Electronic map generation system and method for characteristic agricultural economy

Abstract

The invention relates to the technical field of agricultural information, and discloses an electronic map generation system and method for characteristic agricultural economy, and the method comprises the steps: obtaining a demand text uploaded by a user, extracting keywords, and determining a query region and an agricultural industry; then generating region and industry reports through mining analysis by using related data; combining the two types of data, and calculating an industry economic score by using a mathematical model; and finally, integrating the data and the scoring result through a data association and visualization technology, and generating a comprehensive query report for a user to make a decision and analyze. The method effectively solves the problem of urgent technical demand of characteristic agricultural economic electronic map generation, can assist managers in intuitively studying and judging the current situation and trend of the industry, and promotes high-quality development of characteristic agriculture through scientific decision making.

Description

Technical Field

[0001] The present invention relates to the field of agricultural information technology, and in particular to an electronic map generation system and method for characteristic agricultural economy. Background Art

[0002] As an advanced agricultural data management and analysis tool, the Agricultural Geographic Information System (GIS) integrates a large amount of agricultural-related geographic data and multi-source data analysis functions. For agricultural managers, it is of great significance to be able to conveniently display the data analysis results in the system in the form of reports and save the reports in independent file formats. This can not only improve the work efficiency and scientific decision-making level of agricultural managers, but also ensure the accurate communication and long-term preservation of analysis results.

[0003] However, traditional report generation methods have some significant limitations in the field of agricultural data analysis. First, most report templates produced by existing methods are only for specific file formats (such as Word, Excel, HTML, etc.). This means that if reports in multiple file formats need to be generated, multiple template files often need to be produced separately, which greatly increases the workload of template production and maintenance. Secondly, the data in the reports generated by traditional methods are usually scattered and difficult to effectively retrieve, which makes it difficult to effectively share and exchange the data in the reports, especially in the scenario of cross-system data transmission, affecting the circulation and analysis efficiency of the data.

[0004] Therefore, there is an urgent need to invent a characteristic agricultural economic map generation technology to solve the limitations of traditional report generation methods in supporting multiple file formats, data sharing and exchange, which leads to complex template production, scattered data and difficulty in efficient circulation. Summary of the invention

[0005] In view of this, the present invention proposes an electronic map generation system and method for characteristic agricultural economy, aiming to solve the problems of traditional report generation methods in supporting multiple file formats, data sharing and exchange, which lead to complex template production, scattered data and difficulty in efficient circulation.

[0006] The present invention proposes a method for generating an electronic map for a characteristic agricultural economy, comprising:

[0007] Obtain the demand text uploaded by the user, and obtain the demand keywords in the demand text;

[0008] Determine the query area and query agricultural industry according to the demand keywords, obtain agricultural data and disaster data of the query area according to the query area, and obtain regional data and agricultural data of the query agricultural industry according to the query agricultural industry;

[0009] Generate a regional agricultural report for the query area based on the agricultural data and disaster data of the query area;

[0010] Generate an agricultural industry data report for querying the agricultural industry based on the regional data and agricultural data of the agricultural industry;

[0011] Determine the industry economic score of the queried agricultural industry in the queried area according to the relationship between the regional agricultural report and the agricultural industry data report;

[0012] According to the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, a query report is generated and the query report is output.

[0013] Further, obtaining the demand text uploaded by the user and obtaining the demand keywords in the demand text includes:

[0014] Convert the demand text into a format to be analyzed, and perform word segmentation on the converted demand text;

[0015] Perform keyword recognition on several words after segmentation based on the inverse document word frequency;

[0016] Based on the syntax tree, named entities in several of the keywords are determined, and the named entities are determined as the required keywords.

[0017] Further, according to the required keywords, the query area and the query agricultural industry are determined, including:

[0018] Pre-establish a query word library based on the region name and agricultural industry name;

[0019] The required keywords are matched with the names in the query word library, wherein:

[0020] If the keyword does not match the names, then based on the Levenshtein distance, a distance measure between the keyword and the names is obtained, and synonym replacement is performed on the keyword according to the distance measure, wherein:

[0021] If the demand keyword matches the name after synonym replacement, then according to the attribute of the name, the demand keyword is determined to be the query area or the query agricultural industry;

[0022] If the demand keyword does not match the name after synonym replacement, it is determined that the demand keyword is not the query area or the query agricultural industry.

[0023] Further, when obtaining agricultural data and disaster data of the query area according to the query area, it includes:

[0024] The agricultural data of the query area include the planting distribution, planting area, price trend, growth factors, input cost data, economic benefit data and infrastructure distribution and quantity of each agricultural variety in the query area; the disaster data of the query area include the disaster type, disaster frequency, disaster scale, disaster scope, disaster loss to each agricultural variety and post-disaster production recovery speed.

[0025] Further, when generating a regional agricultural report of the query area according to the agricultural data and disaster data of the query area, it includes:

[0026] Generate a planting score for the agricultural variety in the query area based on the planting distribution, planting area, price trend, growth factors, input cost data, economic benefit data, and infrastructure distribution and quantity of the agricultural variety; the disaster type, frequency, scale, scope, loss of each agricultural variety caused by the disaster, and post-disaster production recovery speed of the query area:

[0027] ;

[0028] Among them, S planting The planting score of the agricultural varieties in the query area, N is the total number of scoring factors, wi is the weight coefficient of the scoring factor, Si is the actual value of the scoring factor, and the actual value of the scoring factor is the planting distribution, planting area, price trend, growth factor, input cost data, economic benefit data and infrastructure distribution and quantity of the agricultural varieties, the disaster type, disaster frequency, disaster scale, disaster scope, disaster losses to each agricultural variety and the speed of production recovery after the disaster in the query area;

[0029] A regional agricultural report for the query area is generated based on the planting scores of each of the agricultural varieties in the query area.

[0030] Further, when obtaining the regional data and agricultural data of the queried agricultural industry according to the queried agricultural industry, it includes:

[0031] The regional data for the agricultural industry being queried includes the distribution areas of the agricultural industry being queried, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, and the agricultural data for the agricultural industry being queried includes the production cycle, production cost, and economic data of each region of the agricultural industry being queried.

[0032] Further, when generating an agricultural industry data report for querying the agricultural industry according to the regional data and agricultural data of the agricultural industry, the report includes:

[0033] According to the distribution area of ​​the queried agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, the production cycle, production cost, and economic data of each area of ​​the queried agricultural industry, the score of each area of ​​the queried agricultural industry is generated:

[0034] ;

[0035] Wherein, Splanting is the regional score of the agricultural industry, N is the total number of scoring factors, wi is the weight coefficient of the scoring factor, Si is the actual value of the scoring factor, and the actual value of the scoring factor is the distribution area of ​​the queried agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, and the production cycle, production cost, and economic data of each region of the queried agricultural industry;

[0036] The scores of each region of the queried agricultural industry are sorted in positive order, and based on the sorted scores of each region, an agricultural industry data report of the queried agricultural industry is generated.

[0037] Further, according to the relationship between the regional agricultural report and the agricultural industry data report, when determining the industry economic score of the queried agricultural industry in the queried area, it includes:

[0038] Obtaining the planting scores of the agricultural varieties in the queried area and the regional scores of the queried area for the agricultural varieties in the queried agricultural industry;

[0039] According to the planting score and the regional score, the industry economic score of the queried agricultural industry in the queried area is determined:

[0040] H = b1·S+b2·s;

[0041] H is the industry economic score, S is the planting score, s is the regional score, b1 and b2 are weight coefficients, and the sum of b1 and b2 is 1.

[0042] Furthermore, based on the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, when generating a query report, it includes:

[0043] Establishing a correlation based on the query area, query agricultural industry, regional agricultural report, agricultural industry data report and industry economic score;

[0044] The correlation formula is converted into Word, Excel or PDF format, and based on the correlation formula converted into Word, Excel or PDF format using a data visualization tool, a chart of the agricultural indicators of the region is generated for output.

[0045] Compared with the prior art, the beneficial effect of the present invention is that by automatically processing the demand text uploaded by the user, the key information of the query area and the agricultural industry can be accurately extracted, and then the relevant agricultural data and disaster data can be quickly obtained, reducing the cumbersome steps of manual intervention in the traditional method. The method can accurately locate the area and industry queried by the user by extracting the demand keywords, ensure the efficient acquisition and analysis of data, avoid data errors caused by inaccurate information extraction, and thus improve the accuracy and efficiency of agricultural economic analysis. In addition, based on the acquired agricultural data and disaster data, the generated query area agricultural report and agricultural industry data report can fully display the economic status, planting conditions, price trends and other key information of the relevant regions and industries. This report generation method can not only provide a real-time reference basis for agricultural decision-making, but also generate customized reports according to the needs of different users, thereby improving the personalization and flexibility of agricultural management work. Furthermore, by performing correlation analysis on the query area agricultural report and the agricultural industry data report, and combining the industry economic score, a multi-dimensional decision support tool is provided. The introduction of the industry economic score enables agricultural managers to conduct a comprehensive assessment of the agricultural industry from the aspects of overall economic performance, industry development potential, and disaster impact. This scoring mechanism helps managers quickly identify the advantages and potential of regional agricultural development and take timely countermeasures, thereby improving the sustainability and disaster resistance of agricultural production. Finally, the method of the present invention has an efficient data output function and can generate query reports in different formats (such as Word, Excel, PDF, etc.) to meet the usage requirements in different demand scenarios. Compared with traditional report generation methods, this method not only avoids the problem of needing to make different templates for each file format, but also ensures the centralization and operability of data in the report, and improves the ability to share and exchange data. This multi-format output function enables agricultural economic analysis reports to circulate quickly between different departments and different systems, promotes rapid information sharing and collaboration, and improves the overall efficiency of the agricultural management system.

[0046] On the other hand, the present application also provides an electronic map generation system for characteristic agricultural economy, comprising:

[0047] An upload module is configured to obtain a demand text uploaded by a user and obtain demand keywords in the demand text;

[0048] A data collection module is electrically connected to the upload module, and is configured to determine the query area and the query agricultural industry according to the demand keyword, obtain the agricultural data and disaster data of the query area according to the query area, and obtain the regional data and agricultural data of the query agricultural industry according to the query agricultural industry;

[0049] A report generation module is electrically connected to the data acquisition module, and the report generation module is configured to generate a regional agricultural report of the query area according to the agricultural data and disaster data of the query area; the report generation module is also configured to generate an agricultural industry data report of the query agricultural industry according to the regional data and agricultural data of the query agricultural industry;

[0050] An output module is electrically connected to the report generation module, and the output module is configured to determine the industry economic score of the queried agricultural industry in the queried area based on the relationship between the regional agricultural report and the agricultural industry data report; the output module is also configured to generate a query report based on the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, and output the query report.

[0051] It can be understood that the electronic map generation system and method for characteristic agricultural economy in the above-mentioned embodiments of the present invention also have the same beneficial effects in the field of characteristic agricultural industries (such as tea industry, bee industry, Chinese medicinal materials industry, edible fungus industry, etc.), and will not be elaborated. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0053] Figure 1 A flowchart of a method for generating an electronic map for agricultural economy provided by an embodiment of the present invention;

[0054] Figure 2 A functional block diagram of an electronic map generation system for agricultural economy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0056] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a method for generating an electronic map for a characteristic agricultural economy, including:

[0057] Step S100: Obtain the demand text uploaded by the user, and obtain demand keywords in the demand text.

[0058] Specifically, when obtaining the demand text uploaded by the user and the demand keywords in the demand text, the method includes: converting the demand text into a format to be analyzed, and performing word segmentation on the converted demand text. Performing keyword recognition on the segmented words based on the inverse document word frequency. Based on the syntax tree, determining the named entities in the keywords, and determining the named entities as demand keywords.

[0059] It is understandable that by converting the demand text uploaded by the user into an analyzable format. This step usually includes text preprocessing, such as removing irrelevant characters, punctuation marks, etc., so that the text is more standardized and clear in subsequent analysis. The converted text will be segmented, that is, the text is decomposed into multiple independent words or phrases to facilitate the subsequent extraction and analysis of keywords. This process usually adopts the word segmentation method in natural language processing (NLP) technology to ensure that the text in Chinese or other languages ​​can be effectively segmented and processed. Next, keyword identification is performed based on the inverse document frequency (TF-IDF) method. TF-IDF is a commonly used text analysis technology that selects the most representative keywords by calculating the importance of each word in the text. TF-IDF can effectively identify important words in the text by measuring the relationship between the frequency (TF) of a word in a specific text and the rarity (IDF) of the word in the entire corpus, thereby providing a basis for keyword extraction. The core of this process is to select the most informative keywords for the demand text through statistical analysis to ensure accurate understanding of the text. Finally, the accuracy of keyword extraction is further optimized based on the analysis of the syntax tree. Syntax tree analysis is a grammatical analysis method that can identify grammatical structures in text and extract named entities. Named entities usually refer to words with specific meanings, such as place names, people, time, industry names, etc., which can clearly indicate important information in the text. Through syntax tree analysis, we can accurately identify which words belong to named entities and use these named entities as demand keywords, thereby improving the accuracy of demand text analysis.

[0060] It can be seen that by converting the demand text uploaded by users into the format to be analyzed, noise information can be eliminated and effective text preprocessing can be performed, laying a solid foundation for subsequent keyword extraction. By segmenting the text into words, long texts can be decomposed into the smallest operable units, so as to better identify and analyze the core content in the text. This method can ensure the accuracy of text analysis, make subsequent processing more efficient and accurate, and avoid subjective bias and errors in manual processing. Secondly, the inverse document frequency (TF-IDF) method is used for keyword recognition. By calculating the importance of words in the document, high-frequency and representative keywords can be effectively screened out. This can not only improve the accuracy of keyword extraction, but also avoid the interference of irrelevant words on the analysis results. The application of the TF-IDF method can help the system extract the most critical words for demand analysis from a large amount of text, improve the depth and breadth of text understanding, and provide accurate information support for further analysis. Finally, the analysis based on the syntactic tree can further improve the accuracy of keyword extraction, especially when identifying named entities. Named entities usually contain information that is critical to user needs, such as specific regions, industries, time, etc. These key information can be accurately identified through syntax tree analysis. Using named entities as demand keywords not only improves the accuracy of keywords, but also ensures that the analysis results are more in line with the actual needs of users, helping the system to better perform subsequent data queries, report generation, and decision support.

[0061] Step S200: determine the query area and query agricultural industry according to the demand keywords, obtain agricultural data and disaster data of the query area according to the query area, and obtain regional data and agricultural data of the query agricultural industry according to the query agricultural industry.

[0062] Specifically, when determining the query region and the query agricultural industry based on the demand keyword, it includes: pre-establishing a query word library based on the region name and the agricultural industry name. Matching the demand keyword with each name in the query word library, wherein: if the keyword does not match each name, then based on the Levenshtein distance, obtaining the distance measurement between the keyword and each name, and performing synonym replacement on the keyword based on the distance measurement, wherein: if the demand keyword matches the name after synonym replacement, then according to the attribute of the name, determining that the demand keyword is the query region or the query agricultural industry. If the demand keyword does not match the name after synonym replacement, then determining that the demand keyword is not the query region or the query agricultural industry.

[0063] Specifically, when obtaining agricultural data and disaster data of the query area according to the query area, it includes: the agricultural data of the query area includes the planting distribution, planting area, price trend, growth factors, input cost data, economic benefit data and infrastructure distribution and quantity of each agricultural variety in the query area. The disaster data of the query area includes the disaster type, disaster frequency, disaster scale, disaster scope, disaster loss to each agricultural variety and post-disaster production recovery speed of the query area.

[0064] Specifically, when querying the agricultural industry, obtaining regional data and agricultural data of the agricultural industry includes: querying the regional data of the agricultural industry includes querying the distribution areas of the agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area; querying the agricultural data of the agricultural industry includes querying the production cycle, production cost, and economic data of each region of the agricultural industry.

[0065] It is understandable that effective matching of demand keywords is achieved by establishing a query word library. The query word library includes the names of regions and agricultural industries. After receiving the demand text uploaded by the user, the demand keywords are first accurately matched with the names in the word library. If there is no direct match, Levenshtein Distance is used for fuzzy matching to calculate the similarity between the demand keywords and each name. This distance measurement method can quantify the edit distance between two words, thereby evaluating their similarity in spelling. If the demand keyword matches the name after synonym replacement, the keyword is confirmed to be the identifier of the query region or agricultural industry. This process ensures that even if the keywords provided by the user have spelling differences or different expressions are used with the names in the system word library, the user's intention can still be accurately identified. Secondly, the agricultural data and disaster data of the query area ensure the comprehensiveness and multidimensionality of the data by modeling the detailed characteristics of the region. Agricultural data includes data information in multiple dimensions such as the distribution of agricultural varieties, planting area, price trends, etc. in the region. These data can provide support for the planning and forecasting of agricultural production and help analyze the planting environment, input costs, and the potential for economic benefits. Disaster data covers a variety of data such as disaster types, frequencies, scopes, and the extent of losses caused by disasters to agricultural varieties, which can provide agricultural decision makers with real-time disaster warnings and impact analysis. By integrating these complex and diverse data, users can be provided with a more accurate regional agricultural analysis report. Finally, querying regional data of the agricultural industry and obtaining agricultural data further refine the multi-level information of the industry. The regional data of the industry not only covers the proportion of the agricultural industry in various distribution areas, environmental conditions and infrastructure conditions, but also involves employment and economic data in these areas, providing a complete regional economic background for the development of the industry. The agricultural data of the agricultural industry includes key data such as production cycle and production cost, which can help users better understand the economic contribution and production efficiency of various regions in the industry. By combining these different dimensions of data, users can be provided with a comprehensive and in-depth analysis of the agricultural industry, providing strong data support for subsequent decision-making and management.

[0066] It can be seen that through the pre-established query word library of regional names and agricultural industry names, accurate matching can be performed according to the keywords entered by the user, ensuring the rapid and accurate identification of the query region or agricultural industry required by the user. In addition, the use of Levenshtein distance for fuzzy matching, combined with the mechanism of synonym replacement, can effectively handle spelling errors, language differences or different expressions in user input, making the system more intelligent and efficient in handling different user needs. Secondly, by obtaining agricultural data and disaster data in the query area, comprehensive regional agricultural information can be provided to help agricultural managers conduct accurate regional analysis. The agricultural data in the query area involves information at multiple levels, including the planting distribution, planting area, economic benefits, etc. of agricultural varieties, which provides an important basis for agricultural planning, production management and market forecasting. The acquisition of disaster data provides key support for disaster warning, agricultural insurance, loss assessment, etc. By comprehensively considering agricultural and disaster data, a more comprehensive and scientific regional agricultural development report can be provided to decision makers. Finally, based on the regional data and agricultural data of the query agricultural industry, the overall development status of the agricultural industry can be deeply analyzed, and users can obtain detailed data within the industry. For example, regional data of an industry not only provides information about the distribution of the industry in different regions, but also covers employment data and infrastructure construction in various regions, which is crucial for understanding the economic vitality, resource allocation and potential analysis of the industry. Agricultural data of the agricultural industry helps users understand information such as production cycles, cost control and regional economic development.

[0067] Step S300: Generate a regional agricultural report for the query area based on the agricultural data and disaster data of the query area.

[0068] Specifically, when generating a regional agricultural report for the query area based on the agricultural data and disaster data of the query area, it includes: planting distribution, planting area, price trend, growth factors, input cost data, economic benefit data, and infrastructure distribution and quantity of agricultural varieties. The type of disaster, frequency, scale, scope, loss of each agricultural variety caused by the disaster, and production recovery speed after the disaster in the query area are used to generate the planting score of the agricultural varieties in the query area:

[0069] .

[0070] Among them, Splanting is the planting score of agricultural varieties in the query area, N is the total number of scoring factors, wi is the weight coefficient of the scoring factor, Si is the actual value of the scoring factor, and the actual value of the scoring factor is the planting distribution, planting area, price trend, growth factor, input cost data, economic benefit data and infrastructure distribution and quantity of agricultural varieties, the type of disaster, frequency of disasters, scale of disasters, scope of disasters, losses of various agricultural varieties caused by disasters, and the speed of production recovery after disasters in the query area. According to the planting scores of various agricultural varieties in the query area, a regional agricultural report for the query area is generated.

[0071] It is understandable that the planting score (Splanting) of agricultural varieties is calculated by weighted average of multiple scoring factors (such as planting distribution, planting area, price trend, etc. of agricultural varieties). Each scoring factor has a weight coefficient wi, which represents the importance of the factor in the overall score. The actual value of the scoring factor Si is quantified according to specific agricultural data and disaster data, which include the growth environment, economic benefits, etc. of agricultural varieties, as well as the type, frequency, and scale of disasters. Through such a multi-dimensional weighted scoring model, the planting situation of agricultural varieties in the query area can be evaluated more comprehensively and accurately. Secondly, the selection and weight allocation process of scoring factors involves normalization and weighted optimization of various types of data. This means that different types of data (such as economic benefits, planting area, infrastructure, etc.) need to be converted through unified standards so that their influence in the score can be reasonably compared and merged. For example, growth factors and price trends may directly affect the economic benefits of agricultural varieties, while the distribution of infrastructure affects the basic conditions of agricultural production. In this way, different types of agricultural data are converted into comparable scoring factors, thereby ensuring that the generated agricultural variety planting scores can reflect the comprehensive planting situation. Finally, the inclusion of disaster data makes the scoring system more dynamic and accurate. Agricultural production is not only affected by the natural environment, but the inclusion of disaster factors takes into account unpredictable risk factors, which is particularly important for the evaluation of agricultural varieties. For example, factors such as the frequency, scale and recovery speed of disasters can significantly affect the growth cycle and economic benefits of crops. Adding disaster data to the scoring factors can further enhance the authenticity and practicality of the scoring results, and help decision makers understand the growth potential and risks of different agricultural varieties in specific areas. Through such a comprehensive analysis, the agricultural report generated for the query area can provide more valuable data support for agricultural management.

[0072] Step S400: Generate an agricultural industry data report for querying the agricultural industry based on the regional data and agricultural data for querying the agricultural industry.

[0073] Specifically, when generating an agricultural industry data report based on querying the regional data and agricultural data of the agricultural industry, it includes: generating scores for each region of the agricultural industry based on querying the distribution area of ​​the agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, and querying the production cycle, production cost, and economic data of each region of the agricultural industry:

[0074] .

[0075] Among them, Splanting is the regional score of the agricultural industry, N is the total number of scoring factors, wi is the weight coefficient of the scoring factor, Si is the actual value of the scoring factor, and the actual value of the scoring factor is the distribution area of ​​the query agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, and the production cycle, production cost, and economic data of each region of the query agricultural industry. The scores of each region in the query agricultural industry are sorted in positive order, and based on the sorted scores of each region, the agricultural industry data report for querying the agricultural industry is generated.

[0076] It is understandable that based on multi-dimensional agricultural industry data, a comprehensive scoring model is used to score each distribution area, thereby generating regional scores for the agricultural industry. These scoring factors include regional distribution of the agricultural industry, planting proportion, disaster data, planting environment, employment data, infrastructure conditions, and other factors, and each factor has a weight coefficient wi. The actual value Si of the scoring factor is obtained through quantitative analysis of various agricultural data, which covers the production cycle, production cost, and economic data of various regions in the agricultural industry. Through this multi-dimensional data analysis, the impact of various factors on the agricultural industry is comprehensively considered to form scores for each region, which can fully reflect the development status of the agricultural industry in different regions. Secondly, the weighted average method in the scoring model enables different types of data to be compared and aggregated in a unified scoring system. Different scoring factors have different importance. For example, planting proportion, production cycle, and production cost may have a greater impact on the economic benefits of the agricultural industry, while disaster data and planting environment may have more impact on the sustainability of the industry. By assigning appropriate weights to each scoring factor, it is ensured that the scoring results can truly reflect the overall performance of the agricultural industry in different regions. In addition, the selection of scoring factors and the weight allocation process need to be reasonably designed according to the characteristics of the industry and the relevance of the data to ensure that the generated scores have high accuracy and practicality. Finally, the application of the sorting mechanism further enhances the value of the data report. When generating an agricultural industry data report to query the agricultural industry, by sorting the scores of each region in ascending order, it is possible to intuitively show which regions have outstanding performance in the development of the agricultural industry and which regions have potential for improvement. This scoring-based sorting method not only makes the report have scientific scoring results, but also provides targeted guidance for decision makers, thereby playing an effective supporting role in policy making, resource allocation, and other aspects.

[0077] Step S500: Determine the industry economic score of the agricultural industry in the query area based on the relationship between the regional agricultural report and the agricultural industry data report.

[0078] Specifically, according to the relationship between the regional agricultural report and the agricultural industry data report, the industry economic score of the queried agricultural industry in the queried area is determined, including: obtaining the planting score of the agricultural variety in the queried area and the regional score of the queried area of ​​the agricultural variety in the queried agricultural industry. According to the planting score and the regional score, the industry economic score of the queried agricultural industry in the queried area is determined: H=b1·S+b2·s. H is the industry economic score, S is the planting score, s is the regional score, b1 and b2 are weight coefficients, and the sum of b1 and b2 is 1.

[0079] It can be understood that by combining the planting score in the regional agricultural report and the regional score in the agricultural industry data report, the economic performance of the agricultural industry in the query area can be evaluated from multiple dimensions. The planting score (S) reflects the performance of agricultural varieties in the region, while the regional score (s) evaluates the agricultural industry potential of the region from a wider range of regional characteristics (such as environment, economy, etc.). By weighting the two scores, the information of the two can be effectively integrated to give a more comprehensive industry economic score (H) to more accurately reflect the overall situation of the industry economy. Secondly, this section uses a weighted average to integrate the scores from different sources. The weight coefficients b1 and b2 respectively represent the importance of the planting score and the regional score in the final industry economic score. By setting the sum of b1 and b2 to 1, the balance of the weight coefficients is ensured, and the influence of each scoring factor can be flexibly adjusted. For example, if the planting factor is more important in a specific region, the weight of b1 can be adjusted accordingly to strengthen the impact of the planting score on the industry economic score. Through this weighting mechanism, the calculation of the score can be adjusted according to the specific situation to ensure that the score result is consistent with the actual situation. Finally, this industry economic evaluation method based on weighted scoring has strong adaptability. For different agricultural industries or different regions, their economic performance may be affected by different factors. By reasonably setting the weights of the scoring factors, the system can be adjusted according to the characteristics of specific regions and industries, so that the scoring model can adapt to various changing environments and needs. At the same time, this method not only provides quantitative scoring results, but also provides decision makers with evaluation results with practical reference significance, helping them make more targeted decisions in resource allocation, policy formulation, etc.

[0080] Step S600: Generate a query report based on the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, and output the query report.

[0081] Specifically, based on the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, when generating the query report, it includes: establishing a correlation based on the query region, the query agricultural industry, the regional agricultural report, the agricultural industry data report and the industry economic score. The correlation is converted into Word, Excel or PDF format, and based on the data visualization tool and the correlation converted into Word, Excel or PDF format, a chart of the regional agricultural indicators is generated for output.

[0082] It is understandable that a comprehensive database model is formed by establishing the association between regional agricultural reports, agricultural industry data reports and industry economic scores. The construction of this association enables the data to be connected to each other to form a logically clear relationship structure. Based on this structure, relevant information can be automatically retrieved according to the user's query requirements, and this information can be combined to generate targeted and systematic query reports. Secondly, the output format after the report is generated supports a variety of common document formats (such as Word, Excel and PDF), which means that it can not only meet the needs of different users, but also be effectively viewed and edited on multiple platforms and devices. The generated report is not only a display of static text information, but also can dynamically generate corresponding charts based on the data in the association. This process relies on the application of data visualization tools, so that the key information in the report can be intuitively presented in a graphical way, thereby improving the efficiency of data understanding and the accuracy of decision-making. Finally, by combining data visualization with document format conversion, the generated query report has both comprehensive information and good user interactivity and display effect. The generation of data visualization charts helps to display complex data relationships, such as the growth trends of agricultural varieties, the agricultural economic conditions of a region, etc., so that the report is not just a pure text file, but also a comprehensive data display tool to help decision makers make more scientific decisions in agricultural management, policy making, etc.

[0083] In the above embodiment, by automatically processing the demand text uploaded by the user, the key information of the query area and the agricultural industry can be accurately extracted, and then the relevant agricultural data and disaster data can be quickly obtained, reducing the cumbersome steps of manual intervention in the traditional method. The method can accurately locate the area and industry queried by the user by extracting the demand keywords, ensuring the efficient acquisition and analysis of data, avoiding data errors caused by inaccurate information extraction, thereby improving the accuracy and efficiency of agricultural economic analysis. In addition, based on the acquired agricultural data and disaster data, the generated query area agricultural report and agricultural industry data report can fully display the economic status, planting conditions, price trends and other key information of the relevant regions and industries. This report generation method can not only provide real-time reference for agricultural decision-making, but also generate customized reports according to the needs of different users, thereby improving the personalization and flexibility of agricultural management work. Further, by performing correlation analysis on the query area agricultural report and agricultural industry data report, and combining the industry economic score, a multi-dimensional decision support tool is provided. The introduction of the industry economic score enables agricultural managers to conduct a comprehensive assessment of the agricultural industry from the aspects of overall economic performance, industry development potential, and disaster impact. This scoring mechanism helps managers quickly identify the advantages and potential of regional agricultural development and take timely countermeasures, thereby improving the sustainability and disaster resistance of agricultural production. Finally, the method of the present invention has an efficient data output function and can generate query reports in different formats (such as Word, Excel, PDF, etc.) to meet the usage requirements in different demand scenarios. Compared with traditional report generation methods, this method not only avoids the problem of needing to make different templates for each file format, but also ensures the centralization and operability of data in the report, and improves the ability to share and exchange data. This multi-format output function enables agricultural economic analysis reports to circulate quickly between different departments and different systems, promotes rapid information sharing and collaboration, and improves the overall efficiency of the agricultural management system.

[0084] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides an electronic map generation system for characteristic agricultural economy, including: an upload module, a data collection module, a report generation module and an output module.

[0085] Specifically, the upload module is configured to obtain the demand text uploaded by the user and obtain the demand keywords in the demand text. The data acquisition module is electrically connected to the upload module, and the data acquisition module is configured to determine the query area and the query agricultural industry according to the demand keywords, obtain the agricultural data and disaster data of the query area according to the query area, and obtain the regional data and agricultural data of the query agricultural industry according to the query agricultural industry. The report generation module is electrically connected to the data acquisition module, and the report generation module is configured to generate a regional agricultural report of the query area according to the agricultural data and disaster data of the query area. The report generation module is also configured to generate an agricultural industry data report of the query agricultural industry according to the regional data and agricultural data of the query agricultural industry. The output module is electrically connected to the report generation module, and the output module is configured to determine the industry economic score of the query agricultural industry in the query area according to the relationship between the regional agricultural report and the agricultural industry data report. The output module is also configured to generate a query report according to the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, and output the query report.

[0086] It is understandable that an electronic map generation system and method for characteristic agricultural economy also has the same beneficial effects in the field of characteristic agricultural industries (such as tea industry, bee industry, Chinese medicinal materials industry, edible fungus industry, etc.), which will not be elaborated.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for generating an electronic map for a characteristic agricultural economy, characterized in that: include: Obtain the demand text uploaded by the user, and obtain the demand keywords in the demand text; Determine the query area and query agricultural industry according to the demand keywords, obtain agricultural data and disaster data of the query area according to the query area, and obtain regional data and agricultural data of the query agricultural industry according to the query agricultural industry; Generate a regional agricultural report for the query area based on the agricultural data and disaster data of the query area; Generate an agricultural industry data report for querying the agricultural industry based on the regional data and agricultural data of the agricultural industry; Determine the industry economic score of the queried agricultural industry in the queried area according to the relationship between the regional agricultural report and the agricultural industry data report; According to the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, a query report is generated and the query report is output.

2. The method for generating an electronic map for characteristic agricultural economy according to claim 1, characterized in that: Obtaining the demand text uploaded by the user and obtaining the demand keywords in the demand text includes: Convert the demand text into a format to be analyzed, and perform word segmentation on the converted demand text; Perform keyword recognition on several words after segmentation based on the inverse document word frequency; Based on the syntax tree, named entities in several of the keywords are determined, and the named entities are determined as the required keywords.

3. The method for generating an electronic map for characteristic agricultural economy according to claim 2, characterized in that: According to the demand keywords, determine the query area and query the agricultural industry, including: Pre-establish a query word library based on the region name and agricultural industry name; The required keywords are matched with the names in the query word library, wherein: If the keyword does not match the names, then based on the Levenshtein distance, a distance measure between the keyword and the names is obtained, and synonym replacement is performed on the keyword according to the distance measure, wherein: If the demand keyword matches the name after synonym replacement, then according to the attribute of the name, the demand keyword is determined to be the query area or the query agricultural industry; If the demand keyword does not match the name after synonym replacement, it is determined that the demand keyword is not the query area or the query agricultural industry.

4. The method for generating an electronic map for characteristic agricultural economy according to claim 3, characterized in that: When obtaining agricultural data and disaster data of the query area according to the query area, it includes: The agricultural data of the query area include the planting distribution, planting area, price trend, growth factors, input cost data, economic benefit data and infrastructure distribution and quantity of each agricultural variety in the query area; the disaster data of the query area include the disaster type, disaster frequency, disaster scale, disaster scope, disaster loss to each agricultural variety and post-disaster production recovery speed.

5. The method for generating an electronic map for characteristic agricultural economy according to claim 4, characterized in that: When generating a regional agricultural report of the query area according to the agricultural data and disaster data of the query area, it includes: Generate a planting score for the agricultural variety in the query area based on the planting distribution, planting area, price trend, growth factors, input cost data, economic benefit data, and infrastructure distribution and quantity of the agricultural variety; the disaster type, frequency, scale, scope, loss of each agricultural variety caused by the disaster, and post-disaster production recovery speed of the query area: ; Among them, S planting The planting score of the agricultural varieties in the query area, N is the total number of scoring factors, wi is the weight coefficient of the scoring factor, Si is the actual value of the scoring factor, and the actual value of the scoring factor is the planting distribution, planting area, price trend, growth factor, input cost data, economic benefit data and infrastructure distribution and quantity of the agricultural varieties, the disaster type, disaster frequency, disaster scale, disaster scope, disaster losses to each agricultural variety and the speed of production recovery after the disaster in the query area; A regional agricultural report for the query area is generated based on the planting scores of each of the agricultural varieties in the query area.

6. The method for generating an electronic map for characteristic agricultural economy according to claim 1, characterized in that: When obtaining the regional data and agricultural data of the queried agricultural industry according to the queried agricultural industry, it includes: The regional data for the agricultural industry being queried includes the distribution areas of the agricultural industry being queried, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, and the agricultural data for the agricultural industry being queried includes the production cycle, production cost, and economic data of each region of the agricultural industry being queried.

7. The method for generating an electronic map for characteristic agricultural economy according to claim 6, characterized in that: According to the query of the regional data and agricultural data of the agricultural industry, when generating the agricultural industry data report for querying the agricultural industry, it includes: According to the distribution area of ​​the queried agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, the production cycle, production cost, and economic data of each area of ​​the queried agricultural industry, the score of each area of ​​the queried agricultural industry is generated: ; Wherein, Splanting is the regional score of the agricultural industry, N is the total number of scoring factors, wi is the weight coefficient of the scoring factor, Si is the actual value of the scoring factor, and the actual value of the scoring factor is the distribution area of ​​the queried agricultural industry, the planting proportion of each distribution area, the disaster data of each distribution area, the planting environment of each distribution area, the employment data of each distribution area, the infrastructure and quantity of each distribution area, and the production cycle, production cost, and economic data of each region of the queried agricultural industry; The scores of each region of the queried agricultural industry are sorted in positive order, and based on the sorted scores of each region, an agricultural industry data report of the queried agricultural industry is generated.

8. The method for generating an electronic map for characteristic agricultural economy according to claim 1, characterized in that: Determining the industry economic score of the queried agricultural industry in the queried area according to the relationship between the regional agricultural report and the agricultural industry data report includes: Obtaining the planting scores of the agricultural varieties in the queried area and the regional scores of the queried area for the agricultural varieties in the queried agricultural industry; According to the planting score and the regional score, the industry economic score of the queried agricultural industry in the queried area is determined: H = b1·S+b2·s; H is the industry economic score, S is the planting score, s is the regional score, b1 and b2 are weight coefficients, and the sum of b1 and b2 is 1.

9. The method for generating an electronic map for characteristic agricultural economy according to claim 1, characterized in that: According to the relationship between the regional agricultural report, agricultural industry data report and industry economic score, when generating a query report, it includes: Establishing a correlation based on the query area, query agricultural industry, regional agricultural report, agricultural industry data report and industry economic score; The correlation formula is converted into Word, Excel or PDF format, and based on the correlation formula converted into Word, Excel or PDF format using a data visualization tool, a chart of the agricultural indicators of the region is generated for output.

10. An electronic map generation system for characteristic agricultural economy, using the electronic map generation method for characteristic agricultural economy as claimed in any one of claims 1 to 9, characterized in that: include: An upload module is configured to obtain a demand text uploaded by a user and obtain demand keywords in the demand text; A data collection module is electrically connected to the upload module, and is configured to determine the query area and the query agricultural industry according to the demand keyword, obtain the agricultural data and disaster data of the query area according to the query area, and obtain the regional data and agricultural data of the query agricultural industry according to the query agricultural industry; A report generation module, electrically connected to the data acquisition module, and configured to generate a regional agricultural report of the query area according to the agricultural data and disaster data of the query area; The report generation module is further configured to generate an agricultural industry data report for querying the agricultural industry based on the regional data and agricultural data for querying the agricultural industry; An output module is electrically connected to the report generation module, and the output module is configured to determine the industry economic score of the queried agricultural industry in the queried area based on the relationship between the regional agricultural report and the agricultural industry data report; the output module is also configured to generate a query report based on the relationship between the regional agricultural report, the agricultural industry data report and the industry economic score, and output the query report.

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