A dynamic adjustment system and method for agricultural product technical service popularization

Abstract

This application provides a dynamic adjustment system and method for the promotion of agricultural product technology services, comprising: analyzing the potential impact of a categorized set of environmental changes on acceptance criteria using a decision tree algorithm to determine the impact weight distribution; extracting evaluation data from historical service records under similar activities for the preliminary adaptability criteria, calculating deviation values ​​using a regression algorithm to obtain deviation correction coefficients; updating the preliminary adaptability criteria using the deviation correction coefficients to generate dynamic acceptance criteria for differences in the external environment and activities; extracting key indicators, such as health, survival, or quality thresholds, from the dynamic acceptance criteria, determining the applicability of the criteria if the indicators match current farmer data, and obtaining the final promotion criteria; and obtaining real-time farmer feedback data based on the final promotion criteria, and determining the effect evaluation results by integrating the feedback data with the standard indicators.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a dynamic adjustment system and method for promoting agricultural product technology services. Background Technology

[0002] In the process of agricultural modernization, the promotion of agricultural technology services has irreplaceable value in improving agricultural production efficiency and farmers' income. This field is not only an important pillar for ensuring food security, but also a key link in promoting rural economic development. However, the promotion of technology services is not always smooth sailing. How to adapt the service content to the needs of different regions and environments has become a pressing practical problem that needs to be solved.

[0003] Current methods for extending technical services often face a common dilemma: a lack of flexibility in responding to changes in the external environment. Many solutions, when formulating service acceptance standards, neglect the impact of uncontrollable factors in agricultural production, such as sudden weather changes or fluctuations in market demand. This results in overly rigid standards that fail to align with actual production scenarios. More importantly, this approach fails to adequately consider the unique characteristics of different types of agricultural activities, often using uniform indicators to measure the effectiveness of diverse technical services in planting, breeding, and processing, leading to a disconnect between evaluation results and actual needs. Against this backdrop, core technical challenges have gradually emerged, primarily manifested in the insufficient adaptability of the acceptance standards. As a crucial basis for evaluating the effectiveness of technical services, if these standards are not adjusted promptly to changes in external conditions, some regions or farmers may fail to achieve their goals in special circumstances, thereby impacting their confidence and participation in technology promotion. A deeper issue lies in the significant differences in the emphasis placed on acceptance standards across different agricultural activities. For example, planting focuses on crop health, animal husbandry emphasizes animal survival, and processing stresses product quality. This diversity necessitates that standards be tailored to specific needs; otherwise, evaluation biases may occur, affecting the fairness and effectiveness of the promotion efforts. Therefore, how to develop acceptance standards that can adapt to changes in the external environment and reflect the unique characteristics of different agricultural activities during the promotion of technical services has become a critical issue that urgently needs to be addressed. This problem is particularly prominent in actual operations. For example, in years with frequent extreme weather events, farmers may experience a decline in yield due to force majeure. If the acceptance standards are not flexibly adjusted, the technical service may be deemed a failure, thereby discouraging farmers' enthusiasm for participation. These interconnected issues, ranging from the complexity of the external environment to the adaptability of acceptance standards, and the diverse needs of different agricultural activities, together constitute the core challenges in the promotion of technical services, requiring new ideas and methods to address them. Summary of the Invention

[0004] This invention provides a dynamic adjustment system and method for the promotion of agricultural product technology services, mainly comprising: External environmental data for the current region, including weather records and market demand indicators, are obtained from a pre-set agricultural database. This data is then grouped using a clustering algorithm to obtain a categorized set of environmental changes. Based on the classified set of environmental changes, a decision tree algorithm is used to analyze its potential impact on the acceptance criteria and determine the impact weight distribution. Obtain data on the type of specific agricultural activity, such as planting, breeding, or processing. By comparing the distribution of influence weights with the activity type data, determine if the weight distribution exceeds a preset threshold, then adjust the standard parameters to obtain a preliminary adaptive standard. Based on the initial adaptability criteria, evaluation data from similar activities were extracted from historical service records, and a regression algorithm was used to calculate the deviation value to obtain the deviation correction coefficient. The initial adaptability criteria are updated by the deviation correction factor to generate dynamic acceptance criteria for differences in the external environment and activities; Key indicators, such as health, survival, or quality thresholds, are extracted from the dynamic acceptance criteria. If the indicators match the current farmer data, the applicability of the criteria is confirmed, and the final promotion criteria are obtained. Based on the final promotion standards, real-time farmer feedback data is obtained, and the effectiveness evaluation results are determined by integrating the feedback data with the standard indicators.

[0005] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a dynamic adjustment system and method for agricultural product technology service promotion. Addressing the adaptability issues of acceptance standards arising from changes in the external environment, fluctuations in market demand, and differences in farmer feedback during agricultural activities, this invention proposes a dynamic adjustment mechanism. By acquiring weather records and market demand indicators, this invention uses a clustering algorithm to group environmental data, combines this with a decision tree algorithm to analyze the influence weights on acceptance standards, and adjusts standard parameters according to the type of agricultural activity to form preliminary adaptability standards. Subsequently, a regression algorithm is used to calculate the deviation correction coefficient of historical data, update the standards, generate dynamic acceptance standards, and confirm applicability through key indicator matching. Finally, real-time farmer feedback is integrated to determine the effect evaluation results. The core innovation of this invention lies in achieving dynamic adjustment of standards through the fusion of multiple algorithms, ensuring a high degree of alignment between standards and the environment, activities, and feedback, thereby improving the accuracy and practicality of promotion services and contributing to the scientific management and sustainable development of agricultural production. Attached Figure Description

[0006] Figure 1 This is a flowchart of a dynamic adjustment system and method for promoting agricultural product technology services according to the present invention.

[0007] Figure 2 This is a schematic diagram of a dynamic adjustment system and method for promoting agricultural product technology services according to the present invention.

[0008] Figure 3 This is another schematic diagram of a dynamic adjustment system and method for promoting agricultural product technology services according to the present invention. Detailed Implementation

[0009] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] like Figures 1-3 This embodiment of a dynamic adjustment system and method for promoting agricultural product technology services may specifically include: Step S101: Obtain the external environmental data of the current region from the preset agricultural database, including weather records and market demand indicators. Group these data using a clustering algorithm to obtain a classified set of environmental changes.

[0011] The system acquires external environmental data for the current region through a pre-defined agricultural database interface, including weather records and market demand indicators, to obtain an initial environmental information set. Based on this initial set, a clustering algorithm is used to group the weather records and market demand indicators, determining the categorized environmental change types. For each categorized environmental change type, the system obtains the weather record characteristics and market demand indicator characteristics, assessing the differences between categories. If the weather record characteristics and market demand indicator characteristics of a certain category deviate from a pre-defined threshold, it is marked as an anomalous category. From the marked anomalous categories, the corresponding weather records and market demand indicator data are extracted to identify the key influencing factors within these categories. Based on these key influencing factors, the system analyzes the correlation between the anomalous categories and the current region's environmental change set, obtaining the impact weight of the anomalous categories on the overall environmental change. Using these impact weights, the data distribution priority within the categorized environmental change types is adjusted to obtain an optimized environmental change set. For the optimized environmental change set, corresponding classification labels are generated and stored in the pre-defined agricultural database, completing the data update process.

[0012] For example, in a smart wheat planting scenario, the system first retrieves multidimensional data of the current planting area through a preset interface. This includes not only weather records such as rainfall, air humidity, and soil pH over seven consecutive days, but also market demand indicators such as the volatility of wheat futures prices in the region and the purchasing intention index of downstream flour mills. This raw data constitutes the initial environmental information set. Subsequently, clustering algorithms such as K-means or DBSCAN are used to map these heterogeneous data into a high-dimensional feature space. The algorithm automatically identifies the similarities between data, grouping data with "high temperature and low rainfall with stable market demand" into one category and data with "low temperature and high humidity with surging demand" into another, thereby determining different categories of environmental changes. This processing method can transform the originally chaotic agricultural big data into a statistically significant structured pattern, laying the foundation for subsequent precise analysis.

[0013] Specifically, based on the classification results, the system further calculates the feature vector for each category. For example, if a category shows rainfall exceeding 50mm for three consecutive days while market demand for high-quality wheat decreases by 20%, the system compares the feature value of that category with a preset historical average threshold. If the calculated deviation value reaches 0.75, exceeding the 0.5 warning line, the category is marked as an anomaly. This process aims to quickly capture atypical situations such as extreme weather events or severe market fluctuations, preventing them from being overwhelmed by regular data.

[0014] In one possible implementation, for the "flood-sluggish sales" category marked as anomalous, the system extracts key influencing factors using principal component analysis. The analysis might show that soil moisture saturation is the dominant factor causing this anomaly, contributing 85%, while temperature changes contribute only 10%. Based on this, the system analyzes the correlation between this anomaly category and the overall crop growth cycle in the current region, assigning it a high influence weight, such as 0.9. This means that in subsequent decision-making models, this anomaly will be considered a core variable.

[0015] Understandably, based on the aforementioned weights, the system reconstructs the environmental change dataset. The original chronological data distribution is adjusted, and high-weighted outlier categories are prioritized for processing, resulting in an optimized environmental change dataset. Finally, the system generates classification labels such as "Severe Flood Risk - Market Downturn" and stores this optimized dataset and labels in the agricultural database. The technical benefit of this approach is that when users query again or the system issues disaster warnings, it can prioritize retrieving the most valuable key risk data, significantly improving the timeliness and accuracy of agricultural production guidance.

[0016] Step S102: Based on the classified set of environmental changes, use the decision tree algorithm to analyze its potential impact on the acceptance criteria and determine the impact weight distribution.

[0017] By initially organizing the categorized data on environmental changes, key change factors are identified. Using pre-established screening rules, a set of primary change factors is determined. For this set, a decision tree algorithm is used for in-depth analysis, constructing a correlation model between each factor and the acceptance criteria to obtain a preliminary impact weight distribution. Based on this distribution, change factors with higher weights are identified. Combining this with the threshold conditions for standard evaluation, if a factor's weight exceeds a preset threshold, it is marked as a key focus, creating a list of factors requiring priority handling. For this list of key factors, historical change records are extracted from a data analysis perspective, and statistical tools are used to calculate their specific impact on the acceptance criteria, determining the impact assessment results. Based on these assessments, the actual impact of each factor on the acceptance criteria is obtained. Using the logic of weight calculation, if a factor's impact degree is inconsistent with the weight distribution, a secondary analysis is performed to obtain an adjusted impact ranking. Based on this adjusted ranking and the characteristics of the factor distribution, an optimization suggestion list for the acceptance criteria is generated, determining the final processing priority sequence.

[0018] For example, in the process of agricultural environmental data processing, the initial sorting stage will extract core variables from massive meteorological observations and agricultural product transaction records.

[0019] For example, regarding fluctuations in rainfall, sunlight intensity, and wholesale market purchase prices in specific planting areas, pre-defined screening rules are used to eliminate minor daily random fluctuations, retaining only indicators with trend-based impacts, thus forming a set of key changing factors. This process ensures that subsequent analysis can focus on the key variables that truly affect output quality and market returns.

[0020] In one possible implementation, decision tree algorithms are used to perform in-depth modeling of these key factors.

[0021] For example, historical meteorological data can be used as the input node, and the agricultural product acceptance pass rate as the target leaf node. By calculating the information gain, it can be found that when the temperature consistently exceeds 38 degrees Celsius and the soil moisture is below 20%, the pass rate of the acceptance standard will decrease significantly. This correlation model can quantify the contribution of each factor to the acceptance result, thereby deriving a preliminary distribution of influence weights and providing data support for management decisions.

[0022] For example, when identifying key areas of focus, the system compares the weight distribution with preset evaluation thresholds. If the export order volume weight in the market demand indicator reaches 0.45, exceeding the warning threshold of 0.3, this factor is marked as a key area of ​​focus. By extracting the historical data of this factor over the past three years, statistical tools are used to analyze its impact on the price protection clauses in the acceptance criteria. If the analysis shows that for every 10% decrease in order volume, the price reduction rate at acceptance increases by 15%, then the actual impact of this factor is confirmed as critical. If, at this point, the factor is found to be ranked only fourth in the initial weight distribution, which is inconsistent with its high sensitivity to final returns, the system immediately conducts a secondary analysis. This process may introduce market volatility as a correction parameter to reassess its weight value, thereby obtaining an adjusted impact ranking to ensure that high-risk factors are placed in a more prominent position.

[0023] Specifically, based on the adjusted impact ranking and the distribution characteristics of each factor over time, an optimization suggestion list for the acceptance criteria is generated. For situations involving market demand fluctuations, the suggestion list might include locking in guaranteed purchase agreements for specific months or adjusting planting cycles to avoid demand troughs. The final determined priority sequence will guide agricultural managers to prioritize addressing high-weight market matching issues, followed by secondary environmental fine-tuning issues. This data-driven dynamic adjustment mechanism not only improves the pass rate of the acceptance criteria but also significantly enhances the adaptability of agricultural production to complex environmental changes, ensuring maximum output efficiency.

[0024] Step S103: Obtain data on the type of specific agricultural activities, such as planting, breeding, or processing. By comparing the influence weight distribution with the activity type data, determine if the weight distribution exceeds a preset threshold, then adjust the standard parameters to obtain a preliminary adaptive standard.

[0025] By extracting type data from an agricultural activity database, and classifying and organizing it according to planting, animal husbandry, and processing categories, a structured activity dataset is obtained. Based on this structured dataset, the influence weights of each activity category are calculated, and a pre-established weight evaluation model is used to determine the weight distribution for each category. If the weight distribution exceeds a preset threshold, the standard parameters are dynamically adjusted by using a parameter optimization tool to obtain the adjusted parameter values. Based on the adjusted parameter values ​​and the weight distribution of each activity category, a preliminary adaptive standard dataset is generated. Data consistency verification is performed on the preliminary adaptive standard dataset by comparing it with historical data records to determine if there are any abnormal deviations. If abnormal deviations are found during verification, the adaptive standard dataset is locally corrected using a data smoothing method to obtain the final adaptive standard result. By storing and classifying the final adaptive standard results, a standard reference library applicable to different agricultural activity categories is generated.

[0026] In one possible implementation, the structured organization of agricultural activity databases is the cornerstone of building a precise standards system.

[0027] Specifically, the system first needs to identify the characteristic fields in the raw data and divide the mixed agricultural logs into clear business segments.

[0028] For example, records related to fertilizer application and irrigation cycles are categorized under planting, while records related to feed input and livestock density are categorized under livestock farming. Energy consumption and emissions data from agricultural product cleaning and packaging are categorized under processing. This classification not only physically isolates the data but also allows for the establishment of independent analytical dimensions for different categories of environmental characteristics. Through this structured processing, it is possible to clearly quantify whether nitrogen and phosphorus loss from planting activities or organic waste emissions from livestock farming dominate within a specific region, thus providing solid data support for weighting calculations.

[0029] Specifically, the weighted assessment model aims to dynamically capture the sensitivity of various activities to their environmental impact. For example, if the frequency of aquaculture activities increases significantly during a monitoring period, the model calculates its impact weight to be 0.75, while the preset concern threshold is 0.60. At this point, the system determines that this type of activity has become a critical risk source, thus triggering a dynamic adjustment mechanism for standard parameters. The parameter optimization tool does not use general static standards but tightens the key indicators in the acceptance criteria based on the excess weight range.

[0030] For example, the previously permissible limit for ammonia nitrogen emissions was automatically adjusted from 5.0 mg / L to 3.5 mg / L to accommodate the environmental pressures brought about by high-intensity aquaculture activities. This dynamic adjustment mechanism ensures that standard parameters can respond in real time to the actual intensity of agricultural production, avoiding the lag caused by "one-size-fits-all" management.

[0031] For example, after generating the initial adaptation standard, the data consistency verification step is crucial, acting as a "filter" to prevent standard deviation. The system compares the adjusted 3.5 mg / L with historical data from the past five years for the region. If historical records show that this indicator has never fallen below 4.0 mg / L, and the current environmental background value has not changed abruptly, then the sudden drop to 3.5 mg / L may stem from short-term data fluctuations rather than a trend change. In this case, the system determines that there is an abnormal deviation and initiates a local correction procedure. Using data smoothing methods, combined with historical averages and current calculated values, a weighted algorithm corrects the standard value to a more reasonable 3.8 mg / L. This process eliminates the interference of extreme data noise, ensuring that the final adaptation standard reflects current stringent control requirements while maintaining technical feasibility. The resulting classification standard reference library can then provide scientific and stable guidance for different types of agricultural activities.

[0032] Step S104: Based on the preliminary adaptability standard, extract evaluation data from similar activities in historical service records, use a regression algorithm to calculate the deviation value, and obtain the deviation correction coefficient.

[0033] By extracting data from historical service data for similar activities, we obtain the original dataset related to the evaluation metrics, establishing a preliminary analytical foundation. Based on the extracted original dataset, we compare the correlation between similar activities and the evaluation metrics, using regression algorithms to process deviation analysis and obtain the distribution of deviation values. Starting from the distribution of deviation values, we calculate correction coefficients based on the deviation analysis results, determining whether they meet a preset threshold range. If they exceed the threshold range, we re-extract service data to adjust the deviation values, obtaining the adjusted deviation values. Based on the adjusted deviation values ​​and the calculation logic of the correction coefficients, we perform a secondary verification of the accuracy of the calculation results, obtaining the final correction coefficient value. Using the final correction coefficient value, we correct the deviations in the historical service data, determining the corrected evaluation metric dataset. We obtain the corrected evaluation metric dataset, combine it with the comparison results of similar activities, and perform data mapping to assess the stability of the algorithm application, obtaining the final deviation correction model. Based on the final deviation correction model, we store the corrected evaluation metric dataset according to the subsequent service data processing logic, determining the available deviation correction reference.

[0034] For example, in the scenario of refining agricultural service data, extracting service data from historical records is the first step in building the analytical foundation.

[0035] For example, for a specific agricultural activity like wheat cultivation, the system will sift through a massive historical database to find raw datasets relevant to the current evaluation indicators, such as yield records per acre over the past five years, soil moisture data, and corresponding agricultural inputs. This process ensures that subsequent analysis is based on the characteristics of similar business operations, providing reliable data support for accurate evaluation.

[0036] Specifically, after acquiring the raw dataset, the system uses regression algorithms to conduct in-depth analysis of the correlations between similar activities. Assuming that there is a non-linear relationship between irrigation frequency and final yield in certain areas during past planting seasons, regression analysis will reveal the distribution of this deviation, thereby quantifying the difference between actual service data and theoretical models and identifying potential drift trends in the data.

[0037] In one possible implementation, based on the aforementioned deviation distribution, the system calculates a correction coefficient to calibrate the data. If the calculated deviation value shows that the deviation of an evaluation indicator for a certain type of service reaches 0.15, the system will further determine whether the value is within a preset threshold range of 0.05 to 0.20. Once the deviation value is found to exceed this range, such as due to abnormal fluctuations in historical data caused by sudden extreme weather, the system will automatically trigger a re-extraction mechanism to remove extreme noise and re-acquire the adjusted deviation value. This dynamic adjustment mechanism effectively avoids analytical distortion caused by a single data source or occasional events, ensuring the robustness of the correction logic.

[0038] Understandably, secondary verification is necessary to ensure the rigor of the correction coefficients. The system combines the adjusted deviation values ​​with the correction logic to verify the accuracy of the calculation results, ultimately determining a final correction coefficient value such as 0.98 or 1.02. Subsequently, this value is used to globally correct historical service data, generating a corrected assessment index dataset. This step not only eliminates systematic errors in historical data but also, through data mapping technology, solidifies the corrected data into the final deviation correction model. This model is stored in the system's core database as a standard reference for subsequent processing of similar agricultural service data, thereby significantly improving the stability and predictive accuracy of agricultural production assessments and providing a reusable digital benchmark for the scientific guidance of agricultural activities.

[0039] Step S105: Update the preliminary adaptability standard by the deviation correction coefficient to generate a dynamic acceptance standard for differences in the external environment and activities.

[0040] The process involves acquiring relevant data on the external environment and recording information on activity differences. A pre-established data acquisition module extracts real-time environmental parameters and activity change data from multiple sources to obtain an initial environmental and activity dataset. For this initial dataset, a deviation correction coefficient is used to standardize the data. If outliers exceed a preset threshold, they are smoothed to determine the corrected dataset. Using this corrected dataset, the impact of external environmental and activity differences on the initial standards is analyzed. A support vector machine (SVM) algorithm is used to classify the data and identify key influencing factors of environmental and activity changes. Based on these key influencing factors, adjustment parameters for the initial standards are generated. If these adjustment parameters do not match the preset adaptability standard range, new parameter values ​​are generated through iterative calculation to obtain suitable environmental adjustment parameters. The initial standards are dynamically updated using these suitable environmental adjustment parameters to generate intermediate standards that conform to external environmental and activity differences, thus determining the updated standard framework. Based on the updated standard framework and the requirements of the dynamic acceptance standards, the final acceptance standards are generated. If the final standards deviate from the preset target values, the intermediate standards are adjusted through data backtracking to obtain the final applicable dynamic acceptance standards. Based on the final applicable dynamic acceptance criteria, standardized results are output for different environments and activities. The results data are saved through the data storage module, completing the entire process of standard adaptation.

[0041] In one possible implementation, for maintenance services of large electromechanical equipment, a pre-established data acquisition module acquires relevant data on the external environment and records information on activity differences in real time. The acquisition module extracts real-time environmental parameters, such as an ambient temperature of 35.5 degrees Celsius, and activity change data, such as fluctuations in maintenance duration, from sensors and operation logs to obtain an initial environmental and activity dataset.

[0042] It should be noted that the initial data may contain abnormal fluctuations. In this case, the data is standardized using the deviation correction coefficient obtained earlier. If a temperature data point is detected to suddenly change to 99.9 degrees, exceeding the preset normal threshold range, the abnormal value is smoothed using normal data from adjacent time points to determine the corrected data set.

[0043] For example, using the corrected dataset, the system analyzes the impact of external environmental and activity differences on the initial maintenance acceptance criteria. A support vector machine algorithm is used to classify the data, mapping the multi-dimensional environmental and activity data to a high-dimensional space to find the optimal hyperplane, identifying high temperature as a key factor leading to increased maintenance time. Based on the classified key factors, the system generates adjustment parameters for the initial criteria. If the generated time adjustment parameter is an increase of 0.5 hours, but this value does not match the preset adaptability standard range, the weights are continuously fine-tuned through iterative calculations to generate new parameter values, ultimately obtaining a suitable environmental adjustment parameter, such as an increase of 0.3 hours.

[0044] In one possible implementation, the initial maintenance acceptance criteria are dynamically updated by adjusting parameters to adapt to the environment, generating intermediate criteria that conform to the current external environment and activity differences. Based on the updated standard framework and the requirements of the dynamic acceptance criteria, the final acceptance criteria are generated. If the equipment operational stability target value in the final criteria deviates from the preset target value, a data backtracking mechanism is used to re-examine the weighting of environmental parameters and adjust the intermediate criteria to obtain the final applicable dynamic acceptance criteria. Based on the final applicable dynamic acceptance criteria, standardized results for different temperature and humidity environments and maintenance activity differences are output. The results data are saved through a data storage module, completing the entire process of standard adaptation and effectively improving the accuracy of service assessment.

[0045] Step S106: Extract key indicators, such as health, survival, or quality thresholds, from the dynamic acceptance criteria. If the indicators match the current farmer data, confirm the applicability of the criteria and obtain the final promotion criteria.

[0046] The specific value range of acceptance indicators is obtained from a pre-set dynamic standard database, covering key aspects such as health status, survival rate, and quality benchmarks. A comparison algorithm is used to determine the completeness of the extracted indicators, resulting in a preliminary set of indicators. For this preliminary set, corresponding field values ​​from farmer data are obtained. Field mapping technology is used to map farmer data to the indicator set one-to-one, determining for missing or abnormal data. Missing data is marked as needing completion, resulting in matched data pairs. Based on the matched data pairs, the deviation ratio between indicator values ​​and farmer data values ​​is calculated. If the deviation ratio exceeds a preset threshold, it is marked as inconsistent; otherwise, it is marked as consistent, determining the matching status between the indicators and the data. Based on the analysis of the matching status, consistent indicator combinations are obtained. A logical filtering method is used to remove inconsistent indicators, resulting in a subset of indicators applicable to the current farmers. For this subset, a template framework for the extension specifications is obtained. The indicator subset is embedded into the template, and an automatic filling technique is used to form a preliminary draft of the extension specifications. Based on the initial draft of the promotion guidelines, similar scenario data from historical promotion cases are obtained. By comparing the execution effects of similar scenarios, the feasibility of the subset of indicators in the draft is determined, resulting in the final confirmed promotion guidelines. The final confirmed promotion guidelines are then entered into the system database using data storage technology, automatically generating corresponding promotion execution plans and determining the basis for implementing the promotion guidelines.

[0047] Specifically, the process of obtaining acceptance indicators from the dynamic standard database is not just a simple reading of numerical values, but an in-depth analysis of dimensions such as health status, survival rate, and quality benchmarks, aiming to build a comprehensive and instructive indicator system.

[0048] For example, in the context of promoting aquaculture, the system extracts specific indicators such as "shrimp body color redness value" as a quality benchmark and "seedling survival rate of 85% to 90%" as a range for the survival rate. By comparing these indicators with algorithms, the system ensures that the subsequently generated specifications will not have loopholes at the implementation level due to the lack of key parameters (such as disease resistance indicators), thus guaranteeing the rigor of the standards.

[0049] Understandably, field mapping technology acts as a bridge connecting theoretical standards with actual production data. The system maps the initially selected set of indicators one by one with the production logs actually uploaded by farmers.

[0050] For example, the "daily feed conversion rate" field in the standard is mapped to the "feeding amount and weight gain records" in the farmer's data. If a record regarding "water transparency" is missing from the farmer's data, the system immediately marks it as needing to be completed. This mechanism effectively avoids decision-making errors caused by data blind spots, ensuring the integrity and usability of the data pairs. Based on the matched data pairs, the system further calculates the deviation ratio between the indicator value and the farmer's actual data. Assuming the standard requires a water pH of 7.5 to 8.0, while the farmer's historical data shows that it has consistently remained at 7.8, the deviation is minimal, and the indicator is marked as consistent. If the farmer's "average yield per acre" is far below the lower limit required by the standard, and the deviation ratio exceeds a preset 20% threshold, the indicator will be judged as inconsistent and removed. This logical screening method ensures that the final subset of indicators meets both high standard requirements and is achievable by farmers under current production conditions through effort, greatly improving the applicability of the standard and the farmer's acceptance.

[0051] In one possible implementation, embedding the filtered subset of metrics into the promotion specification template is achieved through automation. This not only improves efficiency but also ensures the consistency of the specification format. More importantly, the system will call similar scenario data from historical promotion cases for secondary verification.

[0052] For example, the system retrieves data showing that excessively high stocking densities have led to frequent disease outbreaks under similar seasonal and water temperature conditions. Therefore, even if the index deviates from the acceptable range in the initial calculation, the system will still issue warnings or make adjustments to the draft's operability based on historical lessons. Once the finalized promotion specifications are entered into the system, the automatically generated promotion implementation plan will no longer be just a piece of paper, but will be an action guide containing specific implementation criteria such as "a certain dissolved oxygen level must be reached in the third week," achieving a closed loop from standard setting to implementation.

[0053] Step S107: Based on the final promotion standards, obtain real-time farmer feedback data, and determine the effect evaluation results by integrating the feedback data with the standard indicators.

[0054] By collecting farmers' opinions in real time, a feedback data collection process was established to obtain an initial dataset. Based on this initial dataset, data cleaning tools were used to remove invalid records, resulting in a processed feedback dataset. For this processed dataset, a pre-established indicator system was invoked for data comparison to determine whether it met the promotion standards. If the data comparison results showed deviations between the feedback dataset and the standard indicators, a classification algorithm was used to group the deviation data and determine the deviation category. Based on the deviation category, corresponding standard indicator adjustment rules were obtained to generate matching evaluation criteria. A second comparison between the evaluation criteria and the feedback dataset yielded the final effect evaluation conclusion. For the effect evaluation conclusion, storage tools were used to save the analysis records, forming a traceable data archive.

[0055] Specifically, during the real-time collection of farmers' opinions, the system obtains farmers' feedback on the planting of new varieties through mobile terminals.

[0056] For example, during the promotion of a new corn variety, 500 sets of raw data on emergence rate and disease resistance were collected. These data included farmers' subjective evaluations and actual measured values.

[0057] In one embodiment, the data cleaning tool preprocesses these records.

[0058] For example, if a record shows a germination rate of 150 or a negative number, or if the geographic coordinates are missing, it is considered an invalid record and is removed. After cleaning, 480 valid feedback data records were ultimately retained, ensuring the accuracy of subsequent analysis.

[0059] In one embodiment, the processed feedback dataset is compared with a preset promotion indicator system.

[0060] For example, the standard requires that the survival rate of new varieties in arid regions should reach above 92.0%. If the feedback data shows that the actual survival rate is only 88.5%, the deviation judgment logic is triggered.

[0061] Specifically, classification algorithms analyze the causes of bias.

[0062] For example, by correlating meteorological data, it was discovered that the region experienced extreme high temperatures during the growing season, resulting in a failure to meet survival standards. In this case, the deviation was categorized as an environmental anomaly, rather than a variety quality issue.

[0063] In one embodiment, the corresponding standard indicator adjustment rule is invoked based on the deviation category.

[0064] For example, in response to environmental anomalies, the system automatically retrieves correction coefficients for extreme weather conditions, dynamically adjusting the survival rate threshold from 92.0% to 85.0%. This method generates assessments that are more consistent with actual production environments.

[0065] Specifically, the adjusted indicators are compared with the feedback data a second time to arrive at the final assessment conclusion that the variety performs well under extreme weather conditions.

[0066] In one embodiment, all analysis records and evaluation conclusions are fully preserved.

[0067] For example, the system records the complete logical chain from the initial 88.5% survival rate to the final qualification assessment, including the correction coefficients used and the classification criteria. These data archives provide a scientific decision-making reference for subsequent promotion in similar climate regions, achieving traceability of the promotion process.

[0068] If the technical solution of this application involves the collection, storage, use, processing, transmission, provision, disclosure, or deletion of personal information, the products using this technical solution have clearly and understandably informed the users of the personal information processing rules before processing personal information, and have obtained the individuals' voluntary consent in accordance with the law. If the technical solution of this application involves sensitive personal information (such as biometrics, religious beliefs, specific identities, medical and health information, financial accounts, and location tracking), the products using this solution have obtained the individuals' separate consent before processing sensitive personal information, and have also met the requirement of "express consent," ensuring that individuals make authorization decisions voluntarily based on full knowledge.

[0069] Specific implementation methods include, but are not limited to, the following: setting up clear and prominent signs at personal information collection devices such as cameras and sensors to inform relevant personnel that they have entered the scope of personal information collection and that their personal information will be collected and processed. If an individual voluntarily enters the collection scope after being informed, it is deemed that they have agreed to the collection of their personal information; or using obvious icons, text descriptions, or other means on the terminal device or system interface for personal information processing to inform them of the rules for personal information processing, and obtaining the individual's explicit authorization through interactive methods such as pop-up prompts, check confirmation boxes, or asking the individual to upload their personal information themselves.

[0070] The aforementioned personal information processing rules should include, but are not limited to, the name and contact information of the personal information processor, the specific purpose of personal information processing, the processing method, the types of personal information processed, the retention period, and the methods and procedures for individuals to exercise their relevant rights.

[0071] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A dynamic adjustment system and method for promoting agricultural product technology services, characterized in that, The method includes: External environmental data for the current region, including weather records and market demand indicators, are obtained from a pre-set agricultural database. This data is then grouped using a clustering algorithm to obtain a categorized set of environmental changes. Based on the classified set of environmental changes, a decision tree algorithm is used to analyze its potential impact on the acceptance criteria and determine the impact weight distribution. Obtain data on the type of specific agricultural activity, such as planting, breeding, or processing. By comparing the distribution of influence weights with the activity type data, determine if the weight distribution exceeds a preset threshold, then adjust the standard parameters to obtain a preliminary adaptive standard. Based on the initial adaptability criteria, evaluation data from similar activities were extracted from historical service records, and a regression algorithm was used to calculate the deviation value to obtain the deviation correction coefficient. The initial adaptability criteria are updated by the deviation correction factor to generate dynamic acceptance criteria for differences in the external environment and activities; Key indicators, such as health, survival, or quality thresholds, are extracted from the dynamic acceptance criteria. If the indicators match the current farmer data, the applicability of the criteria is confirmed, and the final promotion criteria are obtained. Based on the final promotion standards, real-time farmer feedback data is obtained, and the effectiveness evaluation results are determined by integrating the feedback data with the standard indicators.

2. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The process involves acquiring current regional external environmental data from a pre-set agricultural database, including weather records and market demand indicators, and grouping this data using a clustering algorithm to obtain a categorized set of environmental changes, including: By using a pre-defined agricultural database interface, external environmental data for the current region is obtained, including weather records and market demand indicators, to obtain an initial set of environmental information. Based on the initial set of environmental information, a clustering algorithm is used to group weather records and market demand indicators to determine the categories of environmental changes after classification. For each category of environmental change, the weather record characteristics and market demand index characteristics are obtained to determine the differences between categories. If the weather record characteristics and market demand index characteristics of a certain category deviate from the preset threshold, it is marked as an abnormal category. Extract corresponding weather records and market demand index data from the marked anomaly categories to identify the key influencing factors within the anomaly categories; Based on key influencing factors, the correlation between anomaly categories and the current set of environmental changes in the region is analyzed to obtain the impact weight of anomaly categories on overall environmental changes; By influencing the weights, the priority of data distribution in the classified environmental change categories is adjusted to obtain an optimized set of environmental changes. For the optimized set of environmental changes, corresponding classification labels are generated and stored in a pre-set agricultural database to complete the data update process.

3. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The step of analyzing the potential impact of the categorized environmental changes on the acceptance criteria using a decision tree algorithm, and determining the impact weight distribution, includes: By initially organizing the classified data of environmental changes, key change factors are identified, and pre-established screening rules are used to determine the main set of change factors. For the set of main changing factors, a decision tree algorithm is used for in-depth analysis to construct a correlation model between each factor and the acceptance criteria, and to obtain a preliminary distribution of influence weights. Based on the preliminary impact weight distribution, the factors with higher weights are identified. Combined with the threshold conditions of the standard evaluation, if the weight of a certain factor exceeds the preset threshold, it is marked as a key focus object, and a list of factors that need to be prioritized is determined. For the list of key factors, extract their historical change records from a data analysis perspective, use statistical tools to calculate their specific impact on the acceptance criteria, and determine the results of the impact assessment. Based on the results of the effect assessment, the actual impact of each factor on the acceptance criteria is obtained. Combined with the logic of weight calculation, if the impact of a certain factor is inconsistent with the weight distribution, a secondary analysis is performed to obtain the adjusted impact ranking. By adjusting the impact ranking and combining the characteristics of factor distribution, an optimization suggestion list for the acceptance criteria is generated, and the final processing priority sequence is determined.

4. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The process of acquiring data on specific agricultural activities, such as planting, breeding, or processing, involves comparing the influence weight distribution with the activity type data. If the weight distribution exceeds a preset threshold, the standard parameters are adjusted to obtain a preliminary adaptability standard, including: By extracting type data from the agricultural activity database, and classifying and organizing it according to planting, breeding and processing categories, a structured activity dataset is obtained; Based on the structured activity dataset, the influence weight of each category of activity is calculated, and a pre-established weight evaluation model is used to determine the weight distribution of each category. If the weight distribution exceeds the preset threshold, the standard parameters are dynamically adjusted by calling the parameter optimization tool to obtain the adjusted parameter values. Based on the adjusted parameter values ​​and the weight distribution of each category of activities, a preliminary adaptive standard dataset is generated. For the initial adaptive standard dataset, a data consistency check is performed by comparing it with historical data records to determine if there are any abnormal deviations. If abnormal deviations are found during the verification, the adaptive standard dataset is locally corrected and a data smoothing method is used to obtain the final adaptive standard result. By storing and classifying the final adaptive standard results, a standard reference library applicable to different categories of agricultural activities is generated.

5. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The preliminary adaptability standard involves extracting evaluation data from similar activities in historical service records, using a regression algorithm to calculate the deviation value, and obtaining the deviation correction coefficient, including: By extracting data from service history records for similar activities, we can obtain raw datasets related to evaluation metrics and establish a preliminary analytical basis. Based on the extracted original dataset, activities were compared to assess the correlation between similar activities and evaluation indicators. A regression algorithm was used to process the deviation analysis and obtain the distribution of the deviation values. Based on the distribution of deviation values, a correction coefficient is calculated according to the results of the deviation analysis to determine whether it meets the preset threshold range. If it exceeds the threshold range, the service data is re-extracted to adjust the number of deviation values ​​and obtain the adjusted number of deviation values. Based on the adjusted deviation value and the calculation logic of the correction coefficient, a second verification is performed on the accuracy of the result calculation to obtain the final correction coefficient value. By using the final correction coefficient values, bias corrections are applied to the service data in the historical records to determine the corrected evaluation index dataset. Obtain the corrected evaluation index dataset, combine it with the comparison results of similar activities, perform data mapping on the stability of algorithm application, and obtain the final bias correction model; Starting from the final deviation correction model, based on the processing logic of subsequent service data, the corrected evaluation index dataset is stored, and the deviation correction reference basis that can be called is determined.

6. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The process of updating the initial adaptability criteria through deviation correction coefficients to generate dynamic acceptance criteria for differences in the external environment and activities includes: Acquire relevant data and activity difference records of the external environment, and extract real-time environmental parameters and activity change data from multiple sources through a pre-established data acquisition module to obtain an initial environmental and activity dataset; For the initial environment and activity dataset, a deviation correction coefficient is used to standardize the data. If outliers in the data are detected to exceed a preset threshold, the outliers are smoothed to determine the corrected dataset. By using the revised dataset, we analyze the impact of differences in the external environment and activities on the preliminary standards, and use the support vector machine algorithm to classify the data and identify the key influencing factors of environmental and activity changes. Based on the key influencing factors after classification, adjustment parameters for the preliminary standard are generated. If the adjustment parameters do not match the preset adaptability standard range, new parameter values ​​are generated through iterative calculation to obtain the appropriate environmental adjustment parameters. By adjusting parameters to adapt to the environment, the initial standards are dynamically updated to generate intermediate standards that conform to the differences in the external environment and activities, and the updated standard framework is determined. Based on the updated standard framework and the requirements of the dynamic acceptance standard, the final acceptance standard is generated. If the final standard deviates from the preset target value, the intermediate standard is adjusted through data backtracking to obtain the final applicable dynamic acceptance standard. Based on the final applicable dynamic acceptance criteria, standardized results are output for different environments and activities. The results data are saved through the data storage module, completing the entire process of standard adaptation.

7. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The process involves extracting key indicators from dynamic acceptance standards, such as health, survival, or quality thresholds. If these indicators match current farmer data, the applicability of the standard is confirmed, leading to the final promotion standard. This includes: The specific value range of the acceptance indicators is obtained from the preset dynamic standard database, covering key contents such as health status, survival rate and quality benchmark. The completeness of the extracted indicators is determined by the comparison algorithm, and a preliminary set of indicators is obtained. For the initially selected set of indicators, the corresponding field values ​​in the farmer data are obtained. Field mapping technology is used to match the farmer data with the set of indicators one by one. It is determined whether there is any missing or abnormal data. If there is any missing data, it is marked as to be completed, and the matched data pairs are obtained. Based on the matched data pairs, the deviation ratio between the indicator value and the farmer's data value is calculated. If the deviation ratio exceeds the preset threshold, it is marked as inconsistent; otherwise, it is marked as consistent, thus determining the matching status between the indicator and the data. By analyzing the matching status, we obtain a combination of indicators with consistent labels, and use a logical filtering method to remove inconsistent indicators, thus obtaining a subset of indicators suitable for the current farmers. For the subset of indicators applicable to current farmers, obtain a template framework for the extension specifications, embed the subset of indicators into the template, and form a preliminary draft of the extension specifications through automatic filling technology; Based on the preliminary draft of the promotion guidelines, similar scenario data from historical promotion cases are obtained. By comparing the execution effects of similar scenarios, it is determined whether the subset of indicators in the draft is operable, and the final confirmed promotion guidelines are obtained. Once the promotion guidelines are finalized, they are entered into the system database using data storage technology, automatically generating corresponding promotion execution plans and determining the basis for implementing the promotion guidelines.

8. The dynamic adjustment system and method for agricultural product technology service promotion according to claim 1, characterized in that, The process involves obtaining real-time farmer feedback data based on the final promotion standards, and then integrating this feedback data with the standard indicators to determine the effectiveness evaluation results, including: By collecting farmers' opinions in real time, a feedback data collection process was established to obtain an initial dataset; Based on the initial dataset, invalid records are removed using data cleaning tools to obtain the processed feedback dataset; For the processed feedback dataset, a pre-established indicator system is invoked to compare the data and determine whether it meets the promotion standards. If the data comparison results show that the feedback dataset deviates from the standard indicator, the deviation data is grouped by a classification algorithm to determine the deviation category; Based on the deviation category, obtain the corresponding standard indicator adjustment rules and generate matching evaluation criteria; By comparing the evaluation criteria with the feedback dataset twice, the final effect evaluation conclusion is obtained; Based on the results of the effectiveness evaluation, storage tools are used to save the analysis records and form a traceable data archive.

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