One-to-many remote full-automatic intelligent operation and control system

Abstract

The invention provides a one-to-many remote full-automatic intelligent operation and control system, and relates to the technical field of ship unloaders, and the system comprises a data obtaining module which is used for obtaining the operation parameters of each ship unloader in the system and the scanning data of a target ship in real time, and outputting first data; the preprocessing module is used for preprocessing the first data by utilizing a preset preprocessing method to obtain to-be-analyzed data; the data analysis module is used for performing control analysis and risk analysis on the to-be-analyzed data to respectively obtain a control analysis result, a risk analysis result and a strategy and an instruction corresponding to the corresponding results; and the optimization module is used for performing optimization analysis on the control analysis result and the risk analysis result in combination with a preset optimization index, and optimizing a control strategy and a risk control strategy of each ship unloader. The operation process of the ship unloader is monitored and controlled in real time, risks in the operation process are recognized and processed in time, and the operation efficiency and safety of the ship unloader are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship unloaders, and in particular to a one-to-many remote fully automatic intelligent operation and control system. Background Art

[0002] Shipping by cargo ship is an efficient and low-cost mode of transportation. Cargo ships can transport a variety of goods, including granular or powdered goods such as coal and grain. At the port, the cargo needs to be unloaded by a ship unloader and transported to the shore via a conveyor belt.

[0003] At present, in the process of extracting goods through the ship unloader, the unloader is generally manually operated to control the material head to extract the goods. Among them, the goods are unevenly distributed in the cargo hold of the cargo ship, and the staff need to perform the extraction operation according to the distribution of the goods. This cargo extraction method not only requires high professional ability and coordination ability of the ship unloader staff, but also has high labor costs. In addition, the operation process mainly relies on manual monitoring, the monitoring level is low, and there are safety hazards. At the same time, the working efficiency of this method is low, and it is difficult to meet the port's growing cargo throughput.

[0004] Therefore, the present invention provides a one-to-many remote fully automatic intelligent operation and control system. Summary of the invention

[0005] The present invention provides a one-to-many remote fully automatic intelligent operation and control system, which is used to monitor and control the operation process of the ship unloader in real time, and to promptly identify and handle the risks in the operation process, thereby improving the operation efficiency and safety of the ship unloader.

[0006] The present invention provides a one-to-many remote fully automatic intelligent operation and control system, comprising:

[0007] A data acquisition module, used for acquiring the operating parameters of each ship unloader in the system and the scanning data of the target ship in real time, and outputting first data;

[0008] A preprocessing module, used to preprocess the first data using a preset preprocessing method to obtain data to be analyzed;

[0009] A data analysis module, used to perform control analysis on the data to be analyzed, output control strategies and control instructions based on the control analysis results, control the corresponding operating parameters of each ship unloader, and output control process data, perform risk analysis on the data to be analyzed and the control process data, obtain risk control strategies and risk control instructions matching the risk analysis results, and perform corresponding risk control operations on the operating parameters of each ship unloader based on the risk control strategies and risk control instructions;

[0010] The optimization module is used to optimize the control analysis results and risk analysis results in combination with preset optimization indicators, and optimize the control strategy and risk control strategy of each ship unloader based on the optimization analysis results.

[0011] Preferably, the data acquisition module includes:

[0012] An operation data acquisition submodule is used to acquire the operation parameters of multiple ship unloaders in the system in real time and output real-time operation data;

[0013] A scanning data acquisition submodule is used to scan the ship type information of the target ship and the cargo information loaded in the target ship in real time, and output the scanning data;

[0014] The comprehensive output submodule is used to output first data based on the real-time operation data and the scanning data.

[0015] Preferably, the comprehensive output submodule further includes:

[0016] An environmental parameter acquisition unit is used to acquire the environmental parameters around each ship unloader in real time and output environmental data;

[0017] A classification unit, used for classifying and analyzing the real-time operation data, scanning data and environmental parameter data to obtain sub-data corresponding to each ship unloader;

[0018] The summarizing unit is used for summarizing and outputting the sub-data corresponding to each ship unloader to obtain the first data.

[0019] Preferably, the preprocessing module comprises:

[0020] An abnormality analysis submodule, used to perform abnormality analysis on the first data, and obtain corresponding abnormal features by matching in an abnormality feature database based on the abnormality analysis results;

[0021] A method selection submodule is used to select a matching preprocessing method from a method database based on the abnormal features and in combination with a feature-method comparison table;

[0022] The preprocessing submodule is used to preprocess the first data based on the preprocessing method and output the data to be analyzed.

[0023] Preferably, the preprocessing submodule includes:

[0024] A method-data matching unit, used for matching the preprocessing method with the first data, preprocessing the first data satisfying the preset matching condition by the preprocessing method to obtain data to be output;

[0025] The to-be-analyzed data output unit is used to determine the to-be-analyzed data that meets a preset threshold condition as the to-be-analyzed data and output it.

[0026] Preferably, the data analysis module includes:

[0027] A data analysis submodule, used for analyzing the data to be analyzed to obtain analysis subdata corresponding to each ship unloader;

[0028] The control analysis submodule is used to perform control analysis on the analytical subdata corresponding to each ship unloader, output the control analysis result, and obtain the control strategy and control instruction matching the control analysis result in the strategy-instruction database in combination with the result-strategy-instruction comparison table, control the corresponding operating parameters of each ship unloader, and at the same time, obtain the change data of the operating parameters of each ship unloader during the control process, and output the control process data;

[0029] The risk control analysis submodule is used to perform risk analysis on the analytical sub-data and control process data corresponding to each ship unloader, output the risk analysis results, and obtain the risk control strategies and risk control instructions matching the risk analysis results in the strategy-instruction database in combination with the result-strategy-instruction comparison table, and perform corresponding risk control operations according to the corresponding operating parameters of each ship unloader.

[0030] Preferably, the control analysis submodule includes:

[0031] A trolley data acquisition unit, used to acquire trolley data corresponding to the trolley of the ship unloader, trolley data corresponding to the trolley of the ship unloader, and grab bucket data corresponding to the grab bucket of the ship unloader in the parsed sub-data of each ship unloader;

[0032] a ship data acquisition unit, used for acquiring ship data corresponding to a target ship in the parsed sub-data of each ship unloader;

[0033] A comparison table generating unit is used for acquiring ship unloader information corresponding to each target ship based on the ship data, and outputting a ship-ship unloader comparison table;

[0034] A time alignment unit is used to time align the ship data of the target ship and the trolley data, car data and grab bucket data under each ship unloader corresponding to the target ship based on the ship-ship unloader comparison table, and obtain the time delay information between the trolley data, car data, grab bucket data and ship data in combination with the preset standard time, and output a data time delay analysis table;

[0035] The port vessel information acquisition unit is used to acquire the information of vessels to be operated inside and outside the port and the unloading demand information of the corresponding vessels, and to sort the vessels to be operated according to the preset sorting rules, and output the vessel queue information;

[0036] A collaborative control analysis unit, configured to perform collaborative control analysis on all ship unloaders in the port based on the ship-unloader comparison table, the data delay analysis table and the ship queue information, and in combination with a preset collaborative analysis method, and output a collaborative control analysis result;

[0037] A strategy matching unit, used to obtain control strategies and control instructions that meet preset screening conditions in a strategy-instruction database in combination with the result-strategy-instruction matching table;

[0038] An instruction execution unit, used for controlling the operation parameters corresponding to each ship unloader in the port based on the control strategy and the control instruction;

[0039] The process data output unit is used to obtain the change data of the corresponding operating parameters of each ship unloader during the execution of the control instruction and output the control process data.

[0040] Preferably, the risk control analysis submodule includes:

[0041] A data matching unit, used for performing cluster analysis on the analytical sub-data and the control process data to obtain second data corresponding to each ship unloader;

[0042] a feature extraction unit, configured to extract features from the second data and construct a first feature set based on the extracted features;

[0043] A feature matching unit, configured to match the first feature set with risk features in a risk database, and output a risk feature set based on a matching result;

[0044] A risk identification unit is used to obtain the risk type corresponding to the risk feature set and the risk level under the corresponding risk type in combination with the feature-risk comparison table, and output the risk identification result;

[0045] A result output unit is used to determine the risk status of each ship unloader in the system based on the risk identification result and in combination with a preset result-status mapping table, calculate the risk coefficient of each ship unloader through a preset algorithm, and comprehensively output the risk analysis result;

[0046]

[0047] Among them, R i represents the risk coefficient of the i-th ship unloader in the system; mi represents the total number of cyclic grabs of the grab bucket of the i-th ship unloader during the operation process; e represents the exponential function; τ ij represents the delay and packet loss error adjustment factor corresponding to the j-th grabbing process of the i-th ship unloader; T ij1represents the starting time of the i-th ship unloader when performing the j-th grabbing process; T ij2 represents the end time of the jth grabbing process of the i-th ship unloader; dev ijX (t) represents the torsion angle offset of the i-th ship unloader in the X-axis direction at time t during the j-th grabbing process; dev ijY (t) represents the torsion angle offset of the i-th ship unloader in the Y-axis direction at time t during the j-th grabbing process; dev ijZ (t) represents the torsion angle offset of the i-th ship unloader in the Z-axis direction at time t during the j-th grabbing process, where dev ijX (t), dev ijY (t), dev ijZ (t) are all functions of time t; σ ij1 represents the influence factor between the grab bucket deviation and the vehicle travel when the i-th ship unloader performs the j-th grab operation; σ ij2 represents the influence factor between the grab bucket deviation and the trolley motion when the i-th ship unloader performs the j-th grab operation; σ ij3 represents the influencing factor between the grab bucket deviation and the weather when the i-th ship unloader performs the j-th grab operation;

[0048] A risk control strategy matching unit, configured to select a matching risk control strategy and a risk control instruction under the corresponding risk control strategy from a strategy-instruction database based on the risk analysis result and in combination with the result-strategy-instruction comparison table;

[0049] The wind control unit is used to perform wind control operations on the corresponding operating parameters of each ship unloader based on the wind control strategy and the wind control instructions.

[0050] The present invention provides a one-to-many remote fully automatic intelligent operation and control system, which realizes fully automatic intelligent control and optimization of ship unloader operation through the synergy of modules such as data acquisition, preprocessing, data analysis, risk control and optimization analysis. The present invention can improve the operation efficiency of the ship unloader, reduce the risk of the ship unloader during operation, ensure the safety and stability of the operation, and at the same time, through intelligent analysis and optimization, a more efficient operation process can be achieved, and the quality and efficiency of the overall loading and unloading operation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a schematic diagram of the framework of a one-to-many remote fully automatic intelligent operation and control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, an embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, including:

[0055] A data acquisition module, used for acquiring the operating parameters of each ship unloader in the system and the scanning data of the target ship in real time, and outputting first data;

[0056] A preprocessing module, used to preprocess the first data using a preset preprocessing method to obtain data to be analyzed;

[0057] The data analysis module is used to perform control analysis on the data to be analyzed, output control strategies and control instructions based on the control analysis results, control the corresponding operating parameters of each ship unloader, and output control process data, perform risk analysis on the data to be analyzed and the control process data, obtain risk control strategies and risk control instructions that match the risk analysis results, and perform corresponding risk control operations on the operating parameters of each ship unloader based on the risk control strategies and risk control instructions;

[0058] The optimization module is used to optimize the control analysis results and risk analysis results in combination with preset optimization indicators, and optimize the control strategy and risk control strategy of each ship unloader based on the optimization analysis results.

[0059] In this embodiment, the operating parameters are real-time operating data of the ship unloader, such as speed, load condition, etc.;

[0060] In this embodiment, the target vessel refers to the vessel to be loaded or unloaded with cargo;

[0061] In this embodiment, scanning data refers to the information of the vessel and the cargo loaded on the vessel obtained by the scanning device, such as the vessel type, cargo type, quantity, etc.;

[0062] In this embodiment, the first data: namely, summary data including the operating parameters of the ship unloader and the scanning data of the target vessel;

[0063] In this embodiment, the preset preprocessing method refers to a specific method or algorithm used by the preprocessing module to preprocess the first data;

[0064] In this embodiment, data to be analyzed: data to be further analyzed after being processed by the preprocessing module;

[0065] In this embodiment, the control analysis result is the result obtained by the data analysis module after performing control analysis on the data to be analyzed, which is used to determine the control strategy and control instructions;

[0066] In this embodiment, control strategies and control instructions: strategies and instructions for controlling the operating parameters of each ship unloader, for example, adjusting the speed and load of the ship unloader;

[0067] In this embodiment, control process data: that is, data generated during the control process, used to monitor and analyze the operation process;

[0068] In this embodiment, the risk analysis result is the result obtained after risk analysis of the data to be analyzed and the control process data, and is used to determine the risk control strategy and risk control instructions;

[0069] In this embodiment, risk control strategies and risk control instructions: strategies and instructions for executing risk control operations, such as shutdown and speed reduction;

[0070] In this embodiment, risk control operation: an operation performed according to risk control strategies and instructions to reduce risks or avoid accidents;

[0071] In this embodiment, preset optimization index: an index preset by the system for optimization analysis, which may be an index in terms of efficiency, safety, etc.;

[0072] In this embodiment, the optimization analysis result: the result obtained after optimizing the control analysis result and the risk analysis result is used to optimize the control strategy and risk control strategy of each ship unloader.

[0073] The implementation principle and beneficial effects of this embodiment: The present invention realizes fully automatic intelligent control and optimization of ship unloader operation through the synergy of modules such as data acquisition, preprocessing, data analysis, risk control and optimization analysis. The present invention can improve the operating efficiency of the ship unloader, reduce the risk of the ship unloader during operation, ensure the safety and stability of the operation, and at the same time, through intelligent analysis and optimization, a more efficient operation process can be achieved, and the quality and efficiency of the overall loading and unloading operation can be improved.

[0074] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, a data acquisition module, including:

[0075] An operation data acquisition submodule is used to acquire the operation parameters of multiple ship unloaders in the system in real time and output real-time operation data;

[0076] A scanning data acquisition submodule is used to scan the ship type information of the target ship and the cargo information loaded in the target ship in real time, and output the scanning data;

[0077] The comprehensive output submodule is used to output first data based on the real-time operation data and the scanning data.

[0078] In this embodiment, real-time operation data refers to the operation parameters of multiple ship unloaders in the system, such as the speed, load condition, working status, etc. of the ship unloaders. These data can help monitor and adjust the operation status of the ship unloaders.

[0079] In this embodiment, the ship type information refers to the ship type characteristics of the target ship, such as the type, size, hull structure, etc. of the ship, which is very important for the adjustment and adaptation of the ship unloader during cargo loading and unloading operations;

[0080] In this embodiment, cargo information refers to detailed information of the cargo loaded in the target vessel, such as cargo type, quantity, weight, volume, etc. This information is crucial for the planning and execution of the loading and unloading operation of the ship unloader.

[0081] The implementation principle and beneficial effects of this embodiment: The present invention obtains the operating parameters of the ship unloader and the ship type information and cargo information of the target ship in real time through the operation data acquisition submodule and the scanning data acquisition submodule, and then comprehensively outputs these data as the first data. The present invention can more accurately monitor the operating status of the ship unloader and improve the operating efficiency and safety by acquiring the operating data and the information of the target ship in real time. At the same time, the comprehensively output first data provides data support for subsequent data analysis, risk control and optimization analysis.

[0082] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, the comprehensive output submodule also includes:

[0083] An environmental parameter acquisition unit is used to acquire the environmental parameters around each ship unloader in real time and output environmental data;

[0084] A classification unit is used to classify and analyze the real-time operation data, scanning data and environmental parameter data to obtain sub-data corresponding to each ship unloader;

[0085] The summarizing unit is used to summarize and output the sub-data corresponding to each ship unloader to obtain the first data.

[0086] In this embodiment, environmental parameters refer to the environmental conditions and parameters around each ship unloader, such as wind speed, temperature, humidity, light, etc. These environmental parameters may affect the operation of the ship unloader and the cargo loading and unloading operation;

[0087] In this embodiment, environmental data refers to the data of environmental parameters around each ship unloader acquired in real time by the environmental parameter acquisition unit. These data are of great significance for the system to monitor the environmental conditions in real time and adjust the operation strategy.

[0088] In this embodiment, classification analysis is a process in which the classification unit classifies and analyzes the real-time operation data, the scanning data, and the environmental parameter data. Through classification analysis, the system can summarize and organize different types of data to provide a basis for subsequent processing and decision-making;

[0089] In this embodiment, sub-data refers to sub-data for each ship unloader obtained by the classification unit according to classification analysis, including operating parameters of the ship unloader, information of the target vessel, surrounding environment parameters, etc. These sub-data are the basis for personalized analysis and processing of each ship unloader.

[0090] The implementation principle and beneficial effects of this embodiment: The present invention acquires environmental parameter data in real time through the environmental parameter acquisition unit, and then classifies and analyzes the real-time operation data, scanning data and environmental parameter data through the classification unit to obtain sub-data corresponding to each ship unloader, and finally summarizes and outputs these sub-data as first data through the summary unit. By acquiring environmental parameter data in real time and classifying and analyzing the data, the system can more comprehensively consider the impact of environmental factors on operations, thereby facilitating the formulation and optimization of more reasonable control strategies and risk control strategies, which helps to improve the overall operating efficiency and safety of the system.

[0091] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, a preprocessing module, including:

[0092] An abnormality analysis submodule, used to perform abnormality analysis on the first data, and obtain corresponding abnormal features by matching in an abnormality feature database based on the abnormality analysis results;

[0093] The method selection submodule is used to select a matching preprocessing method from the method database based on abnormal features and in combination with a feature-method comparison table;

[0094] The preprocessing submodule is used to preprocess the first data based on a preprocessing method and output the data to be analyzed.

[0095] In this embodiment, anomaly analysis: that is, the process of performing anomaly detection and analysis on the first data to identify possible abnormal situations or problems;

[0096] In this embodiment, the abnormality analysis result: the result obtained after performing abnormality analysis on the first data, which may include information such as the type, location, and severity of the abnormality;

[0097] In this embodiment, the abnormal feature database: a database storing various abnormal features, which is used to match with the abnormal analysis results to better understand and handle abnormal situations;

[0098] In this embodiment, anomaly features: are abnormal characteristics or patterns in the data identified during the anomaly analysis process, which are helpful for further processing and solving problems;

[0099] In this embodiment, the feature-method comparison table: a table containing the comparison relationship between abnormal features and pre-processing methods, which can help the system select an appropriate pre-processing method to handle abnormal situations;

[0100] In this embodiment, the method database: a database storing various preprocessing methods, used to select a suitable preprocessing method according to the abnormal characteristics;

[0101] In this embodiment, the preprocessing method is a method for processing the first data in preparation for further analysis, including but not limited to operations such as data cleaning, feature extraction, and data conversion.

[0102] The implementation principle and beneficial effects of this embodiment are as follows: the first data is analyzed for abnormality by the abnormality analysis submodule, and then the corresponding abnormal features are matched in the abnormal feature database according to the abnormal features, and then the method selection submodule selects the matching preprocessing method in the method database in combination with the feature-method comparison table, and finally the first data is preprocessed by the preprocessing submodule, and the data to be analyzed is output. Through abnormality analysis and preprocessing, the system of the present invention can timely identify and process abnormal situations in the data, improve data quality, and provide accurate data support for subsequent data analysis.

[0103] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, the preprocessing submodule includes:

[0104] A method-data matching unit, used for matching a preprocessing method with the first data, preprocessing the first data satisfying a preset matching condition by the preprocessing method to obtain data to be output;

[0105] The to-be-analyzed data output unit is used to determine the to-be-analyzed data that meets a preset threshold condition as the to-be-analyzed data and output it.

[0106] In this embodiment, the preset matching condition refers to a pre-set condition used to determine which first data needs to be pre-processed, and these conditions may be specific data features, value ranges, abnormal conditions, etc.;

[0107] In this embodiment, the data to be output: the data obtained after being processed by the preprocessing method, ready to enter the next stage of analysis or processing;

[0108] In this embodiment, the preset threshold condition is used to determine whether the data to be output meets the required conditions. When the data to be output meets the preset threshold condition, it will be determined as data to be analyzed and output.

[0109] The implementation principle and beneficial effects of this embodiment: The present invention matches the preprocessing method with the first data according to the preset matching condition to obtain the data to be output. The data output unit to be analyzed determines which data to be output meets the condition according to the preset threshold condition, determines it as the data to be analyzed and outputs it. The present invention can automatically select the data to be preprocessed and determine which data is suitable for further analysis by setting the preset matching condition and the preset threshold condition, which can improve the intelligence of the system, reduce manual intervention, speed up data processing and analysis, and thus improve work efficiency and accuracy.

[0110] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, a data analysis module, including:

[0111] A data analysis submodule is used to analyze the data to be analyzed and obtain the analysis subdata corresponding to each ship unloader;

[0112] The control analysis submodule is used to perform control analysis on the analytical subdata corresponding to each ship unloader, output the control analysis results, and obtain the control strategy and control instructions matching the control analysis results in the strategy-instruction database in combination with the result-strategy-instruction comparison table, control the corresponding operating parameters of each ship unloader, and at the same time, obtain the change data of the operating parameters of each ship unloader during the control process, and output the control process data;

[0113] The risk control analysis submodule is used to perform risk analysis on the analytical sub-data and control process data corresponding to each ship unloader, output the risk analysis results, and obtain the risk control strategies and risk control instructions that match the risk analysis results in the strategy-instruction database in combination with the result-strategy-instruction comparison table, and perform corresponding risk control operations according to the corresponding operating parameters of each ship unloader.

[0114] In this embodiment, the analytical sub-data: in cargo loading and unloading operations, the analytical sub-data may include information such as the working status of each ship unloader, the type and quantity of cargo, etc.;

[0115] In this embodiment, the result-strategy-instruction comparison table includes a comparison table of the mapping relationship between the control analysis results, the control strategy, and the control instructions, and a comparison table of the mapping relationship between the risk analysis results, the risk control strategy, and the risk control instructions, so that the system can automatically adjust the operating parameters of each ship unloader to perform automatic operation and risk control;

[0116] In this embodiment, the strategy-instruction database: a database including control strategies, control instructions, and risk control strategies and risk control instructions;

[0117] In this embodiment, risk analysis: based on the analytical sub-data and the control process data, the operation risk of each ship unloader is evaluated, for example, possible equipment failures and safety hazards of the ship unloader.

[0118] The implementation principle and beneficial effects of this embodiment: The present invention parses the data to be analyzed into sub-data corresponding to each ship unloader, then controls and analyzes the sub-data through the control analysis submodule and outputs the results, and then performs risk analysis on the results through the risk control analysis submodule and executes risk control operations. Through this systematic data analysis and risk control process, the present invention can monitor and adjust the operating parameters of the ship unloader in real time, improve operating efficiency and safety, and at the same time, through automated control analysis and risk analysis, reduce human errors and discover potential problems in advance, thereby improving the intelligence of the system and operating efficiency.

[0119] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, the control analysis submodule includes:

[0120] A trolley data acquisition unit, used to acquire trolley data corresponding to the trolley of the ship unloader, trolley data corresponding to the trolley of the ship unloader, and grab bucket data corresponding to the grab bucket of the ship unloader in the parsed sub-data of each ship unloader;

[0121] A ship data acquisition unit, used to acquire ship data corresponding to a target ship in the parsed sub-data of each ship unloader;

[0122] A comparison table generating unit: used for obtaining the ship unloader information corresponding to each target ship based on the ship data, and outputting a ship-ship unloader comparison table;

[0123] A time alignment unit is used to time align the ship data of the target ship and the trolley data, car data and grab bucket data under each ship unloader corresponding to the target ship based on the ship-ship unloader comparison table, and obtain the time delay information between the trolley data, car data, grab bucket data and ship data in combination with the preset standard time, and output a data delay analysis table;

[0124] The port vessel information acquisition unit is used to acquire the information of vessels to be operated inside and outside the port and the unloading demand information of the corresponding vessels, and to sort the vessels to be operated according to the preset sorting rules, and output the vessel queue information;

[0125] A collaborative control analysis unit is used to perform collaborative control analysis on all ship unloaders in the port based on the ship-unloader comparison table, the data delay analysis table and the ship queue information, and in combination with a preset collaborative analysis method, and output the collaborative control analysis results;

[0126] A strategy matching unit, used to obtain control strategies and control instructions that meet preset screening conditions in the strategy-instruction database by combining the result-strategy-instruction matching table;

[0127] The instruction execution unit is used to control the operating parameters corresponding to each ship unloader in the port based on the control strategy and control instructions;

[0128] The process data output unit is used to obtain the change data of the corresponding operating parameters of each ship unloader during the execution of the control instruction and output the control process data.

[0129] In this embodiment, the trolley data refers to data related to the ship unloader trolley, which may include information such as the position, speed, and load of the trolley;

[0130] In this embodiment, the trolley data refers to data related to the ship unloader trolley, such as the movement track and working status of the trolley;

[0131] In this embodiment, grab bucket data: data related to the grab bucket of the ship unloader, such as the opening and closing state of the grab bucket, cargo loading and unloading conditions, etc.;

[0132] In this embodiment, the vessel data: includes data related to the target vessel, such as the size of the vessel, the type of cargo, unloading requirements, etc.;

[0133] In this embodiment, the ship unloader information refers to the relevant information of each ship unloader, such as model, working capacity, current status, etc.;

[0134] In this embodiment, the ship-unloader comparison table: a table that matches ships with corresponding ship unloaders, used to indicate which ship unloader should be used to operate each target ship;

[0135] In this embodiment, time alignment: aligning different data in time to facilitate subsequent analysis and control;

[0136] In this embodiment, preset standard time: a preset time standard used for timestamps of reference and comparison data;

[0137] In this embodiment, delay information refers to the time delay information between different data, that is, the time difference between them;

[0138] In this embodiment, the data delay analysis table: records the delay information between data, which is helpful for analyzing and optimizing system operation;

[0139] In this embodiment, the vessel information: that is, the information of all vessels to be operated inside and outside the port, including the type, size, cargo capacity, etc. of the vessel;

[0140] In this embodiment, the unloading demand information includes demand information such as the type, quantity, and time limit of the cargo that the ship needs to unload;

[0141] In this embodiment, the preset sorting rule is a rule for sorting the unloading order of the ships according to factors such as the urgency of the ships and the destination, which is preset;

[0142] In this embodiment, the ship queue information: the sequence table of ships in operation and waiting for operation generated after sorting is arranged according to preset rules;

[0143] In this embodiment, a collaborative analysis method is preset: a specific method used for collaborative control analysis, for example, a priority-based scheduling algorithm;

[0144] In this embodiment, the collaborative control analysis result: the operation scheduling result of each ship unloader obtained according to the collaborative analysis method;

[0145] In this embodiment, the preset screening conditions, ie, the screening conditions used to select control strategies and control methods in the strategy-instruction database, are preset.

[0146] The implementation principle and beneficial effects of this embodiment: The present invention realizes the matching of ships and ship unloaders, time alignment of data, control analysis and instruction execution through the coordinated work of multiple units, so as to realize intelligent operation control. By acquiring and analyzing various data in real time, the system can more effectively dispatch and control the ship unloading operation, improve the operation efficiency and reduce the risk, and can make timely adjustments and optimizations according to the real-time data, so as to improve the intelligence level and efficiency of the overall operation.

[0147] The embodiment of the present invention provides a one-to-many remote fully automatic intelligent operation and control system, and a risk control analysis submodule, including:

[0148] A data matching unit, used for performing cluster analysis on the analytical sub-data and the control process data to obtain second data corresponding to each ship unloader;

[0149] A feature extraction unit, configured to extract features from the second data and construct a first feature set based on the extracted features;

[0150] A feature matching unit, configured to match the first feature set with risk features in a risk database, and output a risk feature set based on a matching result;

[0151] The risk identification unit is used to combine the feature-risk comparison table, obtain the risk type corresponding to the risk feature set and the risk level under the corresponding risk type, and output the risk identification result;

[0152] A result output unit is used to determine the risk status of each ship unloader in the system based on the risk identification result and in combination with a preset result-status mapping table, and calculate the risk coefficient of each ship unloader through a preset algorithm, and comprehensively output the risk analysis result;

[0153]

[0154] Among them, R i represents the risk coefficient of the i-th ship unloader in the system; mi represents the total number of cyclic grabs of the grab bucket of the i-th ship unloader during the operation process; e represents the exponential function; τ ij represents the delay and packet loss error adjustment factor corresponding to the j-th grabbing process of the i-th ship unloader; T ij1 represents the starting time of the i-th ship unloader when performing the j-th grabbing process; T ij2 represents the end time of the jth grabbing process of the i-th ship unloader; dev ijX (t) represents the torsion angle offset of the i-th ship unloader in the X-axis direction at time t during the j-th grabbing process; dev ijY (t) represents the torsion angle offset of the i-th ship unloader in the Y-axis direction at time t during the j-th grabbing process; dev ijZ (t) represents the torsion angle offset of the i-th ship unloader in the Z-axis direction at time t during the j-th grabbing process, where dev ijX (t), dev ijY (t), dev ijZ (t) are all functions of time t; σ ij1 represents the influence factor between the grab bucket deviation and the vehicle travel when the i-th ship unloader performs the j-th grab operation; σ ij2 represents the influence factor between the grab bucket deviation and the trolley motion when the i-th ship unloader performs the j-th grab operation; σ ij3 represents the influencing factor between the grab bucket deviation and the weather when the i-th ship unloader performs the j-th grab operation;

[0155] A risk control strategy matching unit is used to select a matching risk control strategy and a risk control instruction under the corresponding risk control strategy from a strategy-instruction database based on the risk analysis results and in combination with the result-strategy-instruction comparison table;

[0156] The risk control unit is used to perform risk control operations on the corresponding operating parameters of each ship unloader based on the risk control strategy and risk control instructions.

[0157] In this embodiment, cluster analysis: a data analysis method for clustering the analytical sub-data and the control process data to obtain second data corresponding to each ship unloader;

[0158] In this embodiment, the second data: data obtained by cluster analysis, associated with each ship unloader, is used for subsequent feature extraction and risk analysis;

[0159] In this embodiment, the first feature set is a set constructed by extracting features from the second data;

[0160] In this embodiment, risk database: a database storing various risk characteristics;

[0161] In this embodiment, risk characteristics: describe characteristics that may cause risks, such as data anomalies, equipment failures, etc.;

[0162] In this embodiment, risk feature set: a set consisting of risk features obtained by matching;

[0163] In this embodiment, the feature-risk comparison table: a table containing the direct correspondence between risk features and risk types and risk levels;

[0164] In this embodiment, risk type: describes different types or categories of risks, such as equipment failure, operational error, weather risk, etc.;

[0165] In this embodiment, risk level: for each risk type, different risk levels can be defined to assess the severity of the risk;

[0166] In this embodiment, the risk identification result: according to the feature-risk comparison table, the specific risk type and risk level corresponding to the risk feature set are identified;

[0167] In this embodiment, a preset result-state mapping table: a table including a mapping relationship between risk identification results and risk states of each ship unloader;

[0168] In this embodiment, risk status: describes the current risk status of each ship unloader in the system, such as low risk, medium risk, high risk, etc.;

[0169] In this embodiment, risk coefficient: a coefficient used to quantify the risk level of each ship unloader.

[0170] Implementation principle and beneficial effects of this embodiment: The present invention can monitor risk situations in real time and take corresponding measures to ensure operational safety by performing cluster analysis, feature extraction, risk identification and risk control operations on data. The present invention can help identify potential risks and take preventive measures to improve operational safety, reduce the probability of accidents, and improve operational efficiency and system stability. Through risk analysis and control, the system can better protect the safety of equipment, goods and personnel.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A one-to-many remote fully automatic intelligent operation and control system, characterized in that: include: A data acquisition module, used for acquiring the operating parameters of each ship unloader in the system and the scanning data of the target ship in real time, and outputting first data; A preprocessing module, used to preprocess the first data using a preset preprocessing method to obtain data to be analyzed; A data analysis module, used to perform control analysis on the data to be analyzed, output control strategies and control instructions based on the control analysis results, control the corresponding operating parameters of each ship unloader, and output control process data, perform risk analysis on the data to be analyzed and the control process data, obtain risk control strategies and risk control instructions matching the risk analysis results, and perform corresponding risk control operations on the operating parameters of each ship unloader based on the risk control strategies and risk control instructions; The optimization module is used to optimize the control analysis results and risk analysis results in combination with preset optimization indicators, and optimize the control strategy and risk control strategy of each ship unloader based on the optimization analysis results.

2. According to claim 1, a one-to-many remote fully automatic intelligent operation and control system is characterized in that: The data acquisition module comprises: The operation data acquisition submodule is used to acquire the operation parameters of multiple ship unloaders in the system in real time and output real-time operation data; A scanning data acquisition submodule is used to scan the ship type information of the target ship and the cargo information loaded in the target ship in real time, and output the scanning data; The comprehensive output submodule is used to output first data based on the real-time operation data and the scanning data.

3. According to claim 2, a one-to-many remote fully automatic intelligent operation and control system is characterized in that: The comprehensive output submodule further includes: An environmental parameter acquisition unit is used to acquire the environmental parameters around each ship unloader in real time and output environmental data; A classification unit, used for classifying and analyzing the real-time operation data, scanning data and environmental parameter data to obtain sub-data corresponding to each ship unloader; The summarizing unit is used for summarizing and outputting the sub-data corresponding to each ship unloader to obtain the first data.

4. According to claim 1, a one-to-many remote fully automatic intelligent operation and control system is characterized in that: The preprocessing module comprises: An abnormality analysis submodule, used to perform abnormality analysis on the first data, and obtain corresponding abnormal features by matching in an abnormality feature database based on the abnormality analysis results; A method selection submodule is used to select a matching preprocessing method from a method database based on the abnormal features and in combination with a feature-method comparison table; The preprocessing submodule is used to preprocess the first data based on the preprocessing method and output the data to be analyzed.

5. A one-to-many remote fully automatic intelligent operation and control system according to claim 4, characterized in that: The preprocessing submodule comprises: A method-data matching unit, used for matching the preprocessing method with the first data, preprocessing the first data satisfying the preset matching condition by the preprocessing method to obtain data to be output; The to-be-analyzed data output unit is used to determine the to-be-analyzed data that meets a preset threshold condition as the to-be-analyzed data and output it.

6. The one-to-many remote fully automatic intelligent operation and control system according to claim 1 is characterized in that: The data analysis module comprises: A data analysis submodule, used for analyzing the data to be analyzed to obtain analysis subdata corresponding to each ship unloader; The control analysis submodule is used to perform control analysis on the analytical subdata corresponding to each ship unloader, output the control analysis result, and obtain the control strategy and control instruction matching the control analysis result in the strategy-instruction database in combination with the result-strategy-instruction comparison table, control the corresponding operating parameters of each ship unloader, and at the same time, obtain the change data of the operating parameters of each ship unloader during the control process, and output the control process data; The risk control analysis submodule is used to perform risk analysis on the analytical sub-data and control process data corresponding to each ship unloader, output the risk analysis results, and obtain the risk control strategies and risk control instructions matching the risk analysis results in the strategy-instruction database in combination with the result-strategy-instruction comparison table, and perform corresponding risk control operations according to the corresponding operating parameters of each ship unloader.

7. A one-to-many remote fully automatic intelligent operation and control system according to claim 6, characterized in that: The control analysis submodule includes: A trolley data acquisition unit, used to acquire trolley data corresponding to the trolley of the ship unloader, trolley data corresponding to the trolley of the ship unloader, and grab bucket data corresponding to the grab bucket of the ship unloader in the parsed sub-data of each ship unloader; a ship data acquisition unit, used for acquiring ship data corresponding to a target ship in the parsed sub-data of each ship unloader; A comparison table generating unit is used for acquiring ship unloader information corresponding to each target ship based on the ship data, and outputting a ship-ship unloader comparison table; A time alignment unit is used to time align the ship data of the target ship and the trolley data, car data and grab bucket data under each ship unloader corresponding to the target ship based on the ship-ship unloader comparison table, and obtain the time delay information between the trolley data, car data, grab bucket data and ship data in combination with the preset standard time, and output a data time delay analysis table; The port vessel information acquisition unit is used to acquire the information of vessels to be operated inside and outside the port and the unloading demand information of the corresponding vessels, and to sort the vessels to be operated according to the preset sorting rules, and output the vessel queue information; A collaborative control analysis unit, configured to perform collaborative control analysis on all ship unloaders in the port based on the ship-unloader comparison table, the data delay analysis table and the ship queue information, and in combination with a preset collaborative analysis method, and output a collaborative control analysis result; A strategy matching unit, used to obtain control strategies and control instructions that meet preset screening conditions in a strategy-instruction database in combination with the result-strategy-instruction matching table; An instruction execution unit, used for controlling the operation parameters corresponding to each ship unloader in the port based on the control strategy and the control instruction; The process data output unit is used to obtain the change data of the corresponding operating parameters of each ship unloader during the execution of the control instruction and output the control process data.

8. The one-to-many remote fully automatic intelligent operation and control system according to claim 6 is characterized in that: The risk control analysis submodule includes: A data matching unit, used for performing cluster analysis on the analytical sub-data and the control process data to obtain second data corresponding to each ship unloader; A feature extraction unit, configured to extract features from the second data and construct a first feature set based on the extracted features; A feature matching unit, configured to match the first feature set with risk features in a risk database, and output a risk feature set based on a matching result; A risk identification unit is used to obtain the risk type corresponding to the risk feature set and the risk level under the corresponding risk type in combination with the feature-risk comparison table, and output the risk identification result; A result output unit is used to determine the risk status of each ship unloader in the system based on the risk identification result and in combination with a preset result-status mapping table, calculate the risk coefficient of each ship unloader through a preset algorithm, and comprehensively output the risk analysis result; Among them, R i represents the risk coefficient of the i-th ship unloader in the system; mi represents the total number of cyclic grabs of the grab bucket of the i-th ship unloader during the operation process; e represents the exponential function; τ ij represents the delay and packet loss error adjustment factor corresponding to the j-th grabbing process of the i-th ship unloader; T ij1 represents the starting time of the i-th ship unloader when performing the j-th grabbing process; T ij2 represents the end time of the jth grabbing process of the i-th ship unloader; dev ijX (t) represents the torsion angle offset of the i-th ship unloader in the X-axis direction at time t during the j-th grabbing process; dev ijY (t) represents the torsion angle offset of the i-th ship unloader in the Y-axis direction at time t during the j-th grabbing process; dev ijZ (t) represents the torsion angle offset of the i-th ship unloader in the Z-axis direction at time t during the j-th grabbing process, where dev ijX (t), dev ijY (t), dev ijz (t) are all functions of time t; σ ij1 represents the influence factor between the grab bucket deviation and the vehicle travel when the i-th ship unloader performs the j-th grab operation; σ ij2 represents the influence factor between the grab bucket deviation and the trolley motion when the i-th ship unloader performs the j-th grab operation; σ ij3 represents the influencing factor between the grab bucket deviation and the weather when the i-th ship unloader performs the j-th grab operation; A risk control strategy matching unit, configured to select a matching risk control strategy and a risk control instruction under the corresponding risk control strategy from a strategy-instruction database based on the risk analysis result and in combination with the result-strategy-instruction comparison table; The wind control unit is used to perform wind control operations on the corresponding operating parameters of each ship unloader based on the wind control strategy and the wind control instructions.

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