Port dynamic threshold generation method based on data association analysis

Abstract

The invention discloses a port dynamic threshold generation method based on data association analysis, and relates to the technical field of port data analysis, and the method comprises the steps: obtaining a port production and operation historical database through the utilization of port production and operation data; performing association analysis according to the port production and operation historical database to obtain a port production and operation index association relationship; according to the port production and operation index association relationship, obtaining the change trend of the port production and operation indexes by using the port production and operation historical database; and obtaining a port dynamic threshold according to the port production and operation index association relationship and the change trend of the port production and operation indexes. By means of associated data analysis, deep learning and the like, the analysis capability of the complex relationship between the indexes is improved, dynamic generation and adjustment of the port service threshold are realized, dynamic changes of port services can be better adapted, and the efficiency and benefit of port production operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of port data analysis, and in particular to a method for generating a port dynamic threshold based on data association analysis. Background Art

[0002] In port operations, the proper setting of thresholds is a key factor in ensuring efficient production. However, traditional threshold setting methods and some existing threshold adjustment technologies based on data analysis have numerous flaws, which have a significant negative impact on port operations in terms of efficiency, cost, and adaptability.

[0003] In terms of efficiency, traditional threshold setting methods based on static rules or manual experience lack dynamic analysis of multi-dimensional data and therefore cannot accurately match actual port operations. For example, when setting time thresholds for ship loading and unloading operations, if fixed durations are set based solely on past experience without considering factors such as differences in ship type, cargo type, weather conditions, and the real-time status of port equipment, inaccurate loading and unloading time estimates can easily occur. This can lead to early or delayed arrival of loading and unloading equipment at the work site, resulting in idle equipment waiting or poor operational coordination, extending ship stays in port and reducing overall port cargo turnover efficiency. Furthermore, some existing threshold adjustment methods based on data analysis are limited to single indicators or simple rules and fail to fully consider the complex interrelationships between indicators. For example, when adjusting port berth allocation thresholds, they focus solely on ship arrival time and berth availability, ignoring factors such as the volume of cargo loaded and unloaded, the required loading and unloading equipment, and subsequent operational plans. This results in irrational berth allocation, with some berths potentially overcrowded while others remain idle. This disrupts the overall operational process, makes equipment and manpower allocation difficult, and further reduces port efficiency.

[0004] In terms of costs, inaccurate threshold settings directly lead to wasted resources and increased port operating costs. Taking labor costs as an example, because traditional threshold settings cannot accurately reflect actual operational needs, ports may schedule too many or too few staff. When the threshold is set too high, exceeding actual operational needs, it will cause idle personnel and increase unnecessary labor expenditures. Conversely, if the threshold is too low, insufficient staff will lead to delays in operational progress, and additional overtime may be required to catch up. In terms of equipment costs, unreasonable threshold settings also have a significant impact. For example, if the operating threshold of loading and unloading equipment is improperly set without considering the equipment's maintenance cycle and actual usage, it may lead to excessive use of the equipment, accelerated equipment wear, shortened equipment life, and increased equipment repair and replacement costs. At the same time, unreasonable equipment allocation will also lead to energy waste, further increasing operating costs.

[0005] In terms of adaptability, the port operating environment is complex and ever-changing, affected by a variety of factors such as market demand, policies and regulations, and weather conditions. The static nature of traditional threshold setting methods and the limitations of some existing data analysis methods make it difficult for ports to quickly adapt to these changes. When market demand fluctuates, such as a sudden increase or decrease in the transportation demand for a certain type of cargo, ports may not be able to properly allocate resources to meet market demand because traditional thresholds cannot be adjusted in a timely manner according to market changes. This will not only lead to a decline in customer satisfaction, but may also cause ports to miss out on business opportunities and be at a disadvantage in market competition. For example, during the peak season, if the operating thresholds cannot be dynamically adjusted according to the increase in cargo transportation volume and manpower and equipment investment cannot be reasonably increased, cargo backlogs will occur; and if the thresholds cannot be lowered accordingly during the off-season, idle resources will be wasted.

[0006] In summary, the shortcomings of traditional threshold setting methods and some existing data analysis methods have severely restricted port operational efficiency, increased operating costs, and reduced ports' adaptability to complex and changing environments. Therefore, a method for generating dynamic port thresholds based on data association analysis is urgently needed to address the shortcomings of existing technologies. Summary of the Invention

[0007] The purpose of this invention is to propose a method for generating dynamic thresholds for ports based on data association analysis. The method aims to dynamically generate business threshold setting suggestions through multi-dimensional, multi-parameter, and multi-condition data association analysis, thereby more accurately guiding port production operations, improving terminal operation data and resource utilization, and achieving business goals.

[0008] To achieve the above object, the present invention provides a method for generating port dynamic thresholds based on data association analysis, comprising the following steps: S1. Using the port production and operation data, obtain the port production and operation history database; S2. performing correlation analysis based on the port production and operation history database to obtain correlation relationships between port production and operation indicators; S3. Obtaining a change trend of the port production and operation indicators using the port production and operation history database according to the correlation relationship of the port production and operation indicators; S4. Obtaining a port dynamic threshold value according to the correlation relationship between the port production and operation indicators and the change trend of the port production and operation indicators.

[0009] Optionally, S1. Utilize the port production and operation data to obtain a port production and operation history database, including: Acquire port real-time data, port planning data and port operation data as port production and operation data; Using the port production and operation data, obtaining corresponding port production and operation historical data; Constructing a target port production and operation history database based on the port production and operation history data; Performing time sorting processing on the target port production and operation history database to obtain the port production and operation history database; Among them, the real-time port data includes actual ship dynamic data, actual ship berthing data and actual ship departure data; the port planning data includes ship dynamic planning data, ship berthing planning data and ship departure planning data; the port operation data includes quay crane operation data, yard crane operation data, port collection and distribution operation data and gate data.

[0010] Optionally, constructing a target port production and operation history database based on the port production and operation history data includes: Using the port production and operation historical data, a historical port basic data set is obtained, wherein the historical port basic data set includes a plurality of historical port basic data, each of which includes cargo information, basic ship information, shoreline information, berth information, quay crane information, storage yard information, and yard crane information; Matching the port production and operation historical data with the historical port basic data set to construct an initial port production and operation historical database, wherein the initial port production and operation historical database includes a plurality of port production and operation historical data sets; According to the initial port production and operation history database, a first port production and operation history data set, a second port production and operation history data set, and a third port production and operation history data set are obtained; Determine whether the first port production and operation history data set is completely consistent with the second port production and operation history data set, and if so, perform a first operation; otherwise, perform a second operation; The first operation is to determine whether the first port production and operation history data set is completely consistent with the third port production and operation history data set; if so, obtain the initial port production and operation history database with the second port production and operation history data set and the third port production and operation history data set deleted as the basic port production and operation history database, and perform the third operation; otherwise, obtain the initial port production and operation history database with the second port production and operation history data set deleted as the basic port production and operation history database, and perform the third operation; The second operation is: determining whether the second port production and operation history data set is completely consistent with the third port production and operation history data set; if so, obtaining the initial port production and operation history database with the third port production and operation history data set deleted as the basic port production and operation history database, and performing the third operation; otherwise, obtaining the initial port production and operation history database as the basic port production and operation history database, and performing the third operation; The third operation is to determine whether the basic port production and operation history database contains the same port production and operation history data set; if so, use the basic port production and operation history database as the initial port production and operation history database and return to the fourth operation; otherwise, execute the fifth operation; The fourth operation is: obtaining a first port production and operation history data set, a second port production and operation history data set, and a third port production and operation history data set according to the initial port production and operation history database; The fifth operation is: determining whether the basic port production and operation history database contains a port production and operation history data set of the same historical port basic data; if so, integrating the port production and operation history data set of the same historical port basic data according to the basic port production and operation history database, and obtaining the integration result of the basic port production and operation history database as the target port production and operation history database; otherwise, obtaining the basic port production and operation history database as the target port production and operation history database.

[0011] Optionally, S2, performing correlation analysis based on the port production and operation history database to obtain correlation relationships among port production and operation indicators, including: Obtaining basic information of the port production and operation indicators by utilizing the port production and operation history database according to the port production and operation indicators; Performing correlation analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain correlation results of the port production and operation indicators; Obtaining a correlation relationship between the port production and operation indicators according to the correlation result of the port production and operation indicators; Wherein, obtaining the port production and operation indicator association relationship according to the port production and operation indicator association result includes: Utilizing the port production and operation indicator correlation result and the corresponding historical port production and operation indicator correlation result to obtain a judgment criterion for the port production and operation indicator correlation result; Determine whether the port production and operation indicator association results all meet the port production and operation indicator association result discrimination criteria; if so, obtain the port production and operation indicator association results as the port production and operation indicator association relationship; otherwise, perform the sixth operation; The sixth operation is to determine whether the port production and operation indicator association result partially meets the judgment criteria of the port production and operation indicator association result. If so, obtain the port production and operation indicator association result that meets the port production and operation indicator association result threshold as the port production and operation indicator association relationship, and use the port production and operation indicator association result that does not meet the port production and operation indicator association result threshold to update the port production and operation indicator association result, and return to the seventh operation. Otherwise, directly return to the seventh operation. The seventh operation is: using the port production and operation indicator correlation result and the corresponding historical port production and operation indicator correlation result to obtain a judgment standard for the port production and operation indicator correlation result.

[0012] Optionally, basic information of the port production and operation indicators is obtained by utilizing the port production and operation history database according to the port production and operation indicators, including: Obtain port operation data, port operation efficiency, port direct call rate, port direct departure rate and port shoreline occupancy rate as port production and operation indicators. The port operation data includes container operation data, cargo operation data, cruise operation data and bulk cargo operation data. The port operation efficiency includes berthing efficiency, average single bridge efficiency and target efficiency. Utilizing the port production and operation database, obtaining port production and operation history rules; Determine whether all the port production and operation data comply with the port production and operation history rules; if so, perform rule analysis on the port production and operation indicators using the port production and operation history rules to obtain indicator definitions of the port production and operation indicators, and execute the eighth, ninth, and tenth operations; otherwise, delete the port production and operation data that does not comply with the port production and operation history rules based on the port production and operation data, and return to the eleventh operation; The eighth operation is: using the port production and operation data according to the indicator definition of the port production and operation indicator and the port production and operation history rule to obtain the indicator formula and indicator analysis dimensions of the port production and operation indicator, wherein the indicator analysis dimensions include a time dimension, a space dimension, a business entity dimension, and a cargo dimension; The ninth operation is: performing positioning processing according to the port production and operation indicators to obtain the indicator data source of the port production and operation indicators; The tenth operation is: obtaining the port production and operation indicator and the indicator definition, indicator formula, indicator analysis dimension, and indicator data source of the port production and operation indicator as basic information of the port production and operation indicator; The eleventh operation is: using the port production and operation data to obtain corresponding port production and operation historical data.

[0013] Optionally, performing correlation analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain correlation results of the port production and operation indicators includes: Performing dimension analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain indicator dimensions of the port production and operation indicators; Obtaining a port production and operation indicator matrix by using the basic information of the port production and operation indicators according to the indicator dimensions of the port production and operation indicators; Performing linear analysis based on the port production and operation indicator matrix to obtain linear analysis results of the port production and operation indicators; Determine whether the linear analysis result of the port production and operation indicator is a linear relationship. If so, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform Pearson correlation coefficient analysis to obtain the correlation result of the port production and operation indicator. Otherwise, perform the twelfth operation. Among them, the twelfth operation is: determine whether the linear analysis result of the port production and operation indicator is a curve relationship. If so, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform polynomial regression analysis to obtain the correlation result of the port production and operation indicator; otherwise, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform nonlinear regression analysis to obtain the correlation result of the port production and operation indicator.

[0014] Optionally, S3, using the port production and operation history database according to the port production and operation indicator association relationship, to obtain a change trend of the port production and operation indicator, including: According to the port production and operation indicator correlation relationship, the port production and operation history database is used to obtain the corresponding historical port production and operation indicator correlation relationship; Utilizing the historical port production and operation indicator correlation relationship and the port production and operation history database, a port production and operation indicator prediction model is constructed; The port production and operation indicator prediction model is used according to the port production and operation indicator correlation relationship and the port production and operation data to obtain the change trend of the port production and operation indicator.

[0015] Optionally, obtaining the corresponding historical port production and operation indicator correlation relationship by using the port production and operation history database according to the port production and operation indicator correlation relationship includes: Obtaining basic port production and operation historical data using the port production and operation historical database according to the historical port production and operation indicator correlation relationship; According to the basic port production and operation historical data, obtain the corresponding historical port real-time data and historical port planning data; Obtaining the historical port production and operation indicator correlation relationship and historical port real-time data as a training set; Taking the training set as input and the changing trend of the historical port production and operation indicators corresponding to the training set as output, an initial port production and operation indicator prediction model is constructed based on a convolutional neural network; Obtaining the historical port production and operation indicator correlation relationship and the historical port planning data as a validation set; Inputting the verification set into the initial port production and operation indicator prediction model to obtain the change trend of the historical port production and operation indicators corresponding to the verification set; Determine whether the change trend of the historical port production and operation indicators corresponding to the validation set is completely consistent with the change trend of the historical port production and operation indicators corresponding to the training set; if so, obtain the initial port production and operation indicator prediction model as the port production and operation indicator prediction model; otherwise, update the training set using the change trend of the historical port production and operation indicators corresponding to the validation set, and return to execute the thirteenth operation; Among them, the thirteenth operation is: taking the training set as input, the changing trend of the historical port production and operation indicators corresponding to the training set as output, and constructing an initial port production and operation indicator prediction model based on a convolutional neural network.

[0016] Optionally, obtaining a change trend of the port production and operation indicator by using the port production and operation indicator prediction model according to the port production and operation indicator correlation relationship and the port production and operation data includes: Acquiring initial port production and operation data using the port production and operation data according to the port production and operation indicator correlation relationship; Using the initial port production and operation data, corresponding port real-time data and port planning data are obtained as target port real-time data and target port planning data respectively; According to the correlation relationship between the port production and operation indicators and the real-time data of the target port, the port production and operation indicator prediction model is input to obtain the change trend of the initial port production and operation indicators; According to the correlation relationship between the port production and operation indicators and the target port plan data, the port production and operation indicator prediction model is input to obtain a change trend threshold of the port production and operation indicator; Determine whether the changing trend of the initial port production and operation indicator meets the changing trend threshold of the port production and operation indicator; if so, obtain the changing trend of the initial port production and operation indicator as the changing trend of the port production and operation indicator; otherwise, update the training set according to the changing trend of the initial port production and operation indicator, and return to execute the thirteenth operation.

[0017] Optionally, S4, obtaining a port dynamic threshold value according to the correlation relationship between the port production and operation indicators and the change trend of the port production and operation indicators, including: Utilizing the port production and operation indicator correlation relationship and the corresponding historical port production and operation indicator correlation relationship to obtain a historical port production and operation indicator correlation relationship threshold; Obtaining an initial threshold value of the port production and operation indicator according to the port production and operation indicator correlation relationship using the historical port production and operation indicator correlation relationship threshold value; Utilizing the changing trends of the port production and operation indicators, obtaining the influencing factors of the port production and operation indicators; Obtaining a threshold adjustment strategy for the port production and operation indicator by using the influencing factors of the port production and operation indicator according to the initial threshold of the port production and operation indicator; The initial threshold value of the port production and operation indicator is dynamically adjusted according to the threshold value adjustment strategy of the port production and operation indicator, and the dynamically adjusted threshold value of the port production and operation indicator is obtained as the port dynamic threshold value.

[0018] Compared with the closest prior art, the present invention has the following beneficial effects: This application uses multi-dimensional, multi-parameter, and multi-condition data association analysis to comprehensively consider the impact of multiple factors on port production, which can provide in-depth insights into the root causes of business changes, improve the accuracy and adaptability of threshold settings, and thus more accurately guide port production operations; the present invention realizes the dynamic setting and release of thresholds. The dynamically generated thresholds can enable port production operations to be adjusted in a timely manner according to actual conditions, and can also respond to changes in the port production environment in a timely manner, better meet the needs of port operation management, and help improve terminal operation data and operation efficiency, and improve the overall port operation efficiency; the present invention reduces the manual judgment and reprocessing of data statistical analysis results, reduces labor costs, and avoids errors that are easy to occur during manual processing. This application improves the analysis ability of complex relationships between indicators through means such as association data analysis and deep learning, realizes the dynamic generation and adjustment of port business thresholds, can better adapt to the dynamic changes of port business, and improve the efficiency and benefits of port production operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 The present invention provides a flowchart of a method for generating dynamic port thresholds based on data association analysis. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] The terms used in the embodiments of the present invention are only used to explain the specific embodiments of the present invention and are not intended to limit the present invention.

[0023] Traditional port data statistical analysis suffers from numerous limitations. Analysis is often confined to a single dimension and limited conditions, such as the common practice of "monthly / annual statistics for group operations or a specific secondary terminal." This analytical model requires human judgment and reprocessing before the results can be applied to specific operations, such as adjusting performance assessment thresholds.

[0024] This not only results in a massive statistical workload but is also prone to errors during manual processing. Furthermore, this model lacks flexibility and relies on post-analysis, failing to provide timely and effective guidance for businesses. Furthermore, due to the single-minded nature of the analysis process, the results are of low value, making it difficult to gain insight into the root causes of business changes, let alone provide specific and effective measures and recommendations tailored to the specific business.

[0025] Taking the factors that affect port operation data as an example, they cover multiple aspects. The geographical location of the port includes the spatial distance from hinterland customers, the degree of logistics convenience (railway, highway and other transportation conditions), the convenience of ships entering and leaving the port, etc.; the hinterland economy of the port involves the port customer base, surrounding customer base, cross-provincial / regional customer base, and the current local business environment and policies and regulations; port charging (preferential) policies, such as loading and unloading fees, storage fees, port facility security fees, etc.; the number of port customers is also a key factor, including the number of shippers, freight forwarders, shipowners, shipping agents, traders, etc.; port service conditions, such as high loading and unloading efficiency, less unplanned downtime, fast response, convenience of online services, efficiency of customs and maritime services, etc. However, only analyzing the single data of annual port operation data (tons or TEUs) cannot deeply understand the fundamental reasons that affect the changes in operation data, let alone provide targeted guidance for port operations. In response to the above problems, the present invention proposes a method for generating dynamic port thresholds based on data association analysis.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a method for generating a port dynamic threshold based on data association analysis, comprising the following steps: S1. Using the port production and operation data, obtain the port production and operation history database; By comprehensively collecting various data generated during the daily production and operation of the port, such as ship arrival and departure times, quay crane operation times, and ship voyages, this scattered data is integrated, cleaned, and stored to build a historical database of port production and operations. This process provides a rich, accurate, and structured data foundation for subsequent data analysis, allowing various types of port operation information to be stored in an orderly manner, facilitating subsequent in-depth mining of data value and avoiding analytical biases caused by missing or disorganized data.

[0027] S2. performing correlation analysis based on the port production and operation history database to obtain correlation relationships between port production and operation indicators; Based on an established historical database of port production and operations, data analysis algorithms and techniques are used to conduct correlation analysis on various production and operation indicators, revealing the potential interactions and dependencies between different indicators. This process can help port managers gain a deeper understanding of the inherent connections between various aspects of port operations, clarify which indicator changes will have a significant impact on other indicators, and provide a basis for optimizing port operation strategies and avoiding blind decisions.

[0028] S3. Obtaining a change trend of the port production and operation indicators using the port production and operation history database according to the correlation relationship of the port production and operation indicators; After clarifying the correlations between port production and operation indicators, the corresponding historical correlations of port production and operation indicators are extracted from the port production and operation historical database. This correlation and the historical database are then used to construct an indicator prediction model. Finally, the prediction model, correlations, and real-time production and operation data are used to determine the changing trends of port production and operation indicators. This process combines historical experience with real-time data to accurately predict the future development trends of various port production and operation indicators. This allows port managers to gain early insight into potential operational changes, flexibly adjust resource allocation strategies, and calmly respond to production peaks and troughs. This significantly improves the foresight and adaptability of port operations and enhances the port's competitiveness in complex market environments.

[0029] S4. Obtaining a port dynamic threshold value based on the correlation relationship between the port production and operation indicators and the change trend of the port production and operation indicators; By comprehensively analyzing the correlations and changing trends of port production and operation indicators and applying scientific calculation methods, dynamic thresholds for each port production and operation indicator are determined. These thresholds are not fixed but are dynamically adjusted based on the correlations and development trends between indicators. For example, if a strong correlation is found between cargo operation data and the number of loading and unloading equipment, and cargo operation data is on an upward trend, the appropriate threshold range for the number of loading and unloading equipment will be adjusted accordingly. This process establishes scientific early warning boundaries for port operations. When indicators exceed the dynamic threshold range, timely warnings can be issued, helping port managers quickly identify anomalies and take targeted measures to ensure the stable and efficient operation of port production and operation activities.

[0030] As a possible implementation, in the above embodiment, step S1 may specifically include the following steps: S1-1. Acquire port real-time data, port planning data and port operation data as port production and operation data; Port production and operations data is collected from multiple systems related to port production and operations, integrating them based on actual conditions. This data includes, but is not limited to, real-time port data, port planning data, and port operations data. These systems include, but are not limited to, port production and operations management systems, vessel scheduling systems, cargo loading and unloading systems, and equipment maintenance systems. This process comprehensively collects data from multiple dimensions of port operations, covering immediate operational conditions, pre-planned goals, and specific operational execution. This ensures data integrity and timeliness, providing rich and accurate raw data support for subsequent analysis.

[0031] In this embodiment, the real-time port data includes actual dynamic data of ships, actual berthing data of ships, actual data of ship departure, etc., wherein the actual dynamic data of ships include the ship number, ship name, ship entry and exit voyages, dynamic type (entry, departure, shifting), dynamic actual time, dynamic change, change reason, change time, actual entry terminal, etc.; the actual berthing data of ships include the berthed ship number, berthing berth, actual time of tying the first cable, actual time of tying the last cable, actual berthing time, etc.; the actual data of ship departure include the departure ship number, berthing berth, actual time of releasing the first cable, actual time of releasing the last cable, actual departure time, etc.

[0032] Port planning data includes ship dynamic planning data, ship berthing planning data, ship departure planning data, etc. Among them, ship dynamic planning data includes ship number, ship name, ship entry and exit voyages, dynamic type (entry, departure, shifting), dynamic planning time, planned entry terminal, etc. Ship berthing planning data includes berthing ship number, berthing berth, planned time for tying the first cable, planned time for tying the last cable, planned berthing duration, etc.; ship departure planning data includes departing ship number, berthing berth, planned time for releasing the first cable, planned time for releasing the last cable, planned departure duration, etc.

[0033] Port operation data includes quay crane operation data, yard crane operation data, port collection and distribution operation data, gate data, etc. Among them, quay crane operation data includes quay crane number, quay crane operation data, quay crane operation start time, quay crane operation end time, quay crane operation duration, actual single-bridge inlet / export operation data, actual start and end time of single-bridge inlet / export, actual single-bridge downtime, actual downtime reasons (such as equipment downtime, system downtime, difficult operation downtime, quay crane oversized container operation, personnel operation downtime, etc.); yard crane operation data includes yard crane number, yard crane operation data, yard crane operation start time, The end time of yard crane operation, yard crane operation duration, actual yard crane stop time, reason for yard crane stop time, etc.; port collection and distribution operation data includes yard operation data and site container transfer data. Yard operation data includes shifts, operation box information, empty box information, full box information, oversized box information, refrigerated box information, advance port collection box information, reserved box information, operation time period and operation type (port collection, container pickup, transshipment), etc. Site container transfer includes shifts, container transfer information and operation time period; gate data includes fleet, vehicles, entry and exit time, container collection / pickup / transshipment, terminal, container number, ship name, ship number, gate number, etc.

[0034] S1-2. Using the port production and operation data, obtain corresponding port production and operation historical data; Based on the acquired port production and operation data, data storage and archiving technologies are used to save current real-time, planned, and operational data in chronological order, converting them into historical data. This process preserves the exact time records of each ship's arrival and departure, as well as detailed data on every cargo loading and unloading operation. This process, by establishing a historical archive of port operation data, provides a rich source of historical data for subsequent in-depth analysis of port production and operation patterns and comparison of operating conditions over time, preventing data loss over time.

[0035] S1-3. Constructing a target port production and operation history database based on the port production and operation history data; The collected historical port production and operation data is organized using database management technology to build a historical database of the target port's production and operation. This process transforms scattered historical data into an organized, easy-to-manage and query database, improving data organization and accessibility. This facilitates subsequent efficient correlation analysis and in-depth mining of port production and operation data, providing a stable data platform for data-driven decision-making.

[0036] S1-4, performing time sorting processing on the target port production and operation history database to obtain the port production and operation history database; The data in the constructed target port production and operation history database is sorted according to the timestamps generated by the data, ensuring that the data is arranged in chronological order, thereby obtaining the final port production and operation history database. This process gives the data in the database a clear time logic, providing a standardized data structure for subsequent time series-based data analysis, such as trend forecasting and periodic pattern mining. This facilitates more accurate analysis of the changes in port production and operation indicators over time, enhancing the scientific nature and reliability of data analysis.

[0037] As a possible implementation, in the above embodiment, step S1-3 may specifically include the following steps: S1-3-1. Utilizing the port production and operation historical data, obtain a historical port basic data set, wherein the historical port basic data set includes a plurality of historical port basic data, each of which includes cargo information, basic vessel information, shoreline information, berth information, quay crane information, storage yard information, and yard crane information; In this embodiment, cargo data includes the name, code, description, parent cargo category, terminal, etc. of the cargo; basic ship information includes the name, code, nationality, trade category, width, load, route, length, etc. of the ship; shoreline data includes the name, code, terminal, length, description, etc. of the shoreline; berth data includes the name, code, terminal, shoreline, length, description, etc. of the berth; quay crane data includes the name, code, model, rated load, whether automated, shoreline, berth, terminal, etc. of the quay crane; the yard data includes the name, code, location, capacity, yard area (name, code, capacity), block (name, code, capacity), terminal, etc. of the yard; and yard crane data includes the name, code, model, rated load, whether automated, etc. of the yard crane.

[0038] S1-3-2. Matching the port production and operation historical data with the historical port basic data set to construct an initial port production and operation historical database, wherein the initial port production and operation historical database includes a plurality of port production and operation historical data sets; Using the historical port basic data set as the reference standard, the port production and operation historical data are compared and matched one by one, and the qualified data are combined into port production and operation historical data sets, thereby constructing the initial port production and operation historical database.

[0039] S1-3-3. Acquire a first port production and operation history data set, a second port production and operation history data set, and a third port production and operation history data set based on the initial port production and operation history database; Data partitioning operations help to split large-scale data, reduce the amount of data processed at a single time, facilitate subsequent detailed comparison and inspection of the data, improve the accuracy and efficiency of data processing, and also provide more flexible operating space for data optimization and screening.

[0040] S1-3-4. Determine whether the first port production and operation history data set is completely consistent with the second port production and operation history data set. If so, execute S1-3-5; otherwise, directly execute S1-3-6; This judgment process can quickly identify duplicate content in the data set, and it can promptly discover redundant data, providing a basis for subsequent data streamlining and optimization, avoiding duplicate data occupying storage space and affecting the accuracy of data analysis results.

[0041] S1-3-5. Determine whether the first port production and operation history data set is completely consistent with the third port production and operation history data set. If so, obtain the initial port production and operation history database with the second port production and operation history data set and the third port production and operation history data set deleted as the basic port production and operation history database, and directly execute S1-3-7. Otherwise, obtain the initial port production and operation history database with the second port production and operation history data set deleted as the basic port production and operation history database, and directly execute S1-3-7. This process eliminates data redundancy to the greatest extent possible, making the data in the database more refined and improving the database storage efficiency and the speed of data query and analysis.

[0042] S1-3-6. Determine whether the second port production and operation history data set is completely consistent with the third port production and operation history data set. If so, obtain the initial port production and operation history database with the third port production and operation history data set deleted as the basic port production and operation history database, and execute S1-3-7. Otherwise, obtain the initial port production and operation history database as the basic port production and operation history database, and execute S1-3-7. This step further improves the data screening and optimization process, ensures the uniqueness of data in the database, avoids analysis errors caused by data duplication, and provides guarantees for the accuracy and reliability of subsequent data.

[0043] S1-3-7. Determine whether the basic port production and operation history database contains the same port production and operation history data set. If so, use the basic port production and operation history database as the initial port production and operation history database and return to S1-3-3. Otherwise, execute S1-3-8. This step implements cyclical checking and processing of data redundancy, ensuring that there is no duplicate data in the database after multiple screenings, continuously optimizing the database quality, and ensuring the purity and validity of the data.

[0044] S1-3-8. Determine whether the basic port production and operation history database contains a port production and operation history data set of the same historical port basic data. If so, integrate the port production and operation history data set of the same historical port basic data according to the basic port production and operation history database, and obtain the integration result of the basic port production and operation history database as the target port production and operation history database; otherwise, obtain the basic port production and operation history database as the target port production and operation history database.

[0045] This process further optimizes the data structure, eliminates data dispersion and duplication, and forms a target port production and operation history database with clear structure, complete data and no redundancy, providing high-quality data support for port production and operation data analysis and decision-making.

[0046] As a possible implementation, in the above embodiment, step S1-3-2 may specifically include the following steps: S1-3-2-1. Use the historical port basic data set to match the port production and operation historical data, and obtain the historical port basic data and the corresponding port real-time data, port plan data, and port operation data as the port production and operation historical data set; S1-3-2-2. Determine whether the port production and operation historical data set contains historical port basic data. If so, execute S1-3-2-3. Otherwise, obtain the corresponding port production and operation historical data based on the port production and operation historical data set that does not contain historical port basic data, and return to S1-3-1. S1-3-2-3. Determine whether the port production and operation historical data set contains the port real-time data, port planning data, and port operation data corresponding to the historical port basic data. If so, construct the initial port production and operation historical database based on the port production and operation historical data set. Otherwise, construct the initial port production and operation historical database based on the port production and operation historical data set containing the port real-time data, port planning data, and port operation data corresponding to the historical port basic data.

[0047] In summary, steps S1-3-2-1 to S1-3-2-3 have standardized the data structure and improved data quality through layer-by-layer screening and strict verification. This not only enhances the logic and correlation between data, but also provides an accurate and reliable data basis for subsequent in-depth data analysis, indicator correlation research, and port operation decision-making, avoiding analysis deviations due to missing or incorrect data, and improving the overall efficiency and scientific nature of port data management and operation analysis.

[0048] As a possible implementation, in the above embodiment, step S2 may specifically include the following steps: S2-1. Obtain basic information of the port production and operation indicators using the port production and operation history database according to the port production and operation indicators; Guided by port production and operation indicators, we search and extract basic information from the established port production and operation historical database. This step accurately links scattered data in the database with specific indicators, providing a detailed and targeted data foundation for subsequent correlation analysis, avoiding information loss or deviation during analysis and ensuring that the indicator research is based on sufficient data support.

[0049] S2-2. performing correlation analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain correlation results of the port production and operation indicators; Based on the basic information obtained about port production and operation indicators, we use statistical methods, data analysis, and other technical means to conduct an in-depth analysis of the relationships between various indicators. This process can reveal potential causal relationships, synergistic relationships, or constraints between indicators, presenting the complex connections between indicators to port managers, helping them break through the limitations of traditional empirical cognition and identify key influencing factors in operations from a data perspective.

[0050] S2-3. Obtaining a correlation relationship between port production and operation indicators based on the correlation result of the port production and operation indicators; S2-3-1. Utilizing the port production and operation index correlation result and the corresponding historical port production and operation index correlation result, obtaining a judgment criterion for the port production and operation index correlation result; S2-3-2. Determine whether the port production and operation indicator association results all meet the judgment criteria for the port production and operation indicator association results. If so, obtain the port production and operation indicator association results as the port production and operation indicator association relationship. Otherwise, execute S2-3-3. S2-3-3. Determine whether the port production and operation indicator association result partially meets the judgment criteria of the port production and operation indicator association result. If so, obtain the port production and operation indicator association result that meets the port production and operation indicator association result threshold as the port production and operation indicator association relationship, and use the port production and operation indicator association result that does not meet the port production and operation indicator association result threshold to update the port production and operation indicator association result, and return to S2-3-1. Otherwise, directly return to S2-3-1.

[0051] In this embodiment, the Pearson correlation coefficient analysis is performed on the port production and operation indicators with linear relationships, the polynomial regression analysis is performed on the port production and operation indicators with nonlinear relationships and curvilinear relationships, and the nonlinear regression analysis is performed on the port production and operation indicators with nonlinear relationships but no curvilinear relationships. For the port production and operation indicators whose correlation results are Pearson correlation coefficients, the judgment standard is the Pearson correlation coefficient. rThe value range of is between [-1, 1], where | r| The closer it is to 1, the stronger the linear correlation between the two variables; | r| The closer it is to 0, the weaker the linear correlation. For the port production and operation index correlation results that are determination coefficients, the determination coefficients of the port production and operation indexes are calculated. R 2 To evaluate the fitting degree of port production and operation indicators, among which the determination coefficient R 2 The closer the value is to 1, the better the fit of the port production and operation indicators, that is, the more significant the nonlinear relationship between the port production and operation indicators. For port production and operation indicator correlation results that are mean square errors, the mean square error (MSE) of the port production and operation indicators is used to evaluate the fitting effect of the port production and operation indicators. The lower the MSE, the better the fit of the port production and operation indicators.

[0052] In summary, steps S2-3-1 to S2-3-3 determine the correlation between port production and operation indicators. First, the current indicator correlation results are compared with similar historical results in multiple dimensions, and the judgment criteria are formulated based on factors such as data fluctuation range and relationship stability. Then, the correlation results are comprehensively checked according to the standards. If all of them meet the standards, they are directly identified as the final correlation relationship, otherwise, the next step is entered. Finally, it is judged whether the correlation results partially meet the standards. If they partially meet the standards, the qualified results are extracted as the correlation relationship, and the unqualified results are used to update the overall correlation results and then return to re-determine the judgment criteria. If all of them do not meet the standards, the correlation results are directly updated and the judgment criteria are determined again. Through this dynamic iterative optimization mechanism, abnormal correlations can be effectively identified, ensuring that the final correlation relationships are true and reliable, providing accurate and practical support for port operation decisions.

[0053] As a possible implementation, in the above embodiment, step S2-1 may specifically include the following steps: S2-1-1. Obtain port operation data, port operation efficiency, port direct call rate, port direct departure rate and port shoreline occupancy rate as port production and operation indicators; In this embodiment, port operation data refers to information on various types of cargo, including containerized cargo, bulk cargo, and general cargo, that enter and exit the port via waterways and are loaded and unloaded during a specific period. This includes container operation data, cargo operation data, cruise ship operation data, and general cargo operation data. Port operation efficiency includes berthing efficiency, average single-bridge efficiency, and target efficiency (M-efficiency). These data reflect port operation efficiency from different perspectives. By analyzing these data, factors affecting operational efficiency can be identified, and operational processes can be optimized to improve overall port operational efficiency. Berthing efficiency refers to the average hourly loading and unloading operation data during the vessel's berthing time (the time from tying the first line to untying the last line); average single-bridge efficiency refers to the average hourly loading and unloading operation data for a single operating line during the vessel's operating time; and M-efficiency refers to the average hourly loading and unloading operation data for the operating line with the most operation data during the vessel's operating time. The port's direct call rate refers to the proportion of vessel data that arrive within two hours (inclusive) of a cargo liner's arrival, compared to all cargo liner vessel data that arrive at the port. The port's direct departure rate is the ratio of ships that departed within two hours (inclusive) of the cargo liner's completion to the total number of cargo liner ships departing. The port's shoreline occupancy rate is the ratio of dock shoreline occupied by berthed ships. Furthermore, in this embodiment, the dynamic ship fulfillment rate, container pickup and delivery times of less than 30 minutes, container pickup and delivery times of less than 60 minutes, and average vehicle demurrage time can all be used as port production and operation indicators.

[0054] S2-1-2. Utilizing the port production and operation database, obtaining port production and operation history rules; In the port production and operation database, we deeply explore the data patterns and business logic accumulated during past operations to obtain historical rules of port production and operation, such as the fluctuation patterns of cargo operation data in different seasons and the changing trends of port operation efficiency in specific time periods.

[0055] S2-1-3. Determine whether all the port production and operation data conform to the port production and operation history rules. If so, perform rule analysis on the port production and operation indicators using the port production and operation history rules to obtain indicator definitions of the port production and operation indicators, and execute S2-1-4. Otherwise, delete the port production and operation data that does not conform to the port production and operation history rules based on the port production and operation data, and return to S1-2. The collected port production and operation data are compared and verified with historical rules, and the rationality and validity of the data are judged based on historical laws, avoiding analysis deviations caused by abnormal data, ensuring the accuracy of indicator definitions, and laying a solid and reliable data foundation for subsequent analysis.

[0056] S2-1-4. Obtaining an indicator formula and an indicator analysis dimension for the port production and operation indicator using the port production and operation data according to the indicator definition of the port production and operation indicator and the port production and operation historical rules; Based on clear indicator definitions and historical rules, combined with port production and operation data, mathematical modeling, statistical analysis, and other methods are used to derive the calculation methods for each indicator, forming corresponding indicator formulas, such as the formula for calculating port operating efficiency. At the same time, analytical perspectives such as time, space, business entities, and cargo are determined. This step quantifies and deconstructs the indicators in multiple dimensions, providing standardized calculation methods and multi-dimensional research perspectives for port operation analysis, facilitating in-depth analysis of port operations from different angles and mining the value of data.

[0057] In this embodiment, the time dimension analyzes indicators at different time scales, including year, month, and day. For example, for ship operation efficiency indicators such as berthing efficiency and average single-bridge efficiency, daily analysis can promptly identify efficiency differences in daily operations and facilitate timely adjustment of operation arrangements. The spatial dimension analyzes different areas or geographical locations within the port, including group and terminal levels. Due to different geographical locations and facility conditions, different terminals have different operating efficiency and operating data. By comparing the indicator data of each terminal, it is possible to identify the strong and weak terminals, providing a basis for resource allocation and terminal construction planning. The business entity dimension analyzes various entities involved in port operations, including shipping companies, routes, and ship numbers. For example, analyzing indicators such as the efficiency of ships of different shipping companies at berthing and the average single-bridge efficiency can help ports and shipping companies better collaborate and optimize operational processes. The cargo dimension analyzes cargo-related attributes, such as cargo type and cargo loading conditions. Different cargo types have different loading and unloading requirements and efficiency. For example, the loading and unloading methods and efficiency of containerized cargo and bulk cargo vary greatly. By analyzing operational data, loading and unloading efficiency and other indicators of different cargo types, loading and unloading equipment and operational processes can be adjusted in a targeted manner to improve operational efficiency.

[0058] S2-1-5. Perform positioning processing based on the port production and operation indicators to obtain the indicator data source of the port production and operation indicators; For each port's production and operation indicator, by sorting out the port's information system, business processes, and data storage architecture, we can accurately locate the source and storage location of its data, provide clear guidance for subsequent data collection, updating, and maintenance, and avoid data loss or errors caused by confusion in data sources.

[0059] S2-1-6. Obtain the port production and operation indicators and their definitions, formulas, analysis dimensions, and data sources as basic information of the port production and operation indicators.

[0060] As a possible implementation, in the above embodiment, step S2-2 may specifically include the following steps: S2-2-1. Performing dimensional analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain indicator dimensions of the port production and operation indicators; Based on the basic information obtained about port production and operation indicators, we conduct an in-depth analysis of the characteristics of each indicator in terms of time, space, business entities, and cargo, and comprehensively sort out the components and influencing factors of the indicators. For example, we analyze the performance differences of port operation data in the monthly time dimension and the spatial dimension of different port areas, and clarify the influence of factors such as the business entity of the shipping company and the type of cargo. This avoids the one-sided analysis caused by missing dimensions and lays the foundation for accurately exploring indicator correlations.

[0061] S2-2-2. Obtain a port production and operation indicator matrix using the basic information of the port production and operation indicators according to the indicator dimensions of the port production and operation indicators; Based on the indicator dimensions, port production and operation indicators and their related attributes (definitions, formulas, data sources, etc.) are structured and integrated to construct a port production and operation indicator matrix. The matrix forms a two-dimensional, data-based, and visual table format with indicators as rows and dimension attributes as columns. For example, indicators of port operation efficiency, such as berthing efficiency and average single-bridge efficiency, are entered into corresponding matrix cells according to dimensional attributes such as time and space.

[0062] S2-2-3. Perform linear analysis based on the port production and operation indicator matrix, and obtain a linear analysis result between any two port production and operation indicator matrices as a port production and operation indicator linear analysis result; For the constructed port production and operation indicator matrix, linear analysis methods such as linear regression and principal component analysis are used to explore whether there is a linear relationship between any two indicator matrices, such as analyzing the degree of linear correlation between port operation data and port operation efficiency matrix.

[0063] S2-2-4. Determine whether the linear analysis result of the port production and operation indicator is a linear relationship. If so, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform Pearson correlation coefficient analysis to obtain the Pearson correlation coefficient of the corresponding port production and operation indicator as the port production and operation indicator correlation result. Otherwise, execute S2-2-5. The linear analysis results are judged. If the two indicator matrices show a linear relationship, the corresponding port production and operation indicators are selected, and the Pearson correlation coefficient analysis method is used to calculate the correlation coefficient between the indicators to quantify the degree of linear correlation between the indicators.

[0064] S2-2-5. Determine whether the linear analysis result of the port production and operation indicator is a curvilinear relationship. If so, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform a polynomial regression analysis to obtain the coefficient of determination of the corresponding port production and operation indicator as the port production and operation indicator association result. Otherwise, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform a nonlinear regression analysis to obtain the mean square error of the corresponding port production and operation indicator as the port production and operation indicator association result. This step comprehensively covers different types of correlations between indicators, completely reveals the complex nonlinear correlations between indicators, and provides a more comprehensive and accurate decision-making basis for port operation optimization.

[0065] As a possible implementation, in the above embodiment, step S3 may specifically include the following steps: S3-1. Obtain corresponding historical port production and operation indicator correlation relationships using the port production and operation history database according to the port production and operation indicator correlation relationships; Based on the determined correlation between port production and operation indicators (such as linear, curvilinear or nonlinear relationships), data is screened and extracted from the port production and operation historical database to obtain historical data that is consistent with the current indicator correlation pattern. For example, historical data pairs that are consistent with the linear correlation pattern of current port operation data and operation efficiency are found in the historical database.

[0066] S3-2. Constructing a port production and operation indicator prediction model using the historical port production and operation indicator correlation relationship and the port production and operation history database; By combining the acquired historical data on the correlation between port production and operation indicators with relevant data in the port production and operation history database, a prediction model for port production and operation indicators is constructed using techniques such as time series analysis and machine learning algorithms (such as neural networks and random forests). For example, based on the correlation between historical operation data and operation efficiency, a neural network algorithm is used to train the model to learn the patterns of change between the data.

[0067] S3-3. Obtaining a change trend of the port production and operation indicator using the port production and operation indicator prediction model based on the port production and operation indicator correlation relationship and the port production and operation data; Based on the determined correlation between port production and operation indicators, the current port production and operation data is input into the constructed prediction model. Through the calculation and analysis of the model, the future change trend of port production and operation indicators is output, such as predicting the growth or decline trend of port container operation data in the next three months.

[0068] As a possible implementation, in the above embodiment, step S3-2 may specifically include the following steps: S3-2-1. Using the port production and operation history database according to the historical port production and operation indicator correlation relationship, obtain port production and operation history data corresponding to the historical port production and operation indicator correlation relationship as basic port production and operation history data; Based on the established correlations between historical port production and operation indicators, we conduct in-depth searches and screening within the port production and operation history database to accurately extract historical data that matches these correlations. For example, we extract data corresponding to the historical curvilinear correlation between port cargo loading and unloading data and vessel turnaround time, and integrate this data into the basic port production and operation historical data. This step effectively eliminates irrelevant data interference, ensuring that the acquired data closely aligns with the indicator correlations, providing highly targeted data samples for subsequent model construction, enhancing the adaptability of data to model requirements, and improving data quality and the effectiveness of model training.

[0069] S3-2-2. Based on the basic port production and operation historical data, obtain corresponding historical port real-time data and historical port planning data; The acquired basic port production and operation historical data are carefully classified and analyzed to separate historical port real-time data (such as the number of ships berthing at historical moments, the actual progress of cargo loading and unloading, etc.) and historical port plan data (such as pre-established ship arrival plans, cargo loading and unloading plans, etc.).

[0070] S3-2-3. Obtain the historical port production and operation indicator correlation relationship and historical port real-time data as a training set; The training set for model training is constructed by organically integrating historical data on the correlation between port production and operation indicators with historical real-time port data. For example, historical correlation data on port operation data and operational efficiency is combined with real-time data from actual operations within the corresponding time period to form input samples for model training. This step constructs a data set that conforms to the model's learning logic, providing structured training data for the convolutional neural network. This allows the model to learn the correlation and change patterns between indicators from real-world operational scenario data. By training with a large number of data samples, the model's ability to fit the dynamic changes in port production and operation indicators and its prediction accuracy are improved.

[0071] S3-2-4. Using the training set as input and the changing trend of the historical port production and operation indicators corresponding to the training set as output, constructing an initial port production and operation indicator prediction model based on a convolutional neural network; Using a training set as input data and corresponding historical port production and operation indicator trends (e.g., growth trends in historical operational data and fluctuations in operational efficiency) as output targets, the method leverages the powerful feature extraction and pattern recognition capabilities of convolutional neural networks. By constructing and adjusting parameters across multiple convolutional, pooling, and fully connected layers, the model generates an initial prediction model for port production and operation indicators. Compared to traditional methods, this step more accurately captures underlying patterns in the data, providing a more intelligent and efficient tool for port operation indicator forecasting.

[0072] S3-2-5. Obtain the historical port production and operation indicator correlation relationship and the historical port plan data as a verification set; The validation set is composed of historical data on the correlation between port production and operation indicators and historical port planning data. By integrating planning data, the validation set includes planning information for port operations. For example, historical correlation data on ship arrival plans and port operational efficiency are combined to test the model's ability to predict indicator changes under planning scenarios.

[0073] S3-2-6. Input the verification set into the initial port production and operation indicator prediction model to obtain the change trend of the historical port production and operation indicators corresponding to the verification set; This step intuitively demonstrates the model's predictive capabilities in actual operation planning scenarios through the input of validation set data, provides specific quantitative results for evaluating model performance, and helps determine whether the model meets the actual needs of port operation indicator prediction.

[0074] S3-2-7. Determine whether the changing trend of the historical port production and operation indicators corresponding to the validation set is completely consistent with the changing trend of the historical port production and operation indicators corresponding to the training set. If so, obtain the initial port production and operation indicator prediction model as the port production and operation indicator prediction model. Otherwise, update the training set using the changing trend of the historical port production and operation indicators corresponding to the validation set, and return to 3-2-4. This step continuously adjusts and improves the model through an iterative optimization mechanism to ensure that the model can adapt to the differences between actual data and planned data in port operations, improve the accuracy and adaptability of model predictions, and ultimately enable the model to reach an ideal state that meets the prediction needs of port production and operation indicators.

[0075] As a possible implementation, in the above embodiment, step S3-3 may specifically include the following steps: S3-3-1. Using the port production and operation data according to the port production and operation indicator correlation relationship, obtain the port production and operation data corresponding to the port production and operation indicator correlation relationship as initial port production and operation data; Based on the determined correlation between port production and operation indicators, corresponding valid data is screened out from the huge port production and operation data, ensuring that the acquired data is closely centered around the indicator correlation to improve data processing efficiency and the effectiveness of model input data.

[0076] S3-3-2. Using the initial port production and operation data, obtain corresponding port real-time data and port planning data as target port real-time data and target port planning data, respectively; S3-3-3. Input the port production and operation indicator prediction model according to the correlation relationship between the port production and operation indicators and the real-time data of the target port, and obtain the change trend of the corresponding port production and operation indicator as the change trend of the initial port production and operation indicator; The data on the correlation between port production and operation indicators is combined with the target port's real-time data and fed into a trained port production and operation indicator prediction model. By extracting and analyzing data features, the model outputs the corresponding port production and operation indicator trend. For example, it predicts the growth trend of port operation data over the next 24 hours, which serves as the initial port production and operation indicator trend. This step utilizes the trained model to analyze and predict real-time data, providing timely and accurate indicator trend reference for port operations.

[0077] S3-3-4. Input the port production and operation indicator prediction model according to the correlation relationship between the port production and operation indicators and the target port plan data to obtain a change trend threshold of the port production and operation indicators; The port production and operation indicator correlation data and the target port plan data are input into the port production and operation indicator prediction model. The model then calculates the trend of the corresponding port production and operation indicator and sets this trend as the threshold for the port production and operation indicator change. For example, based on the ship arrival plan for the next week, the corresponding range of operating efficiency changes is predicted and used as the trend threshold. This step establishes a standard for measuring the rationality of indicator changes. Through the model's analysis of the planned data, the reasonable range of indicator changes is determined, providing a reference for subsequent evaluation of the forecast results, helping to determine whether the actual operating data conforms to the expected plan, and enhancing the risk warning capabilities of port operations management.

[0078] S3-3-5. Determine whether the change trend of the initial port production and operation indicator meets the change trend threshold of the port production and operation indicator. If so, obtain the change trend of the initial port production and operation indicator as the change trend of the port production and operation indicator. Otherwise, update the training set according to the change trend of the initial port production and operation indicator, and return to 3-2-4. This step establishes a dynamic optimization mechanism to ensure the accuracy and reliability of the prediction results through comparative verification. At the same time, it uses new data to continuously optimize the model so that it can adapt to dynamic changes in the port operation process, continuously improve the model's prediction capabilities, and ensure the scientific nature and effectiveness of port operation decisions.

[0079] As a possible implementation, in the above embodiment, step S4 may specifically include the following steps: S4-1. Obtaining a threshold value of the historical port production and operation indicator correlation relationship by using the port production and operation indicator correlation relationship and the corresponding historical port production and operation indicator correlation relationship; Based on established correlations between port production and operation indicators (such as the relationship between vessel berthing time and cargo handling efficiency), we review the range of variation in the corresponding indicator correlations in historical data. By statistically analyzing historical data, we determine the upper and lower limits of fluctuations in each indicator correlation under different operating scenarios, thereby deriving thresholds for the historical correlations between port production and operation indicators. For example, we analyzed the correlation data between vessel berthing time and cargo handling efficiency over the past five years to calculate the fluctuation range of the correlation between the two.

[0080] S4-2. Obtaining an initial threshold value of the port production and operation indicator based on the port production and operation indicator correlation relationship using the historical port production and operation indicator correlation relationship threshold value; Based on the correlations between port production and operation indicators and combined with historical thresholds for correlations between these indicators, mathematical modeling or data derivation is used to convert these thresholds into initial thresholds for individual port production and operation indicators. For example, the initial threshold range for cargo handling efficiency can be calculated based on the correlation threshold between vessel berthing time and cargo handling efficiency. This step establishes preliminary quantitative standards for port production and operation indicators, providing a baseline for subsequent evaluation of indicator changes. This allows port operations managers to clearly understand the normal fluctuation range of these indicators, facilitating operational monitoring and management.

[0081] S4-3. Utilizing the changing trend of the port production and operation indicators, obtaining the influencing factors of the port production and operation indicators; We conduct in-depth analysis of the changing trends of port production and operation indicators, applying data analysis and machine learning methods to identify key factors influencing these changes. For example, by analyzing the changing trends of port operation data, we found that seasonal factors, international trade policies, and ship capacity have significant impacts. This step clarifies the drivers of indicator changes, providing a basis for subsequent adjustments to indicator thresholds, enabling port operations managers to formulate strategies to address these influencing factors. It also helps optimize the port production and operation indicator forecasting model and improve forecast accuracy.

[0082] S4-4. Obtaining a threshold adjustment strategy for the port production and operation indicator based on the initial threshold of the port production and operation indicator and utilizing the influencing factors of the port production and operation indicator; This step establishes the connection between influencing factors and threshold adjustment, making the threshold setting more flexible and adaptable. It can dynamically adjust the indicator threshold according to changes in the actual operating environment, ensuring that the threshold always meets the actual needs of port operations and improving the dynamic response capabilities of port operation management.

[0083] S4-5. Dynamically adjust the initial threshold of the port production and operation indicator according to the threshold adjustment strategy of the port production and operation indicator, and obtain the dynamically adjusted threshold of the port production and operation indicator as the port dynamic threshold; Based on the established threshold adjustment strategy for port production and operation indicators, the initial thresholds are dynamically adjusted in real time, and the adjusted thresholds are used as the port's dynamic thresholds. For example, thresholds for indicators such as operational data and loading and unloading efficiency are continuously updated based on real-time changes in ship arrival data and cargo loading and unloading demand. This step ensures that the evaluation criteria for port operation indicators can adapt to real-time changes in actual operational data, avoiding assessment bias caused by static thresholds. This provides a more accurate reference for port operation decision-making, effectively improving the refinement and scientific nature of port operation management.

[0084] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for generating port dynamic thresholds based on data association analysis, characterized in that: The specific steps include: S1. Using the port production and operation data, obtain the port production and operation history database; S2. performing correlation analysis based on the port production and operation history database to obtain correlation relationships between port production and operation indicators; S3. Obtaining a change trend of the port production and operation indicators using the port production and operation history database according to the correlation relationship of the port production and operation indicators; S4. Obtaining a port dynamic threshold value according to the correlation relationship between the port production and operation indicators and the change trend of the port production and operation indicators.

2. The method for generating port dynamic thresholds based on data association analysis according to claim 1, characterized in that: S1. Utilize port production and operation data to obtain a port production and operation history database, including: Acquire port real-time data, port planning data and port operation data as port production and operation data; Using the port production and operation data, obtaining corresponding port production and operation historical data; Constructing a target port production and operation history database based on the port production and operation history data; Performing time sorting processing on the target port production and operation history database to obtain the port production and operation history database; Among them, the real-time port data includes actual ship dynamic data, actual ship berthing data and actual ship departure data; the port planning data includes ship dynamic planning data, ship berthing planning data and ship departure planning data; the port operation data includes quay crane operation data, yard crane operation data, port collection and distribution operation data and gate data.

3. The method for generating port dynamic thresholds based on data association analysis according to claim 2 is characterized in that: Based on the port production and operation history data, a target port production and operation history database is constructed, including: Using the port production and operation historical data, a historical port basic data set is obtained, wherein the historical port basic data set includes a plurality of historical port basic data, each of which includes cargo information, basic ship information, shoreline information, berth information, quay crane information, storage yard information, and yard crane information; Matching the port production and operation historical data with the historical port basic data set to construct an initial port production and operation historical database, wherein the initial port production and operation historical database includes a plurality of port production and operation historical data sets; According to the initial port production and operation history database, a first port production and operation history data set, a second port production and operation history data set, and a third port production and operation history data set are obtained; Determine whether the first port production and operation history data set is completely consistent with the second port production and operation history data set, and if so, perform a first operation; otherwise, perform a second operation; The first operation is to determine whether the first port production and operation history data set is completely consistent with the third port production and operation history data set; if so, obtain the initial port production and operation history database with the second port production and operation history data set and the third port production and operation history data set deleted as the basic port production and operation history database, and perform the third operation; otherwise, obtain the initial port production and operation history database with the second port production and operation history data set deleted as the basic port production and operation history database, and perform the third operation; The second operation is: determining whether the second port production and operation history data set is completely consistent with the third port production and operation history data set; if so, obtaining the initial port production and operation history database with the third port production and operation history data set deleted as the basic port production and operation history database, and performing the third operation; otherwise, obtaining the initial port production and operation history database as the basic port production and operation history database, and performing the third operation; The third operation is to determine whether the basic port production and operation history database contains the same port production and operation history data set; if so, use the basic port production and operation history database as the initial port production and operation history database and return to the fourth operation; otherwise, execute the fifth operation; The fourth operation is: obtaining a first port production and operation history data set, a second port production and operation history data set, and a third port production and operation history data set according to the initial port production and operation history database; The fifth operation is: determining whether the basic port production and operation history database contains a port production and operation history data set of the same historical port basic data; if so, integrating the port production and operation history data set of the same historical port basic data according to the basic port production and operation history database, and obtaining the integration result of the basic port production and operation history database as the target port production and operation history database; otherwise, obtaining the basic port production and operation history database as the target port production and operation history database.

4. The method for generating port dynamic thresholds based on data association analysis according to claim 2, characterized in that: S2. Performing correlation analysis based on the port production and operation history database to obtain correlation relationships between port production and operation indicators, including: Obtaining basic information of the port production and operation indicators by utilizing the port production and operation history database according to the port production and operation indicators; Performing correlation analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain correlation results of the port production and operation indicators; Obtaining a correlation relationship between the port production and operation indicators according to the correlation result of the port production and operation indicators; Wherein, obtaining the port production and operation indicator association relationship according to the port production and operation indicator association result includes: Utilizing the port production and operation indicator correlation result and the corresponding historical port production and operation indicator correlation result to obtain a judgment criterion for the port production and operation indicator correlation result; Determine whether the port production and operation indicator association results all meet the port production and operation indicator association result discrimination criteria; if so, obtain the port production and operation indicator association results as the port production and operation indicator association relationship; otherwise, perform the sixth operation; The sixth operation is to determine whether the port production and operation indicator association result partially meets the judgment criteria of the port production and operation indicator association result. If so, obtain the port production and operation indicator association result that meets the port production and operation indicator association result threshold as the port production and operation indicator association relationship, and use the port production and operation indicator association result that does not meet the port production and operation indicator association result threshold to update the port production and operation indicator association result, and return to the seventh operation. Otherwise, directly return to the seventh operation. The seventh operation is: using the port production and operation indicator correlation result and the corresponding historical port production and operation indicator correlation result to obtain a judgment standard for the port production and operation indicator correlation result.

5. The method for generating port dynamic thresholds based on data association analysis according to claim 4 is characterized in that: According to the port production and operation indicators, the port production and operation history database is used to obtain basic information of the port production and operation indicators, including: Obtain port operation data, port operation efficiency, port direct call rate, port direct departure rate and port shoreline occupancy rate as port production and operation indicators. The port operation data includes container operation data, cargo operation data, cruise operation data and bulk cargo operation data. The port operation efficiency includes berthing efficiency, average single bridge efficiency and target efficiency. Utilizing the port production and operation database, obtaining port production and operation history rules; Determine whether all the port production and operation data comply with the port production and operation history rules; if so, perform rule analysis on the port production and operation indicators using the port production and operation history rules to obtain indicator definitions of the port production and operation indicators, and execute the eighth, ninth, and tenth operations; otherwise, delete the port production and operation data that does not comply with the port production and operation history rules based on the port production and operation data, and return to the eleventh operation; The eighth operation is: using the port production and operation data according to the indicator definition of the port production and operation indicator and the port production and operation history rule to obtain the indicator formula and indicator analysis dimensions of the port production and operation indicator, wherein the indicator analysis dimensions include a time dimension, a space dimension, a business entity dimension, and a cargo dimension; The ninth operation is: performing positioning processing according to the port production and operation indicators to obtain the indicator data source of the port production and operation indicators; The tenth operation is: obtaining the port production and operation indicator and the indicator definition, indicator formula, indicator analysis dimension, and indicator data source of the port production and operation indicator as basic information of the port production and operation indicator; The eleventh operation is: using the port production and operation data to obtain corresponding port production and operation historical data.

6. The method for generating port dynamic thresholds based on data association analysis according to claim 4 is characterized in that: The basic information of the port production and operation indicators is used to perform correlation analysis on the port production and operation indicators to obtain correlation results of the port production and operation indicators, including: Performing dimension analysis on the port production and operation indicators using the basic information of the port production and operation indicators to obtain indicator dimensions of the port production and operation indicators; Obtaining a port production and operation indicator matrix by using the basic information of the port production and operation indicators according to the indicator dimensions of the port production and operation indicators; Performing linear analysis based on the port production and operation indicator matrix to obtain linear analysis results of the port production and operation indicators; Determine whether the linear analysis result of the port production and operation indicator is a linear relationship. If so, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform Pearson correlation coefficient analysis to obtain the correlation result of the port production and operation indicator. Otherwise, perform the twelfth operation. Among them, the twelfth operation is: determine whether the linear analysis result of the port production and operation indicator is a curve relationship. If so, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform polynomial regression analysis to obtain the correlation result of the port production and operation indicator; otherwise, use the linear analysis result of the port production and operation indicator to obtain the corresponding port production and operation indicator, and perform nonlinear regression analysis to obtain the correlation result of the port production and operation indicator.

7. The method for generating port dynamic thresholds based on data association analysis according to claim 4 is characterized in that: S3. Utilizing the port production and operation history database based on the correlation relationship of the port production and operation indicators, obtaining a change trend of the port production and operation indicators, including: According to the port production and operation indicator correlation relationship, the port production and operation history database is used to obtain the corresponding historical port production and operation indicator correlation relationship; Utilizing the historical port production and operation indicator correlation relationship and the port production and operation history database, a port production and operation indicator prediction model is constructed; The port production and operation indicator prediction model is used according to the port production and operation indicator correlation relationship and the port production and operation data to obtain the change trend of the port production and operation indicator.

8. The method for generating port dynamic thresholds based on data association analysis according to claim 7 is characterized in that: According to the port production and operation indicator correlation relationship, the port production and operation history database is used to obtain the corresponding historical port production and operation indicator correlation relationship, including: Obtaining basic port production and operation historical data using the port production and operation historical database according to the historical port production and operation indicator correlation relationship; According to the basic port production and operation historical data, obtain the corresponding historical port real-time data and historical port planning data; Obtaining the historical port production and operation indicator correlation relationship and historical port real-time data as a training set; Taking the training set as input and the changing trend of the historical port production and operation indicators corresponding to the training set as output, an initial port production and operation indicator prediction model is constructed based on a convolutional neural network; Obtaining the historical port production and operation indicator correlation relationship and the historical port planning data as a validation set; Inputting the verification set into the initial port production and operation indicator prediction model to obtain the change trend of the historical port production and operation indicators corresponding to the verification set; Determine whether the change trend of the historical port production and operation indicators corresponding to the validation set is completely consistent with the change trend of the historical port production and operation indicators corresponding to the training set; if so, obtain the initial port production and operation indicator prediction model as the port production and operation indicator prediction model; otherwise, update the training set using the change trend of the historical port production and operation indicators corresponding to the validation set, and return to execute the thirteenth operation; Among them, the thirteenth operation is: taking the training set as input, the changing trend of the historical port production and operation indicators corresponding to the training set as output, and constructing an initial port production and operation indicator prediction model based on a convolutional neural network.

9. The method for generating port dynamic thresholds based on data association analysis according to claim 8, characterized in that: The port production and operation indicator prediction model is used according to the port production and operation indicator correlation relationship and the port production and operation data to obtain a change trend of the port production and operation indicator, including: Acquiring initial port production and operation data using the port production and operation data according to the port production and operation indicator correlation relationship; Using the initial port production and operation data, corresponding port real-time data and port planning data are obtained as target port real-time data and target port planning data respectively; According to the correlation relationship between the port production and operation indicators and the real-time data of the target port, the port production and operation indicator prediction model is input to obtain the change trend of the initial port production and operation indicators; According to the correlation relationship between the port production and operation indicators and the target port plan data, the port production and operation indicator prediction model is input to obtain a change trend threshold of the port production and operation indicator; Determine whether the changing trend of the initial port production and operation indicator meets the changing trend threshold of the port production and operation indicator; if so, obtain the changing trend of the initial port production and operation indicator as the changing trend of the port production and operation indicator; otherwise, update the training set according to the changing trend of the initial port production and operation indicator, and return to execute the thirteenth operation.

10. The method for generating port dynamic threshold based on data association analysis according to claim 1, characterized in that: S4. Obtaining a port dynamic threshold value based on the correlation relationship between the port production and operation indicators and the change trend of the port production and operation indicators, including: Utilizing the port production and operation indicator correlation relationship and the corresponding historical port production and operation indicator correlation relationship to obtain a historical port production and operation indicator correlation relationship threshold; Obtaining an initial threshold value of the port production and operation indicator according to the port production and operation indicator correlation relationship using the historical port production and operation indicator correlation relationship threshold value; Utilizing the changing trends of the port production and operation indicators, obtaining the influencing factors of the port production and operation indicators; Obtaining a threshold adjustment strategy for the port production and operation indicator by using the influencing factors of the port production and operation indicator according to the initial threshold of the port production and operation indicator; The initial threshold value of the port production and operation indicator is dynamically adjusted according to the threshold value adjustment strategy of the port production and operation indicator, and the dynamically adjusted threshold value of the port production and operation indicator is obtained as the port dynamic threshold value.

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