A port operation optimization method based on internet of things and big data intelligent analysis

Abstract

The present application relates to port operation, big data processing and internet of things technical field, disclose a kind of based on internet of things and big data intelligent analysis's port operation optimization method, comprising: based on internet of things gateway collection various data, according to the various data after pre-processing standard data set is built;The data in standard data set is carried out feature mining and analysis, determines predicted to optimize link and predicted to optimize data, and generates first predicted optimization strategy;First predicted optimization strategy is simulated, and simulation optimization coefficient is generated according to simulation result and whether first predicted optimization strategy is modified is judged, if yes, second predicted optimization strategy is obtained;Second predicted optimization strategy is judged whether there is conflict, if there is, priority coefficient is calculated, and port operation optimization strategy is generated according to priority coefficient and second predicted optimization strategy, improve port operation efficiency.

Description

Technical Field

[0001] The present application relates to the fields of port operations, big data processing and Internet of Things technology, and in particular to a port operations optimization method based on the Internet of Things and big data intelligent analysis. Background Art

[0002] With the continuous development of global trade, ports, as important hubs connecting sea and land transportation, have a significant impact on the overall logistics chain through their operational efficiency and management level. However, traditional port operations are plagued by numerous problems, such as irrational resource scheduling, low data collection accuracy, and poor optimization of business processes. These issues limit further improvement in port operational efficiency. Therefore, a port operation optimization method based on the Internet of Things and intelligent big data analysis is urgently needed to improve port operational efficiency and ensure smooth operation. Summary of the Invention

[0003] In order to solve the above technical problems, the present application provides a port operation optimization method based on the Internet of Things and big data intelligent analysis. By collecting various types of data in the port operation process and conducting feature mining and analysis, the predicted links to be optimized and the predicted data to be optimized are determined, the corresponding first prediction optimization strategy is determined and simulated to obtain the simulation optimization coefficient, the first prediction optimization strategy is corrected to obtain a second prediction optimization strategy, and a port operation optimization strategy is generated according to the second prediction optimization strategy and the optimization priority order to improve the port operation efficiency.

[0004] In some embodiments of the present application, a port operation optimization method based on the Internet of Things and big data intelligent analysis is provided, including:

[0005] Based on the IoT gateway, various data from the port operation process of the current monitoring node are collected, pre-processed, and a standard data set is constructed based on the pre-processed data.

[0006] Perform feature mining and analysis on the data in the standard data set, determine the predicted link to be optimized and the predicted data to be optimized of the current monitoring node based on the feature mining and analysis results, and generate a first prediction optimization strategy corresponding to the predicted link to be optimized in combination with a preset optimization model;

[0007] Performing simulation on the first prediction optimization strategy, generating a simulation optimization coefficient according to the simulation result, and determining whether to modify the first prediction optimization strategy according to the simulation optimization coefficient, and if so, obtaining a second prediction optimization strategy;

[0008] Determine whether there is a conflict between the second prediction optimization strategies of multiple prediction links to be optimized. If so, calculate the priority coefficient and generate the port operation optimization strategy based on the priority coefficient and the second prediction optimization strategy.

[0009] In some embodiments of the present application, a standard data set is constructed according to pre-processed various types of data, including:

[0010] Port operation businesses in the current monitoring period are obtained, and each port operation business is divided into multiple operation links;

[0011] The various types of data are pre-processed, and the pre-processing includes time unit unification, removal of outliers, filling of missing values, and data cleaning processing;

[0012] The data sources and data attributes of the pre-processed various types of data are obtained, and the various types of data are classified according to the data sources and data attributes, to obtain internal data and external data of different operation links;

[0013] The standard data set is constructed according to the internal data and external data of the multiple operation links.

[0014] In some embodiments of the present application, the predicted optimization link and the predicted optimization data of the current monitoring node are determined according to the feature mining and analysis results, including:

[0015] A plurality of evaluation indexes of each operation link are pre-set;

[0016] The historical monitoring logs of each internal data and external data in the standard data set are obtained, the historical change characteristics of each internal data and external data in the historical monitoring logs are obtained, and the correlation degree of the historical change characteristics and the historical evaluation values of the plurality of evaluation indexes of the corresponding operation link is evaluated;

[0017] The internal data with a correlation degree greater than a pre-set first correlation degree threshold is set as internal correlation data of the corresponding evaluation index;

[0018] The external data with a correlation degree greater than a pre-set second correlation degree threshold is set as external correlation data of the corresponding evaluation index;

[0019] Demand information of each operation link is obtained, and the demand information includes a standard time node and a plurality of standard data meeting the demand;

[0020] The actual change characteristics of the internal correlation data and the external correlation data of all evaluation indexes of the same operation link in the current monitoring period are obtained, and the predicted change characteristics of the corresponding internal correlation data and external correlation data before the standard time node are generated in combination with the historical change characteristics;

[0021] The predicted internal correlation data and the predicted external correlation data at the standard time node are generated according to the predicted change characteristics and the internal correlation data and the external correlation data of the current monitoring node;

[0022] Comparing the predicted internal correlation data and the predicted external correlation data with the corresponding standard data respectively to obtain a first data difference between the predicted internal correlation data and the corresponding standard data and a second data difference between the predicted external correlation data and the corresponding standard data;

[0023] generating a predicted operating condition coefficient of a corresponding operating link according to a plurality of first data differences and second data differences;

[0024] Pre-set operating condition coefficient thresholds;

[0025] If the predicted operating condition coefficient is less than the operating condition coefficient threshold, the corresponding operating link is set as the predicted link to be optimized;

[0026] The internal associated data whose first data difference is greater than the preset difference threshold and the external associated data whose second data difference is greater than the preset difference threshold in the predicted link to be optimized are set as the predicted data to be optimized.

[0027] In some embodiments of the present application, generating a predicted operating condition coefficient of a corresponding operating link based on a plurality of first data differences and second data differences includes:

[0028] Performing a comprehensive analysis on the first data differences corresponding to the predicted internal correlation data of the same evaluation indicator in each operation link, and screening out the predicted internal correlation data whose first data differences are greater than a preset difference threshold;

[0029] Comprehensively analyzing the second data differences corresponding to the predicted external correlation data of the same evaluation indicator in the operation link, and screening out the predicted external correlation data whose second data difference is greater than a preset difference threshold;

[0030] Generate the prediction difference coefficient of the corresponding evaluation index at the standard time node based on the screened prediction internal correlation data and prediction external correlation data;

[0031] The calculation formula of the predicted difference coefficient is:

[0032] ;

[0033] Among them, Y is the prediction difference coefficient, y0 is the difference conversion coefficient, x1 is the first weight coefficient, n1 is the number of predicted internal correlation data screened out, and N1 is the total number of predicted internal correlation data of the current evaluation index. is the value of the ith first data difference greater than the preset difference threshold, d1i is the weight coefficient of the ith predicted internal correlation data, x2 is the second weight coefficient, n2 is the number of screened predicted external correlation data, and N2 is the total number of predicted external correlation data of the current evaluation index. is the value of the s-th second data difference greater than the preset difference threshold, and d2s is the weight coefficient of the s-th predicted external correlation data;

[0034] Generate the predicted operating status coefficient of the corresponding operating link based on the predicted difference coefficient of all evaluation indicators in each operating link at the standard time node;

[0035] The calculation formula of the predicted operating condition coefficient is:

[0036] ;

[0037] Among them, Y2 is the predicted operating status coefficient, y2 is the operating status conversion coefficient, m is the total number of evaluation indicators of the current operation link, Y1v is the predicted difference coefficient of the vth evaluation indicator, and bv is the weight coefficient of the vth evaluation indicator.

[0038] In some embodiments of the present application, simulation of the first prediction optimization strategy includes:

[0039] Constructing a preset optimization model for the corresponding predicted link to be optimized based on the historical optimization log of the predicted link to be optimized;

[0040] Inputting the prediction data to be optimized of each prediction link to be optimized into the corresponding preset optimization model to obtain the first prediction optimization strategy for the corresponding prediction link to be optimized;

[0041] Determine the location and structural information of the equipment involved in the predicted link to be optimized, lock the location of the preset panoramic map, and generate a static simulation scene corresponding to the predicted link to be optimized based on the locked location information;

[0042] Acquire multiple dynamic nodes of a first prediction optimization strategy for a link to be optimized;

[0043] Based on the predicted dependency relationship between the internal and external associated data of the link to be optimized, dynamic dependency features of the internal and external associated data at each dynamic node are established;

[0044] Establish the dynamic operation characteristics of the corresponding equipment at each dynamic node according to the operation relationship between the involved equipment;

[0045] According to the dynamic dependency relationship and dynamic operation relationship under multiple identical dynamic nodes, a position connection is established with the static simulation scene, and a simulation model of the first prediction optimization strategy corresponding to the predicted link to be optimized is constructed.

[0046] In some embodiments of the present application, generating a simulation optimization coefficient according to simulation results includes:

[0047] Preset the test time of the first prediction optimization strategy for the link to be optimized, and set the data collection node according to the preset time interval;

[0048] Collecting simulation internal correlation data and simulation external correlation data in a corresponding simulation model according to a data collection node, and mapping them to a corresponding inspection duration to obtain a simulation data change curve graph, wherein the simulation data change curve graph includes a plurality of first simulation data change curves for predicting data to be optimized and a second simulation data change curve for predicting data;

[0049] Generate a standard data line from multiple standard data in the predicted link to be optimized, and map it to the corresponding simulation data change curve graph, compare the standard data line with the corresponding first simulation data change curve, and determine whether the first simulation data change curve and the corresponding standard data line have an intersection within the test time;

[0050] If it exists, the simulation optimization period is determined based on the initial time node where the intersection exists;

[0051] If not, obtain the simulation data change trend corresponding to the first simulation data change curve, perform trend extrapolation according to the simulation data change trend until an intersection with the corresponding standard data line is found, obtain the predicted time node where the intersection exists, and determine the predicted simulation optimization period;

[0052] Generating a first simulation optimization coefficient of the first prediction optimization strategy for the predicted data to be optimized according to the simulation optimization period and the predicted simulation optimization period;

[0053] Acquire a simulation data change trend of the second simulation data change curve, and determine a distance relationship between the simulation data change trend and a corresponding standard data line, wherein the distance relationship includes a close relationship and a distant relationship;

[0054] When the relationship is far away, the mean value of the data difference between the predicted data corresponding to the second simulation data change curve and the corresponding standard data line is calculated;

[0055] When the relationship is close, the predicted closeness between the simulation data change trend corresponding to the second simulation data change curve and the corresponding standard data line is calculated, and compared with the actual closeness before optimization to obtain the closeness difference;

[0056] Generating a second simulation optimization coefficient of the first prediction optimization strategy for the remaining prediction data according to the data difference mean and the proximity difference;

[0057] Generate a compensation coefficient based on the predicted optimization cost of the first predicted optimization strategy of the link to be optimized;

[0058] generating a simulation optimization coefficient according to the first simulation optimization coefficient, the second simulation optimization coefficient, and the compensation coefficient;

[0059] Pre-set the simulation optimization coefficient threshold;

[0060] If the simulation optimization coefficient is less than the simulation optimization coefficient threshold, a correction instruction corresponding to the first prediction optimization strategy is generated.

[0061] In some embodiments of the present application, the calculation formula of the first simulation optimization coefficient is:

[0062] ;

[0063] Among them, H1 is the first simulation optimization coefficient, h1 is the simulation optimization conversion coefficient of the simulation optimization period, u1 is the number of prediction data to be optimized that have intersections within the test time in the current prediction to be optimized link, T0 is the test time, is the simulation optimization period for the z1th predicted data to be optimized, is the weight coefficient of the z1th predicted data to be optimized, h2 is the simulation optimization conversion coefficient of the predicted simulation optimization period, u2 is the number of predicted data to be optimized in the current predicted optimization link that has no intersection within the test time, The prediction simulation optimization period for the z2th prediction data to be optimized, is the weight coefficient of the z2th predicted data to be optimized;

[0064] The calculation formula of the second simulation optimization coefficient is:

[0065] ;

[0066] Among them, H2 is the second simulation optimization coefficient, h3 is the simulation optimization conversion coefficient of the difference in the degree of proximity of the predicted data, and u3 is the number of predicted data with a close relationship. is the difference in the degree of closeness of the z3th predicted data, is the weight coefficient of the z3th predicted data, h4 is the simulation optimization conversion coefficient of the mean value of the data difference of the predicted data, u3 is the number of predicted data far away from the relationship, is the mean value of the data difference of the z4th predicted data, is the weight coefficient of the z4th predicted data;

[0067] The calculation formula of the simulation optimization coefficient is:

[0068] ;

[0069] Among them, H0 is the simulation optimization coefficient, f0 is the compensation coefficient, f1 is the weight coefficient of the first simulation optimization coefficient, and f2 is the weight coefficient of the second simulation optimization coefficient.

[0070] In some embodiments of the present application, determining whether there is a conflict between the second prediction optimization strategies of multiple prediction links to be optimized includes:

[0071] Obtain the second prediction optimization strategy of all the prediction links to be optimized for the current monitoring node, and extract the optimization target, optimization period, and optimization constraint conditions of each second prediction optimization strategy;

[0072] A conflict analysis is performed on the optimization objectives, optimization periods, and optimization constraints of different second prediction optimization strategies to obtain conflicting second prediction optimization strategies and prediction links to be optimized of the second prediction optimization strategies.

[0073] In some embodiments of the present application, if present, calculating the priority coefficient includes:

[0074] Obtain each conflicting predicted link to be optimized, and randomly select one predicted link to be optimized as the target link;

[0075] Obtain the historical evaluation log of the target link, obtain multiple historical comprehensive evaluation values ​​of the target link based on the historical evaluation log, and calculate the difference between the historical comprehensive evaluation values;

[0076] Filter out the historical comprehensive evaluation value differences with increasing differences, and construct the historical comprehensive evaluation value difference sequence of the target link;

[0077] Determine the corresponding adjacent preset time nodes according to multiple historical evaluation nodes in the historical comprehensive evaluation value difference sequence, and obtain the historical comprehensive evaluation values ​​of other operation links according to the adjacent preset time nodes;

[0078] According to the time sequence of the historical evaluation nodes of the historical comprehensive evaluation value difference sequence of the target link, the historical comprehensive evaluation value difference sequence of the corresponding time nodes of other operation links is constructed;

[0079] Calculate the sequence similarity between the historical comprehensive evaluation value difference sequence of each operating link and the historical comprehensive evaluation value difference sequence of the target link. If the sequence similarity is greater than the preset similarity threshold, the corresponding operating link is determined to be the associated link of the target link, and the correlation coefficient between the associated link and the target link is calculated;

[0080] Generate a priority coefficient of a target link according to the number of associated links, the associated coefficient, and the weight coefficient of the corresponding associated link;

[0081] Generate the priority coefficient of each conflicting prediction link to be optimized in turn.

[0082] In some embodiments of the present application, generating a port operation optimization strategy based on the priority coefficient and the second prediction optimization strategy includes:

[0083] Setting the optimization order of the corresponding second prediction optimization strategy according to the priority coefficient of the conflicting prediction link to be optimized;

[0084] The port operation optimization strategy of the current monitoring node is generated according to the optimization sequence and the second prediction optimization strategy of the link to be optimized.

[0085] The port operation optimization method based on the Internet of Things and big data intelligent analysis in the embodiment of the present application has the following beneficial effects compared with the existing technology:

[0086] By collecting various types of data during the port operation process and conducting feature mining and analysis, the predicted links and data to be optimized are determined, the corresponding first prediction optimization strategy is determined and simulated, and the simulation optimization coefficient is obtained. The first prediction optimization strategy is corrected to obtain the second prediction optimization strategy. According to the second prediction optimization strategy and the optimization priority, the port operation optimization strategy is generated to improve the port operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a flow chart of a port operation optimization method based on the Internet of Things and big data intelligent analysis in an embodiment of the present application. DETAILED DESCRIPTION

[0088] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0089] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0090] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0091] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0092] like Figure 1 As shown, a port operation optimization method based on the Internet of Things and big data intelligent analysis in an embodiment of the present application includes:

[0093] Step S101: collecting various data during the port operation process of the current monitoring node based on the Internet of Things gateway, preprocessing the various data, and constructing a standard data set based on the preprocessed data;

[0094] Step S102: performing feature mining and analysis on the data in the standard data set, determining the predicted link to be optimized and the predicted data to be optimized of the current monitoring node based on the feature mining and analysis results, and generating a first prediction optimization strategy corresponding to the predicted link to be optimized in combination with a preset optimization model;

[0095] Step S103: Simulate the first prediction optimization strategy, generate a simulation optimization coefficient based on the simulation result, and determine whether to modify the first prediction optimization strategy based on the simulation optimization coefficient. If so, obtain a second prediction optimization strategy.

[0096] Step S104: determine whether there is a conflict between the second prediction optimization strategies of multiple prediction links to be optimized. If so, calculate the priority coefficient and generate a port operation optimization strategy based on the priority coefficient and the second prediction optimization strategy.

[0097] In this embodiment, the IoT gateway collects various data from all aspects of port logistics in real time, including RFID tag information, sensor data (such as temperature, humidity, pressure, etc.), vehicle GPS location information, etc.

[0098] In this embodiment, the preset optimization model is constructed based on the historical optimization data of each link predicted to be optimized and the corresponding historical business needs. For example, the link predicted to be optimized is the loading and unloading operation. The neural network training is performed based on the historical ship arrival time set, the historical cargo type set and the corresponding historical loading and unloading demand set to obtain the preset optimization model of the loading and unloading operation. The preset optimization model outputs the optimized loading and unloading operation sequence and schedule, which reduces waiting time and resource waste and realizes the optimization of loading and unloading operations.

[0099] In some embodiments of the present application, a standard data set is constructed based on various preprocessed data, including:

[0100] Obtain the port operations in the current monitoring period and divide each port operation into multiple operation links;

[0101] Preprocessing of various data types, including standardization of time units, removal of outliers, filling of missing values, and data cleaning;

[0102] Obtain the data sources and data attributes of various types of pre-processed data, classify the data according to the data sources and data attributes, and obtain internal and external data of different operation links;

[0103] Build standard data sets based on internal and external data from multiple operational aspects.

[0104] In this embodiment, the port operation process refers to monitoring the operation links of multiple port operation businesses in the current monitoring period to ensure the smooth operation of the port. The port operation business includes transportation business, cargo loading and unloading and warehousing business, tallying business, equipment leasing business, etc. The operation link refers to multiple processes in the port operation business, for example, the loading link, transportation link, stacking and picking up link in the cargo loading and unloading business.

[0105] In this embodiment, data cleaning can be performed according to the data cleaning formula: Clean Data = Remove Redundant And Error Data (Raw Data) where: Raw Data is a collection of various data types; RemoveRedundant And Error Data is a function used to remove redundant and erroneous data; Clean Data is the cleaned data.

[0106] In this embodiment, the operation links corresponding to various types of data are determined based on the data source, and the data of each operation link are divided into internal data and external data based on the data attributes. Internal data refers to the data collected by the corresponding operation link in the port, and external data refers to the data collected during the operation of the corresponding operation link. For example, internal data includes container location, yard occupancy, etc., and external data includes ship arrival time, cargo throughput, etc.

[0107] In this embodiment, by determining the internal and external data of each operation link and constructing a standard data set, the credibility of the data is improved, and the foundation is laid for subsequent feature mining and determining the prediction optimization link.

[0108] In some embodiments of the present application, determining the predicted link to be optimized and the predicted data to be optimized of the current monitoring node based on the feature mining and analysis results includes:

[0109] Pre-set multiple evaluation indicators for each operation link;

[0110] Obtain the historical monitoring logs of each internal data and external data in the standard data set, obtain the historical change characteristics of each internal data and external data in the historical monitoring logs, and evaluate the degree of correlation between the historical change characteristics and the historical evaluation values ​​of multiple evaluation indicators of the corresponding operation links;

[0111] Setting the internal data whose correlation degree is greater than a preset first correlation degree threshold as the internal correlation data corresponding to the evaluation indicator;

[0112] Setting the external data with a correlation degree greater than a preset second correlation degree threshold as the external correlation data corresponding to the evaluation indicator;

[0113] Obtain demand information for each operation link, including standard time nodes and several standard data that meet the demand;

[0114] Based on the actual change characteristics of the internal and external correlation data of all evaluation indicators of the same operation link in the current monitoring period and combined with the historical change characteristics, the predicted change characteristics of the corresponding internal and external correlation data before the standard time node are generated;

[0115] Generate predicted internal correlation data and predicted external correlation data at a standard time node according to the predicted change characteristics and the internal correlation data and external correlation data of the current monitoring node;

[0116] Comparing the predicted internal correlation data and the predicted external correlation data with the corresponding standard data respectively to obtain a first data difference between the predicted internal correlation data and the corresponding standard data and a second data difference between the predicted external correlation data and the corresponding standard data;

[0117] generating a predicted operating condition coefficient of a corresponding operating link according to a plurality of first data differences and second data differences;

[0118] Pre-set operating condition coefficient thresholds;

[0119] If the predicted operating condition coefficient is less than the operating condition coefficient threshold, the corresponding operating link is set as the predicted link to be optimized;

[0120] The internal associated data whose first data difference is greater than the preset difference threshold and the external associated data whose second data difference is greater than the preset difference threshold in the predicted link to be optimized are set as the predicted data to be optimized.

[0121] In the embodiment, the evaluation index of each operation link refers to an index for evaluating the service quality, cost and benefit, operation safety and stability, and the like of the corresponding operation link, for example, a vehicle working rate index, a vehicle intact rate index, a real load rate index, and the like in the distribution link, reflecting the utilization rate and distribution efficiency of vehicles or ships in the distribution process.

[0122] In the embodiment, the historical change feature refers to the change trend, change rate, and fluctuation degree of the historical data change curve of the corresponding internal data or external data in the historical monitoring log, and the correlation degree of the historical change feature and the historical evaluation value is evaluated, that is, the influence degree of the historical change feature of the historical data change curve on the historical evaluation value change curve is evaluated. If one or more of the change trend, change rate, and fluctuation degree of the historical data change curve of the internal data or external data change greatly, the historical evaluation value at the corresponding time node in the historical evaluation value change curve is greatly affected, and the great change and great influence are quantized to obtain the corresponding correlation degree.

[0123] In the embodiment, the preset first correlation degree threshold is set according to the minimum correlation degree of the historical internal correlation data in the port link, and the preset second correlation degree threshold is set according to the minimum correlation degree of the historical external correlation data in the operation process of the port link.

[0124] In the embodiment, the demand information refers to the operation target to be achieved by each operation link, the standard time node refers to the best time node for completing the operation target, and the standard data refers to the standard internal correlation data and standard external correlation data corresponding to the completion of the operation target.

[0125] In the embodiment, the predicted operation status coefficient refers to the evaluation of whether the corresponding operation link can achieve the demand operation target at the standard time node, and the running status coefficient threshold is according to the minimum running status coefficient of the corresponding operation link achieving the demand operation target at the standard time node.

[0126] In the embodiment, by determining the predicted internal correlation data and predicted external correlation data at the standard time node, and generating the predicted operation status coefficient, the predicted to-be-optimized link and the predicted to-be-optimized data are selected, laying a foundation for subsequent generation of optimization strategies, optimizing the operation link, and improving the port operation efficiency.

[0127] In some embodiments of the present application, the predicted operation status coefficient of the corresponding operation link is generated according to the plurality of first data difference values and second data difference values, comprising:

[0128] Performing a comprehensive analysis on the first data differences corresponding to the predicted internal correlation data of the same evaluation indicator in each operation link, and screening out the predicted internal correlation data whose first data differences are greater than a preset difference threshold;

[0129] Comprehensively analyzing the second data differences corresponding to the predicted external correlation data of the same evaluation indicator in the operation link, and screening out the predicted external correlation data whose second data difference is greater than a preset difference threshold;

[0130] Generate the prediction difference coefficient of the corresponding evaluation index at the standard time node based on the screened prediction internal correlation data and prediction external correlation data;

[0131] The calculation formula of the predicted difference coefficient is:

[0132] ;

[0133] Among them, Y is the prediction difference coefficient, y0 is the difference conversion coefficient, x1 is the first weight coefficient, n1 is the number of predicted internal correlation data screened out, and N1 is the total number of predicted internal correlation data of the current evaluation index. is the value of the ith first data difference greater than the preset difference threshold, d1i is the weight coefficient of the ith predicted internal correlation data, x2 is the second weight coefficient, n2 is the number of screened predicted external correlation data, and N2 is the total number of predicted external correlation data of the current evaluation index. is the value of the s-th second data difference greater than the preset difference threshold, and d2s is the weight coefficient of the s-th predicted external correlation data;

[0134] Generate the predicted operating status coefficient of the corresponding operating link based on the predicted difference coefficient of all evaluation indicators in each operating link at the standard time node;

[0135] The calculation formula of the predicted operating condition coefficient is:

[0136] ;

[0137] Among them, Y2 is the predicted operating status coefficient, y2 is the operating status conversion coefficient, m is the total number of evaluation indicators of the current operation link, Y1v is the predicted difference coefficient of the vth evaluation indicator, and bv is the weight coefficient of the vth evaluation indicator.

[0138] In this embodiment, the difference conversion coefficient refers to converting the first data difference and the second data difference of the predicted internal correlation data and the predicted external correlation data of the same evaluation index, which are greater than the preset difference threshold, into a value of the same dimension as the predicted difference coefficient. The larger the value, the larger the corresponding predicted difference coefficient, and vice versa.

[0139] In this embodiment, the operating status conversion coefficient refers to converting the predicted difference coefficients of all evaluation indicators into a coefficient with the same dimension as the predicted operating status coefficient. When the predicted difference coefficient is larger and the weight coefficient of the corresponding evaluation indicator is larger, the corresponding predicted operating status coefficient is smaller, and vice versa.

[0140] In this embodiment, by evaluating the degree of difference between the predicted internal correlation data and the predicted external correlation data of each evaluation indicator and the standard data, a predicted operating status coefficient is generated, the prediction accuracy of the status of each operating link is improved, and the predicted links and predicted data to be optimized are screened out, laying the foundation for the subsequent formulation of optimization strategies and improving port operation efficiency.

[0141] In some embodiments of the present application, simulation of the first prediction optimization strategy includes:

[0142] Constructing a preset optimization model for the corresponding predicted link to be optimized based on the historical optimization log of the predicted link to be optimized;

[0143] Inputting the prediction data to be optimized of each prediction link to be optimized into the corresponding preset optimization model to obtain the first prediction optimization strategy for the corresponding prediction link to be optimized;

[0144] Determine the location and structural information of the equipment involved in the predicted link to be optimized, lock the location of the preset panoramic map, and generate a static simulation scene corresponding to the predicted link to be optimized based on the locked location information;

[0145] Acquire multiple dynamic nodes of a first prediction optimization strategy for a link to be optimized;

[0146] Based on the predicted dependency relationship between the internal and external associated data of the link to be optimized, dynamic dependency features of the internal and external associated data at each dynamic node are established;

[0147] Establish the dynamic operation characteristics of the corresponding equipment at each dynamic node according to the operation relationship between the involved equipment;

[0148] According to the dynamic dependency relationship and dynamic operation relationship under multiple identical dynamic nodes, a position connection is established with the static simulation scene, and a simulation model of the first prediction optimization strategy corresponding to the predicted link to be optimized is constructed.

[0149] In this embodiment, the dependency relationship refers to the change of internal related data and external related data with the change of other internal related data and other external related data in the same operation link, and the degree of change is greater than the preset change degree threshold. The dynamic dependency feature refers to the degree of dependency corresponding to the dependency relationship. The greater the degree of change, the greater the corresponding degree of dependency.

[0150] In this embodiment, the operating relationship refers to the correlation between the operating conditions of the devices involved, that is, changes in the operating conditions of the devices cause significant changes in the operating conditions of other devices in the same operation link. The dynamic operating characteristics refer to the degree of correlation between the devices with operating relationships. The greater the change in the operating conditions, the greater the corresponding degree of correlation.

[0151] In this embodiment, by constructing a simulation of the first prediction optimization strategy for predicting the link to be optimized, the first prediction optimization strategy is accurately simulated, and the optimization effect of the corresponding first prediction optimization strategy is accurately evaluated based on the simulation results, thereby improving the port operation efficiency.

[0152] In some embodiments of the present application, generating a simulation optimization coefficient according to simulation results includes:

[0153] Preset the test time of the first prediction optimization strategy for the link to be optimized, and set the data collection node according to the preset time interval;

[0154] Collecting simulation internal correlation data and simulation external correlation data in a corresponding simulation model according to a data collection node, and mapping them to a corresponding inspection duration to obtain a simulation data change curve graph, wherein the simulation data change curve graph includes a plurality of first simulation data change curves for predicting data to be optimized and a second simulation data change curve for predicting data;

[0155] Generate a standard data line from multiple standard data in the predicted link to be optimized, and map it to the corresponding simulation data change curve graph, compare the standard data line with the corresponding first simulation data change curve, and determine whether the first simulation data change curve and the corresponding standard data line have an intersection within the test time;

[0156] If it exists, the simulation optimization period is determined based on the initial time node where the intersection exists;

[0157] If not, obtain the simulation data change trend corresponding to the first simulation data change curve, perform trend extrapolation according to the simulation data change trend until an intersection with the corresponding standard data line is found, obtain the predicted time node where the intersection exists, and determine the predicted simulation optimization period;

[0158] Generating a first simulation optimization coefficient of the first prediction optimization strategy for the predicted data to be optimized according to the simulation optimization period and the predicted simulation optimization period;

[0159] Acquire a simulation data change trend of the second simulation data change curve, and determine a distance relationship between the simulation data change trend and a corresponding standard data line, wherein the distance relationship includes a close relationship and a distant relationship;

[0160] When the relationship is far away, the mean value of the data difference between the predicted data corresponding to the second simulation data change curve and the corresponding standard data line is calculated;

[0161] When the relationship is close, the predicted closeness between the simulation data change trend corresponding to the second simulation data change curve and the corresponding standard data line is calculated, and compared with the actual closeness before optimization to obtain the closeness difference;

[0162] Generating a second simulation optimization coefficient of the first prediction optimization strategy for the remaining prediction data according to the data difference mean and the proximity difference;

[0163] Generate a compensation coefficient based on the predicted optimization cost of the first predicted optimization strategy of the link to be optimized;

[0164] generating a simulation optimization coefficient according to the first simulation optimization coefficient, the second simulation optimization coefficient, and the compensation coefficient;

[0165] Pre-set the simulation optimization coefficient threshold;

[0166] If the simulation optimization coefficient is less than the simulation optimization coefficient threshold, a correction instruction corresponding to the first prediction optimization strategy is generated.

[0167] In this embodiment, the inspection duration is obtained by averaging the required optimization times of the historical preferred optimization strategies. The historical preferred optimization strategies refer to strategies with better optimization effects among the historical optimization strategies. The required optimization time is the time required for the historical operating conditions of the links to be optimized to meet the historical operating goals.

[0168] In this embodiment, the predicted data refers to other data in the predicted link to be optimized except for the predicted data to be optimized.

[0169] In this embodiment, the simulation optimization period refers to the length of time required to predict the simulation change of the data to be optimized to the corresponding standard data. The simulation optimization period is less than the inspection time. The predicted simulation optimization period refers to the length of time required to predict the standard data corresponding to the simulation change value of the data to be optimized. The predicted simulation optimization period is greater than the inspection time.

[0170] In this embodiment, the correction instruction refers to extracting the predicted data to be optimized that needs to be optimized again to fine-tune the first prediction optimization strategy. If the number of predicted data that needs to be optimized increases, the first prediction optimization strategy is newly formulated and simulated until the simulation results meet the simulation optimization coefficient threshold, and the corresponding second prediction optimization strategy is determined.

[0171] In this embodiment, a simulation optimization coefficient is obtained by comprehensively evaluating the optimization effects of the predicted data to be optimized and the remaining predicted data in the simulation model. The optimization effect of the first prediction optimization strategy is accurately evaluated based on the simulation optimization coefficient, and the corresponding optimization strategy is discovered and adjusted in time to improve the overall operational efficiency of the port.

[0172] In some embodiments of the present application, the calculation formula of the first simulation optimization coefficient is:

[0173] ;

[0174] Among them, H1 is the first simulation optimization coefficient, h1 is the simulation optimization conversion coefficient of the simulation optimization period, u1 is the number of prediction data to be optimized that have intersections within the test time in the current prediction to be optimized link, T0 is the test time, is the simulation optimization period for the z1th predicted data to be optimized, is the weight coefficient of the z1th predicted data to be optimized, h2 is the simulation optimization conversion coefficient of the predicted simulation optimization period, u2 is the number of predicted data to be optimized in the current predicted optimization link that has no intersection within the test time, The prediction simulation optimization period for the z2th prediction data to be optimized, is the weight coefficient of the z2th predicted data to be optimized;

[0175] The calculation formula of the second simulation optimization coefficient is:

[0176] ;

[0177] Among them, H2 is the second simulation optimization coefficient, h3 is the simulation optimization conversion coefficient of the difference in the degree of proximity of the predicted data, and u3 is the number of predicted data with a close relationship. is the difference in the degree of closeness of the z3th predicted data, is the weight coefficient of the z3th predicted data, h4 is the simulation optimization conversion coefficient of the mean value of the data difference of the predicted data, u3 is the number of predicted data far away from the relationship, is the mean value of the data difference of the z4th predicted data, is the weight coefficient of the z4th predicted data;

[0178] The calculation formula of the simulation optimization coefficient is:

[0179] ;

[0180] Among them, H0 is the simulation optimization coefficient, f0 is the compensation coefficient, f1 is the weight coefficient of the first simulation optimization coefficient, and f2 is the weight coefficient of the second simulation optimization coefficient.

[0181] In this embodiment, the simulation optimization conversion coefficient refers to the coefficient of the same dimension converted from the simulation optimization period, the prediction simulation optimization period, the closeness difference of the prediction data, and the data difference mean of the prediction data to the simulation optimization coefficient.

[0182] In this embodiment, when the difference between the inspection time and the simulation optimization period is greater, the corresponding first simulation optimization coefficient is greater, and vice versa. When the difference between the predicted simulation optimization period and the inspection time is greater, the corresponding first simulation optimization coefficient is smaller, and vice versa. When the difference in the degree of closeness of the predicted data is greater, the corresponding second simulation optimization coefficient is greater, and vice versa. When the mean value of the data difference of the predicted data is greater, the corresponding second simulation optimization coefficient is smaller, and vice versa.

[0183] In this embodiment, when the prediction optimization cost is greater, the compensation coefficient is smaller, and vice versa. The value range of the compensation coefficient is (0.8, 1.2).

[0184] In this embodiment, when the first simulation optimization coefficient, the second simulation optimization coefficient and the compensation coefficient are larger, the corresponding simulation optimization coefficient is larger, that is, the corresponding optimization effect of the first prediction optimization strategy is better.

[0185] In some embodiments of the present application, determining whether there is a conflict between the second prediction optimization strategies of multiple prediction links to be optimized includes:

[0186] Obtain the second prediction optimization strategy of all the prediction links to be optimized for the current monitoring node, and extract the optimization target, optimization period, and optimization constraint conditions of each second prediction optimization strategy;

[0187] A conflict analysis is performed on the optimization objectives, optimization periods, and optimization constraints of different second prediction optimization strategies to obtain conflicting second prediction optimization strategies and prediction links to be optimized of the second prediction optimization strategies.

[0188] In some embodiments of the present application, if present, calculating the priority coefficient includes:

[0189] Obtain each conflicting predicted link to be optimized, and randomly select one predicted link to be optimized as the target link;

[0190] Obtain the historical evaluation log of the target link, obtain multiple historical comprehensive evaluation values ​​of the target link based on the historical evaluation log, and calculate the difference between the historical comprehensive evaluation values;

[0191] Filter out the historical comprehensive evaluation value differences with increasing differences, and construct the historical comprehensive evaluation value difference sequence of the target link;

[0192] Determine the corresponding adjacent preset time nodes according to multiple historical evaluation nodes in the historical comprehensive evaluation value difference sequence, and obtain the historical comprehensive evaluation values ​​of other operation links according to the adjacent preset time nodes;

[0193] According to the time sequence of the historical evaluation nodes of the historical comprehensive evaluation value difference sequence of the target link, the historical comprehensive evaluation value difference sequence of the corresponding time nodes of other operation links is constructed;

[0194] Calculate the sequence similarity between the historical comprehensive evaluation value difference sequence of each operating link and the historical comprehensive evaluation value difference sequence of the target link. If the sequence similarity is greater than the preset similarity threshold, the corresponding operating link is determined to be the associated link of the target link, and the correlation coefficient between the associated link and the target link is calculated;

[0195] Generate a priority coefficient of a target link according to the number of associated links, the associated coefficient, and the weight coefficient of the corresponding associated link;

[0196] Generate the priority coefficient of each conflicting prediction link to be optimized in turn.

[0197] In this embodiment, the historical comprehensive evaluation value refers to a historical comprehensive evaluation value obtained by weighting the historical evaluation values ​​of multiple evaluation indicators in the corresponding historical evaluation log, and is used to comprehensively evaluate the operating effect of the corresponding link.

[0198] In this embodiment, the historical comprehensive evaluation value differences with increasing differences are differences between multiple historical comprehensive evaluation values ​​and the same historical comprehensive evaluation value, and the differences show an increasing trend.

[0199] In this embodiment, the adjacent preset time node refers to the next adjacent time node of each historical evaluation node of the target link. The historical comprehensive evaluation values ​​of other operating links are collected based on the adjacent preset time nodes to evaluate the degree of influence of the historical operating effect of the target link on the historical operating effects of other operating links.

[0200] In this embodiment, sequence similarity refers to the similarity analysis of the difference fluctuation degree of the historical comprehensive evaluation value difference in the historical comprehensive evaluation value difference sequence of the target link and the difference fluctuation degree of the historical comprehensive evaluation value difference at the corresponding time node in the historical comprehensive evaluation value difference sequence of other operating links. The greater the similarity of the difference fluctuation degree and the more numbers in the sequence, the greater the corresponding sequence similarity.

[0201] In this embodiment, as the change in the historical comprehensive evaluation value difference of the target link is smaller, the corresponding correlation coefficient becomes larger when the historical comprehensive evaluation value difference of the corresponding time node of the associated link is larger, and vice versa.

[0202] In this embodiment, the importance of each conflicting predicted link to be optimized is evaluated by determining the associated links of each target link, thereby obtaining a priority coefficient and setting a priority, generating a port operation optimization strategy, and ensuring the coordinated and efficient operation of the port.

[0203] In some embodiments of the present application, generating a port operation optimization strategy based on the priority coefficient and the second prediction optimization strategy includes:

[0204] Setting the optimization order of the corresponding second prediction optimization strategy according to the priority coefficient of the conflicting prediction link to be optimized;

[0205] The port operation optimization strategy of the current monitoring node is generated according to the optimization sequence and the second prediction optimization strategy of the link to be optimized.

[0206] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. A port operation optimization method based on the Internet of Things and big data intelligent analysis, characterized in that: include: Based on the IoT gateway, various data from the port operation process of the current monitoring node are collected, pre-processed, and a standard data set is constructed based on the pre-processed data. Perform feature mining and analysis on the data in the standard data set, determine the predicted link to be optimized and the predicted data to be optimized of the current monitoring node based on the feature mining and analysis results, and generate a first prediction optimization strategy corresponding to the predicted link to be optimized in combination with a preset optimization model; Performing simulation on the first prediction optimization strategy, generating a simulation optimization coefficient according to the simulation result, and determining whether to modify the first prediction optimization strategy according to the simulation optimization coefficient, and if so, obtaining a second prediction optimization strategy; Determine whether there is a conflict between the second prediction optimization strategies of multiple prediction links to be optimized. If so, calculate the priority coefficient and generate the port operation optimization strategy based on the priority coefficient and the second prediction optimization strategy; Generate simulation optimization coefficients based on simulation results, including: Preset the test time of the first prediction optimization strategy for the link to be optimized, and set the data collection node according to the preset time interval; Collecting simulation internal correlation data and simulation external correlation data in a corresponding simulation model according to a data collection node, and mapping them to a corresponding inspection duration to obtain a simulation data change curve graph, wherein the simulation data change curve graph includes a plurality of first simulation data change curves for predicting data to be optimized and a second simulation data change curve for predicting data; Generate a standard data line from multiple standard data in the predicted link to be optimized, and map it to the corresponding simulation data change curve graph, compare the standard data line with the corresponding first simulation data change curve, and determine whether the first simulation data change curve and the corresponding standard data line have an intersection within the test time; If it exists, the simulation optimization period is determined based on the initial time node where the intersection exists; If not, obtain the simulation data change trend corresponding to the first simulation data change curve, perform trend extrapolation according to the simulation data change trend until an intersection with the corresponding standard data line is found, obtain the predicted time node where the intersection exists, and determine the predicted simulation optimization period; Generating a first simulation optimization coefficient of the first prediction optimization strategy for the predicted data to be optimized according to the simulation optimization period and the predicted simulation optimization period; Acquire a simulation data change trend of the second simulation data change curve, and determine a distance relationship between the simulation data change trend and a corresponding standard data line, wherein the distance relationship includes a close relationship and a distant relationship; When the relationship is far away, the mean value of the data difference between the predicted data corresponding to the second simulation data change curve and the corresponding standard data line is calculated; When the relationship is close, the predicted closeness between the simulation data change trend corresponding to the second simulation data change curve and the corresponding standard data line is calculated, and compared with the actual closeness before optimization to obtain the closeness difference; Generating a second simulation optimization coefficient of the first prediction optimization strategy for the remaining prediction data according to the data difference mean and the proximity difference; Generate a compensation coefficient based on the predicted optimization cost of the first predicted optimization strategy of the link to be optimized; generating a simulation optimization coefficient according to the first simulation optimization coefficient, the second simulation optimization coefficient, and the compensation coefficient; Pre-set the simulation optimization coefficient threshold; If the simulation optimization coefficient is less than the simulation optimization coefficient threshold, generating a correction instruction corresponding to the first prediction optimization strategy; The calculation formula of the first simulation optimization coefficient is: ； Among them, H1 is the first simulation optimization coefficient, h1 is the simulation optimization conversion coefficient of the simulation optimization period, u1 is the number of prediction data to be optimized that have intersections within the test time in the current prediction to be optimized link, T0 is the test time, is the simulation optimization period for the z1th predicted data to be optimized, is the weight coefficient of the z1th predicted data to be optimized, h2 is the simulation optimization conversion coefficient of the predicted simulation optimization period, u2 is the number of predicted data to be optimized in the current predicted optimization link that has no intersection within the test time, The prediction simulation optimization period for the z2th prediction data to be optimized, is the weight coefficient of the z2th predicted data to be optimized; The calculation formula of the second simulation optimization coefficient is: ； Among them, H2 is the second simulation optimization coefficient, h3 is the simulation optimization conversion coefficient of the difference in the degree of proximity of the predicted data, and u3 is the number of predicted data with a close relationship. is the difference in the degree of closeness of the z3th predicted data, is the weight coefficient of the z3th predicted data, h4 is the simulation optimization conversion coefficient of the mean value of the data difference of the predicted data, u3 is the number of predicted data far away from the relationship, is the mean value of the data difference of the z4th predicted data, is the weight coefficient of the z4th predicted data; The calculation formula of the simulation optimization coefficient is: ； Among them, H0 is the simulation optimization coefficient, f0 is the compensation coefficient, f1 is the weight coefficient of the first simulation optimization coefficient, and f2 is the weight coefficient of the second simulation optimization coefficient.

2. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 1 is characterized in that: Construct standard data sets based on various preprocessed data, including: Obtain the port operations in the current monitoring period and divide each port operation into multiple operation links; Preprocessing of various data types, including standardization of time units, removal of outliers, filling of missing values, and data cleaning; Obtain the data sources and data attributes of various types of pre-processed data, classify the data according to the data sources and data attributes, and obtain internal and external data of different operation links; Build standard data sets based on internal and external data from multiple operational aspects.

3. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 2 is characterized in that: Based on the feature mining and analysis results, the predicted optimization links and data for the current monitoring node are determined, including: Pre-set multiple evaluation indicators for each operation link; Obtain the historical monitoring logs of each internal data and external data in the standard data set, obtain the historical change characteristics of each internal data and external data in the historical monitoring logs, and evaluate the degree of correlation between the historical change characteristics and the historical evaluation values ​​of multiple evaluation indicators of the corresponding operation links; Setting the internal data whose correlation degree is greater than a preset first correlation degree threshold as the internal correlation data corresponding to the evaluation indicator; Setting the external data with a correlation degree greater than a preset second correlation degree threshold as the external correlation data corresponding to the evaluation indicator; Obtain demand information for each operation link, including standard time nodes and several standard data that meet the demand; Based on the actual change characteristics of the internal and external correlation data of all evaluation indicators of the same operation link in the current monitoring period and combined with the historical change characteristics, the predicted change characteristics of the corresponding internal and external correlation data before the standard time node are generated; Generate predicted internal correlation data and predicted external correlation data at a standard time node according to the predicted change characteristics and the internal correlation data and external correlation data of the current monitoring node; Comparing the predicted internal correlation data and the predicted external correlation data with the corresponding standard data respectively to obtain a first data difference between the predicted internal correlation data and the corresponding standard data and a second data difference between the predicted external correlation data and the corresponding standard data; generating a predicted operating condition coefficient of a corresponding operating link according to a plurality of first data differences and second data differences; Pre-set operating condition coefficient thresholds; If the predicted operating condition coefficient is less than the operating condition coefficient threshold, the corresponding operating link is set as the predicted link to be optimized; The internal associated data whose first data difference is greater than the preset difference threshold and the external associated data whose second data difference is greater than the preset difference threshold in the predicted link to be optimized are set as the predicted data to be optimized.

4. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 3 is characterized in that: Generate a predicted operating condition coefficient of a corresponding operating link according to a plurality of first data differences and second data differences, including: Performing a comprehensive analysis on the first data differences corresponding to the predicted internal correlation data of the same evaluation indicator in each operation link, and screening out the predicted internal correlation data whose first data differences are greater than a preset difference threshold; Comprehensively analyzing the second data differences corresponding to the predicted external correlation data of the same evaluation indicator in the operation link, and screening out the predicted external correlation data whose second data difference is greater than a preset difference threshold; Generate the prediction difference coefficient of the corresponding evaluation index at the standard time node based on the screened prediction internal correlation data and prediction external correlation data; The calculation formula of the predicted difference coefficient is: ； Among them, Y is the prediction difference coefficient, y0 is the difference conversion coefficient, x1 is the first weight coefficient, n1 is the number of predicted internal correlation data screened out, and N1 is the total number of predicted internal correlation data of the current evaluation index. is the value of the ith first data difference greater than the preset difference threshold, d1i is the weight coefficient of the ith predicted internal correlation data, x2 is the second weight coefficient, n2 is the number of screened predicted external correlation data, and N2 is the total number of predicted external correlation data of the current evaluation index. is the value of the s-th second data difference greater than the preset difference threshold, and d2s is the weight coefficient of the s-th predicted external correlation data; Generate the predicted operating status coefficient of the corresponding operating link based on the predicted difference coefficient of all evaluation indicators in each operating link at the standard time node; The calculation formula of the predicted operating condition coefficient is: ； Among them, Y2 is the predicted operating status coefficient, y2 is the operating status conversion coefficient, m is the total number of evaluation indicators of the current operation link, Y1v is the predicted difference coefficient of the vth evaluation indicator, and bv is the weight coefficient of the vth evaluation indicator.

5. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 4 is characterized in that: The first prediction optimization strategy is simulated, including: Constructing a preset optimization model for the corresponding predicted link to be optimized based on the historical optimization log of the predicted link to be optimized; Inputting the prediction data to be optimized of each prediction link to be optimized into the corresponding preset optimization model to obtain the first prediction optimization strategy for the corresponding prediction link to be optimized; Determine the location and structural information of the equipment involved in the predicted link to be optimized, lock the location of the preset panoramic map, and generate a static simulation scene corresponding to the predicted link to be optimized based on the locked location information; Acquire multiple dynamic nodes of a first prediction optimization strategy for a link to be optimized; Based on the predicted dependency relationship between the internal and external associated data of the link to be optimized, dynamic dependency features of the internal and external associated data at each dynamic node are established; Establish the dynamic operation characteristics of the corresponding equipment at each dynamic node according to the operation relationship between the involved equipment; According to the dynamic dependency relationship and dynamic operation relationship under multiple identical dynamic nodes, a position connection is established with the static simulation scene, and a simulation model of the first prediction optimization strategy corresponding to the predicted link to be optimized is constructed.

6. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 5 is characterized in that: Determine whether there is a conflict between the second prediction optimization strategies of multiple prediction links to be optimized, including: Obtain the second prediction optimization strategy of all the prediction links to be optimized for the current monitoring node, and extract the optimization target, optimization period, and optimization constraint conditions of each second prediction optimization strategy; A conflict analysis is performed on the optimization objectives, optimization periods, and optimization constraints of different second prediction optimization strategies to obtain conflicting second prediction optimization strategies and prediction links to be optimized of the second prediction optimization strategies.

7. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 6 is characterized in that: If present, calculate the priority coefficient, including: Obtain each conflicting predicted link to be optimized, and randomly select one predicted link to be optimized as the target link; Obtain the historical evaluation log of the target link, obtain multiple historical comprehensive evaluation values ​​of the target link based on the historical evaluation log, and calculate the difference between the historical comprehensive evaluation values; Filter out the historical comprehensive evaluation value differences with increasing differences, and construct the historical comprehensive evaluation value difference sequence of the target link; Determine the corresponding adjacent preset time nodes according to multiple historical evaluation nodes in the historical comprehensive evaluation value difference sequence, and obtain the historical comprehensive evaluation values ​​of other operation links according to the adjacent preset time nodes; According to the time sequence of the historical evaluation nodes of the historical comprehensive evaluation value difference sequence of the target link, the historical comprehensive evaluation value difference sequence of the corresponding time nodes of other operation links is constructed; Calculate the sequence similarity between the historical comprehensive evaluation value difference sequence of each operating link and the historical comprehensive evaluation value difference sequence of the target link. If the sequence similarity is greater than the preset similarity threshold, the corresponding operating link is determined to be the associated link of the target link, and the correlation coefficient between the associated link and the target link is calculated; Generate a priority coefficient of a target link according to the number of associated links, the associated coefficient, and the weight coefficient of the corresponding associated link; Generate the priority coefficient of each conflicting prediction link to be optimized in turn.

8. The port operation optimization method based on the Internet of Things and big data intelligent analysis according to claim 7 is characterized in that: Generate port operation optimization strategy based on priority coefficient and second prediction optimization strategy, including: Setting the optimization order of the corresponding second prediction optimization strategy according to the priority coefficient of the conflicting prediction link to be optimized; The port operation optimization strategy of the current monitoring node is generated according to the optimization sequence and the second prediction optimization strategy of the link to be optimized.

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