A cultivated land spatial layout optimization management system and method based on multi-source data fusion

Abstract

The application discloses a cultivated land spatial layout optimization management system and method based on multi-source data fusion, relates to the cultivated land spatial optimization technical field, and acquires cultivated land patch data, irrigation water source data and ecological protection elements in an evaluation area; analyzes basic characteristics of each cultivated land patch; collects cultivated land change data in a historical period, and constructs a training data set containing stable cultivated land and changed cultivated land; analyzes the weight of each basic characteristic based on a stable fitting model; based on each real-time basic characteristic after processing and the weight of each basic characteristic, weighted summation is carried out, and the optimization comprehensive score of each cultivated land patch is calculated; the classification threshold of the optimization comprehensive score is determined according to an administrator-selected threshold analysis method; the cultivated land patch is divided according to the classification threshold, and an optimization attribute list is generated and sent to the administrator. The subjectivity of the uncertainty of weighting is avoided, and the accuracy of system analysis is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cultivated land space optimization, and in particular to a cultivated land space layout optimization management system and method based on multi-source data fusion. BACKGROUND

[0002] Cultivated land is the fundamental guarantee of national food security and important agricultural product supply, and is a strategic resource supporting sustainable economic and social development. Scientifically optimizing cultivated land space layout is an important link to coordinate the relationship between food security and economic and social development and ecological protection, promote the overall revitalization of rural areas, and is an inevitable requirement to implement the most strict cultivated land protection system, improve the comprehensive productivity of cultivated land, and adapt to the development of agricultural modernization.

[0003] Existing technologies rely on qualitative analysis and expert experience decision-making, and lack objective and quantitative decision-making support systems. When determining the importance of different characteristics, the weight is usually determined by methods such as analytic hierarchy process that rely on expert subjective scoring. This method is easily affected by personal preferences and knowledge limitations of experts, resulting in a strong subjective color in weight distribution, and the conclusions obtained by different regions or different expert teams differ greatly, making it difficult to ensure the scientificity and universality of the planning scheme.

[0004] Therefore, the present application discloses a cultivated land space layout optimization management system and method based on multi-source data fusion to solve the above problems. SUMMARY

[0005] The present application aims to provide a cultivated land space layout optimization management system and method based on multi-source data fusion to solve the problems in the prior art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a cultivated land space layout optimization management method based on multi-source data fusion, which comprises the following steps:

[0007] S1: obtaining cultivated land patch data, irrigation water source data and ecological protection elements in the evaluation area; analyzing the basic characteristics of each cultivated land patch;

[0008] S2: collecting historical cultivated land change data, constructing a training data set containing stable cultivated land and changing cultivated land; analyzing the weight of each basic characteristic based on a stable fitting model;

[0009] S3: based on the processed real-time basic characteristics and the weight of each basic characteristic, performing weighted summation to calculate the optimized comprehensive score of each cultivated land patch;

[0010] S4: determining the classification threshold of the optimized comprehensive score according to the threshold analysis method selected by the administrator; dividing the cultivated land patches according to the classification threshold to generate an optimized attribute list and send it to the administrator.

[0011] According to the above scheme, in S1, the following is included:

[0012] S101: Obtain cultivated land patch data in the evaluation area, the cultivated land patch data including geometric shape, spatial position, cultivated land area and cultivated land quality and other attribute values of the cultivated land patch; obtain irrigation water source data and ecological protection elements; the irrigation water source data including spatial positions of various irrigation water sources; the irrigation water sources including canal systems, river channels, water distribution outlets and reservoirs; the ecological protection elements including ecological protection red lines;

[0013] S102: Analyze the basic characteristics of each cultivated land patch;

[0014] For each cultivated land patch, all cultivated land patches with a cultivated land patch spacing less than a search distance threshold are extracted to form a potential contiguous cultivated land patch set of each cultivated land patch; the cultivated land patch spacing is equal to the shortest Euclidean distance between the polygon boundaries of two cultivated land patches, and if the polygon boundaries of two cultivated land patches intersect or contact, the cultivated land patch spacing is equal to zero; the cultivated land areas of all cultivated land patches in the contiguous cultivated land patch set are summed up, and the sum is recorded as a contiguous expansion potential; the contiguous expansion potentials of all cultivated land patches are normalized;

[0015] The cultivated land quality attribute values of all cultivated land patches in the evaluation area are extracted, the cultivated land quality attribute values are normalized, and the difference between one and the normalized cultivated land quality attribute value is recorded as a quality improvement potential;

[0016] The optimal irrigation distance of each cultivated land patch to the nearest irrigation water source is analyzed; the optimal irrigation distance is equal to the shortest Euclidean distance between the polygon boundary of the cultivated land patch and the irrigation water source; the optimal irrigation distance is normalized, and the difference between one and the normalized optimal irrigation distance is recorded as an irrigation guarantee potential;

[0017] For each cultivated land patch, the overlapping relationship with the ecological protection red line is judged by spatial overlay analysis; if the cultivated land patch overlaps with the ecological protection red line, the ecological restriction strength of the cultivated land patch is recorded as zero; otherwise, the ecological restriction strength of the cultivated land patch is recorded as one.

[0018] The present application comprehensively considers cultivated land patches, irrigation water sources and ecological protection elements and other multi-source data, ensures the integrity and representativeness of feature extraction, and provides reliable input for subsequent analysis; through the quantification of contiguous expansion potential, quality improvement potential and other indicators, the abstract spatial relationship is converted into a calculable feature, and the operability of the method is enhanced; the feature values are normalized, the dimension influence is eliminated, and the comparison and fusion of different features are more fair and accurate.

[0019] According to the above scheme, in S2: the following is included:

[0020] S201: Extract the cultivated land change data within the preset historical time window, identify the cultivated land patches that have always remained as high-quality cultivated land within the preset historical time window as positive samples, and identify the cultivated land patches that have been abandoned, converted into construction land, or severely degraded as negative samples; meanwhile, extract the basic feature values corresponding to the beginning of the preset time window of the cultivated land patches to form a training data set, denoted as D trian ={(X i i )|i∈[1,I]};wherein I represents the total number of cultivated land patches in the training data set; X i represents the feature vector of the i-th cultivated land patch in the training data set; X i =(contig i , qual i , irrig i , eco i ); wherein contig i represents the contiguous expansion potential of the i-th cultivated land patch; qual i represents the quality improvement potential of the i-th cultivated land patch; irrig i represents the irrigation guarantee potential of the i-th cultivated land patch; eco i represents the ecological restriction intensity of the i-th cultivated land patch; Y i represents the label value of the i-th cultivated land patch, wherein the label value of the positive sample is 1; the label value of the negative sample is 0;

[0021] S202: Construct a stable fitting model based on the training data set, and the specific formula of the stable fitting model is:

[0022] Ln(p / (1-p))=β0+β1×contig i +β2×qual i +β3×irrig i +β4×eco i ;

[0023] wherein p represents the predicted label value of the cultivated land patch, β0 represents the intercept term, β1, β2, β3, and β4 represent the fitting coefficients; the stable fitting model is trained using the maximum likelihood estimation method, and the final value of the fitting coefficient is generated through the optimization algorithm, so that the difference between the predicted label value and the true label value based on the feature vector of the stable fitting model is minimized;

[0024] S203: Normalize the absolute value of the final value of the fitting coefficient, analyze the weight of each basic feature, and the weight of each basic feature is equal to the ratio of the normalized value of the absolute value of the corresponding final value of the fitting coefficient to the sum of the normalized values of the absolute values of the final values of the fitting coefficients.

[0025] ​The application trains a model by using historical cultivated land stability data, automatically determines feature weights by fitting coefficients, avoids the uncertainty of subjective weighting, and improves the scientificity and repeatability of the weights; the method allows the model to be retrained as new data is updated, so that the weights can adapt to regional conditions and time changes, and has dynamic adjustment capability.

[0026] According to the above scheme, in S3, the following is included:

[0027] S301: Obtain real-time basic features of each cultivated land patch, and perform forward transformation and extreme value standardization processing on each real-time basic feature of the cultivated land patch.

[0028] S302: Based on the processed each real-time basic feature and the weight of each basic feature, weighted summation is performed to calculate the optimized comprehensive score of each cultivated land patch, and the optimized comprehensive score of the jth cultivated land patch is recorded as LOSS j ; LOSS j =W c ×contig j +W q ×qual j +W i ×irrig j +W e ×eco j ;

[0029] Wherein, W c , W q , W i and W e are the weights of the contiguous expansion potential, the quality improvement potential, the irrigation guarantee potential and the ecological restriction intensity; contig j , qual j , irrig j and eco j are the contiguous expansion potential, the quality improvement potential, the irrigation guarantee potential and the ecological restriction intensity of the jth cultivated land patch.

[0030] The application fuses multiple features into a single score by weighted summation, simplifying the complex decision-making process; the weights come from historical data, but the score is applied to current data, so that the method can inherit historical laws and respond to changes in the present situation.

[0031] According to the above scheme, in S4, the following is included:

[0032] S401: statistics of the distribution of the optimization comprehensive score is counted, and various statistical quantities including mean, median, quantile and sample variance are calculated; the first threshold and the second threshold are determined according to the threshold analysis method selected by the administrator; if the target area method is used, the target total area used and the cumulative logic are recorded; if the expert decision method is used, the expert weight and the decision basis are recorded;

[0033] If the optimization comprehensive score is greater than or equal to the first threshold, the corresponding cultivated land patch is marked as a priority optimization area candidate; if the optimization comprehensive score is less than the first threshold and greater than or equal to the second threshold, the corresponding cultivated land patch is marked as a general optimization area; if the optimization comprehensive score is less than the second threshold, the corresponding cultivated land patch is marked as a status maintenance area; if all optimization comprehensive scores are less than the first threshold, a certain percentage selected by the quantile method is used as a backup; if all optimization comprehensive scores are greater than or equal to the first threshold, the target area method is used to limit the total area of the candidate;

[0034] S402: the number, spatial position and cultivated land area of each cultivated land patch marked as a priority optimization area candidate are extracted; and the optimization attribute list is integrated and sent to the administrator.

[0035] The present application provides various threshold analysis methods (such as the target area method and the expert decision method), which are suitable for different management needs and enhance the practicability and adaptability of the method; when the score distribution is abnormal (such as all being higher or lower than the threshold), a backup method (such as the quantile method) is used to ensure that the partition scheme is always feasible, and the robustness of the method is improved; the accuracy of system analysis is improved.

[0036] In another aspect of the present application, a cultivated land spatial layout optimization management system based on multi-source data fusion is provided, which is applied to the above-mentioned cultivated land spatial layout optimization management method based on multi-source data fusion, and the system comprises a basic feature analysis module, a feature weight analysis module, an optimization comprehensive score analysis module and a cultivated land division module.

[0037] The basic feature analysis module is used to obtain cultivated land patch data, irrigation water source data and ecological protection elements in the evaluation area; and the basic features of each cultivated land patch are analyzed.

[0038] The feature weight analysis module is used to collect historical cultivated land change data, construct a training data set containing stable cultivated land and changed cultivated land; and the weights of various basic features are analyzed based on a stable fitting model.

[0039] The optimization comprehensive score analysis module is used to perform weighted summation based on the processed real-time basic features and the weights of the basic features, and calculate the optimization comprehensive score of each cultivated land patch.

[0040] The cultivated land division module is configured to determine a grading threshold of the optimized comprehensive score according to a threshold analysis method selected by the administrator, divide the cultivated land patches according to the grading threshold, and generate an optimized attribute list and send the list to the administrator.

[0041] According to the above scheme, the basic feature analysis module includes a cultivated land data acquisition unit and a feature analysis unit.

[0042] The cultivated land data acquisition unit is configured to acquire cultivated land patch data in an evaluation area, acquire an irrigation water source set, and acquire an ecological protection element.

[0043] The feature analysis unit is configured to analyze the basic features of each cultivated land patch. The basic features include contiguous expansion potential, quality improvement potential, irrigation guarantee potential, and ecological restriction intensity.

[0044] According to the above scheme, the feature weight analysis module includes a sample acquisition unit, a model fitting unit, and a weight analysis unit.

[0045] The sample acquisition unit is configured to extract cultivated land change data in a preset historical time window, identify cultivated land patches that have always remained high-quality cultivated land in the preset historical time window as positive samples, and identify cultivated land patches that have been abandoned, converted into construction land, or severely degraded as negative samples. Meanwhile, the sample acquisition unit extracts the basic feature values corresponding to the beginning of the preset time window of the cultivated land patches to form a training data set.

[0046] The model fitting unit is configured to build a stable fitting model for the training data set, train the stable fitting model using a maximum likelihood estimation method, and generate a final value of the fitting coefficient through an optimization algorithm, so that the difference between the predicted label value and the true label value of the stable fitting model based on the feature vector is minimized.

[0047] The weight analysis unit is configured to normalize the absolute value of the final value of the fitting coefficient and analyze the weight of each basic feature.

[0048] According to the above scheme, the optimized comprehensive score analysis module includes a real-time data processing unit and a score analysis unit.

[0049] The real-time data processing unit is configured to acquire real-time basic features of each cultivated land patch, and perform positive transformation and extreme value standardization on each real-time basic feature of the cultivated land patch.

[0050] The score analysis unit is configured to perform weighted summation based on the processed real-time basic features and the weight of each basic feature, and calculate the optimized comprehensive score of each cultivated land patch.

[0051] According to the above scheme, the cultivated land division module includes a cultivated land division unit and a list integration unit.

[0052] The cultivated land division unit is used for determining a first threshold value and a second threshold value according to a threshold value analysis method selected by an administrator; and dividing the cultivated land patches based on the first threshold value and the second threshold value.

[0053] The list integration unit is used for extracting the number, spatial position and cultivated land area of each cultivated land patch marked as a priority optimization area candidate; and integrating into an optimization attribute list and sending to the administrator.

[0054] Compared with the prior art, the present application has the beneficial effects that: the present application integrates multi-source data such as cultivated land patches, irrigation water sources and ecological protection elements, ensures the completeness and representativeness of feature extraction, and provides reliable input for subsequent analysis; through the quantification of indexes such as contiguous expansion potential and quality improvement potential, the abstract spatial relationship is converted into a calculable feature, enhancing the operability of the method; the normalization of the feature value eliminates the influence of dimension, making the comparison and fusion of different features more fair and accurate; the present application uses historical cultivated land stability data to train the model, automatically determines the feature weight through the fitting coefficient, avoids the uncertainty of subjective weighting, and improves the scientificity and repeatability of the weight; the method allows the model to be retrained as new data is updated, so that the weight can adapt to regional conditions and time changes, and has dynamic adjustment capability; the present application fuses multiple features into a single score through weighted summation, simplifying the complex decision-making process; the weight comes from historical data, but the score is applied to current data, so that the method can inherit historical laws and respond to present changes; the present application provides multiple threshold value analysis methods, adapts to different management needs, and enhances the practicality and adaptability of the method; when the score distribution is abnormal, the alternative method is used to ensure that the partition scheme is always feasible, improving the accuracy of system analysis. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application together with the embodiments thereof, and explain the present application, but do not limit the present application. In the drawings:

[0056] Figure 1 is a flowchart of a cultivated land spatial layout optimization management method based on multi-source data fusion of the present application;

[0057] Figure 2 is a structural schematic diagram of a cultivated land spatial layout optimization management system based on multi-source data fusion of the present application. DETAILED DESCRIPTION

[0058] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application. Figure 1 The present application provides a technical solution: a cultivated land spatial layout optimization management method based on multi-source data fusion, which comprises the following steps:

[0060] S1: obtaining cultivated land patch data, irrigation water source data and ecological protection elements in an evaluation area; analyzing the basic characteristics of each cultivated land patch;

[0061] In S1, the following contents are included:

[0062] S101: obtaining cultivated land patch data in the evaluation area, the cultivated land patch data including the geometric shape, spatial position, cultivated land area and cultivated land quality of the cultivated land patch; obtaining irrigation water source data and ecological protection elements; the irrigation water source data including the spatial position of each irrigation water source; the irrigation water source including a canal system, a river channel, a water distribution port and a reservoir; the ecological protection elements including an ecological protection red line;

[0063] S102: analyzing the basic characteristics of each cultivated land patch;

[0064] For each cultivated land patch, all cultivated land patches with a cultivated land patch distance less than a search distance threshold are extracted to form a potential contiguous cultivated land patch set of each cultivated land patch; the cultivated land patch distance is equal to the shortest Euclidean distance between the polygon boundaries of two cultivated land patches, and if the polygon boundaries of two cultivated land patches intersect or contact, the cultivated land patch distance is equal to zero; the cultivated land areas of all cultivated land patches in the contiguous cultivated land patch set are summed to form a contiguous expansion potential; the contiguous expansion potentials of all cultivated land patches are normalized;

[0065] The cultivated land quality grades of all cultivated land patches in the evaluation area are extracted, the cultivated land quality grades are normalized, and the difference between one and the normalized cultivated land quality grade is taken as a quality improvement potential;

[0066] The optimal irrigation distances of each cultivated land patch to the nearest irrigation water source are analyzed; the optimal irrigation distance is equal to the shortest Euclidean distance between the polygon boundary of the cultivated land patch and the irrigation water source; the optimal irrigation distance is normalized, and the difference between one and the normalized optimal irrigation distance is taken as an irrigation guarantee potential;

[0067] For each cultivated land parcel, spatial overlay analysis is used to determine its overlap with the ecological protection red line. If the cultivated land parcel overlaps with the ecological protection red line, the ecological restriction intensity of the cultivated land parcel is recorded as zero; otherwise, the ecological restriction intensity of the cultivated land parcel is recorded as one.

[0068] S2: Collect historical data on changes in cultivated land and construct a training dataset containing both stable and changed cultivated land; analyze the weights of each basic feature based on a stable fitting model.

[0069] S2 includes the following:

[0070] S201: Extract farmland change data within a preset historical time window, identify farmland patches that have consistently remained high-quality farmland within the preset historical time window as positive samples, and farmland patches that have been abandoned, converted to construction land, or severely degraded as negative samples; simultaneously, extract the basic feature values ​​corresponding to the farmland patches at the beginning of the preset time window to form a training dataset, denoted as D. trian ={(X i Y i )|i∈[1,I]}; where I represents the total number of cultivated map patches in the training dataset; X i X represents the feature vector of the i-th cultivated patch in the training dataset; i =(contig i qual i irrig i eco i ); where contig i This represents the contiguous expansion potential of the i-th cultivated plot; qual i Indicates the potential for quality improvement of the i-th cultivated plot; irrig i Eco represents the irrigation security potential of the i-th cultivated plot; i Y represents the ecological limitation intensity of the i-th cultivated land patch; i This represents the label value of the i-th cultivated patch, where the label value of a positive sample is 1 and the label value of a negative sample is 0.

[0071] S202: Construct a stable fitting model based on the training dataset. The specific formula for the stable fitting model is:

[0072] Ln(p / (1-p))=β0+β1×contig i +β2×qual i +β3×irrig i +β4×eco i ;

[0073] Wherein, p represents the predicted label value of the cultivated land patch, β0represents the intercept term, β1, β2, β3and β4represent the fitting coefficients; the stable fitting model is trained using the maximum likelihood estimation method, and the final value of the fitting coefficient is generated by the optimization algorithm, so that the stable fitting model minimizes the difference between the predicted label value and the true label value based on the feature vector;

[0074] S203: The absolute value of the final value of the fitting coefficient is normalized, and the weight of each basic feature is analyzed, and the weight of each basic feature is equal to the ratio of the normalized value of the absolute value of the corresponding final value of the fitting coefficient to the sum of the normalized values of the absolute values of the final values of the fitting coefficients.

[0075] S3: Based on the processed real-time basic features and the weights of the basic features, weighted summation is performed, and the optimized comprehensive score of each cultivated land patch is calculated;

[0076] In S3, the following contents are included:

[0077] S301: Obtain the real-time basic features of each cultivated land patch, and perform forward transformation and extreme value standardization on each real-time basic feature of the cultivated land patch;

[0078] S302: Based on the processed real-time basic features and the weights of the basic features, weighted summation is performed, and the optimized comprehensive score of each cultivated land patch is calculated, and the optimized comprehensive score of the jth cultivated land patch is recorded as LOSS j ; LOSS j = W c ×contig j + W q ×qual j + W i ×irrig j + W e ×eco j ;

[0079] Wherein, W c , W q , W i and W e are the weights of the contiguous expansion potential, the quality improvement potential, the irrigation guarantee potential and the ecological restriction intensity; contig j , qual j , irrig j and eco j are the contiguous expansion potential, the quality improvement potential, the irrigation guarantee potential and the ecological restriction intensity of the jth cultivated land patch.

[0080] S4: Determine the classification threshold of the optimized comprehensive score according to the threshold analysis method selected by the administrator; divide the cultivated land patch according to the classification threshold, and generate an optimized attribute list and send it to the administrator.

[0081] In S4, the following is included:

[0082] S401: Statistics of the distribution of the optimization comprehensive score are counted, and various statistical quantities are calculated, including the mean, median, quantile, and sample variance; the first threshold and the second threshold are determined according to the threshold analysis method selected by the administrator; if the target area method is used, the target total area used and the cumulative logic are recorded; if the expert decision method is used, the expert weight and the decision basis are recorded;

[0083] Embodiment 1: In this embodiment, the threshold analysis method includes the target area method, the quantile method, the clustering division method, and the expert decision method;

[0084] The target area method is that the administrator presets the target total area of the priority optimization area, and in the optimization comprehensive score distribution, the first optimization comprehensive score that makes the cumulative area greater than or equal to the target total area is taken as the first threshold according to the cumulative area from high to low, and the second threshold is equal to the optimization comprehensive score corresponding to the secondary target total area;

[0085] The quantile method uses the optimization comprehensive score corresponding to the quantile of the optimization comprehensive score as the threshold, and in this embodiment, the optimization comprehensive scores corresponding to 50% and 90% are used;

[0086] The clustering division method is that the optimization comprehensive scores of all cultivated land patches are subjected to k-means clustering, the classification boundary is determined according to the cluster center and the cluster distance, and finally the highest class is marked as the priority optimization area, the middle class is the general optimization area, and the lowest class is the maintenance area;

[0087] The expert decision method is that the first threshold and the second threshold are determined manually by experts according to the planning target and the social and economic constraints, and the opinions and weight distribution of the expert group are recorded.

[0088] If the optimization comprehensive score is greater than or equal to the first threshold, the corresponding cultivated land patch is marked as a priority optimization area candidate; if the optimization comprehensive score is less than the first threshold and greater than or equal to the second threshold, the corresponding cultivated land patch is marked as a general optimization area; if the optimization comprehensive score is less than the second threshold, the corresponding cultivated land patch is marked as a present situation maintenance area; if all optimization comprehensive scores are less than the first threshold, a certain percentage selected by the quantile method is used as a backup; if all optimization comprehensive scores are greater than or equal to the first threshold, the total area of the candidate is limited by the target area method;

[0089] S402: The number, spatial position, and cultivated land area of each cultivated land patch marked as a priority optimization area candidate are extracted, and are integrated into an optimization attribute list and sent to the administrator.

[0090] See Figure 2The application provides a technical scheme: a cultivated land spatial layout optimization management system based on multi-source data fusion, which comprises a basic feature analysis module, a feature weight analysis module, an optimization comprehensive score analysis module and a cultivated land division module;

[0091] The basic feature analysis module is used for acquiring cultivated land patch data, irrigation water source data and ecological protection elements in an evaluation area and analyzing the basic features of each cultivated land patch.

[0092] The feature weight analysis module is used for collecting cultivated land change data in a historical period, constructing a training data set containing stable cultivated land and changed cultivated land, and analyzing the weights of each basic feature based on a stable fitting model.

[0093] The optimization comprehensive score analysis module is used for performing weighted summation based on each real-time basic feature after processing and the weight of each basic feature, and calculating the optimization comprehensive score of each cultivated land patch.

[0094] The cultivated land division module is used for determining the classification threshold of the optimization comprehensive score according to an administrator-selected threshold analysis method, classifying the cultivated land patches according to the classification threshold, and generating an optimization attribute list and sending the optimization attribute list to the administrator.

[0095] The basic feature analysis module comprises a cultivated land data acquisition unit and a feature analysis unit.

[0096] The cultivated land data acquisition unit is used for acquiring cultivated land patch data in an evaluation area, acquiring an irrigation water source set and ecological protection elements.

[0097] The feature analysis unit is used for analyzing the basic features of each cultivated land patch. The basic features include contiguous expansion potential, quality improvement potential, irrigation guarantee potential and ecological restriction intensity.

[0098] The feature weight analysis module comprises a sample acquisition unit, a model fitting unit and a weight analysis unit.

[0099] The sample acquisition unit is used for extracting cultivated land change data in a preset historical time window, identifying cultivated land patches that have always remained as high-quality cultivated land in the preset historical time window as positive samples, and identifying cultivated land patches that have been abandoned, converted into construction land or seriously degraded as negative samples. Meanwhile, the basic feature values corresponding to the cultivated land patches at the beginning of the preset time window are extracted to form a training data set.

[0100] The model fitting unit is used for constructing a stable fitting model for the training data set, training the stable fitting model by using a maximum likelihood estimation method, and generating a final fitting coefficient value by using an optimization algorithm, so that the difference between the predicted label value and the real label value of the feature vector based on the stable fitting model is minimized.

[0101] The weight analysis unit is configured to normalize the absolute values of the final fitting coefficients, and analyze the weights of the basic features.

[0102] The optimization comprehensive score analysis module comprises a real-time data processing unit and a score analysis unit.

[0103] The real-time data processing unit is configured to acquire real-time basic features of each cultivated land patch, and perform forward transformation and extreme value standardization on each real-time basic feature of the cultivated land patch.

[0104] The score analysis unit is configured to perform weighted summation based on the processed real-time basic features and the weights of the basic features, and calculate the optimization comprehensive scores of the cultivated land patches.

[0105] The cultivated land division module comprises a cultivated land division unit and a list integration unit.

[0106] The cultivated land division unit is configured to determine a first threshold value and a second threshold value according to the threshold value analysis method selected by the administrator, and divide the cultivated land patches based on the first threshold value and the second threshold value.

[0107] The list integration unit is configured to extract the numbers, spatial positions and cultivated land areas of the cultivated land patches marked as candidates of the priority optimization area, and integrate the numbers, spatial positions and cultivated land areas into an optimization attribute list and send the optimization attribute list to the administrator.

[0108] It should be noted that the relational terms such as first and second and the like are used merely to differentiate one entity or action from another, without necessarily requiring or implying any actual such relationship or order between or among the entities or actions. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0109] It will be apparent to those skilled in the art that the present application is not limited to the details of the above-exemplified embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Thus, the embodiments are to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims shall be construed as limiting the claims to the exact elements or steps to which the respective reference signs are given.

Claims

1. A method for optimizing the spatial layout management of arable land based on multi-source data fusion, characterized in that, The method includes the following steps: S1: Obtain farmland patch data, irrigation water source data, and ecological protection elements within the evaluation area; analyze the basic characteristics of each farmland patch; the basic characteristics of farmland patches include the potential for contiguous expansion, the potential for quality improvement, the potential for irrigation security, and the intensity of ecological constraints; The basic characteristics of each cultivated land patch include the following: For each cultivated land patch, extract all cultivated land patches whose spacing is less than the search distance threshold to form a potential contiguous cultivated land patch set for each cultivated land patch; the spacing between cultivated land patches is equal to the shortest Euclidean distance between the polygonal boundaries of two cultivated land patches, and the spacing between cultivated land patches is zero if the polygonal boundaries of two cultivated land patches intersect or contact; sum the cultivated land areas of all cultivated land patches in the contiguous cultivated land patch set, and record it as the contiguous expansion potential; normalize the contiguous expansion potential of all cultivated land patches. Extract the farmland quality grade values ​​of all farmland patches within the evaluation area, normalize the farmland quality grade values, and record the difference between the normalized farmland quality grade value and the normalized farmland quality grade value as the potential for quality improvement. Analyze the optimal irrigation distance from each cultivated land patch to the nearest irrigation water source; the optimal irrigation distance is equal to the shortest Euclidean distance between the polygonal boundary of the cultivated land patch and the irrigation water source; normalize the optimal irrigation distance, and record the difference between the normalized and the normalized optimal irrigation distance as the irrigation guarantee potential. For each cultivated land parcel, spatial overlay analysis is used to determine its overlap with the ecological protection red line; if the cultivated land parcel overlaps with the ecological protection red line, the ecological restriction intensity of the cultivated land parcel is recorded as zero; otherwise, the ecological restriction intensity of the cultivated land parcel is recorded as one. S2: Collect historical data on changes in cultivated land and construct a training dataset that includes both stable and changed cultivated land. The weights of each basic feature are analyzed based on a stable fitting model; In S2: Includes the following: S201: Extract farmland change data within a preset historical time window, identify farmland patches that have consistently remained high-quality farmland within the preset historical time window as positive samples, and farmland patches that have been abandoned, converted to construction land, or severely degraded as negative samples; simultaneously, extract the basic feature values ​​corresponding to the farmland patches at the beginning of the preset time window to form a training dataset, denoted as D. trian ={(X i Y i )|i∈[1,I]}; where I represents the total number of cultivated map patches in the training dataset; X i X represents the feature vector of the i-th cultivated patch in the training dataset; i =(contig) i qual i irrig i eco i ); where contig i This represents the contiguous expansion potential of the i-th cultivated plot; qual i Indicates the potential for quality improvement of the i-th cultivated plot; irrig i Eco represents the irrigation security potential of the i-th cultivated plot; i Y represents the ecological limitation intensity of the i-th cultivated land patch; i This represents the label value of the i-th cultivated patch, where the label value of a positive sample is 1 and the label value of a negative sample is 0. S202: Construct a stable fitting model based on the training dataset. The specific formula for the stable fitting model is: Ln(p / (1-p))=β0+β1×contig i +β2×qual i + β3 × cut i +β4×echo i 4 Where p represents the predicted label value of cultivated land patch, β0 represents the intercept term, and β1, β2, β3 and β4 represent the fitting coefficients; the maximum likelihood estimation method is used to train a stable fitting model, and the final value of the fitting coefficients is generated by the optimization algorithm to minimize the difference between the predicted label value and the true label value based on the feature vector of the stable fitting model. S203: Normalize the absolute values ​​of the final values ​​of the fitting coefficients, and analyze the weights of each basic feature. The weight of each basic feature is equal to the ratio of the normalized value of the absolute value of the corresponding final value of the fitting coefficient to the sum of the normalized values ​​of the absolute values ​​of the final values ​​of the fitting coefficients. S3: Based on the processed real-time basic features and the weights of each basic feature, perform weighted summation to calculate the optimized comprehensive score of each cultivated land patch; S4: Determine the grading threshold for the comprehensive optimization score based on the threshold analysis method selected by the administrator; divide the cultivated land patches according to the grading threshold, generate an optimization attribute list and send it to the administrator.

2. The method for optimizing and managing the spatial layout of cultivated land based on multi-source data fusion according to claim 1, characterized in that: S1 also includes the following: Acquire farmland patch data within the evaluation area, including the geometric shape, spatial location, farmland area, and farmland quality grade of the farmland patches; acquire irrigation water source data and ecological protection elements; the irrigation water source data includes the spatial location of each irrigation water source; the irrigation water sources include canals, rivers, water outlets, and reservoirs; the ecological protection elements include ecological protection red lines.

3. The method for optimizing and managing the spatial layout of cultivated land based on multi-source data fusion according to claim 2, characterized in that: S3 includes the following: S301: Obtain the real-time basic features of each cultivated land patch, and perform positiveization and extreme value standardization processing on each real-time basic feature of the cultivated land patch. S302: Based on the processed real-time basic features and their weights, perform a weighted summation to calculate the optimized comprehensive score for each cultivated land patch. The optimized comprehensive score for the j-th cultivated land patch is denoted as LOSS. j LOSS j =W c ×contig j +W q ×qual j +W i ×irrig j +W e ×eco j ; Among them W c W q W i and W e The weights for contiguous expansion potential, quality improvement potential, irrigation security potential, and ecological constraint intensity are respectively. j qual j irrig j and eco j These represent the contiguous expansion potential, quality improvement potential, irrigation guarantee potential, and ecological limitation intensity of the j-th cultivated land patch, respectively.

4. The method for optimizing and managing the spatial layout of cultivated land based on multi-source data fusion according to claim 3, characterized in that: S4 includes the following: S401: Statistically optimize the distribution of the comprehensive score and calculate various statistics, including the mean, median, quantiles and sample variance; determine the first threshold and the second threshold according to the threshold analysis method selected by the administrator; if the target area method is used, record the total target area and cumulative logic used; if the expert decision method is used, record the expert weights and decision basis. If the comprehensive optimization score is greater than or equal to the first threshold, the corresponding cultivated land patch is marked as a priority optimization candidate; if the comprehensive optimization score is less than the first threshold but greater than or equal to the second threshold, the corresponding cultivated land patch is marked as a general optimization area; if the comprehensive optimization score is less than the second threshold, the corresponding cultivated land patch is marked as a status quo maintenance area; if all comprehensive optimization scores are less than the first threshold, a certain percentage is selected as candidates using the quantile method; if all comprehensive optimization scores are greater than or equal to the first threshold, the total candidate area is limited using the target area method. S402: Extract the ID, spatial location, and cultivated land area of ​​each cultivated land patch marked as a candidate for priority optimization area; integrate them into an optimization attribute list and send it to the administrator.

5. A farmland spatial layout optimization management system based on multi-source data fusion, wherein the system is applied to the farmland spatial layout optimization management method based on multi-source data fusion as described in any one of claims 1-4, characterized in that, The system includes a basic feature analysis module, a feature weight analysis module, an optimized comprehensive scoring analysis module, and a farmland classification module; The basic feature analysis module is used to acquire farmland patch data, irrigation water source data, and ecological protection elements within the evaluation area; and to analyze the basic features of each farmland patch. The feature weight analysis module is used to collect historical data on changes in cultivated land, construct a training dataset containing both stable and changed cultivated land, and analyze the weights of each basic feature based on a stable fitting model. The optimized comprehensive scoring analysis module is used to perform weighted summation based on the processed real-time basic features and the weights of each basic feature to calculate the optimized comprehensive score of each cultivated land patch. The cultivated land division module is used to determine the grading threshold of the comprehensive optimization score based on the threshold analysis method selected by the administrator; the cultivated land patches are divided according to the grading threshold, and an optimized attribute list is generated and sent to the administrator.

6. The farmland spatial layout optimization management system based on multi-source data fusion according to claim 5, characterized in that: The basic feature analysis module includes a farmland data acquisition unit and a feature analysis unit; The cultivated land data acquisition unit is used to acquire cultivated land patch data within the evaluation area, and to acquire irrigation water source collection and ecological protection elements; The feature analysis unit is used to analyze the basic features of each cultivated land patch; the basic features include the potential for contiguous expansion, the potential for quality improvement, the potential for irrigation security, and the intensity of ecological constraints; For each cultivated land patch, extract all cultivated land patches whose spacing is less than the search distance threshold to form a potential contiguous cultivated land patch set for each cultivated land patch; the spacing between cultivated land patches is equal to the shortest Euclidean distance between the polygonal boundaries of two cultivated land patches, and the spacing between cultivated land patches is zero if the polygonal boundaries of two cultivated land patches intersect or contact; sum the cultivated land areas of all cultivated land patches in the contiguous cultivated land patch set, and record it as the contiguous expansion potential; normalize the contiguous expansion potential of all cultivated land patches. Extract the farmland quality grade values ​​of all farmland patches within the evaluation area, normalize the farmland quality grade values, and record the difference between the normalized farmland quality grade value and the normalized farmland quality grade value as the potential for quality improvement. Analyze the optimal irrigation distance from each cultivated land patch to the nearest irrigation water source; the optimal irrigation distance is equal to the shortest Euclidean distance between the polygonal boundary of the cultivated land patch and the irrigation water source; normalize the optimal irrigation distance, and record the difference between the normalized and the normalized optimal irrigation distance as the irrigation guarantee potential. For each cultivated land parcel, spatial overlay analysis is used to determine its overlap with the ecological protection red line. If the cultivated land parcel overlaps with the ecological protection red line, the ecological restriction intensity of the cultivated land parcel is recorded as zero; otherwise, the ecological restriction intensity of the cultivated land parcel is recorded as one.

7. A farmland spatial layout optimization management system based on multi-source data fusion as described in claim 5, characterized in that: The feature weight analysis module includes a sample acquisition unit, a model fitting unit, and a weight analysis unit. The sample collection unit is used to extract farmland change data within a preset historical time window, identify farmland patches that have always remained high-quality farmland within the preset historical time window as positive samples, and farmland patches that have been abandoned, converted to construction land, or severely degraded as negative samples; at the same time, it extracts the basic feature values ​​corresponding to the farmland patches at the beginning of the preset time window to form a training dataset. The model fitting unit is used to train the dataset to build a stable fitting model. The stable fitting model is trained using the maximum likelihood estimation method, and the final value of the fitting coefficients is generated through the optimization algorithm, so that the difference between the predicted label value and the true label value based on the feature vector of the stable fitting model is minimized. The weight analysis unit is used to normalize the absolute value of the final value of the fitting coefficients and analyze the weight of each basic feature.

8. A farmland spatial layout optimization management system based on multi-source data fusion according to claim 5, characterized in that: The optimized comprehensive scoring analysis module includes a real-time data processing unit and a scoring analysis unit; The real-time data processing unit is used to acquire the real-time basic features of each cultivated land patch and perform positiveization and extreme value standardization processing on each real-time basic feature of the cultivated land patch. The scoring analysis unit is used to perform weighted summation based on the processed real-time basic features and the weights of each basic feature to calculate the optimized comprehensive score of each cultivated land patch.

9. A farmland spatial layout optimization management system based on multi-source data fusion as described in claim 5, characterized in that: The cultivated land division module includes a cultivated land division unit and a list integration unit; The cultivated land division unit is used to determine a first threshold and a second threshold according to the threshold analysis method selected by the administrator; and to divide cultivated land patches based on the first threshold and the second threshold. The list integration unit is used to extract the number, spatial location, and cultivated land area of ​​each cultivated land patch marked as a candidate for priority optimization area; and integrate them into an optimization attribute list and send it to the administrator.

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