A method and system for synchronizing port loading and unloading data

Abstract

This invention provides a method and system for synchronizing port loading and unloading data. By deploying a processor on the industrial control side, the operating status of loading and unloading equipment is semantically abstracted and tagged, forming standardized semantic variables and a first set of scheduling instructions. Simultaneously, a virtual scheduling model is constructed on the production scheduling system side to generate a second set of scheduling instructions. The processor compares the tag matching degree of the two types of instructions and selects execution or feedback adjustment based on the matching result, achieving bidirectional data synchronization between the industrial control system and the management system. This method can reduce the data silo problem between heterogeneous systems and improve the real-time performance of data synchronization while ensuring on-site safety. It is suitable for port bulk cargo loading and unloading operations.

Description

Technical Field

[0001] This invention relates to the field of port loading and unloading operations, and more specifically to a method for synchronizing port loading and unloading data, as well as a system employing this method. Background Technology

[0002] Currently, bulk cargo handling operations at ports involve various large pieces of equipment such as bucket wheel excavators, belt conveyors, and ship loaders. The on-site control layer typically employs industrial control systems such as Siemens WinCC to monitor equipment operating status in real time and generate a large amount of high-frequency dynamic data, including instantaneous flow rate of belt conveyors, equipment current, cumulative loading / unloading volume, and fault alarm codes. The enterprise management layer uses a production scheduling system based on a relational database (such as Oracle) to store structured business data such as ship plans, loading / unloading tasks, cargo inventory, and scheduling instructions.

[0003] Due to significant differences in data models, communication protocols, and access methods, these two types of systems form a typical heterogeneous system architecture, resulting in severe barriers to data interaction between them. WinCC-type systems employ a real-time database architecture with a data model of "variable-timestamp-value" triples, while relational databases such as Oracle organize data in two-dimensional tables, making direct communication impossible. Traditional methods often rely on manual data entry or periodic batch imports, with data synchronization delays typically exceeding 30 minutes. This makes it difficult for production scheduling systems to monitor real-time on-site work progress, leading to a lack of timeliness and accuracy in scheduling decisions, thus creating data interaction problems.

[0004] To address these issues, the industry has attempted to achieve data integration through middleware or interface programs. However, due to differences in data models and incompatible communication protocols among heterogeneous systems, extensive customization is often required, resulting in poor system scalability and difficulty in ensuring the security and real-time performance of command issuance. Particularly during the process of dispatching commands from management to field execution, the lack of a mechanism to verify the executability of commands in the industrial control environment can easily lead to safety hazards or work interruptions due to mismatches between commands and the actual field operating conditions.

[0005] Therefore, there is an urgent need for a method that can solve the above-mentioned technical problems and achieve efficient, reliable, and low-latency two-way data synchronization between industrial control systems and management systems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings shown in the background art.

[0007] To achieve the above objectives, the technical solution provided by the present invention is as follows.

[0008] The first aspect is a method for synchronizing port loading and unloading data, which includes the following steps: S1. For the loading and unloading equipment in the port, several detection modules are deployed. The monitoring modules acquire the operating status of the loading and unloading equipment and perform semantic abstraction processing on the operating status through the processor, and encapsulate it into standardized semantic variables according to business semantics. S2. The processor presets several labels for the standardized semantic variables and presets a first scheduling instruction set for the labels; S3. The processor synchronously uploads the semantic variable data that meets the reporting conditions to the production scheduling system. The production scheduling system receives and stores the semantic variables and updates the loading and unloading operation progress. S4. The production scheduling system has a virtual scheduling model preset for the loading and unloading equipment, and generates a second scheduling instruction based on the virtual scheduling model; S5. The processor generates an actual operating model of the corresponding loading and unloading equipment based on the real-time operating status of the equipment collected by the monitoring module, and selects a first scheduling instruction based on the actual operating model. S6. The processor compares the tags of the first scheduling instruction and the second scheduling instruction to determine the degree of matching between them; If the matching degree meets the preset conditions, then the second scheduling instruction is executed; If the matching degree does not meet the preset conditions, the first scheduling instruction is executed or reported, and the execution result is fed back to the production scheduling system. The production scheduling system adjusts the virtual scheduling model according to the feedback result.

[0009] As a preferred technical solution, in step S1, the semantic abstraction processing includes: constructing a semantic variable registration center to centrally manage the metadata of all semantic variables; the metadata includes semantic code, semantic name, data type, value range, threshold definition, and source mapping rules; the processor converts one or more underlying raw parameters into semantic variable values ​​through arithmetic operations, logical operations, cumulative operations, or state machine mapping according to the source mapping rules.

[0010] As a preferred technical solution, in step S2, the tags include data type tags, equipment association tags, response time tags, and operation scenario tags; the data type tags include at least one of real-time data, cumulative data, event data, and status data; the equipment association tags include at least one of bucket wheel excavators, belt conveyors, and ship loaders; the response time tags include at least one of second-level response, minute-level response, and hour-level response; and the operation scenario tags include at least one of normal operation scenarios, congested operation scenarios, equipment maintenance scenarios, and fault handling scenarios.

[0011] As a preferred technical solution, in step S4, the virtual scheduling model generates a second scheduling instruction based on the ship plan, cargo inventory, loading and unloading tasks, and equipment rated parameters.

[0012] As a preferred technical solution, in step S5, the processor collects the real-time feedback of the operating status of the loading and unloading equipment and the real-time status vector set of the production equipment from the monitoring module in a time window, and calculates the expected changes of each parameter within the time window to form an actual operating model.

[0013] As a preferred technical solution, in step S6, the processor extracts the tag set corresponding to the first scheduling instruction, extracts the target device, target parameters, and expected effect corresponding to the second scheduling instruction, and compares the second scheduling instruction with the tag set of the first scheduling instruction; if the target device of the second scheduling instruction matches the device association tag of the first scheduling instruction, and the target parameters of the second scheduling instruction are within the range of the safety parameters corresponding to the tag of the first scheduling instruction, then it is determined that the matching degree meets the preset conditions; otherwise, it is determined that the matching degree does not meet the preset conditions.

[0014] As a preferred technical solution, the second scheduling instruction includes a main decision channel and several special decision channels set in parallel; the main decision channel generates the second scheduling instruction using the virtual scheduling model and performs matching degree verification in conjunction with the actual operation model; the special decision channels correspond to each label dimension of the semantic variables, and each special decision channel generates a corresponding special instruction using its corresponding label.

[0015] As a preferred technical solution, the method further includes constructing a decision difference vector to quantify the decision deviation between the main decision channel and each specific decision channel; the decision difference vector is represented as: V = [v1, v2, …, v n ] Among them, the difference v of the i-th special decision-making channel i Calculated using the following formula: v i = ‖D main - D speciali || In the formula, D main The vector magnitude of the main decision channel, D speciali Let D be the vector modulus of the i-th special decision-making channel; when the D main When the value is below the preset decision threshold, the special decision channel corresponding to the largest vector modulus in the decision difference vector V is identified as the priority decision basis in the current scenario and is used as a candidate for the first or second scheduling instruction.

[0016] As a preferred technical solution, the method further includes constructing a reverse coefficient vector, wherein the reverse coefficient vector is represented as: K = [k1, k2, …, kn ] Among them, each inverse coefficient k i Based on the current job scenario label assignment, when the job scenario label is an abnormal job scenario, the inverse coefficient kfault value corresponding to its special channel is greater than the inverse coefficients of other channels; the decision difference vector is weighted and corrected using the inverse coefficient vector to obtain the corrected weighted difference vector: V' = K ⊙ V = [k1·v1, k2·v2, …, k n ·v n ] In the formula, ⊙ represents the Hadamard product; when the comprehensive decision score Dmain is lower than the preset decision threshold, the special decision channel corresponding to the maximum value in the weighted difference vector V' is identified as the priority decision basis in the current scenario.

[0017] Secondly, the present invention provides a port loading and unloading data synchronization system, comprising: The monitoring module corresponds to the deployment of loading and unloading equipment in the port; The processor, which is communicatively connected to the monitoring module, is used to receive the operating status acquired by the monitoring module, perform semantic abstraction processing, and encapsulate it into standardized semantic variables according to business semantics; it presets several tags for the standardized semantic variables and presets a first scheduling instruction set for the tags; The production scheduling system is communicatively connected to the processor, has a preset virtual scheduling model corresponding to the loading and unloading equipment, and generates a second scheduling instruction based on the virtual scheduling model; it also receives the execution results fed back by the processor.

[0018] The advantages and beneficial effects of this invention are as follows: In this invention, the processor and the production scheduling system remain logically and functionally independent of each other. They achieve loosely coupled bidirectional data synchronization through standardized semantic variables and tagged instruction sets. The processor, as a node on the industrial control side, independently completes real-time acquisition, semantic abstraction, and tagged encapsulation of the operating status of the underlying loading and unloading equipment, forming a first scheduling instruction set that can be understood by the upper layer. Meanwhile, the production scheduling system independently generates a second scheduling instruction based on a virtual scheduling model, without directly intervening in the heterogeneous industrial control environment. This allows both parties to cooperate through semantic alignment and instruction matching mechanisms without changing their own architecture.

[0019] Building upon this foundation, this invention establishes a closed-loop instruction verification and model optimization mechanism through the processor's dynamic construction of the actual operating model and the scheduling system's feedback adjustment of the virtual model. The processor independently selects a first scheduling instruction, which, in conjunction with the second scheduling instruction issued by the scheduling system, performs a matching degree determination at the tag level. This ensures the safe and executable nature of the scheduling instructions in the industrial control field and enables the scheduling system to continuously optimize the virtual scheduling model based on the execution results. Each component undertakes a portion of the data processing, avoiding the difficulties of direct coupling caused by system heterogeneity and achieving low-latency, highly reliable data synchronization and scheduling closed-loop, thereby improving the real-time performance of port loading and unloading operations and the accuracy of scheduling decisions. Detailed Implementation

[0020] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, specific embodiments of this application are described in detail. It is understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0021] The terms “comprising” and “having”, and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly or implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides a method and system for synchronizing port loading and unloading data. By deploying a processor on the industrial control side, the operating status of loading and unloading equipment is semantically abstracted and tagged, forming standardized semantic variables and a first set of scheduling instructions. Simultaneously, a virtual scheduling model is constructed on the production scheduling system side to generate a second set of scheduling instructions. The processor compares the tag matching degree of the two types of instructions and selects execution or feedback adjustment based on the matching result, achieving bidirectional data synchronization between the industrial control system and the management system. This method can reduce the data silo problem between heterogeneous systems and improve the real-time performance of data synchronization while ensuring on-site safety. It is suitable for port bulk cargo loading and unloading operations.

[0024] In port bulk cargo handling operations, the field control layer (industrial control system) generates high-frequency, heterogeneous, and equipment-oriented raw data. Taking the Siemens WinCC system as an example, its data model is a triple structure of "variable-timestamp-value". Variable names typically use address encoding, directly reflecting the data storage location within the PLC, rather than business semantics. These raw parameters include instantaneous flow readings of belt scales, motor current, equipment status bits, fault alarm codes, etc. The characteristics of this type of data are: large volume, ambiguous semantics, and heterogeneous format.

[0025] Meanwhile, production scheduling systems (such as production management systems based on Oracle databases) process business-oriented two-dimensional table data, such as loading and unloading plans (including vessel name, planned loading and unloading volume, and planned start / end time), cargo inventory (yard location, cargo type, and inventory quantity), and equipment scheduling instructions (target equipment, target parameters, and expected results). These two types of systems are incompatible in terms of data models, communication protocols, and access methods, thus preventing them from interacting.

[0026] To address the aforementioned issues, this invention deploys a processor on the industrial control side to perform semantic abstraction processing. The core of this processing lies in constructing a semantic variable center to centrally manage the metadata of all semantic variables. The metadata includes: semantic code (e.g., "BLT_FLOW_INST"), semantic name (e.g., "B1 conveyor belt instantaneous flow"), data type (e.g., floating-point), value range (e.g., 0~5000 t / h), threshold definition (e.g., high alarm threshold 4500 t / h), and source mapping rules.

[0027] Source mapping rules define how to convert underlying raw parameters into semantic variable values, supporting multiple conversion modes, including but not limited to: By weighted averaging of readings from multiple similar sensors, random errors are eliminated. For example, a long-distance conveyor belt may be equipped with multiple belt scales. The processor performs a weighted average based on the calibration coefficients of each scale to generate a semantic variable of "comprehensive instantaneous flow rate." This processing method can effectively suppress data anomalies caused by single sensor failures or instantaneous fluctuations, improving data reliability.

[0028] Logical operations: The processor comprehensively determines the equipment status based on multiple Boolean state variables. For example, to determine the semantic variable "belt conveyor is running", three conditions must be met simultaneously: "motor contactor closed", "speed switch has signal", and "no emergency stop trigger". The processor generates this variable through a logical "AND" operation.

[0029] Cumulative calculation: Integrating instantaneous flow over time generates a cumulative semantic variable. For example, the processor integrates the instantaneous flow of the belt scale by second and accumulates it to obtain the "shift cumulative throughput". This variable can be directly compared with the planned quantity in the production scheduling system without requiring the scheduling system to calculate it itself, thus reducing the computational burden on the upper-level system.

[0030] State machine mapping: This transforms continuous analog quantities into discrete state semantics using a state machine. For example, based on the slewing angle range of a bucket wheel excavator (0°~90°), the semantic variable "bucket wheel excavator position state" is defined: 0°~30° is the "left end zone," 30°~60° is the "middle zone," and 60°~90° is the "right end zone." This transformation allows operational personnel to intuitively understand the equipment's working area without needing to interpret specific angle values.

[0031] Through the aforementioned semantic abstraction process, the raw parameters, which were originally only interpretable by industrial control professionals, are transformed into standardized semantic variables that business personnel can directly understand. Semantic abstraction converts the data model of the industrial control system into the data model of the production scheduling system, forming a common language that can be mutually recognized between heterogeneous systems. Specifically, it solves the following technical problems: First, it addresses the data semantic gap by eliminating semantic-level understanding barriers; second, it addresses data redundancy and noise by simplifying high-frequency sampled raw data into business-meaningful variables while filtering out sensor noise and instantaneous fluctuations; and third, it addresses the coupling problem of heterogeneous systems by eliminating the need for the production scheduling system to directly interface with various heterogeneous industrial control devices, instead allowing it to interact with the processor using standardized semantic variables, thus achieving loose coupling between systems and avoiding extensive custom interface development.

[0032] After completing semantic abstraction, this invention further pre-defines multi-dimensional labels for standardized semantic variables. The label system is designed based on in-depth analysis of port loading and unloading operation scenarios, covering at least one of four dimensions: data characteristics, equipment type, timeliness requirements, and scenario features.

[0033] Data type labels include real-time data, such as the instantaneous flow rate of a conveyor belt, which changes frequently and is used for real-time control and cumulative data; single-ship loading / unloading volume, used for progress statistics and event data, such as conveyor belt fault alarms, which occur discontinuously and are used for anomaly notification; and status data, such as the standby status of a ship loader, which remains stable and is used for operating condition identification. This classification allows the processor to adopt different processing strategies based on the different characteristics of the data. For example, real-time data needs to be reported frequently, while cumulative data can be reported only after reaching a certain increment.

[0034] Equipment-related tags include: bucket wheel excavators, material handling capacity, slewing angle, and pitch angle; belt conveyors, flow rate, motor current, speed, and misalignment status; and ship loaders, telescopic position, chute angle, and loading capacity.

[0035] Response timeliness tags: including second-level response, minute-level response, and hour-level response.

[0036] Operational scenario tags include normal operational scenarios, congested operational scenarios, equipment maintenance scenarios, and fault handling scenarios. These tags enable the instruction set to dynamically adjust according to different scenarios. For example, in fault handling scenarios, safety isolation instructions are prioritized; in congested operational scenarios, traffic coordination control instructions are prioritized.

[0037] Based on the aforementioned tagging system, the processor pre-defines a first scheduling instruction set for each tag. This first scheduling instruction set is a set of control instructions that the processor can independently generate based on the actual operating status of the site, targeting specific equipment. Each first scheduling instruction is associated with a tag for its related semantic variables, forming an instruction-tag mapping relationship. For example, the instruction "reduce the flow rate of conveyor belt B1" is bound to the tag {equipment association tag: conveyor type; response time tag: minute-level response; safety parameter range: flow rate 0~4500 t / h}. When the processor needs to select a first scheduling instruction, it matches the tag features extracted from the current actual operating model with the tag bound to the instruction, selecting the most suitable instruction.

[0038] By encapsulating semantic variables with the conditions, scope, and timeliness of instruction execution, a field-understandable and executable instruction system is constructed. Traditional dispatch instructions are often generated based on ideal models of the management layer, without fully considering the actual operating capabilities of field equipment. This invention binds instructions to equipment type, safety parameter range, and response timeliness through a tagging system, ensuring that the first dispatch instruction always matches the actual field conditions. Port operation scenarios are complex and ever-changing; this invention enables the instruction set to be dynamically adjusted according to different scenarios through operation scenario tags. Furthermore, by distinguishing instructions with different timeliness requirements through response timeliness tags, it ensures that emergency instructions are prioritized.

[0039] This invention constructs two models on the industrial control side and the production scheduling system side respectively, forming a two-way mapping between the field status and scheduling control.

[0040] The processor generates the actual operating model of the corresponding loading and unloading equipment based on the real-time equipment operating status collected by the monitoring module. Specifically, the processor collects real-time feedback on the equipment operating status from the monitoring module within a time window (e.g., the past 5 minutes), generating a real-time equipment status vector set. This vector set includes time-series data for key parameters, such as belt conveyor flow rate sequences, motor current sequences, and bucket wheel excavator slewing angle sequences. The processor calculates the expected changes of each parameter within the time window, including the mean, variance, slope, and extreme values. These statistical characteristics are then combined to form the actual operating model.

[0041] For example, the actual operating model of the B1 belt conveyor can be described as: "Average flow rate 785 t / h, flow rate fluctuation variance 120, flow rate trend slope +5 t / h / min, peak current 380A (85% of rated current)".

[0042] Meanwhile, the production scheduling system has a pre-set virtual scheduling model for the loading and unloading equipment, used to generate the second scheduling instruction. The virtual scheduling model is constructed as follows: the production scheduling system generates management-level scheduling instructions based on the ship's schedule (shipment date, cargo type, planned loading / unloading volume), cargo inventory (yard distribution, inventory level), loading / unloading tasks (operating equipment, operation sequence), and equipment rated parameters (rated flow rate, rated power, health status) using algorithms (such as linear programming and genetic algorithms). For example, the model obtains the ship's schedule "A certain ship needs to complete the loading of 5000 tons of coal within 6 hours," and combined with the rated flow rate of the belt conveyor of 1500 t / h, calculates the required average flow rate as 833 t / h, thus generating the second scheduling instruction: "Set the target flow rate of belt conveyor B1 to 830 t / h."

[0043] By establishing a bidirectional mapping between the actual operation model on the industrial control side and the virtual scheduling model on the management side, this invention addresses the issue of deviations in the actual operation model. Traditionally, after management issues instructions, it cannot obtain real-time information on the execution status of the instructions on-site or perceive changes in the on-site state. This invention enables the scheduling system to see the true state of the on-site environment through the actual operation model, and allows the on-site environment to understand the decision-making intent through the virtual scheduling model. Since the virtual scheduling model is built based on rated parameters and historical data, it deviates from the actual operating state. This invention continuously adjusts the virtual scheduling model based on the execution results fed back from the actual operation model, making it increasingly closer to the actual operating conditions. Regarding the safety assurance of instruction execution, the actual operation model includes information such as the equipment's safety parameter range, current load status, and changing trends, providing a safety verification basis for instruction matching.

[0044] The processor of this invention compares the tags of a first scheduling instruction and a second scheduling instruction to determine their matching degree. The matching degree determination logic is as follows: the processor extracts the tag set corresponding to the first scheduling instruction, extracts the target device, target parameters, and expected effect corresponding to the second scheduling instruction, and compares the second scheduling instruction with the tag set of the first scheduling instruction. If the target device of the second scheduling instruction matches the device association tag of the first scheduling instruction, and the target parameters of the second scheduling instruction are within the range of safety parameters corresponding to the tags of the first scheduling instruction, then the matching degree is determined to meet the preset conditions; otherwise, it is determined not to meet the conditions.

[0045] If the matching degree meets the preset conditions, the processor executes the second scheduling instruction, sending the instruction to the corresponding equipment for execution. If the matching degree does not meet the preset conditions (for example, the second scheduling instruction requires a flow rate of 830 t / h, but the actual operation model shows that the current equipment status can only safely operate up to 750 t / h), the processor executes the first scheduling instruction or reports an anomaly, and feeds the execution result back to the production scheduling system. The production scheduling system adjusts the virtual scheduling model based on the feedback result, for example, correcting the rated flow parameters of the belt conveyor, or regenerating a scheduling instruction that better matches the on-site capabilities.

[0046] By employing an instruction matching mechanism, operational efficiency is maximized while ensuring safety. When management instructions conflict with on-site safety constraints, traditional systems lack automatic adjudication mechanisms, often relying on manual judgment, which carries risks of delays and misjudgments. This invention achieves automated instruction adjudication through label matching. Overly conservative instruction execution reduces operational efficiency, while overly aggressive instruction execution can lead to safety accidents. This invention provides a quantitative boundary for instruction matching by setting safety parameter ranges, enabling the system to maximize efficiency within the safety boundaries. Furthermore, a feedback mechanism allows the production scheduling system to perceive the actual situation of instruction execution and adjust the virtual scheduling model accordingly, giving the system the ability to continuously learn and self-optimize.

[0047] To further enhance the system's adaptability in complex scenarios, this invention designs a main decision-making channel and several specialized decision-making channels configured in parallel within the second scheduling instruction. The main decision-making channel uses a virtual scheduling model to generate a comprehensive decision, representing the overall decision based on a global optimization model. The specialized decision-making channels correspond to the various label dimensions of semantic variables, with each specialized decision-making channel using its corresponding label as the primary driver to generate its respective specialized instruction. Data Type-Specific Channel: Focuses on real-time and event data, generating instructions driven by data changes. For example, when the instantaneous flow rate of the belt scale changes abruptly, this channel generates an instruction to "check if the belt conveyor is blocked."

[0048] Equipment Association Dedicated Channel: This channel focuses on the collaborative relationships between equipment, generating instructions aimed at equipment linkage. For example, when the bucket wheel excavator's material handling capacity increases, this channel generates an instruction to "start the downstream conveyor belt in advance to prevent material accumulation."

[0049] Response Timeliness Dedicated Channel: This channel focuses on the timeliness requirements of instructions, generating instructions aimed at rapid response. For example, in a fault scenario, this channel generates a second-level instruction to "immediately stop upstream and downstream devices of the faulty device."

[0050] Dedicated channel for specific work scenarios: This channel focuses on the current work scenario tags and generates scenario-appropriate instructions. For example, in a congested work scenario, this channel generates an instruction to "reduce traffic to all collaborative devices to avoid congestion."

[0051] Based on this, the present invention constructs a decision difference vector V, and the calculation of its vector magnitude in multiple dimensions of the decision result, such as target device, target parameters, execution time, etc., is calculated by Euclidean distance or Manhattan distance.

[0052] When D main When the decision intensity is below the preset decision threshold, it indicates that the main decision channel's decision strength is weak and may not be able to effectively guide on-site operations. In this case, the processor identifies the specialized decision channel corresponding to the largest vector modulus in the decision difference vector V as the priority decision basis for the current scenario.

[0053] To further improve scenario adaptability, this invention also constructs a reverse coefficient vector K. Each reverse coefficient k... i Values ​​are assigned based on the current task scenario label. For example, when the task scenario label is "fault handling scenario," the inverse coefficient value corresponding to the fault handling-related special channel is greater than the inverse coefficient values ​​of other channels. The decision difference vector is then weighted and corrected using the inverse coefficient vector to obtain the corrected weighted difference vector V'. When the comprehensive decision score D... main When the value is below the preset decision threshold, the special decision channel corresponding to the maximum value in the weighted difference vector V' is identified as the priority decision basis in the current scenario.

[0054] For example, in equipment maintenance scenarios, the reverse coefficient of the equipment-associated special channel is increased. Even if its difference v is not the maximum, the weighted k·v may become the maximum value, thus making the instructions of the equipment-associated special channel take priority and ensuring the safe conduct of maintenance operations.

[0055] By using multi-channel parallel decision-making and quantification of differences, in special scenarios such as congested operations, equipment maintenance, and fault handling, virtual scheduling models based on global optimization may not be able to respond quickly to changes in the scenario. Specialized decision-making channels make decisions based on specific label dimensions, which can adapt to scenario requirements more quickly and accurately. When multiple decision-making channels conflict, how to select the optimal decision basis is a problem. This invention achieves the selection of decision basis through weighted correction of difference vector and inverse coefficient vector. Thirdly, there is the problem of quantitative expression of scenario features. The focus of decision-making should be different in different operation scenarios. This invention quantifies scenario features into weight coefficients through inverse coefficient vector, so that decision preferences can be adjusted with changes in the scenario.

[0056] The present invention also provides a port loading and unloading data synchronization system for implementing the technical solutions of the above method embodiments.

[0057] The system comprises a monitoring module, a processor, and a production scheduling system. The monitoring module corresponds to the port's loading and unloading equipment deployment, including belt scales, current transformers, vibration sensors, limit switches, and PLCs, used to collect real-time data on the equipment's operating status. The monitoring module communicates with the processor via industrial Ethernet or fieldbus, supporting industry standard protocols such as Modbus TCP, PROFINET, and OPC UA.

[0058] The production scheduling system communicates with the processor. Deployed at the group or terminal management level, the system is built on a relational database (such as Oracle) and runs on a server cluster. The system has pre-set virtual scheduling models corresponding to the loading and unloading equipment and generates second scheduling instructions based on these models; it also receives execution results from the processor and adjusts the virtual scheduling model accordingly.

[0059] The purpose of this system architecture is to: achieve cooperation between the industrial control side and the management side through a hierarchical architecture, solve the integration difficulties caused by system heterogeneity, and enable the processor, as the edge node of the industrial control side, to undertake computing tasks such as data conversion, semantic abstraction, and instruction matching, so that the production scheduling system does not have to directly face the heterogeneous industrial control environment, thus reducing the complexity of system integration; the industrial control side has extremely high requirements for real-time performance, while the management side focuses more on data integrity and traceability. The processor, through mechanisms such as local caching, network disconnection reconnection, and data retransmission, ensures both real-time performance on site and data integrity and reliability; when the port adds loading and unloading equipment or changes the process flow, only semantic variables and labels need to be added or modified on the processor side, and the production scheduling system does not need to be modified, demonstrating good scalability.

[0060] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0061] Example 1 This embodiment provides a basic implementation method for port loading and unloading data synchronization.

[0062] In step S1, several detection modules are deployed corresponding to the loading and unloading equipment at the port. Taking bulk cargo loading and unloading operations at a port as an example, detection modules such as belt scales, current transformers, and vibration sensors are deployed on key equipment such as bucket wheel excavators, belt conveyors, and ship loaders. After receiving the raw parameters, the processor constructs a semantic variable registration center. For example, for belt conveyor flow data, the processor converts the raw readings of multiple belt scales into the semantic variable "B1 belt conveyor instantaneous flow" through arithmetic operations (weighted average). Its metadata includes: semantic code "BLT_FLOW_INST", semantic name "B1 belt conveyor instantaneous flow", data type floating point, value range 0~5000 t / h, high alarm threshold 4500 t / h, and source mapping rule "belt scale A reading × 0.6 + belt scale B reading × 0.4".

[0063] In step S2, the processor pre-defines labels for standardized semantic variables. For example, the label set for "instantaneous flow rate of B1 conveyor belt" is: {Data type label: real-time data; Equipment association label: conveyor belt type; Response timeliness label: minute-level response; Operation scenario label: normal operation scenario}. The processor pre-defines a first set of scheduling instructions for these labels, such as the instruction "reduce the flow rate of B1 conveyor belt" which is bound to the labels {Equipment association label: conveyor belt type; Response timeliness label: minute-level response; Safety parameter range: flow rate 0~4500 t / h}.

[0064] In step S3, the processor synchronously uploads semantic variable data that meets the reporting conditions to the production scheduling system. The reporting condition is set to be reported once every 10 seconds. After receiving the semantic variables, the production scheduling system stores them in the corresponding data table in the Oracle database and updates the current loading and unloading operation progress accordingly.

[0065] In step S4, the production scheduling system generates a second scheduling instruction through a virtual scheduling model based on the ship plan (a ship needs to load 5,000 tons of coal within 6 hours), cargo inventory (sufficient coal inventory in the stockpile), loading and unloading tasks (bucket wheel excavator A, B1 / B2 belt conveyors, ship loader C), and equipment rated parameters (rated flow rate of B1 belt conveyor 1,500 t / h). The instruction is: "Target flow rate of B1 belt conveyor 830 t / h".

[0066] In step S5, the processor collects real-time feedback on the equipment operating status from the monitoring module within a 5-minute time window and generates an actual operating model. For example, the actual operating model for the B1 belt conveyor is: "Average flow rate 785 t / h, flow rate fluctuation variance 120, flow rate trend slope +5 t / h / min, peak current 380A (85% of rated current)". Based on this model, the processor selects "reduce the flow rate of the B1 belt conveyor to 750 t / h" as the first scheduling instruction from the first scheduling instruction set.

[0067] In step S6, the processor compares the tags of the first scheduling instruction and the second scheduling instruction. The target device of the second scheduling instruction is belt conveyor B1, which matches the device association tag "belt conveyor type" of the first scheduling instruction; however, the target parameter of 830 t / h exceeds the recommended safety upper limit (750 t / h) based on the actual operation model within the safety parameter range (0~4500 t / h) of the first scheduling instruction, so the matching degree is determined not to meet the preset conditions. The processor executes the first scheduling instruction "reduce the flow rate of belt conveyor B1 to 750 t / h" and feeds back the execution result to the production scheduling system. The production scheduling system adjusts the virtual scheduling model according to the feedback result, lowering the rated flow rate parameter of belt conveyor B1.

[0068] Example 2 The difference between this embodiment and Embodiment 1 is that the labeling system in step S2 has been adjusted.

[0069] In actual port operations, the operational scenario changes dynamically with the progress of operations and the status of equipment. For example, when multiple vessels are operating at a berth simultaneously, the operational scenario label changes from "normal operational scenario" to "congested operational scenario." After receiving the scenario switching instruction from the production scheduling system, the processor updates the operational scenario labels of relevant semantic variables in batches through the label management thread.

[0070] In congested operation scenarios, the processor's preset first scheduling instruction set changes. For example, in normal operation scenarios, the first scheduling instruction set includes "increase the conveyor belt flow" to improve efficiency; while in congested operation scenarios, the first scheduling instruction set is adjusted to focus on flow restriction and coordinated control, including instructions such as "reduce the flow of all coordinated devices" and "maintain stable flow to avoid fluctuations." This dynamic adjustment allows the system to automatically adapt decision-making strategies in different scenarios, preventing congestion from being exacerbated by flow fluctuations.

[0071] Example 3 The difference between this embodiment and embodiment 2 is that the parallel decision-making channel mechanism in step S6 has been adjusted.

[0072] The second scheduling instruction sets up one main decision channel and four specialized decision channels in parallel. The main decision channel uses a virtual scheduling model to generate a comprehensive decision: "Target flow rate of conveyor B1: 830 t / h". The data type-specific channel generates the instruction "Current flow rate fluctuates significantly; it is recommended to maintain the current flow rate and wait for it to stabilize" based on real-time data changes. The equipment association-specific channel generates the instruction "Downstream ship loader currently has limited processing capacity; it is recommended that the flow rate not exceed 800 t / h" based on equipment coordination relationships. The response timeliness-specific channel generates the instruction "Currently not in an emergency; adjustments can be made in minutes" based on timeliness requirements. The operation scenario-specific channel generates the instruction "It is recommended that the flow rate of all coordinated equipment be limited to below 700 t / h" based on the current "Congested Operation Scenario" label.

[0073] The processor calculates the difference between each specialized decision channel and the main decision channel. The decision vector magnitude of the main decision channel is calculated based on the target parameter of 830 t / h; the decision vector magnitude of the equipment-related specialized channel is calculated based on 800 t / h, with a difference of v=30; and the decision vector magnitude of the work scenario specialized channel is calculated based on 700 t / h, with a difference of v=130. Since the main decision channel is below the preset decision threshold, the processor identifies the work scenario specialized channel with the highest difference as the priority decision criterion and adopts the instruction of "limiting the flow rate below 700 t / h" to ensure operational safety in congested scenarios.

[0074] Example 4 The difference between this embodiment and Embodiment 3 is that a weighted correction mechanism for the inverse coefficient vector is further introduced.

[0075] The current task scenario is labeled "Fault Handling Scenario". The processor constructs an inverse coefficient vector K. Based on the characteristics of the fault handling scenario, the inverse coefficient of the response timeliness-specific channel is set to 0.8, and the coefficients of other channels are set to 0.2. The original difference vector V = [15, 30, 40, 25] (corresponding to data type, equipment association, response timeliness, and task scenario, respectively). After weighted correction, the weighted difference vector V' = [15×0.2=3, 30×0.2=6, 40×0.8=32, 25×0.2=5]. At this point, the weighted difference of 32 for the response timeliness-specific channel becomes the maximum value and is identified as the priority decision criterion.

[0076] In fault handling scenarios, response time becomes the most critical decision-making factor. Even if the original difference between the main decision-making channel and other channels is not significant, the instructions of the response time-specific channel are prioritized and executed through the weighting of the reverse coefficient, ensuring the safety and timeliness of fault handling.

[0077] Example 5 This embodiment provides a specific implementation of a port loading and unloading data synchronization system.

[0078] The system comprises monitoring modules, a processor, and a production scheduling system. The monitoring modules include current transformers and angle sensors deployed on the bucket wheel excavator, belt scales and speed sensors deployed on the belt conveyor, and position sensors and level gauges deployed on the ship loader. All monitoring modules communicate with the processor via industrial Ethernet.

[0079] The processor uses a Siemens industrial computer and is equipped with a real-time data processing engine. The engine includes threads for data acquisition, semantic transformation, label management, model generation (calculating the actual running model in a 5-minute time window), instruction matching, and communication management. The processor has an internal 128GB solid-state drive for local caching, supporting data retransmission after network disconnection and reconnection.

[0080] The production scheduling system is deployed in the group's data center, based on an Oracle 19c database, and runs on a cluster of two servers. The system has a pre-set virtual scheduling model. The model's inputs include ship plans, cargo inventory, loading and unloading tasks, and equipment rated parameters, and the output is a second scheduling instruction.

[0081] During continuous operation testing, the system reduced data synchronization latency from over 30 minutes to less than 8 seconds, and the command matching accuracy reached 99.2%, effectively supporting the digital upgrade of port bulk cargo loading and unloading operations.

[0082] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for synchronizing port loading and unloading data, characterized in that, Includes the following steps: S1. For the loading and unloading equipment in the port, several detection modules are deployed. The monitoring modules acquire the operating status of the loading and unloading equipment and perform semantic abstraction processing on the operating status through the processor, and encapsulate it into standardized semantic variables according to business semantics. S2. The processor presets several labels for the standardized semantic variables and presets a first scheduling instruction set for the labels; S3. The processor synchronously uploads the semantic variable data that meets the reporting conditions to the production scheduling system. The production scheduling system receives and stores the semantic variables and updates the loading and unloading operation progress. S4. The production scheduling system has a virtual scheduling model preset for the loading and unloading equipment, and generates a second scheduling instruction based on the virtual scheduling model; S5. The processor generates an actual operating model of the corresponding loading and unloading equipment based on the real-time operating status of the equipment collected by the monitoring module, and selects a first scheduling instruction based on the actual operating model. S6. The processor compares the tags of the first scheduling instruction and the second scheduling instruction to determine the degree of matching between them; If the matching degree meets the preset conditions, then the second scheduling instruction is executed; If the matching degree does not meet the preset conditions, the first scheduling instruction is executed or reported, and the execution result is fed back to the production scheduling system. The production scheduling system adjusts the virtual scheduling model according to the feedback result.

2. The port loading and unloading data synchronization method according to claim 1, characterized in that, In S1, the semantic abstraction process includes: constructing a semantic variable registration center to centrally manage the metadata of all semantic variables; the metadata includes semantic code, semantic name, data type, value range, threshold definition, and source mapping rules; the processor converts one or more underlying raw parameters into semantic variable values ​​through arithmetic operations, logical operations, cumulative operations, or state machine mapping according to the source mapping rules.

3. The port loading and unloading data synchronization method according to claim 1, characterized in that, In step S2, the tags include data type tags, equipment association tags, response time tags, and operation scenario tags; the data type tags include at least one of real-time data, cumulative data, event data, and status data; the equipment association tags include at least one of bucket wheel excavator, belt conveyor, and ship loader; the response time tags include at least one of second-level response, minute-level response, and hour-level response; and the operation scenario tags include at least one of normal operation scenario, congested operation scenario, equipment maintenance scenario, and fault handling scenario.

4. The port loading and unloading data synchronization method according to claim 1, characterized in that, In S4, the virtual scheduling model generates a second scheduling instruction based on the ship plan, cargo inventory, loading and unloading tasks, and equipment rated parameters.

5. The port loading and unloading data synchronization method according to claim 1, characterized in that, In S5, the processor collects the real-time feedback of the monitoring module on the operating status of the loading and unloading equipment and the real-time status vector set of the production equipment in a time window, and calculates the expected changes of each parameter within the time window to form an actual operating model.

6. The port loading and unloading data synchronization method according to claim 1, characterized in that, In step S6, the processor extracts the tag set corresponding to the first scheduling instruction, extracts the target device, target parameters, and expected effect corresponding to the second scheduling instruction, and compares the second scheduling instruction with the tag set of the first scheduling instruction. If the target device of the second scheduling instruction matches the device association tag of the first scheduling instruction, and the target parameters of the second scheduling instruction are within the range of the safety parameters corresponding to the tag of the first scheduling instruction, then it is determined that the matching degree meets the preset conditions. Otherwise, the matching degree is determined to be insufficient to meet the preset conditions.

7. The port loading and unloading data synchronization method according to claim 1, characterized in that, The second scheduling instruction includes a main decision channel and several special decision channels set in parallel; the main decision channel generates the second scheduling instruction using the virtual scheduling model and performs matching degree verification in conjunction with the actual operation model; the special decision channels correspond to each label dimension of the semantic variables, and each special decision channel generates a corresponding special instruction using its corresponding label.

8. The port loading and unloading data synchronization method according to claim 7, characterized in that, The method further includes constructing a decision difference vector to quantify the decision deviation between the main decision channel and each specific decision channel; the decision difference vector is represented as: V = [v1, v2, …, v n ] Among them, the difference v of the i-th special decision-making channel i Calculated using the following formula: v i = ‖D main - D speciali ‖ In the formula, D main The vector magnitude of the main decision channel, D speciali Let D be the vector modulus of the i-th special decision-making channel; when the D main When the value is below the preset decision threshold, the special decision channel corresponding to the largest vector modulus in the decision difference vector V is identified as the priority decision basis in the current scenario and is used as a candidate for the first or second scheduling instruction.

9. The port loading and unloading data synchronization method according to claim 8, characterized in that, The method further includes constructing a reverse coefficient vector, which is represented as follows: K = [k1, k2, …, k n ] Among them, each inverse coefficient k i Based on the current job scenario label assignment, when the job scenario label is an abnormal job scenario, the inverse coefficient kfault value corresponding to its special channel is greater than the inverse coefficients of other channels; the decision difference vector is weighted and corrected using the inverse coefficient vector to obtain the corrected weighted difference vector: V' = K ⊙ V = [k1·v1, k2·v2, …, k n ·v n ] In the formula, ⊙ represents the Hadamard product; when the comprehensive decision score Dmain is lower than the preset decision threshold, the special decision channel corresponding to the maximum value in the weighted difference vector V' is identified as the priority decision basis in the current scenario.

10. A port loading and unloading data synchronization system, characterized in that, include: The monitoring module corresponds to the deployment of loading and unloading equipment in the port; The processor, which is communicatively connected to the monitoring module, is used to receive the operating status acquired by the monitoring module, perform semantic abstraction processing, and encapsulate it into standardized semantic variables according to business semantics; it presets several tags for the standardized semantic variables and presets a first scheduling instruction set for the tags; The production scheduling system is communicatively connected to the processor, has a preset virtual scheduling model corresponding to the loading and unloading equipment, and generates a second scheduling instruction based on the virtual scheduling model; it also receives the execution results fed back by the processor.