Smart port tallying data processing method and system, medium and product

Abstract

A tallying data processing method and system for an intelligent port, a medium and a product relate to the field of electric digital data processing, and the method comprises the following steps: obtaining a ship loading and unloading plan, tallying operation records and cost accounting data according to a preset period, and generating standard tallying data; extracting feature data in a pre-time window from the standard tallying data; inputting the feature data into a prediction calculation model, and generating a plurality of groups of loading and unloading plan change prediction scenes according to a historical operation mode; performing parallel calculation on the multiple groups of prediction scenes to obtain scene prediction data; comparing the real-time tallying data acquired at the port site with the scene prediction data, and calculating to obtain a data goodness of fit; selecting the most matched prediction scene as a current execution scene based on the data goodness of fit, and storing other prediction scenes into a standby scene library; and generating a tallying operation instruction and expense settlement data according to the current execution scene. By implementing the application, the efficiency of port multi-link data processing can be improved.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing, and in particular to a method, system, medium and product for processing tally data in a smart port. Background Art

[0002] With the rapid development of port logistics business, the transportation volume of containers and general cargo continues to grow, and the tally operation plays an increasingly important role in port operation. The tally operation involves multiple links such as cargo handover, inspection, counting, and recording, and a large amount of operation information needs to be accurately recorded and managed. At the same time, the tally business also needs to perform data interaction with multiple systems such as the terminal operating system and the financial system, and provide corresponding business services to customers.

[0003] In the related art, the port tally management system uses information technology means for operation management. The system receives ship arrival information and loading and unloading plans through an electronic data exchange interface, and tally clerks use handheld terminal devices to collect on-site operation data. The dispatching system automatically generates operation plans and personnel scheduling tables according to preset rules. In terms of document management, the system supports the generation of electronic documents in standard formats and performs data exchange with relevant parties through EDI (Electronic Data Interchange). The cost management module can automatically calculate operation costs according to the rates agreed in the contract, generate electronic bills, and complete payments through the bank-enterprise direct connection system.

[0004] However, the related art adopts a linear data processing mode, and data updates in each link need to be processed and verified in a fixed order in sequence. When the loading and unloading plan changes, the data updates in subsequent tally operations, cost accounting and other links are slower, which will affect the timeliness of on-site dispatching decisions. Summary of the Invention

[0005] This application provides a method, system, medium and product for processing tally data in a smart port, which is used to improve the efficiency of multi-link data processing in the port.

[0006] In a first aspect, the present application provides a method for processing tally data in a smart port, which is applied to a tally management system. The method includes: obtaining a ship loading and unloading plan, a tally operation record, and cost accounting data from a terminal operating system and a financial system according to a preset period, and generating standard tally data including cargo attributes, operation volume attributes, and time attributes based on a preset mapping relationship; extracting time series attributes, cargo loading and unloading attributes, and personnel allocation attributes within a pre-set time window from the standard tally data to generate feature data representing the tally operation status; inputting the feature data into a prediction calculation model to generate multiple sets of prediction scenarios for changes in the loading and unloading plan according to the historical operation mode; each set of prediction scenarios includes an operation scheduling sequence and a cost change sequence; performing parallel calculations on multiple sets of prediction scenarios to obtain scenario prediction data including the tally operation time sequence and the cost accounting value; comparing the real-time tally data collected at the port site with the scenario prediction data, and calculating the data matching degree of each prediction scenario; selecting the most matching prediction scenario as the current execution scenario based on the data matching degree, and storing the other prediction scenarios in a standby scenario library; generating a tally operation instruction and cost settlement data according to the current execution scenario.

[0007] In the above embodiment, the tally management system obtains tally data according to a preset period and performs standardized processing, extracts feature data and inputs it into a prediction model to generate multiple sets of prediction scenarios, performs parallel calculations on the prediction scenarios to obtain prediction data, compares it with real-time data, and selects the most matching scenario for execution; improving the efficiency of port tally data processing, and multi-scenario prediction enables the system to quickly respond to plan changes, timely adjust operation instructions and cost settlement, and ensure the efficient and orderly progress of port operations.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after the step of comparing the real-time tally data collected at the port site with the scenario prediction data and calculating the data matching degree of each prediction scenario, the method further includes: when all the data matching degrees are less than a preset matching threshold, triggering a scenario reconstruction instruction; in response to the scenario reconstruction instruction, transmitting the real-time tally data back to the prediction calculation model to regenerate a new prediction scenario for changes in the loading and unloading plan.

[0009] In the above embodiment, when the data matching degrees of the tally management system are all lower than the threshold, it triggers scenario reconstruction, transmits the real-time data back to the prediction model to regenerate the scenario, can adjust the prediction model in a timely manner according to the actual operation situation, avoid serious deviation of the prediction scenario from the actual situation, ensure the accuracy and timeliness of the tally operation scheduling decision, and improve the system's ability to handle abnormal situations.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of triggering a scenario reconstruction instruction when all data matching degrees are less than a preset matching threshold, the method further includes: in response to the scenario reconstruction instruction, obtaining historical scenario data with the same operation processes and cargo categories from a backup scenario library; based on the operation time nodes in the historical scenario data, calibrating and compensating the operation time nodes in the real-time tallying data to generate scenario deviation data including operation quantity differences and cost differences; and optimizing the prediction calculation model based on the scenario deviation data.

[0011] In the above embodiments, the tally management system obtains historical similar scenario data to calibrate and compensate the real-time data, generates deviation data for optimizing the prediction model, and makes the scenario prediction more in line with the actual operation characteristics.

[0012] In combination with some embodiments of the first aspect, in some embodiments, before the step of extracting time series attributes, cargo handling attributes, and personnel allocation attributes within a lead time window from the standard tallying data to generate feature data representing the tallying operation status, the method further includes: receiving a change application for the ship loading and unloading plan; the change application includes cargo quantity adjustment information and operation time adjustment information; based on the real-time personnel allocation and the change application, determining the number of tallying personnel and the number of operation tools for the changed operation plan; and generating a personnel grouping plan and a tool configuration plan based on the number of tallying personnel and the number of operation tools.

[0013] In the above embodiments, the tally management system dynamically adjusts the personnel and tool configuration plans according to the change application, can quickly respond to the plan change requirements, reasonably allocate operation resources, avoid waste of manpower and material resources, and improve the port resource utilization efficiency.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of selecting the most matching prediction scenario as the current execution scenario based on the data matching degree and storing the other prediction scenarios in the backup scenario library, the method further includes: based on a preset scenario display template, converting the operation scheduling sequence in the current execution scenario into a task flow chart; the task flow chart includes operation link nodes and data flow directions between the operation link nodes; extracting the tallying personnel grouping information and operation tool configuration information corresponding to each operation link node to generate link execution cards including operation start and end times, operation quantities, and cost accounting values; real-time monitoring the change in the operation quantity in the link execution cards, and when it is detected that the change in the operation quantity exceeds a preset fluctuation range, triggering an operation warning signal and retrieving an alternative execution scenario from the backup scenario library.

[0015] In the above embodiments, the tally management system visually displays the execution scenario, real-time monitors the change in the operation quantity and gives an intelligent warning, which is convenient for management personnel to master the operation progress, timely discover abnormal situations and take countermeasures, and improves the controllability and safety of port operations.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating the tally operation instruction and the cost settlement data according to the current execution scenario, the method further includes: determining the job completion degree of the current execution scenario based on a preset settlement condition, and when the job completion degree reaches a preset progress threshold, marking the current execution scenario as the final settlement scenario; extracting the goods category, the operation volume, and the operation duration information from the final settlement scenario, calculating according to the preset rate rule, and generating a target bill including the cost breakdown, the billing rule, and the preferential policy; performing electronic signature encryption on the target bill, and pushing it to the business terminal through a preset data interface.

[0017] In the above embodiments, the tally management system automatically executes the cost settlement and bill generation processes, improving the settlement efficiency and accuracy. At the same time, the electronic signature encryption ensures the security of the settlement data, providing a better service experience for customers.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of performing electronic signature encryption on the target bill and pushing it to the business terminal through a preset data interface, the method further includes: receiving the bill confirmation information returned by the business terminal, extracting the receivable and payable amounts in the bill confirmation information, and generating an electronic statement of account including the details of receipts and payments and the payment deadline; performing signing confirmation on the electronic statement of account based on the electronic signature rule, and generating an electronic contract including the operation volume clause and the settlement clause according to the preset contract template; storing the electronic statement of account and the electronic contract in the blockchain system, and generating a bill transaction record and a pending confirmation invoice.

[0019] In the above embodiments, the tally management system realizes electronic reconciliation and contract management based on the blockchain, ensuring the immutability and traceability of transaction records, improving the standardization and transparency of port operations, and effectively preventing business risks.

[0020] In a second aspect, an embodiment of the present application provides a tally management system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the tally management system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, and when the above computer program product runs on the tally management system, it enables the above tally management system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] Fourthly, an embodiment of the present application provides a computer-readable storage medium, including instructions, which, when running on the tally management system, cause the tally management system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the tally management system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Since a parallel data processing method based on multi-scenario prediction is adopted, including technical features such as periodically obtaining standardized tally data, extracting feature data, generating multiple groups of prediction scenarios for parallel calculation, real-time data comparison, and scenario selection, the system can process multiple prediction scenarios simultaneously, quickly respond to plan changes, and timely adjust operation instructions, effectively solving the problems of slow data update and lagging scheduling decisions caused by the linear processing mode in the prior art. Furthermore, the efficient processing of port tally data is realized, and the operation efficiency and resource utilization rate are significantly improved.

[0025] 2. Since a dynamic scenario reconstruction mechanism is adopted, including technical features such as coincidence threshold judgment, scenario reconstruction trigger, real-time data feedback, and generation of new scenarios, the system has an adaptive ability and can adjust the prediction model in a timely manner according to the actual operation situation, effectively solving the problems of fixed prediction scenarios and difficulty in coping with abnormal situations in the prior art. Furthermore, the dynamic optimization of the prediction model is realized, and the accuracy of scenario prediction is improved.

[0026] 3. Since an intelligent cost settlement mechanism is adopted, including technical features such as judgment of operation completion degree, application of cost calculation rules, electronic signature encryption, and data interface push, the system can automatically complete the whole process from operation confirmation to bill generation, effectively solving the problems of low efficiency and easy errors in manual settlement in the prior art. Furthermore, the automation and standardization of tally cost settlement are realized, the settlement efficiency and accuracy are improved, and at the same time, data security is ensured through electronic signatures, optimizing the customer service experience. Description of the Drawings

[0027] Figure 1 is a flowchart of a method for processing tally data of a smart port in an embodiment of the present application; Figure 2 is another flowchart of a method for processing tally data of a smart port in an embodiment of the present application; Figure 3It is a schematic structural diagram of an entity device in the tally management system according to an embodiment of the present application. Detailed implementation manners

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the" and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] A large port container terminal processes the loading and unloading operations of hundreds of containers every day. The traditional tally operation management mainly relies on manual experience for plan formulation and adjustment. When the shipping schedule changes or the quantity of goods changes, the tally management personnel need to formulate a new operation plan. For example, a cargo ship originally expected to load 500 standard containers suddenly increases the loading demand by 100 containers and requires the loading to be completed 2 hours in advance. In this case, the management personnel need to re-evaluate the manpower allocation, adjust the operation sequence, and re-calculate the costs. The whole process takes a long time and is prone to omissions, affecting the operation efficiency and customer satisfaction.

[0032] In the related art, a fixed-rule operation plan generation method and a simple manual calculation method can be adopted to realize tally operation management and cost settlement. The scenario of using the tally data processing method of a smart port in the related art is introduced below.

[0033] A certain port uses a simple automated system for tally management. The system generates an operation plan according to preset fixed rules. When a cargo ship carrying 300 refrigerated containers is delayed in arriving at the port due to weather reasons and some containers need to be temporarily changed in the loading position, the system can only regenerate the plan according to the preset rules and cannot dynamically adjust the operation priority and resource allocation according to the actual situation. At the same time, the system's re-calculation of costs is also relatively mechanical and cannot flexibly apply various preferential policies, resulting in an increase in customer complaints and affecting the port's market competitiveness.

[0034] By adopting the tally data processing method of the intelligent port in the embodiments of the present application, through the establishment of a prediction calculation model and a scenario parallel processing mechanism, the intelligent generation and rapid adjustment of operation plans are realized, which not only improves the efficiency and accuracy of plan formulation, but also can effectively cope with various sudden change situations. The following introduces the scenarios using the tally data processing method of the intelligent port in the present application.

[0035] In the port tally management system adopting this solution, when handling a mixed cargo ship with 400 general containers and 100 dangerous goods containers, after receiving the demand from the shipowner to temporarily add 50 general containers, the system quickly retrieves similar situations from the historical scenario library and automatically generates multiple groups of prediction scenarios. By parallel computing and comparing the feasibility of each scenario, the system selects the optimal solution: adjusting the loading sequence of dangerous goods containers, optimizing the personnel grouping, and reasonably arranging the use of equipment. At the same time, the system automatically calculates the cost changes and generates a new settlement plan, and the whole process only takes a few minutes to complete, greatly improving the operation efficiency and customer satisfaction.

[0036] It can be seen that by adopting the tally data processing method of the intelligent port in the embodiments of the present application, while realizing the intelligent management of tally operations, it can also effectively solve problems such as slow plan adjustment, unreasonable resource allocation, and inaccurate cost accounting in the traditional solution, thereby realizing the efficient management and accurate settlement of tally operations.

[0037] For the convenience of understanding, the following combines the above scenarios to describe the process of the method provided in this embodiment. Please refer to Figure 1 , which is a schematic flowchart of the tally data processing method of the intelligent port in the embodiments of the present application.

[0038] S101. Obtain the ship loading and unloading plan, tally operation record, and cost accounting data from the terminal operation system and the financial system according to a preset period, and generate standard tally data including cargo attributes, operation volume attributes, and time attributes based on a preset mapping relationship.

[0039] Among them, the preset period represents the time interval for the tally management system to regularly obtain data, such as every 30 minutes, every hour, etc.; the terminal operation system refers to the information system used to manage port loading and unloading operations, and the financial system refers to the system used to process financial data related to port operations; the ship loading and unloading plan represents the cargo loading and unloading time sequence arrangement after the ship berths; the tally operation record refers to the detailed record of information such as the quantity and quality during the cargo loading and unloading process; the cost accounting data is used to represent the details of various costs related to the tally operation; the preset mapping relationship represents the corresponding rules for converting data from different sources into a standard format; the cargo attributes include characteristics such as cargo type, weight, and packaging; the operation volume attribute represents the workload index of the loading and unloading operation; the time attribute is used to represent the time node information of the operation.

[0040] The tally management system executes this step at the beginning of each preset cycle to obtain and integrate operation-related data. Specifically, the tally management system first obtains the latest ship loading and unloading plan from the terminal operating system through the system interface, including ship schedule, berth, cargo information, etc.; at the same time, it obtains operation records from the tally operation site collection system, and the record content covers links such as cargo handover, inspection, and counting; in addition, it obtains cost accounting data from the financial system, including operation rates, preferential policies, etc. The tally management system standardizes the obtained raw data according to predefined mapping rules, extracts the basic characteristics of the goods, operation volume indicators, and time node information, and generates standard format data that is convenient for subsequent processing.

[0041] In some embodiments, data standardization processing can be achieved in various ways: Optionally, the tally management system can adopt methods such as data cleaning, field mapping, and format conversion. First, it performs outlier detection and missing value processing on the raw data, then maps the field names of different systems to standard fields uniformly, and finally converts the data format to a standard format such as JSON or XML; Optionally, the tally management system can also adopt a semantic analysis method, identify the conceptual relationships in different data sources by establishing an ontology model, extract key information, and integrate it into standard data according to predefined semantic rules. It can be understood that other data standardization methods can also be adopted, which are not limited here. In addition, the preset mapping relationship can be dynamically adjusted and optimized according to actual business needs.

[0042] In practical applications, the data formats and update cycles of different systems may vary, resulting in data asynchronization problems. To this end, the tally management system adopts a data version control mechanism, assigns a timestamp and version number to each piece of data, preferentially uses the latest version of the data during data integration, and supplements and updates the asynchronous historical data. At the same time, it ensures the integrity and accuracy of the data in each system through data consistency verification, and triggers an alarm and starts the data synchronization process when data inconsistency is found.

[0043] S102. Extract the time series attributes, cargo loading and unloading attributes, and personnel allocation attributes within the leading time window from the standard tally data to generate characteristic data representing the tally operation status.

[0044] Among them, the leading time window represents the time range for analyzing historical data, such as the previous 4 hours, the previous 24 hours, etc.; the time series attributes include time series characteristics such as operation start time, completion time, and interval duration; the cargo loading and unloading attributes refer to loading and unloading operation-related characteristics such as cargo category, quantity, and operation method; the personnel allocation attributes represent human resource characteristics such as the number of tally personnel, division of labor, and working hours; the characteristic data is used to represent the overall status and trend characteristics of the tally operation, including key indicators such as operation efficiency and resource utilization rate.

[0045] The tally management system executes this step after completing data standardization processing, which is used to extract key features reflecting the operation status. Specifically, the tally management system first determines the range of the lead time window and dynamically adjusts the window size based on the operation cycle and data update frequency; then extracts the operation records of each time node from the standard tally data in time series, including operation type, duration, completion status, etc.; at the same time, extracts features such as operation methods, equipment utilization, and operation efficiency during the cargo loading and unloading process; in addition, obtains the configuration of tally personnel, including information such as the number of personnel, professional division of labor, and working hours. The tally management system combines and correlates these extracted features to construct a multi-dimensional feature vector for characterizing the overall status of the current tally operation.

[0046] In some embodiments, feature extraction and status characterization can be achieved in various ways: Optionally, the tally management system can adopt a sliding time window mechanism, by setting a time window of a fixed size and gradually sliding it, calculating the statistical values, change trends, and correlations of various features within the window, so as to capture the dynamic features of the operation status; Optionally, the tally management system can also adopt a deep learning method, by constructing a feature extraction network to automatically learn the implicit features in the data and achieve a high-dimensional representation of the operation status. It can be understood that other feature engineering methods can also be used to achieve the feature expression of the operation status, which is not limited here.

[0047] During the feature extraction process, there may be a problem that the feature dimension is too high, resulting in an increase in computational complexity. To address this problem, the tally management system adopts feature selection and dimensionality reduction techniques. First, it evaluates the importance of each feature based on indicators such as information gain, and screens out the key features that make significant contributions to the operation status characterization; then maps the high-dimensional features to a low-dimensional space through dimensionality reduction methods such as principal component analysis (PCA), reducing the computational complexity while retaining the main information. Through feature screening and dimensionality reduction, both the accuracy of the status characterization is ensured and the subsequent processing efficiency is improved.

[0048] S103. Input the feature data into the prediction calculation model to generate multiple prediction scenarios for the change of the loading and unloading plan according to the historical operation mode.

[0049] Among them, the historical operation mode refers to the typical operation rules and patterns summarized from historical data; the prediction scenario is used to represent the possible change situations of the loading and unloading plan; the change of the loading and unloading plan includes the adjustment of the operation time sequence, the adjustment of the personnel and equipment configuration, etc.; the operation scheduling sequence represents the execution order of the operation links; the cost change sequence refers to the cost adjustment situation caused by the change of the operation plan.

[0050] The tally management system executes this step after obtaining the feature data, which is used to predict possible planned change scenarios. Specifically, the tally management system first inputs the extracted feature data into a pre-trained prediction calculation model. This model learns the operation rules and change patterns by analyzing historical data. Then, based on the current feature status, the model predicts the possible planned changes in the future period, including the adjustment of operation sequence, the change of personnel allocation, the change of equipment usage, etc. At the same time, considering the influence of external factors such as weather and shipping schedule, multiple prediction scenarios with different change characteristics are generated. For each prediction scenario, the model also generates the corresponding operation scheduling sequence and cost change sequence, which are used to evaluate the feasibility and economy of scenario implementation.

[0051] It should be noted that the prediction calculation model adopts a deep learning architecture. In the model training stage, based on historical tally operation data, the inputs include operation time series features (such as loading and unloading rates, operation durations), cargo features (such as categories, weights, volumes), resource allocation features (such as the number of personnel, equipment types), and environmental features (such as weather conditions, changes in shipping schedules). The actually occurred operation plan changes and the corresponding execution effects are used as training labels, and the model is optimized by minimizing the mean square error between the prediction error and the actual result. At the same time, an L2 regularization term is introduced to prevent overfitting. The model itself consists of multiple layers of neural networks, including a time series feature extraction layer (using a bidirectional LSTM structure to capture long-term and short-term time series dependencies), a multi-head attention layer (setting 8 attention heads to respectively focus on feature associations at different time scales), and a prediction output layer (using a fully connected layer to map to the prediction space). In the design of the attention layer, position encoding is specifically added to maintain the integrity of time series information. In the model usage stage, when inputting the feature vector of the current operation status (including the operation records in the recent 4 hours and the known plan in the next 2 hours), the model will output multiple possible planned change scenarios. Each scenario includes an operation scheduling sequence (predicting the specific operation arrangements in the next 4 hours, accurate to a 15-minute granularity) and the corresponding cost change sequence (predicting the adjustment values of various costs). For example, when a cargo ship loaded with 500 standard containers temporarily adds 100 more containers, the model can, based on historical data and the current status, predict multiple feasible operation plans, including specific execution suggestions such as adjusting the operation sequence, optimizing personnel allocation, and re-arranging equipment usage, and predict the possible operation time extension and cost changes for each plan.

[0052] In addition, with regard to the impact of external factors such as weather and shipping schedules, the tally management system can handle them by establishing a multi-source data fusion mechanism. The system accesses the meteorological forecast API to obtain weather prediction data, and retrieves shipping schedule change information from the ship management system, aligning and fusing this external factor data with the operation characteristic data. The system sets impact weights and rule templates for external factors, and dynamically adjusts operation parameters during scenario generation. For example, it extends the estimated operation time during severe weather warnings and optimizes personnel allocation when the shipping schedule is advanced.

[0053] In some embodiments, the prediction scenario generation can be achieved in multiple ways: Optionally, the tally management system can adopt a rule-based scenario generation method, enumerating all possible change combinations through preset business rules and constraints, screening out valid scenarios that meet the conditions, and calculating the probability and impact of each scenario; Optionally, the tally management system can also adopt a deep reinforcement learning method, exploring different change strategies in a virtual environment by constructing a decision-making model and learning the optimal scenario generation strategy. It can be understood that other intelligent algorithms can also be used to achieve the automatic generation of prediction scenarios, which is not limited here.

[0054] During the scenario generation process, problems such as an excessive number of scenarios or low scenario quality may occur. To address this issue, the tally management system adopts a scenario evaluation and screening mechanism. First, it defines scenario evaluation indicators, including dimensions such as feasibility, economy, and stability; then it conducts multi-dimensional evaluation and scoring on the generated scenarios, screening out high-quality scenarios with higher scores; at the same time, it sets an upper limit on the number of scenarios to ensure that the system resource occupancy is within a reasonable range. Through scenario evaluation and screening, both the quality of the prediction scenarios is ensured and the consumption of computing resources is controlled.

[0055] S104. Perform parallel computing on multiple groups of prediction scenarios to obtain scenario prediction data including tally operation timings and cost accounting values.

[0056] Among them, parallel computing refers to a computing method that processes multiple groups of prediction scenarios simultaneously; the tally operation timings refer to the execution time points and sequence arrangements of each operation link; the cost accounting values include the calculation results of various costs such as loading and unloading costs, tally costs, and warehousing costs; the scenario prediction data is used to represent the specific execution plan and expected effects of each prediction scenario; the time accuracy of the prediction data represents the time interval granularity of the prediction results, such as minute-level, hour-level, etc.

[0057] The tally management system executes this step after generating multiple groups of prediction scenarios to efficiently calculate the execution effects of each scenario. Specifically, the tally management system first assigns the prediction scenarios to multiple computing nodes and starts parallel computing tasks; for each scenario, the system simulates the execution of the job scheduling sequence, calculates the start time, completion time, and duration of each link, and forms a complete job timing arrangement; at the same time, according to the change in the volume of work and the rate policy, the system calculates the adjustment values of various fees, including basic fees, additional fees, and preferential amounts; in addition, the system also evaluates the resource requirements and constraints for the execution of the scenario to ensure the feasibility of the prediction results; finally, the timing arrangement and the results of the fee calculation are integrated into scenario prediction data.

[0058] In some embodiments, parallel computing can be achieved in various ways: Optionally, the tally management system can adopt a distributed computing framework, divide the prediction scenarios into multiple subtasks and assign them to a computing cluster, and each node independently calculates and summarizes the results to achieve an even distribution of the computing load; Optionally, the tally management system can also adopt GPU accelerated computing, utilize the parallel computing ability of the graphics processor to process multiple scenarios simultaneously, and greatly improve the computing efficiency. It can be understood that other high-performance computing methods can also be used to improve the scenario computing efficiency, which is not limited here.

[0059] During the parallel computing process, there may be problems such as computing node failures or uneven load distribution. To address this issue, the tally management system adopts a task scheduling and fault tolerance mechanism. First, it performs dynamic load balancing on the computing tasks, adjusts the task allocation in a timely manner according to the node performance and load conditions; when a node failure is detected, the system automatically migrates the task to a standby node for continued calculation; at the same time, it establishes a checkpoint mechanism for the calculation results, regularly saves the intermediate calculation status for easy fault recovery. In addition, the system also monitors the computing progress, adjusts the priority or splits the tasks of the slower-executing tasks to ensure that all scenarios can be calculated in a timely manner.

[0060] S105. Compare the real-time tally data collected at the port site with the scenario prediction data, and calculate the data matching degree of each prediction scenario.

[0061] Among them, the real-time tally data refers to the real-time operation information collected from the port site equipment and personnel; the data matching degree represents the matching degree between the prediction scenario and the actual situation; the data comparison dimensions include multiple aspects such as operation progress, resource utilization, and fee calculation; the error tolerance represents the acceptable prediction deviation range; the weight coefficient is used to represent the importance of different comparison dimensions; the calculation period represents the time interval for data comparison.

[0062] The tally management system executes this step after completing the calculation of scenario prediction data, which is used to evaluate the accuracy of the prediction results. Specifically, the tally management system first collects real-time tally data from the port site, including operation progress, personnel and equipment status, cargo information, etc.; then it conducts multi-dimensional comparisons between the real-time data and the data of each prediction scenario, including the deviation of operation time sequence, the difference in resource allocation, the error in cost calculation, etc.; the system sets weight coefficients for each comparison dimension to reflect the importance of this dimension in the overall evaluation; through weighted calculation, a comprehensive matching degree index is obtained, which is used to measure the matching degree between the prediction scenario and the actual situation; at the same time, the system will record the key difference points in the comparison process to provide a basis for subsequent scenario optimization.

[0063] In some embodiments, data comparison and matching degree calculation can be achieved in various ways: Optionally, the tally management system can adopt a similarity calculation method based on distance, convert the real-time data and the prediction data into feature vectors, calculate the Euclidean distance or cosine similarity between the vectors, and obtain a numerical matching degree; Optionally, the tally management system can also adopt a fuzzy logic evaluation method, and through setting a set of fuzzy rules, comprehensively evaluate the matching conditions of multiple dimensions to obtain a more practical matching degree judgment. It can be understood that other data matching measurement methods can also be used to evaluate the prediction effect, which is not limited here.

[0064] During the data comparison process, problems such as real-time data delay or unstable data quality may occur. To address this issue, the tally management system adopts a data preprocessing and exception handling mechanism. First, it performs time alignment and data cleaning on the real-time data to eliminate the influence of acquisition delay and noise interference; when data anomalies are detected, the system will start a data repair process, and handle the outliers through methods such as interpolation or replacement with neighboring data; at the same time, the system will dynamically adjust the error tolerance range, while ensuring the evaluation accuracy, adapting to the fluctuation characteristics of the real-time data. For continuously abnormal data sources, the system will issue an alarm and temporarily reduce their weights in the comprehensive evaluation to ensure the reliability of the matching degree calculation.

[0065] S106. Select the most matching prediction scenario as the current execution scenario based on the data matching degree, and store other prediction scenarios in the standby scenario library.

[0066] Among them, the most matching prediction scenario refers to the scenario plan with the highest data matching degree; the current execution scenario refers to the operation plan selected for actual execution; the standby scenario library is used to store other prediction scenarios with reference value; the scenario screening threshold represents the lowest matching degree requirement for scenario selection; the scenario priority refers to the execution importance ranking of different scenarios; the scenario storage structure includes information such as scenario features, execution conditions, and association relationships.

[0067] The tally management system executes this step after obtaining the data matching degrees of each prediction scenario, which is used to determine the optimal execution plan. Specifically, the tally management system first sorts all prediction scenarios in descending order of matching degree, selects the scenario with the highest matching degree and exceeding the screening threshold as the current execution scenario; at the same time, it evaluates the reference values of other scenarios, including the feature similarity of the scenarios, the convertibility of execution conditions, etc.; for scenarios with reference value, the system stores their complete information in the standby scenario library, including scenario features, execution conditions, associated scenarios, etc.; the system also establishes an association index between scenarios to facilitate quickly finding alternative solutions when the execution scenario changes; and regularly maintains the standby scenario library to clean up expired or less valuable scenario data.

[0068] In some embodiments, the scenario selection and storage management can be implemented in multiple ways: Optionally, the tally management system can adopt a multi-objective optimization method, considering multiple objectives such as matching degree, execution cost, resource occupancy, etc. at the same time, and select the best execution scenario through Pareto optimal selection, and other non-dominated solutions as standby scenarios; Optionally, the tally management system can also adopt a knowledge graph method, construct the scenario information into a semantic network, store the hierarchical relationship and conversion rules between scenarios through a graph structure, and realize the intelligent management and rapid retrieval of scenarios. It can be understood that other decision optimization methods can also be adopted to implement the selection and management of scenarios, which are not limited here.

[0069] During the scenario management process, problems such as the expansion of the standby scenario library capacity or the low efficiency of scenario retrieval may occur. To address this problem, the tally management system adopts a scenario compression and index optimization mechanism. First, it extracts features and performs dimensionality reduction processing on the scenario data to reduce the storage space occupation; then it establishes a multi-level index structure, including a time index, a feature index, and an association index, to improve the scenario retrieval efficiency; at the same time, the system will regularly evaluate the usage frequency and conversion success rate of scenarios, archive or clean up low-value scenarios; in addition, the system will also establish a cache for frequently used scenarios to improve the access speed of hot scenarios. Through these optimization measures, both the availability of standby scenarios is ensured and the efficiency of scenario management is improved.

[0070] S107. Generate a tally operation instruction and cost settlement data according to the current execution scenario.

[0071] Among them, the tally operation instruction represents the specific operation task arrangement and execution requirements; the cost settlement data includes the calculation result of the operation cost and the settlement basis; the operation task elements include information such as the operation object, operation content, execution personnel, equipment tools, etc.; the cost calculation rules include pricing elements such as the basic rate, preferential policies, additional costs, etc.; the data push interface is used to transmit instructions and settlement information to relevant systems; the data version identifier represents the timeliness of the instructions and settlement data.

[0072] The tally management system executes this step after determining the current execution scenario, which is used to generate actual operation guidance. Specifically, the tally management system first parses the job scheduling sequence in the current execution scenario, extracts the specific task elements of each link, including information such as job time, location, personnel, and equipment; then converts the task elements into job instructions in a standard format, clarifying the work content and quality requirements of each executor; at the same time, calculates various expenses according to the job volume and rate policy, and generates expense data including expense details, calculation basis, and settlement rules; the system pushes the job instructions to the on-site execution terminal through a preset data interface, and pushes the expense data to the financial system; all the pushed data is attached with a version identifier to ensure the timeliness and consistency of the data.

[0073] In some embodiments, the instruction generation and expense calculation can be implemented in various ways: Optionally, the tally management system can adopt a templated instruction generation method, preset standard instruction templates for different types of jobs, and automatically fill in the template content according to specific task elements to generate standardized job instructions; Optionally, the tally management system can also adopt an intelligent decision tree method, and automatically derive the optimal instruction combination and expense calculation scheme according to job conditions and constraints by constructing multi-level decision rules. It can be understood that other intelligent processing methods can also be used to realize the automatic generation of instruction and expense data, which is not limited here.

[0074] During the instruction execution process, problems such as instruction conflicts or expense calculation errors may occur. To address this issue, the tally management system adopts an instruction verification and expense audit mechanism. First, it performs conflict detection on the generated job instructions to ensure that there are no conflicts in the time and resource arrangements between different instructions; when potential conflicts are detected, the system will automatically adjust the instruction sequence or provide alternative solutions; at the same time, it performs multiple verifications on the expense calculation results, including rate applicability checks, preferential policy verifications, and total amount rationality judgments; if abnormalities are found, the system will trigger a manual audit process and retain detailed calculation process records for easy problem tracing and handling. Through these safeguard measures, the executability of the job instructions and the accuracy of the expense settlement are ensured.

[0075] The following further describes the more specific process of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of the tally data processing method for the intelligent port in the embodiment of the present application.

[0076] S201. Obtain the ship loading and unloading plan, tally operation record, and expense accounting data from the terminal operating system and the financial system according to a preset cycle, and generate standard tally data including cargo attributes, job volume attributes, and time attributes based on a preset mapping relationship.

[0077] Referring to step S101, the tally management system will perform data acquisition and standardization.

[0078] S202. Extract the time series attributes, cargo handling attributes, and personnel allocation attributes within the lead time window from the standard tally data to generate feature data representing the tally operation status.

[0079] Referring to step S102, the tally management system performs data feature extraction.

[0080] In some embodiments, the tally management system enables an intelligent resource scheduling mechanism, that is, the tally management system receives a change application for the ship loading and unloading plan; the change application includes cargo quantity adjustment information and operation time adjustment information; based on the real-time personnel allocation and the change application, determine the number of tally personnel and the number of operation tools for the operation plan after the change; based on the number of tally personnel and the number of operation tools, generate a personnel grouping plan and a tool configuration plan.

[0081] Among them, the change application represents a request to modify the original loading and unloading plan; the cargo quantity adjustment information refers to the quantity of goods to be changed; the operation time adjustment information represents the operation time arrangement after the plan change; the real-time personnel allocation is used to represent the current available status of tally personnel; the number of tally personnel refers to the number of human resources required to execute the operation after the change; the number of operation tools represents the required quantity of equipment tool configuration; the personnel grouping plan is used to represent the work assignment plan of tally personnel; the tool configuration plan refers to the usage arrangement of operation tools.

[0082] The tally management system executes this step when receiving a change application for the ship loading and unloading plan, and is used to adjust the operation resource configuration. Specifically, the tally management system first analyzes the content of the change application to obtain the increase or decrease of the cargo quantity and the adjustment range of the operation time; then queries the on-the-job status, professional qualifications, and work load of the current tally personnel, and calculates the required number of tally personnel in combination with the operation volume after the change; at the same time, evaluates the available status and usage efficiency of the operation tools to determine the number of tools required for the operation after the change; based on the personnel and tool requirements, the tally management system comprehensively considers factors such as personnel professional expertise, operation experience, and working hours to generate an optimized personnel grouping plan; for operation tools, the system formulates a detailed tool configuration and rotation usage plan according to the tool performance characteristics and operation requirements.

[0083] In some embodiments, resource allocation optimization can be achieved in various ways: Optionally, the tally management system can use integer programming methods for resource allocation, constructing factors such as personnel professional levels, working hours, and tool usage efficiency as constraints, with the maximum operation efficiency as the objective function, solving for the optimal resource allocation plan, and handling integer constraints through the branch and bound algorithm; Optionally, the tally management system can also use heuristic algorithms to explore the resource allocation space through simulated annealing or genetic algorithms, balancing the workloads of each group of personnel on the premise of ensuring operation quality to achieve reasonable resource allocation. It can be understood that other optimization algorithms can also be used to achieve the optimal allocation of resources, which is not limited here.

[0084] During the resource allocation process, problems may occur where personnel or tools are temporarily unavailable, resulting in the inability to execute the plan. To address this issue, the tally management system adopts a dynamic adjustment mechanism. First, it establishes a resource status monitoring mechanism to continuously track the arrival of personnel and the usage status of tools; when resource anomalies are detected, the system will initiate an emergency adjustment process, including calculating alternative solutions for available resources, adjusting operation groups and tool configurations; at the same time, the system will evaluate the impact of the adjustment plan on operation efficiency and, if necessary, ensure the operation progress by extending working hours or temporarily allocating resources; in addition, the system will also maintain a standby resource pool to provide resource support for emergencies. Through these measures, the reliability and adaptability of the resource allocation plan are ensured.

[0085] S203. Input the feature data into the prediction calculation model to generate prediction scenarios for multiple sets of loading and unloading plan changes based on historical operation patterns.

[0086] Referring to step S103, the tally management system will perform scenario prediction.

[0087] S204. Perform parallel calculations on multiple sets of prediction scenarios to obtain scenario prediction data including tally operation timings and cost accounting values.

[0088] Referring to step S104, the tally management system will implement parallel calculations for scenario simulation.

[0089] S205. Compare the real-time tally data collected at the port with the scenario prediction data, and calculate the data matching degree of each prediction scenario.

[0090] Referring to step S105, the tally management system will perform data comparison.

[0091] S206. When all data matching degrees are less than the preset matching threshold, trigger a scenario reconstruction instruction.

[0092] Among them, the preset matching threshold represents the lowest standard for judging whether a scenario is available; the scenario reconstruction instruction is used to trigger the prediction model to regenerate the scenario; the data matching degree evaluation period represents the time interval for checking the data matching situation; the system alarm level is used to represent the severity of the scenario mismatch.

[0093] The tally management system executes this step after calculating the matching degree of each scenario, and is used to handle the situation of prediction failure. Specifically, the tally management system first checks whether the data matching degrees of all predicted scenarios are lower than the preset threshold, indicating that the current prediction model may no longer be applicable; when it is confirmed that all scenarios do not match, the system generates a scenario reconstruction instruction, including information such as the current operation status and the reason for the mismatch; at the same time, the system issues a warning signal to notify relevant personnel to pay attention to the abnormal operation situation; the system will also temporarily freeze the execution of the current predicted scenario and wait for the generation of a new scenario.

[0094] In some embodiments, the scenario reconstruction trigger can be achieved in various ways: Optionally, the tally management system can adopt a multi-level threshold judgment mechanism, set matching degree thresholds of different severities, and adopt corresponding processing strategies according to different levels; Optionally, the tally management system can also adopt a trend analysis method, by monitoring the change trend of the matching degree, pre-judging possible scenario failures in advance, and realizing preventive scenario reconstruction. It can be understood that other intelligent judgment methods can also be used to achieve the timely trigger of scenario reconstruction, which is not limited here.

[0095] During the scenario reconstruction process, there may be a problem that frequent reconstruction triggers lead to system instability. To address this problem, the tally management system adopts a reconstruction control mechanism. First, it sets the minimum reconstruction interval time to avoid overly frequent reconstruction operations; at the same time, it records the reconstruction history, analyzes the reasons for reconstruction, and conducts root cause analysis and optimization of frequently occurring problems; in addition, the system will evaluate the impact on the operation before reconstruction and adopt a progressive reconstruction strategy when necessary to ensure the stable operation of the system.

[0096] In some embodiments, the tally management system will establish a dynamic scenario indexing mechanism, that is, the tally management system will, in response to the scenario reconstruction instruction, obtain historical scenario data with the same operation process and cargo category from the standby scenario library; based on the operation time nodes in the historical scenario data, calibrate and compensate the operation time nodes in the real-time tally data to generate scenario deviation data including the operation volume difference and cost difference; based on the scenario deviation data, optimize the prediction calculation model.

[0097] Among them, the scenario reconstruction instruction represents a system command for regenerating the predicted scenario; the alternative scenario library refers to a database storing historical predicted scenarios; the operation process represents the execution step sequence of the tally operation; the cargo category refers to the classification type of the goods; the historical scenario data represents the operation records in past similar situations; the operation time node is used to represent the time points of each operation link; the calibration compensation refers to the correction and adjustment of the time data; the operation volume difference represents the deviation between the actual operation volume and the predicted value; the cost difference is used to represent the difference between the actual cost and the predicted cost; the scenario deviation data refers to the statistical information reflecting the prediction error; the prediction calculation model represents the algorithm model for generating the predicted scenario.

[0098] The tally management system executes this step when receiving the scenario reconstruction instruction, which is used to optimize the prediction model. Specifically, the tally management system first retrieves the historical scenarios that match the current operation process and cargo category from the alternative scenario library, including complete information such as operation arrangements, resource allocations, and cost calculations; then compares the time nodes in the historical scenarios with those in the real-time tally data, calculates the time deviation and performs compensation adjustment; at the same time, analyzes the differences between the actual values and predicted values of the operation volume and cost, and generates scenario deviation data representing the prediction error; finally, the tally management system uses the scenario deviation data to adjust and optimize the parameters of the prediction calculation model to improve the accuracy of subsequent predictions.

[0099] In some embodiments, the model optimization can be achieved in various ways: Optionally, the tally management system can adopt the gradient descent method for model optimization. First, calculate the gradient of the prediction error with respect to the model parameters, and then iteratively update the parameter values along the gradient direction until the error converges to an acceptable range. During this period, it is also necessary to dynamically adjust the learning rate to balance the optimization efficiency and stability; Optionally, the tally management system can also adopt the Bayesian optimization method, which describes the parameter space by constructing a Gaussian process model and uses the acquisition function to guide the parameter search direction, achieving a balance between exploration and exploitation, and gradually finding the optimal parameter configuration. It can be understood that other optimization algorithms can also be used to improve the performance of the prediction model, which is not limited here.

[0100] During the model optimization process, there may be a problem that over-optimization leads to a decline in the generalization ability of the model. To address this problem, the tally management system adopts a regularization control mechanism. First, introduce a regularization term into the optimization objective to limit the complexity of the model parameters; at the same time, use the cross-validation method to evaluate the model performance, divide the data set into a training set and a validation set, and monitor the optimization process through the performance of the validation set; when it is found that the performance of the validation set decreases, the system will timely adjust the regularization strength or terminate the optimization process in advance; in addition, the system will also save the model snapshots during the optimization process and can roll back to the version with the best performance when necessary. Through these measures, the effectiveness and reliability of the model optimization are ensured.

[0101] S207. In response to the scenario reconstruction instruction, transmit the real-time tally data back to the prediction calculation model to regenerate a new prediction scenario for the change of the loading and unloading plan.

[0102] Among them, the new prediction scenario represents the new scenario plan generated after reconstruction; the real-time tally data transmission interface is used to input the current operation data into the prediction model; the data transmission content includes information such as operation status, resource configuration, and execution effect; the prediction model reloading means updating the model status with new data; the scenario generation parameters include configuration items such as prediction duration, number of scenarios, and constraint conditions.

[0103] The tally management system executes this step after receiving the scenario reconstruction instruction, which is used to generate a new prediction plan. Specifically, the tally management system first inputs the real-time tally data into the prediction calculation model through the transmission interface, and the data includes the current operation status and recent change trends; then the model re-performs scenario prediction based on the latest data to generate a new set of scenarios that conform to the current situation; the system conducts a preliminary evaluation of the newly generated scenarios to ensure their relevance to the current operation status; at the same time, it saves the trigger conditions and processing procedures for scenario reconstruction for subsequent model optimization.

[0104] In some embodiments, new scenario generation can be achieved in multiple ways: Optionally, the tally management system can adopt an incremental learning method to locally update the prediction model using the newly added real-time data to quickly adapt to operation changes; Optionally, the tally management system can also adopt a combinatorial optimization method to construct a hybrid scenario that conforms to the new situation by recombining high-quality parts of the existing scenarios. It can be understood that other scenario generation methods can also be used to achieve dynamic adjustment of the prediction model, which is not limited here.

[0105] During the new scenario generation process, problems such as slow generation speed or unstable quality may occur. To address this problem, the tally management system adopts a fast response mechanism. First, it quickly generates a basic scenario using the cached scenario template; at the same time, it starts a parallel computing task to optimize and improve on the basis of the basic scenario; the system also dynamically adjusts the generation parameters to find a balance between scenario quality and generation speed. Through these optimization measures, the timeliness and effectiveness of scenario reconstruction are ensured.

[0106] S208. Select the most matching prediction scenario as the current execution scenario based on the data matching degree, and store other prediction scenarios in the standby scenario library.

[0107] Referring to step S106, the tally management system will perform scenario determination and standby storage.

[0108] In some embodiments, the tally management system employs a visual monitoring engine. That is, the tally management system converts the job scheduling sequence in the current execution scenario into a task flow chart based on a preset scenario display template. The task flow chart includes job link nodes and the data flow between the job link nodes. The tally personnel grouping information and job tool configuration information corresponding to each job link node are extracted to generate a link execution card containing the start and end times of the job, the job volume, and the cost calculation value. The job volume change in the link execution card is monitored in real time. When it is detected that the job volume change exceeds the preset fluctuation range, a job warning signal is triggered, and an alternative execution scenario is retrieved from the standby scenario library.

[0109] Among them, the scenario display template represents a preset visual display format; the job scheduling sequence refers to the execution order arrangement of the tally job; the task flow chart is used to represent the logical relationship of the job links; the job link node represents a specific job task point; the data flow refers to the information transfer relationship between the nodes; the link execution card is used to represent the execution information of a specific job task; the start and end times of the job represent the start and end time points of the task; the job volume represents specific workload data; the cost calculation value refers to the corresponding cost calculation result; the preset fluctuation range represents an acceptable job volume change interval; the job warning signal is used to represent a prompt message for abnormal situations; the alternative execution scenario refers to an alternative solution that can be switched to.

[0110] The tally management system executes this step after determining the current execution scenario, which is used to visually monitor the job execution status. Specifically, the tally management system first converts the job scheduling sequence into an intuitive task flow chart according to the preset display template, clearly showing the sequence and dependency relationships between the job links. Then, the corresponding execution information is associated with each job link node, including the grouped tally personnel in charge, the required job tool configuration, etc. The system generates a detailed link execution card, which displays the job start time, the estimated completion time, the current job volume, and the cost calculation situation in real time. The tally management system continuously monitors the job volume changes of each link. When it is detected that the change amplitude exceeds the preset range, an alarm is immediately triggered and a suitable alternative solution is automatically retrieved from the standby scenario library to provide decision support for job adjustment.

[0111] In some embodiments, visual monitoring can be achieved in various ways: Optionally, the tally management system can adopt dynamic flowchart technology, use node coloring to represent the job status, use the thickness of the connection lines to represent the data flow, combine with animation effects to display the job progress, and support node expansion to view detailed information, so as to achieve multi-level visual display; Optionally, the tally management system can also adopt the data dashboard method, display key indicators such as job volume, progress, and cost through multi-dimensional charts, cooperate with the warning threshold line to reflect abnormal situations in real time, and support data drilling down to analyze specific reasons. It can be understood that other visualization methods can also be used to achieve job monitoring, which is not limited here.

[0112] During the visual monitoring process, there may be problems such as delayed data update resulting in untimely display. To address this issue, the tally management system adopts a real-time push mechanism. First, it establishes an efficient data channel and uses technologies such as WebSocket to achieve real-time data transmission; at the same time, it implements an incremental update strategy, only transmitting the changed data content to reduce transmission latency; the system will also prioritize the display content to ensure that important information is updated first; when a network anomaly is detected, the system will enable the local cache mechanism and automatically synchronize and update after the network is restored to ensure the continuity of the display. Through these measures, the real-time nature and reliability of the monitoring display are ensured.

[0113] S209. Generate a tally operation instruction and cost settlement data according to the current execution scenario.

[0114] Referring to step S107, the tally management system will perform data prediction based on the scenario.

[0115] S210. Determine the job completion degree of the current execution scenario based on preset settlement conditions. When the job completion degree reaches the preset progress threshold, mark the current execution scenario as the final settlement scenario.

[0116] Among them, the preset settlement conditions represent the combination of conditions for triggering cost settlement; the job completion degree refers to the completion ratio of the current job progress relative to the plan; the preset progress threshold represents the minimum completion progress at which cost settlement can be carried out; the final settlement scenario is used to determine the basis for cost calculation; the completion degree calculation rules include the progress calculation methods for different job types.

[0117] The tally management system continuously executes this step during the execution of the current scenario to determine the cost settlement timing. Specifically, the tally management system first checks the current job status according to the preset settlement conditions, including the cargo loading and unloading progress, document integrity, equipment return situation, etc.; then, according to the completion degree calculation rules for different cargo types, comprehensively evaluates the overall job progress; when the system confirms that the job completion degree reaches the preset threshold, locks the current execution scenario as the final settlement scenario; at the same time, the system will record the time point and basis for determining the settlement scenario to ensure the traceability of the settlement process.

[0118] In some embodiments, the completion degree evaluation can be achieved in various ways: Optionally, the tally management system can adopt a weighted calculation method, set weight coefficients for different operation links, and obtain the overall completion degree through weighted summation; Optionally, the tally management system can also adopt critical path analysis to identify the critical nodes in the operation process and judge the overall progress based on the completion status of the critical nodes. It can be understood that other progress evaluation methods can also be used to accurately calculate the operation completion degree, which is not limited herein.

[0119] During the completion degree evaluation process, problems such as operation interruption or progress rollback may occur. To address this issue, the tally management system adopts a status check mechanism. First, it establishes a checkpoint system for the operation progress, regularly records and verifies the progress status; when an abnormal change is detected, the system will start an exception handling process, analyze the reasons and adjust the completion degree calculation; at the same time, the system will keep the dynamic update of the final settlement scenario until the operation is truly completed. Through these measures, the accuracy and reliability of the determined settlement scenario are ensured.

[0120] S211. Extract the cargo category, operation volume, and operation duration information from the final settlement scenario, calculate according to the preset rate rules, and generate a target bill including expense items, billing rules, and preferential policies.

[0121] Among them, the cargo category represents the classification identifier of different types of goods; the operation volume includes billing bases such as loading and unloading quantity and storage time; the operation duration represents the actual operation duration; the preset rate rules include pricing schemes such as basic rates and tiered rates; the expense items represent the detailed composition of various expenses; the billing rules include calculation formulas and applicable conditions; the preferential policies include discount schemes and preferential conditions.

[0122] The tally management system executes this step after determining the final settlement scenario for generating a formal bill. Specifically, the tally management system first extracts the basic data required for expense calculation from the settlement scenario, including cargo specifications, operation types, operation times, etc.; then calculates each expense according to the preset rate rules, considering factors such as basic expenses, additional expenses, and preferential discounts; the system will record the calculation process of each expense in detail, including billing standards, calculation formulas, applicable policies, etc.; finally, it integrates all expense items into a target bill in a standard format to ensure the integrity and accuracy of the bill content.

[0123] In some embodiments, the fee calculation can be achieved in various ways: Optionally, the tally management system can adopt a multi-level pricing model, apply different billing rules according to different business scenarios and customer types, and achieve flexible fee accounting; Optionally, the tally management system can also adopt an intelligent pricing algorithm, dynamically adjust the rate parameters by analyzing historical transaction data and market conditions, and optimize the revenue. It can be understood that other billing methods can also be used to achieve reasonable fee calculation, which is not limited here.

[0124] During the bill generation process, problems such as incorrect fee calculation or improper application of preferential policies may occur. To address this issue, the tally management system adopts a multiple verification mechanism. First, it verifies the accuracy of the extracted basic data; then it checks the rationality of the calculation results of each fee, including fee range checking, historical comparison analysis, etc.; at the same time, the system will verify the stacking logic of preferential policies to avoid duplicate or conflicting preferential treatments; for abnormal situations, the system will generate an audit prompt and require manual review and confirmation. Through these verification measures, the accuracy and compliance of the target bill are ensured.

[0125] S212. Electronically sign and encrypt the target bill, and push it to the business terminal through a preset data interface.

[0126] Among them, electronically signing and encrypting means performing digital signature and encryption processing on the bill data; the preset data interface includes a push target and a communication protocol; the business terminal includes a client system, a financial system, a management terminal, etc.; the data encryption algorithm includes symmetric encryption and asymmetric encryption schemes; the signature verification mechanism is used to ensure data integrity and non-repudiation; the data push status represents the processing results of bill transmission and reception.

[0127] The tally management system executes this step after generating the target bill for secure transmission of bill data. Specifically, the tally management system first digitally signs the target bill, calculates the signature of the bill content using a preset key; then encrypts the signed bill data to ensure data security during transmission; the system pushes the encrypted bill to the relevant business terminals through a preset data interface, and at the same time transmits the necessary decryption and verification information; the system will monitor the push process, record the bill delivery status and reception confirmation information to ensure the reliability of data transmission.

[0128] In some embodiments, secure transmission can be achieved in various ways: Optionally, the tally management system can adopt a hierarchical encryption strategy, using different encryption schemes for bill information of different importance levels to balance security and efficiency; Optionally, the tally management system can also adopt blockchain technology, record the bill transmission process through a distributed ledger to ensure the immutability of data. It can be understood that other secure transmission schemes can also be used to achieve reliable push of bill data, which is not limited here.

[0129] During the data push process, problems such as network interruption or terminal offline may occur. To address this issue, the tally management system adopts a reliable transmission mechanism. First, it establishes a retry mechanism for data push and automatically retransmits when a transmission failure is detected. At the same time, the system will maintain a push queue to cache and manage the bills that have not been successfully delivered. In addition, the system also provides multi-channel push options and can switch to an alternative channel for continuous transmission when the primary channel is unavailable. The system will regularly clean up expired push records to keep the push queue running efficiently. Through these safeguard measures, the reliable delivery and timely processing of bill data are ensured.

[0130] In some embodiments, the tally management system will construct an intelligent settlement chain. That is, the tally management system will receive the bill confirmation information returned by the business terminal, extract the receivable and payable amounts in the bill confirmation information, and generate an electronic statement of account containing the details of receipts and payments and the payment deadline. It will sign and confirm the electronic statement of account based on the electronic signature rules and generate an electronic contract containing the workload terms and settlement terms according to the preset contract template. The electronic statement of account and the electronic contract will be deposited into the blockchain system to generate a bill transaction record and a pending invoice.

[0131] Among them, the bill confirmation information represents the confirmation result of the business terminal for the bill; the receivable and payable amount refers to the amount of fees that need to be collected or paid; the electronic statement of account is used to represent the digital reconciliation voucher; the details of receipts and payments represent the detailed information of the income and expenditure of the fees; the payment deadline refers to the deadline requirement for the settlement of the fees; the electronic signature rules are used to represent the authentication standards of digital signatures; the preset contract template represents the standardized contract document format; the workload terms refer to the agreed content about the workload in the contract; the settlement terms represent the agreed content about the calculation and payment of the fees; the blockchain system is used to represent the distributed ledger storage system; the bill transaction record refers to the full process information of bill processing; the pending invoice represents the electronic invoice to be issued.

[0132] The tally management system executes this step after receiving the bill confirmation information to complete the fee settlement and contract signing. Specifically, the tally management system first analyzes the bill confirmation information returned by the business terminal and extracts the confirmed receivable and payable amount information. Then it generates an electronic statement of account in a standard format, detailing the details of the receipt and payment of each fee, the settlement basis, and the payment deadline. At the same time, according to the preset contract template, it fills the confirmed workload information and fee settlement information into the corresponding clause positions to generate a complete electronic contract document. The tally management system digitally signs the document according to the electronic signature rules to ensure the legal effect of the document. Finally, the signed electronic statement of account and the electronic contract are deposited into the blockchain system to generate an immutable transaction record and initiate the invoice issuance process.

[0133] In some embodiments, electronic document processing can be achieved in various ways: Optionally, the tally management system can adopt the PKI system for electronic signature, verify the identity of the signature subject through digital certificates, generate signature values using asymmetric encryption algorithms, and add timestamp information during signature to ensure the validity and non-repudiation of the signature; Optionally, the tally management system can also adopt the federated blockchain technology, automatically execute the reconciliation and settlement processes through smart contracts, ensure the consistency of transaction records using the consensus mechanism, and guarantee the security and traceability of data through distributed storage. It can be understood that other encryption and evidence-preserving methods can also be used to achieve the secure processing of electronic documents, which are not limited herein.

[0134] During the electronic document processing, problems such as signature verification failure or blockchain write timeout may occur. To address this issue, the tally management system adopts a multi-guarantee mechanism. First, a pre-verification step is added during the signature process to ensure the integrity and format correctness of the signature data; when a signature anomaly is detected, the system will automatically retry the signature process while saving the original data for verification; for blockchain writing, the system adopts a hierarchical storage strategy, first storing the data in local secure storage and clearing the local copy only after successfully writing to the blockchain; the system also monitors the blockchain network status and automatically adjusts the submission strategy in case of congestion to ensure the final successful writing of the data. Through these measures, the reliability and integrity of electronic document processing are guaranteed.

[0135] In the embodiments of this application, due to the adoption of the intelligent prediction technology based on historical scenarios and the parallel computing optimization method, multiple groups of prediction scenarios can be quickly generated and evaluated, and the prediction accuracy can be continuously improved through real-time data comparison and dynamic optimization mechanisms; at the same time, by introducing blockchain technology and electronic signature mechanisms, the security and trustworthiness of fee settlement are achieved; in addition, through the visual monitoring and early warning mechanisms, the real-time supervision of the operation process is guaranteed, effectively solving problems such as low efficiency of plan adjustment, unreasonable resource allocation, inaccurate fee accounting, and poor data security in traditional tally operation management. Furthermore, the intelligent management, precise settlement, and full-process controllability of tally operations are realized, significantly improving the port operation efficiency and service quality.

[0136] The following describes the tally management system in the embodiments of this invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the tally management system in the embodiments of this application.

[0137] It should be noted that Figure 3 The structure of the tally management system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of this invention.

[0138] As Figure 3As shown, the tally management system includes a CPU 301, which can perform various appropriate actions and processes according to the program stored in the ROM 302 or the program loaded into the RAM 303 from the storage section 308, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0139] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0140] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, various functions defined in the present invention are executed.

[0141] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0142] Specifically, the tally management system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, the tally data processing method of the intelligent port provided in the above embodiment is implemented.

[0143] As another aspect, the present invention also provides a computer-readable storage medium. This storage medium can be included in the tally management system described in the above embodiment; or it can exist separately without being assembled into the tally management system. The above storage medium carries one or more computer programs. When the one or more computer programs are executed by a processor of the tally management system, the tally management system implements the tally data processing method of the intelligent port provided in the above embodiment.

[0144] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0145] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...". Similarly, depending on the context, the phrase "upon determining..." or "if (the stated condition or event) is detected" can be interpreted to mean "if determined..." or "in response to determining..." or "when (the stated condition or event) is detected" or "in response to detecting (the stated condition or event)".

Claims

1. A method for handling tally data in an intelligent port, characterized in that Applied to the tally management system, the method includes: Obtain the ship loading and unloading plan, tally operation records, and cost accounting data from the terminal operating system and the financial system according to a preset cycle, and generate standard tally data including cargo attributes, operation volume attributes, and time attributes based on a preset mapping relationship; Extract the time series attributes, cargo loading and unloading attributes, and personnel allocation attributes within the lead time window from the standard tally data to generate feature data representing the tally operation status; Input the feature data into the prediction calculation model to generate multiple prediction scenarios for changes in the loading and unloading plan according to the historical operation mode; each group of the prediction scenarios includes an operation scheduling sequence and a cost change sequence; Perform parallel calculations on multiple groups of the prediction scenarios to obtain scenario prediction data including the tally operation time sequence and the cost accounting value; Compare the real-time tally data collected at the port with the scenario prediction data, and calculate the data matching degree of each prediction scenario; Select the most matching prediction scenario as the current execution scenario based on the data matching degree, and store the other prediction scenarios in the standby scenario library; Generate tally operation instructions and cost settlement data according to the current execution scenario.

2. The method according to claim 1, characterized in that After the step of comparing the real-time tally data collected at the port with the scenario prediction data and calculating the data matching degree of each prediction scenario, the method further includes: When all the data matching degrees are less than a preset matching threshold, trigger a scenario reconstruction instruction; In response to the scenario reconstruction instruction, transmit the real-time tally data back to the prediction calculation model to regenerate new prediction scenarios for changes in the loading and unloading plan.

3. The method according to claim 2, characterized in that, After the step of triggering a scenario reconstruction instruction when all the data matching degrees are less than a preset matching threshold, the method further includes: In response to the scenario reconstruction instruction, obtain historical scenario data with the same operation processes and cargo categories from the standby scenario library; Based on the operation time nodes in the historical scenario data, calibrate and compensate the operation time nodes in the real-time tally data to generate scenario deviation data including operation volume differences and cost differences; Optimize the prediction calculation model based on the scenario deviation data.

4. The method according to claim 1, wherein Before the step of extracting the time series attributes, cargo loading and unloading attributes, and personnel allocation attributes within the lead time window from the standard tally data to generate feature data representing the tally operation status, the method further includes: Receive a change application for the ship loading and unloading plan; the change application includes cargo quantity adjustment information and operation time adjustment information; Based on the real-time personnel allocation and the change application, determine the number of tally personnel and the number of operation tools for the changed operation plan; Generate a personnel grouping plan and a tool configuration plan based on the number of tally personnel and the number of operation tools.

5. The method according to claim 1, wherein After the step of selecting the most matching prediction scenario as the current execution scenario based on the data matching degree and storing the other prediction scenarios in the standby scenario library, the method further includes: Based on a preset scenario display template, convert the job scheduling sequence in the current execution scenario into a task flow chart; the task flow chart includes job link nodes and data flow directions between job link nodes. Extract the tally personnel grouping information and job tool configuration information corresponding to each job link node, and generate a link execution card including job start and end times, job volume, and cost accounting values. Real-time monitor the change in the job volume in the link execution card. When it is detected that the change in the job volume exceeds the preset fluctuation range, trigger a job warning signal and retrieve an alternative execution scenario from the backup scenario library.

6. The method according to claim 1, wherein After the step of generating the tally job instruction and cost settlement data according to the current execution scenario, the method further includes: Determine the job completion degree of the current execution scenario based on preset settlement conditions. When the job completion degree reaches the preset progress threshold, mark the current execution scenario as the final settlement scenario. Extract the cargo category, job volume, and job duration information from the final settlement scenario, calculate according to the preset rate rules, and generate a target bill including cost items, billing rules, and preferential policies. Perform electronic signature encryption on the target bill and push it to the business terminal through a preset data interface.

7. The method according to claim 6, wherein After the step of performing electronic signature encryption on the target bill and pushing it to the business terminal through a preset data interface, the method further includes: Receive the bill confirmation information returned by the business terminal, extract the receivable and payable amounts in the bill confirmation information, and generate an electronic statement of account including details of receipts and payments and payment deadlines. Perform signing confirmation on the electronic statement of account based on the electronic signature rules, and generate an electronic contract including job volume terms and settlement terms according to the preset contract template. Store the electronic statement of account and the electronic contract in the blockchain system to generate a bill transaction record and a pending invoice.

8. A tally management system, characterized in that, The tally management system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the tally management system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the tally management system, enable the tally management system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the tally management system, enable the tally management system to execute the method according to any one of claims 1-7.

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