Logistics information management method for grain industry

Abstract

The invention discloses a logistics information management method for the grain industry. The method comprises the following steps: obtaining logistics contract information and extracting requirements; generating a contract-commission-order-waybill four-dimensional business entity based on the multimodal transport characteristics; disassembling the task through an intelligent stowage algorithm and matching own and third-party logistics resources, and generating a feasible transportation sub-path; comprehensively evaluating a path by using a multimodal transport cost optimization algorithm, and selecting an optimal scheme for execution; generating an elastic delegation task and a waybill, collecting logistics data in real time, and constructing a'process element 'for dynamic management; timeliness and cargo quality deterioration risks are monitored based on a multi-dimensional early warning model; and integrating task execution data, and optimizing a logistics management closed loop. The transportation efficiency and the resource utilization rate are improved, the transportation cost is reduced, the grain quality is guaranteed, the controllability of the transportation process is enhanced, the system adapts to complex transportation scenes, and continuous optimization management is achieved through data driving.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics information management, and in particular relates to a logistics information management method for the grain industry. Background Art

[0002] As a special bulk commodity, grain has the characteristics of diversified supply and demand, huge transportation volume, strong fluctuations in transportation seasons, and the product's freshness and quality are easily deteriorated. Grain logistics serves the field of grain transportation and thus has its own unique business characteristics, such as:

[0003] (1) The geographical span of transportation is large and the transportation methods are complex. Multimodal transport, multi-route transshipment, and the participation of multiple transport entities are common forms in the industry.

[0004] (2) High timeliness and time sensitivity in transportation. Grain is a fresh commodity that requires timeliness in transportation. It is sensitive to the environment and is prone to deterioration and mold.

[0005] (3) The product value is low and the transportation cost is sensitive. The unit value of grain is low and it is sensitive to the freight incurred during transportation, which determines the complexity of its transportation decision.

[0006] (4) Data tracking is difficult, and it is difficult to form a closed-loop supervision during the transportation process. The level of informatization in the grain industry is relatively low, and a lot of data is difficult to collect. In addition, the transportation forms are diverse, the transportation methods are flexible, and there are many transportation and transshipment nodes. Information management and supervision breakpoints are very likely to occur during the transportation process, making it impossible to form a closed-loop traceability, and it is difficult to see the quantity and quality of goods.

[0007] However, most mainstream logistics systems currently in use are based on standard cargo and simple transportation processes. These systems fail to fully account for the characteristics of grain transportation, such as fresh products, diverse sources, and numerous transportation spans and nodes. These simple processes make it difficult to handle complex transportation processes, assess the impact of timeliness on products, accurately calculate transportation costs, dynamically manage cargo, and generate closed-loop data flows for managing cargo logistics under complex transportation conditions. This results in difficulties in tracking the quantity and quality of grain transport, leading to frequent cargo loss, unclear sources, and deterioration and mold. Summary of the Invention

[0008] The purpose of the present invention is to provide a logistics information management method for the grain industry to solve the above-mentioned technical problems, realize all-round, three-dimensional, full-chain management of logistics information from formulation and execution to feedback summary under complex transportation conditions, and realize a closed-loop data management of logistics information.

[0009] To solve the above technical problems, the specific technical solution of the logistics information management method for the grain industry of the present invention is as follows:

[0010] A logistics information management method for the grain industry comprises the following steps:

[0011] S1, obtain logistics contract information and extract contract requirements;

[0012] S2, based on the characteristics of multimodal transport, multi-transfer, and multi-transportation entities in actual logistics business, generates and establishes

[0013] Based on the four-dimensional business entity of contract-commission-order-waybill;

[0014] S3, based on the intelligent loading algorithm, breaks down the transport into one or more transport tasks and compares them with the service scope of the logistics information management system and third-party logistics providers to obtain multiple feasible transport sub-routes;

[0015] S4, based on the multimodal transport cost optimization algorithm, comprehensively evaluates the sub-paths and combines multiple desired paths.

[0016] Select the best route to perform logistics transportation tasks;

[0017] S5, generating flexible commission tasks to accommodate logistics service transportation orders generated under the optimal path;

[0018] S6, based on the service transport order and the actual transport situation in the system, disassemble and generate a waybill specific to the specific vehicle;

[0019] S7, collects logistics information during system operation and generates "process elements" based on the waybill;

[0020] S8, based on a multi-dimensional early warning model, analyzes multi-dimensional data in the process of shipping order execution in real time, combines the external environment and the product's own conditions, and evaluates the status of the goods in real time based on the deterioration risk coefficient of various goods in the system in different environments and scenarios, and warns of potential quality deterioration risks of products.

[0021] The data of the device is used to estimate the timeliness of the vehicle in real time;

[0022] S9 integrates task execution status information, summarizes it in real time, and displays logistics information execution status and data. The information executed by the system is used as training data to continue to optimize the management of logistics information.

[0023] Furthermore, the S1 includes the following steps:

[0024] Through system input or OCR analysis of the transport contract structure, the basic information of the transport contract is extracted.

[0025] It includes the cargo's place of departure, destination, transport quantity, transport timeliness requirements, and quality standards.

[0026] Furthermore, the S3 includes the following steps:

[0027] (1) Obtain the time constraint in the contract, the transportation start date t b , deadline t e , obtain the time constraint T, T = t e -t b ;

[0028] (2) Obtain the transportation starting point L of the goods in the contract a , end point L b , combined with the important service node set of self-owned logistics and third-party logistics in the method, it is decomposed into the service area range R S and service provider service area R j Sub-transport segment D i ;

[0029] (3) The disassembled sub-transportation section and the service area range R S and service provider service area R j Perform a secondary comparison, construct the intersection of the route and the service area, and obtain the set of candidate transport segments D, D = {D1, D2, ..., D i},and or Arrange the candidate sub-transportation segments in order and generate all possible continuous segmented routes to ensure that the complete path is covered;

[0030] (4) Mark the feasible vehicle type in each segment and obtain the time information T required for the vehicle in the segment i , according to the time constraint T, remove the paths whose total time is greater than T;

[0031] That is, SUM(T i )=T d1 +T d2 ...T dn ≤T, where SUM(T i ) is the sum of the transportation time of the specific path of the sub-transportation segment under a specific vehicle, T dn The required transportation time for the vehicle for the nth segment;

[0032] (5) Extract the carrier information of the service provider corresponding to each segment, and combine the cargo type I, cargo weight W, cargo volume V to determine the required number of carriers m;

[0033] m1=W / W max

[0034] Where W is the weight of the cargo, Wmax is the maximum load capacity of the carrier, m1 is the required carrier capacity calculated based on the weight;

[0035] m2=V / V max =W / (ρ i ·V max )

[0036] Where V is the cargo volume, V max is the maximum transport volume of the vehicle, W is the weight of the cargo, ρ i is the bulk density of the corresponding grain variety, and m2 is the required carrier volume calculated based on the volume;

[0037]

[0038] m = max(m1, m2), that is, take the maximum value of m1 and m2, and according to the subsidized vehicle type, obtain the minimum number of vehicles required for the corresponding sub-transport segment;

[0039] m=max(m1,m2)

[0040] (6) Output the set P of all feasible transport paths as the input parameter of S4, the multimodal transport cost optimization algorithm;

[0041]

[0042] where {D i1 ,D i2 ,…,D im} is a candidate route, and the sub-transportation segments are arranged in order.

[0043] Furthermore, the S4 includes the following steps:

[0044] The output includes the top n1 routes ranked by Cost from lowest to highest, the top n2 routes ranked by Time with the shortest total transport time, and the top n3 routes ranked by Reliability with the highest reliability. n1, n2, and n3 are set as needed and must all be greater than or equal to 1.

[0045] Output the component transport segments of the corresponding route, arranged in order from the starting point to the end point;

[0046] Output the cost, time, and execution reliability of the entire process;

[0047] Output the number of transport segments, vehicle types, and vehicle quantities for the corresponding route;

[0048] Users choose the path they need;

[0049]

[0050] Among them D iis the i-th sub-transport segment, Cself(D i ) is owned by the specific single vehicle in this sub-section D i The transportation cost, C 3rd (D i ) is a tripartite, the specific single vehicle in this sub-section D i Transportation cost; m(D i ) is sub-segment D i The number of vehicles required, Te(D i ) is sub-segment D i The transportation time, r j (D i ) is the service provider in subsection D i historical fulfillment rate.

[0051] Furthermore, the S5 synchronizes the corresponding transportation task requirements to the corresponding self-owned vehicles or third-party service providers in the system according to the sub-transportation segments to form a transportation order; the single vehicle information is in the form of a waybill, and data is collected based on the sub-transportation segment order as the data entity.

[0052] Furthermore, the S6 will disassemble and generate a waybill specific to the detailed vehicle execution based on the service transportation order and the basic information of the service provider, and hand it over to the actual logistics service unit for execution and summarize the data.

[0053] Furthermore, the step S7 includes the following steps:

[0054] Generate "process metadata" based on waybills. This is the smallest unit of a business process and a collection of various data collected based on waybills. It is also the smallest unit for data aggregation. With flexible delegation, process metadata can be added, deleted, redirected, and merged according to business needs, and then aggregated under delegation.

[0055] Get real-time data of waybills;

[0056] Construct the attribute information of the "process element": waybill ID, vehicle ID, cargo attributes on the vehicle, and time constraint T;

[0057] Construct dynamic information of "process elements": vehicle GPS coordinates, cargo temperature, ambient temperature and humidity;

[0058] Build business node events for "process elements": transportation start, end, extension events, waybill cancellation, and waybill reassignment; add timestamps to process elements based on a time window sorting algorithm;

[0059] According to the elastic algorithm, the "process metadata" data under the order is elastically scaled and unnecessary event information is eliminated;

[0060] Using the timestamp as the primary key, the process metadata of the waybill layer is traversed, cleaned, aggregated, and integrated into order data;

[0061] According to the order starting point arrangement sequence, the order layer information is traversed, summarized, and integrated into the entrustment data.

[0062] Furthermore, the step S8 includes the following steps:

[0063] Timeliness warning:

[0064] Based on sub-transport segment D i Establish timely early warning information;

[0065] Provide timeliness warning for the currently executed transport segment;

[0066] Get the current transportation time T y , if the current time is greater than or equal to the time required for this segment T i , prompting that the transportation has exceeded the time limit;

[0067] Get the current transportation time T y , if the current time is lower than the time required for this segment T i , intercept the current positioning position, calculate the remaining distance and remaining time T1, T1 = T i -T y , get the estimated time T for the remaining journey g , if T g ≥T1, indicating transportation timeout;

[0068] If any sub-transport segment triggers an alert, the contract is marked as having a delay risk.

[0069] If more than Q% of sub-transport segments trigger an alert, the contract is marked as having a high delay risk, and the relevant responsible person is notified to intervene in the management. Q% is a value set according to the actual situation.

[0070] Quality deterioration early warning model:

[0071] After the goods are transported, the goods type I, goods type S, real-time temperature T, real-time humidity H, deterioration coefficient L, temperature T C ;

[0072] Constructing a quality deterioration early warning model:

[0073]

[0074] Where L(I) is the deterioration coefficient of the variety. In an open variable environment, f env (t n ) is the environmental impact factor value obtained by fitting based on the real-time ambient temperature and real-time ambient humidity;

[0075] In a closed environment, f env (t n ) is the environmental impact factor value obtained by fitting the cargo environment temperature and cargo environment humidity; λ is the time decay factor;

[0076] f core (t n ) is the transport t n The impact factor of the temperature change of the goods at the moment on the product quality, here t n The change value actually represents the result of a comprehensive query of comprehensive factors based on the timeliness node. It is determined by multiple factors, including the initial temperature and initial moisture content of the cargo, the trend of the change in the cargo temperature with the change in the external environment, and the trend of the change in the cargo humidity with the change in the external environment. The specific value is based on laboratory measurements, and the coefficient is searched according to the corresponding conditions over time to calculate the risk score.

[0077] Get f at fixed time intervals (Δt) core (t n ),f env (t n ), and finally obtain t n R(t n ) value and compare it with the warning threshold table corresponding to the variety to determine the risk of quality deterioration of the goods and issue an early warning prompt.

[0078] The logistics information management method for the grain industry of the present invention has the following advantages:

[0079] 1. Improve transportation efficiency

[0080] Through intelligent loading algorithms and multimodal transport cost optimization algorithms, the system can quickly break down transportation tasks and generate multiple feasible routes, comprehensively considering cost, time and reliability, significantly improving transportation efficiency and ensuring that food is delivered on time.

[0081] 2. Reduce transportation costs

[0082] By optimizing route selection and vehicle allocation, the system reduces unnecessary transportation links and resource waste, effectively lowering overall transportation costs. It is particularly suitable for transporting grain with lower cargo value.

[0083] 3. Ensure food quality

[0084] By real-time monitoring of environmental data such as cargo temperature and humidity, and combining it with a deterioration warning model, the system can promptly detect and warn of potential quality deterioration risks, ensuring that the grain remains in optimal condition during transportation.

[0085] 4. Enhance the controllability of the transportation process

[0086] The system dynamically collects and analyzes transportation data through "process elements", realizing refined management and real-time monitoring of the entire transportation process, avoiding information breakpoints, and improving the traceability and transparency of the transportation process.

[0087] 5.Flexible adaptation to complex transportation scenarios

[0088] The system supports complex transportation modes such as multimodal transport, multi-route transshipment and multiple transport entities. It can dynamically adjust transportation plans according to actual conditions to adapt to the cross-regional and multi-node transportation needs of the grain industry.

[0089] 6. Optimize resource allocation

[0090] By integrating its own and third-party logistics resources, the system can efficiently match service providers and vehicles, reasonably allocate transportation tasks, maximize the use of existing resources, and improve overall logistics operation efficiency.

[0091] 7. Data-driven continuous optimization

[0092] The system uses data from the execution process as training samples to continuously optimize algorithms and models, improve the accuracy of path planning, timeliness prediction, and risk warning, and form a virtuous cycle of management.

[0093] 8. Improve user decision-making capabilities

[0094] The system provides multi-dimensional path evaluation and real-time warning functions to help users make scientific decisions quickly, reduce transportation risks, and improve the intelligence level of logistics management.

[0095] In summary, the present invention effectively solves the problems of efficiency, cost, quality and controllability in grain logistics through intelligent and refined management means, and provides the grain industry with an efficient, reliable and low-cost logistics information management solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 The present invention is a flow chart of the steps of the logistics information management system and method for the grain industry.

[0097] Figure 2 Schematic diagram of the principle of the method of the present invention. DETAILED DESCRIPTION

[0098] In order to better understand the purpose, structure and function of the present invention, the following further describes in detail a logistics information management method for the grain industry of the present invention in conjunction with the accompanying drawings.

[0099] First set the necessary parameters of the method

[0100] (1) Transport mission information

[0101] ① Cargo attributes: cargo type I, cargo weight W, cargo volume V, cargo type S, and time constraint T.

[0102] ②Cargo quality: moisture content W c , the initial temperature of the cargo itself is T c .

[0103] ③ Geographic information: call the standard map interface and obtain the full distance information L, starting point L a , end point L b , mark the important service node set of self-owned logistics and third-party logistics, L c ={L1,L2,L3,...L n}, L c L is a collection of important service nodes in the country. n The specific geographical location of the service node.

[0104] (2) Logistics business resource information

[0105] ① Own logistics attributes: vehicle type C, full load weight W max , Full load volume V max 、Service area range R s , vehicle timeliness information within the service range T lim , Vehicle cost information C self .

[0106] ② Third-party logistics attributes: service provider set S p {S p1 ,S p2 ,S p3 ...S pn}, service provider service area range R j , Vehicle Type C j 、Quotation information of this vehicle type C 3rd , the transport time information T of a certain section of the vehicle type i , the historical service reputation of logistics companies.

[0107] like Figure 1 Figure 2 As shown, a logistics information management method for the grain industry of the present invention includes the following steps:

[0108] S1: The basic information of the transport contract is obtained through system input or OCR structured analysis of the transport contract, including the place of departure, destination, transport quantity, transport timeliness requirements, quality standards (grain grade, moisture content), etc.

[0109] For example, if the departure place of the goods is Changchun City, the destination is Ningbo City, the type of goods is corn, the transportation quantity is 100 tons, the type of goods is bulk grain, the transportation time is within 10 days, the quality standard is grain grade 1, the moisture content is 13%, and the bulk density is 720g / L.

[0110] S2: Create a four-dimensional business entity, including contract, commission, order, and waybill. This four-dimensional entity is used by the logistics information management system to decompose logistics tasks and to collect and aggregate subsequent data.

[0111] The system automatically creates a four-dimensional structure of contract, entrustment, order and waybill based on the contract.

[0112] The contract is a transport contract and is the basis for conducting business.

[0113] The delegation is a summary of the dynamic data generated by the contract, and a display of the dynamic data collection and progress of the contract execution status.

[0114] The order is based on sub-transport segment D i The generated transport tasks are shown in S3.

[0115] The waybill is based on the vehicle, and the specific tasks of the order are broken down, executed and summarized.

[0116] S3: Based on the intelligent loading algorithm, it is disassembled into one or more transportation tasks, and compared with the service scope of the own and third-party logistics suppliers in the logistics information management system to obtain multiple feasible transportation sub-paths.

[0117] (1) Obtain the time constraints in the contract, the transportation start date (format is year, month, day, hour, minute, second) b , deadline (format is year month day hour minute second) t e , obtain the time constraint T (i.e. the longest transportation time), T = t e -t b .

[0118] T=t e -t b

[0119] (2) Obtain the transportation starting point L of the goods in the contract a , end point L b , combined with the important service node set of self-owned logistics and third-party logistics in the method, it is decomposed into the service area range R S and service provider service area R j Sub-transport segment based on i ).

[0120] For example, if we want to obtain the contract's cargo transportation starting point, Changchun, and ending point, Ningbo, we can read the set of important service nodes {Dalian, Jinan, Shanghai, Ningbo, Hangzhou} for our own logistics and third-party logistics, and decompose them into sub-transportation segments D1 = {Changchun-Dalian | Supplier 1, Supplier 2, ..., Supplier n}, D2 = {Dalian-Ningbo | Supplier 1, Supplier 2, ..., Supplier m}, and D3 = {Shanghai-Ningbo | Supplier 1, Supplier 2, ..., Supplier k}, where n, m, and k are all ≥ 1.

[0121] (3) The disassembled sub-transportation section and the service area range R S and service provider service area R j Perform a secondary comparison, construct the intersection of the route and the service area, and obtain the set of candidate transport segments D, D = {D1, D2, ..., D i},and or The candidate sub-transportation segments are arranged in order to generate all possible continuous segmented routes to ensure that the complete path is covered.

[0122] Based on the above D i Disassemble the result and get the D set. a ={Changchun-Dalian, Dalian-Ningbo}, D b ={Changchun-Shanghai, Shanghai-Ningbo}, D c = {Changchun-Dalian, Dalian-Shanghai, Shanghai-Changchun}. a 、D b 、D c Represents the specific generated path. It should be pointed out here that abc here is an example representation order, and the generated solution in the actual method is greater than or equal to 1.

[0123] (4) Mark the feasible vehicle type in each segment and obtain the time information T required for the vehicle in the segment i , according to the time constraint T, remove the paths whose total time is greater than T.

[0124] That is, SUM(T i )=T d1 +T d2 ...T dn ≤T. Where SUM(T i ) is the sum of the transportation time of the specific path of the sub-transportation segment under a specific vehicle. dn The required transportation time for the vehicle in the nth segment.

[0125] Such as D a Gather together, D a = {Changchun-Dalian, Dalian-Ningbo}, obtain the comprehensive time information of different carriers in this section, obtain different transportation modes, and form Dim , i represents the transportation path, the same i represents the same transportation path, and m represents different transportation combinations.

[0126] Solve SUM(T i ) and obtain the total time consumed by different vehicle combinations. a1 ={(Changchun-Dalian, road transport, 9 hours), (Dalian-Ningbo, rail transport, 30 hours)}, D a2 ={(Changchun-Dalian, railway, 8 hours), (Dalian-Ningbo, water transport, 96 hours)}. in ={(L1-L2, Cj1, Td1),...,(L n-1 -Ln, C jn , T dn )}, where L1 is the starting point of transportation, L2-L n-1 is the transit node, L n The destination of transportation. jn T is the mode of transport used for the nth segment. dn It is the comprehensive time required for this section of transport under this mode of transport. a1 For example, SUM(T i )=9+30=39h.

[0127] (5) Extract the carrier information of the service provider corresponding to each segment, and combine the cargo type I, cargo weight W, cargo volume V, and the required number of carriers (m).

[0128] m1=W / W max

[0129] Where W is the weight of the cargo, W max is the maximum load capacity of the carrier, and m1 is the required carrier capacity calculated based on the weight.

[0130] m2=V / V max =W / (ρ i ·V max )

[0131] Where V is the cargo volume, V max is the maximum transport volume of the vehicle, W is the weight of the cargo, ρ i is the bulk density of the corresponding grain variety. m2 is the required carrier volume calculated based on the volume.

[0132]

[0133] m = max(m1, m2), which is the maximum value of m1 and m2. Based on the subsidized vehicle type, the minimum number of vehicles required for the corresponding sub-transport segment is obtained.

[0134] m=max(m1, m2) (6) Output the set P of all feasible transportation paths as the input parameter of S4, the multimodal transport cost optimization algorithm.

[0135]

[0136] where {D i1 ,D i2 ,…,D im} is a candidate route, and the sub-transportation segments are arranged in order.

[0137] D ik =L a →L b , indicating that the route and the mode of transport exist in the route.

[0138] S4: Based on the multimodal transport cost optimization algorithm, a comprehensive evaluation is performed on the sub-paths, and multiple desired paths are combined to select the best path to perform the logistics transportation task.

[0139] The output includes the top n1 routes ranked by cost (cost), the top n2 routes by time (time), and the top n3 routes by reliability (reliability). n1, n2, and n3 can be set as needed and must all be greater than or equal to 1.

[0140] Output the component transport segments of the corresponding route, arranged in order from the starting point to the end point.

[0141] Output the cost, time and execution reliability of the entire process.

[0142] Output the number of transport segments, vehicle types, and number of vehicles for the corresponding path.

[0143] Users select the path they need.

[0144]

[0145] Among them D i is the i-th sub-transport segment, Cself(D i ) is owned by the specific single vehicle in this sub-section D i The transportation cost. 3rd (D i ), for three parties, a specific single vehicle in this sub-section D i transportation costs.

[0146] m(D i ), for sub-segment D i The number of vehicles required, Te(D i ), for sub-segment D i Transportation time (hours), r j(D i ), the service provider in sub-segment D i The historical fulfillment rate (0-1).

[0147] As in Example D a1 ={(Changchun-Dalian, road transport, 9 hours), (Dalian-Ningbo, rail transport, 30 hours)}. Based on the above output results and the relevant parameters of the method, it is obtained that 100 tons requires 4 trucks. Changchun-Dalian is self-owned logistics.

[0148] For road transport, the cost per vehicle is 2,000 yuan, with a historical fulfillment rate of 100%. For the Dalian-Ningbo route, third-party rail transport requires four carriages for 100 tons, with a cost of 2,800 yuan per carriage. The historical fulfillment rate is 95%.

[0149] Cost=∑D i (C self (D i )·m(D i )+C 3rd (D i ))=4*2000+2800*4=19200 yuan.

[0150] Time=∑D i T e ( Di )=9+30=39h.

[0151] Reliability=∏D i r j (D i )=100%*95%=95%.

[0152] S5: Based on the results of S4, generate flexible delegation tasks and generate an overall task delegation order.

[0153] According to the sub-transportation segments, the corresponding transportation task requirements will be synchronized to the corresponding self-owned vehicles or third-party service providers in the system to form a transportation order.

[0154] As in the example, the lowest-cost option is chosen, and the transport demand order is sent to Complex Logistics, which has its own logistics, and the third-party logistics company A with the lowest bid, which is responsible for the Dalian-Ningbo section.

[0155] Single carrier information is collected in the form of waybills, based on sub-transport segment orders as data entities.

[0156] S6: Collect the actual response of our own and third-party service providers to transportation business.

[0157] The actual logistics data generated by transportation is disassembled and generated into waybills specific to detailed vehicles based on the service transportation order and combined with the basic information of the service provider. The waybills are then handed over to the actual logistics service unit for execution and the data is summarized.

[0158] For example, after receiving an order through its own logistics system, it will create four shipping documents for each specific vehicle. Company A, based on the actual train situation, does not split the order and uses four carriages of a train to aggregate the data for a single shipping document per train.

[0159] S7: Collect logistics information during system operation.

[0160] Generate "process metadata" based on waybills. A process metadata is the smallest unit of a business process, a collection of various data collected based on waybills and the smallest unit for data aggregation. With flexible delegation, process metadata can be added, deleted, redirected, merged, and more based on business needs, and aggregated under the delegation.

[0161] Get real-time data on waybills.

[0162] Construct the attribute information of the "process element": waybill ID, vehicle ID, cargo attributes on the vehicle, and time constraint T.

[0163] Build dynamic information of "process elements": vehicle GPS coordinates, cargo temperature, ambient temperature and humidity.

[0164] Build business node events of "process element": transportation start, end, extension event, waybill cancellation, and waybill reassignment.

[0165] A sorting algorithm based on a time window adds a timestamp to the process element.

[0166] According to the elastic algorithm, the "process metadata" data under the elastic scaling order is elastically expanded and contracted, and unnecessary event information is eliminated.

[0167] Using the timestamp as the primary key, the process metadata of the waybill layer is traversed, cleaned, aggregated, and integrated into order data.

[0168] According to the order starting point arrangement sequence, the order layer information is traversed, summarized, and integrated into the entrustment data.

[0169] S8: Based on a multi-dimensional early warning model, it analyzes multiple dimensions of data, such as timeliness and IoT, during the waybill execution process in real time. Combined with the external environment and the product's own conditions, it assesses the cargo's status in real time based on the deterioration risk factors of various cargoes in the system in different environments and scenarios, providing early warnings of potential product quality deterioration risks. It also estimates the timeliness of the vehicle in real time based on the vehicle's estimated arrival time and data from the vehicle's onboard positioning device.

[0170] Timeliness warning:

[0171] Based on sub-transport segment D i Establish timely early warning information.

[0172] Provide timeliness warning for the currently executed transport segment.

[0173] Get the current transportation time T y , if the current time is greater than or equal to the time required for this segment T i , prompting that the transportation has timed out.

[0174] Get the current transportation time T y , if the current time is lower than the time required for this segment T i , intercept the current positioning position, calculate the remaining distance and remaining time T1, T1 = T i -T y Get the estimated time T for the remaining journey g , if T g ≥T1, indicating transportation timeout.

[0175] If any sub-transport segment triggers an alert, the contract is marked as having a delay risk.

[0176] If more than Q% of sub-transport segments trigger an early warning, the contract will be marked as having a high delay risk, and the relevant responsible persons will be notified to intervene in the management. Q% is a value set according to the actual situation.

[0177] Quality deterioration early warning model:

[0178] After the goods are transported, the goods type I, goods type S, real-time temperature T, real-time humidity H, deterioration coefficient L, temperature T C .

[0179] Build a quality deterioration early warning model

[0180]

[0181] Where L(I) is the deterioration coefficient of the variety. In an open variable environment, f env (t n ) is the environmental impact factor value obtained by fitting the real-time ambient temperature and real-time ambient humidity.

[0182] In a closed environment, f env (t n ) is the environmental impact factor value obtained by fitting the cargo environment temperature and cargo environment humidity.

[0183] λ is the time decay factor.

[0184] f core (t n ), is the transport t nThe impact factor of the temperature change of the cargo at a certain moment on the product quality is comprehensively measured by factors such as the variety characteristics, the change trend of the cargo temperature and cargo humidity, the change value, and time. n The actual change represents the result of a comprehensive query of multiple factors at that time point. It should be noted again that the risk score is determined by multiple factors, including the cargo's initial temperature, initial moisture content, the trend of the cargo's temperature change with external changes, and the trend of the cargo's humidity change with external changes. The specific values ​​are based on laboratory measurements, and the coefficients used to calculate the risk score are searched over time according to the corresponding conditions.

[0185] Get f at fixed time intervals (Δt) core (t n ),f env (t n ), and finally obtain t n R(t n ) value and compare it with the warning threshold table corresponding to the variety to determine the risk of quality deterioration of the goods and issue an early warning prompt.

[0186] To calculate the risk assessment coefficient for a two-hour truck transport of bulk cargo, the process is as follows. (The following data are random numbers for ease of explanation and do not represent actual conditions. The product deterioration coefficient, environmental impact factor, time decay factor, and the impact of cargo temperature change on cargo quality are all experimentally measured. They are built into the system and can be called upon as needed.)

[0187] For example, the coefficient of deterioration of corn varieties is 0.6, f env (t n ) The first fitting value is 0.1, the second fitting value is 0.2, ..., the λ table value is 1, the time value step is 10min (1 / 6h), fcore(2) is 0.24, and the final value of R(2) is 0.173. From the table, we can see that it is lower than the deterioration value of 0.5 for this variety, which means that at this moment, this variety of goods has no risk of deterioration in 2 hours under this transportation method.

[0188] S9: During system operation, the system and method aggregate system content data in real time for business personnel to analyze and make decisions. Upon task completion, task execution status information is integrated and summarized in real time, displaying logistics information execution status and data. This system execution information serves as training data to further optimize logistics information management and improve information such as supplier historical fulfillment rates and service timeliness.

[0189] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A logistics information management method for the grain industry, characterized in that: The steps include: S1, obtain logistics contract information and extract contract requirements; S2, based on the characteristics of multimodal transport, multi-transfer, and multiple transport entities in actual logistics business, generates and establishes a four-dimensional business entity based on contract-commission-order-waybill; S3, based on the intelligent loading algorithm, breaks down the transport into one or more transport tasks and compares them with the service scope of the logistics information management system and third-party logistics providers to obtain multiple feasible transport sub-routes; S4, based on the multimodal transport cost optimization algorithm, comprehensively evaluates the sub-paths, combines multiple desired paths, and selects the best path to perform the logistics transportation task; S5, generating flexible commission tasks to accommodate logistics service transportation orders generated under the optimal path; S6, based on the service transport order and the actual transport situation in the system, disassemble and generate a waybill specific to the specific vehicle; S7 collects logistics information during system operation and generates "process elements" based on the waybill; S8, based on a multi-dimensional early warning model, conducts real-time analysis of multi-dimensional data during the waybill execution process. Combining the external environment with the product's own conditions, and based on the deterioration risk coefficients of various goods in the system under different environments and scenarios, it conducts real-time assessments of the cargo's status, providing early warnings of potential product quality deterioration risks. It also provides real-time estimates of the vehicle's timeliness based on the vehicle's estimated arrival time and data from the vehicle's onboard positioning device. S9 integrates task execution status information, summarizes it in real time, and displays logistics information execution status and data. The information executed by the system is used as training data to continue to optimize the management of logistics information.

2. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S1 comprises the following steps: Through system input or OCR analysis of the transport contract structure, the basic information of the transport contract is extracted, including the origin and destination of the goods, the transport quantity, the transport timeliness requirements, and the quality standards.

3. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S3 comprises the following steps: (1) Obtain the time constraint in the contract, the transportation start date t b , deadline t e , get the time constraint T, T=t e -t b ; (2) Obtain the transportation starting point L of the goods in the contract a , end point L b , combined with the important service node set of self-owned logistics and third-party logistics in the method, it is decomposed into the service area range R S and service provider service area R j Sub-transport segment D i ; (3) The disassembled sub-transportation section and the service area range R S and service provider service area R j Perform a secondary comparison, construct the intersection of the route and the service area, and obtain the set of candidate transport segments D, D = {D1, D2, ..., D i },and or Arrange the candidate sub-transportation segments in order and generate all possible continuous segmented routes to ensure that the complete path is covered; (4) Mark the feasible vehicle type in each segment and obtain the time information T required for the vehicle in the segment i , according to the time constraint T, remove the paths whose total time is greater than T; That is, SUM(T i )=T d1 +T d2 ...T dn ≤T, where SUM(T i ) is the sum of the transportation time of the specific path of the sub-transportation segment under a specific vehicle, T dn The required transportation time for the vehicle for the nth segment; (5) Extract the carrier information of the service provider corresponding to each segment, and combine the cargo type I, cargo weight W, cargo volume V to determine the required number of carriers m; m1=W / W max Where W is the weight of the cargo, W max is the maximum load capacity of the carrier, m1 is the required carrier capacity calculated based on the weight; m2=V / V max =W / (ρ i ·In max ) Where V is the cargo volume, V max is the maximum transport volume of the vehicle, W is the weight of the cargo, ρ i is the bulk density of the corresponding grain variety, and m2 is the required carrier volume calculated based on the volume; m = max(m1, m2), that is, take the maximum value of m1 and m2, and according to the subsidized vehicle type, obtain the minimum number of vehicles required for the corresponding sub-transport segment; m=max(m1,m2) (6) Output the set P of all feasible transport paths as the input parameter of S4, the multimodal transport cost optimization algorithm; where {D i1 ,D i2 ,…,D im } is a candidate route, and the sub-transportation segments are arranged in order.

4. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S4 comprises the following steps: The output includes the top n1 routes ranked by Cost from lowest to highest, the top n2 routes ranked by Time with the shortest total transport time, and the top n3 routes ranked by Reliability with the highest reliability. n1, n2, and n3 are set as needed and must all be greater than or equal to 1. Output the component transport segments of the corresponding route, arranged in order from the starting point to the end point; Output the cost, time, and execution reliability of the entire process; Output the number of transport segments, vehicle types, and vehicle quantities for the corresponding route; Users select the path they need; Among them D i is the i-th sub-transport segment, Cself(D i ) is owned by the specific single vehicle in this sub-section D i The transportation cost, C 3rd (D i ) is a tripartite, the specific single vehicle in this sub-section D i Transportation cost; m(D i ) is sub-segment D i The number of vehicles required, Te(D i ) is sub-segment D i The transportation time, r j (D i ) is the service provider in subsection D i historical fulfillment rate.

5. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S5 synchronizes the corresponding transportation task requirements to the corresponding self-owned vehicles or third-party service providers in the system according to the sub-transportation segments to form a transportation order; the single vehicle information is in the form of a waybill, and data is collected based on the sub-transportation segment order as the data entity.

6. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S6 will disassemble and generate a waybill specific to the detailed vehicle execution based on the service transportation order and the basic information of the service provider, and hand it over to the actual logistics service unit for execution and summarize the data.

7. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S7 comprises the following steps: Generate a "process element" based on the waybill. This element is the smallest unit of the business process and a collection of various data collected based on the waybill. It is also the smallest unit for data aggregation. With flexible delegation, process metadata can be added, deleted, redirected, and merged according to business needs, and then aggregated under the delegation. Get real-time data of waybills; Construct the attribute information of the "process element": waybill ID, vehicle ID, cargo attributes on the vehicle, and time constraint T; Construct dynamic information of "process elements": vehicle GPS coordinates, cargo temperature, ambient temperature and humidity; Build business node events for "process elements": transportation start, end, delay events, waybill cancellation, and waybill reassignment; A sorting algorithm based on a time window adds timestamps to process elements; According to the elastic algorithm, the "process metadata" data under the order is elastically scaled and unnecessary event information is eliminated; Using the timestamp as the primary key, the process metadata of the waybill layer is traversed, cleaned, aggregated, and integrated into order data; According to the order starting point arrangement sequence, the order layer information is traversed, summarized, and integrated into the entrustment data.

8. The method for managing logistics information for the food industry according to claim 1, characterized in that: The S8 comprises the following steps: Timeliness warning: Based on sub-transport segment D i Establish timely early warning information; Provide timeliness warning for the currently executed transport segment; Get the current transportation time T y , if the current time is greater than or equal to the time required for this segment T i , prompting that the transportation has exceeded the time limit; Get the current transportation time T y , if the current time is lower than the time required for this segment T i , intercept the current positioning position, calculate the remaining distance and remaining time T1, T1 = T i -T y , get the estimated time T for the remaining journey g , if T g ≥T1, indicating transportation timeout; If any sub-transport segment triggers an alert, the contract is marked as having a delay risk. If more than Q% of sub-transport segments trigger an alert, the contract is marked as having a high delay risk, and the relevant responsible person is notified to intervene in the management. Q% is a value set according to the actual situation. Quality deterioration early warning model: After the goods are transported, the goods type I, goods type S, real-time temperature T, real-time humidity H, deterioration coefficient L, temperature T C ; Constructing a quality deterioration early warning model: Where L(I) is the deterioration coefficient of the variety. In an open variable environment, f env (t n ) is the environmental impact factor value obtained by fitting based on the real-time ambient temperature and real-time ambient humidity; In a closed environment, f env (t n ) is the environmental impact factor value obtained by fitting the cargo environment temperature and cargo environment humidity; λ is the time decay factor; f core (t n ) is the transport t n The impact factor of the temperature change of the goods at the moment on the product quality, here t n The change value actually represents the result of a comprehensive query of comprehensive factors based on the timeliness node. It is determined by multiple factors, including the initial temperature and initial moisture content of the cargo, the trend of the change in the cargo temperature with the change in the external environment, and the trend of the change in the cargo humidity with the change in the external environment. The specific value is based on laboratory measurements, and the coefficient is searched according to the corresponding conditions over time to calculate the risk score. Get f at fixed time intervals (Δt) core (t n ),f env (t n ), and finally obtain t n R(t n ) value and compare it with the warning threshold table corresponding to the variety to determine the risk of quality deterioration of the goods and issue an early warning prompt.

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