Operation and maintenance management system and method for integrated agricultural product supply chain

Abstract

The present invention relates to the field of supply chain operation and maintenance management technology, specifically to an operation and maintenance management system and method for the integration of agricultural product supply chains, in which an integrated monitoring data acquisition module acquires each process link included in the agricultural product supply chain, as well as link monitoring data of agricultural products in each process link of the supply chain, to construct agricultural product integrated management information. The present invention takes into account the summary results of abnormal factors in the traceability results of each process link, and then constructs an abnormal risk analysis model for each agricultural product based on each process link; at the same time, it also takes into account the correlation of monitoring data in different process links, and combines the feature extraction results of each link to calculate the abnormal event risk bias coefficient of each process link based on the agricultural product integrated management information, and obtains a quantitative judgment of the abnormal risk association link for the abnormal event, and then accurately conducts risk responsibility link warning.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain operation and maintenance management, and in particular to an operation and maintenance management system and method for integrating an agricultural product supply chain. Background Art

[0002] The agricultural product supply chain involves many links, such as production, processing, transportation, warehousing, sales, etc. Building an agricultural product supply chain can monitor the entire process of agricultural products from planting to selling to users, and provide strong data support for agricultural product food safety and traceability. However, since the agricultural product supply chain involves many process links and the types of monitoring data are complex, in order to effectively manage the agricultural product supply chain data, an operation and maintenance management system for the integrated agricultural product supply chain has appeared on the market.

[0003] In the field of agricultural product supply chain, traditional operation and maintenance management systems mostly focus on a single link (such as production, logistics or sales), lacking the ability to coordinate and integrate the entire chain, leading to problems such as information islands and low efficiency. At the same time, it is impossible to combine the summary results of abnormal factors in the traceability results of each process link and the correlation of monitoring data in different process links, and thus it is impossible to determine the abnormal risk-related links for abnormal events, and thus it is impossible to accurately warn of risk rights and responsibilities. Therefore, the existing technology has major defects. Summary of the invention

[0004] The purpose of the present invention is to provide an operation and maintenance management system and method for the integration of agricultural product supply chains to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: an operation and maintenance management method for an integrated agricultural product supply chain, the method comprising the following steps: S1. Obtain the monitoring data of each process link included in the agricultural product supply chain, as well as the process links of agricultural products in each process link of the supply chain, and build integrated management information of agricultural products; S2. Based on the agricultural product types corresponding to the agricultural product integrated management information and the abnormal factors in the traceability results of each process link when the corresponding agricultural product types in the historical data are managed abnormally, the abnormal factors in the traceability results of each process link for each agricultural product are counted, and an abnormal risk analysis model based on each process link is constructed for each agricultural product; S3. Combine the data types in the abnormal risk analysis model of each process link for each agricultural product, extract the link features of the agricultural product integrated management information; combine the correlation between the data types in the feature extraction results of each link, and generate abnormal risk-related link binding pairs; S4. Based on the feature extraction results of each link, the obtained abnormal risk-related link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link, the abnormal event risk bias coefficient of each process link based on the agricultural product integrated management information is generated, and fed back to the operation and maintenance management personnel for early warning of risk rights and responsibilities.

[0006] Furthermore, the integrated management information of agricultural products includes each process link in the agricultural product supply chain and the link monitoring data in each process link; the link monitoring data in each process link of the supply chain includes each sensor number corresponding to the corresponding process link, the type of data monitored by each numbered sensor and the monitoring data of each numbered sensor at each time point.

[0007] Further, the S2 includes: The i-th agricultural product in the agricultural product category corresponding to the agricultural product integrated management information is recorded as Ai; in the process of obtaining the i-th agricultural product supply in the historical data, each time the management is abnormal, the abnormal factors in the traceability results of each process link are summarized and recorded as the link traceability abnormal factor summary set {[D (i,1,1) , D (i,1,2) , ..., D (i,1,m) ],[D (i,2,1) , D (i,2,2) , ..., D (i,2,m) ], ..., [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ]},D (i,n,m) It represents the number of management anomalies of the mth abnormal factor in the traceability result of the nth process link in the process of supplying the i-th agricultural product in the historical data; the statistical result of the abnormal factor in the traceability result of the i-th agricultural product in the nth process link is recorded as B （i，n） , B （i，n） Indicates [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ] The corresponding elements whose values ​​are not 0 correspond to the types of abnormal factors and the summary set of the monitoring values ​​of each sensor whose corresponding values ​​correspond to the types of abnormal factors; In the process of obtaining the supply of the i-th agricultural product in the historical data of the present invention, when summarizing the abnormal factors in the traceability results of each process link each time the abnormality is managed, the abnormal factors in the traceability results of each process link each time the abnormality is managed are obtained through database query, and the data in the database are obtained and stored in the subsequent abnormality tracing analysis.

[0008] The method steps for constructing an abnormal risk analysis model for the i-th agricultural product based on the n-th process link are as follows: S21. Get [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ], the array consisting of the elements whose corresponding values ​​are not 0, is recorded as the abnormal element analysis group of the i-th agricultural product based on the n-th process link; S22. Obtain the number of elements in the abnormal element analysis group of the i-th agricultural product based on the n-th process link, denoted as C （i，n） ; Calculate the average value of the elements in the abnormal element analysis group of the i-th agricultural product based on the n-th process link, recorded as Q （i，n） ; S23, calculate the relationship between each element in the abnormal element analysis group of the i-th agricultural product based on the n-th process link and Q （i，n） The absolute value of the difference between When C （i，n） ≥2, the abnormal element analysis group of the i-th agricultural product based on the n-th process link is divided into Q （i，n） The abnormal element analysis group of the i-th agricultural product based on the n-th process link is denoted as Y （i，n） ; Y （i，n） The average value of the elements in is denoted as Q1 （i，n） ; If |Q （i，n） -Q1 （i，n） |≤μ, then Q1 （i，n） The corresponding abnormal element analysis group is output to step S24; If |Q （i，n） -Q1 （i，n） |>μ, then Y （i，n） Output to step S22; When C （i，n） <2, then Q （i，n） The corresponding abnormal element analysis group is output to step S24; S24, receiving the abnormal element analysis group transmitted by S23, constructing an abnormal risk analysis model for the i-th agricultural product based on the n-th process link, wherein the constructed abnormal risk analysis model includes a plurality of association mapping pairs, wherein the first element in each association mapping pair represents the type of abnormal factor corresponding to any one element in the obtained abnormal element analysis group; the second element in each association mapping pair represents the type of abnormal factor corresponding to any one element in B （i，n） A summary of the monitoring values ​​of each sensor in .

[0009] Furthermore, S3 includes: The summary set of agricultural product types corresponding to the agricultural product integrated management information is obtained, which is recorded as the agricultural product set to be analyzed; the summary set of abnormal factor types in the abnormal risk analysis model of various agricultural products based on the nth process link in the agricultural product set to be analyzed is obtained, which is recorded as the first extracted category set; among the link feature extraction results of the agricultural product integrated management information, the nth link feature extraction result is the sensor monitoring result whose data type belongs to the first extracted category set; In the process of generating abnormal risk-related link binding pairs, the data type in the feature extraction results of each link is obtained, and different process links in which the sets constituted by the data types in the corresponding link feature extraction results have intersections are bound, and the intersection between the sets constituted by the data types in the link feature extraction results of each element in the binding result is recorded as the risk-related element set; it is determined whether there is a correlation between the same data type in the link feature extraction results of each element in the binding result that belongs to the corresponding risk-related element set.

[0010] Furthermore, the S4 includes: S41, obtaining the feature extraction results of each link in the agricultural product integrated management information, the obtained abnormal risk associated link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link; S42. Obtain the risk bias coefficient of abnormal events of the i-th agricultural product in the agricultural product integrated management information in the n-th process link, and the calculation formula is as follows: ; Among them, F （i，n） represents the risk bias coefficient of abnormal events of the nth process link based on the i-th agricultural product in the agricultural product integrated management information; the array consisting of the abnormal risk-related link binding pairs containing the nth process link, except the nth process link, is recorded as Gn; the intersection of the risk-related element sets corresponding to each abnormal risk-related link binding pair containing the nth process link and the jth element in Gn is recorded as TG （n，j） ; FP1 （i，n） The value is equal to the nth link feature extraction result of agricultural product integrated management information that does not belong to TG （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the i-th agricultural product based on the n-th process link; FP2 （i，n） Indicates FR1 （i，n） With FR2 （i，n） The maximum value among FR1 （i，n） The value is equal to the TG in the feature extraction result of the nth link of agricultural product integrated management information （n，j）The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the i-th agricultural product based on the n-th process link; FR2 （i，n） The value is equal to the process link in the agricultural product integrated management information that corresponds to the process link corresponding to the jth element of Gn. The feature extraction results of the link that belongs to TG （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the process link corresponding to the jth element in Gn for the i-th agricultural product; β represents the weight factor, and β is a preset constant; S43, when i is different values, the corresponding F （i，n） The maximum value of is taken as the risk bias coefficient of abnormal events based on the integrated management information of agricultural products in the nth process link.

[0011] Furthermore, in the process of obtaining the monitoring data corresponding to each data type compared with the abnormal risk assessment coefficient of the corresponding abnormal risk analysis model, the associated mapping pair containing the corresponding data type in the corresponding abnormal risk analysis model is screened, which is recorded as E; the normal state monitoring interval preset by the sensor corresponding to the first element in E is obtained, which is recorded as EZ; the array consisting of all elements in the summary set corresponding to the second element in E that are greater than the maximum value in EZ is recorded as ED1, and the array consisting of all elements in the summary set corresponding to the second element in E that are less than the minimum value in EZ is recorded as ED2; The calculation formula for obtaining the abnormal risk assessment coefficient of the monitoring data corresponding to each data type compared with the corresponding abnormal risk analysis model is as follows: When g1=0, then FW=0; otherwise, ; FW represents the abnormal risk assessment coefficient of the monitoring data corresponding to the corresponding data type compared with the corresponding abnormal risk analysis model; g1 represents the total number of monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type; FWg represents the abnormal risk assessment coefficient of the g-th monitoring data that does not belong to EZ in the monitoring data corresponding to the corresponding data type compared with the corresponding abnormal risk analysis model; the value of FWg is equal to the quotient of the sum of the number of elements in ED1 that are less than or equal to the g-th monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type and 1 divided by the sum of the total number of elements in ED1 and 1, or the quotient of the sum of the number of elements in ED2 that are greater than or equal to the g-th monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type and 1 divided by the sum of the total number of elements in ED2 and 1.

[0012] An operation and maintenance management system for an integrated agricultural product supply chain, the system comprising the following modules: An integrated monitoring data acquisition module, which acquires monitoring data of each process link included in the agricultural product supply chain and the agricultural products in each process link of the supply chain, and constructs integrated management information of agricultural products; A risk model building module, which is based on the agricultural product types corresponding to the agricultural product integrated management information, and the abnormal factors in the traceability results of each process link are summarized for each abnormal management of the corresponding agricultural product types in the historical data, and the abnormal factors in the traceability results of each process link of each agricultural product are counted, and an abnormal risk analysis model for each agricultural product based on each process link is constructed; An abnormal risk-related link analysis module, which extracts link features of agricultural product integrated management information based on the data types in the abnormal risk analysis models of each process link for each agricultural product; generates abnormal risk-related link binding pairs based on the correlation between the data types in the feature extraction results of each link; The risk warning feedback module generates abnormal event risk bias coefficients for each process link based on the integrated management information of agricultural products based on the feature extraction results of each link, the obtained abnormal risk-related link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link, and feeds back to the operation and maintenance management personnel for risk responsibility link warning.

[0013] Furthermore, the risk model building module includes an abnormal factor statistics unit and an abnormal risk analysis model building unit. The abnormal factor statistical unit is based on the agricultural product types corresponding to the agricultural product integrated management information, and the abnormal factor summary results of each process link traceability result for each agricultural product type in the historical data when the corresponding agricultural product type is managed abnormally each time, and the abnormal factors of each agricultural product in the traceability result of each process link are counted; The abnormal risk analysis model construction unit constructs an abnormal risk analysis model for each agricultural product based on each process link based on the statistical results of the abnormal factor statistical unit.

[0014] Furthermore, the abnormal risk associated link analysis module includes a link feature extraction unit and an abnormal risk associated link binding pair acquisition unit. The link feature extraction unit extracts link features of agricultural product integrated management information based on the data type of each agricultural product in the abnormal risk analysis model of each process link; The abnormal risk associated link binding pair acquisition unit generates abnormal risk associated link binding pairs by combining the correlation between data types in the feature extraction results of each link.

[0015] Furthermore, the risk warning feedback module includes an abnormal event risk bias coefficient calculation unit and a feedback warning unit. The abnormal event risk bias coefficient calculation unit generates abnormal event risk bias coefficients for each process link based on agricultural product integrated management information based on the feature extraction results of each link, the obtained abnormal risk associated link binding pairs, and the abnormal risk analysis models of each agricultural product based on each process link; The feedback warning unit feeds back each abnormal event risk bias coefficient obtained by the abnormal event risk bias coefficient calculation unit to the operation and maintenance management personnel to issue a risk responsibility link warning.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention takes into account the summary results of abnormal factors in the traceability results of each process link, and then constructs an abnormal risk analysis model for each agricultural product based on each process link; at the same time, it also takes into account the correlation of monitoring data in different process links, and combines the feature extraction results of each link to calculate the abnormal event risk bias coefficient of each process link based on the integrated management information of agricultural products, and obtains a quantitative judgment of the abnormal risk-related links for abnormal events, and then accurately performs risk responsibility link warnings, thereby realizing effective management of integrated operation and maintenance information of the agricultural product supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a structural schematic diagram of the operation and maintenance management system for agricultural product supply chain integration of the present invention; Figure 2 It is a flow chart of the operation and maintenance management method for the integrated supply chain of agricultural products of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 The present invention provides a technical solution: an operation and maintenance management system for an integrated agricultural product supply chain, the system comprising the following modules: An integrated monitoring data acquisition module, which acquires monitoring data of each process link included in the agricultural product supply chain and the agricultural products in each process link of the supply chain, and constructs integrated management information of agricultural products; A risk model building module, wherein the risk model building module includes an abnormal factor statistics unit and an abnormal risk analysis model building unit, The abnormal factor statistical unit is based on the agricultural product types corresponding to the agricultural product integrated management information, and the abnormal factor summary results of each process link traceability result for each agricultural product type in the historical data when the corresponding agricultural product type is managed abnormally each time, and the abnormal factors of each agricultural product in the traceability result of each process link are counted; The abnormal risk analysis model construction unit constructs an abnormal risk analysis model for each agricultural product based on each process link based on the statistical results of the abnormal factor statistical unit; An abnormal risk-related link analysis module, the abnormal risk-related link analysis module includes a link feature extraction unit and an abnormal risk-related link binding pair acquisition unit, The link feature extraction unit extracts link features of agricultural product integrated management information based on the data type of each agricultural product in the abnormal risk analysis model of each process link; The abnormal risk associated link binding pair acquisition unit generates an abnormal risk associated link binding pair by combining the correlation between the data types in the feature extraction results of each link; The risk warning feedback module includes an abnormal event risk bias coefficient calculation unit and a feedback warning unit. The abnormal event risk bias coefficient calculation unit generates abnormal event risk bias coefficients for each process link based on agricultural product integrated management information based on the feature extraction results of each link, the obtained abnormal risk associated link binding pairs, and the abnormal risk analysis models of each agricultural product based on each process link; The feedback warning unit feeds back each abnormal event risk bias coefficient obtained by the abnormal event risk bias coefficient calculation unit to the operation and maintenance management personnel to issue a risk responsibility link warning.

[0020] like Figure 2 As shown, an operation and maintenance management method for an integrated agricultural product supply chain includes the following steps: S1. Acquire each process link included in the agricultural product supply chain, as well as the link monitoring data of the agricultural products in each process link of the supply chain, and construct integrated management information of agricultural products; the integrated management information of agricultural products includes each process link in the agricultural product supply chain and the link monitoring data in each process link; the link monitoring data in each process link of the supply chain includes each sensor number corresponding to the corresponding process link, the type of data monitored by each numbered sensor, and the monitoring data of each numbered sensor at each time point.

[0021] S2. Based on the agricultural product types corresponding to the agricultural product integrated management information and the abnormal factors in the traceability results of each process link when the corresponding agricultural product types in the historical data are managed abnormally, the abnormal factors in the traceability results of each process link for each agricultural product are counted, and an abnormal risk analysis model based on each process link is constructed for each agricultural product; The S2 includes: The i-th agricultural product in the agricultural product category corresponding to the agricultural product integrated management information is recorded as Ai; in the process of obtaining the i-th agricultural product supply in the historical data, the abnormal factors in the traceability results of each process link are summarized each time the management is abnormal (the abnormal factors in the traceability results of each process link are obtained through database query each time the management is abnormal, and the data in the database are obtained and stored in the subsequent abnormal traceability analysis), which is recorded as the link traceability abnormal factor summary set {[D (i,1,1) , D (i,1,2) , ..., D (i,1,m) ],[D (i,2,1) , D (i,2,2) , ..., D (i,2,m) ], ..., [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ]},D (i,n,m) It represents the number of management anomalies of the mth abnormal factor in the traceability result of the nth process link in the process of supplying the i-th agricultural product in the historical data; the statistical result of the abnormal factor in the traceability result of the i-th agricultural product in the nth process link is recorded as B （i，n） , B （i，n） Indicates [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ] The corresponding elements whose values ​​are not 0 correspond to the types of abnormal factors and the summary set of the monitoring values ​​of each sensor whose corresponding values ​​correspond to the types of abnormal factors; In this embodiment, if i=2, n=1 and m=5, then [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) The specific expression of ] is [D (2,1,1), D (2,1,2) , D (2,1,3) , D (2,1,4) , D (2,1,5) ]; If D (2,1,2) and D (2,1,5) The values ​​of are all 0, and D (2,1,1) , D (2,1,3) and D (2,1,4) The values ​​of are not equal to 0; If D (2,1,1) The type of abnormal factor is recorded as TYPE 甲 , and TYPE 甲 The array of sensor monitoring values ​​of each abnormal factor type is recorded as MON 甲 ; If D (2,1,3) The type of abnormal factor is recorded as TYPE 乙 , and TYPE 乙 The array of sensor monitoring values ​​of each abnormal factor type is recorded as MON 乙 ; If D (2,1,4) The type of abnormal factor is recorded as TYPE 丙 , and TYPE 丙 The array of sensor monitoring values ​​of each abnormal factor type is recorded as MON 丙 ; Then judge B （2，1） ={[TYPE 甲 , MON 甲 ], [TYPE 乙 , MON 乙 ], [TYPE 丙 , MON 丙 ]}; The method steps for constructing an abnormal risk analysis model for the i-th agricultural product based on the n-th process link are as follows: S21. Get [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ], the array consisting of the elements whose corresponding values ​​are not 0, is recorded as the abnormal element analysis group of the i-th agricultural product based on the n-th process link; S22. Obtain the number of elements in the abnormal element analysis group of the i-th agricultural product based on the n-th process link, denoted as C （i，n） ; Calculate the average value of the elements in the abnormal element analysis group of the i-th agricultural product based on the n-th process link, recorded as Q （i，n） ; S23, calculate the relationship between each element in the abnormal element analysis group of the i-th agricultural product based on the n-th process link and Q （i，n） The absolute value of the difference between When C （i，n） ≥2, the abnormal element analysis group of the i-th agricultural product based on the n-th process link is divided into Q （i，n） The abnormal element analysis group of the i-th agricultural product based on the n-th process link is denoted as Y （i，n） ; Y （i，n） The average value of the elements in is denoted as Q1 （i，n） ; If |Q （i，n） -Q1 （i，n） |≤μ, then Q1 （i，n） The corresponding abnormal element analysis group is output to step S24; If |Q （i，n） -Q1 （i，n） |>μ, then Y （i，n） Output to step S22; When C （i，n） <2, then Q （i，n） The corresponding abnormal element analysis group is output to step S24; In this embodiment, whether step S23 jumps to step S22 or step S24, the content transmitted is the abnormal element analysis group; when jumping to step S22, the abnormal element analysis group of the adjusted i-th agricultural product based on the n-th process link is iteratively analyzed; |Q （i，n） -Q1 （i，n） |≤μ and C （i，n） <2 is equivalent to the end condition of this iteration method.

[0022] S24, receiving the abnormal element analysis group transmitted by S23, constructing an abnormal risk analysis model for the i-th agricultural product based on the n-th process link, wherein the constructed abnormal risk analysis model includes a plurality of association mapping pairs, wherein the first element in each association mapping pair represents the type of abnormal factor corresponding to any one element in the obtained abnormal element analysis group; the second element in each association mapping pair represents the type of abnormal factor corresponding to any one element in B （i，n） A summary of the monitoring values ​​of each sensor in .

[0023] S3. Combine the data types in the abnormal risk analysis model of each process link for each agricultural product, extract the link features of the agricultural product integrated management information; combine the correlation between the data types in the feature extraction results of each link, and generate abnormal risk-related link binding pairs; The S3 includes: The summary set of agricultural product types corresponding to the agricultural product integrated management information is obtained, which is recorded as the agricultural product set to be analyzed; the summary set of abnormal factor types in the abnormal risk analysis model of various agricultural products based on the nth process link in the agricultural product set to be analyzed is obtained, which is recorded as the first extracted category set; among the link feature extraction results of the agricultural product integrated management information, the nth link feature extraction result is the sensor monitoring result whose data type belongs to the first extracted category set; In the process of generating abnormal risk-related link binding pairs, the data type in the feature extraction results of each link is obtained, and different process links in which the sets constituted by the data types in the corresponding link feature extraction results have intersections are bound, and the intersection between the sets constituted by the data types in the link feature extraction results of each element in the binding result is recorded as the risk-related element set; it is determined whether there is a correlation between the same data type in the link feature extraction results of each element in the binding result that belongs to the corresponding risk-related element set.

[0024] S4. Based on the feature extraction results of each link, the obtained abnormal risk-related link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link, the abnormal event risk bias coefficient of each process link based on the agricultural product integrated management information is generated, and fed back to the operation and maintenance management personnel for early warning of risk rights and responsibilities; The S4 includes: S41, obtaining the feature extraction results of each link in the agricultural product integrated management information, the obtained abnormal risk associated link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link; S42. Obtain the risk bias coefficient of abnormal events of the i-th agricultural product in the agricultural product integrated management information in the n-th process link, and the calculation formula is as follows: ; Among them, F （i，n） represents the risk bias coefficient of abnormal events of the nth process link based on the i-th agricultural product in the agricultural product integrated management information; the array consisting of the abnormal risk-related link binding pairs containing the nth process link, except the nth process link, is recorded as Gn; the intersection of the risk-related element sets corresponding to each abnormal risk-related link binding pair containing the nth process link and the jth element in Gn is recorded as TG （n，j） ; FP1 （i，n） The value is equal to the nth link feature extraction result of agricultural product integrated management information that does not belong to TG （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the i-th agricultural product based on the n-th process link; FP2 （i，n） Indicates FR1（i，n） With FR2 （i，n） The maximum value among FR1 （i，n） The value is equal to the TG in the feature extraction result of the nth link of agricultural product integrated management information （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the i-th agricultural product based on the n-th process link; FR2 （i，n） The value is equal to the process link in the agricultural product integrated management information that corresponds to the process link corresponding to the jth element of Gn. The feature extraction results of the link that belongs to TG （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the process link corresponding to the jth element in Gn for the i-th agricultural product; β represents the weight factor, and β is a preset constant; S43, when i is different values, the corresponding F （i，n） The maximum value of is taken as the risk bias coefficient of abnormal events based on the integrated management information of agricultural products in the nth process link.

[0025] In the process of obtaining the monitoring data corresponding to each data type compared with the abnormal risk assessment coefficient of the corresponding abnormal risk analysis model, the associated mapping pairs containing the corresponding data types in the corresponding abnormal risk analysis model are screened, which are recorded as E; the normal state monitoring interval preset by the sensor corresponding to the first element in E is obtained, which is recorded as EZ; the array consisting of all elements in the summary set corresponding to the second element in E that are greater than the maximum value in EZ is recorded as ED1, and the array consisting of all elements in the summary set corresponding to the second element in E that are less than the minimum value in EZ is recorded as ED2; The calculation formula for obtaining the abnormal risk assessment coefficient of the monitoring data corresponding to each data type compared with the corresponding abnormal risk analysis model is as follows: When g1=0, then FW=0; otherwise, ; FW represents the abnormal risk assessment coefficient of the monitoring data corresponding to the corresponding data type compared with the corresponding abnormal risk analysis model; g1 represents the total number of monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type; FWg represents the abnormal risk assessment coefficient of the g-th monitoring data that does not belong to EZ in the monitoring data corresponding to the corresponding data type compared with the corresponding abnormal risk analysis model; the value of FWg is equal to the quotient of the sum of the number of elements in ED1 that are less than or equal to the g-th monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type and 1 divided by the sum of the total number of elements in ED1 and 1, or the quotient of the sum of the number of elements in ED2 that are greater than or equal to the g-th monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type and 1 divided by the sum of the total number of elements in ED2 and 1.

[0026] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0027] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An operation and maintenance management method for an integrated agricultural product supply chain, characterized in that: The method comprises the following steps: S1. Obtain the monitoring data of each process link in the agricultural product supply chain and each process link of the agricultural product in the supply chain, and build the integrated management information of agricultural products; S2. Based on the agricultural product types corresponding to the agricultural product integrated management information and the abnormal factors in the traceability results of each process link when the corresponding agricultural product types in the historical data are managed abnormally, the abnormal factors in the traceability results of each process link for each agricultural product are counted, and an abnormal risk analysis model based on each process link is constructed for each agricultural product; S3. Combine the data types in the abnormal risk analysis model of each process link for each agricultural product, extract the link features of the agricultural product integrated management information; combine the correlation between the data types in the feature extraction results of each link, and generate abnormal risk-related link binding pairs; S4. Based on the feature extraction results of each link, the obtained abnormal risk-related link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link, the abnormal event risk bias coefficient of each process link based on the agricultural product integrated management information is generated, and fed back to the operation and maintenance management personnel for early warning of risk rights and responsibilities.

2. The operation and maintenance management method for integrated agricultural product supply chain according to claim 1, characterized in that: The integrated management information of agricultural products includes each process link in the agricultural product supply chain and the link monitoring data in each process link; the link monitoring data in each process link of the supply chain includes each sensor number corresponding to the corresponding process link, the type of data monitored by each numbered sensor and the monitoring data of each numbered sensor at each time point.

3. The operation and maintenance management method for integrated agricultural product supply chain according to claim 2, characterized in that: The S2 includes: The i-th agricultural product in the agricultural product category corresponding to the agricultural product integrated management information is recorded as Ai; in the process of obtaining the i-th agricultural product supply in the historical data, each time the management is abnormal, the abnormal factors in the traceability results of each process link are summarized and recorded as the link traceability abnormal factor summary set {[D (i,1,1) , D (i,1,2) , ..., D (i,1,m) ],[D (i,2,1) , D (i,2,2) , ..., D (i,2,m) ], ..., [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ]}, D (i,n,m) It represents the number of management anomalies of the mth abnormal factor in the traceability result of the nth process link in the process of supplying the i-th agricultural product in the historical data; the statistical result of the abnormal factor in the traceability result of the i-th agricultural product in the nth process link is recorded as B （i，n） , B （i，n） Indicates [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ] The corresponding elements whose values ​​are not 0 correspond to the types of abnormal factors and the summary set of the monitoring values ​​of each sensor whose corresponding values ​​correspond to the types of abnormal factors; The method steps for constructing an abnormal risk analysis model for the i-th agricultural product based on the n-th process link are as follows: S21. Get [D (i,n,1) , D (i,n,2) , ..., D (i,n,m) ], the array consisting of the elements whose corresponding values ​​are not 0, is recorded as the abnormal element analysis group of the i-th agricultural product based on the n-th process link; S22. Obtain the number of elements in the abnormal element analysis group of the i-th agricultural product based on the n-th process link, denoted as C （i，n） ; Calculate the average value of the elements in the abnormal element analysis group of the i-th agricultural product based on the n-th process link, recorded as Q （i，n） ; S23, calculate the relationship between each element in the abnormal element analysis group of the i-th agricultural product based on the n-th process link and Q （i，n） The absolute value of the difference between When C （i，n） ≥2, the abnormal element analysis group of the i-th agricultural product based on the n-th process link is divided into Q （i，n） The abnormal element analysis group of the i-th agricultural product based on the n-th process link is denoted as Y （i，n） ; Y （i，n） The average value of the elements in is denoted as Q1 （i，n） ; If |Q （i，n） -Q1 （i，n） |≤μ, then Q1 （i，n） The corresponding abnormal element analysis group is output to step S24; If |Q （i，n） -Q1 （i，n） |>μ, then Y （i，n） Output to step S22; When C （i，n） <2, then Q （i，n） The corresponding abnormal element analysis group is output to step S24; S24, receiving the abnormal element analysis group transmitted by S23, constructing an abnormal risk analysis model for the i-th agricultural product based on the n-th process link, wherein the constructed abnormal risk analysis model includes a plurality of association mapping pairs, wherein the first element in each association mapping pair represents the type of abnormal factor corresponding to any one element in the obtained abnormal element analysis group; the second element in each association mapping pair represents the type of abnormal factor corresponding to any one element in B （i，n） A summary of the monitoring values ​​of each sensor in .

4. The operation and maintenance management method for integrated agricultural product supply chain according to claim 1, characterized in that: The S3 includes: The summary set of agricultural product types corresponding to the agricultural product integrated management information is obtained, which is recorded as the agricultural product set to be analyzed; the summary set of abnormal factor types in the abnormal risk analysis model of various agricultural products based on the nth process link in the agricultural product set to be analyzed is obtained, which is recorded as the first extracted category set; among the link feature extraction results of the agricultural product integrated management information, the nth link feature extraction result is the sensor monitoring result whose data type belongs to the first extracted category set; In the process of generating abnormal risk-related link binding pairs, the data type in the feature extraction results of each link is obtained, and different process links in which the sets constituted by the data types in the corresponding link feature extraction results have intersections are bound, and the intersection between the sets constituted by the data types in the link feature extraction results of each element in the binding result is recorded as the risk-related element set; it is determined whether there is a correlation between the same data type in the link feature extraction results of each element in the binding result that belongs to the corresponding risk-related element set.

5. The operation and maintenance management method for integrated agricultural product supply chain according to claim 4 is characterized in that: The S4 includes: S41, obtaining the feature extraction results of each link in the agricultural product integrated management information, the obtained abnormal risk associated link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link; S42. Obtain the risk bias coefficient of abnormal events of the i-th agricultural product in the agricultural product integrated management information in the n-th process link, and the calculation formula is as follows: ； Among them, F （i，n） represents the risk bias coefficient of abnormal events of the nth process link based on the i-th agricultural product in the agricultural product integrated management information; the array consisting of the abnormal risk-related link binding pairs containing the nth process link, except the nth process link, is recorded as Gn; the intersection of the risk-related element sets corresponding to each abnormal risk-related link binding pair containing the nth process link and the jth element in Gn is recorded as TG （n，j） ; FP1 （i，n） The value is equal to the nth link feature extraction result of agricultural product integrated management information that does not belong to TG （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the i-th agricultural product based on the n-th process link; FP2 （i，n） Indicates FR1 （i，n） With FR2 （i，n） The maximum value among FR1 （i，n） The value is equal to the TG in the feature extraction result of the nth link of agricultural product integrated management information （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the i-th agricultural product based on the n-th process link; FR2 （i，n） The value is equal to the process link in the agricultural product integrated management information that corresponds to the process link corresponding to the jth element of Gn. The feature extraction results of the link that belongs to TG （n，j） The monitoring data corresponding to each data type is compared with the maximum value of the abnormal risk assessment coefficient of the abnormal risk analysis model of the process link corresponding to the jth element in Gn for the i-th agricultural product; β represents the weight factor, and β is a preset constant; S43, when i is different values, the corresponding F （i，n） The maximum value of is taken as the risk bias coefficient of abnormal events based on the integrated management information of agricultural products in the nth process link.

6. The operation and maintenance management method for integrated agricultural product supply chain according to claim 5, characterized in that: In the process of obtaining the monitoring data corresponding to each data type compared with the abnormal risk assessment coefficient of the corresponding abnormal risk analysis model, the associated mapping pairs containing the corresponding data types in the corresponding abnormal risk analysis model are screened, which are recorded as E; the normal state monitoring interval preset by the sensor corresponding to the first element in E is obtained, which is recorded as EZ; the array consisting of all elements in the summary set corresponding to the second element in E that are greater than the maximum value in EZ is recorded as ED1, and the array consisting of all elements in the summary set corresponding to the second element in E that are less than the minimum value in EZ is recorded as ED2; The calculation formula for obtaining the abnormal risk assessment coefficient of the monitoring data corresponding to each data type compared with the corresponding abnormal risk analysis model is as follows: When g1=0, then FW=0; otherwise, ； FW represents the abnormal risk assessment coefficient of the monitoring data corresponding to the corresponding data type compared with the corresponding abnormal risk analysis model; g1 represents the total number of monitoring data that do not belong to EZ in the monitoring data corresponding to the corresponding data type; FWg represents the abnormal risk assessment coefficient of the g-th monitoring data that does not belong to EZ among the monitoring data corresponding to the corresponding data type compared with the corresponding abnormal risk analysis model; the value of FWg is equal to the quotient of the sum of the number of elements in ED1 that are less than or equal to the g-th monitoring data that does not belong to EZ among the monitoring data corresponding to the corresponding data type and 1 divided by the sum of the total number of elements in ED1 and 1, or the quotient of the sum of the number of elements in ED2 that are greater than or equal to the g-th monitoring data that does not belong to EZ among the monitoring data corresponding to the corresponding data type and 1 divided by the sum of the total number of elements in ED2 and 1.

7. An operation and maintenance management system for an integrated agricultural product supply chain, applying the operation and maintenance management method for an integrated agricultural product supply chain as described in any one of claims 1 to 6, characterized in that: The system includes the following modules: An integrated monitoring data acquisition module, which acquires monitoring data of each process link included in the agricultural product supply chain and the agricultural products in each process link of the supply chain, and constructs integrated management information of agricultural products; A risk model building module, which is based on the agricultural product types corresponding to the agricultural product integrated management information, and the abnormal factors in the traceability results of each process link are summarized for each abnormal management of the corresponding agricultural product types in the historical data, and the abnormal factors in the traceability results of each process link of each agricultural product are counted, and an abnormal risk analysis model for each agricultural product based on each process link is constructed; An abnormal risk-related link analysis module, which extracts link features of agricultural product integrated management information based on the data types in the abnormal risk analysis models of each process link for each agricultural product; generates abnormal risk-related link binding pairs based on the correlation between the data types in the feature extraction results of each link; The risk warning feedback module generates abnormal event risk bias coefficients for each process link based on the integrated management information of agricultural products based on the feature extraction results of each link, the obtained abnormal risk-related link binding pairs, and the abnormal risk analysis model of each agricultural product based on each process link, and feeds back to the operation and maintenance management personnel for risk responsibility link warning.

8. The operation and maintenance management system for agricultural product supply chain integration according to claim 7 is characterized in that: The risk model building module includes an abnormal factor statistics unit and an abnormal risk analysis model building unit. The abnormal factor statistical unit is based on the agricultural product types corresponding to the agricultural product integrated management information, and the abnormal factor summary results of each process link traceability result for each agricultural product type in the historical data when the corresponding agricultural product type is managed abnormally each time, and the abnormal factors of each agricultural product in the traceability result of each process link are counted; The abnormal risk analysis model construction unit constructs an abnormal risk analysis model for each agricultural product based on each process link based on the statistical results of the abnormal factor statistical unit.

9. The operation and maintenance management system for agricultural product supply chain integration according to claim 7, characterized in that: The abnormal risk associated link analysis module includes a link feature extraction unit and an abnormal risk associated link binding pair acquisition unit. The link feature extraction unit extracts link features of agricultural product integrated management information based on the data type of each agricultural product in the abnormal risk analysis model of each process link; The abnormal risk associated link binding pair acquisition unit generates abnormal risk associated link binding pairs by combining the correlation between data types in the feature extraction results of each link.

10. The operation and maintenance management system for agricultural product supply chain integration according to claim 7, characterized in that: The risk warning feedback module includes an abnormal event risk bias coefficient calculation unit and a feedback warning unit. The abnormal event risk bias coefficient calculation unit generates abnormal event risk bias coefficients for each process link based on agricultural product integrated management information based on the feature extraction results of each link, the obtained abnormal risk associated link binding pairs, and the abnormal risk analysis models of each agricultural product based on each process link; The feedback warning unit feeds back each abnormal event risk bias coefficient obtained by the abnormal event risk bias coefficient calculation unit to the operation and maintenance management personnel to issue a risk responsibility link warning.

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