Postpartum loss evaluation system and method for grain safety

Abstract

The invention relates to the technical field of grain safety, and particularly discloses a postpartum loss evaluation system and method for grain safety, and the system comprises a grouping module which obtains the number a of grain loss technologies in any processing link; obtaining a plurality of identical grain samples, and dividing the grain samples into a groups; the processing module is used for acquiring indexes of the processed grains in each group; and the evaluation module determines the weight of each index through an entropy weight method, calculates the evaluation coefficient of the grain loss technology, obtains the maximum value of the evaluation coefficient, and takes the grain loss technology corresponding to the maximum value as the grain loss technology with the best effect in the fth link of the ith class. According to the method, different indexes can be corrected according to the purposes of the grains so as to determine the technology with the best loss reduction effect.

Description

Technical Field

[0001] The present invention relates to the technical field of food security, and particularly relates to a post-harvest loss assessment system and method for food security. Background Art

[0002] Post-harvest losses of grains refer to the losses and wastes in the processes of drying, storage, transportation, etc. after the grains are harvested. Post-harvest losses of grains will bring economic losses to farmers, have a negative impact on the environment, and also affect global food security and the stability of the food market. In order to reduce post-harvest losses of grains, there are various different technologies, but the loss reduction effects of different technologies are also different. Therefore, it is very important to evaluate these different technologies and then select the technology with the best loss reduction effect.

[0003] A Chinese patent with the application number 2021102115508 discloses an evaluation method for post-harvest losses of grains. In its solution, various indicators of grains after the application of a certain technology are compared with various indicators of grains under the traditional method, and then the loss reduction effect of the technology is judged, which can more intuitively show the loss reduction effect of the technology on various indicators. However, in the same processing link of grains, the emphasis on different indicators is not the same. For example, in the drying link, more attention may be paid to reducing the moisture content, but other indicators cannot be ignored. If there is a technology with a very significant effect on reducing the moisture content, but it will cause a large increase in indicators such as imperfect grains. However, when evaluating this technology, only judging that this technology has a good effect based on reducing the moisture content is obviously unreasonable. In the prior art, there is a lack of comprehensive evaluation of grain loss reduction technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide a post-harvest loss assessment system and method for food security to solve the above technical problems.

[0005] The purpose of the present invention can be achieved by the following technical solutions: A post-harvest loss assessment system for food security includes: A grouping module, which obtains the processing links of grains and obtains the methods that can reduce grain losses in different processing links, and defines them as grain loss reduction technologies; Obtain the number a of grain loss reduction technologies in any one processing link, and obtain several identical grain samples. Divide the grain samples into a groups, each group contains the same number of grain samples, and define any one group as the reference group and the remaining groups as the comparison groups; The processing module obtains the indexes of the grains in each group after processing. The indexes include the apparent loss rate AL (%), the net loss rate QL (%), the reduction rate of imperfect grains U (%), and the bulk density loss rate VW (%). The reduction rate of imperfect grains represents the effect in reducing imperfect grains. The process of obtaining the reduction rate of imperfect grains U (%) specifically includes: Step 1: In any one of the comparison groups, screen the imperfect grains in each grain sample before processing and weigh them using weighing equipment. Dc represents the weight of the imperfect grains in the c-th grain sample; Screen the imperfect grains in each grain sample after processing and weigh them using weighing equipment. Dc' represents the weight of the imperfect grains in the c-th grain sample after processing; Calculate the first increase rate Pc = (Dc' - Dc) / tc, where Pc represents the first increase rate of the c-th grain sample and tc represents the total weight of the c-th grain sample; Step 2: Repeat Step 1 to obtain the first increase rate in the reference group and denote it as Pj; Step 3: Calculate the reduction rate of imperfect grains within this comparison group. The calculation formula is: ; where m represents the number of grain samples in each group; The evaluation module determines the weight of each index through the entropy weight method. T AL represents the weight of the apparent loss rate; Calculate the evaluation coefficient k of the grain loss reduction technology h = AL h * T AL + QL h * T QL + U h * T U + VW h * T VW , where k h represents the evaluation coefficient of the grain loss reduction technology used in the h-th comparison group, AL h represents the apparent loss rate in the h-th comparison group, QL h represents the net loss rate in the h-th comparison group, UG h represents the reduction rate of imperfect grains in the h-th comparison group, VW h represents the bulk density loss rate in the h-th comparison group; Obtain the maximum value k' = max(k1, k2,..., ka) of the evaluation coefficients, and use the grain loss reduction technology corresponding to the maximum value k' as the grain loss reduction technology with the best effect in the current processing link.

[0006] As a further solution of the present invention: in the grouping module, when there is no such comparison group in a certain category, this classification does not participate in the subsequent steps.

[0007] As a further solution of the present invention: during the process of weighing the weight of the imperfect grains, obtain the results of several weighings and calculate the average value as the weight of the imperfect grains.

[0008] As a further solution of the present invention: when the difference between the result i of a certain weighing and the average value is greater than the preset value, discard the result i of the weighing and recalculate the average value.

[0009] As a further solution of the present invention: in the first step, when the ratio f = Dk / tk of the weight Dk of the imperfect grains in a certain sample to the total weight tk of the grains in this sample is greater than the preset value, this sample is resampled.

[0010] As a further solution of the present invention: in the evaluation module, when there are two or more evaluation coefficients of the above-mentioned technologies that are the same and are the maximum values, perform the following steps: Define the same and maximum evaluation coefficient as the to-be-determined evaluation coefficient. Obtain the link y where the to-be-determined evaluation coefficient is located, and obtain the weight of each index in the link y. Obtain the maximum value b of the weight and the index b' corresponding to the maximum value b, and calculate the sorting score Bf = b * b', where Bf represents the sorting score of the f-th to-be-determined evaluation coefficient. Obtain the maximum value Bmax of the sorting scores of the to-be-determined coefficients, and take the evaluation coefficient corresponding to the maximum value Bmax as the maximum value k'.

[0011] As a further solution of the present invention: in the evaluation module, the analytic hierarchy process can also be used to determine the grain loss reduction technology with the best effect.

[0012] A method for evaluating post-harvest losses for food security includes the following steps: S1: Obtain the processing links of grains, and obtain the methods that can reduce grain losses in different processing links, which are defined as grain loss reduction technologies. Obtain the number a of grain loss reduction technologies in any one processing link, and obtain several identical grain samples. Divide the grain samples into a groups, each group contains the same number of grain samples, and define any one group as the reference group and the remaining groups as the comparison groups. S2: Obtain the indexes of the grain in each group after processing. The indexes include apparent loss rate AL (%), net loss rate QL (%), reduction rate of imperfect grains U (%), and bulk density loss rate VW (%). The reduction rate of imperfect grains is the effect in reducing imperfect grains. The process of obtaining the reduction rate of imperfect grains U (%) specifically includes: Step 1: In any one of the comparison groups, screen the imperfect grains in each grain sample before processing and weigh them using weighing equipment. Dc represents the weight of the imperfect grains in the c-th grain sample; Screen the imperfect grains in each grain sample after processing and weigh them using weighing equipment. Dc' represents the weight of the imperfect grains in the c-th grain sample after processing; Calculate the first increase rate Pc = (Dc' - Dc) / tc, where Pc represents the first increase rate of the c-th grain sample, and tc represents the total weight of the c-th grain sample; Step 2: Repeat Step 1 to obtain the first increase rate in the reference group and denote it as Pj; Step 3: Calculate the reduction rate of imperfect grains within this comparison group. The calculation formula is: ; where m represents the number of grain samples in each group; S3: Determine the weight of each index through the entropy weight method. T AL represents the weight of the apparent loss rate; Calculate the evaluation coefficient k of the grain loss reduction technology h = AL h * T AL + QL h * T QL + U h * T U + VW h * T VW , where k h represents the evaluation coefficient of the grain loss reduction technology used in the h-th comparison group, AL h represents the apparent loss rate in the h-th comparison group, QL h represents the net loss rate in the h-th comparison group, UG h represents the reduction rate of imperfect grains in the h-th comparison group, VW h represents the bulk density loss rate in the h-th comparison group; Obtain the maximum value k' = max(k1, k2,..., ka) of the evaluation coefficient, and use the grain loss reduction technology corresponding to the maximum value k' as the grain loss reduction technology with the best effect in the current processing link.

[0013] Advantages of the present invention: In the present invention, in the same process, several samples of grains are obtained, and the number of groups is determined according to the number of grain loss reduction technologies in this process; different grain loss reduction technologies are used to process the divided groups. The advantage of such an operation is that in the same process, the degree of emphasis on the same index is the same, which can solve the problem that different processing links of grains have different degrees of emphasis on the same index; various indexes are obtained, which is the basis for comprehensively evaluating the effects of grain loss reduction technologies subsequently; the weights of different indexes are determined according to the entropy weight method; then the evaluation coefficients of different technologies are calculated, and the best grain loss reduction technology is determined according to the magnitudes of the evaluation coefficients. The present invention can comprehensively evaluate the effects of grain loss reduction technologies, and further determine the best grain loss reduction technology in the processing link. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 is a schematic flowchart of a post-harvest loss assessment system for food security according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Please refer to Figure 1 as shown, the present invention is a post-harvest loss assessment system for food security, including: a grouping module, which obtains the processing links of grains and obtains the methods that can reduce grain losses in different processing links, defined as grain loss reduction technologies; In any one of the processing links, obtain the number a of grain loss reduction technologies, and obtain several identical grain samples. Divide the grain samples into a groups, each group contains the same number of grain samples, and define any one of the groups as the reference group, and the remaining groups as the comparison groups; a processing module, which obtains the indexes of the grains in each group after processing. The indexes include the apparent loss reduction rate AL (%), the net loss reduction rate QL (%), the reduction rate U (%) of imperfect grains, and the volume weight loss reduction rate VW (%). The reduction rate U (%) of imperfect grains is the effect in reducing imperfect grains. The process of obtaining the reduction rate U (%) of imperfect grains specifically includes: Step 1: In any one of the said comparison groups, screen out the imperfect grains in each grain sample before treatment and weigh them using a weighing device. Dc represents the weight of the imperfect grains in the c-th grain sample; Screen out the imperfect grains in each grain sample after treatment and weigh them using a weighing device. Dc' represents the weight of the imperfect grains in the c-th grain sample after treatment; Calculate the first increase rate Pc = (Dc' - Dc) / tc, where Pc represents the first increase rate of the c-th grain sample, and tc represents the total weight of the c-th grain sample; Step 2: Repeat Step 1 above to obtain the first increase rate in the said reference group and denote it as Pj; Step 3: Calculate the reduction rate of imperfect grains within this comparison group. The calculation formula is: ; where m represents the number of grain samples in each group; Evaluation module, determine the weight of each index through the entropy weight method, T AL represents the weight of the apparent loss rate; Calculate the evaluation coefficient k of the grain loss reduction technology h =AL h *T AL +QL h *T QL +U h *T U +VW h *T VW , where k h represents the evaluation coefficient of the grain loss reduction technology used in the h-th comparison group, AL h represents the apparent reduction rate in the h-th comparison group, QL h represents the net reduction rate in the h-th comparison group, UG h represents the reduction rate of imperfect grains in the h-th comparison group, VW h represents the reduction rate of bulk density in the h-th comparison group; Obtain the maximum value k' = max(k1, k2,..., ka) of the said evaluation coefficient, and take the grain loss reduction technology corresponding to the maximum value k' as the grain loss reduction technology with the best effect in the current treatment link.

[0018] In the same step, obtain several samples of grains, and determine the number of groups according to the number of grain loss reduction technologies in this step; process the divided groups using different grain loss reduction technologies. The advantage of such an operation is that in the same step, the degree of emphasis on the same indicator is the same, which can solve the problem that the degree of emphasis on the same indicator in different processing steps of grains is different; obtain various indicators, which is the basis for subsequent comprehensive evaluation of the effectiveness of grain loss reduction technologies; determine the weights of different indicators according to the entropy weight method; then calculate the evaluation coefficients of different technologies, and determine the grain loss reduction technology with the best effect according to the magnitudes of the evaluation coefficients.

[0019] In another preferred embodiment of the present invention, in the grouping module, when there is no such comparison group in a certain category, this classification does not participate in the subsequent steps.

[0020] It is worth noting that at this time, it indicates that there is only one traditional method for processing in this classification. This classification not participating in the subsequent steps can reduce the amount of data to be processed subsequently.

[0021] In another preferred embodiment of the present invention, during the process of weighing the weight of the imperfect grains, obtain the results of several weighings and calculate the average value as the weight of the imperfect grains.

[0022] It can be understood that by weighing multiple times and calculating the average value, the influence of individual errors can be reduced, thereby improving the accuracy of the results.

[0023] In another preferred embodiment of the present invention, when the difference between the result i of a certain weighing and the average value is greater than the preset value, discard the result i of the weighing and recalculate the average value.

[0024] It should be noted that this is to exclude possible outliers or significantly deviated data to ensure that the finally obtained average value is more accurate and reliable.

[0025] In another preferred embodiment of the present invention, in step one, when the ratio f = Dk / tk of the weight Dk of the imperfect grains in a certain sample to the total weight tk of the grains in this sample is greater than the preset value, this sample is resampled.

[0026] It should be noted that at this time, it indicates that there may be a problem with the sampling, so this sample is resampled.

[0027] In another preferred embodiment of the present invention, in the evaluation module, when there are two or more evaluation coefficients of the technologies that are the same and are the maximum values, perform the following steps: Define the same and maximum evaluation coefficient as the undetermined evaluation coefficient. Define the same and maximum evaluation coefficient as the to-be-determined evaluation coefficient. Obtain the link y where the to-be-determined evaluation coefficient is located, and obtain the weight of each index in the link y. Obtain the maximum value b of the weight and the corresponding index b' of the maximum value b, and calculate the sorting score Bf = b * b', where Bf represents the sorting score of the f-th to-be-determined evaluation coefficient. Obtain the maximum value Bmax of the sorting scores of the to-be-determined coefficients, and use the evaluation coefficient corresponding to the maximum value Bmax as the maximum value k'.

[0028] In another preferred embodiment of the present invention, in the evaluation module, the analytic hierarchy process can also be used to determine the best food loss reduction technology.

[0029] A method for evaluating post-harvest losses for food security includes the following steps: S1: Obtain the same kind of food harvested in the same batch, and classify the obtained food according to the use of the food. Obtain the number of types a of food loss reduction technologies in the f-th link of the i-th category, where the link is a step of processing the food. Obtain several samples of food, divide the samples of the food into a + 1 groups, each group contains m samples of food, and define the group processed by the traditional method as the reference group and the group processed by the food loss reduction technology as the comparison group. S2: Obtain the indexes of the food in each group after processing. The indexes include the apparent loss rate AL (%), net loss rate QL (%), imperfect grain reduction rate U (%), and volume weight loss rate VW (%). The imperfect grain reduction rate is the effect in reducing imperfect grains. The process of obtaining the imperfect grain reduction rate U (%) specifically includes: Step 1: In any one of the comparison groups, screen the imperfect grains in each sample before processing and weigh them using weighing equipment. Dc represents the weight of the imperfect grains in the c-th sample. Screen the imperfect grains in each sample after processing and weigh them using weighing equipment. Dc' represents the weight of the imperfect grains in the c-th sample after processing. Calculate the first increase rate Pc = (Dc' - Dc) / tc, where Pc represents the first increase rate of the c-th sample, and tc represents the total weight of the food in the c-th sample. Step 2: Repeat Step 1 to obtain the first increase rate in the reference group and denote it as Pj. Step 3: Calculate the imperfect grain reduction rate within this comparison group. The calculation formula is: ; S3: Determine the weight of each index in the i-th category by the entropy weight method, T AL represents the weight of the apparent loss rate in the current classification; Calculate the evaluation coefficient k of the food loss reduction technology h =AL h *T AL +QL h *T QL +U h *T U +VW h *T VW , where k h represents the evaluation coefficient of the food loss reduction technology used in the h-th comparison group, AL h represents the apparent loss reduction rate in the h-th comparison group, QL h represents the net loss reduction rate in the h-th comparison group, UG h represents the reduction rate of imperfect grains in the h-th comparison group, VW h represents the bulk density loss rate in the h-th comparison group; Obtain the maximum value k' = max(k1, k2,..., ka) of the evaluation coefficient, and use the food loss reduction technology corresponding to the maximum value k' as the food loss reduction technology with the best effect in the f-th link of the i-th category.

[0030] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A post-harvest loss assessment system for food security, characterized in that: include: The grouping module obtains the food processing links and the methods that can reduce food losses in different processing links, which are defined as food loss reduction technologies; Obtain the number a of grain loss reduction technologies in any processing link, and obtain a number of identical grain samples, divide the grain samples into a groups, each group contains the same number of grain samples, and define any one of the groups as a reference group, and the remaining groups as comparison groups; The processing module obtains the indexes of the grains in each group after processing, wherein the indexes include the apparent loss rate AL (%), the net loss rate QL (%), the imperfect grain reduction rate U (%) and the bulk weight loss rate VW (%). The imperfect grain reduction rate is the effect of reducing imperfect grains. The process of obtaining the imperfect grain reduction rate U (%) specifically includes: Step 1: In any of the comparison groups, the imperfect grains in each grain sample before treatment are screened and weighed using a weighing device, where Dc represents the weight of the imperfect grains in the cth grain sample; The imperfect grains in each grain sample after processing are screened and weighed using a weighing device, and Dc' represents the weight of the imperfect grains in the cth grain sample after processing; Calculate the first increase rate Pc=(Dc'-Dc) / tc, where Pc represents the first increase rate of the cth grain sample, and tc represents the total weight of the cth grain sample; Step 2: Repeat step 1 to obtain the first increase rate in the reference group and record it as Pj; Step 3: Calculate the reduction rate of imperfect grains in the comparison group. The calculation formula is: ； Where m represents the number of grain samples in each group; Evaluation module, determines the weight of each indicator through entropy weight method, T AL represents the weight of the apparent loss rate; Calculation of the evaluation coefficient k of food loss reduction technology h =AL h *T AL +QL h *T QL +U h *T U +VW h *T VW , where k h represents the evaluation coefficient of the food loss reduction technology used in the hth comparison group, AL h represents the apparent impairment rate in the hth comparison group, QL h represents the net loss rate in the hth comparison group, UG h represents the reduction rate of imperfect grains in the hth comparison group, VW h represents the test weight loss rate in the hth comparison group; The maximum value k'=max(k1, k2, ..., ka) of the evaluation coefficient is obtained, and the grain loss reduction technology corresponding to the maximum value k' is used as the grain loss reduction technology with the best effect in the current processing link.

2. A post-harvest loss assessment system for food security according to claim 1, characterized in that: In the grouping module, when there is a category in which the comparison group does not exist, the category does not participate in the subsequent steps.

3. A post-harvest loss assessment system for food security according to claim 1, characterized in that: In the process of weighing the imperfect grains, several weighing results are obtained, and the average is calculated as the weight of the imperfect grains.

4. A post-harvest loss assessment system for food security according to claim 3, characterized in that: When the difference between a certain weighing result i and the mean value is greater than a preset value, the weighing result i is discarded and the mean value is recalculated.

5. A post-harvest loss assessment system for food security according to claim 1, characterized in that: In the step 1, when the ratio f=Dk / tk of the weight Dk of imperfect grains in a sample to the total weight tk of grains in the sample is greater than a preset value, the sample is resampled.

6. A post-harvest loss assessment system for food security according to claim 1, characterized in that: In the evaluation module, when there are two or more technologies whose evaluation coefficients are the same and are the maximum value, the following steps are performed: The same evaluation coefficient with the maximum value is defined as the undetermined evaluation coefficient. Obtain the link y where the undetermined evaluation coefficient is located, and obtain the weight of each indicator in the link y; Obtain the maximum value b of the weight and the index b' corresponding to the maximum value b, and calculate the ranking score Bf=b*b', where Bf represents the ranking score of the f-th undetermined evaluation coefficient; The maximum value Bmax of the ranking scores of the undetermined coefficients is obtained, and the evaluation coefficient corresponding to the maximum value Bmax is taken as the maximum value k'.

7. A post-harvest loss assessment system for food security according to claim 1, characterized in that: In the evaluation module, the analytic hierarchy process can also be used to determine the most effective food loss reduction technology.

8. A post-harvest loss assessment method for food security, characterized in that: The following steps are involved: S1: Obtain the food processing links and the methods that can reduce food losses in different processing links, which are defined as food loss reduction technologies; Obtain the number a of grain loss reduction technologies in any processing link, and obtain a number of identical grain samples, divide the grain samples into a groups, each group contains the same number of grain samples, and define any one of the groups as a reference group, and the remaining groups as comparison groups; S2: Obtaining the indexes of the grains in each group after processing, the indexes include the apparent loss rate AL (%), the net loss rate QL (%), the imperfect grain reduction rate U (%) and the bulk weight loss rate VW (%). The imperfect grain reduction rate is the effect of reducing imperfect grains. The process of obtaining the imperfect grain reduction rate U (%) specifically includes: Step 1: In any of the comparison groups, the imperfect grains in each grain sample before treatment are screened and weighed using a weighing device, where Dc represents the weight of the imperfect grains in the cth grain sample; The imperfect grains in each grain sample after processing are screened and weighed using a weighing device, and Dc' represents the weight of the imperfect grains in the cth grain sample after processing; Calculate the first increase rate Pc=(Dc'-Dc) / tc, where Pc represents the first increase rate of the cth grain sample, and tc represents the total weight of the cth grain sample; Step 2: Repeat step 1 to obtain the first increase rate in the reference group and record it as Pj; Step 3: Calculate the reduction rate of imperfect grains in the comparison group. The calculation formula is: ； Where m represents the number of grain samples in each group; S3: Determine the weight of each indicator by entropy weight method, T AL represents the weight of the apparent loss rate; Calculation of the evaluation coefficient k of food loss reduction technology h =AL h *T AL +QL h *T QL +U h *T U +VW h *T VW , where k h represents the evaluation coefficient of the food loss reduction technology used in the hth comparison group, AL h represents the apparent impairment rate in the hth comparison group, QL h represents the net loss rate in the hth comparison group, UG h represents the reduction rate of imperfect grains in the hth comparison group, VW h represents the test weight loss rate in the hth comparison group; The maximum value k'=max(k1, k2, ..., ka) of the evaluation coefficient is obtained, and the grain loss reduction technology corresponding to the maximum value k' is used as the grain loss reduction technology with the best effect in the current processing link.

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