A method, system, and readable medium for post-harvest loss assessment of oilseed crops based on monte carlo simulation
Patent Information
- Application Number
- CN202611066604.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-17
- Publication Date
- 2026-10-23
AI Technical Summary
现有产后损失评估方法多为针对单一作物、单一环节的分散研究，缺乏覆盖主要油料作物且贯穿五个产后环节的系统性评估方法
1、本发明中方案提高了油料作物产后环节技术损失率评估结果的可靠性；避免了由于油料作物数据不易获得，导致的各环节不同环节损失率的不确定性，并可实现对油料作物分品种、分省域以及全国范围内油料作物产后损失评估。
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Figure CN122965814A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of oilseed crop loss prediction technology, and specifically relates to a method, system and readable medium for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation. Background Technology
[0002] my country is the world's largest consumer of oilseed crops and edible vegetable oils. In 2012, my country's actual oilseed imports exceeded 100 million tons for the first time. The country's dependence on imported vegetable oils has long been high, and the oilseed trade has consistently faced the severe situation of "high dependence on imports and concentrated import sources." Under the rigid constraints of arable land resources, post-harvest loss reduction has become an important path to increase the effective supply of oilseeds.
[0003] Accurately assessing oilseed crop losses and clarifying key loss mechanisms are fundamental to implementing post-harvest loss reduction measures. Currently, the academic community has conducted extensive research on losses in the three major staple grains (corn, rice, and wheat) and soybeans. However, due to differences in assessment methods and environmental factors, a consensus on post-harvest losses in grains has not yet been reached. Research on losses in oilseed crops such as rapeseed and peanuts is even scarcer, and studies on losses at different stages primarily focus on harvesting and farmer storage losses. The extent and key stages of post-harvest losses in oilseed crops remain unclear. Overall, current research on post-harvest losses in oilseed crops suffers from unclear baseline data, ambiguous loss mechanisms, and a lack of systematic research, which to some extent hinders the implementation of post-harvest loss reduction pathways.
[0004] Specifically, the existing post-harvest loss assessment for oilseed crops has the following problems: 1. Lack of a unified and quantifiable assessment method. my country has a rich variety of oilseed crops with wide applications and a relatively complex supply chain. Looking at the current utilization status of domestically produced oilseed crops in my country, apart from rapeseed and camellia seeds which are mainly used for oil production, soybeans, peanuts, sunflower seeds, walnuts, sesame seeds, and other oilseed crops all have the dual attribute of being used for edible oil. The post-harvest process of oilseed crops is the process of transforming raw materials into commercial products, including harvesting, drying and storage, transportation, processing, sales, and consumption. The reduction in oilseed quantity due to factors such as technological level and management conditions in the first five stages is defined as post-harvest loss. Existing post-harvest loss assessment methods are mostly fragmented studies targeting single crops and single stages, lacking a systematic assessment method that covers major oilseed crops and spans all five post-harvest stages.
[0005] 2. Difficulty in obtaining post-harvest loss data. Obtaining post-harvest loss data usually relies on field trials, farmer surveys, or expert estimates, which suffers from problems such as small sample size, limited coverage, and inconsistent data quality. Limited sample data is difficult to accurately reflect the overall post-harvest loss situation.
[0006] 3. Postpartum loss assessment involves a large number of parameters, including the loss rate of different technologies at each stage and the proportion of each technology used. These parameters often have a certain range of values rather than fixed values. Traditional deterministic models cannot effectively handle this uncertainty, making it difficult to judge the reliability of the assessment results. Summary of the Invention
[0007] To address the aforementioned issues, the present invention aims to provide a method, system, and readable medium for assessing post-harvest losses of oil crops based on Monte Carlo simulation. It establishes a complete system for assessing post-harvest losses of oil crops, solves the uncertainty in oil crop loss assessment caused by insufficient samples, and improves the reliability of loss rate assessment for oil crops under different technical conditions.
[0008] To achieve the above objectives, the present invention proposes the following technical solution: a method for assessing post-harvest losses of oil crops based on Monte Carlo simulation, comprising the following steps: establishing a material flow analysis model for oil crops at each post-harvest stage; Each of the aforementioned steps includes several technical methods. A weighted analysis is performed on the technical methods to obtain the loss rate of each step. The loss rates of each stage are input into a Monte Carlo simulation based on a Beta distribution to obtain the technical loss rate distribution of each stage. Based on the material flow analysis model and the technical loss rate distribution of each stage, the comprehensive post-harvest loss rate of a single oilseed crop is obtained. Based on the comprehensive post-harvest loss rate of the single oilseed crop, the comprehensive post-harvest loss rate of the oilseed crop within the province and / or the whole country is obtained through a province-to-nation weighted calculation model. The key loss stages in the post-harvest chain are identified by the contribution of the comprehensive post-harvest loss rate of the oilseed crop within the province and / or the whole country.
[0009] Furthermore, the post-harvest stages include harvesting, drying, storage, transportation, and processing. Between each stage, the outflow from the previous stage equals the inflow from the next stage.
[0010] Furthermore, the loss rates for each stage in the material flow analysis model are:
[0011] in, It is the loss rate of the i-th stage. It is the output of the i-th stage; The comprehensive post-harvest loss rate of the single oilseed crop is:
[0012] in, This is the theoretical yield of a single oilseed crop. It is the output of the processing stage. It is the comprehensive postpartum loss rate.
[0013] Furthermore, the harvesting process includes manual harvesting, segmented harvesting, and combined harvesting; the drying process includes natural sun-drying and mechanical drying; the storage process includes simple farmer storage, bulk storage in flat warehouses, and temperature-controlled storage; the transportation process includes bulk transportation, bagged transportation, and boxed / refrigerated transportation; and the processing process includes traditional pressing, pre-treatment pressing, pre-pressing-leaching, and cold pressing. The i-th stage usually has One technical approach, let the first The loss rate of this technical method The proportion adopted is Then the loss rate of the i-th stage is:
[0014] in, .
[0015] Furthermore, in the aforementioned province-to-national weighted calculation model, within the provincial scope, oilseed crops... Postpartum loss for:
[0016] Nationwide, oil crops Postpartum loss for:
[0017] Oil crops The national postpartum comprehensive loss rate for:
[0018] in, It is the first p The province, the first v The comprehensive post-harvest loss rate of oilseed crops, It is the first p The province, the first v The theoretical yield of an oilseed crop, It is the first p The province, the first v Loss rate of the i-th stage of an oilseed crop; The comprehensive post-harvest loss rate for all oilseed crops is:
[0019] in, It is the overall loss rate of all oilseed crops, where n is the number of types of oilseed crops.
[0020] Furthermore, the method for obtaining the technical loss rate distribution of each stage by inputting the loss rate of each stage into a Monte Carlo simulation based on the Beta distribution is as follows: using triangular distribution parameters... The uncertainty of the loss rate is expressed; the triangular distribution parameters are converted into Beta distribution parameters; the Beta distribution parameters are subjected to Monte Carlo sampling to obtain the technical loss rate distribution of each stage.
[0021] Furthermore, the triangular distribution parameters The method for expressing the uncertainty of the loss rate is as follows: for the th Step, First loss rate of this technology The uncertainty range is expressed using three parameters of a triangular distribution: It is the minimum loss rate. It is the most likely value of the loss rate. It is the maximum value of the loss rate; the method to convert the triangular distribution parameters into Beta distribution parameters is as follows: Calculate the most probable value of the standardized loss rate using a, m, and b. ;
[0022] The most likely value of the standardized loss rate Calculate the mean of the triangular distribution. and variance ;
[0023] According to the mean and variance By inversely deriving the shape parameters of the Beta distribution,
[0024]
[0025] in, It is a bridge parameter connecting the triangular distribution moment and the Beta shape parameter. It is the first shape parameter. It is the second shape parameter.
[0026] Furthermore, the contribution of each stage to the overall postpartum loss rate. for:
[0027] in, This refers to the comprehensive post-harvest loss rate of all oilseed crops, and the contribution of each stage to the comprehensive post-harvest loss rate. The larger the value, the greater the contribution of that stage to postpartum losses.
[0028] This invention also discloses a post-harvest loss assessment system for oilseed crops based on Monte Carlo simulation, comprising: a material flow analysis model establishment module for establishing a material flow analysis model for oilseed crops at each post-harvest stage; a loss rate acquisition module for performing weighted analysis on several technical methods included in each stage to obtain the loss rate of each stage; a Monte Carlo simulation module for inputting the loss rates of each stage into a Monte Carlo simulation based on a Beta distribution to obtain the technical loss rate distribution of each stage; a single crop post-harvest comprehensive loss rate acquisition module for obtaining the comprehensive post-harvest loss rate of a single oilseed crop based on the material flow analysis model and the technical loss rate distribution of each stage; a post-harvest comprehensive loss rate acquisition module for obtaining the comprehensive post-harvest loss rate of the oilseed crop within a province and / or nationwide using a province-to-nation weighted calculation model based on the comprehensive post-harvest loss rate of the single oilseed crop; and a stage loss contribution module for identifying key loss stages in the post-harvest chain by the contribution of the comprehensive post-harvest loss rate of the oilseed crop within a province and / or nationwide.
[0029] The present invention also discloses a computer-readable storage medium storing a computer program that is executed by a processor to implement the Monte Carlo simulation-based method for assessing post-harvest losses of oil crops as described in any of the preceding claims.
[0030] The technical solution of the present invention has at least the following technical effects or advantages: 1. The solution in this invention improves the reliability of the assessment results of technical loss rate in the post-harvest stage of oil crops; avoids the uncertainty of loss rate in different stages due to the difficulty in obtaining oil crop data, and can realize the assessment of post-harvest loss of oil crops by variety, by province, and nationwide.
[0031] 2. This invention couples the proportion of technologies adopted in each post-harvest stage of oilseed crops with the loss rate, analyzes the key links of post-harvest loss of oilseed crops and evaluates the loss reduction effect that depends on technological improvements.
[0032] 3. The solution in this invention is not only applicable to the three major oil crops, but can also be extended to the post-harvest loss assessment of other agricultural products such as rice, wheat, and corn by adjusting the technical parameters and crop characteristic parameters, and has broad application prospects. Attached Figure Description
[0033] Figure 1 This is a flowchart of a method for assessing post-harvest loss of oil crops based on Monte Carlo simulation in one embodiment of the present invention; Figure 2This is a comparison chart of the simulated average loss rates of the three major oilseed crops at each stage after harvest, according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the contribution of soybean post-harvest loss in one embodiment of the present invention. Detailed Implementation
[0034] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention is described in detail through specific embodiments. However, it should be understood that the specific embodiments are provided only for a better understanding of the present invention and should not be construed as limiting the present invention. In the description of the present invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0035] To address the problems in existing technologies, such as the lack of a unified and quantifiable method for assessing oilseed crop losses, difficulties in obtaining post-harvest loss data, and the inability to quantify parameter uncertainties, this invention proposes a method, system, and readable medium for assessing post-harvest losses of oilseed crops based on Monte Carlo simulation. The method includes the following steps: establishing a material flow analysis model for each oilseed crop at each stage; performing a weighted analysis of the technical methods at each stage to obtain the loss rate at each stage; using Monte Carlo simulation based on Beta distribution to solve the uncertainty problem of different technical loss rates at each stage and obtain the range of technical loss rates at each stage; obtaining the comprehensive post-harvest loss rate of a single oilseed crop based on the material flow analysis model; and obtaining the comprehensive post-harvest loss rate at the provincial and / or national level, the comprehensive post-harvest loss rate of all oilseed crops, and the comprehensive post-harvest loss rate of all oilseed crops based on the comprehensive post-harvest loss rate of a single oilseed crop. This invention improves the reliability of the assessment results of technical loss rates at post-harvest stages of oilseed crops and enables the assessment of post-harvest losses of the three major oilseed crops by variety, province, and nationwide, as well as key loss stages. The invention will now be described in detail with reference to the accompanying drawings.
[0036] Example 1 This embodiment discloses a method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation, such as... Figure 1 As shown, it includes the following steps: S1 establishes a material flow analysis model for oilseed crops at various post-harvest stages.
[0037] The definition of post-harvest loss of oil crops is: any reduction in edible oil content caused by spillage, quality degradation, low processing technology, or other objective factors before the distribution and circulation of oil crops (excluding distribution and circulation). Specifically, it can be divided into oil crop harvesting loss, drying and storage loss, transportation loss, and processing loss. The weight reduction of oil crops due to moisture reduction during post-harvest processing is not included in the loss.
[0038] In this embodiment, the post-harvest process of oilseed crops includes the complete chain from field harvesting to final processing output, starting from the end of oilseed crop production and ending at the point of retail, excluding the retail and consumption stages of the final products. The post-harvest process of oilseed crops includes five stages: harvesting, drying, storage, transportation, and processing. Each stage follows a material flow balance relationship, that is, the outflow of the previous stage equals the inflow of the next stage. The 2024 yields of oilseed crops soybeans, rapeseed, and peanuts in this embodiment are shown in Table 1.
[0039] Table 1. Oilseed crop production in 2024 (10,000 tons)
[0040] S2 includes several technical methods in each stage. A weighted analysis of the technical methods is performed to obtain the loss rate of each stage.
[0041] The loss rate at each stage is defined as the ratio of the amount of material lost at each stage to the amount of material entering that stage. The calculation formula is:
[0042] in, It is the loss rate of the i-th stage. It is the output of the i-th stage, where i = 1, 2, 3, 4, 5 correspond to the harvesting, drying, storage, transportation, and processing stages, respectively.
[0043] There may be multiple technical methods used in each stage of the postpartum process, and the loss rates of different technical methods vary. The loss rate of each stage is obtained by weighted averaging of the loss rates of different technical methods within that stage, with the weighting weight being the proportion of each technical method used.
[0044] The comprehensive post-harvest loss rate of a single oilseed crop is:
[0045] in, This is the theoretical yield of a single oilseed crop. It is the output of the processing stage. It is the comprehensive postpartum loss rate.
[0046] As shown in Table 2, the harvesting process includes manual harvesting, segmented harvesting, and combined harvesting; the drying process includes natural sun-drying and mechanical drying; the storage process includes simple storage by farmers, bulk storage in flat warehouses, and temperature-controlled storage; the transportation process includes bulk transportation, bagged transportation, and boxed / refrigerated transportation; and the processing process includes traditional pressing, pre-treatment pressing, pre-pressing-leaching, and cold pressing.
[0047] The definition of the i-th stage usually includes: One technical approach, let the first The loss rate of this technical method The proportion adopted is ( Then, the overall loss rate of the i-th stage is:
[0048] Multiple techniques may be used in each postpartum stage, and the loss rates of different techniques vary. The loss rate for each stage is obtained by weighted averaging of the loss rates of different techniques within that stage, with the weighting factor being the proportion of each technique used.
[0049] Table 2. Technical methods included in each stage
[0050] The harvesting process includes One technical approach. Let the first... The loss rate of this technical method The proportion adopted is ( Then, the overall loss rate during the harvesting stage is:
[0051] The overall loss rate during the harvesting stage is ,
[0052] The overall loss rate in the drying process is ,
[0053] The overall loss rate during storage is ,
[0054] The overall loss rate in the transportation process is ,
[0055] The overall loss rate in the processing stage is , .
[0056] The post-harvest comprehensive loss rate is defined as the proportion of total loss from field yield to final processed product, and its calculation formula is as follows:
[0057] in, This is the theoretical yield of a single oilseed crop. It is the output of the processing stage. This is the postpartum comprehensive loss rate. The above formula reflects the cumulative effect of losses in each stage of the postpartum process. That is, the comprehensive loss rate is not a simple sum of the loss rates of each stage, but a chain effect that takes into account the impact of losses in each stage on the base of subsequent stages.
[0058] Table 3. Parameters of Technical Loss Rate at Each Stage of Soybean Processing
[0059] S3 inputs the loss rate of each stage into a Monte Carlo simulation based on the Beta distribution to obtain the technical loss rate distribution of each stage.
[0060] The method for predicting the technology loss rate at each stage using Monte Carlo simulation based on the Beta distribution is as follows: S3.1 Using triangular distribution parameters Expressing the uncertainty of the loss rate.
[0061] For the Step, First loss rate of this technology The uncertainty range is expressed using three parameters of a triangular distribution: It is the minimum loss rate, i.e. the most optimistic estimate, derived from the lower limit reported in the literature or the measured minimum. It is the most likely value of the loss rate, derived from typical values reported in the literature or expert judgment; This represents the maximum, most pessimistic estimate of the loss rate, derived from the upper limit reported in the literature or the maximum measured value. The technical loss rate parameters for each stage of soybean, rapeseed, and peanut production are shown in Tables 3, 4, and 5, respectively.
[0062] Table 4. Technical Loss Rate Parameters for Each Stage of Rapeseed Processing
[0063] S3.2 Convert the triangular distribution parameters to Beta distribution parameters; Calculate the most probable value of the standardized loss rate using a, m, and b. ;
[0064] The most likely value of the standardized loss rate Calculate the mean of the triangular distribution. and variance ;
[0065] Table 5. Parameters of Technical Loss Rate at Each Stage of Peanut Processing
[0066] According to the mean and variance By inversely deriving the shape parameters of the Beta distribution,
[0067]
[0068] Among them, among them, It is a bridge parameter connecting the triangular distribution moment and the Beta shape parameter. It is the first shape parameter. It is the second shape parameter.
[0069] S3.3 Monte Carlo sampling is performed on the Beta distribution parameters, and statistical analysis is conducted on the sample sequences obtained through iteration to obtain the final technical loss rate of each stage.
[0070] Monte Carlo sampling is a form of random sampling. In each Monte Carlo sampling iteration, the formula for independently sampling each technical loss rate is:
[0071] in, The random number is uniformly distributed in the interval (0,1). The field yield of each variety in each province, the proportion of technology adopted in each stage, and the oil extraction ratio of oil crops are fixed values. The proportion of technology adopted in each stage and the oil extraction ratio of oil crops can be obtained through field surveys, expert assignment, or literature data.
[0072] Each Monte Carlo simulation uses BETAINV independent sampling for each technology loss rate parameter to obtain the sample value of the technology loss rate for that iteration. The technology samples are then aggregated into a comprehensive loss rate for each product category using the national weighted average technology ratio. Statistical analysis is performed on the sample sequences obtained from N iterations to calculate the sample mean, standard deviation, and 95% confidence interval, in order to quantify the uncertainty of the results. A key aspect of Monte Carlo simulation is determining a sufficient number of simulation iterations. Insufficient simulations will lead to unstable results, while excessive simulations will waste computational resources.
[0073] The method for detecting whether a Monte Carlo model has converged is as follows: 1. Sample mean convergence test For varieties , define the first Cumulative average of overall loss rate after each simulation for:
[0074] If for ,have (Pick If the mean has converged, then it is considered that the mean has converged.
[0075] 2. Quantile convergence test Check P5 and whether it is stable:
[0076]
[0077] in, It is the P5 quantile. It is P 95 Quantiles.
[0078] The results of the simulation of the average loss rate of the three major oilseed crops at each stage after harvest are as follows: Figure 2 As shown.
[0079] S4 obtains the comprehensive post-harvest loss rate of a single oilseed crop based on the material flow analysis model and the distribution of technical loss rates at each stage.
[0080] Assuming the post-harvest process of oil crops involves Provinces ( ), One variety, in this embodiment These represent soybeans, rapeseed, and peanuts, respectively, but are not limited to these; they can also be used for other oilseed crops. This embodiment only uses my country's three major oilseed crops as examples for illustration. Postpartum care ( The loss rates for each stage are as follows: The output of each stage is as follows:
[0081]
[0082]
[0083]
[0084]
[0085] In this embodiment, the technical methods used in each stage of the main soybean, rapeseed, and peanut producing provinces are compared and shown in Tables 6-20.
[0086] Table 6. Proportion of Harvesting Techniques Used in Major Soybean Producing Provinces
[0087] Table 7. Proportion of Drying Technology Used in Major Soybean Producing Provinces
[0088] Table 8. Proportion of Soybean Storage Technology Used in Major Soybean Producing Provinces
[0089] Table 9. Proportion of Transportation Technologies Used in Major Soybean-Producing Provinces
[0090] Table 10. Proportion of Processing Technology Used in Major Soybean Producing Provinces
[0091] Table 11. Proportion of Harvesting Techniques Used in Major Rapeseed Producing Provinces
[0092] Table 12 Proportion of Drying Techniques Used in Major Rapeseed Producing Provinces
[0093] Table 13 Proportion of Storage Techniques Used in Major Rapeseed Producing Provinces
[0094] Table 14. Proportion of Transportation Technologies Used in Major Rapeseed-Producing Provinces
[0095] Table 15 Proportion of Processing Technology Used in Major Rapeseed Producing Provinces
[0096] Table 16. Proportion of Harvesting Techniques Used in Major Peanut-Producing Provinces
[0097] Table 17 Proportion of Drying Techniques Used in Major Peanut-Producing Provinces
[0098] Table 18: Proportion of Storage Techniques Used in Major Peanut-Producing Provinces
[0099] Table 19. Proportion of Transportation Technologies Used in Major Peanut-Producing Provinces
[0100] Table 20: Proportion of Processing Technology Used in Major Peanut-Producing Provinces
[0101] S5 calculates the comprehensive post-harvest loss rate of a single oilseed crop using a province-to-nation weighted calculation model, obtaining the comprehensive post-harvest loss rate for oilseed crops at the provincial and / or national levels. By assessing the contribution of the comprehensive post-harvest loss rate at the provincial and / or national levels, key loss links in the post-harvest chain are identified.
[0102] In the provincial-national weighted calculation model, the comprehensive postpartum loss rate is calculated within the provincial scope. for:
[0103] Within the province, conduct a comprehensive assessment of post-harvest losses of oilseed crops. for:
[0104] The overall loss rate and loss amount for each province are shown in Table 21.
[0105] Nationwide, conduct a comprehensive assessment of post-harvest losses in oilseed crops. for:
[0106] Comprehensive post-harvest loss rate of oil crops nationwide for:
[0107] in, It is the first p The province, the first v The total post-harvest loss rate of each oilseed crop. It is the first p The province, the first v The theoretical yield of an oilseed crop, It is the first p The province, the first v Loss rate of the i-th stage of an oilseed crop; It is the first p The province, the first v The yield of an oilseed crop at the i-th stage, i=1,2,3,4,5, corresponding to the harvesting, drying, storage, transportation and processing stages, respectively.
[0108] Table 21: Loss Rate and Loss Amount of Postpartum Losses in Each Province
[0109] The overall loss rate for all oilseed crops was:
[0110] in, It is the overall loss rate of all oilseed crops, where n is the number of types of oilseed crops.
[0111] Loss contribution analysis was used to identify key loss links in the postpartum chain. The contribution of each link to the overall postpartum loss was analyzed. for:
[0112] After simplification, we get:
[0113] The contribution of each stage to the total postpartum loss. The larger the value, the greater the contribution of that stage to the total post-harvest loss. In this embodiment, the contribution of the soybean post-harvest loss stage is as follows: Figure 3 As shown.
[0114] Example 2 Based on the same inventive concept, this embodiment discloses a post-harvest loss assessment system for oilseed crops based on Monte Carlo simulation, including: The material flow analysis model building module is used to build material flow analysis models for oil crops in various post-harvest stages. The loss rate acquisition module is used to perform weighted analysis on various technical methods in each stage to obtain the loss rate of each stage. The Monte Carlo simulation module is used to input the loss rate of each stage into a Monte Carlo simulation based on the Beta distribution to obtain the technical loss rate distribution of each stage; The module for obtaining the comprehensive post-harvest loss rate of a single crop is used to obtain the comprehensive post-harvest loss rate of a single oilseed crop based on the material flow analysis model and the distribution of technical loss rates at each stage. The post-harvest comprehensive loss rate acquisition module is used to obtain the post-harvest comprehensive loss rate of oil crops within the province and / or the whole country based on the post-harvest comprehensive loss rate of a single oil crop through a province-national weighted calculation model. The segment loss contribution module is used to identify key loss segments in the post-harvest chain by assessing their contribution to the overall post-harvest loss rate of oilseed crops at the provincial and / or national levels.
[0115] Example 3 Based on the same inventive concept, this embodiment discloses a computer-readable storage medium storing a computer program, which is executed by a processor to implement the Monte Carlo simulation-based method for assessing post-harvest losses of oil crops as described above.
[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific embodiments of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.
Claims
1. A method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation, characterized in that, Includes the following steps: Establish a material flow analysis model for oilseed crops at each post-harvest stage; Each of the aforementioned steps includes several technical methods. A weighted analysis is performed on the technical methods to obtain the loss rate of each step. The loss rate of each stage is input into a Monte Carlo simulation based on the Beta distribution to obtain the technical loss rate distribution of each stage; Based on the material flow analysis model and the distribution of technical loss rates at each stage, the comprehensive post-harvest loss rate of a single oilseed crop is obtained. Based on the comprehensive post-harvest loss rate of the single oilseed crop, the comprehensive post-harvest loss rate of the oilseed crop within the province and / or the whole country is obtained through a province-national weighted calculation model. By assessing the contribution of comprehensive post-harvest loss rates to oilseed crops at the provincial and / or national levels, key loss links in the post-harvest chain can be identified.
2. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 1, characterized in that, The post-harvest stages include harvesting, drying, storage, transportation, and processing. The outflow from one stage is equal to the inflow from the next stage.
3. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 1, characterized in that, The loss rates of each stage described in the material flow analysis model are: in, It is the loss rate of the i-th stage. It is the output of the i-th stage; The comprehensive post-harvest loss rate of the single oilseed crop is: in, This is the theoretical yield of a single oilseed crop. It is the output of the processing stage. It is the comprehensive postpartum loss rate.
4. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 2, characterized in that, The harvesting process includes manual harvesting, segmented harvesting, and combined harvesting; the drying process includes natural sun-drying and mechanical drying; the storage process includes simple farmer storage, bulk storage in flat warehouses, and temperature-controlled storage; the transportation process includes bulk transportation, bagged transportation, and boxed / refrigerated transportation; and the processing process includes traditional pressing, pre-treatment pressing, pre-pressing-leaching, and cold pressing. The i-th stage usually has One technical approach, let the first The loss rate of this technical method The proportion adopted is Then the loss rate of the i-th stage is: in, .
5. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 3, characterized in that, In the aforementioned provincial-national weighted calculation model, within the provincial scope, oilseed crops Postpartum loss for: Nationwide, oil crops Postpartum loss for: Oil crops The national postpartum comprehensive loss rate for: in, It is the first p The province, the first v The comprehensive post-harvest loss rate of oilseed crops, It is the first p The province, the first v The theoretical yield of an oilseed crop, It is the first p The province, the first v Loss rate of the i-th stage of an oilseed crop; The comprehensive post-harvest loss rate for all oilseed crops is: in, It is the overall loss rate of all oilseed crops, where n is the number of types of oilseed crops.
6. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 1, characterized in that, The method for obtaining the technical loss rate distribution of each stage by inputting the loss rate of each stage into a Monte Carlo simulation based on the Beta distribution is as follows: Using triangular distribution parameters The uncertainty of the expressed loss rate; Convert the triangular distribution parameters into Beta distribution parameters; Monte Carlo sampling was performed on the Beta distribution parameters to obtain the distribution of technical loss rates for each stage.
7. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 6, characterized in that, The triangular distribution parameters The method for expressing the uncertainty of the loss rate is as follows: For the Step, First loss rate of this technology The uncertainty range is expressed using three parameters of a triangular distribution: It is the minimum loss rate. It is the most likely value of the loss rate. It is the maximum value of the loss rate; The method for converting the triangular distribution parameters into Beta distribution parameters is as follows: Calculate the most probable value of the standardized loss rate using a, m, and b. ; The most likely value of the standardized loss rate Calculate the mean of the triangular distribution. and variance ; According to the mean and variance By inversely deriving the shape parameters of the Beta distribution, in, It is a bridge parameter connecting the triangular distribution moment and the Beta shape parameter. It is the first shape parameter. It is the second shape parameter.
8. The method for assessing post-harvest loss of oilseed crops based on Monte Carlo simulation as described in claim 3, characterized in that, Contribution of each stage to the overall postpartum loss rate for: in, This refers to the comprehensive post-harvest loss rate of all oilseed crops, and the contribution of each stage to the comprehensive post-harvest loss rate. The larger the value, the greater the contribution of that stage to postpartum losses.
9. A post-harvest loss assessment system for oilseed crops based on Monte Carlo simulation, characterized in that, include: The material flow analysis model building module is used to build material flow analysis models for oil crops in various post-harvest stages. The loss rate acquisition module is used to perform weighted analysis on the various technical methods included in each of the aforementioned stages to obtain the loss rate of each stage. The Monte Carlo simulation module is used to input the loss rate of each stage into a Monte Carlo simulation based on the Beta distribution to obtain the technical loss rate distribution of each stage; The module for obtaining the comprehensive post-harvest loss rate of a single crop is used to obtain the comprehensive post-harvest loss rate of a single oilseed crop based on the material flow analysis model and the distribution of technical loss rates at each stage. The post-harvest comprehensive loss rate acquisition module is used to obtain the post-harvest comprehensive loss rate of the oilseed crop within the province and / or the whole country by using a province-to-nation weighted calculation model based on the post-harvest comprehensive loss rate of the single oilseed crop. The segment loss contribution module is used to identify key loss segments in the post-harvest chain by assessing their contribution to the overall post-harvest loss rate of oilseed crops at the provincial and / or national levels.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method for assessing post-harvest loss of oil crops based on Monte Carlo simulation as described in any one of claims 1-8.