Method for Reducing Invoice Errors and Integrated System Management through Courier API Integration

Abstract

The present invention relates to a method for reducing invoice errors and managing integrated computer systems through integration with a courier company API, and more specifically, to a technology that controls the load on physical hardware by processing data generated during the logistics process, and improves the operational efficiency of logistics infrastructure by dynamically analyzing user input behavior, terminal error history, and the physical characteristics of cargo. In one embodiment, the present invention relates to a logistics operation control technology that controls the distribution of computational resources by considering the backlog of connection requests, communication delays, and physical hardware loads occurring during the connection process with an external courier system, selectively triggers an invoice pre-verification procedure by reflecting modification events, input time, and the complexity of the address structure occurring during the user's address input process, calculates the priority of alternative transport routes based on the dwell time at the logistics hub, weather conditions, remaining capacity for cargo, and recent error history, and classifies cargo handling methods by reflecting the weight error rate of cargo per customer, the proportion of items requiring caution for damage, packaging preparation time, variability in outgoing volume, and return history.

Description

Technology Field

[0001] The present invention relates to a method for reducing invoice errors and managing integrated computer systems through integration with a courier company API, and more specifically, to a technology that controls the load on physical hardware by processing data generated during the logistics process, and improves the operational efficiency of logistics infrastructure by dynamically analyzing user input behavior, terminal error history, and the physical characteristics of cargo.

[0002] In one embodiment, the present invention relates to a logistics operation control technology that controls the distribution of computational resources by considering the backlog of connection requests, communication delays, and physical hardware loads occurring during the connection process with an external courier system, selectively triggers an invoice pre-verification procedure by reflecting modification events, input time, and the complexity of the address structure occurring during the user's address input process, calculates the priority of alternative transport routes based on the dwell time at the logistics hub, weather conditions, remaining capacity for cargo, and recent error history, and classifies cargo handling methods by reflecting the weight error rate of cargo per customer, the proportion of items requiring caution for damage, packaging preparation time, variability in outgoing volume, and return history. Background Technology

[0003] Conventional logistics management systems often remain limited to the role of information relay, merely aggregating customer order data, transmitting it to courier servers, and receiving tracking numbers. When external connection requests surge during periods of high cargo volume, the systems fail to organically control communication delays and server processor overheating, leading to the depletion of server resources and the shutdown of the entire computer network.

[0004] Furthermore, conventional technology primarily relies on a method of simply comparing the completed string with a zip code database to prevent errors when users enter a delivery address. This approach has limitations, as it fails to account for the confusion or fatigue users experience during the input process, leading to indiscriminate computational load, or it cannot prevent misdeliveries in advance if a typo happens to match another address.

[0005] Even when calculating a detour route due to physical bottlenecks occurring during transportation, conventional methods simply push cargo to alternative terminals that are closer in distance or have more remaining space. This simple assignment does not take into account the processing quality of the alternative terminal, leading to a problem where it causes secondary delays resulting in large-scale misclassification at the alternative terminal.

[0006] In particular, the conventional method cannot combine the recent processing accuracy of alternative terminals, the frequency of misdeliveries, or the remaining capacity of volume at the current time into a single judgment structure, so a phenomenon may occur where volume is repeatedly assigned to a base that is outwardly close or a base with large capacity, and consequently, there is a problem in that secondary bottlenecks or reclassification delays occur continuously even after the primary bottleneck is resolved.

[0007] Furthermore, conventional logistics systems determine cargo handling methods by assigning static grades based solely on a customer's total cargo volume. Consequently, there are limitations in that cargo from customers with poor packaging practices or those who under-declare weight is fed into standard conveyor lines, causing damage to other goods and degrading the processing speed and quality of the overall logistics infrastructure.

[0008] Furthermore, conventional methods have limitations in that they establish cargo handling policies based on total shipment volume per customer or static grade information, failing to reflect quantitative indicators directly related to the likelihood of logistics accidents, such as the degree of discrepancy between actual and entered weights, the proportion of fragile items, insufficient packaging preparation time, and accumulated return history. As a result, cargo from customers with high fixed grades but a recurring tendency toward packaging defects or weight errors may be fed directly into general lines, potentially lowering the overall processing efficiency of the terminal. The problem to be solved

[0009] The present invention has been devised to overcome the limitations of the prior art described above, and aims to provide a method for reducing invoice errors and managing integrated computer systems through courier API integration, which controls server shutdowns in advance by dynamically calculating the physical hardware load status and communication delay time.

[0010] In addition, another objective of the present invention is to provide a system that reduces unnecessary computational load and improves the quality of logistics data by quantifying user input instability and the complexity of target addresses, and selectively triggering a pre-verification procedure for cargo with a high probability of misdelivery.

[0011] In addition, the present invention has another objective of providing a system that reduces the damage rate and delay of the entire logistics network by calculating a bypass route to prevent secondary bottlenecks by weighting the history of misclassification relative to the capacity of alternative terminals, and by separating cargo with malicious logistics patterns into a special inspection line by calculating the variability of cargo outflow volume and the error rate of packaging weight.

[0012] Furthermore, the present invention has an additional objective of providing a quantitative logistics operation control structure that reflects multiple operating conditions together, rather than static control relying on a single input value, by independently calculating multiple parameters derived from external linkage status, user input process, logistics hub status, and cargo processing history by customer, and then deriving a control index or classification coefficient by applying a proportional relationship, an inverse relationship, a log-proportional relationship, or an exponential reflection relationship between each parameter.

[0013] The problems of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0014] According to an embodiment of the present invention for solving the above problem

[0015] An integrated server unit that controls the distribution of computational resources of physical hardware;

[0016] A courier linkage module that mediates data transmission and reception with an external courier system;

[0017] Logistics management module for collecting order information and managing the logistics ledger;

[0018] A transportation problem processing module that calculates an alternative transportation route based on the dwell time and error history of logistics hubs;

[0019] A customized transport provision module that quantifies the physical handling characteristics of cargo and directs customized handling methods; and

[0020] A computer management module that controls the activation of a pre-verification procedure based on the user's invoice input behavior;

[0021] Includes,

[0022] The integrated server unit may be configured to calculate a resource allocation index by sequentially combining a queue ratio, cargo urgency weight, communication latency, and hardware load index; the computer management module may calculate the need for pre-verification by sequentially combining a dynamic input instability variable, batch order count, past misdelivery history ratio, and address complexity coefficient; the transportation problem processing module may calculate a detour route priority score by reflecting dwell time, weather deterioration weight, travel distance, remaining acceptable cargo volume, and the number of recent misclassifications and misdeliveries; and the customized transportation provision module may be configured to calculate a customer-specific logistics pattern dynamic classification coefficient by reflecting the average weight error rate, the proportion of fragile items, packaging preparation time, outbound volume variability, and return history index.

[0023] The above logistics management module generates a logistics ledger by aggregating individual order data entered through a user terminal or bulk order data uploaded in the form of a file, and

[0024] The above-mentioned courier linkage module converts dispatch request data recorded in the above-mentioned logistics ledger into the data specifications of the above-mentioned external courier system and transmits it, and is characterized by operating to prevent data loss by storing the dispatch request data in an internal waiting area when a communication delay in the external network is detected and then transmitting it in batches when the connection status is normalized.

[0025] The above integrated server unit is,

[0026] The number of courier company linkage requests waiting in the above courier linkage module is divided by the maximum allowable number of linkages per unit time to calculate the queue ratio, and the basic processing requirement is derived by multiplying the calculated queue ratio by a cargo urgency weight assigned according to the cargo delivery deadline.

[0027] The method is characterized by measuring the round-trip time of a test packet transmitted to the external courier system to derive the communication delay time, and then calculating a delay correction value by multiplying the basic processing requirement by the logarithm of the communication delay time to suppress the linear excessive fluctuation of the resource allocation value even if the communication delay time temporarily spikes.

[0028] The above integrated server unit is,

[0029] The processor heat temperature and utilization are collected in real time through temperature sensors and an operating system installed in the system to calculate a hardware load index, and

[0030] A final resource allocation index is derived by combining the inverse value of the hardware load index with the above-derived delay correction value, wherein the final resource allocation index is controlled to decrease rapidly and non-linearly as the hardware load index approaches a set risk threshold.

[0031] The method is characterized by immediately stopping the allocation of new computational resources to the courier linkage module and diverting incoming linkage requests to a temporary queue when the above-mentioned final resource allocation index falls below a preset lower threshold, even if the above-mentioned cargo urgency is calculated to be high, thereby preventing the physical server from shutting down in advance.

[0032] The above computer management module is,

[0033] Calculate a dynamic input instability variable representing the average modification frequency per unit time by dividing the number of times the clear key is activated detected in the address input form displayed on the user terminal by the total input time between the entry and exit points of the address input window, and

[0034] By parsing the input address string, an address complexity coefficient is derived that quantifies the number of depth levels of the administrative district system and the number of special characters included,

[0035] It is characterized by dynamically predicting the probability of invoice errors based on the state of confusion occurring during the physical input process of the user and the structural difficulty of the target address itself,

[0036] The above computer management module is,

[0037] By combining the log value of the number of batch orders received simultaneously through the above logistics management module with the above dynamic input instability variable, an intermediate score is calculated that smoothly reflects fatigue caused by mass work, and

[0038] The ratio of past misdelivery history, which is the ratio of the number of returned items over a past period, is combined with the aforementioned intermediate score in an exponential proportional relationship and weighted accordingly,

[0039] Calculate the final pre-validation necessity score by proportionally combining the address complexity coefficient with the above weighted result,

[0040] If the above-mentioned final pre-verification necessity score exceeds a preset threshold, the system may be characterized by immediately blocking progress to the next step even if the user presses the save button on the screen, and forcibly displaying a pop-up window for address reconfirmation and modification on the screen, thereby selectively controlling verification only for high-risk cargo with a high probability of misdelivery.

[0041] In addition, the transportation problem processing module can calculate a first detour score that is proportional to the current time spent at the sorting terminal or hub and the weight of adverse weather conditions on the route and inversely proportional to the distance to the alternative terminal, and then calculate a final detour transportation route priority score by combining the log-transformed value of the capacity-to-error history ratio, calculated by dividing the remaining cargo capacity of the alternative terminal by the value obtained by adding a reference correction value to the number of misclassifications and misdeliveries in the last week, in a multiplicative form.

[0042] In addition, the customized transport provision module calculates a standard pattern score by dividing the sum of the average weight error rate and the proportion of fragile items per customer by the packaging preparation time per customer, combines the standard pattern score with a log-transformed value of the variability of the weekly cargo shipment volume per customer in a multiplicative manner, and then calculates a dynamic classification coefficient for logistics patterns per customer by reflecting that the result increases exponentially as the return history index based on the cumulative number of return collection cases per customer increases. Effects of the invention

[0043] The method for reducing invoice errors and managing integrated computer systems through courier API integration according to the present invention has the effect of supporting stable computer system connectivity by effectively suppressing resource depletion and operational shutdowns of physical servers even in situations of traffic overload, by comprehensively calculating the queue ratio, cargo urgency, communication delay time, and hardware load index through an integrated server unit.

[0044] In addition, by selectively controlling pre-validation through a computerized management module that combines dynamic input instability variables occurring during the user's physical input process with the number of batch orders, it provides the effect of significantly reducing misdelivery cost losses while reducing server load caused by full inspection.

[0045] In addition, through the transportation problem processing module, not only is there ample space at alternative terminals, but past misclassification history is also normalized into a logarithmic form to calculate a bypass route, thereby mitigating the phenomenon of logistics being excessively concentrated at specific hubs and stably distributing the load of the entire logistics network.

[0046] Furthermore, by dynamically quantifying customers' weight error rates, packaging preparation times, and return history through a customized transportation provision module to identify high-risk groups for packaging defects and assigning them to a special inspection line, it suppresses secondary damage accidents on the conveyor belt and creates the effect of substantially improving the hourly processing volume of the entire terminal.

[0047] In addition, through the simulation or operational history-based comparison results for the integrated server unit, the computer management module, the transportation problem processing module, and the customized transportation provision module, the difference in operational indicators such as the number of system shutdowns, verification server occupancy rates, secondary bottleneck delay times, number of damage claims, or processing volume can be compared between cases where control is performed based on each control index or classification coefficient and cases where it is not performed, so the effect of the present invention can be presented in a manner based on operational data. Brief explanation of the drawing

[0048] Figure 1 illustrates a control flow diagram between the overall system components according to the present invention. FIG. 2 illustrates a dynamic resource allocation control flowchart of an integrated server unit according to the present invention. Figure 3 illustrates a control flowchart for invoice pre-verification activation of a computer management module according to the present invention. Figure 4 illustrates an alternative bypass path control flowchart of a transportation problem processing module according to the present invention. Figure 5 illustrates a dynamic classification flowchart of the logistics pattern of a customized transportation provision module according to the present invention. Specific details for implementing the invention

[0049] Hereinafter, various embodiments are described in more detail with reference to the attached drawings. The embodiments described in this specification may be modified in various ways. Specific embodiments may be depicted in the drawings and described in detail in the detailed description. However, specific embodiments disclosed in the attached drawings are intended only to facilitate understanding of various embodiments. Accordingly, the technical concept is not limited by specific embodiments disclosed in the attached drawings, and it should be understood that it includes all equivalents or substitutions that fall within the spirit and scope of the invention.

[0050] Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but these components are not limited by the aforementioned terms. The aforementioned terms are used solely for the purpose of distinguishing one component from another.

[0051] In this specification, terms such as “comprising” or “having” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. When a component is described as being “connected” or “connected” to another component, it should be understood that it may be directly connected to or connected to that other component, or that there may be other components in between. On the other hand, when a component is described as being “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

[0052] Meanwhile, a "module" or "part" for a component as used in this specification performs at least one function or operation. Furthermore, a "module" or "part" may perform a function or operation by hardware, software, or a combination of hardware and software. Additionally, a plurality of "modules" or a plurality of "parts," excluding a "module" or "part" that must be performed on specific hardware or on at least one processor, may be integrated into at least one module. A singular expression includes a plural expression unless the context clearly indicates otherwise.

[0053] In addition, power, power transmission, and control therefor for the following assembly configurations and embodiments, including "by control," follow conventional technology including terminals, applications, hardware control modules, etc., so they are omitted to avoid redundancy.

[0054] In addition, the operation embodiments and configurations described in a general manner without being explained in detail below follow the prior art and are omitted in order to focus on describing the purpose of the present invention and the resulting effects.

[0055] Furthermore, in describing the present invention, if it is determined that a detailed description of related known functions or configurations may unnecessarily obscure the essence of the invention, such detailed description is abbreviated or omitted.

[0056] However, the above abbreviations or omissions are limited to peripheral configurations such as general user terminal screen configurations, conventional database access methods, or known communication protocols, and the calculation order and combination relationships of the queue ratio, cargo urgency weighting, hardware load index, dynamic input instability variable, address complexity coefficient, detour transport route priority score, and customer-specific logistics pattern dynamic classification coefficient, which are the core of the present invention, are explained in detail below.

[0057] Additionally, the indices, weights, ratios, or coefficients used in this specification may be calculated through normalization, log transformation, or exponential correction procedures to process input values ​​having different unit systems in a comparable manner, and the reference values ​​for said normalization or correction procedures may be set or adjusted based on test operation data from the initial stage of operation or statistical data accumulated during operation.

[0058] The method for reducing invoice errors and managing integrated computer systems through courier API integration according to the present invention operates by organically combining multiple software modules centered around an integrated server unit (100) that controls the resource distribution of physical hardware. Specifically, the system of the present invention includes an integrated server unit (100) that protects physical devices by performing data computation and information processing, a courier linkage module (110) that mediates data transmission and reception with an external courier system, a logistics management module (120) that collects order information and manages the overall flow of logistics by computerizing it, a transportation problem processing module (130) that calculates an alternative terminal when delivery delays or misclassification situations are detected, a customized transportation provision module (140) that quantifies the customer's cargo dispatch pattern and physical handling characteristics to apply operational plans differentially, and a computer management module (150) that controls the activation of a pre-verification procedure based on the user's invoice input behavior.

[0059] The above logistics management module (120) may be configured to manage order data, dispatch history data, and delivery status data in an integrated manner, provide information on the number of concurrently received orders and dispatch history to the computer management module, provide inbound and outbound history and claim history for calculating dwell time to the transportation problem processing module, and provide customer-specific dispatch volume history, return collection history, and weight input data to the customized transportation provision module. Accordingly, each module may be implemented in a structure that operates independently while sharing common basic data centered around the logistics management module.

[0060] The integrated server unit (100) is a major physical device that performs overall data calculations and controls hardware resources for a courier-linked logistics operation automation system according to the present invention. This configuration is intended to support uninterrupted service by managing data bottlenecks that occur during the process of linking with external systems and preventing overloading of physical servers. The integrated server unit (100) calculates a basic processing requirement by proportionally combining the ratio of waiting requests and the cargo urgency. Subsequently, the integrated server unit (100) performs calculations that gradually increase the communication delay time and rapidly decrease the calculated resource allocation index as the physical hardware load index reaches a set threshold. Based on the derived index, the integrated server unit (100) performs control operations such as allocating additional idle processing units or temporarily blocking the inflow of new traffic to divert it to a queue. The integrated server unit (100) obtains queue data by periodically counting the state of the waiting memory within the system. In addition, the integrated server unit (100) measures the round-trip time of a test packet transmitted to an external server to calculate the communication delay time, and collects the heat temperature and utilization of the processor in real time through a temperature sensor mounted on the main board and the task management function of the operating system. In an actual operating environment, during special holiday periods, a situation may occur where requests for courier services surge, exceeding the contracted allowable number of cases. At this time, when the integrated server unit (100) detects that the processor temperature has reached 85 degrees, it prioritizes hardware protection logic despite the high urgency, immediately stops the allocation of new computing resources, and enters a traffic limit mode, thereby preventing the entire server from stopping operation in advance.

[0061] The above-mentioned courier linkage module (110) is a software component that mediates data transmission and reception between the above-mentioned integrated server unit (100) and multiple external courier company computer systems. This module aims to standardize communication with external systems having different specifications and to manage the waiting status of linkage requests. The above-mentioned courier linkage module (110) converts collection request data received from a user into the data specifications required by the relevant courier company and transmits it. In addition, the above-mentioned courier linkage module (110) receives invoice numbers and real-time delivery tracking status data returned from external systems and synchronizes them according to the specifications of the internal database. If a communication delay occurs, the above-mentioned courier linkage module (110) stores the request data in a temporary queue and sequentially performs re-requests according to a delay waiting algorithm. The above-mentioned courier linkage module (110) obtains delivery status data through a data interpretation process that calls an application program interface provided by an external courier company to receive structured string data and converts it into an internal variable. If the response is delayed due to server maintenance by a specific courier company, the above-mentioned courier linkage module (110) does not immediately return an error but stores the dispatch request data in an internal waiting area. Subsequently, the above-mentioned courier linkage module (110) checks the connection status at regular intervals and performs an operation to prevent data loss by sending the stored requests in batches when the courier company server is normalized.

[0062] The above logistics management module (120) is a software component that collects delivery request information from sellers and corporate customers and manages all stages from collection to delivery completion in an integrated manner. This module aims to enhance user convenience by visualizing the flow of logistics on a single screen. The above logistics management module (120) aggregates order data uploaded individually or in bulk and structures it into a systematic logistics ledger. Based on the status information collected through the above courier linkage module (110), the above logistics management module (120) continuously updates the current location of each shipment and measures the number of batch orders received simultaneously to provide as basic data for other judgment modules. The above logistics management module (120) obtains order information by receiving data directly through a web input form displayed on a user terminal or by separating and reading data by row and column when a bulk order form in the format of a spreadsheet file is uploaded. When a corporate customer uploads a file containing multiple recipient addresses, the logistics management module (120) analyzes the data in each row, excludes duplicate received orders, stores valid orders in a database in bulk, and then provides a summary screen showing the status of successful receipt to the user to support efficient business processing.

[0063] The above transportation problem processing module (130) is a software component that detects delivery delays and misclassification situations occurring during the transportation process and calculates an alternative terminal. This aims to resolve bottlenecks where cargo is concentrated at specific points within the logistics network and to reduce delays. The above transportation problem processing module (130) calculates a primary score by calculating the dwell time of a specific cargo, the weighting of adverse weather conditions, and the distance traveled. Subsequently, the above transportation problem processing module (130) derives a final detour priority score by weighting the ratio of the remaining capacity of the alternative terminal and the history of misclassification in a logarithmic form. When the alternative terminal with the highest score is selected, the above transportation problem processing module (130) transmits a route change instruction to the vehicle navigation system and the administrator terminal. The above transportation problem processing module (130) obtains the dwell time by calculating the difference in the inbound and outbound recognition times of the scanner within the logistics terminal and identifies precipitation by receiving public data from the Korea Meteorological Administration. In addition, it collects capacity through the terminal inventory management system and obtains error history data by querying the past claim database. When the functionality of a specific hub terminal is degraded due to heavy rain, the transportation problem processing module (130) calculates an optimal detour route to another terminal that is somewhat far away but has a low error rate and can handle stable processing, instead of an adjacent terminal that is close but has a high error history, thereby preventing additional delivery accidents.

[0064] The customized transport provision module (140) is a software component that quantifies the cargo outbound patterns and physical handling characteristics of each customer and applies operational plans differentially. This module aims to reduce the damage rate of the entire logistics infrastructure by selecting cargo with poor packaging practices. The customized transport provision module (140) calculates a standard score by dividing the error rate between the input weight and the actual weight, and the ratio of items requiring caution for damage, by the packaging preparation time. In addition, the customized transport provision module (140) derives a final classification coefficient by smoothly reflecting the variability of the cargo outbound volume and exponentially weighting the return recovery history. If the coefficient exceeds a risk threshold, the customized transport provision module (140) forcibly assigns the cargo to a special inspection line instead of a general line. The customized transport provision module (140) receives the actual weight value measured by an automatic weight measuring device installed on the logistics center conveyor belt, calculates the packaging preparation time through the time difference between the order acceptance time and the collection request time, and obtains the number of returns by aggregating return processing records. When a customer's cargo is detected that has been repeatedly entered with a low weight in the computer system and has been packaged and accepted within a short period of time, the customized transport provision module (140) classifies this cargo as a high-risk group for packaging defects. Accordingly, the cargo is separated from the automatic sorter and enters a special inspection line where a worker manually adds cushioning material, thereby preventing damage accidents.

[0065] The above computer management module (150) is a software component that monitors the user's invoice input behavior to dynamically predict the probability of incorrect entry and controls pre-verification. This module aims to limit actual misdeliveries while reducing server load caused by indiscriminate address verification. The above computer management module (150) derives the ratio of the number of keyboard modifications by the user and the time required for input, and calculates a pre-verification necessity score by calculating the number of batch orders, the sender's past misdelivery history, and the structural complexity of the address. Only in a risk situation where the score exceeds a preset threshold, the above computer management module (150) forcibly displays an address reconfirmation pop-up window before invoice confirmation. The above computer management module (150) applies a motion detection script to the input form on the user screen to count the number of times the delete key is activated, and parses the address text using a regular expression to obtain the number of administrative district steps and the number of special characters. When a situation is detected where a worker sending a large volume of holiday gifts presses the delete key multiple times while entering a complex local address while fatigued, the computer management module (150) calculates this as a high probability of incorrect input. Subsequently, when the worker presses the save button, the computer management module (150) displays a warning window indicating that an error has been detected in the address system, thereby inducing the worker to verify the address again and controlling computer errors in advance.

[0066] Based on the aforementioned components, the present invention performs the following stepwise control processes to achieve the protection of physical devices and operational efficiency of the logistics network, going beyond simple relay of logistics information.

[0067] First, the integrated server unit (100) comprehensively calculates the queue ratio, cargo urgency, communication delay time, and hardware load index in a traffic overload situation and performs dynamic resource allocation control to prevent physical shutdown of the server.

[0068] Next, the computer management module (150) calculates the user's dynamic input instability, the number of batch orders, the sender's misdelivery history, and the address complexity, and selectively triggers a pre-verification procedure only for high-risk invoices with a high misdelivery rate while reducing the computer processing load.

[0069] Next, the transportation problem processing module (130) calculates an optimal detour route to prevent additional logistics delays by combining cargo dwell time, weather deterioration weighting, travel distance to alternative terminals, and the ratio of misclassification history relative to remaining capacity when a bottleneck occurs at a specific logistics hub.

[0070] Next, the customized transport provision module (140) dynamically calculates the variability of cargo shipment volume per customer, the actual weight error rate, the ratio of items requiring caution for damage, the packaging preparation time, and the cumulative return history to identify malicious logistics patterns and performs customized logistics control by instructing the assignment of a special inspection line or automatic recommendation of packaging materials for the cargo.

[0071] The integrated server unit (100) can dynamically allocate computing resources to mitigate processing delays, request backlogs, and server overload that may occur during the process of linking with an external application program interface. To this end, the integrated server unit (100) can sequentially analyze the linkage request status, external network communication status, cargo processing priority, and server hardware status collected from the courier linkage module (110) to calculate a resource allocation index, and can perform expansion, maintenance, or restriction control of processing units according to the resource allocation index.

[0072] Specifically, the integrated server unit (100) can first calculate a queue ratio by comparing the number of courier company linkage requests waiting at the courier linkage module (110) with the preset maximum allowable number of linkages per unit time. The queue ratio is a reference value indicating the density of linkage requests currently loaded within the system, and as the queue ratio increases, the integrated server unit (100) can determine that the need for additional allocation of processing resources has increased.

[0073] Additionally, the integrated server unit (100) can calculate a cargo urgency weight based on the cargo's delivery deadline, the need to maintain freshness, whether same-day processing is required, or whether it is a scheduled delivery. The cargo urgency weight is a value intended to reflect the physical characteristics of logistics in the process of allocating computational resources, and the integrated server unit (100) can calculate a basic processing requirement by proportionally combining the cargo urgency weight with the queue ratio. Accordingly, even in the same queue state, if cargo with a high need for urgent processing is included, the basic processing requirement may be calculated to be larger.

[0074] Next, the integrated server unit (100) can measure the communication delay time that occurred during the communication process with an external courier server or an external network during the previous cycle. The communication delay time is a value that reflects the network bottleneck or the external server response delay status, and the integrated server unit (100) can reflect the communication delay time in a logarithmic proportional relationship to the basic processing requirement. Accordingly, when the communication delay time increases, the need for resource allocation increases, but the increase is reflected gradually and gradually, so that the phenomenon of the resource allocation amount fluctuating excessively due to a temporary increase in delay can be mitigated.

[0075] Furthermore, the integrated server unit (100) can calculate a hardware load index based on the processor heat temperature and processor occupancy rate. The hardware load index is a value indicating the physical overheating state or computational saturation state of the server equipment, and the integrated server unit (100) can control the final resource allocation index to decrease exponentially as the hardware load index increases, with respect to the intermediate result value reflecting the basic processing demand and communication delay time. Accordingly, even if the queue ratio or the cargo urgency weight is calculated to be high, the final resource allocation index is rapidly lowered when the physical load of the server increases, so that hardware protection can be performed preferentially.

[0076] The integrated server unit (100) can calculate a final resource allocation index by sequentially combining the queue ratio, the cargo urgency weight, the communication delay time, and the hardware load index. Specifically, the integrated server unit (100) can calculate a basic processing requirement by first combining the queue ratio and the cargo urgency weight, derive an intermediate resource requirement value by reflecting the communication delay time in a log-proportional relationship to the basic processing requirement value, and calculate a final resource allocation index by applying an exponential reduction rule according to the increase in the hardware load index to the intermediate resource requirement value.

[0077] Here, the hardware load index may be a value calculated by combining the processor heat temperature and processor occupancy rate after normalizing them according to a reference range, and it is desirable that the resource allocation index be applied such that it continuously decreases as the hardware load index increases. In addition, values ​​such as the processor load rate of 85 percent or the processor temperature of 85 degrees correspond to examples of representative measurements for observing the risk range of the hardware load index, and the actual control standard may be set to the hardware load index itself or a reference value corresponding to the hardware load index.

[0078] The above final resource allocation index can be used as a judgment criterion for determining subsequent control operations of the integrated server unit (100). For example, if the above final resource allocation index is greater than or equal to a preset first threshold value, the integrated server unit (100) may temporarily expand processing capacity by additionally allocating idle processing units to the courier linkage module (110) or by increasing the number of parallel processing threads. Conversely, if the above final resource allocation index falls below a preset second threshold value, the integrated server unit (100) may perform delayed waiting control by temporarily restricting the acceptance of new linkage requests or diverting incoming requests to a queue. Here, the above first threshold value may be set as a resource expansion start criterion, and the above second threshold value may be set as a resource restriction start criterion.

[0079] The process by which the integrated server unit (100) obtains data for each of the above judgment criteria can be implemented as follows. The queue ratio can be obtained by periodically counting the number of connection requests loaded in the courier connection module (110) and comparing it with the maximum allowable number of connections. The cargo urgency weight can be calculated by referring to product classification codes, delivery type codes, or delivery deadline information stored in the order database. The communication delay time can be obtained by measuring the packet round-trip time with the external courier server, the delay in receiving response messages, or the average response time for connection requests. The hardware load index can be calculated based on temperature information and occupancy information obtained through a mainboard temperature sensor, a processor temperature sensor, the resource monitoring function of the operating system, or a separate system monitoring module.

[0080] In one embodiment, the integrated server unit (100) may determine that the basic processing requirement has increased by combining the two values ​​when the queue ratio is calculated to be 0.8 and the cargo urgency weight is calculated to be 1.5. Subsequently, if the external network communication delay time is measured to have increased, the integrated server unit (100) may additionally reflect the need for resource expansion regarding the basic processing requirement. However, if the processor temperature and occupancy rate rise at the same time and the hardware load index is calculated to be a high value, the integrated server unit (100) may lower the final resource allocation index by first applying an exponential reduction rule based on the hardware load index. Accordingly, the integrated server unit (100) may protect the physical server by stopping additional resource allocation and switching to a traffic limit mode or a cooling standby mode.

[0081] In another embodiment, if the communication delay time deviates from the normal range due to an external communication disconnection or an exceedance of the response delay upper limit, the integrated server unit (100) may bypass the general procedure for calculating the resource allocation index, forcibly set the resource allocation index to below the limit value, and switch to an offline backup process or a retry waiting process. Additionally, if part of the hardware status information is received abnormally, such as a temperature sensor error or a occupancy rate collection error, the integrated server unit (100) may continue resource allocation control by switching to other hardware status information that can be received normally as an alternative judgment criterion.

[0082] The operation criteria of the integrated server unit (100) may be adjusted according to a preset reference value or statistical information accumulated during operation. For example, the maximum allowable number of connections per unit time may be set according to the contract conditions with the courier company server or the system design capacity, and the hardware load-related risk criteria may be determined based on the processor's recommended limit temperature, occupancy limit, or long-term average operating load. Additionally, the integrated server unit (100) may be configured to enable resource control suitable for the actual operating environment by analyzing long-term accumulated operating data and adjusting the first reference value, the second reference value, or the hardware load-related risk criteria.

[0083] In this way, the integrated server unit (100) can calculate a resource allocation index by sequentially combining the queue ratio and cargo urgency weighting, which represent logical processing demand, the communication delay time, which represents external connection status, and the hardware load index, which represents physical device status. Accordingly, unlike a method of increasing resources based solely on the number of requests, resource allocation that considers both external network status and hardware status becomes possible, and connection stability can be maintained while reducing the possibility of server equipment overload and overall processing interruption even in situations of a surge in logistics requests.

[0084] In one embodiment, the integrated server unit (100) may perform a simulation based on past operational history to set a risk criterion related to hardware load.

[0085] The above-mentioned risk criteria related to hardware load are not determined solely by the processor load rate, but may be a reference value corresponding to a hardware load index calculated by normalizing the processor occupancy rate and the processor heat temperature and combining them. For example, the integrated server unit (100) may analyze whether the possibility of a surge in communication delay and system shutdown increases together in the interval where the processor load rate reaches 85 percent based on the logistics server operation history accumulated over the past 12 months, and use this as a representative observation value for setting the risk interval of the hardware load index.

[0086] Additionally, the integrated server unit (100) may perform a sequential calculation procedure to calculate a resource allocation index. Specifically, in the first step, the integrated server unit (100) may calculate a queue ratio by dividing the number of pending requests by the maximum allowable number of connections per unit time. In the second step, the integrated server unit (100) may derive a basic processing requirement by combining the cargo urgency weight with the queue ratio. In the third step, the integrated server unit (100) may calculate a delay correction value by reflecting the logarithmic value of the communication delay time in the basic processing requirement. In the fourth step, the integrated server unit (100) may derive a final resource allocation index by applying a hardware load correction value to the delay correction value such that the result value decreases exponentially as the hardware load index increases.

[0087] The following table is an example of the results of 10 simulations that measured changes in system instability indicators as the load increased, using the processor load rate as a representative observation.

[0088]

[0089] The following table is an example of the results comparing a group that restricts new connection requests and performs queue bypass control when the aforementioned hardware load-related risk criteria are reached during a traffic surge, with a group that does not perform said control.

[0090]

[0091] According to the simulation results above, in the section where the processor load is 80 percent or less, the packet loss rate and the rate of system failure are maintained at a relatively low level, but from the point where it reaches 85 percent, a section may be formed in which the rate of communication delay spikes and the rate of system failure increase together. Accordingly, the integrated server unit (100) can set the above section as a representative observation section corresponding to the risk criteria related to hardware load. In addition, it can be confirmed that if new request restrictions and queue bypass control are performed when the risk criteria are reached, the number of system failures can be reduced while increasing the number of successful processing completions per hour.

[0092] The above computer management module (150) can determine in advance the possibility of misdelivery due to incorrect address entry, incorrect administrative district entry, or omission of detailed address that may occur during the invoice input process, and can perform control to selectively trigger a pre-verification procedure for invoices that require verification. To this end, the above computer management module (150) can calculate the degree of need for pre-verification by sequentially combining the circumstances of input instability derived from the user's address input behavior, the order throughput at the same time, the sender's past history of causing misdelivery, and the structural complexity of the address to be entered, and can control whether to proceed with the invoice confirmation procedure according to the degree of need for pre-verification.

[0093] Specifically, the computer management module (150) can first count the number of backspace key inputs, delete key inputs, or address string modifications detected in the address input form of the user terminal, and measure the input time taken from the time of initial entry into the address input form until the address input is completed or the focus moves to another input item. The computer management module (150) can calculate a dynamic input instability variable by dividing the number of modifications by the input time taken. The dynamic input instability variable is a value representing the frequency of repeated modifications per unit time, and as the value increases, it can be determined that there are many instances of typos, hesitation, or re-input during the address input process.

[0094] Next, the computer management module (150) can calculate the number of batch orders by aggregating the number of orders received simultaneously within the same upload session, the same reception request, or a preset time window. The number of batch orders is a value representing the cumulative degree of repetitive input work, and the computer management module (150) can reflect the number of batch orders in a log-proportional relationship with respect to the dynamic input instability variable. Accordingly, when the number of batch orders increases, the need for prior verification increases, but the increase is reflected gradually and gradually, thereby mitigating the excessive amplification of the input work burden occurring in a large-volume order environment.

[0095] Additionally, the computer management module (150) can query the invoice processing history accumulated over a certain past period based on the sender identifier, member identifier, business identifier, or account identifier, and calculate the ratio of past misdelivery history by dividing the number of misdelivery cases determined to be address entry errors, administrative district errors, omission of detailed addresses, or address information discrepancies by the total number of shipments. The computer management module (150) can reflect the ratio of past misdelivery history in an exponential proportional relationship with respect to the intermediate result value reflecting the dynamic input instability variable and the number of batch orders. Accordingly, for senders who have repeatedly caused address-related errors in the past, the need for prior verification may be reflected more significantly, even if the current input status is similar.

[0096] Furthermore, the computer management module (150) can calculate an address complexity coefficient by analyzing the depth of the administrative district system of the input address, the structure of the detailed address entry, and whether special characters are included. For example, the computer management module (150) can determine that the depth of the administrative district system is high if information such as city / province, city / county / district, town / village / dong, road name, building number, and unit number is included sequentially, and can determine that the structural complexity is high if parentheses, slashes, hyphens, or multiple separators are included. The computer management module (150) can calculate the final pre-verification requirement by proportionally combining the address complexity coefficient with an intermediate result value that reflects the ratio of past misdelivery history. Accordingly, even if the user's input behavior appears stable, the pre-verification requirement may increase if the structure of the address itself is complex.

[0097] The computer management module (150) can calculate the final pre-verification requirement by sequentially combining the dynamic input instability variable, the number of batch orders, the ratio of past misdelivery history, and the address complexity coefficient. Specifically, the computer management module (150) can calculate the dynamic input instability variable based on the ratio of the number of modifications and the input time required, derive an intermediate risk level that considers the input workload by reflecting the number of batch orders in a log-proportional relationship to the dynamic input instability variable, derive an intermediate result value that reflects the possibility of repeated errors by exponentially reflecting the ratio of past misdelivery history to the intermediate risk level, and calculate the final pre-verification requirement by additionally reflecting the address complexity coefficient to the intermediate result value.

[0098] Here, it is preferable to define the aforementioned ratio of past misdelivery history not as a simple total return rate, but as a value calculated by dividing the number of misdelivery cases determined to be due to address entry errors, incorrect administrative district input, omission of detailed addresses, or address information discrepancies by the total number of shipments. Additionally, the aforementioned final pre-verification requirement may be normalized to a pre-established score range to ensure comparability, and the aforementioned threshold value may be set within the normalized score range or adjusted according to operational data.

[0099] The above final pre-verification requirement can be used as a criterion for determining subsequent control operations of the computer management module (150). For example, if the above final pre-verification requirement is greater than or equal to a preset threshold, the computer management module (150) may not immediately proceed to the general receipt completion procedure despite the user's request for invoice confirmation, but may trigger a pre-verification procedure by forcibly displaying at least one of an address reconfirmation popup, an administrative district selection re-verification window, a detailed address supplementation request window, or a misdelivery history notification window. Conversely, if the above final pre-verification requirement is less than the threshold, the computer management module (150) may control the execution of the general invoice receipt completion procedure.

[0100] The process by which the computer management module (150) obtains data for each of the above judgment criteria can be implemented as follows. The dynamic input instability variable can be calculated by counting backspace keys, delete keys, or modification events through key event detection logic connected to the address input field of a webpage, application, or dedicated terminal interface, and by measuring the time between the activation and deactivation points of the address input field. The number of batch orders can be calculated based on the number of rows in the uploaded file, the length of the invoice array included in the application interface request, or the number of registrations received simultaneously within a preset time window. The ratio of past misdelivery history can be calculated by querying the dispatch history data stored in the logistics management module (120) or a separate history storage unit. The address complexity coefficient can be calculated through parsing the input address string, decomposing it into administrative district units, determining whether symbols are included, or analyzing the address system pattern.

[0101] In one embodiment, the computer management module (150) may prioritize reflecting the need for prior verification due to increased input workload when a high number of batch orders for a specific sender are calculated in situations where a large number of orders are concentrated, such as during holidays or event periods. In this state, if the number of repeated modifications increases during the process of a user entering a specific recipient's address, the ratio of the sender's past history of causing misdelivery exceeds a preset normal range, and the input target address is determined to have a structure including multiple administrative districts and multiple delimiters, the computer management module (150) may determine that the need for prior verification exceeds a threshold value as a result of sequentially combining the variables. Accordingly, the computer management module (150) may temporarily suspend the invoice confirmation procedure and forcibly trigger a prior verification procedure to induce address reconfirmation or modification.

[0102] In another embodiment, the computer management module (150) may determine that the reliability of the dynamic input instability variable is low when key input events are not sufficiently detected, such as address input using a copy and paste function, address substitution by an auto-complete function, or automatic input by an external program. In this case, the computer management module (150) may control the calculation of the need for prior verification based on the address complexity coefficient and the ratio of past misdelivery history. Additionally, if the input time significantly deviates from the range of physical typing speed or the repetitive input pattern is similar to the input characteristics of an automation program, the computer management module (150) may perform a separate security verification procedure or an additional verification procedure in conjunction.

[0103] The operation criteria of the above computer management module (150) may be adjusted according to a pre-set standard value or historical information accumulated during operation. For example, the standard value for the need for prior verification may be set based on the results of test operations during the initial operation phase, and may be adjusted based on the history of misdelivery corrections, address corrections, or the number of cases where corrections were completed after the verification procedure was triggered, accumulated over a certain period thereafter. In addition, the above computer management module (150) may set or adjust the standard value differently according to address input characteristics by industry, recurring error patterns by sender, or order concentration by season.

[0104] In this way, the computer management module (150) can calculate the need for prior verification by considering not only the address string after input is completed, but also the relationship between the frequency of modifications occurring during the address input process and the input time, the order throughput at the same time, the sender's history of causing misdeliveries, and the structural complexity of the address itself. Accordingly, unnecessary verification procedures can be reduced for normal invoices with low verification needs, while verification resources can be selectively concentrated on invoices with a relatively high probability of misdelivery, thereby enabling the management of both invoice reception efficiency and logistics accuracy.

[0105] In one embodiment, the computer management module (150) may perform a simulation based on past invoice input history and actual misdelivery history to set a threshold value for triggering a pre-verification procedure. The pre-verification necessity score may be normalized and used within a pre-set range for comparison, for example, a range of 0 to 10. In this case, the computer management module (150) may be set to trigger a pre-verification procedure when the normalized pre-verification necessity score is 3.5 or higher.

[0106] Additionally, the computer management module (150) may perform sequential calculation procedures to calculate the pre-verification necessity score. Specifically, in the first step, the computer management module (150) may calculate a dynamic input instability variable by dividing the number of backspace inputs, the number of delete key inputs, or the number of modifications by the total input time required. In the second step, the computer management module (150) may calculate an intermediate score by reflecting the log-converted value of the number of batch orders in the dynamic input instability variable. In the third step, the computer management module (150) may combine the exponential reflection value of the past misdelivery history ratio with the intermediate score. In the fourth step, the computer management module (150) may calculate a final pre-verification necessity score by reflecting the address complexity coefficient in the result value.

[0107] The following table is an example of the results measuring the percentage of popup triggers where actual address errors were confirmed and the percentage where popups were triggered but had valid addresses, when the pre-verification trigger threshold was set differently.

[0108]

[0109] The following table is an example of the results of comparing a group that performs a pre-verification procedure only on invoices with a pre-verification need score of 3.5 or higher with a group that performs a full verification on all invoices.

[0110]

[0111] According to the simulation results above, when the threshold value for triggering pre-verification is 3.0 or lower, the actual address error detection rate may be low and the unnecessary popup exposure rate may be high. On the other hand, when the threshold value reaches the level of 3.5, a range may be formed where the actual address error detection rate increases and the unnecessary popup exposure rate decreases. Accordingly, the computer management module (150) may set 3.5 as an exemplary threshold value for triggering the pre-verification procedure. In addition, it can be confirmed that when performing selective verification by applying the threshold value, the average reception time and verification server occupancy rate can be lowered while maintaining the misdelivery rate at a similar level.

[0112] When a transportation problem is detected within a logistics network, such as a delivery delay, misclassification, or misdelivery, the above-mentioned transportation problem processing module (130) can calculate a detour transportation route priority score by reflecting the current base's status of stay, weather conditions along the route, travel distance to an alternative terminal, capacity of the alternative terminal, and the recent error history of the alternative terminal. Accordingly, the above-mentioned transportation problem processing module (130) can perform route reassignment control that considers not only the travel distance but also the possibility of processing after detour and the possibility of errors occurring.

[0113] Specifically, the transportation problem processing module (130) can calculate the dwell time by first measuring the time the cargo remains at the current sorting terminal or hub after being loaded without being released or processed to the next stage. The dwell time is a value indicating the degree of congestion or bottleneck occurring at the current base, and as the dwell time increases, the transportation problem processing module (130) can determine that the need for detour assignment has increased.

[0114] Additionally, the transportation problem processing module (130) may collect at least one of precipitation, snowfall, freezing information, or whether a weather warning has occurred for the movement route from the current base to each alternative terminal, and calculate a weather deterioration weight based on this. The weather deterioration weight is a value that reflects the difficulty of physical movement or the possibility of transportation delay, and as the weather deterioration weight increases, the transportation problem processing module (130) may determine that the need to reset the detour transportation route at that time has increased.

[0115] Next, the transportation problem processing module (130) can calculate the actual travel distance from the current location to each alternative terminal. The travel distance can be calculated based on the road network-based travel distance, the estimated travel distance, or the length of the possible route for logistics vehicles. The transportation problem processing module (130) can apply an inverse relationship such that the detour transportation route priority score decreases as the travel distance increases, even when the stay time and the weather deterioration weighting are reflected. Accordingly, alternative terminals with excessively long travel distances may be assigned a lower priority compared to other terminals under the same conditions.

[0116] Furthermore, the transportation problem processing module (130) can calculate the remaining capacity for cargo at the current time for each alternative terminal. The remaining capacity for cargo can be calculated based on the difference between the maximum processing capacity of the terminal and the current loading volume or the volume waiting to be processed, and it can be determined that the capacity to accommodate additional cargo increases as the remaining capacity for cargo increases.

[0117] Additionally, the transportation problem processing module (130) can aggregate the number of misclassifications and misdeliveries that occurred in the past week for each alternative terminal. The number of misclassifications and misdeliveries in the past week is a value indicating the recent processing accuracy or error occurrence trend of the terminal, and it can be determined that as the number increases, there is a higher likelihood of additional errors occurring for cargo that has been rerouted to the terminal.

[0118] The above transportation problem processing module (130) can calculate the capacity-to-error history ratio using the remaining capacity to accommodate cargo and the number of misclassifications and misdeliveries in the past week. Specifically, the above transportation problem processing module (130) can calculate the capacity-to-error history ratio by dividing the remaining capacity to accommodate cargo by the value obtained by adding a reference correction value to the number of misclassifications and misdeliveries in the past week. Here, the reference correction value is a reference value added to ensure that the denominator does not become zero even when the number of misclassifications and misdeliveries in the past week is zero, and can typically be set to 1. Accordingly, the ratio can be calculated even for terminals without an error history, and a judgment value reflecting both the remaining capacity to accommodate cargo and the error history can be derived.

[0119] The above transportation problem processing module (130) can convert the capacity-to-error history ratio into a log-proportional relationship. Accordingly, the phenomenon where the priority score increases excessively even if the remaining capacity for cargo is very large can be mitigated, and conversely, the increase in priority for terminals with a low error history can be reflected gradually. That is, by applying the log-proportional relationship, it is possible to control the relative difference between capacity and error history to be reflected in the bypass priority score while reducing the phenomenon of excessive concentration of assignments to specific alternative terminals.

[0120] The above transportation problem processing module (130) can first calculate a primary detour priority score that is proportional to the stay time and the weather deterioration weighting and inversely proportional to the travel distance. Subsequently, the above transportation problem processing module (130) can calculate a final detour transportation route priority score by multiplying the capacity-to-error history ratio, which is converted into the log-proportional relationship, with the primary detour priority score. Accordingly, even if the stay time is long and the weather conditions deteriorate, making detour necessary, the final priority score may be lower if the error history of the alternative terminal is high, and conversely, even if the travel distance is somewhat long, a terminal with sufficient capacity and a low error history may have a relatively high priority score.

[0121] Here, the aforementioned recent error history may be defined as the sum of the number of misclassifications and misdeliveries that occurred during the past week, and the aforementioned reference correction value may be a reference value added to the denominator to enable the calculation of the capacity-to-error history ratio even when the aforementioned recent error history is 0. Additionally, the aforementioned detour transport route priority score may be used as a score for relative comparison between multiple alternative terminals and may be used together with a minimum allowable reference value to permit automatic detour assignment as needed.

[0122] The above final detour transport route priority score can be used as a criterion for determining subsequent control operations of the transport problem processing module (130). For example, the transport problem processing module (130) may calculate the final detour transport route priority score for each of multiple alternative terminals within a radius or a preset search range, and determine the alternative terminal with the highest calculated score as the priority assignment target terminal. Subsequently, the transport problem processing module (130) may provide a route change instruction corresponding to the determined alternative terminal to a logistics vehicle terminal, an operation management unit, or a terminal manager system.

[0123] The process of the transportation problem processing module (130) acquiring data for each of the above judgment criteria can be implemented as follows. The dwell time can be calculated based on the difference between the time of entry and the time of recent processing or departure obtained through a barcode scanner, a radio frequency identification device, or an entry / exit history management unit. The weather deterioration weighting can be calculated based on an external weather information provision server, a weather data linkage module, or regional road condition information. The travel distance can be calculated through a geographic information database, vehicle driving route information, or a road network analysis unit. The remaining capacity for cargo can be calculated based on maximum capacity and current load information stored in a warehouse management unit or a terminal operation unit. The number of misclassifications and misdeliveries in the past week can be calculated by querying an integrated database, claim processing history, return processing history, or quality control history.

[0124] In one embodiment, the transportation problem processing module (130) may determine that the need for detour assignment has increased if the cargo dwell time at a specific hub terminal exceeds the normal range and the weight of adverse weather conditions on the route at that time is calculated to be high. At this time, the transportation problem processing module (130) may set a first alternative terminal and a second alternative terminal as candidate groups and calculate the travel distance, remaining capacity for cargo, and the number of misclassifications and misdeliveries over the past week for each of the candidate groups. Even if the first alternative terminal has a short travel distance and a large remaining capacity for cargo, its final priority score may be lowered if the number of misclassifications and misdeliveries over the past week is tallied at a high level. On the other hand, even if the second alternative terminal has a longer travel distance, it may have a relatively high final priority score if the remaining capacity for cargo is secured at a certain level or higher and the number of misclassifications and misdeliveries over the past week is low. Accordingly, the transportation problem processing module (130) may determine the second alternative terminal as the priority assignment target terminal.

[0125] In another embodiment, if the transportation problem processing module (130) cannot normally calculate the weather deterioration weight due to a failure to receive external weather information, it can calculate a detour transportation route priority score using the remaining judgment criteria while setting the weather deterioration weight as a reference value. Additionally, if some scan information required for measuring the stay time is missing, the transportation problem processing module (130) can calculate the stay time by using the average stay time for the same time period, same route, or same processing line as a correction value. Accordingly, it can be configured so that detour assignment control is not interrupted even if some data collection anomalies occur.

[0126] The operation criteria of the above transportation problem processing module (130) may be adjusted according to pre-set operational criteria or accumulated logistics processing history. For example, the alternative terminal search range may be set according to the vehicle driving distance, transportation conditions by region, or contractually connectable range. In addition, the criteria correction value, the calculation criteria for the weather deterioration weighting, or the aggregation criteria for the number of misclassifications and misdeliveries in the last week may be set according to the actual logistics operation environment.

[0127] In this way, the transportation problem processing module (130) can calculate a detour transportation route priority score by reflecting the dwell time, the weather deterioration weight, the travel distance, the remaining capacity of cargo, and the number of misclassifications and misdeliveries in the past week. Accordingly, unlike a method of simply selecting the nearest terminal or a terminal with a large remaining space, detour assignment is possible by considering both the need to resolve bottlenecks and the processing stability of alternative bases, and the possibility of transportation delays and additional errors can be reduced while reducing the excessive concentration of assignments to a specific terminal.

[0128] In one embodiment, the transport problem processing module (130) may determine the terminal to be prioritized for assignment by comparing priority scores for a plurality of alternative terminals. In this case, the priority score may be a value for determining the relative ranking among candidate terminals and may be used together with a minimum allowable threshold value to allow automatic bypass assignment as needed. For example, the transport problem processing module (130) may use the priority score normalized to a range of 0 to 100 and may be configured to allow automatic bypass assignment only for terminals with a score of 50 or higher.

[0129] Additionally, the transportation problem processing module (130) may perform a sequential calculation procedure to calculate a detour priority score. Specifically, in the first step, the transportation problem processing module (130) may calculate a first detour score by dividing the value combining the cargo dwell time and the weather deterioration weight by the travel distance to the alternative terminal. In the second step, the transportation problem processing module (130) may calculate a capacity-to-error history ratio by dividing the remaining capacity cargo volume by the value obtained by adding a reference correction value to the number of misclassifications and misdeliveries in the past week. Here, the reference correction value may be a reference value added to the denominator to enable the calculation of the ratio even when the number of misclassifications and misdeliveries in the past week is 0, and may be set to, for example, 1. In the third step, the transportation problem processing module (130) may calculate a log-converted value of the capacity-to-error history ratio and derive a final detour transportation route priority score by combining the log-converted value with the first detour score in a multiplicative manner.

[0130] The following table is an example of the results measuring the operational status of alternate terminals when the settings are configured to allow automatic bypass assignment only for terminals with a priority score above a certain threshold.

[0131]

[0132] The following table is an example of the results of comparing a method of selecting the terminal with the highest priority score among multiple replacement terminals and simultaneously 50 or higher as the priority allocation target, and a method of selecting the terminal with the largest remaining space as the priority.

[0133]

[0134] The above terminal concentration index may be an example of a value calculated as the ratio of the maximum allocated terminal to the total bypass allocation volume.

[0135] According to the simulation results above, if the minimum allowable threshold value for automatic bypass assignment is low or non-existent, the secondary misclassification rate and secondary bottleneck delay time may be significantly high. On the other hand, if the threshold value is set to a level of 50, a section may be formed where the acceptance rate converted to the remaining acceptable cargo volume is secured above a certain level and the secondary bottleneck delay time is reduced. Accordingly, the transportation problem processing module (130) can set a priority score of 50 as an exemplary minimum allowable threshold value for automatic bypass assignment. In addition, among multiple alternative terminals, if the terminal with the highest priority score is selected from the candidates satisfying the minimum allowable threshold value, it can be confirmed that the terminal concentration index can be lowered and the number of damage claims and the time required for final customer delivery can be reduced.

[0136] The customized transport provision module (140) can calculate a customer-specific logistics pattern dynamic classification coefficient by reflecting the customer-specific logistics processing history, the physical characteristics of the cargo, and the packaging readiness status together, and can perform subsequent operational control such as recommending packaging materials, assigning to a special inspection line, or assigning to a general line according to the customer-specific logistics pattern dynamic classification coefficient. Accordingly, the customized transport provision module (140) can perform dynamic classification that considers the customer-specific cargo processing characteristics and the possibility of errors together, rather than simply determining the handling method based only on the customer's cumulative number of shipments or fixed grade.

[0137] Specifically, the customized transport provision module (140) can first calculate an average error rate between the actual cargo weight for each customer and the cargo weight entered by the customer in advance. The average error rate can be calculated by comparing the actual cargo weight measured at a logistics center or terminal with the cargo weight entered in the invoice or order information, and as the average error rate increases, it can be determined that the need for attention regarding the customer's packaging condition, weight input accuracy, or the possibility of inspection omission increases.

[0138] Additionally, the custom transport provision module (140) can calculate the ratio of fragile items to total orders per customer as the fragile item inclusion ratio. The fragile items may include glass products, electronic devices, ceramics, precision instruments, or items susceptible to impact, and as the fragile item inclusion ratio increases, it may be determined that the customer's cargo is more likely to be damaged or contaminated during handling.

[0139] Next, the customized transport provision module (140) may set the sum of the average error rate and the ratio of items requiring caution for damage as a reference input value. The reference input value is a value that reflects both the physical risk of the cargo for each customer and the degree of discrepancy in the input information, and as the value increases, it may be determined that the need for subsequent inspection or reinforcement packaging has increased.

[0140] The customized transport provision module (140) can calculate the packaging preparation time for each customer by measuring the time from the time of order acceptance for each customer until the time of collection instruction generation. The packaging preparation time is a value indicating the degree of leeway or urgent processing status of the packaging work for each customer, and it can be determined that the shorter the packaging preparation time, the higher the likelihood that sufficient inspection or packaging reinforcement has not been performed. Accordingly, the customized transport provision module (140) can calculate a standard pattern score by dividing the standard input value by the packaging preparation time. In this case, the standard pattern score may increase as the average error rate and the ratio of items requiring caution for damage are higher, and as the packaging preparation time is shorter.

[0141] Furthermore, the customized transportation provision module (140) can collect the history of shipment volumes for multiple recent weeks for each customer and calculate the weekly cargo shipment volume variability for each customer based on the deviation between the average shipment volume for multiple weeks and the shipment volume for each week. The weekly cargo shipment volume variability for each customer is a value indicating whether the logistics occurrence pattern of a specific customer is stable or irregular; as the variability increases, it can be determined that the inflow volume of the customer is inconsistent, making operational prediction difficult. The customized transportation provision module (140) can calculate a log-converted value of the weekly cargo shipment volume variability for each customer and combine the log-converted value with the reference pattern score in a multiplicative manner. Accordingly, when the weekly cargo shipment volume variability for each customer increases, the classification coefficient increases, but the increase is reflected gradually and gradually, thereby mitigating the phenomenon where the dynamic classification coefficient of the logistics pattern for each customer fluctuates excessively due to temporary fluctuations in cargo volume.

[0142] Additionally, the customized transportation provision module (140) can aggregate the cumulative number of return retrieval cases per customer. The cumulative number of return retrieval cases per customer may include a history of retrieval due to refusal of receipt, damaged return, return due to misdelivery, or poor product condition, and it may be determined that as the cumulative number of return retrieval cases per customer increases, it is highly likely that repetitive problems occurred during the customer's cargo handling process. The customized transportation provision module (140) can convert the cumulative number of return retrieval cases per customer into a return history index according to a pre-set period and standard value. The return history index may be a normalized value that allows comparison without directly using the cumulative number of return retrieval cases per customer, and the result value may be reflected to increase exponentially as the return history index increases.

[0143] The customized transportation provision module (140) can calculate the final customer-specific logistics pattern dynamic classification coefficient by sequentially combining the reference pattern score, the log-transformed value of the weekly cargo shipment volume variability by customer, and the return history index. Specifically, the customized transportation provision module (140) can calculate the reference pattern score by dividing the sum of the average error rate and the ratio of items requiring caution for damage by the packaging preparation time, and then derive the final customer-specific logistics pattern dynamic classification coefficient by multiplying the reference pattern score with the log-transformed value of the weekly cargo shipment volume variability by customer and reflecting the result value to increase exponentially as the return history index increases.

[0144] Here, rather than reflecting the cumulative number of return collection cases per customer as a direct raw count, it is preferable to reflect it after converting it into a return history index that is comparable by reflecting a pre-set period, the total number of shipments per customer, or operational standard values. Additionally, the dynamic classification coefficient of the logistics pattern per customer can be normalized into a score within a pre-set range, and the multiple interval criteria can be set according to the normalized score range.

[0145] The above customer-specific logistics pattern dynamic classification coefficient can be used as a criterion for determining subsequent operational control. For example, the customized transport provision module (140) can classify customer cargo into one of a plurality of management sections by comparing the above customer-specific logistics pattern dynamic classification coefficient with a plurality of pre-set section criteria. If the above customer-specific logistics pattern dynamic classification coefficient corresponds to a section with a low value, the customized transport provision module (140) can perform general line assignment and standard packaging material recommendation. On the other hand, if the above customer-specific logistics pattern dynamic classification coefficient corresponds to a section with a high value, the customized transport provision module (140) can control the performance of at least one of special inspection line assignment, cushioning material reinforcement recommendation, external packaging condition re-inspection request, or weight re-measurement request.

[0146] The process by which the customized transport provision module (140) obtains data for each of the above judgment criteria can be implemented as follows. The average error rate between the actual cargo weight and the input weight for each customer can be calculated based on an automatic weight measurement unit, invoice input data, or order management data. The percentage of items requiring special care for damage for each customer can be calculated based on product category information, item tag information, or product classification code. The packaging preparation time for each customer can be calculated based on the time of order acceptance, the time of packaging completion, the time of collection request, or the time of collection instruction generation. The variability of the weekly cargo shipment volume for each customer can be calculated by querying the shipment volume history of multiple recent weeks. The cumulative number of return retrieval cases for each customer can be calculated by querying the return retrieval history stored in the return processing system, customer service system, or logistics operation database.

[0147] In one embodiment, the customized transport provision module (140) may determine that the standard pattern score is formed high when, for a specific customer, the average error rate between the actual cargo weight and the input weight is high, the proportion of items requiring caution for damage per customer is above a certain level, and the packaging preparation time per customer is calculated to be shorter than the usual range. At this time, if the variability of the weekly cargo shipment volume per customer is high and the return history index based on the cumulative number of return recovery cases per customer is also formed high, the customized transport provision module (140) may determine that the customer corresponds to a section with a high dynamic classification coefficient for the logistics pattern. Accordingly, the customized transport provision module (140) may assign the customer's cargo to a special inspection line rather than a general line and provide instructions to the worker terminal or packaging guidance unit to reinforce cushioning material or reconfirm the packaging condition.

[0148] Conversely, as another embodiment, the customized transport providing module (140) may determine the customer-specific logistics pattern dynamic classification coefficient to be in a low range when, for a specific customer, the average error rate between the actual cargo weight and the input weight is low, the proportion of items requiring special care for damage is low, sufficient packaging preparation time is secured for each customer, the variability in the weekly cargo shipment volume for each customer is low, and the return history index based on the cumulative number of return recovery cases for each customer is calculated to be low. Accordingly, the customized transport providing module (140) can control the assignment to a general line without separate additional inspection.

[0149] In another embodiment, the customized transport provision module (140) may apply the average variability value of a customer group with the same industry group or similar logistics characteristics as a reference value when it is difficult to directly calculate the weekly cargo shipment volume variability for each customer due to insufficient shipment volume history over multiple recent weeks, such as for new subscribers. Additionally, when the actual cargo weight cannot be obtained normally due to sensor errors in the automatic weight measurement unit, the customized transport provision module (140) may be controlled to reduce the weight of the average error rate reflection and calculate a temporary classification coefficient using the remaining judgment criteria. Accordingly, the classification control can be configured so that it is not interrupted even if some input data is missing.

[0150] The operation criteria of the customized transport provision module (140) can be adjusted according to pre-set operational criteria or accumulated logistics processing history. For example, the plurality of segment criteria can be set based on past damage rates, inspection omission history, or return recovery processing statistics. Additionally, the calculation period for the variability of the weekly cargo shipment volume per customer, the normalization criteria for the return history index, or the standard range for the packaging preparation time can be adjusted according to the actual operating environment.

[0151] In this way, the customized transport provision module (140) can calculate a dynamic classification coefficient for logistics patterns for each customer by reflecting together the average error rate between the actual cargo weight and the input weight for each customer, the proportion of items requiring special care for damage for each customer, the packaging preparation time for each customer, the variability of the weekly cargo shipment volume for each customer, and the return history index based on the cumulative number of return recovery cases for each customer. Accordingly, unlike a method that determines the handling method based only on a simple fixed customer grade or cumulative shipment volume, operational control that considers both the physical risk and processing history of the cargo for each customer becomes possible, and processing efficiency for general cargo can be maintained while prioritizing the allocation of inspection and packaging reinforcement resources to cargo with a high probability of damage.

[0152] In one embodiment, the customized transport providing module (140) may perform a simulation to determine whether to assign a special inspection line based on a customer-specific logistics pattern dynamic classification coefficient. The customer-specific logistics pattern dynamic classification coefficient may be normalized to a pre-set range for comparison, for example, a range of 0 to 10, and may be set to perform special inspection line assignment when the coefficient is 5.0 or higher.

[0153] Additionally, the customized transport provision module (140) may perform a sequential calculation procedure to calculate the customer-specific logistics pattern dynamic classification coefficient. Specifically, the customized transport provision module (140) may calculate a standard pattern score by summing the weight average error rate and the ratio of items requiring damage precautions measured in the first step, and dividing this by the packaging preparation time. In the second step, the customized transport provision module (140) may calculate a log-converted value for the variability of the weekly cargo shipment volume for each customer and combine the log-converted value with the standard pattern score in a multiplicative manner. In the third step, the customized transport provision module (140) may calculate a return history index normalized according to a pre-set period and standard value for the cumulative number of return recovery processing cases, and derive the final customer-specific logistics pattern dynamic classification coefficient by reflecting that the result value increases exponentially as the return history index increases.

[0154] The following table is an example of the results of measuring indicators related to packaging defects and secondary damage that occur when a customer's cargo is processed on a general line, based on the dynamic classification coefficient of the logistics pattern for each customer.

[0155]

[0156] The following table is an example of the results of comparing a method of separating customer cargo with a customer logistics pattern dynamic classification coefficient of 5.0 or higher into a special inspection line and a method of feeding all cargo into a general line.

[0157]

[0158] According to the simulation results above, in the section where the dynamic classification coefficient of the logistics pattern per customer is 4.0 or lower, the rate of secondary damage inducing within the line remains relatively low, but from the section where the coefficient reaches the level of 5.0, the actual rate of detection of packaging defects and the rate of secondary damage inducing may show a tendency to increase together. Accordingly, the customized transport provision module (140) can set 5.0 as an exemplary standard value for assigning a special inspection line. In addition, it can be confirmed that if customer cargo exceeding the standard value is separated into a special inspection line, the number of rail stops and the overall cargo damage rate can be reduced while increasing the terminal's hourly processing volume.

[0159] In one embodiment, when the logistics management module receives a bulk order upload, the computer management module calculates the degree of pre-verification required based on the number of concurrently received orders and address input events, and the integrated server unit can calculate a resource allocation index based on the backlog of linkage requests and the external network communication delay status at the same time. Subsequently, if an increase in dwell time at a specific hub is detected during the process of invoice issuance and status synchronization through the courier linkage module, the transportation problem processing module calculates a priority score for a bypass transportation route of alternative terminal candidates, and simultaneously, the customized transportation provision module determines whether to assign a special inspection line based on the dynamic classification coefficient of the customer's logistics pattern, thereby enabling the order reception stage, courier linkage stage, transportation reassignment stage, and cargo handling stage to be controlled continuously.

[0160] The method for reducing invoice errors and managing integrated computer systems through courier API integration according to the present invention supports stable operation by enabling each component to organically exchange data in extreme logistics environments where large volumes of cargo are concentrated in a short period, such as during special holiday seasons. Below, we explain through specific scenarios how four core processes are linked and operate throughout the entire process from cargo acceptance to final delivery.

[0161] As the first step of logistics processing, a situation is assumed where a corporate customer intending to send a large quantity of holiday gift sets accesses the system and enters shipping information. The logistics management module (120) receives 500 batch order data uploaded by the customer and aggregates them into a basic logistics ledger. At the same time, the computer management module (150) activates a pre-verification trigger control logic to prevent the cause of misdelivery from the input stage. The computer management module (150) derives a dynamic input instability variable by calculating the ratio of the number of backspace key inputs and the time taken that occurs during the process of a worker entering a specific recipient's address. The computer management module (150) calculates a final pre-verification necessity score by combining this variable with the fatigue of the large order of 500 items, the ratio of the customer's past misdelivery history, and the structural complexity of the target address. If the calculated score is determined to exceed a preset threshold of 3.5 points, the computer management module (150) forcibly displays an address reconfirmation popup as soon as the worker presses the save button. Through this, the system initially limits the inflow of incorrect address data into the subsequent logistics network, thereby reducing unnecessary delivery recall costs.

[0162] When cargo data verified with a valid address is registered in the system, the customized transport provision module (140) subsequently performs a logistics pattern dynamic classification logic to determine the physical handling method of the cargo. The customized transport provision module (140) calculates the variability of the weekly shipment volume of the corporate customer and calculates the error rate between the actual weight collected from the automatic weight measuring device on the conveyor belt and the computer input weight. The customized transport provision module (140) derives a reference score by dividing this error rate and the ratio of items requiring caution for damage by the short packaging preparation time from order acceptance to collection, and derives a final classification coefficient by exponentially weighting the accumulated return history to this. If the calculation result shows that the classification coefficient of the customer is 5.0 or higher, which is the risk threshold, the customized transport provision module (140) automatically assigns the cargo to a special inspection line instead of a general line and transmits instructions to reinforce cushioning material to the worker terminal. Through this organic linkage, secondary accidents such as poorly packaged cargo contaminating other cargo within the conveyor belt or stopping rail operation are prevented in advance.

[0163] In order to transfer packages that have been packaged and internally inspected to an external courier company, the courier linkage module (110) calls the application program interface of the external courier company server to attempt to generate invoice numbers and link collection. However, as large-scale traffic is concentrated, the communication delay time of the external network increases rapidly to more than 500ms, and a situation occurs where the internal queue reaches a saturation state. At this time, the integrated server unit (100) immediately intervenes with dynamic resource allocation control logic to prevent the entire system from stopping operation. The integrated server unit (100) calculates the necessity of resource allocation by multiplying the queue ratio and the urgency of the packages and reflecting the communication delay time in a logarithmic proportion. However, in this process, if the integrated server unit (100) detects through the mainboard sensor that the processor load rate has reached a risk threshold of 85%, it determines that hardware protection is the top priority and rapidly reduces the calculated resource allocation index in an inverse relationship. As a result, the integrated server unit (100) successfully prevents a full system shutdown caused by overheating of the server processor by blocking the allocation of new threads to the courier linkage module (110) and diverting incoming linkage requests to a temporary queue.

[0164] Afterward, when the hardware load has stabilized, the parcel linkage is completed, and the cargo has entered the actual transport network, a situation is detected by the transport problem processing module (130) where the cargo remains at a specific intermediate sorting hub for more than 10 hours due to unexpected heavy rain. The transport problem processing module (130) performs control logic to assign a detour route for the delayed cargo. The transport problem processing module (130) calculates the weather deterioration weight and the distance to an adjacent alternative terminal, and derives a priority score by combining the ratio of the remaining cargo capacity of the alternative terminal to the recent misclassification history. As a result of the calculation, Terminal A, which is physically close and has a large remaining space but has a frequent recent misclassification history, has its score reduced and is pushed to a lower priority, while Terminal B, which is somewhat far but has almost no error history and can handle stable processing, is calculated as the highest priority detour route. The above transportation problem processing module (130) instructs the dispatch system to change the route based on this result, thereby blocking the balloon effect in which even alternative terminals are paralyzed due to blind short-distance push assignments and stably distributing the logistics flow.

[0165] As described above, the present invention enables the data input stage (computer management module), cargo handling stage (customized transportation provision module), computer linkage stage (integrated server unit and courier linkage module), and physical transportation stage (transportation problem processing module and logistics management module) to operate complementarily while exchanging numerical data in real time without being disconnected. Compared to conventional logistics systems that were limited to simple load balancing or static information provision, this multi-stage organic control structure provides the technical effect of efficiently distributing system resources in a real logistics environment where unpredictable variables are scattered, and significantly reducing delivery delays and damage rates.

[0166] Although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. Various modifications are possible by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention. Explanation of the symbols

[0167] Integrated server unit (100) Parcel delivery linkage module (110) Logistics management module (120) Transport problem handling module (130) Customized transport provision module (140) Computer management module (150)

Claims

Claim 1 In a method for reducing invoice errors and managing computerized integration based on courier API linkage, performed by a system comprising a courier linkage module (110) that mediates data transmission and reception with an external courier system, a logistics management module (120) that manages order information collection and a logistics ledger, and a computerized management module (150) that controls the activation of a pre-verification procedure based on the user's invoice input behavior, wherein the logistics management module (120) collects individual order data entered through a user terminal or bulk order data uploaded in the form of a file to generate a logistics ledger; and the computerized management module (150) calculates a dynamic input instability variable representing the average modification frequency per unit time by dividing the number of times a clear key is activated detected in an address input form displayed on a user terminal by the total input time between the entry time and the exit time of the address input window, and an address complexity coefficient that quantifies the number of depth levels of the administrative district system and the number of special characters included by parsing the address string to be input A derivation step; the computer management module (150) calculates an intermediate score by combining the log value of the number of batch orders simultaneously received through the logistics management module (120) with the dynamic input instability variable, weights the ratio of past misdelivery history by combining it with the intermediate score in an exponential proportional relationship, and calculates a final pre-verification necessity score by proportionally combining the address complexity coefficient with the weighted result;The above computer management module (150) if the final pre-verification necessity score exceeds a preset threshold, does not immediately proceed to the general acceptance completion procedure even if the user's invoice confirmation request is entered, but forcibly displays at least one of an address reconfirmation popup, an administrative district selection reconfirmation window, a detailed address supplementation request window, or a misdelivery history notification window on the screen of the user terminal to trigger a pre-verification procedure; the above integrated server unit (100) calculates a queue ratio by dividing the number of courier company linkage requests waiting at the courier linkage module (110) by the maximum allowable number of linkages per unit time, derives a basic processing requirement by multiplying the calculated queue ratio by a cargo urgency weighting assigned according to the cargo delivery deadline, derives a communication delay time by measuring the round-trip time of a test packet transmitted to the external courier company system, and calculates a delay correction value by multiplying the basic processing requirement by the log value of the communication delay time; the above integrated server unit (100) collects the processor heat temperature and occupancy rate in real time through the temperature sensor and operating system installed in the system and the hardware load index Calculate, and derive a final resource allocation index by combining the derived delay correction value with the inverse value of the hardware load index, wherein the final resource allocation index is controlled to decrease rapidly and non-linearly as the hardware load index approaches a set risk threshold, and when the final resource allocation index falls below a preset lower threshold, even if the cargo urgency weighting is calculated to be high, immediately stop the allocation of new computational resources to the courier linkage module (110) and divert incoming linkage requests to a temporary queue to prevent the physical server from stopping operation in advance;A method for reducing invoice errors and managing integrated computer systems through courier API integration, comprising: a step in which the courier linkage module (110), when the final pre-verification necessity score is less than the threshold value or when a general acceptance completion procedure is performed after undergoing the pre-verification procedure, converts the dispatch request data recorded in the logistics ledger into the data specifications of an external courier system and attempts to generate an invoice number and link collection by calling an application program interface provided by the external courier; and a step in which the courier linkage module (110), when a communication delay in the external network is detected, stores the dispatch request data in an internal waiting area and transmits it in batches when the connection status is normalized to prevent data loss. Claim 2 A method for reducing invoice errors and managing computerized systems through courier API integration according to claim 1, wherein the computer management module (150) calculates the ratio of past misdelivery-causing history by dividing the number of misdelivery cases determined to be address entry errors, administrative district errors, omission of detailed addresses, or address information discrepancies by the total number of shipments, wherein the computer management module (150) calculates the address complexity coefficient by analyzing the depth of the administrative district system, the structure of the detailed address entry, and whether special characters are included in the input address, and wherein the courier linkage module (110) receives invoice numbers and real-time delivery tracking status data returned from the external courier system and synchronizes them according to the specifications of the internal database.

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