0147] The water product recommendation information is determined based on the preset recommendation value and the water product matching information.

[0148] Optionally, obtain a library of pre-set water products, including:

[0149] Obtain information on water products on sale;

[0150] Dividing the water service product information on sale according to preset grouping conditions to obtain the divided water service product information on sale;

[0151] A preset water products library is determined based on the divided water products for sale information.

[0152] Optionally, a feedback report is obtained according to the water industry request information, the water feedback information and the water product recommendation information, including:

[0153] Get the feedback report template;

[0154] The feedback report template is filled in according to the water industry request information, the water feedback information and the water product recommendation information to obtain a feedback report.

[0155] The large-model-based water product recommendation device proposed in the embodiment of the present invention obtains target water key features based on the acquired water industry request information, and then obtains water feedback information and water product recommendation information based on a preset water knowledge base and a preset water product library. Finally, a feedback report is obtained based on the water industry request information, water feedback information and water product recommendation information. It can not only provide professional answers to users in the water industry, but also recommend water industry-related products with a high degree of matching to users based on user intentions, which is conducive to improving user satisfaction and reducing sales costs.

[0156] It should be noted that the device is a device corresponding to the above method, and all implementations in the above method embodiment are applicable to the embodiment of the device and can achieve the same technical effect. This embodiment will not be repeated.

[0157] The embodiment of the present invention further provides a computing device, comprising: a processor, and a memory storing a computer program, wherein when the computer program is executed by the processor, the method described in any one of the above embodiments is executed. All implementations in the above method embodiments are applicable to the embodiments of the device, and can achieve the same technical effects. They will not be described in detail in this embodiment.

[0158] The embodiment of the present invention further provides a computer-readable storage medium on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects. They will not be described in detail in this embodiment.

[0159] It should be noted that, in the above-mentioned embodiments, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the implementation of the above-mentioned embodiments is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0160] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A water product recommendation method based on a large model, characterized in that: include: Obtain information requested by the water industry; Extracting features of the water industry request information to obtain key features of the target water industry; Obtaining water affairs feedback information according to the target water affairs key features and a preset water affairs knowledge base; Obtain water service product recommendation information based on the target water service key features and the preset water service product library; A feedback report is obtained according to the water industry request information, the water feedback information and the water product recommendation information.

2. The water product recommendation method based on a large model according to claim 1 is characterized in that: Get information requested by the water industry, including: Get the original request information; The original request information is screened according to preset screening conditions to obtain water industry request information.

3. The water product recommendation method based on a large model according to claim 1 is characterized in that: Feature extraction is performed on the water industry request information to obtain key features of the target water industry, including: Obtain preset water affairs key feature database; Extracting features from the water industry request information to obtain original key features; The target water affairs key feature is determined according to the preset water affairs key feature library and the original key feature.

4. The water product recommendation method based on a large model according to claim 1 is characterized in that: According to the target water affairs key features and the preset water affairs knowledge base, water affairs feedback information is obtained, including: Access to preset water affairs knowledge base; Obtaining water affairs information according to the target water affairs key features and the preset water affairs knowledge base; The water affairs information is adjusted to obtain water affairs feedback information.

5. The water product recommendation method based on a large model according to claim 1 is characterized in that: According to the target water service key features and the preset water service product library, water service product recommendation information is obtained, including: Get a library of preset water products; Obtaining water service product matching information according to the target water service key features and the preset water service product library; The water product recommendation information is determined based on the preset recommendation value and the water product matching information.

6. The water product recommendation method based on a large model according to claim 5 is characterized in that: Access a library of pre-set water products, including: Obtain information on water products on sale; Dividing the water service product information on sale according to preset grouping conditions to obtain the divided water service product information on sale; A preset water products library is determined based on the divided water products for sale information.

7. The water product recommendation method based on a large model according to claim 1 is characterized in that: According to the water industry request information, the water feedback information and the water product recommendation information, a feedback report is obtained, including: Get the feedback report template; The feedback report template is filled in according to the water industry request information, the water feedback information and the water product recommendation information to obtain a feedback report.

8. A water product recommendation device based on a large model, characterized in that: include: The acquisition module is used to obtain water industry request information; The first processing module is used to extract features from the water industry request information to obtain key features of the target water industry; A second processing module is used to obtain water affairs feedback information according to the target water affairs key features and a preset water affairs knowledge base; A third processing module is used to obtain water service product recommendation information according to the target water service key features and a preset water service product library; The fourth processing module is used to obtain a feedback report according to the water industry request information, the water feedback information and the water product recommendation information.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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