Method for recommending soil and water conservation measures of production construction project based on natural language processing and apriori algorithm

Abstract

A production construction project soil and water conservation measure recommendation method based on natural language processing and Apriori algorithm, comprising: collecting approved production construction project soil and water conservation scheme report data, and performing text preprocessing to obtain report text; generating a first embedding vector of each report text; generating a second embedding vector for a production construction project currently in need of soil and water conservation measure recommendation; indexing the first embedding vectors of all report texts using a vectorization retrieval tool, finding a number of report texts most similar to the current production construction project by matching the second embedding vector; and screening soil and water conservation measures most matching the current regional natural geographical conditions from the similar report texts based on the association rules between the soil and water conservation measures and the natural geographical conditions. The present application can efficiently and accurately recommend the most matching soil and water conservation measures for production construction projects in different regions, and improve the scientificity and operability of soil and water conservation work.

Description

Technical Field

[0001] The present invention relates to the fields of computer science and soil and water conservation technology, and in particular to a method for recommending soil and water conservation measures for production and construction projects by utilizing an SBERT model, a Faiss (FastAI Similarity Search and Retrieval System) vectorized retrieval tool, and an Apriori algorithm. Background Art

[0002] Soil and water conservation refers to a series of measures that protect agricultural production and the ecological environment by rationally utilizing these resources while protecting land resources and preventing soil erosion. With global climate change and the continued intensification of human activities, soil erosion is becoming increasingly serious, severely impacting agricultural production, the ecological environment, and human life. Therefore, researching and implementing effective soil and water conservation measures is of great significance to promoting sustainable development.

[0003] Traditional soil and water conservation measures rely primarily on experience and qualitative analysis. While these methods can address soil erosion to a certain extent, they lack systematicity and scientificity, making them inadequate for the complex and changing natural environment. Furthermore, with technological advancements and the accumulation of data, leveraging big data and intelligent algorithms to make scientific decisions about soil and water conservation measures has become a hot topic and a challenge in current research. Summary of the Invention

[0004] The purpose of this invention is to provide a soil and water conservation measure recommendation method based on the natural language processing model SBERT, the Faiss vectorized retrieval tool and the association rule mining algorithm Apriori. Through efficient similarity search and association rule mining, the most suitable soil and water conservation measures for different regions are recommended, overcoming the limitations of traditional methods.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A method for recommending soil and water conservation measures for production and construction projects based on natural language processing and Apriori algorithm includes the following steps:

[0007] Collecting soil and water conservation plan report materials of production and construction projects that have obtained approval from the water administration department, and performing text preprocessing on the soil and water conservation plan report materials to obtain a report text;

[0008] Generate the first embedding vector of each report text using a natural language processing model;

[0009] For production and construction projects that currently require recommended soil and water conservation measures, a natural language processing model is used to generate a second embedding vector describing the project type and the local physical and geographical conditions, including topography, climate, soil, and vegetation.

[0010] Use a vectorized search tool to index the first embedding vectors of all report texts. By matching them with the second embedding vectors, find the report texts that are most similar to the project types and descriptions of the natural geographical conditions of the production and construction projects currently requiring recommended soil and water conservation measures.

[0011] Apriori algorithm is used to mine association rules between soil and water conservation measures and natural geographical conditions from the most similar report texts.

[0012] Based on the mined association rules, the soil and water conservation measures that best match the natural geographical conditions of the current region are screened out from similar report texts.

[0013] Furthermore, according to the "Standard for Soil and Water Loss Prevention and Control in Production and Construction Projects" (GB / T50434-2018), the effectiveness of the selected soil and water conservation measures in preventing and controlling soil and water loss is evaluated by calculating soil and water loss prevention and control indicators; the soil and water loss prevention and control indicators include soil and water loss control degree, soil loss control ratio, slag protection rate, topsoil protection rate, forest and grass vegetation recovery rate and forest and grass coverage rate.

[0014] Furthermore, the natural language processing model is an SBERT model, which is trained for sentence-level tasks to generate word embeddings representing contextual information of sentences or words. The sentence-level tasks include semantic similarity calculation.

[0015] Furthermore, the vectorized retrieval tool uses Faiss to perform similarity search indexing.

[0016] Furthermore, the text preprocessing of the soil and water conservation program report data includes: word segmentation, stop word removal and stem extraction, wherein word segmentation is to decompose the text into word sequences, stop word removal is to delete common meaningless words in the text, and stem extraction is to restore the words to their stem form. The obtained report text is S i ={w1,w2,...,w n}, where wi is the i-th word in the report text, and the preprocessed report text S i Stored in a relational database.

[0017] Furthermore, generating a first embedding vector for each report text using a natural language processing model includes:

[0018] Step 21: Word Embedding Layer: Convert the sequence of input text after word segmentation into a format accepted by the machine learning model, and map each word to a fixed-length embedding vector e i :

[0019] e i =SBERT(w i )

[0020] Step 22: Position encoding: Transform the position encoding vector p i Add to each embedding vector e i On the above, we get the word vector x i , to include information about the word's position in the sentence:

[0021] x i =e i +p i

[0022] Step 23: Generate the first embedding vector μ i :Add the word vector sequence {x1,x2,...,x n} is input into the Transformer encoder, which consists of multiple self-attention layers and feedforward neural network layers. The word vector sequence is output as the first embedding vector μ after the Transformer encoder. i :

[0023] μ i =Transformer(x1,x2,...,x n ).

[0024] Furthermore, the vectorized search tool is used to index the first embedding vectors of all report texts, and by matching them with the second embedding vectors, several report texts that are most similar to the project type and the description of the natural geographical conditions of the region of the production and construction project for which soil and water conservation measures are currently required are found, including:

[0025] Step 41: Select the index type: Select memory-mapped index as the similarity search index type. The memory-mapped index accelerates the search by partitioning, and each partition has a representative vector.

[0026] Step 42: Similarity search, by calculating the second embedding vector u2 and the first embedding vector centroid μ i The cosine similarity between them is used to perform similarity search to find the most similar vector to the second embedding vector, thereby finding several reports that are most similar to the current region description.

[0027] Furthermore, the cosine similarity is the vector u2 and μ iThe cosine value of the angle θ between them, cos(θ), is calculated as follows:

[0028]

[0029] Where cos(θ) represents the vector u2 and μ i The cosine value of the angle θ between them. The closer the cosine value is to 1, the more similar the two vectors are; the closer the cosine value is to 0, the less similar the two vectors are.

[0030] Furthermore, the Apriori algorithm is used to mine association rules between soil and water conservation measures and natural geographical conditions from the most similar report texts, including:

[0031] Step 51: Generate frequent itemsets

[0032] (1) Starting from a single item, generate a candidate item set C1 of size 1;

[0033] (2) Scan the database, calculate the support of each candidate item set, and find the frequent item set F1 whose support is greater than or equal to the minimum support threshold;

[0034] (3) Generate candidate item sets through frequent item sets: Using frequent item sets F k Generate a candidate item set C of size k+1 k+1 ;

[0035] (4) Scan the database and calculate each candidate item set C k+1 Support, find the frequent itemsets F whose support is greater than or equal to the minimum support threshold k+1 ;

[0036] (5) Repeat steps (3) and (4) until no new frequent itemsets can be generated.

[0037] Step 52: Generate association rules

[0038] (2) For each frequent item set F k , generate all possible association rules X→Y, where and Y = F k \X;

[0039] (2) For each association rule, calculate the support and confidence:

[0040] (3) For each frequent item set, generate all possible association rules X→Y and calculate their support and confidence. Record the support and confidence of each rule, set the minimum support and confidence thresholds, and only retain the rules whose support and confidence both meet the minimum support and minimum confidence thresholds to screen out strong association rules.

[0041] Furthermore, step 52 specifically includes:

[0042] Step 521: Calculate support

[0043] Definition: Support refers to the frequency with which an item set appears in all transactions;

[0044] formula:

[0045]

[0046] Step 522: Calculate confidence

[0047] Confidence refers to the conditional probability that a transaction also contains Y when X is included:

[0048]

[0049] This method uses association rules and big data mining methods to accurately recommend soil and water conservation measures based on specific regional characteristics, effectively reducing the subjectivity of soil and water conservation analysis and improving the matching accuracy of soil and water conservation measure recommendations. This method can efficiently and accurately recommend the most appropriate soil and water conservation measures for production and construction projects in different regions, significantly enhancing the scientific nature and operability of soil and water conservation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flowchart of a method for recommending soil and water conservation measures for production and construction projects based on natural language processing and Apriori algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] With the development of big data technology and artificial intelligence, natural language processing (NLP) technology has shown great potential in text analysis and information extraction. It can extract valuable information from large amounts of text data and automatically analyze text content, thereby providing a basis for decision-making. SBERT (Sentence-BERT) is a NLP model for large-scale text analysis. It encodes sentences to generate vector representations of sentences. It offers advantages such as sentence-level semantic representation, contextual awareness, robustness, transferable learning, and wide applicability, providing an effective tool and technology for NLP tasks. SBERT excels in calculating semantic similarity and is suitable for similarity analysis of large-scale text reports, such as soil and water conservation reports.

[0053] Similarity search is a key technology in information retrieval, aiming to quickly find items similar to the query data within massive amounts of data. Traditional similarity search methods rely primarily on keyword matching within text, which struggles to accurately reflect the text's semantic information. With the development of embedding technology, text embedding vectors generated using deep learning models can better represent the semantic features of text, thereby improving the accuracy of similarity search.

[0054] The Faiss vectorized search tool is a similarity search tool based on embedded vector technology, supporting indexing and searching of large-scale embedded vectors. Using the Faiss vectorized search tool, you can build an efficient similarity search index, enabling rapid retrieval of large numbers of soil and water conservation reports. Finding the reports most similar to the current regional description provides a foundation for subsequent association rule mining and recommended measures.

[0055] The Apriori algorithm is a classic association rule mining algorithm widely used in fields such as data mining and machine learning. It analyzes the relationships between data item sets to discover frequently occurring item sets and association rules. In the field of soil and water conservation, the Apriori algorithm can be used to mine association rules between soil and water conservation measures and natural geographical conditions (topography, climate, soil, and vegetation) from a large number of soil and water conservation reports, providing algorithmic support for recommending appropriate measures for specific regions.

[0056] Figure 1 FIG2 is a flowchart of a method for recommending soil and water conservation measures for production and construction projects based on natural language processing and the Apriori algorithm according to an embodiment of the present invention. The method includes the following steps:

[0057] Step 1: Collect a large amount of soil and water conservation plan report data of production and construction projects that have obtained the reply from the water administration department, and pre-process the soil and water conservation plan report data to obtain the report text S i ={w1,w2,...,w n}, where wi is the i-th word in the report text.

[0058] The text preprocessing in step 1 includes: word segmentation (breaking the text into sequences of words), stop word removal (removing common meaningless words in the text), and stemming (reducing words to their stem form).

[0059] The preprocessed report text S i Stored in a relational database.

[0060] Step 2: Use the natural language processing model SBERT to generate each report text S i The first embedding vector of . Step 2 specifically includes:

[0061] Step 21: Word Embedding Layer: Convert the sequence of input text after word segmentation into a format acceptable to the machine learning model, that is, each word is mapped to a fixed-length embedding vector e i :

[0062] e i =SBERT(w i )

[0063] Step 22: Position encoding: Transform the position encoding vector p i Add to each embedding vector e i On the above, we get the word vector x i , to include information about the word's position in the sentence:

[0064] x i =e i +p i

[0065] Step 23: Generate the first embedding vector μ i :Add the word vector sequence {x1,x2,...,x n} is input into the Transformer encoder, which consists of multiple self-attention layers and feedforward neural network layers. The word vector sequence is output as the first embedding vector μ after the Transformer encoder. i :

[0066] μ i =Transformer(x1,x2,...,x n )

[0067] Step 3: For production and construction projects that currently require recommended soil and water conservation measures, use the natural language processing model SBERT according to the method in step 2 to generate a second embedding vector u2 that describes the project type and the natural geographical conditions of the area where the project is located. The natural geographical conditions include terrain, climate, soil, and vegetation.

[0068] Step 4: Perform a similarity search based on the second embedding vector generated in step 3 and the first embedding vector generated in step 2: Use a vectorized retrieval tool to index the first embedding vectors of all report texts, and by matching them with the second embedding vectors, find several report texts that are most similar to the project types and descriptions of the natural geographical conditions of the production and construction projects that currently require recommended soil and water conservation measures.

[0069] Step 4 specifically includes:

[0070] Step 41: Select the index type. The Faiss vectorized search tool provides multiple index types, each with different performance and applicable scenarios. Common index types include: Flat Index (FlatIndex), which is suitable for small-scale datasets and directly performs brute force searches. Its advantage is high accuracy because it performs accurate Euclidean distance calculations. Its disadvantage is that as the dataset size grows, the search speed slows down and memory consumption increases; Inverted File Index (IVF Index), which is suitable for medium to large-scale datasets and accelerates searches through partitioning (i.e., clustering). Each partition has a representative vector (centroid). Its advantage is that it significantly improves search speed and reduces memory usage while maintaining high accuracy. Its disadvantage is that it takes time to perform cluster training when building the index; HNSW Index (HierarchicalNavigableSmallWorld), which is suitable for large-scale datasets and provides approximate nearest neighbor search based on graph structures. Its advantages are very fast search speed, moderate memory consumption, and high accuracy. Its disadvantage is that it takes more time and memory to build the index.

[0071] Select an appropriate index type based on the dataset size and performance requirements. In this embodiment, an in-memory mapping index (IVF Index) is selected as the similarity search index type. The IVF index accelerates searches by partitioning (i.e., clustering), with each partition having a representative vector (centroid).

[0072] Step 42: Similarity search, by calculating the second embedding vector u2 and the first embedding vector centroid μ i The cosine similarity between them is used to perform similarity search to find the most similar vector to the second embedding vector, thereby finding several reports that are most similar to the current region description.

[0073] In the embodiment of the present invention, the cosine similarity is the vector u2 and μ i The cosine value of the angle θ between them, cos(θ), is calculated as follows:

[0074]

[0075] Where cos(θ) represents the vector u2 and μ i The cosine value of the angle θ between them. The closer the cosine value is to 1, the more similar the two vectors are; the closer the cosine value is to 0, the less similar the two vectors are.

[0076] Step 5: Use the Apriori algorithm to mine association rules between the soil and water conservation measures in the report text extracted in step 4 and the natural geographical conditions (topography, climate, soil, vegetation).

[0077] Step 5 specifically includes:

[0078] Step 51: Generate frequent itemsets: The core idea of ​​the Apriori algorithm is to generate frequent itemsets by gradually increasing the size of the itemsets. The specific steps include:

[0079] (1) Starting from a single item, generate a candidate item set C1 of size 1.

[0080] (2) Scan the database, calculate the support of each candidate item set, and find the frequent item set F1 whose support is greater than or equal to the minimum support threshold.

[0081] (3) Generate candidate item sets through frequent item sets: Using frequent item sets F k Generate a candidate item set C of size k+1 k+1 . This step is called the "connect" operation.

[0082] (4) Scan the database and calculate each candidate item set C k+1 Support, find the frequent itemsets F whose support is greater than or equal to the minimum support threshold k+1 .

[0083] (5) Repeat steps (3) and (4) until no new frequent itemsets can be generated.

[0084] Step 52: Generate association rules

[0085] After finding all frequent itemsets, we can then generate association rules from each frequent itemset.

[0086] (1) For each frequent item set F k , we need to generate all possible association rules X→Y (where and Y = F k \X).

[0087] (2) For each association rule, calculate its support and confidence.

[0088] (3) For each frequent item set, generate all possible association rules X→Y and calculate their support and confidence. Record the support and confidence of each rule. Usually, a minimum support and confidence threshold is set. Only rules whose support and confidence meet the minimum support and minimum confidence threshold are retained to filter out strong association rules. Step 52 specifically includes:

[0089] Step 521: Calculate support (Support):

[0090] Definition: Support refers to the frequency with which an item set appears in all transactions.

[0091] formula:

[0092]

[0093] Step 522: Calculate confidence: Confidence refers to the conditional probability that a transaction contains X and also contains Y.

[0094]

[0095] Step 6: According to step 5, the association rules with the largest support and confidence are screened out, and the most suitable soil and water conservation measures for the area and specific type of production and construction projects that currently need to be recommended are recommended from similar report texts.

[0096] Step 7: Evaluate the effectiveness of the soil and water conservation measures selected in step 6 in preventing and controlling soil erosion.

[0097] Step 7 specifically includes:

[0098] Step 71: Calculate indicators. According to the "Standard for Soil and Water Loss Prevention and Control in Production and Construction Projects" (GB / T50434-2018), calculate the six major indicators: soil and water loss control degree, soil loss control ratio, slag protection rate, topsoil protection rate, forest and grass vegetation recovery rate, and forest and grass coverage rate.

[0099] Step 72: Based on the calculated indicators, evaluate the effectiveness of the recommended soil and water conservation measures on preventing and controlling soil erosion, which will be used by the project construction unit and the design unit to decide whether the recommended soil and water conservation measures can be included in the final design plan.

[0100] This paper proposes an efficient and accurate method for recommending soil and water conservation measures by combining the SBERT model, the Faiss vectorized search tool, and the Apriori algorithm. Through steps such as data collection and preprocessing, embedding vector generation, regional feature input, similarity search, association rule mining, and measure recommendation, this method recommends scientific and reasonable soil and water conservation measures for different regions. This method can improve the accuracy and efficiency of soil and water conservation measure recommendations and has broad application prospects and practical value.

[0101] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for recommending soil and water conservation measures for production and construction projects based on natural language processing and Apriori algorithm, characterized by: The following steps are involved: Collecting soil and water conservation plan report materials of production and construction projects that have obtained approval from the water administration department, and performing text preprocessing on the soil and water conservation plan report materials to obtain a report text; Generate the first embedding vector of each report text using a natural language processing model; For production and construction projects that currently require recommended soil and water conservation measures, a natural language processing model is used to generate a second embedding vector describing the project type and the local physical and geographical conditions, including topography, climate, soil, and vegetation. Use a vectorized search tool to index the first embedding vectors of all report texts. By matching them with the second embedding vectors, find the report texts that are most similar to the project types and descriptions of the natural geographical conditions of the production and construction projects currently requiring recommended soil and water conservation measures. Apriori algorithm is used to mine association rules between soil and water conservation measures and natural geographical conditions from the most similar report texts. Based on the mined association rules, the soil and water conservation measures that best match the natural geographical conditions of the current region are screened out from similar report texts.

2. The method according to claim 1, characterized in that Also includes: According to the Standard for Soil and Water Loss Prevention and Control in Production and Construction Projects (GB / T 50434-2018), the effectiveness of the selected soil and water conservation measures in preventing and controlling soil and water loss is evaluated by calculating soil and water loss prevention and control indicators; the soil and water loss prevention and control indicators include soil and water loss control degree, soil loss control ratio, slag protection rate, topsoil protection rate, forest and grass vegetation recovery rate, and forest and grass coverage rate.

3. The method according to claim 1, characterized in that The natural language processing model is an SBERT model, which is trained for sentence-level tasks to generate word embeddings representing contextual information of sentences or words. The sentence-level tasks include semantic similarity calculation.

4. The method according to claim 1, wherein The vectorized retrieval tool uses Faiss to perform similarity search indexing.

5. The method according to claim 4, characterized in that The text preprocessing of the soil and water conservation program report data includes: word segmentation, stop word removal and stem extraction, wherein word segmentation is to decompose the text into word sequences, stop word removal is to delete common meaningless words in the text, and stem extraction is to restore the words to their stem form. The resulting report text is S i ={w1,w2,...,w n }, where w i is the i-th word in the report text, and the preprocessed report text S i Stored in a relational database.

6. The method according to claim 5, characterized in that Generating a first embedding vector for each report text using a natural language processing model includes: Step 21: Word Embedding Layer: Convert the sequence of input text after word segmentation into a format accepted by the machine learning model, and map each word to a fixed-length embedding vector e i : e i SBERT(w i ) Step 22: Position encoding: Transform the position encoding vector p i Add to each embedding vector e i On the above, we get the word vector x i , to include information about the word's position in the sentence: x i =and i +p i Step 23: Generate the first embedding vector μ i :Add the word vector sequence {x1,x2,...,x n } is input into the Transformer encoder, which consists of multiple self-attention layers and feedforward neural network layers. The word vector sequence is output as the first embedding vector μ after the Transformer encoder. i : μ i =Transform(x1,x2,...,x n )。 7. The method according to claim 6, characterized in that The vectorized search tool is used to index the first embedding vectors of all report texts, and by matching them with the second embedding vectors, several report texts that are most similar to the project type and the description of the natural geographical conditions of the region of the production and construction project that currently requires recommended soil and water conservation measures are found, including: Step 41: Select the index type: Select memory-mapped index as the similarity search index type. The memory-mapped index accelerates the search by partitioning, and each partition has a representative vector. Step 42: Similarity search, by calculating the second embedding vector u2 and the first embedding vector centroid μ i The cosine similarity between them is used to perform similarity search to find the most similar vector to the second embedding vector, thereby finding several reports that are most similar to the current region description.

8. The method according to claim 7, characterized in that The cosine similarity is the vector u2 and μ i The cosine value of the angle θ between them, cos(θ), is calculated as follows: Where cos(θ) represents the vector u2 and μ i The cosine value of the angle θ between them. The closer the cosine value is to 1, the more similar the two vectors are; the closer the cosine value is to 0, the less similar the two vectors are.

9. The method according to claim 1, characterized in that The first embedding vector generation formula is as follows: The use of the Apriori algorithm to mine association rules between soil and water conservation measures and natural geographical conditions from the most similar report texts includes: Step 51: Generate frequent itemsets (1) Starting from a single item, generate a candidate item set C1 of size 1; (2) Scan the database, calculate the support of each candidate item set, and find the frequent item set F1 whose support is greater than or equal to the minimum support threshold; (3) Generate candidate item sets through frequent item sets: Using frequent item sets F k Generate a candidate item set C of size k+1 k+1 ; (4) Scan the database and calculate each candidate item set C k+1 Support, find the frequent itemsets F whose support is greater than or equal to the minimum support threshold k+1 ; (5) Repeat steps (3) and (4) until no new frequent item sets can be generated; Step 52: Generate association rules (1) For each frequent item set F k , generate all possible association rules X→Y, where and Y = F k \X; (2) For each association rule, calculate the support and confidence: (3) For each frequent item set, generate all possible association rules X→Y and calculate their support and confidence. Record the support and confidence of each rule, set the minimum support and confidence thresholds, and only retain the rules whose support and confidence both meet the minimum support and minimum confidence thresholds to screen out strong association rules.

10. The method according to claim 9, characterized in that Step 52 specifically includes: Step 521: Calculate support Definition: Support refers to the frequency with which an item set appears in all transactions; formula: Step 522: Calculate confidence Confidence refers to the conditional probability that a transaction also contains Y when X is included:

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