An AI-based robot job processing method and apparatus

Abstract

The application relates to the technical field of water area resources, in particular to a robot operation processing method and device based on AI, which collects the biodiversity state of a water area to be operated within a preset area, configures a dynamic underwater simulation model that changes, performs water area resource quality evaluation on the range of the water area to be operated within the preset area through the dynamic underwater simulation model that changes, collects the range where abnormal water area resource quality may exist, and performs security and protection operation through a security and protection terminal; robot fishing operation information of the water area to be operated within the preset area is collected according to the security and protection terminal, and capture result analysis and recording are performed; a water area resource quality data intelligent judgment model is configured, and the water area resource quality is intelligently judged and reminded. The application can dynamically analyze the diversity of water area resources in the fishing process, accurately represent the ecological balance in the water area, and improve the protection of species diversity of water resources.

Description

Technical Field

[0001] This application relates to the field of aquatic resource technology, and in particular to an AI-based robotic operation processing method and apparatus. Background Technology

[0002] With the improvement of modern marine fishing capabilities, nearshore fishery resources have experienced unsustainable development problems such as gradual decline. Therefore, fishery resource management departments need to obtain and accurately predict fishery resources in a timely manner in order to formulate effective ecological environment restoration and resource conservation measures to effectively restore marine fishery resources and marine ecosystems.

[0003] Existing technologies, such as patent CN119338078A, include a system and method for predicting the dynamic trend of fishery resources based on time series analysis. This system provides technical support for the sustainable management of fishery resources by predicting the dynamic trend of fishery resources. However, this existing technology only uses a time series dynamic prediction model to predict the future trend of the current standardized fishery resource time series dataset, generating prediction results of the spatial location of resource distribution, the trend of resource abundance change, and the time range of peak and trough periods. It fails to dynamically analyze the diversity of aquatic resources during the fishing process, cannot accurately characterize the ecological balance in the water, and is not conducive to the protection of aquatic biodiversity. Summary of the Invention

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] According to a first aspect of the present invention, the present invention claims protection for an AI-based robot task processing method, comprising the following steps:

[0006] Collect the biodiversity status of the water area to be operated within a predetermined area, and configure an underwater dynamic simulation model to change the biodiversity status of the water area to be operated within the predetermined area.

[0007] The underwater dynamic simulation model is used to evaluate the quality of water resources within the pre-defined area of ​​the water area to be operated, collect the range of water resources that may have abnormal quality, and distribute security terminals in the range of water resources that may have abnormal quality to carry out security operations.

[0008] Based on the information collected by the security terminal regarding the robot fishing operation within a predetermined area of ​​the water area to be fished, and based on the capture result analysis and recording of the robot fishing operation information within the predetermined area of ​​the water area to be fished, capture result analysis and recording are collected.

[0009] Based on the captured results, the system analyzes and records the configuration of an intelligent judgment model for water resource quality data, and intelligently judges the water resource quality according to the intelligent judgment model, and provides reminders on the water resource quality.

[0010] Furthermore, the biodiversity status of the water area to be operated on within a predetermined area is collected, and an underwater dynamic simulation model is configured to change the biodiversity status of the water area to be operated on within the predetermined area, specifically including:

[0011] Collect biodiversity status of the water area to be operated within a predetermined area, and collect underwater information of the water area to be operated within the predetermined area based on the biodiversity status of the water area to be operated within the predetermined area.

[0012] An underwater 3D model is configured based on the underwater information of the water area to be operated within a predetermined area, and regulatory element information data related to water resource quality are collected within a predetermined period based on the biodiversity status of the water area to be operated within the predetermined area.

[0013] The underwater three-dimensional model is optimized based on the information on the regulatory elements associated with the quality of water resources within the preset period, the regulatory element information data associated with the quality of water resources in each fishing and hauling cycle, and the information on the regulatory elements associated with the quality of water resources in each cycle.

[0014] Based on optimization, underwater 3D models for each cycle are collected, and the underwater 3D models for each cycle are sorted and their changes are demonstrated according to temporal characteristics, thus collecting the changing underwater dynamic simulation models.

[0015] Furthermore, the underwater dynamic simulation model is used to evaluate the water resource quality within a pre-defined area of ​​the water area to be operated on, identifying areas where abnormal water resource quality may exist. Security terminals are then deployed within these potentially abnormal water resource quality areas to conduct security operations, specifically including:

[0016] Based on the various optimization ranges of the underwater dynamic simulation model of the changes, water resource quality analysis is performed, water resource quality data of the water area to be operated within the preset area are collected, and water resource quality data threshold values ​​are set.

[0017] Determine whether the water resource quality data of the water area to be operated within the preset area is greater than the water resource quality data threshold. If the water resource quality data of the water area to be operated within the preset area is not greater than the water resource quality data threshold, then the associated range location is taken as the normal range.

[0018] If the water resource quality data of the water area to be operated within the preset area is greater than the water resource quality data threshold, the associated range will be regarded as the range where abnormal water resource quality may exist, and will be presented according to the preset method. Security terminals will be distributed in the range where abnormal water resource quality may exist to carry out security operations.

[0019] Furthermore, based on the information collected by the security terminal regarding the robot fishing operation within a predetermined area of ​​the water area to be fished, and based on the capture result analysis and recording of the robot fishing operation information within the predetermined area of ​​the water area to be fished, the capture result analysis and recording are collected, specifically including:

[0020] Based on the information collected by the security terminal regarding the robot fishing operation within a predetermined area of ​​the water area to be operated, and the coverage area fluctuation data information collected in the robot fishing operation information within the predetermined area of ​​the water area to be operated is also collected.

[0021] The robot fishing operation information is evaluated based on the coverage area fluctuation data collected in the robot fishing operation information within the preset area of ​​the water area to be operated. The robot fishing operation information is also collected on the foreign object situation of the fishing results during the fishing net haul-in and statistical analysis.

[0022] Based on the robot fishing operation information, the prediction of foreign object situation in the fishing results during fishing and statistical analysis is used to predict the collection cycle during fishing and net hauling, and the collection cycle of robot fishing operation information is updated. In each cycle, the updated robot fishing operation information of the water area to be operated is collected within the preset area.

[0023] Based on the updated robot fishing operation information within the preset area of ​​the waters to be fished in each cycle, a capture result analysis record is generated and the capture result analysis record is output.

[0024] Furthermore, based on the captured results analysis record, a smart judgment model for water resource quality data is configured, and the water resource quality is intelligently judged according to the smart judgment model for water resource quality data, specifically including:

[0025] A smart judgment model for water resource quality data is configured by configuring the neural coverage area of ​​a multilayer perceptron, and the capture result analysis record of each cycle is collected. The capture result analysis record of each cycle is used as an input feature, and the input feature is filtered for anomalies. The input feature after anomaly filtering is collected.

[0026] Based on the anomaly-filtered input features, configure anomaly-filtered input feature tuples, input the anomaly-filtered input feature tuples into the ecological balance map for calculation, and calculate the development probability value of each input feature in the anomaly-filtered input feature tuples to develop into another input feature.

[0027] Set a development probability threshold. When the development probability value is greater than the development probability threshold, update the current input feature to another input feature and update the input feature tuple after the anomaly filtering.

[0028] When the development probability value is not greater than the development probability threshold value, the current input features remain unchanged, and the final anomaly-filtered input feature tuple is output. The final anomaly-filtered input feature tuple is then input into the intelligent judgment model for water resource quality data for learning.

[0029] Furthermore, reminders regarding the quality of aquatic resources are issued, specifically including:

[0030] Intelligent judgment is made based on the intelligent judgment model of water resource quality data, and the input feature tuple after real-time final anomaly filtering is collected. The input feature threshold range is set, and the input feature value of the current period is collected based on the input feature tuple after real-time final anomaly filtering.

[0031] Determine whether the input feature value of the current period is within the range of the input feature threshold value. If the input feature value of the current period is within the range of the input feature threshold value, then reduce the frequency of security operations in the associated range.

[0032] When the input feature value of the current period is not within the range of the input feature threshold value, the frequency of security operations in the associated range is increased, and a reminder is given for the associated range.

[0033] According to a second aspect of the present invention, the present invention claims protection for an AI-based robot task processing apparatus, comprising a memory and a processor, wherein the memory includes an AI-based robot task processing method program, which, when executed by the processor, implements the steps of the AI-based robot task processing method.

[0034] This application relates to the field of aquatic resource technology, and in particular to an AI-based robotic operation processing method and apparatus. The method involves collecting biodiversity data of the water area to be operated within a pre-defined area, configuring a dynamic underwater simulation model, evaluating the water resource quality within the pre-defined area using the dynamic underwater simulation model, identifying areas with potentially abnormal water resource quality, and deploying security terminals for security operations. Based on the robotic fishing operation information collected by the security terminals within the pre-defined area, the method analyzes and records the capture results. An intelligent water resource quality data judgment model is configured to intelligently determine the water resource quality and provide alerts. This invention can dynamically analyze the diversity of aquatic resources during the fishing process, accurately characterize the ecological balance within the water area, and improve the protection of aquatic biodiversity. Attached Figure Description

[0035] Figure 1 A flowchart illustrating an AI-based robot task processing method claimed in an embodiment of this application;

[0036] Figure 2 A second flowchart illustrating an AI-based robot task processing method claimed in an embodiment of this application;

[0037] Figure 3 This is a structural block diagram of an AI-based robot task processing device claimed in an embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. All other embodiments obtained by those of ordinary skill in the art through the embodiments of this application without creative effort are within the scope of protection of this application.

[0039] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or terminal that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or terminals.

[0040] 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 mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] like Figure 1 As shown, the first aspect of the present invention provides an AI-based robot task processing method, comprising the following steps:

[0042] S102: Collect the biodiversity status of the water area to be operated within the preset area, and configure the changing underwater dynamic simulation model according to the biodiversity status of the water area to be operated within the preset area.

[0043] S104: Using a dynamic underwater simulation model, the quality of water resources in the area to be operated is evaluated within a pre-defined area. The range where abnormal water resource quality may exist is collected, and security terminals are distributed in the range where abnormal water resource quality may exist to carry out security operations.

[0044] S106: Collect robot fishing operation information within a pre-defined area of ​​the water area to be fished based on the security terminal, and analyze and record the capture results based on the robot fishing operation information within the pre-defined area of ​​the water area to be fished.

[0045] S108: Based on the analysis and recording of the captured results, configure an intelligent judgment model for water resource quality data, and make intelligent judgments on water resource quality according to the intelligent judgment model for water resource quality data, and issue reminders on water resource quality.

[0046] It should be noted that this invention uses a dynamic underwater simulation model to simulate the change of water resource quality within a preset range of open-pit mining. It can analyze the changes in water resource quality within a preset period, thereby selecting the scope of security operations, improving the rationality of security operations, and reducing the cost of security operations.

[0047] Furthermore, in this method, the biodiversity status of the water area to be operated on within a pre-defined area is collected, and an underwater dynamic simulation model is configured to change based on the biodiversity status of the water area to be operated on within the pre-defined area. Specifically, this includes:

[0048] Collect biodiversity data within a pre-defined area of ​​the water area to be operated on, and collect underwater information within the pre-defined area of ​​the water area to be operated on based on the biodiversity data within the pre-defined area.

[0049] An underwater 3D model is configured based on the underwater information of the water area to be operated within a pre-defined area, and information data on regulatory factors related to water resource quality are collected within a pre-defined period based on the biodiversity status of the water area to be operated within the pre-defined area.

[0050] The underwater 3D model is optimized based on the information of regulatory elements related to the quality of water resources in each fishing and hauling cycle, which is based on the information of regulatory elements related to the quality of water resources in each cycle.

[0051] Based on optimization, underwater 3D models for each cycle are collected, and the underwater 3D models for each cycle are sorted and their changes are demonstrated according to temporal characteristics, thus collecting the changing underwater dynamic simulation models.

[0052] It should be noted that the pre-defined area includes both a periodic range and a spatial range. The biodiversity status of the water area to be operated within the pre-defined area includes information on fish, algae, and shellfish. Based on 3D modeling technology, combined with information on regulatory factors related to water resource quality within the pre-defined period and underwater structures, an underwater dynamic simulation model is used to simulate the changes in water resource quality characteristics within the pre-defined area of ​​the water area to be operated, forming a change analysis to analyze the range where abnormal water resource quality may exist.

[0053] Furthermore, in this method, a dynamic underwater simulation model is used to evaluate the water resource quality within a pre-defined area of ​​the work area, identifying areas where abnormal water resource quality may exist. Security terminals are then deployed within these potentially abnormal water resource quality areas to conduct security operations, specifically including:

[0054] Based on the various optimization ranges of the changing underwater dynamic simulation model, water resource quality analysis is performed, water resource quality data of the water area to be operated within the preset area are collected, and water resource quality data threshold values ​​are set.

[0055] Determine whether the water resource quality data of the water area to be operated within the pre-set area is greater than the water resource quality data threshold. If the water resource quality data of the water area to be operated within the pre-set area is not greater than the water resource quality data threshold, then the associated range location is taken as the normal range.

[0056] If the water resource quality data of the water area to be operated is greater than the water resource quality data threshold within the preset area, the associated range will be regarded as the range where abnormal water resource quality may exist, and will be presented according to the preset method. Security terminals will be distributed in the range where abnormal water resource quality may exist to carry out security operations.

[0057] It should be noted that abnormal water resource quality includes abnormal biological and underwater changes. This method can analyze and collect data on abnormal geological conditions.

[0058] like Figure 2 As shown, further, in this method, information on robot fishing operations within a pre-defined area of ​​the water body to be fished is collected by the security terminal, and the capture results are analyzed and recorded based on this information. The capture result analysis records specifically include:

[0059] S202: Collect robot fishing operation information within a preset area of ​​the water area to be fished based on the security terminal, and collect the coverage area fluctuation data information collected in the robot fishing operation information within the preset area of ​​the water area to be fished.

[0060] S204: Evaluate the robot fishing operation information based on the coverage area fluctuation data collected in the robot fishing operation information within the pre-set area of ​​the water area to be operated, and collect the foreign object situation of the fishing results during the fishing and statistical analysis of the robot fishing operation information.

[0061] S206: Based on the information of robot fishing operations, the prediction of foreign objects in the fishing results during fishing and statistical analysis is carried out during the fishing and net-closing process. The collection cycle of robot fishing operation information is updated, and the updated robot fishing operation information of the water area to be operated in each cycle is collected within the preset area.

[0062] S208: Generate a capture result analysis record based on the robot fishing operation information within the preset area of ​​the water area to be operated in each cycle, and output the capture result analysis record.

[0063] It should be noted that the data information on coverage area fluctuations includes information transmission rate, information transmission volume within a unit period, and bit error rate. Based on this method, the collection period during the fishing net haul-in and statistical analysis of robot fishing operation information is predicted to include the presence of foreign objects. This determines the actual data collection period and updates the ranking of robot fishing operation information within a period sequence, improving the accuracy of security operations.

[0064] Furthermore, in this method, an intelligent judgment model for water resource quality data is configured based on the analysis records of the captured results, and the water resource quality is intelligently judged according to the intelligent judgment model for water resource quality data, specifically including:

[0065] A smart judgment model for water resource quality data is configured by configuring the neural coverage area of ​​a multilayer perceptron, and the capture result analysis record of each cycle is collected. The capture result analysis record of each cycle is used as an input feature, and anomaly filtering is performed on the input feature. The anomaly-filtered input feature is then collected.

[0066] Based on the input features after anomaly filtering, configure the input feature tuples after anomaly filtering, input the input feature tuples after anomaly filtering into the ecological balance map for calculation, and calculate the development probability value of each input feature in the input feature tuples after anomaly filtering to develop into another input feature.

[0067] Set a development probability threshold. When the development probability value is greater than the development probability threshold, the current input feature is updated to another input feature, and the input feature tuple after anomaly filtering is also updated.

[0068] When the development probability value is not greater than the development probability threshold, the current input features remain unchanged, and the final anomaly-filtered input feature tuple is output. The final anomaly-filtered input feature tuple is then input into the intelligent judgment model for water resource quality data for learning.

[0069] It should be noted that when the development probability value is greater than the development probability threshold, the current input feature is updated to another input feature, and the input feature tuple after anomaly filtering is updated. When the development probability value is not greater than the development probability threshold, the current input feature remains unchanged, and the final input feature tuple after anomaly filtering is output. This improves the accuracy of security operations for robot fishing information within the target range, making security operations for water resource quality more reasonable.

[0070] Furthermore, this method provides reminders regarding the quality of aquatic resources, specifically including:

[0071] Intelligent judgment is made based on the intelligent judgment model of water resource quality data, and the input feature tuple after real-time final anomaly filtering is collected. The input feature threshold value range is set, and the input feature value of the current period is collected based on the input feature tuple after real-time final anomaly filtering.

[0072] Determine whether the input feature value of the current period is within the input feature threshold range. If the input feature value of the current period is within the input feature threshold range, reduce the frequency of security operations in the associated range.

[0073] When the input feature value of the current period is not within the range of the input feature threshold value, the frequency of security operations in the associated range is increased, and a reminder is given for the associated range.

[0074] In addition, this method also includes:

[0075] Broadcast terminals are deployed within the mining area of ​​the waters to be operated, enabling broadcasting within the mining area and configuring a broadcast coverage area to collect data on fishing vessels operating within the broadcast coverage area and to collect input feature values ​​for the current period.

[0076] When the input feature value of the current period is greater than the preset input feature threshold value, the associated range is taken as the diversity risk range, and it is determined whether the fishing vessel operating in the broadcast coverage area is within the diversity risk range.

[0077] When the fishing vessel operating in the broadcast coverage area is within the range of diversity risks, a real-time underwater 3D model is extracted, and several fishing routes are determined based on the real-time underwater 3D model and the range of diversity risks.

[0078] The broadcasting method of fishing vessels operating in the context of diversity risks is collected. A fishing route with the shortest distance is selected from the fishing routes, and the fishing route with the shortest distance is sent to the broadcasting terminal according to the broadcasting method of fishing vessels operating in the context of diversity risks.

[0079] It should be noted that this method can identify a range of diverse risks, thereby enabling the development of optimal fishing routes and ensuring the safety of fishing vessels.

[0080] In addition, security terminals are deployed in areas where abnormal water resource quality may exist to carry out security operations, specifically including:

[0081] Based on water quality information of the range where abnormal water resource quality may exist during large-scale fishing operations, and biological data of the range where abnormal water resource quality may exist, historical average species diversity restoration period information under each biological data (biological density, biological type, etc.) is collected. Based on the water quality information of the range where abnormal water resource quality may exist and the historical average species diversity restoration period information under each biological data, predicted species diversity restoration period information under each range where abnormal water resource quality may exist is collected.

[0082] Based on the water quality information of the range of potentially abnormal water resources within the preset period and the biological data of the range of potentially abnormal water resources, the predicted species diversity restoration period information is collected to obtain the water quality information within the predicted species diversity restoration period information.

[0083] The fishing and net-catching range of the security terminal is collected under the water quality information within the predicted species diversity restoration period information, and the fishing and net-catching range of the security terminal under the water quality information within the predicted species diversity restoration period information is sorted, and the smallest fishing and net-catching range of the security terminal within the predicted species diversity restoration period information is collected.

[0084] The number and distribution location of security terminals are initialized, and the security terminals are distributed in areas where abnormal water resource quality may exist based on the minimum fishing catch range, the number of security terminals, and the distribution location of the security terminals within the predicted species diversity restoration period information.

[0085] The system collects real-time data on the catch and net-closing area. When the real-time catch and net-closing area is larger than the area where there may be abnormal water resource quality, the system deploys control measures based on the number and distribution of security terminals.

[0086] It should be noted that, since weather conditions are subject to change, the coverage range of wireless sensors for fishing net hauling is determined based on the water quality information within the predicted species diversity restoration period. The minimum coverage range for security operations under the water quality information within the predicted species diversity restoration period is then selected as the benchmark. The number of security terminals and their installation locations are then replanned, so that the biological status of the mine can be monitored under any weather conditions, thus improving the rationality of monitoring. Example

[0087] like Figure 3 As shown, the second embodiment of the present invention provides an AI-based robot task processing device, including a memory 41 and a processor 42. The memory 41 includes an AI-based robot task processing method program. When the AI-based robot task processing method program is executed by the processor 42, it performs the following steps:

[0088] Collect biodiversity data within a pre-defined area of ​​the water body to be operated on, and configure an underwater dynamic simulation model to reflect the changing biodiversity data within the pre-defined area of ​​the water body to be operated on.

[0089] The underwater dynamic simulation model is used to evaluate the quality of water resources in the area to be operated within a pre-defined area, collect the range where abnormal water resource quality may exist, and distribute security terminals in the range where abnormal water resource quality may exist to carry out security operations.

[0090] Based on the information collected by the security terminal regarding the robot fishing operation within the pre-defined area of ​​the water area to be fished, the capture results are analyzed and recorded, and the capture result analysis records are collected.

[0091] Based on the analysis and recording of the captured results, an intelligent judgment model for water resource quality data is configured, and the water resource quality is intelligently judged according to the intelligent judgment model for water resource quality data, and reminders are issued regarding the water resource quality.

[0092] Furthermore, this device collects the biodiversity status of the water area to be operated within a pre-defined area, and configures a dynamic underwater simulation model based on the changing biodiversity status of the water area to be operated within the pre-defined area, specifically including:

[0093] Collect biodiversity data within a pre-defined area of ​​the water area to be operated on, and collect underwater information within the pre-defined area of ​​the water area to be operated on based on the biodiversity data within the pre-defined area.

[0094] An underwater 3D model is configured based on the underwater information of the water area to be operated within a pre-defined area, and information data on regulatory factors related to water resource quality are collected within a pre-defined period based on the biodiversity status of the water area to be operated within the pre-defined area.

[0095] The underwater 3D model is optimized based on the information of regulatory elements related to the quality of water resources in each fishing and hauling cycle, which is based on the information of regulatory elements related to the quality of water resources in each cycle.

[0096] Based on optimization, underwater 3D models for each cycle are collected, and the underwater 3D models for each cycle are sorted and their changes are demonstrated according to temporal characteristics, thus collecting the changing underwater dynamic simulation models.

[0097] Furthermore, in this device, a dynamic underwater simulation model is used to evaluate the water resource quality within a pre-defined area of ​​the work area, identifying areas where abnormal water resource quality may exist. Security terminals are then deployed within these potentially abnormal water resource quality areas to conduct security operations, specifically including:

[0098] Based on the various optimization ranges of the changing underwater dynamic simulation model, water resource quality analysis is performed, water resource quality data of the water area to be operated within the preset area are collected, and water resource quality data threshold values ​​are set.

[0099] Determine whether the water resource quality data of the water area to be operated within the pre-set area is greater than the water resource quality data threshold. If the water resource quality data of the water area to be operated within the pre-set area is not greater than the water resource quality data threshold, then the associated range location is taken as the normal range.

[0100] If the water resource quality data of the water area to be operated is greater than the water resource quality data threshold within the preset area, the associated range will be regarded as the range where abnormal water resource quality may exist, and will be presented according to the preset method. Security terminals will be distributed in the range where abnormal water resource quality may exist to carry out security operations.

[0101] Furthermore, in this device, information on robot fishing operations within a pre-defined area of ​​the water to be fished is collected by a security terminal, and the capture results are analyzed and recorded based on this information. The capture result analysis records specifically include:

[0102] Based on the information collected by the security terminal regarding the robot fishing operation within the pre-defined area of ​​the water area to be operated, and the data on the fluctuation of the coverage area collected in the information on the robot fishing operation within the pre-defined area of ​​the water area to be operated;

[0103] The robot fishing operation information is evaluated based on the coverage area fluctuation data collected in the robot fishing operation information within the pre-defined area of ​​the water area to be operated. The robot fishing operation information is also collected on the foreign object situation of the fishing results during the fishing and statistical analysis.

[0104] Based on the information from robot fishing operations, the prediction of foreign objects in the fishing results and statistical analysis during the fishing net haul-in process is used to predict the collection cycle during the fishing net haul-in process. The collection cycle of robot fishing operation information is updated, and the updated robot fishing operation information in the waters to be operated within the preset area is collected in each cycle.

[0105] Based on the updated robot fishing operation information within the preset area of ​​the waters to be fished in each cycle, a capture result analysis record is generated and output.

[0106] Furthermore, in this device, an intelligent judgment model for water resource quality data is configured based on the analysis records of the captured results, and the water resource quality is intelligently judged according to the intelligent judgment model for water resource quality data, specifically including:

[0107] A smart judgment model for water resource quality data is configured by configuring the neural coverage area of ​​a multilayer perceptron, and the capture result analysis record of each cycle is collected. The capture result analysis record of each cycle is used as an input feature, and anomaly filtering is performed on the input feature. The anomaly-filtered input feature is then collected.

[0108] Based on the input features after anomaly filtering, configure the input feature tuples after anomaly filtering, input the input feature tuples after anomaly filtering into the ecological balance map for calculation, and calculate the development probability value of each input feature in the input feature tuples after anomaly filtering to develop into another input feature.

[0109] Set a development probability threshold. When the development probability value is greater than the development probability threshold, the current input feature is updated to another input feature, and the input feature tuple after anomaly filtering is also updated.

[0110] When the development probability value is not greater than the development probability threshold, the current input features remain unchanged, and the final anomaly-filtered input feature tuple is output. The final anomaly-filtered input feature tuple is then input into the intelligent judgment model for water resource quality data for learning.

[0111] Furthermore, this device provides alerts regarding the quality of aquatic resources, specifically including:

[0112] Intelligent judgment is made based on the intelligent judgment model of water resource quality data, and the input feature tuple after real-time final anomaly filtering is collected. The input feature threshold value range is set, and the input feature value of the current period is collected based on the input feature tuple after real-time final anomaly filtering.

[0113] Determine whether the input feature value of the current period is within the input feature threshold range. If the input feature value of the current period is within the input feature threshold range, reduce the frequency of security operations in the associated range.

[0114] When the input feature value of the current period is not within the range of the input feature threshold value, the frequency of security operations in the associated range is increased, and a reminder is given for the associated range.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, devices, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the presented or discussed mutual couplings, direct couplings, or communication connections may be based on some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into one operating unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0117] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. An AI-based robot task processing method, characterized in that, Includes the following steps: Collect biodiversity status of the water area to be operated within a predetermined area, and collect underwater information of the water area to be operated within the predetermined area based on the biodiversity status of the water area to be operated within the predetermined area. An underwater 3D model is configured based on the underwater information of the water area to be operated within a predetermined area, and regulatory element information data related to water resource quality are collected within a predetermined period based on the biodiversity status of the water area to be operated within the predetermined area. The underwater three-dimensional model is optimized based on the information on the regulatory elements associated with the quality of water resources within the preset period, the regulatory element information data associated with the quality of water resources in each fishing and hauling cycle, and the information on the regulatory elements associated with the quality of water resources in each cycle. Based on optimization, underwater 3D models for each cycle are collected, and the underwater 3D models for each cycle are sorted and their changes are demonstrated according to temporal characteristics, thus collecting the changing underwater dynamic simulation model. The underwater dynamic simulation model is used to evaluate the water resources quality of the area to be operated within a pre-defined area, collect the areas with abnormal water resources quality, and distribute security terminals in the areas with abnormal water resources quality to carry out security operations. Based on the information collected by the security terminal regarding the robot fishing operation within a predetermined area of ​​the water area to be fished, and based on the capture result analysis and recording of the robot fishing operation information within the predetermined area of ​​the water area to be fished, capture result analysis and recording are collected. A smart judgment model for water resource quality data is configured by configuring the neural coverage area of ​​a multilayer perceptron, and the capture result analysis record of each cycle is collected. The capture result analysis record of each cycle is used as an input feature, and the input feature is filtered for anomalies. The input feature after anomaly filtering is collected. Based on the anomaly-filtered input features, configure anomaly-filtered input feature tuples, input the anomaly-filtered input feature tuples into the ecological balance map for calculation, and calculate the development probability value of each input feature in the anomaly-filtered input feature tuples to develop into another input feature. Set a development probability threshold. When the development probability value is greater than the development probability threshold, update the current input feature to another input feature and update the input feature tuple after the anomaly filtering. When the development probability value is not greater than the development probability threshold value, the current input features remain unchanged, and the final anomaly-filtered input feature tuple is output. The final anomaly-filtered input feature tuple is then input into the intelligent judgment model for water resource quality data for learning, and reminders are given regarding water resource quality.

2. The AI-based robot task processing method as described in claim 1, characterized in that, The underwater dynamic simulation model is used to evaluate the water resource quality within a pre-defined area of ​​the water area to be operated on. Areas with abnormal water resource quality are identified, and security terminals are deployed within these areas to conduct security operations. Specifically, this includes: Based on the various optimization ranges of the underwater dynamic simulation model of the changes, water resource quality analysis is performed, water resource quality data of the water area to be operated within the preset area are collected, and water resource quality data threshold values ​​are set. Determine whether the water resource quality data of the water area to be operated within the preset area is greater than the water resource quality data threshold. If the water resource quality data of the water area to be operated within the preset area is not greater than the water resource quality data threshold, then the associated range location is taken as the normal range. If the water resource quality data of the water area to be operated within the preset area is greater than the water resource quality data threshold, the associated range will be regarded as the range where there is abnormal water resource quality, and will be presented according to the preset method. Security terminals will be distributed in the range where there is abnormal water resource quality to carry out security operations.

3. The AI-based robot task processing method as described in claim 1, characterized in that, Based on the information collected by the security terminal regarding robot fishing operations within a pre-defined area of ​​the water area to be fished, and based on the capture result analysis and recording of the robot fishing operation information within the pre-defined area of ​​the water area to be fished, the capture result analysis and recording are collected, specifically including: Based on the information collected by the security terminal regarding the robot fishing operation within a predetermined area of ​​the water area to be operated, and the coverage area fluctuation data information collected in the robot fishing operation information within the predetermined area of ​​the water area to be operated is also collected. The robot fishing operation information is evaluated based on the coverage area fluctuation data collected in the robot fishing operation information within the preset area of ​​the water area to be operated. The robot fishing operation information is also collected on the foreign object situation of the fishing results during the fishing and statistical analysis. Based on the robot fishing operation information, the prediction of foreign object situation in the fishing results during fishing and statistical analysis is used to predict the collection cycle during fishing and net hauling, and the collection cycle of robot fishing operation information is updated. In each cycle, the updated robot fishing operation information of the water area to be operated is collected within the preset area. Based on the updated robot fishing operation information within the preset area of ​​the waters to be fished in each cycle, a capture result analysis record is generated and the capture result analysis record is output.

4. The AI-based robot task processing method as described in claim 1, characterized in that, Reminders regarding the quality of water resources, specifically including: Intelligent judgment is made based on the intelligent judgment model of water resource quality data, and the input feature tuple after real-time final anomaly filtering is collected. The input feature threshold range is set, and the input feature value of the current period is collected based on the input feature tuple after real-time final anomaly filtering. Determine whether the input feature value of the current period is within the range of the input feature threshold value. If the input feature value of the current period is within the range of the input feature threshold value, then reduce the frequency of security operations in the associated range. When the input feature value of the current period is not within the range of the input feature threshold value, the frequency of security operations in the associated range is increased, and a reminder is given for the associated range.

5. An AI-based robotic task processing device, characterized in that, The system includes a memory and a processor. The memory includes an AI-based robot task processing method program. When the AI-based robot task processing method program is executed by the processor, it implements the steps of an AI-based robot task processing method as described in any one of claims 1-4.

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