Node position and number determination method and device, charging downloading square cabin, readable storage medium and chip

Abstract

The invention provides a node position and number determination method and device, a charging downloading square cabin, a readable storage medium and a chip, and relates to the technical field of charging downloading square cabins, and the node position and number determination method comprises the steps: obtaining image data of a plurality of charging downloading cabinets corresponding to the charging downloading square cabin; obtaining an identification result of the node server; determining an image file and an identification result file according to the image data and the identification result; acquiring first node information corresponding to the image file; acquiring second node information corresponding to the identification result file; and determining a checking result according to the first node information and the second node information, wherein the checking result is used for determining whether the actually inserted node position and number of the node seismic instrument are consistent with the identification result. Through the scheme of the invention, the seismic data processing efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of charging and downloading cabin technology, and more specifically, to a method, apparatus, charging and downloading cabin, readable storage medium, and chip for determining the location and number of nodes. Background Technology

[0002] In seismic exploration, nodal seismic instruments are core data acquisition devices, typically charged and downloaded via charging and downloading modules. Each module contains multiple charging and downloading cabinets, each equipped with multiple interfaces for connecting to the nodal instruments. The node-specific software (hereinafter referred to as server software) installed on a server connected to the charging and downloading cabinets can count the number and location of identified nodes. However, due to issues such as interface aging, environmental interference, or poor contact, the actual number and location of inserted nodes often differ from the software's identification results. Traditional manual verification requires comparing each interface individually, which is time-consuming, labor-intensive, and error-prone, severely impacting data integrity and processing efficiency. Summary of the Invention

[0003] In view of this, the present invention aims to solve the problems of insufficient data integrity and processing efficiency when the interface compares data one by one.

[0004] Specifically, the present invention is achieved through the following technical solution: The first aspect of this invention provides a method for determining the location and number of nodes.

[0005] A second aspect of the present invention provides a device for determining the location and number of nodes.

[0006] A third aspect of the present invention provides a charging and downloading cabin.

[0007] A fourth aspect of the present invention provides a readable storage medium.

[0008] The fifth aspect of this invention provides a chip.

[0009] The present invention provides a method for determining the location and number of nodes in a charging and downloading cabin. The charging and downloading cabin includes multiple charging and downloading cabinets, each equipped with multiple interfaces for connecting node seismic instruments. The charging and downloading cabinets are communicatively connected to a node server. The method for determining the location and number of nodes includes: acquiring image data of the multiple charging and downloading cabinets corresponding to the charging and downloading cabin; acquiring the identification results from the node server; determining image files and identification result files based on the image data and identification results; acquiring first node information corresponding to the image files; acquiring second node information corresponding to the identification result files; and determining a verification result based on the first and second node information. The verification result is used to determine whether the actual insertion location and number of nodes in the node seismic instruments are consistent with the identification results.

[0010] The method for determining the location and number of nodes provided by this invention is applied to a charging and downloading container. Multiple charging and downloading cabinets are installed in the container, and these cabinets are connected to nodal seismic instruments via interfaces to acquire seismic exploration data. Furthermore, multiple charging and downloading cabinets are communicatively connected to a node server. Software running on the node server identifies and records the number and location of nodes corresponding to the connection interfaces.

[0011] Based on this, image data corresponding to at least one charging / downloading cabinet is acquired from the charging / downloading container. The acquired image data and recognition results are stored in a specific directory, and image files corresponding to the image data and recognition result files corresponding to the recognition results are determined. A result output directory corresponding to the specific directory is also determined.

[0012] It is important to emphasize that the comparison between the image files and the recognition results will only begin after the image data and recognition results are confirmed to be stored in a specific directory and the corresponding output directory is selected, so as to facilitate the unified processing of subsequent data.

[0013] Image files are processed using image recognition algorithms, and a node list based on visual analysis, i.e., the first node information, is output to identify nodes on multiple charging stations, their corresponding interface locations, and the number of interfaces. The recognition result file is parsed to determine the node list based on node server software analysis, i.e., the second node information. The first node information corresponding to visual recognition and the second node information recognized by the server software are compared, and the resulting difference report is output as the verification result to a designated output directory. All input files, intermediate files, and output files are managed through a file directory, ensuring cleanliness and ease of integration.

[0014] Understandably, image recognition technology can quickly and accurately identify the number and location of nodes on the charging station. Through data comparison, abnormal nodes can be quickly and accurately located and marked, reducing manual review time, minimizing human error, and improving overall work efficiency.

[0015] In some technical solutions, optionally, obtaining the first node information corresponding to the image file includes: obtaining at least one image acquisition device; performing image recognition on the image file based on the image acquisition device to determine the first cabinet number information and node information corresponding to the charging download cabinet, wherein the node information includes the physical location of the node and the number of nodes; or obtaining at least one first external interface to determine the first cabinet number information corresponding to the charging download cabinet based on the mapping relationship between the image acquisition device and the charging download cabinet; determining the first node information based on the physical location of the node and the number of nodes; wherein the first cabinet number information and the first node information are bound together.

[0016] In this solution, at least one first external interface with image recognition capability is obtained. The first external interface performs image recognition on the image file using visual recognition technology to identify the first cabinet number and node information corresponding to the charging and downloading cabinet. The node information includes the physical location and number of nodes corresponding to at least one connected interface.

[0017] In some technical solutions, optionally, before performing image recognition on the image file according to the image acquisition device, the image file is preprocessed, including one or more of intelligent noise reduction, brightness equalization, geometric correction and region cropping.

[0018] In this solution, one or more preprocessing steps are required before image recognition is performed on the image file. These preprocessing steps include intelligent noise reduction, brightness equalization, geometric correction, and region cropping.

[0019] In some technical solutions, optionally, obtaining the second node information corresponding to the recognition result file includes: obtaining at least one second external interface; reading the recognition result file according to the second external interface and determining the text content in the recognition result file; determining the second cabinet number information and the second node information recognized by the node server according to the text content; wherein the second cabinet number information and the second node information are bound together.

[0020] In this solution, the second external interface of the server software reads the identification result file in a specific directory to determine the corresponding second cabinet number information and second node information. The second node information includes the number of nodes and the node location.

[0021] In some technical solutions, optionally, the verification result is determined based on the first node information and the second node information, including: obtaining the mapping relationship between the second cabinet number information and the first cabinet number information; matching and comparing the second node information and the first node information one by one according to the mapping relationship to determine the difference list, which includes the difference information of unidentified node locations, incorrectly identified node locations, and the difference in the number of nodes; and determining the verification result based on the difference information of unidentified node locations, incorrectly identified node locations, and the difference in the number of nodes.

[0022] In this scheme, the image recognition results corresponding to the image file and the parsing results of the recognition result file are compared to generate a comparison result. The comparison result includes information on the location of unrecognized nodes, the location of incorrectly recognized nodes, and the difference in the number of nodes. Based on the information on the location of unrecognized nodes, the location of incorrectly recognized nodes, and the difference in the number of nodes, the verification result is determined to determine whether the node data in the image file and the recognition result file are the same and the corresponding differences.

[0023] In some technical solutions, optionally, after determining the verification result based on the first node information and the second node information, the solution may also include: determining the location of the discrepancy point based on the verification result, and conducting a point-to-point investigation of the discrepancy point location; or reconnecting or marking the discrepancy point location; or performing node data download, charging, and testing operations based on the verification result.

[0024] In this scheme, the abnormal locations are identified and investigated based on the verification results. After eliminating the discrepancies, a series of seismic data processing tasks are performed, such as downloading node data, charging nodes, or testing nodes.

[0025] A second aspect of the present invention provides a device for determining the location and number of nodes. The device includes: an image input module for acquiring image data of multiple charging and downloading cabinets corresponding to a charging and downloading cabin; a server identification module for acquiring the identification results of a node server; a data storage module for determining an image file and an identification result file based on the image data and the identification results; an image recognition module for acquiring first node information corresponding to the image file; an identification result reading module for acquiring second node information corresponding to the identification result file; and an algorithm module for determining a verification result based on the first and second node information, the verification result being used to determine whether the actual insertion location and number of nodes by the node seismic instrument are consistent with the identification results.

[0026] An embodiment of the third aspect of the present invention provides a charging and downloading container, including multiple charging and downloading cabinets, each equipped with multiple interfaces for connecting node seismic instruments, and the charging and downloading cabinets communicating with a node server; the charging and downloading container also includes a device for determining the location and number of nodes as described in the second aspect.

[0027] An embodiment of the fourth aspect of the present invention provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps as described in the first aspect.

[0028] An embodiment of the fifth aspect of the present invention provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run a program or instructions to implement the steps as described in the first aspect.

[0029] The technical solution provided by this invention brings at least the following beneficial effects: This invention proposes a method for determining the location and number of nodes in a charging and downloading cabin. The charging and downloading cabin includes multiple charging and downloading cabinets, each equipped with multiple interfaces for connecting node seismic instruments. The charging and downloading cabinets communicate with a node server. By using artificial intelligence (AI) image recognition and multi-sensor data fusion, the method enables rapid determination of the number and location of nodes, shortening verification time and improving seismic data processing efficiency. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0032] Figure 1 One of the flowcharts illustrating the method for determining the location and number of nodes provided in an embodiment of the present invention; Figure 2 A second flowchart illustrating the method for determining the location and number of nodes provided in an embodiment of the present invention; Figure 3 The third flowchart illustrates the method for determining the location and number of nodes provided in this embodiment of the invention. Figure 4 The fourth flowchart illustrates the method for determining the location and number of nodes provided in this embodiment of the invention. Figure 5 A schematic block diagram of the structure of the device for determining the position and number of nodes provided in an embodiment of the present invention; Figure 6 A schematic block diagram of the structure of the charging and downloading cabin provided in an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the workflow of nodes in related technologies from being placed on the shelf to being removed from the shelf. Figure 8 A schematic diagram illustrating the workflow of a node from the upper shelf to the lower shelf, provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of a device for verifying the number and location of nodes in a charging and downloading container provided in an embodiment of the present invention.

[0033] in, Figure 5 , Figure 6 and Figure 9 The correspondence between component names and their designations is as follows: 1: Image Input Module; 2: Algorithm Module; 3: Result Output Module; 4: Auxiliary Data Input Module; 5: Image Acquisition Equipment; 11: Image Acquisition Submodule; 12: Image Loading Submodule; 13: Image Preprocessing Submodule; 21: Image Recognition Submodule; 22: Auxiliary Data Extraction Submodule; 23: Data Processing Submodule; 31: Result Display Submodule; 32: File Result Output Submodule; 41: Server Software Recognition Result Reading Submodule; 51: Image Acquisition Unit; 100: Charging and Downloading Cabinet Node Quantity and Location Verification Device; 200: Charging and Downloading Cabinet; 202: Charging and Downloading Cabinet; 2022: Interface; 300: Node Seismic Instrument; 400: Node Server; 900: Node Location and Quantity Determination Device; 902: Image Input Module; 904: Server Recognition Module; 906: Data Storage Module; 908: Image Recognition Module; 910: Recognition Result Reading Module; 912: Algorithm Module. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] In seismic exploration, nodal seismic instruments are core data acquisition devices, typically charged and downloaded via a charging and downloading container. Each charging and downloading container contains multiple charging and downloading cabinets, each equipped with multiple interfaces to connect to the nodal instruments. The node-specific software (hereinafter referred to as server software) installed on a server connected to the charging and downloading cabinet can count the number and location of identified nodes. However, due to issues such as interface aging, environmental interference, or poor contact, the actual number and location of inserted nodes often differ from the software's identification results. The existing workflow for nodes moving from the cabinet to the downloading cabinet is as follows: Figure 7 As shown, it includes: Step S200: Node placement; Step S202: Manually verify whether the node in the cabinet is consistent with the node identified by the server software; If the nodes in the cabinet are inconsistent with the nodes identified by the server software after manual verification, proceed to step S204: remove the nodes in the abnormal locations and return to step S202. If the nodes in the cabinet are manually verified to be consistent with the nodes identified by the server software, proceed to step S206: data download, node charging, and node testing. Step S208: Node removal from cabinet.

[0036] Traditional manual verification methods require comparing each interface one by one, which is time-consuming, labor-intensive, and prone to errors, seriously affecting data integrity and processing efficiency.

[0037] The technical problem this invention aims to solve is to provide a method and apparatus for verifying the number and location of nodes in a node instrument charging and downloading container. This addresses issues such as the time-consuming and inefficient verification process, the inability to quickly and accurately locate unidentified nodes, the need to improve the accuracy and stability of intelligent identification technology, the lack of information analysis capabilities for associated equipment, and the high error rate due to the need for extensive manual intervention.

[0038] To address the aforementioned technical problems, this invention provides a method for determining the location and number of nodes.

[0039] See Figure 1 The first aspect of this invention provides a method for determining the location and number of nodes, used in a charging and downloading container. The charging and downloading container includes multiple charging and downloading cabinets, each equipped with multiple interfaces for connecting node seismic instruments. The charging and downloading cabinets are communicatively connected to a node server. The method for determining the location and number of nodes includes the following steps: Step S100: Acquire image data of multiple charging and downloading cabinets corresponding to the charging and downloading cabin; Step S102: Obtain the identification results from the node server; Step S104: Determine the image file and recognition result file based on the image data and recognition results; Step S106: Obtain the first node information corresponding to the image file; Step S108: Obtain the second node information corresponding to the recognition result file; Step S110: Determine the verification result based on the first node information and the second node information. The verification result is used to determine whether the actual insertion location and number of nodes of the nodal seismic instrument are consistent with the identification result.

[0040] The method for determining the location and number of nodes provided by this invention is applied to a charging and downloading container. Multiple charging and downloading cabinets are installed in the container, and these cabinets are connected to nodal seismic instruments via interfaces to acquire seismic exploration data. Furthermore, multiple charging and downloading cabinets are communicatively connected to a node server. Software running on the node server identifies and records the number and location of nodes corresponding to the connection interfaces.

[0041] Based on this, image data corresponding to at least one charging / downloading cabinet is acquired from the charging / downloading container. The acquired image data and recognition results are stored in a specific directory, and image files corresponding to the image data and recognition result files corresponding to the recognition results are determined. A result output directory corresponding to the specific directory is also determined.

[0042] It is important to emphasize that the comparison between the image files and the recognition results will only begin after the image data and recognition results are confirmed to be stored in a specific directory and the corresponding output directory is selected, so as to facilitate the unified processing of subsequent data.

[0043] Image files are processed using image recognition algorithms, and a node list based on visual analysis, i.e., the first node information, is output to identify nodes on multiple charging stations, their corresponding interface locations, and the number of interfaces. The recognition result file is parsed to determine the node list based on node server software analysis, i.e., the second node information. The first node information corresponding to visual recognition and the second node information recognized by the server software are compared, and the resulting difference report is output as the verification result to a designated output directory. All input files, intermediate files, and output files are managed through a file directory, ensuring cleanliness and ease of integration.

[0044] Understandably, image recognition technology can quickly and accurately identify the number and location of nodes on the charging station. Through data comparison, abnormal nodes can be quickly and accurately located and marked, reducing manual review time, minimizing human error, and improving overall work efficiency.

[0045] Specifically, visual evidence is obtained by capturing images of the physical state of the charging cabinet interface using image acquisition devices, such as cameras. This image data reflects the actual physical presence of the interface connection. Meanwhile, node list data is retrieved from the node server software, reflecting the software's identification of the connected nodes corresponding to the interface. This simultaneous collection of both physical and software view data lays the foundation for subsequent comparisons, eliminating discrepancies between the actual number and location of inserted nodes and the software identification results, often caused by interface aging, environmental interference, or poor contact.

[0046] In some embodiments, the specific directory may optionally be a file system directory on the node server or a file system directory on an externally connected device. Using this specific directory as a central hub for data transfer and management allows image data streams and software recognition results to be uniformly converted into file objects, facilitating subsequent processing by modules through a unified interface. During subsequent access to external interfaces, files can be read directly from this specific directory without restrictions on data format or source, improving the efficiency and flexibility of seismic data processing.

[0047] In some embodiments, the image data may optionally include, but is not limited to, visible light images, infrared thermal imaging images, and depth camera images, wherein the infrared thermal imaging images are used to determine whether the node is charging or has overheating anomalies, and the depth camera images are used for three-dimensional spatial positioning.

[0048] In some embodiments, the image data may be generated by at least one fixed industrial camera deployed in front of the charging and downloading cabinet and / or at least one track-mounted camera deployed inside the charging and downloading container.

[0049] In some embodiments, optionally, while acquiring image data and recognition results, circuit signal data from the charging cabinet can also be acquired, such as the current and voltage of each interface, to help determine whether the interface is actually connected to the node. Even if the image recognition or server software fails to recognize the node, an anomaly can still be indicated if current flows through the interface corresponding to the node.

[0050] In some embodiments, the output file of the verification results may optionally include a structured report file, a visual image, an audible alarm signal, or a message sent to a remote monitoring center via a network interface.

[0051] In some embodiments, optionally, after determining the verification results, batch data download or charging instructions are initiated only for node groups that match the verification, while alarms are sent and operations are suspended for abnormal node groups.

[0052] In some embodiments, optionally, such as Figure 2 As shown, the first node information corresponding to the image file is obtained, including: Step S1082: Acquire at least one image acquisition device; Step S1084: Based on the image recognition of the image file by the image acquisition device, determine the first cabinet number information and node information corresponding to the charging download cabinet. The node information includes the physical location of the node and the number of nodes, or obtain at least one first external interface, and determine the first cabinet number information corresponding to the charging download cabinet according to the mapping relationship between the image acquisition device and the charging download cabinet. Step S1086: Determine the information of the first node based on the physical location and number of nodes; Among them, the information of the first cabinet number and the information of the first node are bound together.

[0053] In this embodiment, at least one first external interface with image recognition capability is obtained. The first external interface performs image recognition on the image file using visual recognition technology to identify the first cabinet number information and node information corresponding to the charging and downloading cabinet. The node information includes the physical location and number of nodes corresponding to at least one connected interface.

[0054] Understandably, by calling the image recognition service through the first external interface, a modular design is achieved. When it is necessary to upgrade the image recognition algorithm, change the artificial intelligence model supplier, or fix the recognition function vulnerability, only the independent first external interface needs to be updated. There is no need to modify and redeploy the core verification program, which reduces the complexity and cost of system maintenance, while improving the efficiency of image recognition.

[0055] In some embodiments, the first external interface may optionally include, but is not limited to, local function call interfaces, such as application programming interfaces (APIs) of dynamic link libraries, remote procedure call interfaces, web service interfaces, and message queue interfaces.

[0056] In some embodiments, the image recognition module corresponding to the first external interface may be deployed on a local server, an edge computing device, or a cloud server.

[0057] In some embodiments, the external interface may be invoked in a synchronous manner (waiting for the recognition result to be returned before continuing execution) or an asynchronous manner (returning immediately after sending the image, and obtaining the result through callback or polling).

[0058] In some embodiments, image recognition may be based on one or more deep learning-based object detection models.

[0059] In some embodiments, the target detection model used for image recognition may optionally integrate multiple dedicated models, including: a cabinet detection model for locating and identifying cabinets and extracting cabinet numbers; an interface location model for accurately segmenting the position of each interface; and a node status recognition model for determining whether there are nodes on the interface and further identifying the physical state of the nodes.

[0060] In some embodiments, optionally, after receiving the image recognition result, the image recognition result is post-processed. The post-processing includes non-maximum suppression to remove duplicate boxes and rule-based logic verification, that is, one interface position can only correspond to one node.

[0061] In some embodiments, the first cabinet number information and the first node information are optionally bound in the form of key-value pairs and database records. The binding relationship also includes a timestamp and the image source file path to form a complete traceability record for a recognition task.

[0062] In some embodiments, optionally, before performing image recognition on the image file according to the image acquisition device, the method includes: preprocessing the image file, the preprocessing including one or more of intelligent noise reduction, brightness equalization, geometric correction and region cropping.

[0063] In this embodiment, before performing image recognition on the image file, one or more preprocessing steps are required, including intelligent noise reduction, brightness equalization, geometric correction, and region cropping.

[0064] Understandably, preprocessed image files eliminate environmental interference, lighting differences, and geometric deformations, allowing subsequent image recognition to focus on the essential features of nodes and interfaces, such as shape, color, and texture, rather than being interfered with by irrelevant noise. This greatly reduces the false recognition rate and the missed recognition rate, and improves the reliability and feasibility of seismic data processing.

[0065] Intelligent denoising refers to automatically reducing or eliminating noise in an image by using image processing algorithms or deep learning-based denoising models.

[0066] For example, the environment in which charging and discharging occur may have issues such as insufficient light, sensor thermal noise, and electromagnetic interference, resulting in images containing a large amount of random noise. This noise can interfere with the subsequent extraction of features such as node edges and interface contours by deep learning models, leading to misidentification or missed identification. Intelligent denoising methods analyze image noise features and select appropriate filtering parameters to remove noise interference while preserving as much of the original image file's details as possible.

[0067] In some embodiments, the image processing algorithm may optionally include, but is not limited to, nonlocal mean denoising and Gaussian filtering.

[0068] Brightness equalization adjusts the brightness and contrast of an image to ensure that the brightness and darkness of different areas in the image file corresponding to the charging dock are more consistent. The brightness equalization algorithm automatically evaluates the grayscale distribution of the image, brightens overly dark areas, and suppresses overly bright areas, so that all interfaces and nodes can be observed under a clear and consistent brightness level.

[0069] In some embodiments, the brightness equalization algorithm may optionally include, but is not limited to, gamma transform, histogram equalization, and adaptive histogram equalization.

[0070] Geometric correction corrects image distortion caused by lens aberration or incorrect shooting angles in image acquisition equipment through camera calibration and image transformation. During preprocessing, a correction transformation matrix is ​​calculated using pre-calibrated camera parameters of the image acquisition equipment or identified inherent features of the cabinet. This matrix is ​​then used to straighten the image file corresponding to the charging / downloading cabin and restore it to a standard orthogonal view, ensuring accurate measurement of the position and shape of each interface.

[0071] During the region cropping process, the system automatically identifies the image regions corresponding to multiple charging and download cabinets in the image file, crops these regions, and removes irrelevant backgrounds. Specifically, the image regions corresponding to the multiple charging and download cabinets correspond to the image regions of the charging and download cabin.

[0072] In some embodiments, optionally, when the image data is a video stream, preprocessing includes extracting multiple frames of images from the video stream and performing image fusion (such as mean fusion, sharpness-based weighted fusion) to generate a higher quality, more information-rich synthetic image corresponding to the charging and downloading cabin for identification.

[0073] In some embodiments, the specific parameters of the preprocessing are optionally dynamically adjusted. First, the image quality is assessed, and the preprocessing algorithm and its parameters are adaptively selected based on the assessment results (such as image brightness variance, noise level, and blur).

[0074] In some embodiments, optionally, a pipelined parallel architecture is employed when multiple image files need to be processed. That is, while one image is undergoing geometric correction, the next image can be simultaneously subjected to brightness equalization, making full use of computing resources and significantly improving batch processing efficiency.

[0075] In some embodiments, optionally, such as Figure 3 As shown, the second node information corresponding to the recognition result file is obtained, including: Step S1102: Obtain at least one second external interface; Step S1104: Read the recognition result file according to the second external interface and determine the text content in the recognition result file; Step S1106: Determine the second cabinet number information and the second node information identified by the node server based on the text content; Among them, the second cabinet number information and the second node information are bound together.

[0076] In this embodiment, the second external interface corresponding to the server software reads the identification result file in a specific directory to determine the corresponding second cabinet number information and second node information, wherein the second node information includes the number of nodes and the node location.

[0077] Understandably, by calling the file parsing service through the second external interface, decoupling from the server software output is achieved. For different models of node servers or different versions of server software, the corresponding output formats may be completely different. By decoupling through the second external interface to call the file parsing service, it is only necessary to determine a corresponding external module or parser for each format, which can be compatible with all server file formats, greatly reducing the complexity and cost of system maintenance.

[0078] The second external interface includes, but is not limited to, the identification and reading module corresponding to the server software.

[0079] Specifically, through the second external interface, the command parsing service reads the recognition result files in a specific directory, determines the underlying details such as the file's encoding and format, and converts the content of the recognition result files into a unified, plain text or structured text format for output.

[0080] It converts physical files of various formats into standardized text data streams that can be processed by subsequent logic, thus masking the differences in file formats.

[0081] After receiving the text content, the external parsing service extracts structured information corresponding to the cabinet number, node, and interface from the text according to predefined rules or syntax. This information is known as the second cabinet number information and the second node information. The second cabinet number information indicates the cabinet number of the charging and downloading cabinet, while the second node information is used to parse the list of nodes identified by the server software. This information typically includes the node number, the location of the interface, and the corresponding interface status.

[0082] The cabinet number parsed from the identification result file is associated with the list of server nodes corresponding to the cabinet number, which ensures that accurate, item-by-item comparison of cabinet number can be performed in the subsequent comparison process, thus improving the accuracy of seismic data processing.

[0083] In some embodiments, the second external interface may be invoked either synchronously or asynchronously.

[0084] In some embodiments, the identification result file may be actively pushed to a specific directory by the node server, or actively retrieved by querying the database, calling the server application programming interface, etc.

[0085] In some embodiments, the second node information may include not only the number and location of nodes, but also the node status (e.g., battery level and health status), node number, device model, and firmware version parsed from the identification result file.

[0086] In some embodiments, optionally, the meaning and location of data fields can be dynamically determined by parsing headers or metadata in the recognition result file based on the text content, thereby ensuring compatibility with server files of different formats.

[0087] In some embodiments, optionally, such as Figure 4 As shown, the verification result is determined based on the information from the first node and the information from the second node, including: Step S1122: Obtain the mapping relationship between the second cabinet number information and the first cabinet number information; Step S1124: Match and compare the second node information and the first node information one by one according to the mapping relationship to determine the difference list. The difference list includes the location of unidentified nodes, the location of incorrectly identified nodes, and the difference in the number of nodes. Step S1126: Determine the verification result based on the information of unidentified node locations, incorrectly identified node locations, and differences in the number of nodes.

[0088] In this embodiment, a comparison result is generated by comparing the image recognition result corresponding to the image file with the parsing result of the recognition result file. The comparison result includes information on the location of unidentified nodes, the location of incorrectly identified nodes, and the difference in the number of nodes. The verification result is determined based on the information on the location of unidentified nodes, the location of incorrectly identified nodes, and the difference in the number of nodes, in order to determine whether the node data in the image file and the recognition result file are the same and the corresponding differences.

[0089] Understandably, maintenance personnel no longer need to check all interfaces in all charging and downloading cabins one by one. They can directly determine the verification results based on the report, which greatly reduces the investigation time for seismic data processing, improves the efficiency of seismic data processing, and avoids possible misjudgments from manual analysis.

[0090] Since image recognition and server recognition may use different identifiers to represent the same physical cabinet (e.g., image recognition identifies cabinet A, while the server records the cabinet number as "Cabinet_01"), a mapping table needs to be established to ensure comparisons are made for the same physical cabinet. Assuming the comparison is of the same charging / downloading cabinet, the system begins comparing each node location within the cabinet. It iterates through each interface location of the charging / downloading cabinet, checking if the image recognition and server recognition results match at the interface location. Unrecognized or incorrectly recognized nodes are marked, and the total number of nodes recognized by the image is compared with the total number of nodes recognized by the server to determine any discrepancies in node count.

[0091] For example, an unidentified node location refers to a situation where image recognition detects a node at a certain interface location, but the corresponding interface location in the server's recognition result is empty. This corresponds to an actual interface connection, but the server identifies it as a missed identification fault due to no interface connection.

[0092] For example, an incorrectly identified node location refers to a situation where, at a certain interface location, the server's identification result records a node, but the image recognition result finds that the corresponding interface location is not connected. The corresponding interface is actually not connected, but the server identifies it as a false report or incorrect identification failure that there is an interface connection.

[0093] In some embodiments, optionally, the association features between the image file and the recognition result file are dynamically established based on the cabinet serial number of the charging download cabinet, and mapping is performed based on the association features.

[0094] In some embodiments, optionally, a mapping is automatically established by comparing the unique features shared by the first node information and the second node information. When the unique feature is missing, the physical arrangement order of the charging and downloading cabinets in the charging and downloading cabin obtained by image recognition is matched with the logical order in the server results.

[0095] In some embodiments, the established mapping relationship may be periodically verified. If a large number of unmatched anomalies are found, the mapping relationship reconstruction process is automatically triggered to adapt to changes in the configuration of the charging and downloading cabinet on site.

[0096] In some embodiments, the one-to-one matching comparison may optionally include one or more of the following strategies: a match is considered to be made only when both the location and the node are completely identical; a match is considered to be made as long as the physical location is identical, ignoring differences in node numbers, for the purpose of quickly checking connectivity; or a similarity threshold may be set for matching node numbers, such as image recognition numbers which may have character errors.

[0097] In some embodiments, optionally, during the one-to-one matching and comparison process, not only the location and number of the nodes are compared, but also the status information of the nodes. The status information includes the interface power and health status of the node.

[0098] In some embodiments, optionally, after determining the verification result based on the first node information and the second node information, the method further includes: determining the location of the difference point based on the verification result, and conducting a point-to-point investigation of the difference point location; or reconnecting or marking the difference point location; or performing node data download, charging, and testing operations based on the verification result.

[0099] In this embodiment, the abnormal locations are identified by a fixed-point investigation based on the verification results. After eliminating the discrepancies, a series of seismic data processing tasks are performed, such as node data download, node charging, or node testing.

[0100] The specific anomalies listed in the verification results are converted into executable instructions. Maintenance personnel no longer need to blindly check all interfaces corresponding to the charging dock; instead, they can directly go to the designated charging cabinet to troubleshoot the corresponding interfaces. Based on the troubleshooting, specific repair or marking actions are performed to reconnect or mark the discrepancies.

[0101] For example, for a node with poor contact, unplug the connection at the interface and then plug it back in to try to restore the connection.

[0102] The server only sends data download commands to nodes that match the verified data, ensuring the integrity of data collection and avoiding errors or timeouts due to downloading data from unidentified nodes. The server prioritizes charging nodes that match the verified data and have low battery levels, while suspending charging for nodes at discrepancies until further investigation, thereby improving power safety and work efficiency.

[0103] In some embodiments, optionally, a group of nodes that have been verified is marked as a batch of available data, and batch downloading and processing of the available data batch is initiated.

[0104] In some embodiments, optionally, the power distribution strategy of the charging cabinet can be dynamically adjusted based on the node power difference information to prioritize the normal use of nodes with low power.

[0105] In some embodiments, optionally, after performing node repair operations such as repeated connection, a new round of image acquisition and rapid verification is automatically triggered to verify whether the repair operation corresponding to the node is effective and to form a closed-loop feedback.

[0106] In one specific embodiment, the present invention optionally provides a device for verifying the number and location of nodes in a charging and downloading cabin, and also provides a method for verifying the number and location of nodes in a charging and downloading cabin using the above device (i.e., a method for determining the location and number of nodes).

[0107] like Figure 9 As shown, the charging and downloading cabin node quantity and location verification device 100 includes an image input module 1, an algorithm module 2 (core algorithm module), a result output module 3, an auxiliary data input module 4, and an image acquisition device 5. The image input module 1 is connected to the algorithm module 2, the result output module 3 is connected to the algorithm module 2, the auxiliary data input module 4 is connected to the algorithm module 2, and the image acquisition device 5 is connected to the image input module 1.

[0108] The image input module 1 includes an image acquisition submodule 11, an image loading submodule 12, and an image preprocessing submodule 13. The image acquisition submodule 11 is used to control an external image acquisition device to acquire images, the image loading submodule 12 is used to load image files, and the image preprocessing submodule 13 is used to perform preprocessing such as noise reduction and cropping on the image files. Algorithm module 2 includes image recognition submodule 21, auxiliary data extraction submodule 22 and data processing submodule 23. Image recognition submodule 21 is used to recognize content in the image, including cabinet number, node count, node position and other content. Data processing submodule 23 is used to process the content recognized by image recognition submodule 21 and the content extracted by auxiliary data extraction submodule 22. The result output module 3 includes a result display submodule 31 and a file result output submodule 32. The result display submodule 31 is used to display the results in a graphical user interface (GUI), and the file result output submodule 32 is used to output the results in a file format. Image acquisition device 5 includes multiple image acquisition units 51, which are installed in the node instrument charging and downloading cabin; The auxiliary data input module 4 includes a server software recognition result reading submodule 41. The server software recognition results include image results and text results. The server software recognition result reading submodule 41 is used to read the server software recognition results.

[0109] Among them, the auxiliary data extraction submodule 22 is used to extract the reading results of the server software identification result reading submodule 41, including cabinet number, node count, node position and other contents.

[0110] This invention also provides a method for verifying the location and quantity of nodes in a charging and downloading container. A schematic diagram of the node's workflow from the upper cabinet to the lower cabinet is shown below. Figure 8 As shown, the workflow of a node moving from the upper shelf to the lower shelf includes: Step S300: Node placement; Step S302: Device identification and verification; Step S304: Remove nodes at abnormal locations; Step S306: Data download, node charging, node testing; Step S308: Node removal from cabinet.

[0111] The method for verifying the location and quantity of charging and downloading container nodes is based on the aforementioned device for verifying the location and quantity of charging and downloading container nodes. Specifically: S1. Image acquisition, which includes static graphics and images, as well as dynamic video and animation content; In some embodiments, the image acquisition medium may optionally be a portable image acquisition device, such as a mobile phone or a camera; In some embodiments, the image acquisition medium may optionally be an image acquisition device that is compatible with the node number and location verification device of the node instrument charging and downloading cabin, and the image acquisition unit of the image acquisition device 5 is installed inside the charging and downloading cabin; In some embodiments, optionally, when the image acquisition medium is a portable image acquisition device, after acquiring the image file, the acquired image file is placed in a specific directory; In some embodiments, when the image acquisition medium is an image acquisition device that is compatible with the node instrument charging and downloading cabin node number and location verification device, the image acquisition submodule 11 controls the image acquisition unit to acquire image files and puts the acquired image files into a specific directory; In some embodiments, the above-mentioned image file may be one or more, and the present invention does not limit it; In some embodiments, the image acquisition unit may be one or more, and the present invention does not limit the number of units. S2. Image Input: Place the acquired image file into a specific directory; S3. Auxiliary data input: Place the server software recognition result file into a specific directory; In some embodiments, the server software identification result file may optionally be an online node list file saved by the server software; In some embodiments, the server software identification result file may optionally be an online node diagram file saved by the server software; In some embodiments, optionally, on some open platforms, the auxiliary data input module of the above-mentioned node instrument charging download cabin node number and location verification device can directly read the online node list of the server software, so that it is not necessary to put the server software identification result file into a specific directory; S4. Select the output directory for the results; S5. Confirm that the requirements of S1 to S4 are met, and begin the identification and comparison process; S6. The image loading submodule begins to read the image files in the specific directory in S2 above. The image preprocessing module performs intelligent noise reduction, brightness equalization, geometric correction, and region cropping on the image files, and passes the processed results to the core algorithm module. In some embodiments, optionally, on some open platforms, the auxiliary data input module 4 of the above-mentioned charging download cabin node number and location verification device can directly read the online node list of the server software and pass the reading result to the core algorithm module, so that the server software identification result reading submodule does not need to start reading the server software identification result file in the specific directory mentioned above; S7. The server software identification result reading submodule starts reading the server identification result file in the specific directory in S3 above, and passes the reading result to the core algorithm module; In some embodiments, optionally, the cabinet number in the identification content can be identified by OCR visual recognition technology or by identifying the image source; the present invention does not impose any particular limitation. S8. The image recognition submodule uses artificial intelligence (AI) visual recognition technology to perform image recognition on the results transmitted in S6 above. The recognition content includes cabinet number, node count, node position, etc. S9. The auxiliary data extraction submodule extracts the results passed in S7 above, including cabinet number, node count, node position, etc. S10. The data processing submodule compares the results from S8 and S9 above and generates comparison results, including AI visual recognition results, server software recognition results, whether the two are the same and the differences, and transmits the comparison results to the result output module. S11. After receiving the results in S10 above, the result output module displays the results in the GUI interface, and the file result output module saves the results as files in the result output directory in S4 above. S12. After removing nodes at abnormal locations based on the results obtained in S11, perform a series of tasks such as node data download, node charging, and node testing.

[0112] When the image acquisition medium of the charging and downloading cabin node quantity and location verification device is an image acquisition device that is matched with the charging and downloading cabin node quantity and location verification device, the image acquisition submodule controls the image acquisition device to view images in real time, monitor the status of the node instrument charging and downloading cabin in real time, support multi-terminal visualization display and alarm linkage mechanism, and remotely monitor the status of the charging and downloading cabin in real time to ensure the safety and reliability of data downloading and node charging process.

[0113] It is understood that this invention uses AI image recognition technology to quickly and accurately identify the number and location of nodes on the charging and downloading cabinet. Through data comparison, it can quickly and accurately locate and mark abnormal nodes, reduce manual review time, reduce human error, and improve overall work efficiency. The image recognition algorithm is improved by training with AI datasets, enhancing the model's generalization ability. By combining multi-sensor data and dynamically adjusting recognition parameters, the recognition accuracy in complex environments is significantly improved, the false recognition rate is reduced, and the system stability and reliability are enhanced. By integrating multi-sensor networks and environmental data fusion technology, a multi-dimensional sensing system is constructed to monitor environmental changes in real time, provide comprehensive data analysis, enhance data analysis capabilities, and assist in decision-making. It enables real-time monitoring of the charging and downloading status of node instruments, supports multi-terminal visualization and alarm linkage mechanisms, and allows for remote real-time monitoring of the charging and downloading status of the node, ensuring the safety and reliability of data downloading and node charging processes.

[0114] like Figure 5As shown, a second aspect of the present invention provides a node location and quantity determination device 900, which includes: an image input module 902 for acquiring image data of multiple charging and downloading cabinets corresponding to a charging and downloading cabin; a server identification module 904 for acquiring identification results of node servers; a data storage module 906 for determining an image file and an identification result file based on the image data and identification results; an image identification module 908 for acquiring first node information corresponding to the image file; an identification result reading module 910 for acquiring second node information corresponding to the identification result file; and an algorithm module 912 for determining a verification result based on the first node information and the second node information, wherein the verification result is used to determine whether the actual insertion location and quantity of the node seismic instrument are consistent with the identification result.

[0115] The node location and quantity determination device 900 provided by this invention realizes a method for determining node location and quantity, enabling rapid determination of node quantity and location, shortening verification time, and improving seismic data processing efficiency.

[0116] like Figure 6 As shown, the third aspect of the present invention provides a charging and downloading container 200, which includes multiple charging and downloading cabinets 202, each equipped with multiple interfaces 2022 for connecting node seismic instruments 300, and the charging and downloading cabinets 202 are communicatively connected to a node server 400; the charging and downloading container also includes a device for determining the location and number of nodes.

[0117] The charging and downloading cabin 200 provided by this invention connects to the node seismic instrument 300 through the interface 2022 corresponding to multiple charging and downloading cabinets 202. By using AI image recognition and multi-sensor data fusion, a method for quickly determining the number and location of nodes is achieved, thereby shortening the verification time and improving the efficiency of seismic data processing.

[0118] A fourth aspect of this invention provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the embodiments of the method for determining the location and number of nodes described above, achieving the same technical effects. To avoid repetition, these details will not be repeated here. Furthermore, the readable storage medium improves the data storage capacity and data processing speed of each step in the method for determining the location and number of nodes in this application.

[0119] The methods can be implemented in various ways depending on specific features and / or example applications. For example, these methods can be implemented through a combination of hardware, firmware, and / or software. For instance, in a hardware implementation, the processor can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, electronic devices, other device units for performing the functions described above, and / or combinations thereof.

[0120] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing, but is not limited thereto. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital universal disk (DVD), memory cards, floppy disks, encoding mechanical devices (e.g., punched cards or grooves with raised structures for recording instructions), and any suitable combination of the foregoing. The computer-readable storage medium used herein should not be construed as the transmission of signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media, or electrical signals transmitted through wires.

[0121] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0122] A fifth aspect of this invention provides a chip including a processor and a communication interface, the communication interface and the processor being coupled. The processor is used to run programs or instructions to implement the various processes of the embodiments of the method for determining the location and number of nodes described above, and can achieve the same technical effects. To avoid repetition, these will not be repeated here. Furthermore, the chip improves the data processing speed of each step in the method for determining the location and number of nodes in this application.

[0123] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily used to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0124] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0125] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0126] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method of determining the position and number of nodes, characterized in that, A charging download shelter, the charging download shelter comprises a plurality of charging download cabinets, the charging download cabinet is equipped with a plurality of interfaces, the interface is used for connecting node seismic instrument, the charging download cabinet is connected with node server, the node position and the number of determination method comprises: Obtain the image data of the plurality of charging download cabinets corresponding to the charging download shelter; Obtain the identification result of the node server; Determine the image file and the identification result file according to the image data and the identification result; Obtain the first node information corresponding to the image file; Obtain the second node information corresponding to the identification result file; According to the first node information and the second node information, the checking result is used to determine whether the node position and the number of the node seismic instrument actually inserted are consistent with the identification result.

2. The method of claim 1, wherein, The first node information corresponding to the image file is obtained, comprising: At least one image acquisition device is obtained; According to the image recognition of the image acquisition device on the image file, the first cabinet number information and node information corresponding to the charging download cabinet are determined, the node information includes node physical position and node quantity, or at least one first external interface is obtained, and the first cabinet number information corresponding to the charging download cabinet is determined according to the mapping relationship between the image acquisition device and the charging download cabinet; According to the node physical position and the node quantity, the first node information is determined; Wherein, the first cabinet number information and the first node information are bound.

3. The method of claim 2, wherein Before the image recognition of the image acquisition device on the image file, comprising: The image file is pretreated, and the pretreatment includes one or more of intelligent denoising, brightness equalization, geometric correction and region clipping.

4. The method of claim 1, wherein, The second node information corresponding to the identification result file is obtained, comprising: At least one second external interface is obtained; According to the second external interface, the text content in the identification result file is determined; According to the text content, the second cabinet number information and the second node information identified by the node server are determined; Wherein, the second cabinet number information and the second node information are bound.

5. The method of claim 4, wherein According to the first node information and the second node information, the checking result is determined, comprising: The mapping relationship between the second cabinet number information and the first cabinet number information is obtained; According to the mapping relationship, the second node information and the first node information are matched and compared one by one to determine the difference list, the difference list includes un-identified node position, error-identified node position and node quantity difference information; According to the un-identified node position, the error-identified node position and the node quantity difference information, the checking result is determined.

6. The method of determining the number of nodes and their positions according to any one of claims 1 to 5, characterized in that, After the checking result is determined according to the first node information and the second node information, further comprising: According to the checking result, the difference point position is determined, and the difference point position is fixedly investigated;Or The difference point position is reconnected or marked;Or According to the checking result, node data download, charging, test operation is carried out.

7. A device for determining the position and number of nodes, characterized in that Comprising: An image input module is configured to acquire image data of a plurality of charging and downloading cabinets corresponding to the charging and downloading shelter. A server identification module is configured to acquire an identification result of the node server. A data storage module is configured to determine an image file and an identification result file according to the image data and the identification result. An image identification module is configured to acquire first node information corresponding to the image file. An identification result reading module is configured to acquire second node information corresponding to the identification result file. An algorithm module is configured to determine a checking result according to the first node information and the second node information, the checking result being used to determine whether the node seismic instrument actually inserted node positions and quantities are consistent with the identification result.

8. A charge download shelter, comprising: The charging and downloading shelter comprises a plurality of charging and downloading cabinets, each of which is equipped with a plurality of interfaces for connecting node seismic instruments, and each of which is in communication connection with a node server. The charging and downloading shelter further comprises the node position and quantity determination device according to claim 7.

9. A readable storage medium, characterized by, A readable storage medium stores programs or instructions, which are executed by a processor to implement the steps of the node position and quantity determination method according to any one of claims 1 to 6.

10. A chip, characterized by The chip comprises a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the steps of the node position and quantity determination method according to any one of claims 1 to 6.

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