A spot exposure management system and method based on unitization configuration

Abstract

This invention discloses a spot open position management system and method based on unitized configuration, relating to the field of open position management technology. The key technical solutions include the following steps: obtaining the spot demand of target open position orders under standard fulfillment scenarios; statistically analyzing the comprehensive fulfillment verification conditions of orders based on the location distribution of transportation and warehousing segments in historical transaction orders; processing and analyzing historical logistics records to obtain a first fulfillment correlation coefficient, a second fulfillment correlation coefficient, and a third fulfillment correlation coefficient; obtaining the degree of spot fulfillment delay affected by logistics fluctuations and warehousing area changes during the execution of target open position orders based on the first, second, and third fulfillment correlation coefficients; and selecting fulfillment verification conditions from the comprehensive fulfillment verification conditions to verify the fulfillment efficiency of target open position orders. The effect is to improve the overall management level of enterprises in spot open position business.

Description

Technical Field

[0001] This invention relates to the field of open position management technology, and more specifically, to a spot open position management system and method based on unitized configuration. Background Technology

[0002] In the spot trading sector, exposure management is a critical issue for enterprises. Traditional spot exposure management methods lack a comprehensive consideration of the entire order fulfillment process. They fail to incorporate the locational distribution of transportation and warehousing segments in historical orders to statistically analyze comprehensive fulfillment verification conditions. This results in a lack of targeted and comprehensive fulfillment verification, failing to effectively cover various key stages and potential risks in the order fulfillment process. Regarding the impact of logistics fluctuations and warehousing area changes on fulfillment delays, traditional methods lack correlation coefficients that accurately reflect the degree of correlation between various factors. Consequently, they cannot predict the extent of spot fulfillment delays caused by these factors during the execution of target exposure orders, and it is difficult to develop effective risk response strategies in advance. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a spot open management system and method based on unitized configuration.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for managing spot exposure based on unitized configuration, comprising the following steps:

[0006] Obtain the spot demand for target open orders under standard fulfillment scenarios;

[0007] Based on the location distribution of transportation and warehousing segments in historical transaction orders, the comprehensive fulfillment verification conditions of the orders are calculated.

[0008] The first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient are obtained by processing and analyzing historical logistics records;

[0009] The first, second, and third performance correlation coefficients are used to obtain the spot performance delay value of the target open order during the execution process due to logistics fluctuations and warehouse area changes;

[0010] From the comprehensive performance verification conditions, select the performance verification conditions for verifying the performance efficiency of target open orders;

[0011] After processing and analyzing the data on performance verification conditions and the degree of delay in spot performance, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme for the target open orders are adjusted in a coordinated manner.

[0012] Preferably, the historical logistics records are processed and analyzed to obtain the first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient, specifically including the following steps:

[0013] If the logistics efficiency of different transportation or warehousing segments in historical logistics records is unstable and fluctuates, then extract the first performance correlation coefficient between logistics fluctuation and spot performance efficiency.

[0014] If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows stable fluctuations, then the second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency is extracted; the third fulfillment correlation coefficient between sorting area conversion and spot turnover efficiency is extracted.

[0015] Preferably, obtaining the spot demand for target open orders under standard fulfillment scenarios specifically includes the following steps:

[0016] Obtain the target open orders, current trading parameters, and initial contract volume for the spot transactions to be conducted;

[0017] The spot quality and inventory status of the target open orders are used to obtain the basic characteristics of the spot goods;

[0018] Obtain standard fulfillment data for orders that have completed transactions in standard fulfillment scenarios.

[0019] Establish a fulfillment demand model based on standard order fulfillment data;

[0020] Input the target open order, initial contract volume, basic spot characteristics, and current transaction parameters into the fulfillment demand model to obtain the spot demand of the target open order in a standard fulfillment scenario.

[0021] Preferably, if the logistics efficiency of different transportation or warehousing segments in historical logistics records exhibits unstable fluctuations, then the first fulfillment correlation coefficient between logistics fluctuations and spot fulfillment efficiency is extracted, specifically including the following steps:

[0022] If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows unstable fluctuations, it is marked as fulfillment scenario feature one.

[0023] Based on the performance scenario characteristic 1, select historical order 1 from the historical transaction orders, and obtain the performance efficiency value 1 of historical order 1 that is adjacent in position and is all in the transportation section or warehousing section.

[0024] Based on the corresponding logistics fluctuation amplitude values ​​of the performance efficiency value and performance scenario characteristics, the first performance correlation coefficient between logistics fluctuation and spot performance efficiency is extracted.

[0025] Preferably, if the logistics efficiency of different transportation or warehousing segments in historical logistics records shows stable fluctuations, then a second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency is extracted, specifically including the following steps:

[0026] If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows stable fluctuations, it is marked as fulfillment scenario feature two.

[0027] Based on the second characteristic of the fulfillment scenario, select the second historical order from the historical transaction orders, and obtain the second turnover efficiency value of each of the adjacent historical orders with the front end being the storage area and the back end being the sorting area.

[0028] Based on the stable value of the corresponding storage capacity of the second turnover efficiency value and the second performance scenario characteristic, the second performance correlation coefficient between storage area conversion and spot turnover efficiency is extracted.

[0029] Preferably, the extraction of the third fulfillment correlation coefficient between sorting area conversion and spot inventory turnover efficiency specifically includes the following steps:

[0030] Obtain the turnover efficiency values ​​of each of the adjacent historical orders (where the front section is the sorting area and the back section is the storage area);

[0031] Based on the corresponding stable storage capacity values ​​of the turnover efficiency value 3 and the fulfillment scenario feature 2, a third fulfillment correlation coefficient is extracted between sorting area conversion and spot turnover efficiency.

[0032] Preferably, the degree of spot fulfillment delay affected by logistical fluctuations and warehousing area changes during the execution of the target open order is obtained based on the first fulfillment correlation coefficient, the second fulfillment correlation coefficient, and the third fulfillment correlation coefficient. This specifically includes the following steps:

[0033] Combine performance scenario feature one and performance scenario feature two into a performance feature set one to be trained;

[0034] The first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient are combined to form the second performance feature set to be trained;

[0035] Obtain historical contract volume and historical performance adjustment data for historical transaction orders;

[0036] A dynamic performance model is established based on historical transaction parameters, historical contract volume, and two sets of performance features to be trained.

[0037] Detect the current logistics and warehousing status of the target open orders;

[0038] Input the current transaction parameters, current logistics status, current warehousing status, initial contract volume, and target open order into the performance dynamic model to obtain the spot performance delay value of the target open order affected by logistics fluctuations and warehousing area changes during the execution process.

[0039] Preferably, after processing and analyzing the performance verification conditions and the degree of delay in spot performance, the initial contract quantity, logistics scheduling plan, and warehousing allocation scheme of the target open order are adjusted in a coordinated manner, specifically including the following steps:

[0040] Input the historical orders, current transaction parameters, current logistics status, current warehousing status, and initial contract quantity of the performance verification conditions into the performance dynamic model to obtain the performance adjustment value to be compared;

[0041] If the difference between the spot delivery delay value and the comparable delivery adjustment value is within the preset delivery error range, then the spot demand and the spot delivery delay value will be integrated to obtain the actual demand forecast value 1.

[0042] If the difference between the spot performance delay value and the comparable performance adjustment value is outside the performance error range, then the spot performance delay value is adjusted for error control based on the comparable performance adjustment value to obtain the corrected performance adjustment value; the spot demand and the corrected performance adjustment value are then integrated to obtain the second actual demand forecast value.

[0043] Based on either Actual Demand Forecast 1 or Actual Demand Forecast 2, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme for the target open orders will be adjusted in a coordinated manner.

[0044] A spot exposure management system based on unitized configuration includes:

[0045] Acquisition module: Acquires the spot demand for target open orders under standard fulfillment scenarios;

[0046] Statistics module: Based on the location distribution of transportation and warehousing segments in historical transaction orders, statistics are compiled to determine the comprehensive fulfillment verification conditions of the orders.

[0047] Analysis module: Processes and analyzes historical logistics records to obtain the first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient;

[0048] Processing module: Based on the first fulfillment correlation coefficient, the second fulfillment correlation coefficient, and the third fulfillment correlation coefficient, obtain the spot fulfillment delay value of the target open order during the execution process due to logistics fluctuations and warehouse area changes;

[0049] Filtering module: Filters out the performance verification conditions from the comprehensive performance verification conditions to verify the performance efficiency of the target open orders;

[0050] Management module: After processing and analyzing the data of performance verification conditions and the degree of delay in spot performance, the module coordinates and adjusts the initial contract volume, logistics scheduling plan and warehousing allocation scheme of the target open orders.

[0051] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the aforementioned spot exposure management method based on unitized configuration.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This invention provides a foundational quantitative basis for order management by acquiring the spot demand of target open-end orders under standard fulfillment scenarios. It statistically analyzes the locational distribution of transportation and warehousing segments in historical transaction orders to establish comprehensive fulfillment verification conditions, making fulfillment verification more targeted and reliable. By processing and analyzing historical logistics records, it obtains a first, second, and third fulfillment correlation coefficient, thereby determining the degree of spot fulfillment delay affected by logistics fluctuations and warehousing area changes during the execution of target open-end orders. This helps enterprises anticipate delay risks during order fulfillment. Based on these delay values, it allows for the development of contingency strategies, such as adjusting logistics plans and optimizing warehousing processes, to ensure timely order fulfillment. Fulfillment verification conditions for verifying the fulfillment efficiency of target open-end orders are selected from the comprehensive fulfillment verification conditions, improving the operational efficiency of order management. After processing and analyzing the fulfillment verification condition data and spot fulfillment delay values, it coordinates and adjusts the initial contract quantity, logistics scheduling plan, and warehousing allocation scheme for target open-end orders, achieving comprehensive optimization of multiple order elements. This allows for maximizing resource utilization efficiency while ensuring order fulfillment quality, thereby improving the company's overall management level in spot market operations. Attached Figure Description

[0054] Figure 1 A schematic diagram illustrating the steps of a spot exposure management method based on unitized configuration proposed in this invention;

[0055] Figure 2 This invention presents a schematic diagram of a spot open management system based on unitized configuration.

[0056] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0057] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0058] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0061] Reference Figures 1-3 As shown.

[0062] The embodiments further illustrate the spot open management system and method based on unitized configuration proposed in this invention.

[0063] A method for managing spot exposure based on unitized configuration, comprising the following steps:

[0064] Obtain the spot demand for target open orders under standard fulfillment scenarios;

[0065] Based on the location distribution of transportation and warehousing segments in historical transaction orders, the comprehensive fulfillment verification conditions of the orders are calculated.

[0066] The first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient are obtained by processing and analyzing historical logistics records;

[0067] The first, second, and third performance correlation coefficients are used to obtain the spot performance delay value of the target open order during the execution process due to logistics fluctuations and warehouse area changes;

[0068] From the comprehensive performance verification conditions, select the performance verification conditions for verifying the performance efficiency of target open orders;

[0069] After processing and analyzing the data on performance verification conditions and the degree of delay in spot performance, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme for the target open orders are adjusted in a coordinated manner.

[0070] This application first obtains the spot demand of the target open-end order under the standard fulfillment scenario. Based on the location distribution of transportation and warehousing segments in historical transaction orders, it statistically analyzes the comprehensive fulfillment verification conditions for the corresponding orders, providing a reference standard for subsequent verification of the target order's fulfillment efficiency. The application then processes and analyzes historical logistics records to obtain a first, second, and third fulfillment correlation coefficient. These coefficients quantify the correlation between factors such as logistics fluctuations and warehousing area changes and spot fulfillment efficiency from different perspectives, serving as key evidence for assessing fulfillment delays. Based on these three fulfillment correlation coefficients, the application obtains the degree of spot fulfillment delay affected by logistics fluctuations and warehousing area changes during the execution of the target open-end order, thereby assessing the potential delay risk of the order. Finally, the application selects suitable fulfillment verification conditions from the comprehensive fulfillment verification conditions to verify the fulfillment efficiency of the target open-end order, ensuring the relevance and effectiveness of the verification. The data on performance verification conditions and the degree of delay in spot performance are processed and analyzed. Based on the analysis results, the initial contract volume, logistics scheduling plan and warehousing allocation scheme of the target exposure orders are coordinated and adjusted to ensure that the orders can be fulfilled efficiently and smoothly, and to achieve scientific management of spot exposure.

[0071] The historical logistics records are processed and analyzed to obtain the first, second, and third performance correlation coefficients. This process includes the following steps:

[0072] If the logistics efficiency of different transportation or warehousing segments in historical logistics records is unstable and fluctuates, then extract the first performance correlation coefficient between logistics fluctuation and spot performance efficiency.

[0073] If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows stable fluctuations, then the second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency is extracted; the third fulfillment correlation coefficient between sorting area conversion and spot turnover efficiency is extracted.

[0074] Examine the logistics efficiency performance of different transportation or warehousing segments in historical logistics records. If the logistics efficiency of these segments exhibits unstable fluctuations, extract the first fulfillment correlation coefficient between logistics fluctuations and spot fulfillment efficiency to quantify the impact of such unstable logistics fluctuations on spot fulfillment efficiency. If the logistics efficiency of different transportation or warehousing segments exhibits stable fluctuations, extract the second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency to measure the effect of warehousing area conversion on spot turnover efficiency when logistics efficiency fluctuates stably; and extract the third fulfillment correlation coefficient between sorting area conversion and spot turnover efficiency to clarify the impact of sorting area conversion on spot turnover efficiency under this stable fluctuation scenario. This approach grasps the relationship between logistics-related factors and spot fulfillment and turnover efficiency from different dimensions.

[0075] To obtain the spot demand for target open orders under standard fulfillment scenarios, the following steps are required:

[0076] Obtain the target open orders, current trading parameters, and initial contract volume for the spot transactions to be conducted;

[0077] The spot quality and inventory status of the target open orders are used to obtain the basic characteristics of the spot goods;

[0078] Obtain standard fulfillment data for orders that have completed transactions in standard fulfillment scenarios.

[0079] Establish a fulfillment demand model based on standard order fulfillment data;

[0080] Input the target open order, initial contract volume, basic spot characteristics, and current transaction parameters into the fulfillment demand model to obtain the spot demand of the target open order in a standard fulfillment scenario.

[0081] When obtaining the spot demand for target open orders under standard performance scenarios, the first step is to comprehensively collect basic information, such as the target open order being a rebar order to be traded, including the steel type and delivery location; current transaction parameters include the average market price of rebar on the day, payment cycle and transportation method; for example, the initial contract quantity agreed upon by both parties is 500 tons.

[0082] Testing was conducted on the basic characteristics of the spot market. Rebar quality testing included mechanical properties, chemical composition, and appearance quality, all of which met relevant standards. Regarding inventory status, statistics were compiled on the actual inventory quantity, inventory turnover rate, and storage duration of this type of rebar in the current warehouse; these information collectively constitute the basic characteristics of the spot market.

[0083] Collect standard fulfillment data for orders completed under standard fulfillment scenarios. For example, data on 10 completed orders of the same type of rebar in the past six months, with similar transaction size, delivery area, and market environment, including the actual demand, fulfillment cycle, and number of replenishments due to insufficient inventory for each order. This data is used to construct a fulfillment demand model through regression analysis algorithms.

[0084] For example, by inputting the specific information of the target open order, the initial contract quantity of 500 tons, the basic characteristics of the spot market, and the current transaction parameters into the fulfillment demand model, the spot demand of the target open order in the standard fulfillment scenario is 480 tons. This data comprehensively considers the current inventory status, market fluctuations, and historical fulfillment experience, providing a basis for subsequent adjustments to the contract quantity and the formulation of fulfillment plans.

[0085] If the logistics efficiency of different transportation or warehousing segments in historical logistics records fluctuates unstablely, then the first fulfillment correlation coefficient between logistics fluctuations and spot fulfillment efficiency is extracted, specifically including the following steps:

[0086] If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows unstable fluctuations, it is marked as fulfillment scenario feature one.

[0087] Based on the performance scenario characteristic 1, select historical order 1 from the historical transaction orders, and obtain the performance efficiency value 1 of historical order 1 that is adjacent in position and is all in the transportation section or warehousing section.

[0088] Based on the corresponding logistics fluctuation amplitude values ​​of the performance efficiency value and performance scenario characteristics, the first performance correlation coefficient between logistics fluctuation and spot performance efficiency is extracted.

[0089] First, examine the historical logistics records of rebar. If you find that the logistics efficiency of different transportation or storage areas exhibits unstable fluctuations—for example, the transportation time from steel production enterprises to transit warehouses is normally 2 days, but it can be extended to 5 days during extreme weather, while it can be shortened to 1.5 days during periods of sufficient logistics capacity; and the inventory turnover time of rebar in regional transit warehouses is typically 3 days, which can be extended to 6 days during peak steel demand seasons and shortened to 2 days during off-seasons—this obvious unstable fluctuation in logistics efficiency is marked as fulfillment scenario characteristic one.

[0090] Based on the performance scenario characteristic, historical orders are selected from historical transaction orders. For example, orders involving the same type of rebar within the past two years are selected, as these orders have similar transaction scale and delivery requirements to the target open order. The performance efficiency value is then obtained for adjacent historical orders that are both located in either transportation or warehousing segments. Assuming the adjacent transportation segment is from a regional transit warehouse to a city steel trading market, the performance efficiency value is the average transportation time and the rebar loss rate during multiple transportation processes for that segment. If the adjacent segment is a warehousing segment, such as from the storage area to the loading / unloading area within a regional transit warehouse, the performance efficiency value is the average warehousing time and average outbound time for the rebar in that warehousing segment.

[0091] The first performance correlation coefficient is extracted based on the logistics fluctuation amplitude values ​​corresponding to the performance efficiency value and performance scenario characteristic. Regarding the logistics fluctuation amplitude values, the logistics fluctuation amplitude values ​​for the transportation segment are the maximum fluctuation range of transportation time and the fluctuation range of the average loading rate of transport vehicles; the logistics fluctuation amplitude values ​​for the warehousing segment are the fluctuation range of rebar inventory turnover time and the fluctuation range of rebar storage density within the warehousing area. The first performance correlation coefficient between logistics fluctuations and rebar spot performance efficiency is calculated using correlation analysis methods on the performance efficiency value and logistics fluctuation amplitude values. For example, it is found that for every one-day increase in transportation time fluctuation, the on-time delivery rate of rebar spot decreases by 10%; and for every one-day increase in warehousing turnover time fluctuation, the inventory management cost of rebar spot increases by 5%. This quantifies the impact of logistics fluctuations on the performance efficiency of rebar spot.

[0092] If the logistics efficiency of different transportation or warehousing segments in historical logistics records shows stable fluctuations, then a second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency is extracted, specifically including the following steps:

[0093] If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows stable fluctuations, it is marked as fulfillment scenario feature two.

[0094] Based on the second characteristic of the fulfillment scenario, select the second historical order from the historical transaction orders, and obtain the second turnover efficiency value of each of the adjacent historical orders with the front end being the storage area and the back end being the sorting area.

[0095] Based on the stable value of the corresponding storage capacity of the second turnover efficiency value and the second performance scenario characteristic, the second performance correlation coefficient between storage area conversion and spot turnover efficiency is extracted.

[0096] If the logistics efficiency of different transportation or warehousing segments in historical logistics records shows stable fluctuations, such as the normal transportation time from the steel production base to the regional warehousing center being 3 days, with occasional fluctuations of half a day due to minor traffic adjustments, and the inventory turnover time of rebar in the regional storage area being stable at 4 days with fluctuations not exceeding 1 day, this stable fluctuation in logistics efficiency is marked as fulfillment scenario characteristic two.

[0097] Based on the second characteristic of the fulfillment scenario, historical orders are selected from historical transaction orders. For example, orders involving rebar of the same specification are selected from the past year. These orders have similar transaction patterns and logistics routes to the target open orders to be analyzed. Then, the turnover efficiency value 2 for each of the adjacent historical orders 2, with the preceding segment being a storage area and the following segment being a sorting area, is obtained. Assuming that the adjacent storage area is a large storage area in a regional warehousing center, and the sorting area is the steel sorting operation area within that warehousing center, then the turnover efficiency value 2 represents the average inventory turnover days of rebar in the storage area and the neatness of the goods stacking in the storage area; the turnover efficiency value 2 in the sorting area represents the average sorting time and sorting accuracy of rebar.

[0098] The second performance correlation coefficient is extracted based on the stable values ​​of the corresponding storage capacity for the second turnover efficiency value and the second performance scenario characteristic value. Regarding the stable storage capacity value, for example, the storage capacity of a regional storage area is 50,000 tons of rebar, with a fluctuation range within ±5,000 tons; the sorting capacity of the sorting area is stable at 200 tons of rebar per hour, with a fluctuation range within ±20 tons. The second performance correlation coefficient between the second turnover efficiency value and the stable storage capacity value is calculated using regression analysis. For example, it is found that when the storage capacity of the storage area is stable at 50,000 tons, the inventory turnover efficiency increases by 8% during the conversion of rebar from the storage area to the sorting area; when the sorting capacity of the sorting area is stable at 200 tons per hour, the spot turnover efficiency of rebar can be increased by 10%, thus quantifying the impact of the storage area conversion on the spot turnover efficiency of rebar.

[0099] Extracting the third fulfillment correlation coefficient between sorting area conversion and spot inventory turnover efficiency includes the following steps:

[0100] Obtain the turnover efficiency values ​​of each of the adjacent historical orders (where the front section is the sorting area and the back section is the storage area);

[0101] Based on the corresponding stable storage capacity values ​​of the turnover efficiency value 3 and the fulfillment scenario feature 2, a third fulfillment correlation coefficient is extracted between sorting area conversion and spot turnover efficiency.

[0102] Historical Order 2 refers to orders involving rebar transactions where logistics efficiency fluctuates steadily. Within Historical Order 2, identify adjacent stages where the preceding stage is a sorting area and the following stage is a storage area. Obtain the turnover efficiency value 3 for each of these stages. For example, in a historical order 2, the sorting efficiency of the preceding sorting area is measured by the number of tons of rebar sorted per hour, assuming an average of 50 tons per hour. The turnover efficiency value 3 of the following storage area is reflected by the average time from when the rebar enters the storage area to when its storage arrangement is completed; for example, on average, each batch of rebar completes its storage location within 2 hours of entering the storage area.

[0103] Combined with the second characteristic of the fulfillment scenario, the corresponding stable value of the storage capacity. The second characteristic of the fulfillment scenario is the stable value of the storage capacity corresponding to the stable fluctuation of logistics efficiency. For example, the storage capacity of the storage area is stable at accommodating 1,000 tons of rebar, and the fluctuation range of the storage capacity does not exceed ±50 tons.

[0104] A third fulfillment correlation coefficient is extracted based on the turnover efficiency value and the stable storage capacity value, relating the switching of sorting areas to spot turnover efficiency. Specifically, this involves determining the impact of switching rebar from the sorting area to the storage area on spot turnover efficiency when the turnover efficiency of the sorting area and the storage capacity of the storage area are stable at around 1000 tons. The third fulfillment correlation coefficient is determined through statistical analysis of a large amount of historical data. For example, it involves calculating the overall turnover time and turnover rate of rebar spot goods under different stable sorting efficiencies and storage capacities, thereby quantifying the impact of sorting area switching on rebar spot turnover efficiency.

[0105] The degree of spot fulfillment delay caused by logistical fluctuations and warehousing area changes during the execution of the target open order is obtained based on the first, second, and third fulfillment correlation coefficients. This process includes the following steps:

[0106] Combine performance scenario feature one and performance scenario feature two into a performance feature set one to be trained;

[0107] The first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient are combined to form the second performance feature set to be trained;

[0108] Obtain historical contract volume and historical performance adjustment data for historical transaction orders;

[0109] A dynamic performance model is established based on historical transaction parameters, historical contract volume, and two sets of performance features to be trained.

[0110] Detect the current logistics and warehousing status of the target open orders;

[0111] Input the current transaction parameters, current logistics status, current warehousing status, initial contract volume, and target open order into the performance dynamic model to obtain the spot performance delay value of the target open order affected by logistics fluctuations and warehousing area changes during the execution process.

[0112] First, performance scenario feature one and performance scenario feature two are combined to form the first set of performance features to be trained. At the same time, the first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient are combined to form the second set of performance features to be trained.

[0113] Obtain historical contract volumes for rebar from historical transaction orders, such as the monthly rebar order volume signed by a trader over the past year, as well as historical performance adjustment data. The rebar order volume ranges from 500 to 800 tons. For example, due to logistics delays, the delivery time of some orders was adjusted from 10 days to 12 days; due to faster warehousing turnover, the delivery of some orders was completed 2 days ahead of schedule. Based on historical transaction parameters, historical contract volumes, and two sets of performance features to be trained, a machine learning algorithm is used to establish a dynamic performance model. Historical transaction parameters include historical rebar prices and transaction cycles.

[0114] The system monitors the current logistics and warehousing status of open orders. For example, a construction company plans to purchase 1,000 tons of HRB400 rebar. The current logistics status is that the transport vehicle from the steel mill to the regional warehouse is halfway there and is expected to arrive in one day, with no obvious abnormalities along the way. The regional warehouse has 300 tons of similar rebar in stock, the loading and unloading equipment is operating normally, the storage area occupancy rate of the regional warehouse is 60%, and there is still sufficient storage space. At the same time, the sorting area is fully staffed.

[0115] The current transaction parameters, current logistics status, current warehousing status, initial contract volume, and target open order are input into the fulfillment dynamic model. The fulfillment dynamic model comprehensively analyzes these input factors and, by reusing historical patterns and calculating current parameters, determines the degree of spot fulfillment delay affected by logistics fluctuations and warehousing area changes during the execution of the target open order. For example, it predicts a delivery delay of 0.5 days, thus providing a basis for subsequent adjustments to order volume, logistics scheduling, and warehousing allocation.

[0116] After processing and analyzing the performance verification conditions and the degree of delay in spot performance, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme of the target open orders are adjusted in a coordinated manner, specifically including the following steps:

[0117] Input the historical orders, current transaction parameters, current logistics status, current warehousing status, and initial contract quantity of the performance verification conditions into the performance dynamic model to obtain the performance adjustment value to be compared;

[0118] If the difference between the spot delivery delay value and the comparable delivery adjustment value is within the preset delivery error range, then the spot demand and the spot delivery delay value will be integrated to obtain the actual demand forecast value 1.

[0119] If the difference between the spot performance delay value and the comparable performance adjustment value is outside the performance error range, then the spot performance delay value is adjusted for error control based on the comparable performance adjustment value to obtain the corrected performance adjustment value; the spot demand and the corrected performance adjustment value are then integrated to obtain the second actual demand forecast value.

[0120] Based on either Actual Demand Forecast 1 or Actual Demand Forecast 2, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme for the target open orders will be adjusted in a coordinated manner.

[0121] Collect historical orders, current transaction parameters, current logistics status, current warehousing status, and initial contract quantity related to fulfillment verification conditions, and input this information into the fulfillment dynamic model. Based on previously learned correlations between logistics fluctuations, warehousing conversions, and fulfillment delays, the fulfillment dynamic model calculates and obtains a comparable fulfillment adjustment value, assumed to be 0.8 days.

[0122] The spot fulfillment delay value is compared with the corresponding fulfillment adjustment value. If the difference between the two is within the preset fulfillment error range, the spot demand and the spot fulfillment delay value are integrated to obtain the actual demand forecast value 1.

[0123] For example, if an order is delayed by 1 day due to factors such as logistics fluctuations and warehouse location changes during execution, calculate the difference between the spot fulfillment delay value and the corresponding fulfillment adjustment value, i.e., 1 day minus 0.8 days, resulting in a difference of 0.2 days. Assume the preset fulfillment error range is 0 to 0.3 days. We find that 0.2 days falls within this preset fulfillment error range. Therefore, we need to integrate the spot demand and the spot fulfillment delay value to obtain the first actual demand forecast. The spot demand refers to the quantity of rebar required for the target open order under a standard fulfillment scenario, assumed to be 1000 tons. Integrate the spot demand of 1000 tons and the spot fulfillment delay value of 1 day. This integration can be achieved by adjusting the demand based on the delay level. For example, if the delay is 1 day due to market changes and inventory turnover factors, the demand needs to be slightly adjusted to 980 tons, then the first actual demand forecast is 980 tons.

[0124] First, we define the spot fulfillment delay value and the comparable fulfillment adjustment value. For example, the spot fulfillment delay value for a target open order of rebar is the expected delay in delivery due to factors such as logistics fluctuations and changes in warehousing areas during the execution of the order, which we assume is 2 days. At the same time, we input the historical orders, current transaction parameters, current logistics status, current warehousing status, and initial contract quantity corresponding to the fulfillment verification conditions into the fulfillment dynamic model to obtain the comparable fulfillment adjustment value, which we assume is 0.5 days.

[0125] The difference between the calculated spot fulfillment delay value and the comparable fulfillment adjustment value is 1.5 days. This means the difference is outside the fulfillment error range. The spot fulfillment delay value is then adjusted based on the comparable fulfillment adjustment value to obtain a corrected fulfillment adjustment value. For example, based on the correlation and influencing factors of a comparable fulfillment adjustment value of 0.5 days, the original spot fulfillment delay value of 2 days is adjusted. Combining this with historical data on the stability of logistics and warehousing, the delay is adjusted to 0.8 days, resulting in a corrected fulfillment adjustment value of 0.8 days.

[0126] The second actual demand forecast is obtained by integrating the spot demand and the revised fulfillment adjustment value. The spot demand refers to the quantity of rebar required for the target open order under the standard fulfillment scenario, which is assumed to be 1,000 tons. The spot demand of 1,000 tons and the revised fulfillment adjustment value of 0.8 days are integrated. This integration takes into account the correction of the delay level and adjusts the demand accordingly. For example, considering the improvement of the delay, the demand is adjusted to 950 tons, thus obtaining the second actual demand forecast value of 950 tons. This provides a basis for subsequent adjustments to the initial contract quantity, logistics scheduling plan and warehousing allocation scheme of the target open order.

[0127] Based on either Actual Demand Forecast 1 or Actual Demand Forecast 2, the initial contract quantity, logistics scheduling plan, and warehousing allocation scheme for the target open orders will be adjusted accordingly. For example, if Actual Demand Forecast 1 shows an actual need of 880 tons of rebar, less than the initial contract quantity of 1000 tons, the initial contract quantity can be adjusted to 880 tons. Regarding logistics scheduling, if there is a certain risk of delays as predicted, the departure time of the transport fleet will be adjusted, or a faster transport route will be used to optimize the logistics scheduling plan. For warehousing allocation, if the actual demand forecast indicates significant warehousing pressure, the warehousing areas will be adjusted, allocating rebar to areas with lower occupancy rates and better storage conditions.

[0128] A spot exposure management system based on unitized configuration includes:

[0129] Acquisition module: Acquires the spot demand for target open orders under standard fulfillment scenarios;

[0130] Statistics module: Based on the location distribution of transportation and warehousing segments in historical transaction orders, statistics are compiled to determine the comprehensive fulfillment verification conditions of the orders.

[0131] Analysis module: Processes and analyzes historical logistics records to obtain the first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient;

[0132] Processing module: Based on the first fulfillment correlation coefficient, the second fulfillment correlation coefficient, and the third fulfillment correlation coefficient, obtain the spot fulfillment delay value of the target open order during the execution process due to logistics fluctuations and warehouse area changes;

[0133] Filtering module: Filters out the performance verification conditions from the comprehensive performance verification conditions to verify the performance efficiency of the target open orders;

[0134] Management module: After processing and analyzing the data of performance verification conditions and the degree of delay in spot performance, the module coordinates and adjusts the initial contract volume, logistics scheduling plan and warehousing allocation scheme of the target open orders.

[0135] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a spot exposure management method based on unitized configuration.

[0136] like Figure 3 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a spot exposure management method based on unitized configuration.

[0137] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0138] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute a spot exposure management method based on unitized configuration.

[0139] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a spot exposure management method based on a unitized configuration.

[0140] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing spot exposure based on unitized configuration, characterized in that, The method includes the following steps: Obtain the spot demand for target open orders under standard fulfillment scenarios; Based on the location distribution of transportation and warehousing segments in historical transaction orders, the comprehensive fulfillment verification conditions of the orders are calculated. The historical logistics records are processed and analyzed to obtain the first, second, and third performance correlation coefficients. This process includes the following steps: If the logistics efficiency of different transportation or warehousing segments in historical logistics records is unstable and fluctuates, then extract the first performance correlation coefficient between logistics fluctuation and spot performance efficiency. If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows stable fluctuations, then the second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency is extracted; the third fulfillment correlation coefficient between sorting area conversion and spot turnover efficiency is extracted. The first, second, and third performance correlation coefficients are used to obtain the spot performance delay value of the target open order during the execution process due to logistics fluctuations and warehouse area changes; From the comprehensive performance verification conditions, select the performance verification conditions for verifying the performance efficiency of target open orders; After processing and analyzing the data on performance verification conditions and the degree of delay in spot performance, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme for the target open orders are adjusted in a coordinated manner.

2. The method of managing a spot exposure based on a unitized configuration according to claim 1, wherein, To obtain the spot demand for target open orders under standard fulfillment scenarios, the following steps are required: Obtain the target open orders, current trading parameters, and initial contract volume for the spot transactions to be conducted; The spot quality and inventory status of the target open orders are used to obtain the basic characteristics of the spot goods; Obtain standard fulfillment data for orders that have completed transactions in standard fulfillment scenarios. Establish a fulfillment demand model based on standard order fulfillment data; Input the target open order, initial contract volume, basic spot characteristics, and current transaction parameters into the fulfillment demand model to obtain the spot demand of the target open order in a standard fulfillment scenario.

3. The method of managing a spot exposure based on a unitized configuration according to claim 2, wherein, If the logistics efficiency of different transportation or warehousing segments in historical logistics records shows unstable fluctuations, then the first fulfillment correlation coefficient between logistics fluctuations and spot fulfillment efficiency is extracted, specifically including the following steps: If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows unstable fluctuations, it is marked as fulfillment scenario feature one. Based on the performance scenario characteristic 1, select historical order 1 from the historical transaction orders, and obtain the performance efficiency value 1 of historical order 1 that is adjacent in position and is all in the transportation section or warehousing section. Based on the corresponding logistics fluctuation amplitude values ​​of the performance efficiency value and performance scenario characteristics, the first performance correlation coefficient between logistics fluctuation and spot performance efficiency is extracted.

4. The method of managing a spot exposure based on a unitization configuration according to claim 3, wherein, If the logistics efficiency of different transportation or warehousing segments in historical logistics records shows stable fluctuations, then a second fulfillment correlation coefficient between warehousing area conversion and spot turnover efficiency is extracted, specifically including the following steps: If the logistics efficiency of different transportation or warehousing sections in historical logistics records shows stable fluctuations, it is marked as fulfillment scenario feature two. Based on the second characteristic of the fulfillment scenario, select the second historical order from the historical transaction orders, and obtain the second turnover efficiency value of each of the adjacent historical orders with the front end being the storage area and the back end being the sorting area. Based on the stable value of the corresponding storage capacity of the second turnover efficiency value and the second performance scenario characteristic, the second performance correlation coefficient between storage area conversion and spot turnover efficiency is extracted.

5. The method of managing a spot exposure based on a unitized configuration according to claim 4, wherein, Extracting the third fulfillment correlation coefficient between sorting area conversion and spot inventory turnover efficiency includes the following steps: Obtain the turnover efficiency values ​​of each of the adjacent historical orders (where the front section is the sorting area and the back section is the storage area); Based on the corresponding stable storage capacity values ​​of the turnover efficiency value 3 and the fulfillment scenario feature 2, a third fulfillment correlation coefficient is extracted between sorting area conversion and spot turnover efficiency.

6. The method of managing a spot exposure based on a unitized configuration according to claim 5, wherein, The degree of spot fulfillment delay caused by logistical fluctuations and warehousing area changes during the execution of the target open order is obtained based on the first, second, and third fulfillment correlation coefficients. This process includes the following steps: Combine performance scenario feature one and performance scenario feature two into a performance feature set one to be trained; The first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient are combined to form the second performance feature set to be trained; Obtain historical contract volume and historical performance adjustment data for historical transaction orders; A dynamic performance model is established based on historical transaction parameters, historical contract volume, and two sets of performance features to be trained. Detect the current logistics and warehousing status of the target open orders; Input the current transaction parameters, current logistics status, current warehousing status, initial contract volume, and target open order into the performance dynamic model to obtain the spot performance delay value of the target open order affected by logistics fluctuations and warehousing area changes during the execution process.

7. The method of managing a spot exposure based on a unitized configuration according to claim 6, wherein, After processing and analyzing the performance verification conditions and the degree of delay in spot performance, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme of the target open orders are adjusted in a coordinated manner, specifically including the following steps: Input the historical orders, current transaction parameters, current logistics status, current warehousing status, and initial contract quantity of the performance verification conditions into the performance dynamic model to obtain the performance adjustment value to be compared; If the difference between the spot delivery delay value and the comparable delivery adjustment value is within the preset delivery error range, then the spot demand and the spot delivery delay value will be integrated to obtain the actual demand forecast value 1. If the difference between the spot performance delay value and the comparable performance adjustment value is outside the performance error range, then the spot performance delay value is adjusted for error control based on the comparable performance adjustment value to obtain the corrected performance adjustment value; the spot demand and the corrected performance adjustment value are then integrated to obtain the second actual demand forecast value. Based on either Actual Demand Forecast 1 or Actual Demand Forecast 2, the initial contract volume, logistics scheduling plan, and warehousing allocation scheme for the target open orders will be adjusted in a coordinated manner.

8. A spot exposure management system based on unitization configuration, applied to the spot exposure management method based on unitization configuration in any one of claims 1 to 7, characterized in that, include: Acquisition module: Acquires the spot demand for target open orders under standard fulfillment scenarios; Statistics module: Based on the location distribution of transportation and warehousing segments in historical transaction orders, statistics are compiled to determine the comprehensive fulfillment verification conditions of the orders. Analysis module: Processes and analyzes historical logistics records to obtain the first performance correlation coefficient, the second performance correlation coefficient, and the third performance correlation coefficient; Processing module: Based on the first fulfillment correlation coefficient, the second fulfillment correlation coefficient, and the third fulfillment correlation coefficient, obtain the spot fulfillment delay value of the target open order during the execution process due to logistics fluctuations and warehouse area changes; Filtering module: Filters out the performance verification conditions from the comprehensive performance verification conditions to verify the performance efficiency of the target open orders; Management module: After processing and analyzing the data of performance verification conditions and the degree of delay in spot performance, the module coordinates and adjusts the initial contract volume, logistics scheduling plan and warehousing allocation scheme of the target open orders.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a spot exposure management method based on unitized configuration as described in any one of claims 1 to 7.

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