Intelligent planning and budget evaluation method for high-standard farmland construction in hilly and mountainous areas

Abstract

The invention provides an intelligent planning and budget evaluation method for high-standard farmland construction in hilly and mountainous areas. The method comprises the following steps: collecting current live data of a to-be-planned area; establishing an evaluation model of each planning index for predicting and evaluating each planning index value after different planning schemes are implemented, integrating the values into a multi-objective optimization algorithm, generating a plurality of different planning schemes based on live data, and finding an optimal planning scheme through iteration; values of decision variables are extracted from the optimal planning scheme, layer parameters are adjusted, and optimized planning maps are obtained and fused to obtain a final planning map; and generating an image set according to the final planning image, providing data for a metering center, and generating a budget file according to the data of the metering center and the budget template. According to the method, scientific, reasonable, efficient and accurate planning of the high-standard farmland in the hilly and mountainous areas is realized through the evaluation model and the multi-objective optimization algorithm, and meanwhile, budget management and resource allocation of the farmland construction project are performed in combination with budget evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent farmland planning, and in particular to an intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas. Background Art

[0002] As an important area for agricultural production, farmland construction in hilly and mountainous areas is of great significance for ensuring food security, promoting rural economic development and protecting the ecological environment. In farmland construction planning, farmland construction in hilly and mountainous areas faces many challenges due to its complex terrain, large differences in soil quality, and uneven distribution of water resources.

[0003] At present, farmland construction usually adopts manual planning methods, and traditional manual planning methods often rely on experience and judgment, lack scientificity and systematicness, and are difficult to meet the precision and efficiency requirements of modern high-standard farmland construction. Summary of the invention

[0004] Based on this, the purpose of the present invention is to propose an intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas to solve the above-mentioned problems.

[0005] According to the present invention, a method for intelligent planning and budget assessment of high-standard farmland construction in hilly and mountainous areas is provided, the method comprising:

[0006] Collect current real-time data of the area to be planned, including geospatial data, soil data, meteorological data, water resources data and crop planting data;

[0007] Establish an evaluation model for each planning indicator to predict and evaluate the values ​​of each planning indicator after the implementation of different planning schemes, wherein the planning indicators include soil quality indicators, irrigation and drainage efficiency indicators, road convenience indicators, farmland coverage indicators and current ecological maintenance indicators;

[0008] Integrate the evaluation model into a multi-objective optimization algorithm, and generate a variety of different planning schemes through the multi-objective optimization algorithm based on the current real-time data of the area to be planned, and find the optimal planning scheme through iterative optimization to balance various optimization objectives;

[0009] Extracting the values ​​of each decision variable from the optimal planning scheme to adjust the layer parameters and obtain an optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map;

[0010] The optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map are integrated to obtain the final planning map;

[0011] Generate a system design monomer atlas and a project design monomer atlas according to the final planning map, wherein the final planning map provides design engineering quantity data for the measurement center, and the project design monomer atlas provides unit engineering quantity data for the measurement center;

[0012] Based on the data and budget template of the measurement center, a budget document is generated and adjusted and optimized in real time.

[0013] Furthermore, based on the current real-time data of the area to be planned, a plurality of different planning schemes are generated by the multi-objective optimization algorithm, and the optimal planning scheme is found by iterative optimization to balance various optimization objectives, including:

[0014] Based on the current real-time data of the area to be planned, the particle swarm algorithm is used to search for the optimal solution in the multidimensional space composed of multiple planning indicators. In each iteration, the planning scheme is evaluated and selected according to the fitness function constructed based on the evaluation model to find the optimal planning scheme that comprehensively considers multiple planning objectives.

[0015] Furthermore, based on the current real-time data of the area to be planned, the particle swarm algorithm is used to search for the optimal solution in a multidimensional space composed of multiple planning indicators. In each iteration, the planning scheme is evaluated and selected according to the fitness function constructed based on the evaluation model to find the optimal planning scheme that comprehensively considers multiple planning objectives, including:

[0016] A group of particles are randomly initialized, and a reasonable value range is assigned to the position vector of each initialized particle according to the current actual data of the area to be planned, where each particle represents a potential planning scheme, and the position of each particle is represented by a multidimensional vector, and each dimension corresponds to a planning indicator. The planning indicators include soil quality indicators, irrigation and drainage planning parameters, road planning parameters, farmland layout parameters, and ecological environment protection planning parameters;

[0017] According to the planning objectives, a fitness function is constructed in combination with the evaluation model. The planning objectives include improving soil quality, optimizing irrigation and drainage, improving road conditions, rationally arranging farmland, and strengthening ecological environmental protection;

[0018] Update the particle's velocity based on the particle's current position, individual historical optimal position, global historical optimal position, inertia weight, and learning factor;

[0019] According to the updated particle speed, the particle position is adjusted to generate a new planning scheme, and the fitness of the newly generated planning scheme is evaluated through the fitness function;

[0020] If the current solution is better, it is updated to the individual historical optimal position of the current particle;

[0021] If the current solution is the best solution in the entire particle swarm, it is updated to the global historical best position;

[0022] During the iteration of the particle swarm algorithm, the changes in the global historical optimal position in each iteration are recorded, and each time the global historical optimal position is updated, it is determined that a new planning scheme has been generated;

[0023] Through multiple iterations, multiple different planning schemes are generated, and the final global historical optimal position is determined as the optimal planning scheme.

[0024] Furthermore, the fitness function is constructed according to the planning goal and in combination with the evaluation model, including:

[0025] Quantify each of the planning objectives to obtain the soil quality compliance index, irrigation and drainage compliance index, road design compliance index, farmland layout compliance index and ecological environment compliance index;

[0026] According to the importance and priority of each planning goal, weights are set for the quantitative indicators of each planning goal;

[0027] According to the current actual data of the area to be planned and the specific parameters in the planning scheme, the value of each planning indicator is estimated and calculated through each evaluation model, and weighted and calculated according to the set weights to obtain the fitness value of the planning scheme, wherein the higher the fitness value, the more the planning scheme conforms to the overall planning goal.

[0028] Furthermore, the evaluation model of each planning indicator is established to predict and evaluate the values ​​of each planning indicator after the implementation of different planning schemes, including:

[0029] Collect a large amount of historical planning data related to the construction of high-standard farmland in hilly and mountainous areas from various sources. The historical planning data includes regional actual data, planning scheme data and corresponding actual values ​​of planning indicators. The regional actual data includes regional geographic space data, soil data, meteorological data, water resources data and crop planting data;

[0030] The actual regional data and the specific parameters in the planning scheme are used as input features, and the actual values ​​of the corresponding planning indicators are used as output targets. The regression model is trained to obtain the evaluation model.

[0031] Furthermore, the generating of the system design monomer atlas and the project design monomer atlas according to the final planning diagram includes:

[0032] Extracting key planning elements from the final planning map as basic components of the single-image set, wherein the key planning elements include roads, irrigation systems, drainage facilities, farmland plots, and ecological protection areas;

[0033] For each of the key planning elements, a corresponding monomer template is created, wherein the monomer template includes the basic shape, size, attributes and annotation of the element;

[0034] Organizing all the monomer templates into a system design monomer atlas, wherein the system design monomer atlas contains all monomer types;

[0035] Analyze the planning requirements of the project to determine the required unit types and their quantity, location and attributes based on the content and objectives of the final plan;

[0036] Selecting monomer types that meet planning requirements from the system design monomer atlas, and organizing all selected monomers into a system recommended project monomer atlas;

[0037] According to the specific requirements and standards of the project, each monomer in the monomer atlas of the system recommended project is refined and the project-specific information is added to each monomer;

[0038] Organize all refined monomers into a monomer atlas for this project.

[0039] Furthermore, the budget document is generated based on the data of the project measurement center and the budget template, including:

[0040] Obtain the design engineering quantity from the final planning drawing and match it with the unit engineering quantity in the design unit drawing set of the project;

[0041] Based on the matching data, a preliminary budget template is generated, including cost items, quantity, unit price and total price;

[0042] The preliminary budget template is adjusted and a final budget document is generated, which contains detailed cost composition, total budget and budget allocation information.

[0043] In summary, the intelligent planning and budget evaluation method for high-standard farmland construction in hilly and mountainous areas of the present invention can predict the effects of different planning schemes after implementation by establishing evaluation models for multiple planning indicators such as soil quality, irrigation and drainage efficiency, road convenience, farmland coverage and ecological maintenance, and provide a scientific basis for farmland planning decisions, thereby improving the rationality and feasibility of planning; then, the evaluation model is integrated into the multi-objective optimization algorithm, and the optimal planning scheme is found through iterative optimization. The optimal planning scheme found by the multi-objective optimization algorithm can balance various optimization goals, ensure that the planning scheme can achieve a comprehensive optimal state in multiple dimensions, avoid the one-sidedness and limitations of traditional farmland planning methods, and significantly improve the planning accuracy, efficiency, scientificity and rationality of high-standard farmland construction in hilly and mountainous areas; then, the decision variable values ​​are extracted from the optimal planning scheme to adjust the layer parameters, generate various optimized planning maps, and finally merge them into the final planning map, visualize and intuitively express the planning scheme, and then generate the system design monomer atlas and the project design monomer atlas according to the final planning map, provide the measurement center with design engineering quantity and unit engineering quantity data, and then generate a budget file. The present invention realizes the scientific, reasonable, efficient and accurate planning of high-standard farmland in hilly and mountainous areas through evaluation models and multi-objective optimization algorithms, and at the same time combines budget evaluation to carry out budget management and resource allocation of farmland construction projects.

[0044] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0046] Figure 1 This is a flow chart of a method for intelligent planning and budget assessment of high-standard farmland construction in hilly and mountainous areas according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0048] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0050] See also Figure 1 The present invention proposes an intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas, the method comprising steps S101 to S107:

[0051] S101, collecting current real-time data of the area to be planned, including geographic space data, soil data, meteorological data, water resource data and crop planting data.

[0052] It can be understood that during the data collection stage, this embodiment can collect real-time data on the planning area through methods including satellite remote sensing, drone aerial surveys, and actual survey topographic maps to ensure the comprehensiveness, accuracy, and timeliness of the data, providing a solid foundation for subsequent planning, evaluation, and decision-making.

[0053] Satellite remote sensing technology can obtain surface information over a large area and continuously through sensors on satellites. For complex terrain such as hilly and mountainous areas, satellite remote sensing can provide high-precision topographic maps, vegetation coverage, soil moisture and other remote sensing image data, which are of great significance for understanding the overall geographical spatial layout, vegetation distribution and soil conditions of the area to be planned.

[0054] Drone aerial survey technology uses drones equipped with high-resolution cameras, multispectral sensors and other equipment to fly at low altitudes and capture ground images. Compared with satellite remote sensing, drone aerial survey has higher resolution and flexibility, and can obtain more detailed geospatial data and crop planting conditions. It can also perform fixed-point measurements through drones to obtain detailed data on specific areas.

[0055] The actual survey topographic map is to measure the ground on the spot and draw it through traditional surveying methods, such as total station, GPS, etc. Although this method is time-consuming, it can obtain very accurate terrain data. Especially for areas with complex terrain and large changes, actual survey is an indispensable supplement.

[0056] The current real-time data of the area to be planned collected in this embodiment may include the following aspects:

[0057] Geospatial data: including topographic maps and remote sensing images. Topographic maps can be obtained through satellite remote sensing and actual surveys to understand basic information such as regional terrain undulations, slopes, and slope directions, while remote sensing images can provide macro information such as vegetation coverage, soil moisture, and land use types.

[0058] Soil data: UAV aerial surveys and field sampling and analysis can be used to obtain key indicators such as soil texture, fertility, pH, and organic matter content, providing a basis for soil improvement and crop planting.

[0059] Meteorological data: Meteorological station or satellite remote sensing data can be used to obtain meteorological elements such as temperature, precipitation, wind speed, sunshine, etc. in the planned area to provide decision support for irrigation, drainage and crop growth management.

[0060] Water resource data: Through field surveys and remote sensing technology, we can obtain the water source distribution, water quality, groundwater level, etc. in the planned area, providing a basis for irrigation planning and water resource management.

[0061] Crop planting data: UAV aerial surveys and field surveys can be combined to obtain information such as the current crop planting type, distribution, and growth status, providing guidance for farmland layout planning and crop rotation.

[0062] S102, establishing an evaluation model for each planning indicator, which is used to predict and evaluate the values ​​of each planning indicator after the implementation of different planning schemes, wherein the planning indicators include soil quality indicators, irrigation and drainage efficiency indicators, road convenience indicators, farmland coverage indicators and current ecological maintenance indicators.

[0063] It is understandable that in the intelligent planning process of high-standard farmland construction in hilly and mountainous areas, by establishing an evaluation model for each planning indicator, the possible values ​​of each planning indicator after the implementation of different planning schemes can be predicted and evaluated, thereby providing a scientific basis for subsequent planning decisions and screening out the optimal planning scheme.

[0064] Based on the planning objectives of this embodiment, the planning indicators that need to be evaluated may include soil quality indicators, irrigation and drainage efficiency indicators, road convenience indicators, farmland coverage indicators and current ecological maintenance indicators. Specifically, soil quality indicators can reflect parameters of soil health such as soil fertility, pH, organic matter content, and are the basis for crop growth. Irrigation and drainage efficiency indicators can measure the effectiveness of the irrigation system, the utilization rate of irrigation water, and the smoothness of the drainage system, which is crucial to ensuring the water demand of crops and preventing waterlogging. Road convenience indicators are used to evaluate the accessibility, convenience and transportation capacity of roads around farmland, which is of great significance for the transportation of agricultural production materials and the sale of agricultural products. The farmland coverage index can reflect the utilization degree of farmland and the vegetation coverage, and is an important indicator for evaluating the ecological function and production potential of farmland. The current ecological maintenance index is used to evaluate the impact of the planning scheme on the surrounding ecological environment, including biodiversity protection, soil and water conservation, and landscaping.

[0065] This embodiment applies the established evaluation model to different planning schemes, predicts and evaluates the values ​​of each planning indicator after the implementation of each scheme, and comprehensively constructs a fitness function for evaluating the overall advantages and disadvantages of the planning scheme to find the optimal planning scheme. The evaluation model can foresee the possible problems and risks of the planning scheme in advance. The fitness function constructed based on the evaluation model can comprehensively consider the quality of indicators in multiple aspects such as soil quality, irrigation and drainage efficiency, road convenience, farmland coverage and ecological maintenance, so as to predict and evaluate the overall effect of different planning schemes, so as to more reasonably allocate resources such as land, water, and funds, and improve resource utilization efficiency.

[0066] S103, integrating the evaluation model into a multi-objective optimization algorithm, and generating a plurality of different planning schemes through the multi-objective optimization algorithm based on current real-time data of the area to be planned, and finding the optimal planning scheme through iterative optimization to balance various optimization objectives.

[0067] It can be understood that the evaluation model is integrated into the multi-objective optimization algorithm to use the predictive ability of the evaluation model to construct a fitness function, quantitatively evaluate the effects of different planning schemes after implementation, and find a balance between multiple conflicting or contradictory objectives through the multi-objective optimization algorithm to generate the optimal planning scheme.

[0068] The multi-objective optimization algorithm of this embodiment can process multiple optimization objectives at the same time, and find a solution set that makes multiple objective functions reach the optimal solution under the premise of satisfying certain constraints. In farmland construction planning, the multi-objective optimization algorithm can adopt genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm, etc. These algorithms have the characteristics of strong global search ability and easy parallel computing, and are suitable for processing complex optimization problems.

[0069] First, determine multiple optimization goals. For example, you can use soil quality improvement, irrigation and drainage efficiency improvement, road convenience enhancement, farmland coverage increase, and ecological maintenance improvement as optimization goals. According to the actual situation and planning requirements of the planned area, set corresponding constraints, which may include land use restrictions, water resource protection requirements, and ecological environmental protection regulations.

[0070] The evaluation model is used as a component of the multi-objective optimization algorithm. Based on the current real-time data of the area to be planned, the algorithm is executed and a variety of different planning schemes are generated. During the execution of the algorithm, new planning schemes are generated through continuous iterations, and the fitness function constructed by the evaluation model is used to evaluate the advantages and disadvantages of the new planning schemes. Each iteration will adjust and optimize the scheme according to the evaluation results to gradually approach the optimal solution. After the iterative optimization is completed, the optimal scheme is selected from the generated multiple planning schemes. The selection of the optimal scheme at this time integrates the considerations of multiple optimization objectives, finds a balance between multiple optimization objectives, and improves the scientificity and rationality of farmland planning.

[0071] This embodiment uses the fitness function constructed based on the evaluation model to perform effect prediction and evaluation and the multi-objective optimization algorithm to generate and select solutions, which can reduce the subjective judgment of human factors and enhance the objectivity of planning decisions. And through the automated and intelligent planning process, the efficiency and quality of farmland planning can be greatly improved, and the optimal planning solution that meets actual needs and has long-term benefits can be generated.

[0072] S104, extracting the value of each decision variable from the optimal planning scheme to adjust the layer parameters to obtain an optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map.

[0073] It is understandable that the optimal planning scheme obtained by iterating the multi-objective optimization algorithm in this embodiment includes the optimal values ​​of a series of decision variables. These decision variables directly determine the specific implementation details of key aspects such as soil improvement, irrigation and drainage system layout, road network design, farmland distribution, and ecological environment protection. These decision variables are extracted and converted into operational layer parameters, and then various optimized planning maps are generated to intuitively display the planning results.

[0074] First, all relevant decision variables need to be clearly identified from the optimal planning scheme. These variables may include specific measures for soil improvement (such as fertilizer application amount, type of soil conditioner, etc.), layout parameters of irrigation and drainage systems (such as pipeline direction, pump station location, etc.), planning parameters of road networks (such as road width, road surface type, etc.), layout plans for farmland (such as crop planting type, rotation system, etc.), and measures for ecological environmental protection (such as vegetation restoration areas, soil and water conservation projects, etc.). For each decision variable, its specific value needs to be extracted from the optimal planning scheme. These values ​​are obtained based on iterative calculations of the multi-objective optimization algorithm and represent the optimal choice of each decision variable under the current conditions.

[0075] According to the values ​​of the decision variables for soil improvement, the parameters of the soil quality assessment layer are adjusted. For example, the soil fertility grade distribution is updated according to the amount of fertilizer applied and the type of soil conditioner; the soil pH distribution map is updated according to the soil pH adjustment measures, etc.

[0076] Adjust the parameters of the irrigation and drainage planning layer according to the layout parameters of the irrigation and drainage system, including adding or modifying the location and direction of irrigation pipes and drainage ditches, marking the location of key facilities such as pumping stations and reservoirs, etc.

[0077] According to the planning parameters of the road network, adjust the parameters of the road planning layer, including drawing or modifying the road network, marking the road grade, road surface type, width and other attribute information.

[0078] According to the layout plan of the farmland, adjust the parameters of the farmland layout layer, including dividing different crop planting areas, marking crop types, crop rotation system and other information.

[0079] According to the ecological and environmental protection measures, adjust the parameters of the ecological and environmental protection layer, including marking the location and scope of vegetation restoration areas, soil and water conservation projects, and biodiversity protection areas.

[0080] After adjusting the parameters of all relevant layers, the various layers are superimposed and integrated using the Geographic Information System (GIS) or professional planning software to generate optimized soil quality assessment maps, irrigation and drainage planning maps, road planning maps, farmland layout maps, and ecological environment protection maps. These planning maps intuitively show the specific implementation details of the optimal planning scheme.

[0081] S105, integrating the optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map to obtain a final planning map.

[0082] Understandably, the optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map are integrated to generate a comprehensive final planning map to ensure the coordination and consistency among the various planning elements and provide clear and intuitive guidance for subsequent planning implementation, construction and effect evaluation.

[0083] You can use Geographic Information System (GIS) or professional planning software, such as AutoCAD, ArcGIS, etc., to overlay, integrate and render layers. These software provide powerful layer management, spatial analysis and visualization functions, and can efficiently complete the fusion task.

[0084] First, the optimized planning maps can be imported into GIS or planning software in the form of layers. Make sure that each layer contains the correct geographic coordinates and projection information for accurate superposition and integration. Then, according to the principle of hierarchy, superimpose each layer in turn. During the superposition process, pay attention to checking the spatial relationship between the layers to ensure their coordination and consistency. Then integrate the superimposed layers, merge repeated or redundant information, delete unnecessary layers or elements, and adjust the color, symbol and annotation method of the layers according to the visualization principle to make the final planning map clearer and easier to read. During the integration process, if conflicts or contradictions are found between the layers, they should be detected and processed in time. Ensure the coordination and consistency between the planning elements by adjusting the layer parameters, modifying the planning scheme or making necessary compromises. Finally, export the final planning map after fusion. The final planning map after fusion should be able to comprehensively and intuitively display the overall layout and implementation details of the planning scheme. By comparing the optimized planning maps with the final planning map, the effect and quality of the fusion can be evaluated. If problems or deficiencies are found, they should be corrected and improved in time to ensure the accuracy and operability of the final planning map.

[0085] S106, generating a system design monomer atlas and a current project design monomer atlas according to the final planning diagram, wherein the final planning diagram provides design engineering quantity data for the measurement center, and the current project design monomer atlas provides unit engineering quantity data for the measurement center.

[0086] S107, generating a budget file according to the data of the measurement center and the budget template, and performing real-time adjustment and optimization.

[0087] Understandably, in the intelligent planning process of high-standard farmland construction in hilly and mountainous areas, the final planning map integrates the optimization results of soil quality, irrigation and drainage, roads, farmland layout, and ecological environment protection, which can provide comprehensive and intuitive guidance for the specific implementation of farmland construction projects. In order to transform the farmland planning scheme into a specific construction design and budget basis, it is necessary to further generate the system design monomer atlas and the project design monomer atlas, and compile budget documents based on these data to ensure the detailed design of farmland construction, the accuracy of the budget, and the feasibility of project implementation.

[0088] Based on step S101 to step S107, by establishing an evaluation model for multiple planning indicators such as soil quality, irrigation and drainage efficiency, road convenience, farmland coverage and ecological maintenance, the effects of different planning schemes after implementation can be predicted, providing a scientific basis for farmland planning decisions, thereby improving the rationality and feasibility of planning; then, the evaluation model is integrated into the multi-objective optimization algorithm, and the optimal planning scheme is found through iterative optimization. The optimal planning scheme found by the multi-objective optimization algorithm can balance various optimization goals, ensuring that the planning scheme can achieve a comprehensive optimal state in multiple dimensions, avoiding the one-sidedness and limitations of traditional farmland planning methods, and significantly improving the planning accuracy, efficiency, scientificity and rationality of high-standard farmland construction in hilly and mountainous areas; then, the decision variable values ​​are extracted from the optimal planning scheme to adjust the layer parameters, generate various optimized planning maps, and finally merge them into the final planning map, visualize and intuitively express the planning scheme, and then generate the system design monomer atlas and the project design monomer atlas according to the final planning map, provide the measurement center with design engineering quantity and unit engineering quantity data, and then generate a budget file. The present invention realizes the scientific, reasonable, efficient and accurate planning of high-standard farmland in hilly and mountainous areas through evaluation models and multi-objective optimization algorithms, and at the same time combines budget evaluation to carry out budget management and resource allocation of farmland construction projects.

[0089] The following is a further detailed introduction to a method for intelligent planning and budget assessment of high-standard farmland construction in hilly and mountainous areas according to an embodiment of the present invention:

[0090] Further optionally, in step S103, based on the current real-time data of the area to be planned, a plurality of different planning schemes are generated by the multi-objective optimization algorithm, and the optimal planning scheme is found by iterative optimization to balance various optimization objectives, including:

[0091] Based on the current real-time data of the area to be planned, the particle swarm algorithm is used to search for the optimal solution in the multidimensional space composed of multiple planning indicators. In each iteration, the planning scheme is evaluated and selected according to the fitness function constructed based on the evaluation model to find the optimal planning scheme that comprehensively considers multiple planning objectives.

[0092] Understandably, for planning problems that need to meet multiple conflicting or competing goals at the same time, the particle swarm algorithm of this embodiment can comprehensively consider multiple planning indicators, quickly find the optimal solution that achieves the best balance between multiple goals through iterative search, and is easy to implement and adjust parameters. Through the fitness function built based on the evaluation model, the particle swarm algorithm can accurately evaluate the pros and cons of each planning scheme. This flexibility enables the algorithm to adapt to different planning goals and constraints, so as to find the optimal solution that best meets actual needs.

[0093] Further optionally, based on the current real-time data of the area to be planned, the particle swarm algorithm is used to search for the optimal solution in a multidimensional space composed of multiple planning indicators. In each iteration, the planning scheme is evaluated and selected according to the fitness function constructed based on the evaluation model to find the optimal planning scheme that comprehensively considers multiple planning objectives, including:

[0094] A group of particles are randomly initialized, and a reasonable value range is assigned to the position vector of each initialized particle according to the current actual data of the area to be planned, where each particle represents a potential planning scheme, and the position of each particle is represented by a multidimensional vector, and each dimension corresponds to a planning indicator. The planning indicators include soil quality indicators, irrigation and drainage planning parameters, road planning parameters, farmland layout parameters, and ecological environment protection planning parameters;

[0095] According to the planning objectives, a fitness function is constructed in combination with the evaluation model. The fitness function should take into account all planning indicators and give a comprehensive score. The planning objectives include improving soil quality, optimizing irrigation and drainage, improving road conditions, rationally arranging farmland, and strengthening ecological environmental protection;

[0096] Update the particle's velocity based on the particle's current position, individual historical optimal position, global historical optimal position, inertia weight, and learning factor;

[0097] According to the updated particle speed, the particle position is adjusted to generate a new planning scheme, and the fitness of the newly generated planning scheme is evaluated through the fitness function;

[0098] If the current solution is better, it is updated to the individual historical optimal position of the current particle;

[0099] If the current solution is the best solution in the entire particle swarm, it is updated to the global historical best position;

[0100] During the iteration of the particle swarm algorithm, the changes in the global historical optimal position in each iteration are recorded, and each time the global historical optimal position is updated, it is determined that a new planning scheme has been generated;

[0101] Through multiple iterations, multiple different planning schemes are generated, and the final global historical optimal position is set as the optimal planning scheme. The optimal planning scheme performs best under the fitness function evaluation, and comprehensively considers multiple indicators such as soil quality, irrigation and drainage, road planning, farmland layout and ecological environment protection.

[0102] Understandably, the particle swarm algorithm is used to efficiently search in the multidimensional planning indicator space to find the optimal planning scheme. First, it randomly initializes a group of particles, each particle represents a potential planning scheme, and its position vector is given a reasonable range based on the current actual data of the area to be planned, covering key indicators such as soil quality, irrigation and drainage, road planning, farmland layout and ecological environment protection. Then, a comprehensive fitness function is constructed in combination with the planning objectives to evaluate the comprehensive performance of each scheme. During the iteration process, the algorithm dynamically updates the particle speed based on the current position of the particle, the individual and global historical optimal position, combined with the inertia weight and learning factor, and then adjusts the position to generate a new scheme, and strictly evaluates it through the fitness function. Each iteration records the changes in the global optimal position to ensure that it is constantly approaching the optimal solution. Through multiple iterations, the scheme is not only continuously optimized, but also comprehensively considers all planning indicators. The final global historical optimal position is the optimal planning scheme that performs best in many aspects such as soil quality improvement, irrigation and drainage optimization, road condition improvement, farmland layout rationalization and ecological environment protection.

[0103] Further optionally, constructing a fitness function according to the planning goal and in combination with the evaluation model includes:

[0104] Quantify each of the planning objectives to obtain the soil quality compliance index, irrigation and drainage compliance index, road design compliance index, farmland layout compliance index and ecological environment compliance index;

[0105] According to the importance and priority of each planning goal, weights are set for the quantitative indicators of each planning goal;

[0106] According to the current actual data of the area to be planned and the specific parameters in the planning scheme, the value of each planning indicator is estimated and calculated through each evaluation model, and weighted and calculated according to the set weights to obtain the fitness value of the planning scheme, wherein the higher the fitness value, the more the planning scheme conforms to the overall planning goal.

[0107] Understandably, a fitness function is constructed based on the evaluation model to accurately evaluate the fit between the planning scheme and the overall planning goals. To this end, each planning goal is first carefully quantified to form a compliance index system including soil quality, irrigation and drainage, road design, farmland layout and ecological environment. Subsequently, based on the importance and priority of each planning goal, the weights are reasonably allocated to ensure the emphasis and balance in the evaluation process. Using the real-time data of the area to be planned and the specific parameters of the planning scheme, the actual value of each planning indicator is estimated through the constructed evaluation model, and a weighted sum is performed on this basis to obtain the fitness value of the planning scheme. This fitness value is a key indicator for measuring the pros and cons of the scheme. Its high or low directly reflects the degree to which the planning scheme meets the overall planning goals of the farmland, thereby providing solid data support and decision-making basis for the selection and optimization of subsequent schemes.

[0108] Further optionally, the establishment of an evaluation model for each planning indicator, which is used to predict and evaluate the values ​​of each planning indicator after the implementation of different planning schemes, includes:

[0109] Collect a large amount of historical planning data related to the construction of high-standard farmland in hilly and mountainous areas from various sources. The historical planning data includes regional actual data, planning scheme data and corresponding actual values ​​of planning indicators. Regional actual data includes regional geospatial data (such as topographic maps, remote sensing images), soil data, meteorological data, water resources data and crop planting data;

[0110] The actual regional data and the specific parameters in the planning scheme are used as input features, and the actual values ​​of the corresponding planning indicators are used as output targets. The regression model is trained to obtain the evaluation model.

[0111] It is understandable that by constructing an evaluation model, the values ​​of various planning indicators for high-standard farmland construction in hilly and mountainous areas after the implementation of different planning schemes are predicted and evaluated. To this end, historical planning data closely related to the construction of high-standard farmland in hilly and mountainous areas are first widely collected. These data not only cover detailed regional real-time information, such as geospatial data, soil conditions, meteorological conditions, water resource distribution and crop planting conditions, but also include past planning schemes and their corresponding actual values ​​of planning indicators. On this basis, with the specific parameters of regional real-time data and planning schemes as input features, and the actual planning indicator values ​​as output targets, the regression model is deeply trained and optimized, and finally an evaluation model that can accurately predict various planning indicators (including soil quality, irrigation and drainage efficiency, road convenience, farmland coverage and current ecological maintenance) is obtained. The establishment of this model can provide a scientific basis for planning decisions for the construction of high-standard farmland in hilly and mountainous areas, and select the optimal planning scheme by quantitatively evaluating the expected effects of different schemes.

[0112] Further optionally, in step S106, generating a system design monomer atlas and a project design monomer atlas according to the final planning diagram includes:

[0113] Extracting key planning elements from the final planning map as basic components of the single-body map set, wherein the key planning elements include roads, irrigation systems, drainage facilities, farmland plots, ecological protection areas, etc.;

[0114] For each of the key planning elements, a corresponding monomer template is created, wherein the monomer template includes the basic shape, size, attributes and annotation of the element;

[0115] Organizing all the monomer templates into a system design monomer atlas, wherein the system design monomer atlas contains all monomer types;

[0116] Analyze the planning requirements of the project based on the content and objectives of the final planning map to determine the required unit types and their quantity, location and attributes;

[0117] Selecting monomer types that meet planning requirements from the system design monomer atlas, and organizing all selected monomers into a system recommended project monomer atlas;

[0118] According to the specific requirements and standards of the project, each monomer in the monomer atlas of the system recommended project is refined and the project-specific information is added to each monomer;

[0119] Organize all refined monomers into a monomer atlas for this project.

[0120] Understandably, in order to transform the key elements in the planning map into specific and implementable monomer designs. First, accurately extract key planning elements such as roads, irrigation systems, drainage facilities, farmland plots, and ecological protection areas from the planning map as the basic components of the monomer atlas. Subsequently, use CAD or GIS software to create standardized and reusable monomer templates for these elements, and clarify their basic shapes, sizes, attributes, and annotations. Next, integrate these monomer templates into a system design monomer atlas to achieve comprehensive and orderly management. This atlas covers all possible monomer types, and through classification and naming rules, ensure that the required monomers can be quickly located and used. Then, based on the content and objectives of the final planning map, deeply analyze the specific planning requirements of the project to clarify the type, quantity, location, and attributes of the required monomers. Select monomers that meet the requirements from the system design monomer atlas to form a system recommended project monomer atlas, and make necessary adjustments and optimizations according to the actual situation of the project to ensure that the monomer design is highly consistent with the project requirements. On this basis, the design of each unit is further refined, and project-specific information such as number, name, specification, quantity, etc. is added. The size, shape, material, construction details and other aspects are adjusted according to the project standards and requirements to ensure that each unit not only meets the basic functional requirements, but also fully integrates into the overall project to enhance the practicality and aesthetics of the design. Finally, all the units with detailed designs are integrated into the design unit atlas of this project, providing a detailed and accurate design basis for the construction and subsequent management of farmland planning and construction projects.

[0121] Further optionally, in step S107, generating a budget file according to the data of the project measurement center and the budget template includes:

[0122] Obtain the design engineering quantity from the final planning drawing and match it with the unit engineering quantity in the design unit drawing set of the project;

[0123] Based on the matching data, a preliminary budget template is generated, including cost items, quantity, unit price and total price;

[0124] The preliminary budget template is adjusted and a final budget document is generated, which contains detailed information such as cost composition, total budget and budget allocation.

[0125] It is understandable that the design engineering quantity can be obtained from the final planning drawing. The design engineering quantity reflects the actual quantity of various projects required for the project, such as earthwork volume, material quantity, equipment quantity, etc. By interpreting the final planning drawing, the specific scale and demand of each project can be clarified.

[0126] The unit quantity is obtained from the design drawing set of this project. The design drawing set of this project contains the detailed design of each unit, including size, material, structure, etc., which directly determine the cost of the unit quantity and match it with the design quantity. The matching process is to associate each item in the design quantity with the corresponding item in the unit drawing set to ensure the accuracy and pertinence of the budget.

[0127] Based on the matching data, a preliminary budget template is generated. Specifically, the quantity corresponding to each cost item can be extracted from the design engineering quantity data, and the unit price corresponding to each cost item can be extracted from the unit engineering quantity data. Then the total price of each cost item is calculated using the formula "total price = quantity × unit price", where cost items are the basic units of the budget, such as labor costs, material costs, equipment costs, etc. The quantity reflects the actual required engineering quantity, and the unit price is the unit cost of each cost item. All cost items, quantities, unit prices and total prices are organized into a table or database format to form a preliminary budget template.

[0128] Adjust the preliminary budget template. The preliminary budget template may need to be adjusted according to the actual situation of the project, changes in market prices, policy adjustments and other factors. The adjustment process may involve the increase or decrease of cost items, adjustment of quantities, update of unit prices, etc. The adjusted budget template is organized into a final budget file, which contains a detailed cost structure and shows the specific amount and proportion of each cost item. The total budget is a summary of the total price of all cost items, reflecting the overall budget level of the project. The budget allocation shows the allocation of the budget between different cost items or project stages, which helps project managers to reasonably control and optimize the budget.

[0129] This step achieves accurate, comprehensive and detailed management of the budget for high-standard farmland construction projects in hilly and mountainous areas by obtaining the design project quantities from the planning drawings, matching them with the unit project quantities in the individual drawing collection, generating a preliminary budget template, and generating the final budget document through adjustments.

[0130] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas, characterized in that: The method comprises: Collect current real-time data of the area to be planned, including geospatial data, soil data, meteorological data, water resources data and crop planting data; Establish an evaluation model for each planning indicator to predict and evaluate the values ​​of each planning indicator after the implementation of different planning schemes, wherein the planning indicators include soil quality indicators, irrigation and drainage efficiency indicators, road convenience indicators, farmland coverage indicators and current ecological maintenance indicators; Integrate the evaluation model into a multi-objective optimization algorithm, and generate a variety of different planning schemes through the multi-objective optimization algorithm based on the current real-time data of the area to be planned, and find the optimal planning scheme through iterative optimization to balance various optimization objectives; Extracting the values ​​of each decision variable from the optimal planning scheme to adjust the layer parameters and obtain an optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map; The optimized soil quality assessment map, irrigation and drainage planning map, road planning map, farmland layout map and ecological environment protection map are integrated to obtain the final planning map; Generate a system design monomer atlas and a project design monomer atlas according to the final planning map, wherein the final planning map provides design engineering quantity data for the measurement center, and the project design monomer atlas provides unit engineering quantity data for the measurement center; Based on the data and budget template of the measurement center, a budget document is generated and adjusted and optimized in real time.

2. The intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas according to claim 1 is characterized in that: The method generates a plurality of different planning schemes based on the current real-time data of the area to be planned by the multi-objective optimization algorithm, and finds the optimal planning scheme by iterative optimization to balance various optimization objectives, including: Based on the current real-time data of the area to be planned, the particle swarm algorithm is used to search for the optimal solution in the multidimensional space composed of multiple planning indicators. In each iteration, the planning scheme is evaluated and selected according to the fitness function constructed based on the evaluation model to find the optimal planning scheme that comprehensively considers multiple planning objectives.

3. The intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas according to claim 2 is characterized in that: Based on the current real-time data of the area to be planned, the particle swarm algorithm is used to search for the optimal solution in a multidimensional space composed of multiple planning indicators. In each iteration, the planning scheme is evaluated and selected according to the fitness function constructed based on the evaluation model to find the optimal planning scheme that comprehensively considers multiple planning objectives, including: A group of particles are randomly initialized, and a reasonable value range is assigned to the position vector of each initialized particle according to the current actual data of the area to be planned, where each particle represents a potential planning scheme, and the position of each particle is represented by a multidimensional vector, and each dimension corresponds to a planning indicator. The planning indicators include soil quality indicators, irrigation and drainage planning parameters, road planning parameters, farmland layout parameters, and ecological environment protection planning parameters; According to the planning objectives, a fitness function is constructed in combination with the evaluation model. The planning objectives include improving soil quality, optimizing irrigation and drainage, improving road conditions, rationally arranging farmland, and strengthening ecological environmental protection; Update the particle's velocity based on the particle's current position, individual historical optimal position, global historical optimal position, inertia weight, and learning factor; According to the updated particle speed, the particle position is adjusted to generate a new planning scheme, and the fitness of the newly generated planning scheme is evaluated through the fitness function; If the current solution is better, it is updated to the individual historical optimal position of the current particle; If the current solution is the best solution in the entire particle swarm, it is updated to the global historical best position; During the iteration of the particle swarm algorithm, the changes in the global historical optimal position in each iteration are recorded, and each time the global historical optimal position is updated, it is determined that a new planning scheme has been generated; Through multiple iterations, multiple different planning schemes are generated, and the final global historical optimal position is determined as the optimal planning scheme.

4. The intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas according to claim 1 is characterized in that: The fitness function is constructed according to the planning goal and in combination with the evaluation model, including: Quantify each of the planning objectives to obtain the soil quality compliance index, irrigation and drainage compliance index, road design compliance index, farmland layout compliance index and ecological environment compliance index; According to the importance and priority of each planning goal, weights are set for the quantitative indicators of each planning goal; According to the current actual data of the area to be planned and the specific parameters in the planning scheme, the value of each planning indicator is estimated and calculated through each evaluation model, and weighted and calculated according to the set weights to obtain the fitness value of the planning scheme, wherein the higher the fitness value, the more the planning scheme conforms to the overall planning goal.

5. The intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas according to claim 4 is characterized in that: The evaluation model for each planning indicator is established to predict and evaluate the values ​​of each planning indicator after the implementation of different planning schemes, including: Collect a large amount of historical planning data related to the construction of high-standard farmland in hilly and mountainous areas from various sources. The historical planning data includes regional actual data, planning scheme data and corresponding actual values ​​of planning indicators. The regional actual data includes regional geographic space data, soil data, meteorological data, water resources data and crop planting data; The actual regional data and the specific parameters in the planning scheme are used as input features, and the actual values ​​of the corresponding planning indicators are used as output targets. The regression model is trained to obtain the evaluation model.

6. The intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas according to claim 1 is characterized in that: The generating of the system design monomer atlas and the project design monomer atlas according to the final planning diagram includes: Extracting key planning elements from the final planning map as basic components of the single-image set, wherein the key planning elements include roads, irrigation systems, drainage facilities, farmland plots, and ecological protection areas; For each of the key planning elements, a corresponding monomer template is created, wherein the monomer template includes the basic shape, size, attributes and annotation of the element; Organizing all the monomer templates into a system design monomer atlas, wherein the system design monomer atlas contains all monomer types; Analyze the planning requirements of the project to determine the required unit types and their quantity, location and attributes based on the content and objectives of the final plan; Selecting monomer types that meet planning requirements from the system design monomer atlas, and organizing all selected monomers into a system recommended project monomer atlas; According to the specific requirements and standards of the project, each monomer in the monomer atlas of the system recommended project is refined and the project-specific information is added to each monomer; Organize all refined monomers into a monomer atlas for this project.

7. The intelligent planning and budget assessment method for high-standard farmland construction in hilly and mountainous areas according to claim 1 is characterized in that: The budget document is generated based on the data of the project measurement center and the budget template, including: Obtain the design engineering quantity from the final planning drawing and match it with the unit engineering quantity in the design unit drawing set of the project; Based on the matching data, a preliminary budget template is generated, including cost items, quantity, unit price and total price; The preliminary budget template is adjusted and a final budget document is generated, which contains detailed cost composition, total budget and budget allocation information.

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