Land utilization efficiency analysis method and system

Abstract

The invention relates to the technical field of agricultural land planning, in particular to a land utilization efficiency analysis method and system, and the method comprises the following steps: obtaining planting time sequence data: obtaining historical planting time sequence data of a target land parcel, the data at least comprising the variety of crops planted each time, the corresponding sowing date and the corresponding harvesting date; neutral period calculation: calculating the length of a field neutral period between two adjacent planting in the historical planting time sequence according to the sowing date and the harvesting date; analysis: comparing each field neutral period length obtained by calculation with a preset reasonable neutral period threshold range for a specific combination of preceding and succeeding crops; based on the comparison results, a land utilization efficiency diagnostic report is generated that at least explicitly lists specific crop rotation locations diagnosed as unreasonable. The system comprises a processor and a memory in communication connection with the processor. According to the invention, the compactness and rationality of time utilization can be diagnosed.

Description

Technical Field

[0001] This application relates to the technical field of agricultural land planning, and in particular to a method and system for land use efficiency analysis. Background Technology

[0002] In the fields of agricultural production and land planning, improving land resource utilization efficiency is the core of ensuring food security and sustainable agricultural development. Traditional assessment methods rely heavily on macro-statistical indicators (such as the multiple cropping index) or subjective experience, lacking detailed quantitative analysis of land time utilization intensity, spatial allocation rationality, and the economic adaptability of planting structure. With the development of smart agriculture technology, how to systematically integrate multi-source data to conduct in-depth diagnosis and optimization of land use efficiency has become an urgent technical problem to be solved.

[0003] Several solutions dedicated to digital agricultural management have emerged in the existing technology. For example, Chinese patent application CN117132219A discloses a full-process management system and method for field agriculture. This system collects environmental and agricultural machinery information through a field data acquisition module and presets land, agricultural machinery, and operating specifications information using a basic model input module. Its agricultural management module can automatically or manually release agricultural tasks with expected times and provide feedback on task execution progress through a visualization unit. The intelligent decision-making module can adjust task parameters such as irrigation volume based on real-time field information (such as rainfall). This solution focuses on the automated release, execution supervision, and real-time fine-tuning of predetermined agricultural tasks based on environmental feedback, thereby realizing the digital management of the farming process.

[0004] However, such existing technologies have obvious limitations. First, they lack an evaluation mechanism for the inherent efficiency of planting systems, especially the inability to quantify the "time overlap rate" and "gap period" between crop rotations, and thus cannot diagnose the compactness and rationality of time utilization. Second, their decision-making relies on pre-set fixed operating procedures and real-time environmental sensing, and fails to introduce regional industrialization economic orientation as an analytical dimension. Summary of the Invention

[0005] This application provides a land use efficiency analysis method and system, which can at least partially solve the above-mentioned technical problems.

[0006] Firstly, this application provides a land use efficiency analysis method, which adopts the following technical solution: A land use efficiency analysis method includes the following steps: Planting time series data acquisition: Acquire historical planting time series data of the target plot, the data including at least the types of crops planted each time, the corresponding sowing date and harvest date; Calculation of gap period: Based on the sowing date and harvest date, calculate the length of the field gap period between two adjacent plantings in the historical planting sequence; Analysis: The calculated length of each field gap period is compared with a preset reasonable gap period threshold range for a specific combination of preceding and following crops; based on the comparison results, a land use efficiency diagnosis report is generated, which at least clearly lists the specific cropping locations diagnosed as unreasonable.

[0007] By adopting the above technical solutions, the assessment of land use efficiency is transformed from traditional experience-based judgment or macro-statistics to precise diagnosis based on specific and quantifiable time-series data. This method can automatically identify specific links in the planting plan where time utilization efficiency is low (i.e. unreasonable gaps), providing clear, data-driven decision targets for subsequent targeted optimization, and overcoming the shortcomings of vague perception of time-wasting links in previous management.

[0008] Optionally, an optimization step is provided after the analysis step; Optimization: If there are loose and compressible gaps in the diagnostic report, adjust the crop type, planting mode or operation date and perform iterative calculations to generate at least one recommended planting plan that can improve the time sequence compactness; Recommendation: Send out recommended planting plans and land use efficiency diagnostic reports.

[0009] By adopting the above technical solution, the system identifies loose and compressible gaps based on the diagnostic report. Then, it uses algorithms to adjust crop types, planting patterns, or operation dates for iterative calculations to generate recommended planting plans that improve the timeliness of the schedule. The system then pushes these plans along with the diagnostic report. This achieves an automated closed loop from problem diagnosis to plan generation. The system not only points out efficiency bottlenecks but also proactively provides optimized planting plans, reducing the workload and subjectivity of manual planning and improving the timeliness and automation of decision support.

[0010] Optionally, a matching adjustment step is provided after the optimization step; Matching and Adjustment: Obtain high-economic-benefit time window data for the dominant crops in the region; analyze the overlap between the expected output period of the crops in the historical planting time series data or the recommended planting scheme and the corresponding high-economic-benefit time window, and calculate the time window matching score; Judgment: If the matching score within the judgment time window is greater than the preset matching threshold, the original recommended planting plan is maintained; otherwise, the recommended planting plan is modified and the recommended steps are executed.

[0011] By adopting the above technical solutions, the optimization goal of land use efficiency is transformed from simply maximizing physical time utilization to optimizing the allocation of time resources for economic benefits. This reduces the possibility that the system might propose an ineffective solution that is extremely time-constrained but whose output is entirely concentrated during periods of low market prices. By introducing an economic time window as a core constraint, it ensures that the automatically generated solutions, while pursuing time efficiency, also have a higher probability of realizing market value, thus directly linking land time utilization with economic benefits.

[0012] Optionally, a fertility disturbance step is provided after the gap period calculation step; Fertility disturbance: Obtain soil fertility data for the target plot, including at least the available nitrogen, phosphorus, and potassium nutrient contents before sowing; establish a nutrient budget model based on nutrient uptake per unit yield of different crops, straw return ratio, and nutrient release characteristics; calculate the theoretical change in soil nutrients after one planting season using the following formula. : ; in, This refers to the content of readily available nutrients in the soil before sowing. To absorb the nutrients taken away by the current season's crops, The amount of nutrients that can be mineralized and released from crop residues during the planned off-season; Based on the sign and magnitude of ΔN, determine whether adjustments are needed to the selection of subsequent crops, planting density, or the length of the crop gap period.

[0013] By adopting the above technical solution, the system acquires or calls the latest soil test data of the target plot and combines it with a built-in database containing the absorption and residue characteristics of various crop nutrients. Through a predetermined nutrient balance calculation formula, the system simulates and calculates the changes in available soil nutrients caused by each planting activity. Based on the positive or negative sign and the magnitude of the calculation results, the program automatically determines the trend of the impact of the current planting arrangement on soil fertility.

[0014] This adds a soil nutrient sustainability dimension to land use efficiency analysis. The traditional pursuit of high multiple cropping may come at the cost of depleting soil fertility. By using quantitative models to predict the consumption or replenishment of key soil nutrient pools by different planting patterns, the risk of soil fertility depletion can be warned at the planning stage. This makes efficiency optimization no longer short-sighted, but based on the long-term safety boundary of ensuring sustainable use of resources.

[0015] Optionally, an overlapping calculation step may be included between the judgment step and the recommendation step; Overlap calculation: The high-economic-efficiency time window data is used to define the ideal output overlap window; the proportion of overlapping days between the output period of each crop in the recommended planting scheme and the ideal window is calculated, the recommended planting scheme is corrected according to the proportion of overlapping days, and then the recommendation steps are executed.

[0016] By adopting the above technical solution, the system calculates the overlap ratio between the yield period of each crop in the recommended scheme and the ideal window of that crop. Based on this ratio, the optimization algorithm is driven to fine-tune the crop varieties or sowing dates, for example, prioritizing varieties whose yield periods better cover the ideal window. This achieves fine-grained operation of economic adaptability optimization, decomposing a macro-level matching score target into adjustment instructions for the yield time of each individual crop, making the optimization process more targeted. Compared to judging pass / fail solely based on the final total score, this method can guide the system to make incremental improvements, such as prioritizing the adjustment of crops that are most misaligned with the ideal window, thereby improving the overall economic adaptability of the scheme with higher efficiency.

[0017] Optionally, it also includes a comprehensive overlap rate analysis step, which includes: Expected overlap calculation: Based on the preset ideal planting pattern or regional high-yield planting system, calculate the total number of days of the expected crop growing period within a standard production cycle for the target plot, and calculate the expected time overlap rate; Actual overlap calculation: Based on the historical planting time series data, calculate the total number of days of actual crop growth period within the same standard production cycle, and calculate the actual time overlap rate; Overlap rate deviation analysis: Calculate the deviation between the actual time overlap rate and the expected time overlap rate; if the deviation value is negative and exceeds the first tolerance threshold, a conclusion of insufficient utilization is generated in the land use efficiency diagnosis report, and the key gap period that caused the deviation is identified.

[0018] By adopting the above technical solutions, a target management and gap analysis mechanism for land use efficiency has been established. This transforms efficiency assessment from an absolute value calculation to a relative gap analysis compared with a clear target. This not only lets users know how many days their land has been used, but also how many days are left before reaching full potential and where the gap lies. This target gap-based diagnosis provides a clear sense of direction and urgency for optimization, enabling resource investment to focus on the most critical limiting factors.

[0019] Optionally, after the overlap rate deviation analysis, an adjustment and optimization step is also included; Adjustment and optimization: Based on the land use efficiency diagnosis report and the overlap rate deviation analysis results, at least one planting structure optimization scheme aimed at reducing negative deviation is generated and updated to the recommended planting scheme; each scheme clearly specifies the adjustment instructions for the critical gap period and related crops, the adjustment instructions include: inserting a short-growing-period crop during the critical gap period after the previous crop is harvested, or replacing the previous and next crops with a variety combination that can make the growing season more closely connected.

[0020] By adopting the above technical solutions, automated reasoning from identifying the gap to figuring out how to bridge it is achieved. It effectively solves the common dilemma of knowing the problem but not knowing how to solve it. By directly linking the efficiency gap with specific agronomic operations (species change, intercropping), the system can generate a list of highly actionable improvement suggestions, significantly reducing the technical threshold and decision-making cost of translating diagnostic conclusions into field actions.

[0021] Optionally, after the adjustment and optimization steps, a simulation verification step is also included; Simulation verification: For each generated planting structure optimization scheme, simulate the execution of its planting sequence and calculate its corresponding simulation time overlap rate; Define an ideal overlap interval based on the expected time overlap rate; Verify whether the simulated time overlap rate of each scheme falls within the ideal overlap range; mark the scheme with the simulated time overlap rate closest to the expected time overlap rate and falling within the ideal overlap range as a valid optimized scheme and update it to the recommended planting scheme.

[0022] By adopting the above technical solutions, feedforward verification and quality control are introduced in the solution generation stage. This prevents theoretically feasible solutions from being output that have poor actual simulation results. For example, a solution that compresses gaps by inserting short-growing-period crops may fail because the actual light and temperature conditions required by the crop do not match the climate during the gap period. Although the simulation verification step cannot completely replace real planting, through pre-drilling and quantitative evaluation, obviously unreasonable or ineffective options can be screened out, thereby improving the success rate and reliability of the final recommended solution.

[0023] Optionally, it also includes an interference adjustment step; Interference Adjustment: Acquire or construct a crop interaction knowledge base, which stores known negative interference relationships between crops that may lead to prolonged growth cycle or reduced yield of subsequent crops; The crop time sequence in the effective optimization scheme is matched with the knowledge base to identify specific crop rotation combinations with negative interference relationships; Based on the recognition results, perform at least one of the following adjustments: Extend the field gap period corresponding to the identified crop rotation, with the extension duration preset according to the type of negative interference relationship; Replace the subsequent crop in the identified cropping cycle with a substitute crop variety whose growth cycle is not negatively affected by the previous crop. The adjusted scheme is then updated into the recommended planting scheme and marked as an interference correction optimization scheme.

[0024] By adopting the above technical solutions, biosafety and ecological rationality considerations are incorporated into the optimization of automated planting schemes. It compensates for the biological risks that may be overlooked when optimizing solely based on time, economic, and fertility models. By encoding agronomic experience into machine-executable rules, the system can avoid proposing planting plans that, while numerically efficient, may fail in practice due to pest outbreaks or allelopathic inhibition, thus significantly enhancing the agronomic robustness and feasibility of the optimization schemes.

[0025] Secondly, the land use efficiency analysis system provided in this application adopts the following technical solution: A land use efficiency analysis system includes: a processor and a memory communicatively connected to the processor; The memory is provided with a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the processor processes the computer program stored on the computer-readable storage medium, it implements a land use efficiency analysis method.

[0026] In summary, this application includes at least one of the following beneficial technical effects: 1. The assessment of land use efficiency is transformed from traditional experience-based judgment or macro-statistics to precise diagnosis based on specific and quantifiable time-series data. This method can automatically identify specific links in the planting plan where time utilization efficiency is low (i.e. unreasonable gaps), providing clear, data-driven decision targets for subsequent targeted optimization, and overcoming the shortcomings of vague perception of time-wasting links in previous management. 2. The optimization objective of land use efficiency is shifted from simply maximizing physical time utilization to optimizing the allocation of time resources for economic benefits; this reduces the possibility that the system might propose an ineffective solution that is extremely time-constrained but whose output is entirely concentrated during periods of low market prices; by introducing an economic time window as a core constraint, it ensures that the automatically generated solutions, while pursuing time efficiency, have a higher probability of realizing market value, thus directly linking land time utilization with economic benefits. 3. It incorporates biosafety and ecological rationality considerations into the optimization of automated planting schemes; it compensates for the biological risks that may be overlooked when optimizing solely based on time, economic, and fertility models; by encoding agronomic experience into machine-executable rules, the system can reduce the number of planting plans that, while numerically efficient, may fail in practice due to pest outbreaks or allelopathic inhibition, thus significantly enhancing the agronomic robustness and feasibility of the optimized schemes. Attached Figure Description

[0027] Figure 1 This is a flowchart of the analysis method in Embodiment 1 of this application; Figure 2 This is a flowchart of the analysis method in Embodiment 2 of this application; Figure 3 This is a flowchart of the analysis method in Embodiment 3 of this application; Figure 4 This is a flowchart of the analysis method in Embodiment 4 of this application; Figure 5 This is a flowchart of the analysis method in Embodiment 5 of this application. Detailed Implementation

[0028] The following combination Figures 1 to 5 This application will be described in further detail.

[0029] This embodiment discloses a method for analyzing land use efficiency.

[0030] Example 1: Refer to Figure 1 Land use efficiency analysis methods include the following steps: Planting time series data acquisition: Acquire historical planting time series data of the target plot, the data including at least the types of crops planted each time, the corresponding sowing date and harvest date; Specifically, the system automatically connects to the farm management information system and IoT agricultural machinery monitoring platform of the target plot through an application programming interface (API), or allows users to manually upload standard-format electronic planting logs via a web form. The system parses and extracts structured historical planting records from these data sources, with each record containing at least three fields: Crop_Type (crop type), Sowing_Date (sowing date), and Harvest_Date (harvest date). These date data are required to be accurate to the day.

[0031] How to use: Before the analysis begins, the user needs to specify or select a unique identifier for the target plot (such as plot number). The system will automatically retrieve or require the user to upload all planting records for the plot in at least one full calendar year (e.g., from January 1, 2025 to December 31, 2025).

[0032] Calculation of gap period: Based on the sowing date and harvest date, calculate the length of the field gap period between two adjacent plantings in the historical planting sequence; Specifically, the system sorts the planting records obtained in the planting time series data acquisition step into an ordered planting sequence by ascending order of Harvest_Date; for any two adjacent plantings in the sequence... and Define the previous crop The harvest date is Subsequent crops The sowing date is .

[0033] Calculation formula: Length of the field gap between two adjacent plantings The formula for calculating (unit: days) is: ; In the formula, subtracting 1 indicates that the gap period does not include the harvest day and the sowing day.

[0034] Usage: The system sequentially traverses the planting sequence, applies the above formula to each pair of adjacent planting records, and calculates the gap period for each cropping cycle in the history of this plot. And form a list.

[0035] Analysis: The calculated length of each field gap period is compared with a preset reasonable gap period threshold range for a specific combination of preceding and following crops; based on the comparison results, a land use efficiency diagnosis report is generated, which at least clearly lists the specific cropping locations diagnosed as unreasonable.

[0036] Specifically, the system maintains a "reasonable gap period threshold library". This threshold library is stored in the form of a data table, and each record defines the corresponding reasonable gap period threshold range for a specific "previous crop-next crop" combination. .

[0037] It represents the shortest time required to complete necessary agricultural operations (such as previous crop residue disposal, soil preparation, and application of base fertilizer). This represents the maximum number of days of idle time allowed, typically set based on avoiding seasonal waste and lower limits for agricultural time. These thresholds are determined and preset based on the experience of regional agronomic experts, the physiological characteristics of major crops, and local climatic conditions.

[0038] Usage: For each gap period calculated in the gap calculation step... The system determines the type of the preceding crop based on the type of the preceding crop. and subsequent crop type Retrieve the corresponding threshold from the threshold database. and Then a comparison is performed: like If so, the diagnosis for this crop rotation is "insufficient rest period, posing a risk".

[0039] like If so, the cropping arrangement is diagnosed as "reasonable".

[0040] like If so, the cut surface is diagnosed as "loose and compressible".

[0041] The system summarizes the diagnostic conclusions of all crop rotations and automatically generates a "Land Use Efficiency Diagnostic Report." This report, in the form of structured text and charts, clearly lists the specific locations of all crop rotations diagnosed as "insufficient fallow period" or "loose and compressible" (e.g., "the gap between the harvest of the first rice crop in 2022 and the planting of the second rapeseed crop"), and the corresponding number of days of the gap period. and reasonable thresholds for comparison. .

[0042] Example of threshold value: In the rice-rapeseed rotation area of ​​the middle and lower reaches of the Yangtze River, the reasonable gap period threshold range for the "rice-rapeseed" combination is set to [7, 15] days. Reasoning for this value: This is an estimate of the minimum time required for necessary operations such as field drainage after rice harvest, straw treatment, rotary tillage, and fertilization before rapeseed sowing. This is because the optimal sowing window for rapeseed is considered; an excessively long gap will lead to late sowing of rapeseed, affecting yield.

[0043] For example, taking the planting records of a target plot in a certain city of a certain province in 2022 as an example, the data obtained is: the first crop was rice, and the harvest date was... The planting date was August 10, 2022; the second crop was rapeseed, and the sowing date was... It is September 1, 2022.

[0044] Gaps calculation: .

[0045] Analysis and diagnosis: Query the threshold database for the "rice-rapeseed" combination. ;because The system marks this cut as "loose and compressible" in the diagnostic report and points out that the actual gap period is 6 days longer than the reasonable upper limit.

[0046] This embodiment achieves automated quantitative diagnosis of land time utilization efficiency at the stage of the process. Compared with traditional methods that rely on macro-statistics such as the annual multiple cropping index, this embodiment can accurately pinpoint the specific cropping sequence and the number of days of inefficient idleness or excessive compactness, providing clear and objective data targets for subsequent refined management and optimization.

[0047] Example 2: Refer to Figure 2 The difference between this embodiment and Embodiment 1 is that an optimization step is added after the analysis step. Optimization: If there are loose and compressible gaps in the diagnostic report, adjust the crop type, planting mode or operation date and perform iterative calculations to generate at least one recommended planting plan that can improve the time sequence compactness; Specifically, the system reads the diagnostic report generated in Example 1 and filters out all gap period records marked as "loose and compressible". Based on a built-in crop growth period database, the system optimizes by compressing the total length of these gap periods. It then uses an iterative algorithm (such as heuristic search) to adjust the crop types (selected from a variety library with shorter growth periods) or operation dates (fine-tuning the sowing date within the agricultural time window). Each iteration generates a new planting sequence scheme (i.e., a "recommended planting scheme") and calculates its theoretical total gap period. The iteration continues until N (e.g., N=5) schemes with significantly shortened total gap periods are generated.

[0048] The system will temporarily store the generated N recommended planting plans and their corresponding planting calendars (crop sequences and dates).

[0049] Matching and Adjustment: Obtain high-economic-benefit time window data for the dominant crops in the region; analyze the overlap between the expected output period of the crops in the historical planting time series data or the recommended planting scheme and the corresponding high-economic-benefit time window, and calculate the time window matching score; Specifically, the system obtains or incorporates "high-economic-benefit time window data for dominant crops" from regional agricultural market information platforms; for each crop C, this data defines one or more preferred production periods. For example, "the market price of rapeseed increased by more than 15% between May 10 and May 30".

[0050] Usage: For each recommended planting plan generated in the optimization step, the system calculates the planting date and average growth period of each crop in the plan. Calculate its expected production date Then, calculate the expected yield date of the crop. Its high economic efficiency window The degree of overlap.

[0051] Calculation formula: The formula for calculating the time window matching score M (for a single crop) is as follows: ;in, This indicates the number of days that the expected production period coincides with the window of high efficiency. This indicates the number of days in the crop's expected production period (which can be set according to sales practices, such as 7 days); the overall matching score of the plan. All crops are available The weighted average.

[0052] Preset matching threshold For example, setting it to 0.6 means that the output period of the main crops in the plan should fall within the high-efficiency window for more than 60% of the time.

[0053] Judgment: If the matching score of the time window is greater than the preset matching threshold, the original recommended planting plan is maintained; otherwise, the recommended planting plan is modified and the overlap calculation step is performed.

[0054] Specifically, the system compares each recommended planting plan. and .like If the economic matching degree of the plan is deemed satisfactory, the original plan will be maintained and proceed to the next step; if If so, it is deemed unqualified.

[0055] Overlap calculation: The high-economic-efficiency time window data is used to define the ideal output overlap window; the proportion of overlapping days between the output period of each crop in the recommended planting scheme and the ideal window is calculated, the recommended planting scheme is corrected according to the proportion of overlapping days, and then the recommendation steps are executed.

[0056] Specifically, for solutions deemed unqualified during the judgment step, the system improves... To make corrections to the target; correction methods include: replacing severely mismatched crop varieties with varieties whose maturity periods are better matched. Varieties, or minor adjustments to their sowing dates within permissible limits. After correction, the scheme is recalculated. .

[0057] Usage: This step is part of iterative optimization until a solution is found. achieve Or correct the attempt count has been exhausted.

[0058] Recommendation: Send out recommended planting plans and land use efficiency diagnostic reports.

[0059] Specifically, the system will package and push the final recommended planting plan (which may be one or more) that simultaneously meets the requirements of time-compact optimization and economic matching, together with the diagnostic report generated in Example 1, to the user's client interface.

[0060] Application Scenario Example (Continued from Example 1): In response to the 21-day gap diagnosed in Example 1, the system generates an optimization plan in the optimization step: replace the original rapeseed variety with an early-maturing rapeseed variety with a shorter growth period of 5 days, thereby allowing the sowing date to be postponed to September 6 and the gap period to be compressed to 16 days.

[0061] After adjusting the matching degree in the steps, the system retrieved the local high-efficiency rapeseed window. The target date is May 10-25 of the following year; the original plan (sowing on September 1, growth period 210 days) resulted in a yield in early May, which is a poor match; the new plan (sowing on September 6, early-maturing variety with a growth period of 205 days) results in a yield around May 10, calculated as follows. Significantly increased to over 0.8, exceeding .

[0062] The system determined that the solution was acceptable and ultimately recommended it to the user along with the diagnostic report.

[0063] This embodiment, based on basic efficiency diagnosis, adds the function of automatically generating optimization plans and closely integrates the optimization direction with market demand; it reduces the problem of reduced economic benefits that may be caused by simply pursuing tight schedules, and makes the output planting plan both time-efficient and economically reasonable.

[0064] Example 3: Reference Figure 3 The difference between this embodiment and Embodiment 2 is that a fertility disturbance step is added after the gap period calculation step. Fertility disturbance: Obtain soil fertility data for the target plot, including at least the content of available nitrogen, phosphorus, and potassium nutrients in the soil before sowing; establish a nutrient balance model based on the nutrient uptake per unit yield of different crops, the proportion of straw returned to the field, and nutrient release characteristics; calculate the theoretical change in soil nutrients ΔN after one season of planting using the following formula: ΔN=(Sinitial−Ucrop+Rresidue)−Sinitial; Among them, Sinitial is the content of available nutrients in the soil before sowing, Ucrop is the amount of nutrients absorbed and carried away by the current crop, and Rresidue is the amount of nutrients that can be mineralized and released from the residues of the current crop during the planned gap period. Based on the sign and magnitude of ΔN, determine whether adjustments are needed to the selection of subsequent crops, planting density, or the length of the crop rotation gap; if If soil nutrient levels fall below the preset safe threshold, it is recommended to extend the gap period to allow for longer natural nutrient recovery or to plant green manure crops, or to plant crops with lower nutrient requirements in the subsequent crop; if If the value is significantly higher than the preset threshold, it is recommended to shorten the gap period or plant high-fertilizer-consuming crops in the subsequent crop.

[0065] Specifically, the system obtains the most recent soil testing report data for the target plot through an interface, or through historical data from vehicle-mounted sensors, including at least the available nitrogen content of the soil before sowing. Available phosphorus content Available potassium content (Unit: kg / mu).

[0066] The system has a built-in "crop nutrient budget model," which is a database that records the amount of nitrogen, phosphorus, and potassium nutrients absorbed by the main crops in the region for every 100 kg of economic yield produced. And the proportion of nutrients that can be mineralized and released within a set time period after the straw and other residues are returned to the field. ).

[0067] Usage: For each planting operation j in historical planting records or any recommended planting plan (the crop being planted is...) The expected output is ): Calculate the amount of nutrients removed by crops: Phosphorus and potassium can be calculated similarly.

[0068] Calculate the amount of nutrients that can be released from crop residues: Assuming all straw is returned to the field, then ,in This is the ratio of straw yield to economic output; phosphorus and potassium are calculated similarly.

[0069] Calculate the theoretical changes in soil nutrients after planting this season (taking nitrogen as an example, ΔN): ; Note that this calculation represents the net change in nutrients relative to the planting activities of the current season. It will be dynamically updated during continuous calculations.

[0070] Soil nutrient safety threshold Based on the goal of maintaining moderate soil fertility, such as available nitrogen .

[0071] Judgment logic: The system simulates a continuous planting process. If the simulation reaches a certain cropping stage, the predicted soil value... If the soil nitrogen nutrient level is too low, it is determined that the soil nitrogen nutrient level is insufficient and adjustments are needed. The adjustment recommendation logic is as follows: if the negative value of ΔN is too large, it is recommended to extend the gap period after the current cropping (to provide time for natural mineralization) or to plant green manure crops during the gap period (to increase the nitrogen content of the soil). If the predicted value is far above the safety threshold, it may be recommended to shorten the gap period or plant fertilizer-intensive crops to utilize the surplus nutrients.

[0072] Application scenario example (continued from example 2): Based on the early-maturing rapeseed scheme optimized in example 2, perform fertility interference analysis.

[0073] The system obtains the initial soil data of the plot. Model parameters: Rice (yield 500 kg / mu) absorption g, straw nitrogen release rate Early-maturing rapeseed (yield 180 kg / mu) absorption Straw nitrogen release rate Straw coefficient .

[0074] Rice season: ; End-of-quarter forecast .

[0075] Rapeseed season (16-day gap, natural mineralization ignored): ; End-of-quarter forecast .

[0076] The system determines that although there is a nitrogen surplus, the lack of crop consumption over a long period may lead to nitrogen loss. Combined with the fact that the original 21-day gap in the diagnostic report has been compressed, the system may suggest: "The current plan has a soil nitrogen surplus; it is advisable to consider planting a short-term crop of fast-growing green manure (such as sesbania) during the summer gap after rapeseed harvest to fix nitrogen and prevent loss, or to arrange crops with higher nitrogen requirements in subsequent crop rotations."

[0077] This embodiment, based on time-series and economic optimization, adds constraint analysis of soil nutrient dynamic balance. It can predict and warn of the long-term impact of planting arrangements on soil fertility, prevent soil degradation caused by pursuing short-term efficiency, and make the optimization scheme ecologically sustainable.

[0078] Example 4: Reference Figure 4 The difference between this embodiment and embodiment 3 is that it also includes a comprehensive overlap rate analysis step, which includes: Expected overlap calculation: Based on the preset ideal planting pattern or regional high-yield planting system, calculate the total number of days of the expected crop growing period within a standard production cycle for the target plot, and calculate the expected time overlap rate; Specifically, the system determines an "ideal planting pattern" based on the target plot's climate conditions, light and temperature resources, and a regional database of high-yield and high-efficiency planting patterns (such as a three-crop-a-year system of wheat-corn-soybeans); and calculates the value of this pattern over a standard production cycle. The total number of days in the theoretical growing season of all crops within a 365-day period (e.g., 365 days). .

[0079] Calculation formula: Expected time overlap rate .

[0080] Actual overlap calculation: Based on the historical planting time series data, calculate the total number of days of actual crop growth period within the same standard production cycle, and calculate the actual time overlap rate; Specifically, based on historical planting timeline data, the total number of days in the actual growing season for all crops within the same period is calculated. The crop growing season is defined as the number of days from the sowing date to the harvest date.

[0081] Calculation formula: Actual time overlap rate .

[0082] Overlap rate deviation analysis: Calculate the deviation between the actual time overlap rate and the expected time overlap rate; if the deviation value is negative and exceeds the first tolerance threshold, a conclusion of insufficient utilization is generated in the land use efficiency diagnosis report, and the key gap period that caused the deviation is identified.

[0083] Specifically, calculate the deviation value Set the first tolerance threshold. For example, -10%. If If so, the land is deemed underutilized in terms of time. The system will... , , The correlation analysis of key gaps leading to inefficiency (from the diagnosis in Example 1) was also included in the diagnostic report.

[0084] Following the overlap rate deviation analysis, an adjustment and optimization step is also included; Adjustment and optimization: Based on the land use efficiency diagnosis report and the overlap rate deviation analysis results, at least one planting structure optimization scheme aimed at reducing negative deviation is generated and updated to the recommended planting scheme; each scheme clearly specifies the adjustment instructions for the critical gap period and related crops, the adjustment instructions include: inserting a short-growing-period crop during the critical gap period after the previous crop is harvested, or replacing the previous and next crops with a variety combination that can make the growing season more closely connected.

[0085] Specifically, regarding the "critical gaps" identified by the deviation analysis, the system aims to improve... near With the goal of automatically generating optimization schemes, for example, for a critical gap period, the system selects suitable crops from the short-growing-period crop library for insertion simulation, or evaluates the feasibility of changing the varieties of the preceding and following crops to shorten the connection time, generating multiple candidate "planting structure optimization schemes".

[0086] Simulation verification: For each generated planting structure optimization scheme, simulate the execution of its planting sequence and calculate its corresponding simulation time overlap rate; Define an ideal overlap interval based on the expected time overlap rate; Verify whether the simulated time overlap rate of each scheme falls within the ideal overlap range; mark the scheme with the simulated time overlap rate closest to the expected time overlap rate and falling within the ideal overlap range as a valid optimized scheme and update it to the recommended planting scheme.

[0087] Specifically, for each candidate scheme generated by the adjustment and optimization steps, the system simulates and executes its precise planting sequence, and calculates its simulation time overlap rate. .

[0088] Define an ideal overlap interval ,in To allow for floating values, such as 5%.

[0089] Verification logic: If If the solution is valid, then the system considers the solution effective; among all valid solutions, the system selects the solution that best suits its purpose. The smallest solution is marked as an "effective optimization solution" and updated to the final set of recommended solutions.

[0090] Application scenario examples (continued from example 3): Suppose the target plot is located in a city where two crops a year (rice-rapeseed) is the mainstream model, but theoretically three crops a year (such as early rice-rabbit rice-rapeseed) is the high-yield model for the region.

[0091] The ideal model is "early rice-rabbit rice-rapeseed". It is approximately (120 + 80 + 210) = 410 days. The historical actual model of "rice-rapeseed" It is approximately (130 + 210) = 340 days. ; The system determined that the utilization was insufficient and identified the long gap between rice and rapeseed as a key issue.

[0092] The system attempts to generate optimized solutions, such as inserting a 60-day growing season of fast-growing vegetables into the long gap between rice harvest and rapeseed planting. Simulations calculate the new "rice-fast-growing vegetables-rapeseed" solution. , The result falls within the ideal range [107%, 117%]. This solution is marked as an effective optimization solution.

[0093] This embodiment introduces the expected overlap rate based on the high-yield potential of a region as an evaluation benchmark, giving the efficiency analysis a clear "goal-oriented" nature; combined with simulation verification, it can select the scheme that is closest to the theoretical optimal goal from multiple optimization paths, thereby improving the scientific nature and foresight of the planning.

[0094] Example 5: Refer to Figure 5The difference between this embodiment and embodiment 4 is that it also includes an interference adjustment step; Interference Adjustment: Acquire or construct a crop interaction knowledge base, which stores known negative interference relationships between crops that may lead to prolonged growth cycle or reduced yield of subsequent crops; The crop time sequence in the effective optimization scheme is matched with the knowledge base to identify specific crop rotation combinations with negative interference relationships; Based on the recognition results, perform at least one of the following adjustments: Extend the field gap period corresponding to the identified crop rotation, with the extension duration preset according to the type of negative interference relationship; Replace the subsequent crop in the identified cropping cycle with a substitute crop variety whose growth cycle is not negatively affected by the previous crop. The adjusted scheme is then updated into the recommended planting scheme and marked as an interference correction optimization scheme.

[0095] Specifically, the system constructs or connects a "crop interaction knowledge base". This knowledge base is stored in the form of rules, such as: "Previous crop: watermelon; Subsequent crop: tomato; Interference type: increased risk of soil-borne diseases (wilt); Recommended measures: extend the gap period by at least 30 days or rotate with non-solanaceous crops".

[0096] The system extracts the crop time series from the "effective optimization scheme" obtained in Example 4 and performs pattern matching with the knowledge base.

[0097] Adjustments will be made based on the identification results: If the suggestion to "extend the gap period" is matched, then the gap period of that crop will be extended. Forced increase to the recommended value The extended duration is preset based on the type of interference; for example, 30 days corresponds to disease risk, and 15 days corresponds to allelopathic inhibition.

[0098] If a "Change Crop" suggestion is matched, an alternative crop is selected from the list of non-sensitive crops for that subsequent crop location, and its growth period and economic suitability are re-examined.

[0099] The scheme formed after the above adjustments will be updated to the "Interference Correction and Optimization Scheme".

[0100] The effective optimization scheme in Example 4 is "rice-fast-growing vegetables-rapeseed". In the interference adjustment step, the system matches the sequence with the knowledge base. It is assumed that there are rules in the knowledge base: "Previous crop: certain varieties of fast-growing vegetables (such as certain leafy vegetables); Subsequent crop: rapeseed; Interference type: may increase the risk of rapeseed sclerotinia disease; Recommended measures: extend the gap period by 10 days or strengthen field disinfection".

[0101] The system identified this potential risk. Adjustment measures were taken: the planned gap period between "fast-growing vegetables and rapeseed" (assumed to be 5 days) was extended by 10 days, to 15 days; then the total growth period and overlap rate of this "interference correction optimization scheme" were reassessed to confirm that it still met the requirements.

[0102] This embodiment adds biocompatibility (agronomic safety) constraints to the existing physical, economic, and ecological constraints; it can automatically identify and avoid potential yield reduction risks caused by unfavorable crop interactions (such as continuous cropping obstacles and allelopathy), so that the final optimized scheme is not only efficient in model calculation, but also has higher robustness and success rate in field practice.

[0103] This application also discloses a land use efficiency analysis system.

[0104] A land use efficiency analysis system includes: a processor, and a memory communicatively connected to the processor; The memory is provided with a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the processor processes the computer program stored on the computer-readable storage medium, it implements a land use efficiency analysis method.

[0105] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for analyzing land use efficiency, characterized in that: Includes the following steps: Planting time series data acquisition: Acquire historical planting time series data of the target plot, the data including at least the types of crops planted each time, the corresponding sowing date and harvest date; Calculation of gap period: Based on the sowing date and harvest date, calculate the length of the field gap period between two adjacent plantings in the historical planting sequence; Analysis: The calculated length of each field gap period is compared with a preset reasonable gap period threshold range for a specific combination of preceding and following crops; based on the comparison results, a land use efficiency diagnosis report is generated, which at least clearly lists the specific cropping locations diagnosed as unreasonable.

2. The land use efficiency analysis method according to claim 1, characterized in that: An optimization step is provided after the analysis step; Optimization: If there are loose and compressible gaps in the diagnostic report, adjust the crop type, planting mode or operation date and perform iterative calculations to generate at least one recommended planting plan that can improve the time sequence compactness; Recommendation: Send out recommended planting plans and land use efficiency diagnostic reports.

3. The land use efficiency analysis method according to claim 2, characterized in that: Following the optimization steps, a matching adjustment step is also provided; Matching and Adjustment: Obtain high-economic-benefit time window data for the dominant crops in the region; analyze the overlap between the expected output period of the crops in the historical planting time series data or the recommended planting scheme and the corresponding high-economic-benefit time window, and calculate the time window matching score; Judgment: If the matching score within the judgment time window is greater than the preset matching threshold, the original recommended planting plan is maintained; otherwise, the recommended planting plan is modified and the recommended steps are executed.

4. The land use efficiency analysis method according to claim 1, characterized in that: A fertility disturbance step is set after the gap period calculation step. Fertility disturbance: Obtain soil fertility data for the target plot, including at least the content of available nitrogen, phosphorus, and potassium nutrients in the soil before sowing; establish a nutrient balance model based on the nutrient uptake per unit yield of different crops, the proportion of straw returned to the field, and nutrient release characteristics. The theoretical changes in soil nutrients after one planting season can be calculated using the following formula. : ； in, This refers to the content of readily available nutrients in the soil before sowing. To absorb the nutrients taken away by the current season's crops, The amount of nutrients that can be mineralized and released from crop residues during the planned off-season; Based on the sign and magnitude of ΔN, determine whether adjustments are needed to the selection of subsequent crops, planting density, or the length of the crop gap period.

5. The land use efficiency analysis method according to claim 3, characterized in that: Between the judgment step and the recommendation step, there is also an overlapping calculation step. Overlap calculation: The high-economic-efficiency time window data is used to define the ideal output overlap window; the proportion of overlapping days between the output period of each crop in the recommended planting scheme and the ideal window is calculated, the recommended planting scheme is corrected according to the proportion of overlapping days, and then the recommendation steps are executed.

6. The land use efficiency analysis method according to claim 5, characterized in that: It also includes a comprehensive overlap rate analysis step, which includes: Expected overlap calculation: Based on the preset ideal planting pattern or regional high-yield planting system, calculate the total number of days of the expected crop growing period within a standard production cycle for the target plot, and calculate the expected time overlap rate; Actual overlap calculation: Based on the historical planting time series data, calculate the total number of days of actual crop growth period within the same standard production cycle, and calculate the actual time overlap rate; Overlap rate deviation analysis: Calculate the deviation between the actual time overlap rate and the expected time overlap rate; if the deviation value is negative and exceeds the first tolerance threshold, a conclusion of insufficient utilization is generated in the land use efficiency diagnosis report, and the key gap period that caused the deviation is identified.

7. The land use efficiency analysis method according to claim 6, characterized in that: Following the overlap rate deviation analysis, an adjustment and optimization step is also included; Adjustment and optimization: Based on the land use efficiency diagnosis report and the overlap rate deviation analysis results, at least one planting structure optimization scheme aimed at reducing negative deviation is generated and updated to the recommended planting scheme; Each scheme explicitly specifies adjustment instructions for the critical gap period and related crops, including: inserting a short-growing-period crop during the critical gap period after the previous crop is harvested, or replacing the previous and subsequent crops with a combination of varieties that allows for a closer connection between the growing seasons.

8. The land use efficiency analysis method according to claim 7, characterized in that: Following the adjustment and optimization steps, a simulation verification step is also included; Simulation verification: For each generated planting structure optimization scheme, simulate the execution of its planting sequence and calculate its corresponding simulation time overlap rate; Define an ideal overlap interval based on the expected time overlap rate; Verify whether the simulated time overlap rate of each scheme falls within the ideal overlap range; mark the scheme with the simulated time overlap rate closest to the expected time overlap rate and falling within the ideal overlap range as a valid optimized scheme and update it to the recommended planting scheme.

9. The land use efficiency analysis method according to claim 8, characterized in that: Following the simulation verification step, an interference adjustment step is also included. Interference Adjustment: Acquire or construct a crop interaction knowledge base, which stores known negative interference relationships between crops that may lead to prolonged growth cycle or reduced yield of subsequent crops; The crop time sequence in the effective optimization scheme is matched with the knowledge base to identify specific crop rotation combinations with negative interference relationships; Based on the recognition results, perform at least one of the following adjustments: Extend the field gap period corresponding to the identified crop rotation, with the extension duration preset according to the type of negative interference relationship; Replace the subsequent crop in the identified cropping cycle with a substitute crop variety whose growth cycle is not negatively affected by the previous crop. The adjusted scheme is then updated into the recommended planting scheme and marked as an interference correction optimization scheme.

10. A land use efficiency analysis system, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory is provided with a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the processor processes a computer program stored on the computer-readable storage medium, it implements the method as described in any one of claims 1-9.

Images