Full-process business management and analysis system for agricultural equipment sales

Abstract

The invention relates to the technical field of data processing, in particular to an agricultural equipment sales-oriented full-process business management and analysis system, which comprises a time sequence feature analysis module configured to generate a trend amplification coefficient according to a matching relationship between current time and a preset farming time window, and weight historical sales volume data based on the trend amplification coefficient to obtain a weighted historical sales volume; outputting a dynamic safe water level line; the bimodal arbitration module is configured to compare the current available stock with the dynamic safety water line in real time; a sequence distribution mechanism or a weight filtering mechanism is activated; and the data consistency guarantee module is configured to respond to the allocation of the resource locking authority, generate a temporary token in a memory, and asynchronously trigger data synchronization aiming at the database. According to the method, the problems of resource mismatching and concurrent deadlock under the seasonal pulse demand are solved through the time sequence dynamic water level and bimodal arbitration.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a full-process business management and analysis system for agricultural equipment sales. Background Technology

[0002] With the continuous improvement of agricultural mechanization, agricultural equipment sales are characterized by high unit value, long after-sales service cycles, and market demand being greatly affected by agricultural seasons. Enterprises need to rely on end-to-end business management and analysis systems to handle increasingly complex inventory data, ensuring accurate matching of market supply and demand during critical operational windows such as spring planting and autumn harvest. In actual business scenarios, agricultural equipment sales exhibit strong short-term pulse characteristics; for example, demand for combine harvesters often surges within a short two weeks before the harvest season, placing extremely high demands on inventory management response speed and resource allocation strategies. Traditional general-purpose inventory management systems often struggle to adapt to the high concurrency and time-sensitive nature of this industry, which features a binary, extreme distribution of demand.

[0003] In the prior art, Chinese patent application publication number CN108985691A discloses an automatic replenishment method and system based on dynamic inventory control. This technical solution mainly classifies inventory materials and uses the average and standard deviation of historical sales data over a period of time to calculate a volatility coefficient, thereby setting thresholds for safety stock, maximum stock, and minimum stock. When the actual available inventory falls below the minimum stock threshold, the system automatically triggers replenishment logic and calculates the replenishment quantity. This approach aims to find a balance between inventory costs and stockout risk through statistical models, solving the problem of inaccurate manual replenishment based on experience. It has good applicability and dynamic adjustment capabilities in conventional retail scenarios where demand is relatively stable or fluctuates according to a normal distribution.

[0004] However, in the typical high-pressure, highly seasonal scenario of agricultural equipment sales, the existing technologies relying on long-term statistical smoothing and indiscriminate data scheduling logic expose serious underlying resource mismatch problems. Because the market demand for agricultural equipment exhibits extreme alternations between quiet and explosive periods, long-term historical average smoothing calculations mask instantaneous demand extremes, resulting in a lag in the calculated safety stock level. This prevents early warnings before the arrival of demand peaks during sudden agricultural busy seasons. Even more critically, when such hidden, pulse-like demand instantly drains inventory to a critically low level, existing systems still rely on conventional time-sequence-based underlying database row-locking mechanisms to handle all high-concurrency requests. In extreme cases where hundreds of concurrent requests flood the system to compete for the last few pieces of equipment, the system lacks the ability to identify and degrade the value of data request business conversions. A large number of low-intent requests, only in the consultation or exploratory stages, will preempt database lock resources according to a first-come, first-served rule. This indiscriminate allocation of lock resources causes high-intent orders that urgently need equipment for agricultural production to fail to be processed while waiting in the queue because lock resources are occupied. This results in a substantial mismatch of core resources and business deadlock, making it impossible for the system to guarantee the stringent requirements of high availability and high transaction rate during the extremely short agricultural window. Summary of the Invention

[0005] In order to solve the problems of resource mismatch and concurrent deadlock under seasonal pulse demand by using time-series dynamic water level and bimodal arbitration, this application provides a full-process business management and analysis system for agricultural equipment sales.

[0006] The full-process business management and analysis system for agricultural equipment sales provided in this application adopts the following technical solution: The full-process business management and analysis system for agricultural equipment sales includes a time-series feature analysis module, a dual-modal arbitration module, and a data consistency guarantee module that are coupled together; The time-series feature analysis module is configured to generate a trend amplification coefficient based on the matching relationship between the current time and the preset agricultural time window, and output a dynamic safety water level line that changes dynamically over time based on the historical sales data weighted by the trend amplification coefficient. The dual-modal arbitration module is configured to compare the current available inventory with the dynamic safety level in real time: if the current available inventory is higher than the dynamic safety level, the sequential allocation mechanism is activated to allocate resource locking permissions according to the order of request arrival; if the current available inventory is not higher than the dynamic safety level, the weighted filtering mechanism is activated to allocate the resource locking permissions only to requests with a weight not lower than a preset admission threshold, and to block requests with a weight lower than the preset admission threshold. The data consistency guarantee module is configured to generate a temporary token containing a timeliness parameter in memory in response to the allocation of the resource locking permission, thereby deducting the inventory count in memory and asynchronously triggering data synchronization for the database.

[0007] Optionally, the time-series feature analysis module is configured as follows: The average daily sales within the same sliding time window of historical years are extracted as the historical sales data, and the supply response cycle parameter, which characterizes the physical time lag of the supply chain, is obtained. In response to the current time matching the preset agricultural time window, the trend amplification coefficient is set to a preset amplification threshold, and a product operation is performed on the historical sales data, the supply response cycle parameter and the trend amplification coefficient. The output of the product operation is defined as the dynamic safety water level.

[0008] Optionally, the time-series feature analysis module is configured as follows: If it is detected that the current time does not fall within the preset agricultural time window, the trend amplification coefficient will be anchored to the preset benchmark threshold. If the current time is detected to be within the preset agricultural time window, the trend amplification coefficient is directly switched from the preset baseline threshold to the preset amplification threshold, so as to improve the dynamic safety level by changing the trend amplification coefficient when the historical sales data value remains unchanged.

[0009] Optionally, the bimodal arbitration module is configured to: In response to the current available inventory being higher than the dynamic safety level, a linear first-in-first-out queue anchored to a physical timestamp is established; Access requests are directly mapped to the linear first-in-first-out queue according to their arrival time; Resource locking permissions are issued sequentially according to the position order of the linear first-in-first-out queue.

[0010] Optionally, the bimodal arbitration module is configured to perform the weighted filtering mechanism as follows: Parse the customer identity identifier carried in the request, and retrieve the associated customer level tag to map it to the weight value of the request; In response to the weight value being not lower than the preset access threshold, the resource locking permission is issued to the corresponding request source; In response to the weight value being lower than the preset admission threshold, the inventory deduction operation for the requesting source is blocked, and a pre-sale registration signal is returned to the requesting source.

[0011] Optionally, the bimodal arbitration module is configured to: When issuing the resource locking permission to the corresponding request source, it is configured as follows: The weight value is compared with a preset core threshold, wherein the preset core threshold is greater than the preset admission threshold; In response to the weight value being no less than the preset core threshold, a replenishment instruction is generated simultaneously with the issuance of the resource locking permission.

[0012] Optionally, the data consistency guarantee module is configured to deduct the inventory count in memory as follows: The inventory count is established in memory based on the current available inventory. In response to the allocation of the resource locking permission, a pre-deduction verification is performed on the inventory count; In response to the inventory count after the pre-deduction verification being not less than zero, the inventory allocation is determined to be valid and the temporary token is generated.

[0013] Optionally, the data consistency guarantee module is further configured as follows: Monitor the duration of each temporary token; In response to the target temporary token's duration exceeding a preset survival threshold, the resource locking permission associated with the target temporary token is revoked, and the pre-deducted value in the inventory count is restored.

[0014] Optionally, the data consistency guarantee module is further configured as follows: Obtain the physical inventory value from the associated database at the preset reconciliation node; The baseline inventory value is determined based on the sum of the physical inventory value and the pre-deducted values ​​corresponding to temporary tokens that do not exceed the preset survival threshold. In response to the deviation between the baseline inventory value and the inventory count exceeding a preset fault tolerance threshold, the inventory count is reset based on the baseline inventory value.

[0015] Optionally, the system further includes a visualization decision mapping module that is communicatively connected to the bimodal arbitration module; The visualization decision mapping module is configured as follows when the bimodal arbitration module executes the weight filtering mechanism: Obtain the difference between the dynamic safety level and the current available inventory. Based on the difference, generate inventory early warning graph elements for the target equipment; The rendering color spectrum and flashing frequency of the inventory warning element are adjusted based on the difference.

[0016] In summary, this application includes the following beneficial technical effects: 1. This application utilizes the collaborative operation of a time-series feature analysis module and a dual-modal arbitration module to dynamically generate a trend amplification coefficient and output a dynamic safety water level based on the matching relationship between the current time and the preset agricultural time window. It also compares the current available inventory in real time to automatically switch to a weighted filtering mechanism when inventory is scarce. This fundamentally solves the problem of resource mismatch and concurrent deadlock caused by seasonal pulse demand during the peak sales season of agricultural equipment, ensuring that high-value orders can obtain resource locking permissions first during peak demand periods.

[0017] 2. This application activates a weighted filtering mechanism through a dual-modal arbitration module when the inventory is below the dynamic safety level. Only requests with a weight not lower than the preset admission threshold are allowed to obtain resource locking permissions, while low-weight requests are blocked. This effectively avoids low-intent exploratory requests from preempting database lock resources, ensuring a high transaction rate and supply stability for core customers during the agricultural season window, and significantly improving the system's business processing efficiency in high-concurrency scenarios.

[0018] 3. This application generates a temporary token containing a timeliness parameter in memory through a data consistency guarantee module to deduct inventory count and asynchronously trigger database synchronization. At the same time, it monitors the duration of the temporary token and automatically reclaims inventory resources after the timeout. This achieves high-speed and accurate processing and automatic error correction of inventory data in high-concurrency scenarios, completely avoiding the problems of overselling, inconsistent inventory data and system response delay, and ensuring the reliability of the entire business management process and the integrity of the data closed loop. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall system architecture; Figure 2 This is a schematic diagram of the state switching logic of the dual-modal arbitration module between the normal throughput mode and the high-voltage fuse mode; Figure 3 This is a schematic diagram illustrating the principle of the time-series feature analysis module generating a dynamic safety water level line based on the agricultural time window. Detailed Implementation

[0020] The following is in conjunction with the appendix Figure 1-3 This application will be described in further detail.

[0021] This application discloses a full-process business management and analysis system for agricultural equipment sales. This system specifically optimizes existing technologies to address core deficiencies, exemplified by CN108985691A, entitled "An Automatic Replenishment Method and System Based on Dynamic Inventory Control." This prior art classifies inventory materials, calculates a volatility coefficient based on the average and standard deviation of historical sales data, and then sets a safety stock threshold. When the actual available inventory falls below the threshold, automatic replenishment logic is triggered. While this solution is applicable to stable retail scenarios, it suffers from three unresolved core deficiencies in agricultural equipment sales. The first is the spatiotemporal lag of the static threshold. Agricultural equipment sales demand exhibits a strong seasonality, alternating between quiet and peak periods. Smoothing calculations of long-term historical averages mask instantaneous demand extremes, resulting in a significant lag in safety stock threshold adjustments. This prevents timely warnings and replenishment preparations before peak agricultural demand arrives. The second issue is the scheduling failure under resource scarcity. Existing technologies, even in scenarios with critical inventory shortages, still follow a time-order-based first-in-first-out (FIFO) mechanism to handle all concurrent requests, indiscriminately allocating database lock resources. A large number of low-intent, low-conversion-rate exploratory requests preempt lock resources, causing high-value, high-intent orders to fail due to lock timeouts, resulting in core resource mismatch and business deadlock. The third issue is the lack of data consistency guarantees in high-concurrency scenarios. Existing technologies do not have a dedicated data consistency mechanism designed for the high-concurrency scenarios of peak agricultural equipment sales seasons. Directly manipulating the underlying database for inventory deduction significantly increases system response latency and is highly prone to overselling and inventory data asynchronization. This system systematically addresses these three core defects through the collaborative operation of a time-series feature analysis module, a dual-modal arbitration module, and a data consistency guarantee module.

[0022] like Figure 1 As shown, this system consists of a core processing unit and external interactive components. The core processing unit integrates a coupled time-series feature analysis module, a bimodal arbitration module, and a data consistency guarantee module, and is connected to a visualization decision mapping module. The sales client interacts with the core processing unit through a bidirectional link, sending requests and receiving feedback. The core processing unit also connects to the underlying database (DB) and high-speed memory cache (Cache), ensuring data processing efficiency through read-write separation and asynchronous synchronization mechanisms. This diagram visually illustrates the connection relationships and data flow between the modules of this application.

[0023] The time-series feature analysis module completes the configuration and storage of basic operating parameters. These parameters include preset agricultural time windows, preset amplification thresholds, preset baseline thresholds, and supply response cycle parameters. The preset agricultural time window is determined based on the agricultural time patterns of the corresponding operating scenarios of agricultural equipment, target crop types, and planting areas. The window range encompasses the complete time interval of the pre-demand preparation cycle and the demand surge cycle. For sowing and land preparation equipment in the spring plowing scenarios of the main grain-producing areas of Northeast China and the Huang-Huai-Hai Plain, the preset agricultural time window is set from February 22nd to March 15th each year, with the pre-demand preparation cycle matching a 7-day supply response cycle to ensure replenishment is completed before the demand surge. For harvesting and threshing equipment in the autumn harvest scenarios of the main grain-producing areas, the preset agricultural time window is set from August 25th to September 15th each year. This window covers the core preparation cycle and the demand surge cycle before the concentrated demand surge within the traditional operating cycle of the main grain-producing areas. It allows for pre-adjustment of the safety level before the arrival of the demand peak, avoiding the lag problem of static thresholds failing to match the pulse demand in existing technologies. For specialized agricultural equipment targeting cash crops and specialty planting scenarios, the preset agricultural time window can be adapted and adjusted according to the planting and harvesting cycles of the corresponding crops, ensuring that the solution can cover the sales demand characteristics of all types of agricultural equipment.

[0024] The preset benchmark threshold is set to 1.0. This threshold corresponds to the stable demand period outside the agricultural season window. During this period, market demand does not fluctuate significantly seasonally, and there is no need to make additional weighted adjustments to historical sales data. Therefore, anchoring the benchmark value to 1.0 ensures that the calculated safety level during the stable period matches the actual demand scale perfectly, avoiding ineffective inventory redundancy and capital occupation. The preset amplification threshold is set to 1.5. This threshold corresponds to the demand surge period within the agricultural season window. During this period, market demand exhibits short-term, pulse-like growth. Based on the statistical analysis of the total sales data of all categories of mainstream agricultural equipment across all channels in China over the past 5 years, the average daily sales during the agricultural season window can reach 1.4 to 1.6 times the average daily sales during the same stable period. Therefore, the amplification threshold is set to 1.5. The same stable period refers to the non-seasonal sales cycle within 3 months before and after the agricultural season window, excluding agricultural season-related demand fluctuations. During this cycle, the average daily sales fluctuation does not exceed ±10%, which is considered a stable demand phase. The preset amplification threshold can be customized based on the actual demand growth rate of different types of agricultural equipment, with an adjustment range of 1.2-2.0, ensuring adaptation to the sales characteristics of different equipment. This value can fully cover the normal demand growth rate within the agricultural cycle, raising the safety level in advance to trigger replenishment warnings, while avoiding excessive amplification of demand leading to inventory backlog and working capital occupation, achieving an optimal balance between stockout risk and inventory costs adapted to agricultural scenarios.

[0025] The supply response cycle parameter characterizes the physical time lag in the supply chain, specifically the complete cycle from the formal issuance of a replenishment order to the completion of equipment warehousing and its availability for sale. This parameter is set to 7 days based on the typical production schedules and logistics transportation times of major domestic agricultural equipment manufacturers. This value matches the typical cycle of domestic agricultural equipment production and trunk logistics transportation, ensuring that replenishment orders are completed and warehoused before the peak demand during the agricultural season, filling inventory gaps corresponding to safety levels and avoiding stockout risks caused by supply chain lag. For imported equipment and customized special-purpose equipment, the supply response cycle parameter can be adjusted according to the actual supply chain cycle to ensure that the parameter is fully adapted to the actual business scenario.

[0026] The time-series feature analysis module extracts the average daily sales within a sliding time window of the same historical year as historical sales data. The duration of the sliding time window is consistent with the preset agricultural time window, set to 15 days, and the sliding step size is set to 1 day, updating the window range forward daily. The historical year's corresponding sliding time window refers to the same calendar date range of the historical year that completely corresponds to the start and end dates of the current sliding time window. For example, if the current sliding time window is from February 22, 2026 to March 8, 2026, the historical corresponding time window would be from February 22, 2025, 2024, and 2023 to March 8. The historical year range is the past 3 years. The time-series feature analysis module extracts the daily sales data within the corresponding historical sliding time window of the past 3 years, calculates the average daily sales within the corresponding year window, and takes the arithmetic mean of the average daily sales of the past 3 years as the final historical sales data. This method of value determination can smooth out abnormal sales fluctuations caused by extreme weather, regional planting structure adjustments, and policy changes in a single year, while fully preserving the real demand characteristics within the agricultural cycle, thus avoiding the core defect of existing technologies that use long-term historical average smoothing to mask instantaneous demand extremes.

[0027] For new agricultural equipment that has been on the market for less than 3 years, the time-series feature analysis module matches the historical sales data of already marketed equipment as a substitute data source according to the priority dimension of "same operation type > same suitable crop type > same rated power level > same price range". If there is no matching equipment of the same level, the historical sales data of the equipment of the next higher power level can be extracted and multiplied by a correction factor of 0.8 as a substitute data source. The matching rules and correction factors of the substitute data source can be adjusted by the system administrator according to the characteristics of the equipment to ensure that the newly marketed equipment can also complete the corresponding dynamic safety water level calculation.

[0028] The time-series feature analysis module monitors the current time in real time, determines its matching relationship with the preset agricultural time window, and generates a corresponding trend amplification coefficient. If the time-series feature analysis module detects that the current time does not fall within the preset agricultural time window, it anchors the trend amplification coefficient to a preset benchmark threshold. If the time-series feature analysis module detects that the current time enters the preset agricultural time window, it directly switches the trend amplification coefficient from the preset benchmark threshold to the preset amplification threshold, thereby raising the dynamic safety level by changing the trend amplification coefficient without changing historical sales data. If the time-series feature analysis module detects that the current time exceeds the end node of the preset agricultural time window, it lowers the trend amplification coefficient from the preset amplification threshold back to the preset benchmark threshold. This switching logic strictly follows the claims definition. Simultaneously, by incorporating a pre-stocking cycle within the preset agricultural time window, it achieves early warning and inventory preparation before the surge in agricultural demand, fundamentally solving the core problems of existing technologies such as lagging static threshold adjustments and inability to adapt to pulse-like demand.

[0029] The time-series feature analysis module performs a product operation on historical sales data, supply response cycle parameters, and trend amplification coefficient, defining the output of this product operation as the dynamic safety level. In this calculation logic, historical sales data represents the scale of regular market demand per unit time, the supply response cycle parameter represents the complete time span required for inventory replenishment, and the trend amplification coefficient represents the correction coefficient for demand fluctuations caused by the agricultural season cycle. The dimensions of these three parameters are perfectly matched, and the calculation result accurately represents the minimum inventory level required to ensure market supply within the corresponding period. Example: The average daily sales of a certain model of combine harvester within a 15-day window during the autumn harvest over the past three years is 2 units. The supply response cycle parameter is 7 days, and the trend amplification coefficient is 1.0 during the non-agricultural season window, at which point the dynamic safety level is 14 units. After entering the preset agricultural season window for the autumn harvest, the trend amplification coefficient switches to 1.5, at which point the dynamic safety level is 21 units. The system immediately triggers the replenishment process to ensure that replenishment is completed before the surge in autumn harvest demand. This example visually demonstrates the adaptive adjustment capability of the dynamic safety water level line according to the agricultural season, providing an accurate benchmark for subsequent system operation.

[0030] like Figure 3 As shown, the horizontal axis of the coordinate system represents the time axis, including the non-agricultural window, the pre-stocking cycle, and the agricultural window (peak period); the vertical axis represents numerical values. The figure illustrates the evolution of the historical sales data curve (thin line) and the dynamic safety level line (thick line). When the time enters the pre-stocking cycle, the trend amplification factor jumps from 1.0 to 1.5. Driven by this, the dynamic safety level line rises sharply in advance, before the historical sales data shows significant fluctuations, forming a clear early warning range. This figure intuitively demonstrates how this application utilizes the "trend amplification factor" to overcome the lag defect of existing technologies based on historical averages, achieving advance stocking before the peak demand during the agricultural season.

[0031] The bimodal arbitration module establishes a real-time bidirectional communication link with the time-series feature analysis module, acquiring the dynamic safety waterline output by the time-series feature analysis module in real time. Simultaneously, it acquires the real-time updated inventory count pushed by the data consistency assurance module in memory, using this as the current available inventory. It then performs a real-time comparison between the current available inventory and the dynamic safety waterline, activating the corresponding resource allocation mechanism based on the comparison result. In this system, the current available inventory refers to the real-time sellable inventory count in the memory of the data consistency assurance module, after deducting locked but unsold quantities.

[0032] The bimodal arbitration module configures and stores basic operating parameters, including customer level classification rules, mapping rules between customer levels and weight values, preset admission thresholds, and preset core thresholds. Customer levels are comprehensively evaluated based on three core indicators: total annual purchase amount over the past three consecutive calendar years, annual repurchase rate, and historical order fulfillment rate. These three indicators are weighted at 60%, 25%, and 15% respectively to calculate a comprehensive score. Based on this comprehensive score, customers are classified into three levels: core customers, premium customers, and ordinary customers, with corresponding weight values ​​set at 90, 70, and 50 respectively. The total annual purchase amount directly determines the customer's core value contribution, therefore it has the highest weight. The repurchase rate and fulfillment rate reflect the stability of customer cooperation and the certainty of order conversion, perfectly matching the low-frequency, high-value transaction characteristics of the agricultural equipment sales industry.

[0033] For new customers without transaction data for three consecutive full calendar years, they are defaulted to being classified as ordinary customers with a weighting of 50. If a new customer provides sufficient prepayment or performance bond, their status can be temporarily upgraded to premium customer level based on the purchase volume corresponding to the prepayment amount, with a weighting of 70. This temporary upgrade is valid for 30 calendar days after the completion of the transaction. The temporary rating rules for new customers can be adjusted by system administrators based on actual business scenarios.

[0034] The preset admission threshold is set to 60. The value of 60 is between the weight of 70 for high-quality customers and the weight of 50 for ordinary customers. In scenarios where inventory is scarce, it can accurately filter low-weight requests from ordinary customers and only allow requests from high-quality customers and above with a weight of not less than 60 to enter the resource allocation process. This avoids the limited inventory resources and system computing resources being occupied by trial requests with low conversion probability, while also preventing the admission scope from being excessively narrowed, ensuring that the normal procurement needs of high-quality cooperative customers can be stably responded to.

[0035] The preset core threshold is set to 85. The preset core threshold is always greater than the preset admission threshold to avoid conflicts in logical judgment. The value of 85 is between the weight of 90 for core customers and the weight of 70 for high-quality customers, which can accurately identify the core customer group. This matches the current situation in the agricultural equipment sales industry where the annual procurement contribution of core customers accounts for more than 60%, ensuring the supply priority and stability of core cooperative customers.

[0036] The dual-modal arbitration module sets globally mutually exclusive system operation status flags. The value of the status flag is updated in real time based on the comparison result between the current available inventory and the dynamic safety level. 0 corresponds to the normal throughput state, and 1 corresponds to the high-voltage fuse state. The two states are mutually exclusive and cannot be activated simultaneously. The system performs a full comparison and verification of the current available inventory and the dynamic safety level every 100ms. At the same time, when the quantity of inventory deducted in a single instance exceeds 10% of the dynamic safety level, an additional comparison and verification is immediately triggered to ensure the real-time and accuracy of system operation status switching, while avoiding system performance loss in high-concurrency scenarios.

[0037] like Figure 2 As shown, the system defines two mutually exclusive operating states: normal throughput mode and high-pressure circuit breaker mode. The dual-modal arbitration module drives the state transition based on real-time comparison results: when the current available inventory is greater than the dynamic safety level, the system maintains the normal throughput mode and performs FIFO (First-In-First-Out) queue allocation; once the current available inventory drops to no higher than the dynamic safety level, the system immediately switches to high-pressure circuit breaker mode and performs weighted filtering and current limiting. This state transition diagram intuitively illustrates the system's differentiated scheduling strategies under different inventory pressures.

[0038] If the bimodal arbitration module determines that the current available inventory is higher than the dynamic safety level, it sets the system operation status flag to 0, activates the sequential allocation mechanism, and allocates resource locking permissions according to the order of request arrival. This state typically corresponds to a stable demand period outside the agricultural season window. At this time, the dynamic safety level generated by the time series feature analysis module is at the baseline level, inventory reserves are sufficient, and there is no need to differentiate requests.

[0039] The bimodal arbitration module, in response to the determination that the current available inventory is higher than the dynamic safety level, establishes a linear first-in-first-out queue anchored to a physical timestamp. The timestamp precision of the queue is set to 1ms, which can accurately distinguish the arrival time of different concurrent requests and avoid the problem of request timing confusion in high-concurrency scenarios. If multiple requests have the same physical timestamp precision (arriving within the same 1ms), they are sorted according to the arrival order of the TCP packets received by the underlying system, ensuring that the sorting rule is unique and completely based on the arrival time.

[0040] The bimodal arbitration module directly maps all valid order requests to a linear first-in-first-out queue according to their arrival time, without performing any screening or filtering on the customer level or weight value corresponding to the request, thus ensuring the fairness of all customer requests.

[0041] The dual-modal arbitration module issues resource locking permissions to the corresponding requests in the queue in the order of the linear first-in-first-out queue, until all requests in the queue are processed or the current available inventory falls below the dynamic safety level, triggering a switch in the system's operating state.

[0042] If the bimodal arbitration module determines that the current available inventory is not higher than the dynamic safety level, it sets the system operation status flag to 1, activates the weighted filtering mechanism, and only allocates resource locking permissions to requests with a weight not lower than the preset admission threshold, while blocking requests with a weight lower than the preset admission threshold. This state typically corresponds to the demand surge period within the agricultural season window. At this time, the time series feature analysis module has already raised the dynamic safety level through the trend amplification coefficient, allowing the system to enter the high-pressure circuit breaker state in advance before the demand peak arrives, completing the resource scheduling strategy switch ahead of time, and avoiding the lag defect of passively responding only after the inventory is exhausted in the existing technology.

[0043] In response to the determination that the current available inventory is not higher than the dynamic safety level, the dual-modal arbitration module activates the request filter, performs full parsing on each incoming order request, and extracts the unique customer identifier carried in the request header.

[0044] The bimodal arbitration module retrieves the customer level tag associated with the customer from the customer management database based on the extracted unique customer identifier, and converts the customer level tag into the weight value of the corresponding request according to the pre-configured mapping rules.

[0045] The bimodal arbitration module compares the weight value corresponding to the request with the preset admission threshold. If the weight value is not lower than the preset admission threshold, it issues resource locking permission to the corresponding request source.

[0046] If the bimodal arbitration module determines that the weight value is lower than the preset admission threshold, it directly blocks the inventory deduction operation for the request source and returns a pre-sale registration signal to the request source, preventing it from entering the subsequent inventory deduction process. In this way, the low-value request is forced to be offloaded in the case of inventory shortage, avoiding a large number of low-weight requests from occupying system resources and inventory resources. This solves the problem of high-value order processing failure and core resource mismatch caused by the indiscriminate first-in-first-out mechanism in the existing technology.

[0047] The pre-sale registration signal includes the current inventory status of the target equipment, the estimated replenishment time, the pre-sale registration channel link, and the registration validity period. For requests returning pre-sale registration signals, the system establishes a pre-sale registration queue according to the arrival sequence of the requests, recording the customer's identity, the required equipment model, the purchase quantity, and the registration time. The registration validity period is 15 calendar days. When the system switches to normal throughput mode and the current available inventory is higher than the dynamic safety level, inventory release notifications are pushed to registered customers sequentially according to the pre-sale registration queue sequence. Customers can submit orders and lock inventory within 24 hours of the notification being sent. If an order is not submitted within 24 hours of the registration validity period or after the notification, the corresponding pre-sale registration record is automatically cleared.

[0048] When the bimodal arbitration module issues resource locking permissions to the corresponding request source, it simultaneously compares the weight value corresponding to the request with the preset core threshold.

[0049] If the bimodal arbitration module determines that the weight value is not lower than the preset core threshold, it will issue resource locking permissions and simultaneously generate a replenishment instruction for the corresponding equipment to the supply chain management module, triggering an emergency replenishment process for that equipment. The replenishment quantity calculation formula is: Replenishment Quantity = Dynamic Safety Water Level - Current In-Memory Inventory Count + Quantity Requested by the Core Customer This Time. The lower limit for the replenishment quantity is 0; if the calculation result is negative, replenishment will not be triggered. The replenishment quantity can be manually adjusted by supply chain management personnel based on actual production capacity and logistics cycle. By locking inventory for core customers in advance and replenishing inventory reserves, the long-term supply stability of core customers is further guaranteed.

[0050] The data consistency assurance module establishes a real-time bidirectional communication link with the dual-modal arbitration module, synchronously obtains the system operation status flag and resource locking permission allocation results from the dual-modal arbitration module, and simultaneously receives the current available inventory data collected by the dual-modal arbitration module. The inventory control operation is initiated solely based on the resource locking permission issued by the dual-modal arbitration module.

[0051] The data consistency assurance module completes the configuration and storage of basic operating parameters, including preset survival threshold, preset fault tolerance threshold, and preset reconciliation node.

[0052] The preset survival threshold is set at 15 minutes. This threshold is based on the industry average time for the entire process of filling in routine information, verifying qualifications, and confirming corporate payments for domestic agricultural equipment sales orders. This not only allows customers sufficient time to process orders and ensures smooth order conversion, but also allows for the rapid recovery of inventory resources occupied by overdue unpaid orders, avoiding resource idleness caused by long-term inventory lock-up. It is especially suitable for high-pressure business scenarios with tight inventory resources during the agricultural season.

[0053] The default fault tolerance threshold is set to 1 unit. This threshold is based on the industry characteristics of agricultural equipment, which has high unit value and whose inventory changes can directly affect the regional supply and demand balance and agricultural operation arrangements. It can ensure that small deviations in inventory data can be identified and corrected by the system, avoiding overselling or inventory statistics distortion caused by the continuous expansion of deviations. It is suitable for the low turnover and high value sales characteristics of agricultural equipment.

[0054] The default reconciliation node is set at 00:00 every day. This node is the normal off-peak period for agricultural equipment sales business. Based on the statistical results of the time distribution of agricultural equipment sales orders across all channels in China over the past 3 years, the number of order requests during this period is less than 1% of the daytime peak. The reconciliation operation will not occupy the core computing resources of the system, and can completely avoid the impact of the reconciliation operation on the system response speed of high-concurrency business during the daytime.

[0055] The data consistency guarantee module is based on the current available inventory collected in real time by the dual-modal arbitration module. It establishes a real-time updated inventory count in the server's high-speed memory cache. The initial value of the inventory count is completely consistent with the initial value of the current available inventory (i.e., the physical inventory value in the inventory management database). All inventory pre-deduction operations are preferentially executed in the memory cache without directly operating the underlying database. This can significantly reduce system response latency in high-concurrency scenarios and avoid row lock contention and business blocking problems caused by direct database operations in existing technologies.

[0056] In response to the resource locking permission issued by the dual-modal arbitration module, the data consistency assurance module first parses the equipment purchase quantity in the corresponding order request, performs a pre-deduction verification on the inventory count in memory, and calculates the inventory count value after deducting the purchase quantity.

[0057] If the data consistency assurance module determines that the inventory count after pre-deduction and verification is not less than zero, it considers the current inventory allocation valid. A temporary token containing a unique identifier and expiration parameters is generated in memory. These parameters include the token's generation timestamp and a preset expiration timestamp. The preset expiration timestamp equals the generation timestamp plus a preset survival threshold, and its initial validity period is consistent with the preset survival threshold. Simultaneously, based on the equipment purchase quantity corresponding to the temporary token, the inventory count in memory is deducted, and a data synchronization operation is asynchronously triggered against the inventory management database, synchronizing the inventory lock record corresponding to this pre-deduction operation to the underlying database. If the asynchronous data synchronization operation fails, the system immediately triggers a retry mechanism with a retry interval of 1 second and a maximum of 3 retries. If all 3 retries fail, the system immediately triggers an inventory count rollback operation, restoring the pre-deducted inventory count, revoking the corresponding temporary token and resource lock permissions, and simultaneously pushing a data synchronization failure warning to system administrators to ensure consistency between memory and database data.

[0058] If the data consistency guarantee module determines that the inventory count after pre-deduction and verification is less than zero, it determines that the current inventory allocation is invalid, directly rejects the corresponding inventory deduction application, does not generate a temporary token, and does not perform any inventory count change operation, thus avoiding the overselling problem from the root and adapting to the inventory management needs of high-concurrency request scenarios during the agricultural season window.

[0059] The data consistency guarantee module monitors the duration of all generated temporary tokens in real time. The duration is calculated cumulatively starting from the physical timestamp of the temporary token's generation, and the duration of each temporary token is compared with the preset survival threshold in real time.

[0060] If the data consistency assurance module determines that the duration of a target temporary token exceeds a preset survival threshold, it classifies the order corresponding to the temporary token as an invalid order that has expired and not been paid. It immediately revokes the resource locking permissions associated with the target temporary token and restores the number of devices pre-deducted from the in-memory inventory count, completing the automatic reclamation of inventory resources. The reclaimed inventory resources are synchronously pushed to the bimodal arbitration module, which can immediately respond to new resource locking permission allocation requests issued by the bimodal arbitration module, particularly suitable for dynamic resource scheduling needs in inventory-scarce scenarios. After the temporary token expires, the system asynchronously triggers database synchronization, deletes the corresponding inventory locking record, and releases the locked inventory in the database. When the order corresponding to the temporary token completes payment and transaction closure, the system immediately updates the inventory locking record in the database to the formal outbound deduction record, completing the data closure of the entire transaction process.

[0061] When the preset reconciliation node is reached, the data consistency assurance module automatically triggers the reconciliation and self-healing operation of the full inventory data.

[0062] The data consistency assurance module first obtains the physical inventory value recorded in the inventory management database. At the same time, it calculates the total pre-deduction value corresponding to all valid temporary tokens in the system that have not exceeded the preset survival threshold. The physical inventory value is subtracted from the total pre-deduction value of valid temporary tokens to determine the baseline inventory value. This calculation logic can accurately reflect the current real available inventory scale of the system, eliminate the inventory quantity that has been locked but has not completed the transaction closure, and provide an accurate baseline basis for data consistency verification.

[0063] The data consistency assurance module compares the calculated baseline inventory value with the real-time inventory count in memory and calculates the absolute deviation between the two.

[0064] If the data consistency guarantee module determines that the deviation value exceeds the preset fault tolerance threshold, it determines that there is a data deviation in the memory inventory count. It immediately resets the inventory count in memory based on the baseline inventory value, completes the self-healing correction of data consistency, ensures the logical consistency between the memory inventory count and the physical inventory data in the database, avoids the data deviation problem caused by asynchronous data synchronization in high-concurrency scenarios, and solves the core defect of the lack of data consistency guarantee in high-concurrency scenarios in existing technologies.

[0065] The data consistency assurance module synchronously pushes the real-time updated inventory count data in memory to the bimodal arbitration module, providing accurate data support for the real-time comparison of the current available inventory and the dynamic safety water level of the bimodal arbitration module, forming a closed-loop linkage of the preceding business processes.

[0066] The visualization decision mapping module establishes a real-time bidirectional communication link with the bimodal arbitration module, the time series feature analysis module, and the data consistency guarantee module. It synchronously acquires the system operation status flag of the bimodal arbitration module, the dynamic safety water level output by the time series feature analysis module, and the memory inventory count updated in real time by the data consistency guarantee module. The visualization warning mapping operation is initiated when the bimodal arbitration module activates the weight filtering mechanism and the system operation status flag switches to 1. When the system operation status flag switches to 0 and the weight filtering mechanism is exited, the visualization module automatically clears the current inventory warning element and stops the warning display.

[0067] Upon receiving a trigger signal, the visualization decision mapping module locks the dynamic safety water level of the corresponding agricultural equipment and the current memory inventory count in real time. It calculates the inventory gap difference by subtracting the current memory inventory count from the dynamic safety water level as the baseline value. This difference is always ≥0 in the trigger state, and the larger the value, the higher the degree of inventory shortage.

[0068] The visualization decision mapping module generates inventory warning elements that correspond one-to-one with the target agricultural equipment based on the calculated inventory gap difference. Each inventory warning element is bound to the corresponding equipment's model and specifications, sales region, physical storage location, and associated customer level distribution information. It can be directly mapped to the inventory visualization interface in the system management backend, allowing managers to intuitively locate the inventory status and impact range of the corresponding equipment.

[0069] The visualization decision mapping module linearly adjusts the rendering color spectrum of the inventory warning elements based on the magnitude of the inventory gap difference. The rendering color spectrum follows the human-computer interaction visualization specifications for industrial control scenarios, covering a gradient range from light blue to bright red: when the inventory gap difference is positive and does not exceed 30% of the dynamic safety level, the inventory warning element is rendered in light blue; when the inventory gap difference is greater than 30% but not more than 60% of the dynamic safety level, the inventory warning element is rendered in yellow; when the inventory gap difference is greater than 60% but less than 100% of the dynamic safety level, the inventory warning element is rendered in orange; and when the inventory gap difference reaches or exceeds 100% of the dynamic safety level, the inventory warning element is fixedly rendered in bright red. Through precise changes in the color gradient, the level of inventory shortage is intuitively conveyed to management personnel.

[0070] The visualization decision mapping module synchronously and linearly adjusts the flashing frequency of the inventory warning graph elements based on the size of the inventory gap difference. The flashing frequency adjustment range is set from 0 times / min to 60 times / min. This range is based on the matching degree between human visual recognition comfort and the urgency of the warning information. 0 times / min corresponds to a stable display state with no flashing, while 60 times / min corresponds to the highest level of warning flashing state, avoiding excessive frequency that may cause visual fatigue to managers. When the inventory gap difference is 0, the flashing frequency of the inventory warning graph elements is fixed at 0 times / min to maintain a stable display. When the inventory gap difference is positive, the flashing frequency increases by 6 times / min for every 10% increase in the dynamic safety level. When the inventory gap difference reaches or exceeds 100% of the dynamic safety level, the flashing frequency is fixed at 60 times / min. Through the gradient change in flashing frequency, the warning effect under different shortage levels is enhanced.

[0071] The visualization decision mapping module pushes the generated inventory warning graph to the visualization interface of the system management backend in real time. At the same time, it pushes the warning information of the corresponding inventory shortage level, equipment shortage quantity, and affected sales area to the terminals of the sales manager and supply chain manager of the corresponding equipment. This helps managers to quickly complete decision-making operations such as replenishment scheduling and order priority allocation, adapting to the rapid response needs of agricultural equipment sales during the agricultural season.

[0072] The implementation principle of the full-process business management and analysis system for agricultural equipment sales in this application embodiment is as follows: A time-series feature analysis module generates a trend amplification coefficient based on the matching relationship between the current time and the preset agricultural time window. Based on this coefficient and weighted historical sales data, a dynamic safety water level line that changes dynamically over time is output. This allows for the early warning of inventory thresholds to be raised before the surge in agricultural demand, solving the problem of lagging static threshold adjustments and inability to adapt to pulse-like demand in existing technologies. A dual-modal arbitration module compares the current available inventory with the dynamic safety water level line in real time. When inventory is sufficient, a sequential allocation mechanism is activated to allocate resources according to the order of request arrival. When inventory is tight... The system switches to a weighted filtering mechanism, allocating resource locking permissions only to high-intent requests with weights not lower than a preset threshold and blocking low-weight requests. This ensures priority processing of high-value orders under resource scarcity, avoiding core resource mismatch and business deadlock issues caused by indiscriminate resource locking. The data consistency guarantee module generates a temporary token with a timeliness parameter in memory when allocating resource locking permissions to deduct from the memory inventory count and asynchronously triggers database synchronization. Simultaneously, it ensures data consistency between memory and the database under high-concurrency scenarios by monitoring token expiration and implementing a preset reconciliation node automatic correction mechanism, avoiding overselling and data inconsistency issues. In summary, this system systematically solves the technical defects of static threshold lag, scheduling failure under resource scarcity, and lack of data consistency under seasonal pulse demand in agricultural equipment sales through pre-warning of time-series dynamic water levels, differentiated scheduling with dual-modal arbitration, and asynchronous consistency guarantees between memory and the database.

[0073] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A full-process business management and analysis system for agricultural equipment sales, characterized in that: It includes a coupled time-series feature analysis module, a dual-modal arbitration module, and a data consistency guarantee module; The time-series feature analysis module is configured to generate a trend amplification coefficient based on the matching relationship between the current time and the preset agricultural time window, and output a dynamic safety water level line that changes dynamically over time based on the historical sales data weighted by the trend amplification coefficient. The dual-modal arbitration module is configured to compare the current available inventory with the dynamic safety level in real time: if the current available inventory is higher than the dynamic safety level, the sequential allocation mechanism is activated to allocate resource locking permissions according to the order of request arrival; If the current available inventory is not higher than the dynamic safety level, the weighted filtering mechanism is activated, and the resource locking permission is only assigned to requests with a weight not lower than the preset admission threshold, while requests with a weight lower than the preset admission threshold are blocked. The data consistency guarantee module is configured to generate a temporary token containing a timeliness parameter in memory in response to the allocation of the resource locking permission, thereby deducting the inventory count in memory and asynchronously triggering data synchronization for the database.

2. The system according to claim 1, characterized in that, The time-series feature analysis module is configured as follows: The average daily sales within the same sliding time window of historical years are extracted as the historical sales data, and the supply response cycle parameter, which characterizes the physical time lag of the supply chain, is obtained. In response to the current time matching the preset agricultural time window, the trend amplification coefficient is set to a preset amplification threshold, and a product operation is performed on the historical sales data, the supply response cycle parameter and the trend amplification coefficient. The output of the product operation is defined as the dynamic safety water level.

3. The system according to claim 2, characterized in that, The time-series feature analysis module is configured as follows: If it is detected that the current time does not fall within the preset agricultural time window, the trend amplification coefficient will be anchored to the preset benchmark threshold. If the current time is detected to be within the preset agricultural time window, the trend amplification coefficient is directly switched from the preset baseline threshold to the preset amplification threshold, so as to improve the dynamic safety level by changing the trend amplification coefficient when the historical sales data value remains unchanged.

4. The system according to claim 3, characterized in that, The bimodal arbitration module is configured to perform the sequential allocation mechanism as follows: In response to the current available inventory being higher than the dynamic safety level, a linear first-in-first-out queue anchored to a physical timestamp is established; Access requests are directly mapped to the linear first-in-first-out queue according to their arrival time; Resource locking permissions are issued sequentially according to the position order of the linear first-in-first-out queue.

5. The system according to claim 3, characterized in that, The bimodal arbitration module is configured to perform the weighted filtering mechanism as follows: Parse the customer identity identifier carried in the request, and retrieve the associated customer level tag to map it to the weight value of the request; In response to the weight value being not lower than the preset access threshold, the resource locking permission is issued to the corresponding request source; In response to the weight value being lower than the preset admission threshold, the inventory deduction operation for the requesting source is blocked, and a pre-sale registration signal is returned to the requesting source.

6. The system according to claim 5, characterized in that, The bimodal arbitration module is configured to: When issuing the resource locking permission to the corresponding request source, the bimodal arbitration module is configured as follows: The weight value is compared with a preset core threshold, wherein the preset core threshold is greater than the preset admission threshold; In response to the weight value being no less than the preset core threshold, a replenishment instruction is generated simultaneously with the issuance of the resource locking permission.

7. The system according to claim 1, characterized in that, The data consistency guarantee module is configured to deduct the inventory count in memory as follows: The inventory count is established in memory based on the current available inventory. In response to the allocation of the resource locking permission, a pre-deduction verification is performed on the inventory count; In response to the inventory count after the pre-deduction verification being not less than zero, the inventory allocation is determined to be valid and the temporary token is generated.

8. The system according to claim 7, characterized in that, The data consistency guarantee module is also configured to: Monitor the duration of each temporary token; In response to the target temporary token's duration exceeding a preset survival threshold, the resource locking permission associated with the target temporary token is revoked, and the pre-deducted value in the inventory count is restored.

9. The system according to claim 8, characterized in that, The data consistency guarantee module is also configured to: Obtain the physical inventory value from the associated database at the preset reconciliation node; The baseline inventory value is determined based on the sum of the physical inventory value and the pre-deducted values ​​corresponding to temporary tokens that do not exceed the preset survival threshold. In response to the deviation between the baseline inventory value and the inventory count exceeding a preset fault tolerance threshold, the inventory count is reset based on the baseline inventory value.

10. The system according to claim 5, characterized in that, The system also includes a visualization decision mapping module that is communicatively connected to the dual-modal arbitration module; The visualization decision mapping module is configured as follows when the bimodal arbitration module executes the weight filtering mechanism: Obtain the difference between the dynamic safety level and the current available inventory. Based on the difference, generate inventory early warning graph elements for the target equipment; The rendering color spectrum and flashing frequency of the inventory warning element are adjusted based on the difference.

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