Marine operation task intelligent auditing method, device, equipment and medium

Abstract

The invention discloses a marine operation task intelligent auditing method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining at least one operation plan document; inputting the at least one job plan document into an identification model to obtain first job information; generating a first job table according to the first job information; generating a first operation diagram according to the first operation table; and inputting the first job diagram and the first job table into a first intelligent auditing model, and outputting a first auditing result. According to the method, the fine-grained first job information is obtained by identifying at least one job plan document, the first job table is generated by using the fine-grained first job information, then the first job diagram is generated, and the first job table and the first job diagram are input into the first intelligent auditing model to obtain the first auditing result; and the rationality and effectiveness of the job plan document and the job task are effectively verified.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an intelligent review method, device, equipment and medium for marine operation tasks. Background Art

[0002] The audit of marine operation tasks mainly involves the audit of multiple operation plans prepared by offshore construction personnel. At present, there are two main methods for the audit of marine operation tasks. The first method is the traditional manual audit method, in which a dedicated auditor judges whether the operation plan is reasonable and effective based on human experience. This method not only requires the auditor to have rich professional experience and knowledge, but also requires the auditor to grasp the reliable operation resources and operation area in real time. It is inefficient and has low accuracy. The second method is to obtain an audit model based on artificial intelligence technology training, and verify the operation plan through the audit model. The application of this method in marine operation tasks currently only focuses on the audit of typos and contextual semantics in documents, and does not involve the audit of the effectiveness and rationality of the operation plan in the operation plan document. The audit of effectiveness and rationality still relies on dedicated auditors for auditing, which is inefficient and has low accuracy. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose an intelligent review method, device, equipment and medium for marine operation tasks, so as to efficiently and accurately review the effectiveness and rationality of marine operation tasks.

[0004] To achieve the above purpose, one aspect of an embodiment of the present application proposes an intelligent review method for marine operation tasks, the method comprising the following steps:

[0005] Obtain at least one work plan document;

[0006] Inputting at least one of the operation plan documents into a recognition model to obtain first operation information;

[0007] Generate a first operation table according to the first operation information;

[0008] generating a first operation graph according to the first operation table;

[0009] The first operation diagram and the first operation table are input into a first intelligent audit model, and a first audit result is output.

[0010] In some embodiments, inputting at least one of the operation plan documents into the recognition model to obtain the first operation information comprises the following steps:

[0011] Input at least one of the operation plan documents into the recognition model to identify and obtain the first operation information; wherein the first operation information includes information of multiple sub-projects and dependency information between the sub-projects; each sub-project information includes at least project identification, sub-project identification, operation area, operation volume, operation vessel and estimated operation days;

[0012] The step of generating a first operation table according to the first operation information comprises the following steps:

[0013] The first operation table is generated according to the first operation information in accordance with a preset template.

[0014] In some embodiments, generating a first operation graph according to the first operation table specifically includes:

[0015] extracting the first job information from the first job table;

[0016] At least one operation branch is constructed by taking the sub-project identifier as a root node, the project identifier as a first-level leaf node, and the operation vessel as a second-level leaf node;

[0017] The first job graph is constructed according to at least one of the job branches.

[0018] In some embodiments, constructing the first job graph according to at least one of the job branches comprises the following steps:

[0019] According to the dependency information between the sub-projects, the root nodes in the job branches are connected by edges;

[0020] The first operation graph is constructed by taking the operation volume and the operation area as attributes of the first-level leaf node and taking the estimated operation days as attributes of the second-level leaf node.

[0021] In some embodiments, the first intelligent audit model includes a first encoder, a second encoder, a first fuser, and a first-level decoder; the first-level decoder is composed of a plurality of network blocks and a plurality of fully connected layers, each of the network blocks includes an LSTM layer, a RELU layer, and a pooling layer;

[0022] The step of inputting the first operation diagram and the first operation table into a first intelligent audit model and outputting a first audit result comprises the following steps:

[0023] Inputting the first job graph into the first encoder to obtain a first encoding vector;

[0024] Inputting the first operation table into the second encoder to obtain a second encoding vector;

[0025] Inputting the first encoding vector and the second encoding vector into the first fuser to obtain a first fused vector;

[0026] The first fusion vector is input into the first level decoder to obtain the first audit result.

[0027] In some embodiments, the second encoder includes an extraction sub-model and an encoding sub-model;

[0028] The step of inputting the first operation table into the second encoder to obtain a second encoding vector comprises the following steps:

[0029] Inputting the first operation table into the extraction sub-model to obtain a target key information set;

[0030] The target key information set and weight information corresponding to the key information are input into the encoding sub-model to obtain a second encoding vector.

[0031] In some embodiments, the method further comprises the following steps:

[0032] Modify the first operation information and the first operation table according to the first audit result to obtain second operation information and a second operation table;

[0033] Inputting the second job information into the first job scheduling model to generate first scheduling information;

[0034] generating a first scheduling graph according to the first scheduling information;

[0035] The first scheduling diagram and the second operation table are input into a second intelligent audit model, and a second audit result is output.

[0036] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides an intelligent review device for marine operation tasks, the device comprising:

[0037] An acquisition module, used for acquiring at least one work plan document;

[0038] A recognition module, used for inputting at least one of the operation plan documents into a recognition model to obtain first operation information;

[0039] A first generating module, configured to generate a first operation table according to the first operation information;

[0040] A second generating module, configured to generate a first operation graph according to the first operation table;

[0041] The first audit module is used to input the first operation diagram and the first operation table into a first intelligent audit model and output a first audit result.

[0042] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0043] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0044] The embodiments of the present application include at least the following beneficial effects:

[0045] This application can obtain at least one work plan document; input at least one work plan document into the recognition model to obtain first work information; generate a first work table according to the first work information; generate a first work diagram according to the first work table; input the first work diagram and the first work table into the first intelligent audit model to output a first audit result. This application obtains fine-grained first work information by identifying at least one work plan document, and generates a first work table using the fine-grained first work information, and then generates a first work diagram, and inputs the first work table and the first work diagram into the first intelligent audit model to obtain a first audit result, which effectively verifies the rationality and effectiveness of the work plan document and the work task. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic diagram of a process flow of an intelligent review method for marine operation tasks provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of operation branches provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of an operation diagram provided for an embodiment of the present application;

[0050] Figure 4 A schematic diagram of a first intelligent audit model provided in an embodiment of the present application;

[0051] Figure 5 A flowchart of another intelligent review method for marine operation tasks provided in an embodiment of the present application;

[0052] Figure 6 A schematic diagram of a first scheduling diagram provided in an embodiment of the present application;

[0053] Figure 7 A schematic diagram of the structure of an intelligent review device for marine operation tasks provided in an embodiment of the present application;

[0054] Figure 8 A schematic diagram of the structure of another intelligent review device for marine operation tasks provided in an embodiment of the present application;

[0055] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0057] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0058] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] The embodiments of the present application provide a method, device, equipment and medium for intelligent review of marine operation tasks, and relate to the field of data processing technology. The method, device, equipment and medium for intelligent review of marine operation tasks provided by the embodiments of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements an intelligent review method for marine operation tasks, etc., but is not limited to the above forms.

[0061] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0062] Reference Figure 1 The embodiment of the present application provides an intelligent review method for marine operation tasks, which may include but is not limited to S1 to S5, as follows:

[0063] S1: Obtain at least one work plan document.

[0064] In each operation plan document, the marine operation information will be recorded in detail. The marine operation information includes multiple sub-project information. Each sub-project information at least includes the project identification, sub-project identification, operation area, sub-project operation volume, the operation vessel to be used for the sub-project, and the estimated number of operation days required for the operation vessel to perform the sub-project. Sub-projects are operation projects designed for marine exploration, which may include marine 3D seismic exploration operations, marine quasi-3D seismic exploration operations, marine 2D seismic exploration operations, marine geological sampling operations, marine water extraction operations, marine gravity and magnetic exploration operations, marine heat flow exploration operations, ROV operations, unmanned ship exploration operations, marine multi-beam exploration operations, etc. At the same time, the marine operation information also includes the dependency relationship between sub-projects, which includes whether sub-project A and sub-project B can be executed at the same time, whether sub-project A should be executed before sub-project B, and whether sub-project A should be executed after sub-project B.

[0065] S2: Input at least one operation plan document into the recognition model to obtain first operation information.

[0066] Input the at least one acquired operation plan document into the intelligent recognition model, and obtain the first operation information through the intelligent recognition model. The intelligent recognition model may use an OCR network and / or a deep neural network, input the operation plan document into the recognition model, and obtain the marine operation information through the intelligent recognition model recognition as the first operation information.

[0067] In one embodiment, the job plan document contains multimodal data such as text, image, table, etc. At this time, the intelligent recognition model adopts a combined neural network model, which includes a document structure recognition network, a feature extraction network, a feature fusion network and a decoding network, wherein the feature extraction network includes an image feature extraction subnetwork, a text feature extraction subnetwork and a table feature extraction subnetwork.

[0068] Optionally, inputting at least one of the operation plan documents into a recognition model to obtain first operation information comprises the following steps:

[0069] Input each document image in the job plan document into a document structure recognition network to recognize a text area, an image area, and a table area in each document image;

[0070] Input the text area into the text feature extraction subnetwork to extract the first key feature;

[0071] Input the image region into the image feature extraction subnetwork to extract the second key feature;

[0072] Input the table area into the table feature extraction sub-network to extract the third key feature;

[0073] Inputting the first key feature, the second key feature, and the third key feature into a feature fusion network, and outputting the key feature through the feature fusion network;

[0074] The key features are input into the decoding network to obtain key text information, and the key text information is used as the first operation information.

[0075] Among them, the text feature extraction subnetwork can use BILSTM, BIGRU network, etc., the image feature extraction subnetwork and table feature extraction subnetwork can use convolutional neural network; the decoding network can use BILSTM, BIGRU network. The document structure recognition network uses the commonly used document region division network.

[0076] In another embodiment, the recognition model uses an OCR network and a dictionary matching model. The key fields of the marine operation information are stored in a dictionary model, the operation plan document is recognized as text data through the OCR network, and the text data is matched with the dictionary model to obtain the first operation information.

[0077] S3: Generate a first operation table according to the first operation information.

[0078] After the first operation information is identified and obtained, the first operation information is used to generate a first operation table according to a preset operation table generation template. The first operation table generation template is pre-configured, and the first operation information is filled into the first operation table according to the template fields, thereby generating the first operation table. The template fields at least include a project name field, an operation area field, a sub-project name field, a sub-project workload field, a sub-project intended operation vessel field, an estimated operation days field, and a dependency field.

[0079] In one embodiment, in order to improve the generation efficiency of the first job table, a parallel and / or multi-threaded method is used to obtain the first job table, specifically: pre-configure the recognition model and the job table generation template on the cluster nodes; select multiple nodes on the cluster nodes for job plan document recognition and job table generation as the target node set based on the load balancing algorithm; send multiple job plan documents to each target node in the target node set according to the number of job plan documents; each target node uses the recognition model and the job table generation template to generate multiple first temporary job tables; and splice the multiple first temporary job tables to obtain the first job table.

[0080] For example, there are four work plan documents, wherein the first work plan document records the work plan of project A, the second work plan document records the work plan of project B, the third work plan document records the work plan of project C, and the fourth work plan document records the work plan of project D. The generated first work table is shown in Table 1:

[0081]

[0082]

[0083] Table 1

[0084] In Table 1, A, B, C, and D respectively represent project names or project IDs, a, b, c, and d respectively represent subproject names or subproject IDs, Ar1, Ar2, Ar3, and Ar4 respectively represent operating area names or operating area identifiers; V1, V2, and V3 respectively represent operating vessel names or operating vessel identifiers.

[0085] S4: Generate a first operation diagram according to the first operation table.

[0086] After generating the first operation table, it is necessary to generate a first operation graph according to the first operation table, which specifically includes: extracting the first operation information in the first operation table; constructing at least one operation branch with the sub-project as the root node, the project as the first-level leaf node, and the operation vessel as the second-level leaf node; and constructing an operation graph according to the at least one operation branch. Figure 2 The diagram shows the job branches constructed according to the first job table shown in Table 1.

[0087] After at least one job branch is constructed, a first job graph is constructed according to the at least one job branch, specifically: root nodes in at least one job branch are connected by edges according to dependency information between sub-projects; the workload and the job area are used as attributes of the first-level leaf nodes, and the estimated job days are used as attributes of the second-level leaf nodes to construct the first job graph. Among them, V represents a node, E represents an edge, and X represents a node attribute.

[0088] According to the dependency relationship between the sub-projects, the root nodes in at least one job branch are connected by edges, specifically including: if there is a dependency relationship between the sub-projects, bidirectional directed edges and / or unidirectional directed edges are used for connection, and if there is no dependency relationship, undirected edges are used for connection. Specifically, when the dependency relationship is that sub-project A should be executed before sub-project B and sub-project A is executed after sub-project B, unidirectional directed edges are used for edge connection, and when the dependency relationship is that sub-project A and sub-project B can be executed at the same time, bidirectional directed edges are used for edge connection. Figure 3 Shown is based on Figure 2 The job graph built by the job branches in .

[0089] S5: Input the first operation diagram and the first operation table into the first intelligent audit model, and output a first audit result. Optionally, the first audit result includes whether there is abnormal information in the operation information, and if there is abnormal information, indicates the abnormal information.

[0090] In one embodiment, referring to Figure 4The first intelligent audit model includes a first encoder, a second encoder, a first fuser and a first-level decoder; the first-level decoder is composed of multiple network blocks and multiple layers of fully connected layers, and each network block includes an LSTM layer, a RELU layer and a pooling layer.

[0091] Inputting the first operation diagram and the first operation table into the first intelligent audit model, and outputting the first audit result, specifically including:

[0092] S51: Input the first operation graph into the first encoder to obtain a first encoding vector.

[0093] The first encoder uses a graph neural network GNN to input the first job graph into the graph neural network for encoding to obtain a first encoding vector. Specifically, according to the first job graph Construct the adjacency matrix A (1) , weight W1 (1) and node attributes In constructing weight W1 (1) When a unidirectional directed edge is used between nodes, the weight is 1; when a bidirectional directed edge is used between nodes, the weight is 2; when an undirected edge is used between nodes, the weight is 0; when constructing node attributes When , the attribute of the first-level leaf node is the workload, and the attribute of the second-level leaf node is the expected number of working days. (1) , weight W1 (1) and node attributes Get the first encoding vector

[0094] S52: Input the first operation table to the second encoder to obtain a second encoding vector.

[0095] The second encoder includes an extraction sub-model and an encoding sub-model; the extraction sub-model is used to extract a key information set from the first job table, and the encoding sub-model is used to encode the key information set according to the key information set and the weight information corresponding to the key information to obtain a second encoding vector

[0096] Specifically, the first operation table is input into the second encoder to obtain a second encoding vector, including:

[0097] Input the first worksheet into the extraction sub-model to obtain the target key information set;

[0098] The target key information set and the weight information corresponding to the key information are input into the encoding sub-model to obtain a second encoding vector.

[0099] In one embodiment, the key fields and the weight information corresponding to the key fields are stored in a database in advance in the form of a dictionary (key-value), and the first operation table is input into the extraction sub-model. The extraction sub-model matches the key fields in the dictionary in the first operation table to obtain the key information set and the weight information corresponding to the key information set; the key information set and the weight information corresponding to each key information are input into the encoding sub-model to obtain the second encoding vector:

[0100]

[0101] in, represents the target key information set, Represents weight information.

[0102] S53: Input the first encoding vector and the second encoding vector into the first fuser to obtain a first fused vector.

[0103]

[0104] S54: Input the first fusion vector to the first level decoder to obtain a first audit result. The first level decoder is composed of multiple network blocks and multiple layers of fully connected layers, and each network block includes an LSTM layer, a RELU layer and a pooling layer.

[0105] y (1) =Decoder_BLOCK_LSTM(v (1) );

[0106] By using multiple network blocks in the decoder, the input feature vector can be reduced in multiple levels to extract key entity information and improve computational efficiency. At the same time, by using multiple levels of LSTM network layers, more comprehensive contextual semantic information can be obtained, thereby improving computational accuracy.

[0107] For example, the first-level decoder includes three network blocks and two fully connected layers, each network block includes an LSTM layer, a RELU layer and a pooling layer, and the pooling layer adopts maximum pooling or average pooling; the first fusion vector is input to the first-level decoder to obtain a first audit result, which specifically includes:

[0108] The first fusion vector is input into the first network block to obtain the first network block vector, which is specifically:

[0109]

[0110] The first network block vector is input into the second network block to obtain the second network block vector, specifically:

[0111]

[0112] The third network block vector is input into the third network block to obtain the third network block vector, which is specifically:

[0113]

[0114] The first network block vector, the second network block vector, and the third network block vector are input into the first fully connected layer to obtain the first fully connected layer vector, which is specifically:

[0115]

[0116] The first fully connected layer vector is input into the second fully connected layer to obtain the first audit result, which is:

[0117]

[0118] The first intelligent audit model can detect anomalies in the first operation information and indicate the existence of the anomaly information when the anomaly information is detected. The anomaly information includes whether the dependency between sub-items in the marine operation information is reasonable, whether the operation vessels used to execute the sub-items are reasonable, whether the estimated operation days to complete the sub-items are reasonable, etc. For example, the first operation table 1 and the first operation diagram Figure 3 The input is input into the first intelligent audit model, and anomalies are detected in the operation information. The anomaly information lies in that project identifier C and sub-project identifier c can also be executed by the operation vessel V3; at the same time, due to the actual operation environment of the operation area Ar3, the estimated operation time of sub-project c and sub-project d has obvious deviations and should be appropriately extended.

[0119] Before using the first intelligent audit model to audit marine operation information, it is necessary to train the first intelligent audit model using training samples, which include manually annotated historical sample documents and operation area information obtained through web crawling. Set a loss function, and when the loss function reaches the target threshold, train and obtain the first intelligent audit model.

[0120] This embodiment obtains first operation information by intelligently identifying multiple marine operation plan documents, constructs a first operation table and a first operation diagram with dependency and association relationships based on the first operation information, inputs the first operation table and the first operation diagram into a pre-trained first intelligent audit model to obtain an audit result, thereby improving the audit efficiency and audit accuracy of the marine operation information.

[0121] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0122] (1) The embodiment of the present application proposes an intelligent audit method for marine operation plan documents and marine operation tasks. The method obtains fine-grained operation information by identifying multiple operation plan documents, and uses the fine-grained operation information to generate a first operation table and a first operation diagram. The first operation table and the first operation diagram are input into the intelligent audit model to obtain the audit result, which effectively verifies the rationality and effectiveness of the operation plan documents and the operation tasks. At the same time, the present application uses the first intelligent audit model trained and constructed by prior knowledge and rich external knowledge to perform intelligent audits on operation tasks, further improving the efficiency and accuracy of marine operation task audits.

[0123] (2) The embodiment of the present application generates a first ocean scheduling diagram based on the operation information, and before the first scheduling diagram is generated, the operation information is audited by a first intelligent audit model, and the operation information is adjusted based on the audit results, thereby improving the accuracy of the operation scheduling information. After the operation scheduling diagram is generated, the generated scheduling information is audited by a second intelligent audit model, thereby further improving the accuracy of the operation scheduling.

[0124] (3) When generating an operation diagram, the embodiment of the present application takes into account the affiliation, dependency and importance between the various information in the operation information, takes the sub-project as the root node, the project as the first-level leaf node, and the operation vessel as the second-level leaf node, and constructs at least one operation branch. Then, using the dependency information, the root nodes in the operation branch are connected, the operation volume is used as the attribute of the first-level leaf node, and the operation days are used as the attribute of the second-level leaf node. This can not only visually display the core information in the operation plan document, but also provide important data support for intelligent review.

[0125] Reference Figure 5 The embodiment of the present application further proposes a solution for generating a first scheduling diagram according to the operation information, and performing intelligent audit on the first scheduling diagram according to the second intelligent audit model, which specifically includes the following steps S6 to S9:

[0126] S6: Modify the first operation information and the first operation table according to the first audit result to obtain second operation information and a second operation table.

[0127] After completing the intelligent review of the marine operation task through the first intelligent review model, the first operation information and the first operation table are corrected according to the first review result to obtain the second operation information and the second operation table. During the correction, the abnormal parts in the first operation information and the first operation table are adjusted according to the indication of the abnormal information.

[0128] S7: Input the second job information into the first job scheduling model to generate first scheduling information.

[0129] A first job scheduling model is pre-constructed, and the first job scheduling model includes a scheduling encoding network, a scheduling generation network and a scheduling decision network; the scheduling generation network includes multiple generation sub-networks, and the multiple generation sub-networks are used to generate scheduling information corresponding to sub-project information.

[0130] After the first job scheduling model is trained, the first job scheduling model and the second job information are used to generate first scheduling information, which specifically includes:

[0131] S71: Input multiple sub-item information into the scheduling coding network to obtain multiple sub-item vectors.

[0132] S72: Input multiple sub-project vectors into the scheduling generation network, and generate multiple first scheduling sub-information corresponding to the multiple sub-projects through the generation network; the first scheduling sub-information includes operation vessel information, operation day information and window label information.

[0133] In the actual production process, considering the influence of various factors such as weather and waves, the operation cycle is divided into multiple continuous operation windows by month. For example, March to September of the current year is divided into the first window, and September of the current year to March of the next year is divided into the second window. The first window label and the second window label are used to identify the first window and the second window respectively.

[0134] When constructing a scheduling generation network, multiple generation subnetworks are constructed for multiple work vessels. For example, a first generation subnetwork for the work vessel V1, a second generation subnetwork for the work vessel V2, and a third generation subnetwork for the work vessel V3 are constructed respectively, and labels are set for the multiple generation subnetworks according to the sub-item categories that can be executed by the work vessel. Label a is set for the first generation subnetwork, labels b, c, d are set for the second generation subnetwork, and labels a, b, d are set for the third generation subnetwork. The generation subnetwork adopts an adversarial generation network model.

[0135] Inputting multiple sub-item vectors into a scheduling generation network, and generating multiple first scheduling sub-information corresponding to the multiple sub-items through the scheduling generation network, specifically includes:

[0136] According to the sub-item identifier and the label of the generated sub-network, the corresponding generated sub-network is matched and obtained. For example, according to the sub-item category a, the first generated sub-network and the third generated sub-network are matched and obtained, according to the sub-item category b, the second generated sub-network and the third generated sub-network are matched and obtained, and according to the sub-item category c, the second generated sub-network is matched and obtained.

[0137] The sub-item vector is input to the matching generative sub-network. For example, the sub-item vector corresponding to the sub-item information (A, a, Ar1, 5000) is input to the first generative sub-network and the third generative sub-network, the sub-item information (B, b, Ar2, 300) is input to the second generative sub-network and the third generative sub-network, and the sub-item information (B, c, Ar2, 3000) is input to the second generative sub-network.

[0138] The first scheduling sub-information is outputted by generating a sub-network. The first scheduling sub-information includes a project identifier, a sub-project identifier, an operation area information, an operation ship information, a first window label and first operation day information, a second window label and second operation day information.

[0139] S73: Inputting the dependency data between the plurality of first scheduling sub-information and the sub-items into the scheduling decision network to generate the first scheduling information.

[0140] After obtaining the first scheduling sub-information, the dependencies between the multiple first scheduling sub-information and the sub-projects are input into the scheduling decision network, and the first scheduling information is output through the scheduling decision network; the first scheduling information includes the execution sequence corresponding to the operation vessel, operation project identifier, project sub-identifier, operation area and project sub-identifier.

[0141] The scheduling decision network adopts a multi-network fusion structure based on dependency, including input layer, embedding layer, feature extraction layer, first gating layer, multi-block shared MLP layer, linear layer and output layer, where:

[0142] The input layer is used to receive a plurality of first scheduling sub-information and dependency relationship information between sub-items;

[0143] The embedding layer is used to convert the first scheduling sub-information and the corresponding dependency information into a feature vector to obtain a sub-information feature vector and a dependency feature vector;

[0144] The fusion layer is used to fuse the sub-information feature vector and the dependency feature vector to obtain a fused feature vector;

[0145] The fused feature vector is subjected to feature extraction using multiple feature extraction networks in the feature extraction layer to obtain multiple initial feature vectors; wherein the feature extraction layer may adopt a deep neural network model;

[0146] The first gating layer is used to generate first weight data corresponding to each feature extraction network, and perform weighted summation on the initial feature vectors output by each feature extraction network to obtain a first combined feature vector;

[0147] The multiple shared MLP layers are used to perform task migration processing on the first combined feature vector to obtain a first migration feature vector;

[0148] The linear layer is used to transform the first migration feature vector to obtain first scheduling information;

[0149] The output layer is used to output the first scheduling information.

[0150] In one embodiment, the multi-block shared MLP layer is a multi-level block network structure, each block includes m MLPs and a shared layer, and the shared layer of the i-th block is used to extract the output features of the m MLPs in the current block to obtain m knowledge features, and MLP i k1 The output features of MLP i k2 The extracted knowledge features are fused as MLP i+1 k1 The input of , where m is the number of operating ships, MLP represents the multi-layer perceptron model, k2=1:m and k2≠k1.

[0151] For example, the output of the MLPs in the ith block is: X_MLP i 1 , ..., X_MLP i k1 , ..., X_MLP i m .

[0152] The input is shared to the shared layer, which extracts knowledge and obtains the knowledge feature data of the i-th level as follows:

[0153] Y_MLP i 1 =EXTRACT(X_MLP i 1 ), ..., Y_MLP i k1 =EXTRACT(X_MLP i k

[0154] 1 ), ..., Y_MLP i m =EXTRACT(XMLP i m );

[0155] Then the feature data input to the MLPs in the i+1th level is:

[0156] {Input_MLP i+1 1} = concat(X_MLP i 1 ,Y_MLP i2 ,……,Y_MLP i k1 ,……,Y_MLP i m );

[0157] {Input_MLP i+1 k1} = concat(X_MLP i k1 ,Y_MLP i 1 ,……,Y_MLP i k1-1 ,Y_MLP i k1+1 ,……,Y_MLP i m );

[0158] {Input_MLP i+1 m} = concat(X_MLP i m ,Y_MLP i 1 ,……,Y_MLP i k1 ,……,Y_MLP i m-1 ).

[0159] Through the first scheduling model, the first scheduling information is obtained as shown in Table 2:

[0160] Ship number V1 V2 V3 (V1, A, a, Ar1, 1) (V2, C, c, d, Ar3, 1) (V3, D, d, Ar4, 1) (V1, C, a, Ar3, 2) (V2, B, c, Ar2, 2) (V3, B, a, Ar2, 1) (V3, B, b, Ar2, 3)

[0161] Table 2

[0162] S8: Generate a first scheduling graph according to the first scheduling information.

[0163] After the first scheduling information is generated, the first scheduling graph is constructed with the operating vessel as the root node, the triple consisting of the operation project identifier, project sub-identifier, and operation area as the leaf node, and the execution sequence corresponding to the project sub-identifier as the constraint condition. like Figure 6 As shown, Figure 6 The solid lines in the figure represent the execution order of sub-items for the same ship, and the dotted lines represent the execution order of sub-items between different ships. Figure 6In the example, the operation vessel V3 first completes the operation area Ar2, project identifier B, and sub-project identifier a, then the operation vessel V3 completes the operation area Ar2, project identifier B, and sub-project identifier c, and finally the operation vessel V3 completes the operation area Ar2, project identifier B, and sub-project identifier b. By constructing the first scheduling diagram, the operation scheduling plan can be clearly reflected.

[0164] S9: Input the first scheduling diagram and the second operation table into the second intelligent audit model, and output the second audit result.

[0165] The second intelligent audit model adopts an architecture similar to the first intelligent audit model, which includes a third encoder, a fourth encoder, a second fuser and a second-level decoder; the second-level decoder is also composed of multiple network blocks and multiple layers of fully connected layers, each network block includes an LSTM layer, a RELU layer and a pooling layer.

[0166] Inputting the first scheduling diagram and the second operation table into the second intelligent audit model, and outputting the second audit result, specifically including:

[0167] S91: Input the first scheduling graph to the third encoder to obtain a third encoding vector.

[0168] The third encoder uses a convolutional neural network to input the first scheduling graph into the convolutional neural network for encoding to obtain a third encoding vector:

[0169]

[0170] S92: Input the first operation table to the fourth encoder to obtain a fourth encoding vector.

[0171] The fourth encoder adopts the same architecture as the second encoder, and also includes an extraction sub-model and an encoding sub-model; the extraction sub-model is used to extract the key information set from the second operation table, and the encoding sub-model is used to encode the key information set according to the key information set and the weight information corresponding to the key information, so as to obtain a fourth encoding vector

[0172]

[0173] in, represents the target key information set, Represents weight information.

[0174] S93: Input the third encoding vector and the fourth encoding vector into the second fuser to obtain a second fused vector:

[0175]

[0176] S94: Input the second fusion vector to the second-level decoder to obtain a second audit result:

[0177] y (2) =Decoder_BLOCK_LSTM(v (2) ).

[0178] For example, the second-level decoder includes three network blocks and two fully connected layers, each network block includes an LSTM layer, a RELU layer and a pooling layer, and the pooling layer adopts maximum pooling or average pooling; the second fusion vector is input to the second-level decoder to obtain a second audit result, which specifically includes:

[0179] The second fusion vector is input into the first network block to obtain the first network block vector, which is specifically:

[0180]

[0181] The first network block vector is input into the second network block to obtain the second network block vector, specifically:

[0182]

[0183] The third network block vector is input into the third network block to obtain the third network block vector, which is specifically:

[0184]

[0185] The first network block vector, the second network block vector, and the third network block vector are input into the first fully connected layer to obtain the first fully connected layer vector, which is specifically:

[0186]

[0187] The first fully connected layer vector is input into the second fully connected layer to obtain the second audit result, which is:

[0188]

[0189] The second intelligent audit model can detect anomalies in the operation schedule and indicate the existence of abnormal information when abnormal information is detected. The abnormal information includes whether the execution order between sub-items in the operation information is reasonable, whether the ships used to execute the sub-items are reasonable, whether there are any sub-items missing, etc.

[0190] In this embodiment, before the marine operation scheduling information is generated, the operation information is audited by the first intelligent audit model, and the operation information is adjusted based on the audit result, thereby improving the accuracy of the scheduling information generation; at the same time, after the marine operation scheduling information is generated, the generated scheduling information is audited by the second intelligent audit model, thereby further improving the accuracy of the scheduling information generation.

[0191] Reference Figure 7 The embodiment of the present application also proposes an intelligent review device for marine operation tasks, the device comprising:

[0192] An acquisition module 701 is used to acquire at least one job document;

[0193] The recognition module 702 is used to input at least one of the operation documents into a recognition model to obtain first operation information;

[0194] A first generating module 703, configured to generate a first operation table according to the first operation information;

[0195] A second generating module 703, configured to generate a first operation graph according to the first operation table;

[0196] The first review module 704 is used to input the first operation diagram and the first operation table into a first intelligent review model and output a first review result.

[0197] The identification module 702 identifies at least one job plan document and obtains first job information, which specifically includes:

[0198] At least one operation plan document is input into the recognition model to obtain first operation information; the first operation information includes at least one project information, each project information includes multiple sub-project information and dependency information between sub-projects; the sub-project information includes at least project identification, sub-project identification, operation area, operation volume, operation vessel and operation days.

[0199] The first generating module 703 generates a first operation table according to the first operation information, specifically including: generating the first operation table according to a preset template using the first operation information.

[0200] The second generating module 704 generates a first operation diagram according to the first operation table, specifically including:

[0201] Extracting first job information from the first job table;

[0202] Taking the sub-project as the root node, the project as the first-level leaf node, and the operation vessel as the second-level leaf node, at least one operation branch is constructed;

[0203] Construct a job graph based on at least one job branch, specifically: connect the root nodes in the job branch according to the dependency information; use the job volume and job area as the attributes of the first-level leaf node, and use the job days as the attributes of the second-level leaf node to construct the job graph.

[0204] The first intelligent audit model in the first audit module 705 includes a first encoder, a second encoder, a first fuser and a first-level decoder; the first-level decoder is composed of a plurality of network blocks, each of which includes an LSTM network, a RELU layer and a pooling layer;

[0205] The first review module 705 obtains a first review result according to the first operation diagram and the first operation table, specifically including:

[0206] Inputting the first job graph into the first encoder to obtain a first encoding vector;

[0207] Input the first operation table to the second encoder to obtain a second encoding vector; the second encoder includes an extraction sub-model and an encoding sub-model; input the first operation table to the second encoder to obtain the second encoding vector, specifically: input the first operation table to the extraction sub-model to obtain a target key information set; input the target key information set and the weight information corresponding to the key information to the encoding sub-model to obtain the second encoding vector;

[0208] Inputting the first encoding vector and the second encoding vector into a first fuser to obtain a first fused vector;

[0209] The first fusion vector is input into the first level decoder to obtain a first audit result.

[0210] In another embodiment, referring to Figure 8 , an intelligent review device for marine operation tasks proposed in the embodiment of the present application also includes:

[0211] A correction module 706, configured to correct the first operation information and the first operation table according to the first review result to obtain the second operation information and the second operation table;

[0212] The third generating module 707 is used to input the second job information into the first job scheduling model to generate the first scheduling information;

[0213] A fourth generating module 708, configured to generate a first scheduling graph according to the first scheduling information;

[0214] The second audit module 709 is used to input the first scheduling diagram and the second operation table into the second intelligent audit model and output a second audit result.

[0215] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0216] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method of the embodiment of the present application when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0217] It can be understood that the contents of the above method embodiments are all applicable to the embodiments of the present device, the functions specifically implemented by the embodiments of the present device are the same as those of the method of the present application, and the beneficial effects achieved are also the same as those achieved by the method of the present application.

[0218] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0219] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0220] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 902, and the processor 901 calls and executes the methods of the embodiments of this application;

[0221] Input / output interface 903, used to implement information input and output;

[0222] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0223] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0224] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other through a bus 905 within the device.

[0225] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program implements the method of the present application when executed by a processor.

[0226] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0227] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0228] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0229] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0230] The device embodiments described above are merely illustrative, and the modules described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0231] Those skilled in the art will appreciate that all or some of the steps, devices, and functional modules / units in the above disclosed methods may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0232] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or equipment.

[0233] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0234] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0235] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0236] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0237] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0238] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. An intelligent audit method for marine operation tasks, characterized in that: The method comprises the following steps: Obtain at least one work plan document; Inputting at least one of the operation plan documents into a recognition model to obtain first operation information; Generate a first operation table according to the first operation information; generating a first operation graph according to the first operation table; The first operation diagram and the first operation table are input into a first intelligent audit model, and a first audit result is output.

2. The intelligent audit method for marine operation tasks according to claim 1 is characterized in that: The step of inputting at least one of the operation plan documents into the recognition model to obtain the first operation information comprises the following steps: Input at least one of the operation plan documents into the recognition model to identify and obtain the first operation information; wherein the first operation information includes information of multiple sub-projects and dependency information between the sub-projects; each sub-project information includes at least project identification, sub-project identification, operation area, operation volume, operation vessel and estimated operation days; The step of generating a first operation table according to the first operation information comprises the following steps: The first operation table is generated according to the first operation information in accordance with a preset template.

3. The intelligent audit method for marine operation tasks according to claim 2 is characterized in that: Generating a first operation graph according to the first operation table specifically includes: extracting the first job information from the first job table; At least one operation branch is constructed by taking the sub-project identifier as a root node, the project identifier as a first-level leaf node, and the operation vessel as a second-level leaf node; The first job graph is constructed according to at least one of the job branches.

4. The intelligent audit method for marine operation tasks according to claim 3 is characterized in that: The step of constructing the first job graph according to at least one of the job branches comprises the following steps: According to the dependency information between the sub-projects, the root nodes in the job branches are connected by edges; The first operation graph is constructed by taking the operation volume and the operation area as attributes of the first-level leaf node and taking the estimated operation days as attributes of the second-level leaf node.

5. The intelligent audit method for marine operation tasks according to claim 1 is characterized in that: The first intelligent audit model includes a first encoder, a second encoder, a first fuser and a first-level decoder; the first-level decoder is composed of a plurality of network blocks and a plurality of fully connected layers, each of the network blocks includes an LSTM layer, a RELU layer and a pooling layer; The step of inputting the first operation diagram and the first operation table into a first intelligent audit model and outputting a first audit result comprises the following steps: Inputting the first job graph into the first encoder to obtain a first encoding vector; Inputting the first operation table into the second encoder to obtain a second encoding vector; Inputting the first encoding vector and the second encoding vector into the first fuser to obtain a first fused vector; The first fusion vector is input into the first level decoder to obtain the first audit result.

6. The intelligent audit method for marine operation tasks according to claim 5 is characterized in that: The second encoder includes an extraction sub-model and an encoding sub-model; The step of inputting the first operation table into the second encoder to obtain a second encoding vector comprises the following steps: Inputting the first operation table into the extraction sub-model to obtain a target key information set; The target key information set and weight information corresponding to the key information are input into the encoding sub-model to obtain a second encoding vector.

7. A method for intelligent review of marine operation tasks according to any one of claims 1 to 6, characterized in that: The method further comprises the following steps: Modify the first operation information and the first operation table according to the first audit result to obtain second operation information and a second operation table; Inputting the second job information into the first job scheduling model to generate first scheduling information; generating a first scheduling graph according to the first scheduling information; The first scheduling diagram and the second operation table are input into a second intelligent audit model, and a second audit result is output.

8. An intelligent audit device for marine operation tasks, characterized in that: The device comprises: An acquisition module, used for acquiring at least one work plan document; A recognition module, used for inputting at least one of the operation plan documents into a recognition model to obtain first operation information; A first generating module, configured to generate a first operation table according to the first operation information; A second generating module, configured to generate a first operation graph according to the first operation table; The first audit module is used to input the first operation diagram and the first operation table into a first intelligent audit model and output a first audit result.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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