Port monitoring method, device and equipment based on data cross validation

Abstract

The embodiment of the invention provides a port monitoring method, device and equipment based on data cross validation. The method comprises the following steps: in response to acquired first environment data and second environment data about a target port area, storing the first environment data and the second environment data in a database; in response to determining that the abnormal data exists, calling normal data from a database based on the spatio-temporal information matched with the abnormal data and the type of the data acquisition device, and performing comparison verification on the abnormal data by using a preset data cross validation rule to obtain abnormal judgment information matched with a verification result; updating a three-dimensional digital map about the target port area based on the position information of the plurality of static data acquisition devices, the dynamic position information of the plurality of mobile data acquisition devices and the abnormality judgment information; and pushing the three-dimensional digital map to a user terminal for monitoring display. By using the control method disclosed by the embodiment of the invention, the accuracy and the operation and maintenance efficiency of port monitoring can be remarkably improved.

Description

Technical Field

[0001] This disclosure relates to the fields of digital twin and industrial Internet of Things technologies, specifically to a port monitoring method, apparatus, and equipment based on data cross-validation. Background Technology

[0002] As a core hub of the global supply chain, the operational efficiency and intelligent management level of ports directly affect the overall effectiveness of the logistics chain. Currently, port management relies heavily on basic geographic information platforms for digital support, but existing technological solutions have significant shortcomings in data integration, system architecture, and functional implementation.

[0003] In terms of data infrastructure, the system suffers from inconsistent geographic coordinates, scattered data sources, and varying standards, resulting in low data accuracy, difficulties in integration and sharing, and significantly increasing the complexity and cost of system operation and maintenance.

[0004] In terms of system architecture and processing mechanisms, existing solutions mostly adopt a centralized processing model with central equipment, resulting in low system resource utilization and poor processing efficiency. Simultaneously, there are blind spots in data acquisition and monitoring capabilities: when fixed acquisition devices malfunction, environmental status information for that area cannot be effectively obtained, posing a security risk. More significantly, current solutions completely separate the acquisition and processing of dynamic and static data, configuring independent functions for mobile and fixed devices respectively. This fragmented architecture not only leads to redundant deployment of acquisition equipment but also results in a significant waste of system computing power. Because dynamic and static data cannot be analyzed collaboratively and cross-verified, it is difficult to form a unified situational awareness.

[0005] The aforementioned technical deficiencies make it difficult for ports to achieve refined management of equipment. The 3D map model cannot accurately and in real time reflect the details of the equipment and the overall operating status, which restricts the further improvement of port operation efficiency and safety management level. Summary of the Invention

[0006] To address the aforementioned technical problems, the present disclosure provides a solution. Embodiments of this disclosure offer a port monitoring method, apparatus, and equipment based on data cross-validation.

[0007] According to a first aspect of the present disclosure, a port monitoring method based on data cross-validation is provided, wherein the port monitoring method includes: In response to obtaining first and second environmental data about the target port area, the first and second environmental data are stored in the database; The target port area is the port area that needs to be monitored. The first environmental data is collected by multiple static data acquisition devices in the target port area, and the second environmental data is collected by multiple mobile data acquisition devices in the target port area. The mobile data acquisition devices are deployed on mobile carriers in the target port area. In response to determining that there is abnormal data in the first environmental data or the second environmental data, based on the spatiotemporal information of the abnormal data and the type of data acquisition device, the same type of normal data collected by another type of data acquisition device in the same spatiotemporal context is retrieved from the database, and the abnormal data is compared and verified using a preset data cross-validation rule to obtain abnormal judgment information of the matching verification result. Based on the location information of the multiple static data acquisition devices, the dynamic location information of the multiple mobile data acquisition devices, and the anomaly judgment information, update the three-dimensional digital map of the target port area; The three-dimensional digital map is pushed to the user terminal for monitoring and display.

[0008] According to a second aspect of the present disclosure, a port monitoring device based on data cross-validation is provided, wherein the port monitoring device includes: The data storage unit is configured to: in response to acquiring first environmental data and second environmental data about the target port area, store the first environmental data and second environmental data in a database; The target port area is the port area that needs to be monitored. The first environmental data is collected by multiple static data acquisition devices in the target port area, and the second environmental data is collected by multiple mobile data acquisition devices in the target port area. The mobile data acquisition devices are deployed on mobile carriers in the target port area. The cross-validation judgment unit is configured to: in response to determining that there is abnormal data in the first environmental data or the second environmental data, based on the spatiotemporal information of the abnormal data and the type of data acquisition device, retrieve the same type of normal data collected by another type of data acquisition device in the same spatiotemporal environment from the database, and use preset data cross-validation rules to compare and verify the abnormal data to obtain abnormal judgment information of matching verification results. The map update unit is configured to update the three-dimensional digital map of the target port area based on the location information of the plurality of static data acquisition devices, the dynamic location information of the plurality of mobile data acquisition devices, and the anomaly judgment information. The data push unit is configured to push the three-dimensional digital map to the user terminal for monitoring and display.

[0009] According to a third aspect of the present disclosure, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the port monitoring method based on data cross-validation as described in the present disclosure.

[0010] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, the storage medium storing a computer program for executing the port monitoring method based on data cross-validation as described in the present disclosure.

[0011] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program, when executed by a processor, implements the port monitoring method based on data cross-validation as described in the present disclosure.

[0012] As described above, the port monitoring method based on data cross-validation provided in this disclosure effectively solves problems such as data silos, redundant computing power, and extensive management in port monitoring by constructing a hierarchical system architecture and establishing a dynamic and static data cross-validation mechanism. It ensures the reliability of data sources through equipment classification and control, intelligently identifies real-world environmental anomalies and equipment failures using spatiotemporal correlation rules, and finally achieves accurate visualization in a three-dimensional digital map. This significantly improves the accuracy, operational efficiency, and intelligent management level of port monitoring, providing an efficient technical path for realizing port digital twins. Attached Figure Description

[0013] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0014] Figure 1 This is a flowchart illustrating a port monitoring method based on data cross-validation provided in an exemplary embodiment of this disclosure; Figure 2 This is a schematic diagram of the system architecture that supports the operation of the port monitoring method based on data cross-validation, provided by an exemplary embodiment of this disclosure; Figure 3 This is a public announcement Figure 1 An exemplary flowchart of the port monitoring method based on data cross-validation provided in the embodiments. Figure 1 ; Figure 4 This is a public announcement Figure 1An exemplary flowchart of the port monitoring method based on data cross-validation provided in the embodiments. Figure 2 ; Figure 5 This is a public announcement Figure 1 An exemplary flowchart of the port monitoring method based on data cross-validation provided in the embodiments. Figure 3 ; Figure 6 This is a schematic diagram of the structure of a port monitoring device based on data cross-validation provided in an exemplary embodiment of this disclosure; Figure 7 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed herein. Detailed Implementation

[0015] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present disclosure, and not all embodiments of the present disclosure. It should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0016] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0017] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0018] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0019] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0020] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0021] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0022] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0023] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0024] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0025] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0026] Overview of the inventive concept The inventive concept disclosed herein lies in: constructing a... Figure 2 The system architecture shown, comprising a five-layer structure—acquisition layer, transmission layer, data layer, support layer, and application layer—serves as the "skeleton" for a "port monitoring solution based on data cross-validation." The core of this solution is a dynamic and static data cross-validation mechanism: when any data source detects an anomaly, it automatically retrieves another type of data with spatiotemporal correlation for comparison, intelligently distinguishing between real-world environmental anomalies and equipment malfunctions. This mechanism operates on the aforementioned architecture, relying on the acquisition layer to obtain diverse data, achieving collaborative analysis through data services in the support layer, and ultimately presenting reliable decision-making information in a 3D digital map in the application layer. This fundamentally solves the problems of data fragmentation, computational redundancy, and inefficient management, thereby significantly improving monitoring accuracy and management efficiency.

[0027] Based on the above inventive concept, this disclosure can provide specific solutions as described in the following embodiments.

[0028] Example 1 Figure 2 This is a schematic diagram of a system (geographic information digitization platform) architecture provided in an exemplary embodiment of this disclosure. This architecture can be deployed through software, hardware, or a combination of both. (Refer to...) Figure 2 The system architecture adopts a layered structure, including a data acquisition layer, a transmission layer, a data layer, a support layer, and an application layer. The data acquisition layer accesses internet data and collects both dynamic and static data from within the port. The transmission layer transmits data to a data server via the port's dedicated network and the internet. The data layer stores vector data and other business data. The support layer, as the platform's core, provides information services such as data retrieval and system operation logs. The application layer provides users with business applications such as map browsing and information querying.

[0029] Figure 1 This is a schematic diagram of a port monitoring method based on data cross-validation provided in an exemplary embodiment of this disclosure. This port monitoring method based on data cross-validation can be deployed or subordinate to... Figure 2 The system architecture shown is executed by the server. The server may include a cloud service platform or a locally deployed server.

[0030] Specifically, refer to Figure 1 The port monitoring method based on data cross-validation includes: S110. In response to obtaining first environmental data and second environmental data about the target port area, store the first environmental data and second environmental data in the database.

[0031] The target port area refers to the port area that needs to be monitored.

[0032] As an optional implementation, the data acquisition and storage process described in step S110 is provided by the appendix... Figure 1 The system architecture shown consists of a data acquisition layer, a transmission layer, and a data layer that work together.

[0033] Specifically, the "acquiring of first and second environmental data regarding the target port area" is achieved in the following ways: The first environmental data is collected by static data acquisition devices deployed at fixed locations in the target port area. These static data acquisition devices may include, but are not limited to, temperature and humidity sensors, wind sensors, oxygen concentration sensors, and high-definition cameras installed at wharves, storage yards, and anchorages. These static data acquisition devices continuously collect environmental data at a preset sampling frequency (e.g., once per minute).

[0034] The second environmental data is collected by mobile data acquisition devices deployed on mobile carriers. These mobile carriers mainly refer to port machinery (simply put, mobile machinery is a general term for vehicles and equipment that perform short-distance, mobile operations within the port area. For example, it can include manually driven patrol equipment, manually driven loading and unloading equipment, unmanned patrol equipment, and unmanned loading and unloading equipment). These mobile data acquisition devices also have embedded temperature and humidity sensors, wind sensors, etc., and, leveraging their mobility, collect environmental data about their location in real time and dynamically during operations. Simultaneously, their built-in positioning modules (such as GPS / BeiDou modules) assign precise time and spatial location tags to each piece of environmental data.

[0035] Specifically, the step of "storing the first environmental data and the second environmental data in a database" is achieved in the following way: As attached Figure 1As shown, the dynamic and static environmental data acquired by the acquisition layer are transmitted through the transmission layer (including the port's dedicated network and the public Internet) and finally sent to the data server in the data layer.

[0036] The data layer is configured with a time-series database (for efficiently storing time-series sensor data) and a spatial database (for storing data strongly correlated with spatial location). After format standardization and coordinate unification, the first and second environmental data are categorized and stored in the aforementioned databases, providing a unified and standardized data source for subsequent cross-validation.

[0037] In summary, step S110 achieves automated, standardized collection and centralized storage of dynamic and static environmental data across the entire port area, laying the data foundation for the execution of the entire method.

[0038] As another optional implementation, before performing step S110, the mobile carrier is a flow machine entering the target port area, and the port monitoring method further includes: In response to receiving the port entry registration information of the flow machine equipment, the flow machine equipment is classified into Class I equipment or Class II equipment using the equipment classification service.

[0039] The first type of equipment is authorized to operate within the target port area and is equipped with the mobile data acquisition device; the second type of equipment is restricted to operating in a designated quiet area within the target port area and is equipped with a positioning device.

[0040] As an optional example, see [reference] Figure 2 as well as Figure 3 The above steps can be achieved in the following way: S310. In response to determining that the flow machine equipment is port-owned equipment based on the port entry registration information, the flow machine equipment is classified as the first type of equipment.

[0041] Specifically, refer to Figure 2 Port staff input port entry registration information for the conveyor belt equipment using application-layer terminals (such as PCs or handheld devices). This information is then sent to the server via the transport layer. The equipment management service in the support layer is invoked, comparing the received equipment identifiers (such as equipment IDs and license plate numbers) with the "port asset database" in the data layer.

[0042] If the comparison is successful, the flow machine equipment is confirmed to be a port-owned asset. The equipment management service then marks its classification status as "Class 1 equipment" and writes the classification result, along with detailed equipment information (such as model and department), into the "Port Equipment Management Database" in the data layer. At the same time, the system generates a task instruction to notify staff to install a port-integrated dynamic data acquisition device, including a dynamic data acquisition module and a positioning module, on the equipment.

[0043] S320. In response to determining, based on the port entry registration information, that the flow machine equipment is not owned by the port and that the non-owned equipment has agreed to be registered, classify the non-owned equipment as the first type of equipment.

[0044] Specifically, refer to Figure 2 Once the equipment management service confirms through comparison that the flow machine equipment is not owned by the port, it will initiate a second inquiry through the application layer interface (such as sending a prompt to the driver's handheld terminal), asking whether the user agrees to register and install the data acquisition equipment.

[0045] If the other party confirms "agree to registration" through the interface, the response signal is returned to the support layer. The equipment management service marks its classification status as "Class 1 equipment" and stores the equipment information (such as the company and contact person) and registration agreement status in the "Port Equipment Management Database". The subsequent process is the same as S310, that is, to install a complete set of dynamic data acquisition equipment and authorize it to operate freely within the port area.

[0046] S330. In response to determining, based on the port entry registration information, that the flow machine equipment is not owned by the port and that the non-owned equipment does not agree to registration, the non-owned equipment is classified as the second type of equipment.

[0047] Specifically, in the second inquiry, if the other party confirms "disagrees to registration," the device management service will mark its classification status as "Class II device" and store it in the database. The system will then generate a guidance instruction to guide the device to the designated rest area through the application layer interface.

[0048] Simultaneously, the system generates a task for staff to temporarily attach a simplified positioning device (typically containing only a positioning module and a data transmission module) to the equipment. The location data of this device will be captured by the acquisition layer and transmitted back through the transmission layer for centralized, restricted monitoring of such equipment on a 3D map.

[0049] S120. In response to determining that there is abnormal data in the first environmental data or the second environmental data, based on the spatiotemporal information of the abnormal data and the type of data acquisition device, retrieve the same type of normal data collected by another type of data acquisition device in the same spatiotemporal environment from the database, and use a preset data cross-validation rule to compare and verify the abnormal data to obtain abnormal judgment information of the matching verification result.

[0050] As an optional implementation method, refer to Figure 2 as well as Figure 4 Step S120 can be achieved in the following way: S1210. In response to determining that the abnormal data comes from the static data acquisition device, retrieve similar data reported by the mobile data acquisition device that passed through the area surrounding the static data acquisition device within a preset historical time period from the database as the normal data, and determine the consistency between the normal data and the abnormal data.

[0051] As an optional example, a specific implementation of step S1210 (static data anomaly, dynamic data verification) includes: Ia1, Anomaly Triggering and Service Invocation. Specifically, when the monitoring module in the data layer detects that the data reported by a static data acquisition device (e.g., the temperature sensor at Dock 3) continuously exceeds a threshold (e.g., 60°C), it is determined to be an anomaly. This event triggers the data verification service in the support layer.

[0052] Ia2, Spatiotemporal Correlation Query. Specifically, the data verification service generates a query command based on the spatiotemporal information of the abnormal data (i.e., the location of the "Dock No. 3 Temperature Sensor" and the time period in which the abnormality occurred). This command is sent to the spatiotemporal database of the data layer, and the query content is: "Retrieve temperature data (same type data) reported by all mobile data acquisition devices (i.e., A / B type flow machines) that have passed through the 50-meter area (surrounding area) around the Dock No. 3 sensor within the last 20 minutes (preset historical time period)."

[0053] Ia3. Data Comparison and Verification. Specifically, the data verification service receives several pieces of mobile device temperature data (i.e., the "normal data") returned by the query. Then, a pre-defined cross-validation rule is applied to determine consistency. The core of this rule is to first determine whether the static data itself is significantly abnormal, that is, to calculate the difference between the abnormal data value V_r collected by the static data acquisition device R and the average value V_avg of the normal data collected by its neighboring similar devices during the same period, and then compare it with a pre-defined difference threshold vector. Compare. If (Where i corresponds to a specific data type), and if the retrieved mobile data also supports this abnormal trend, then it is determined to be an environmental anomaly. If However, if the retrieved mobile data does not show consistent anomalies, it is determined that the static data acquisition device R is faulty. If the fluctuation is normal, it will not trigger a subsequent high-level alarm.

[0054] Ia4. Generate Judgment Information. Specifically, based on the comparison and verification results, generate corresponding anomaly judgment information. If the anomaly is determined to be environmental, generate "Environmental Anomaly Confirmation" information. If the anomaly is determined to be device malfunction, generate "Static Data Acquisition Device R Suspected Fault" information.

[0055] It should be noted that the "Class A flow machines and Class B flow machines" mentioned in the above and the following embodiments belong to the "first category of equipment"; and the "Class C flow machine" belongs to the "second category of equipment".

[0056] S1220. In response to determining that the abnormal data comes from the mobile data acquisition device, retrieve from the database the same type of data reported by the static data acquisition device within a preset time period within a preset geographical range of the real-time location where the mobile data acquisition device reported the abnormal data, and determine the consistency between the normal data and the abnormal data.

[0057] As an optional example, a specific implementation of step S1220 (dynamic data anomaly, static data verification) includes: Ib1. Exception Triggering and Service Invocation. Specifically, when a mobile data acquisition device on an operating forklift (Class A flow machine) reports a sudden drop in oxygen concentration at its location (abnormal data), the system triggers a data verification service.

[0058] Ib2. Spatiotemporal Correlation Query. Specifically, the data verification service first obtains the precise GPS coordinates (P1) and time when the forklift reported the anomaly. Then, it initiates a query to the spatial database of the data layer: "Search for the existence of a static data acquisition device (K) within a 10-meter radius of coordinate P1 (preset geographical range), and retrieve oxygen concentration data (similar data) reported by device K within the last 10 minutes (preset time period)." Ib3, Data Comparison and Verification. Specifically, After receiving the historical data sequence V_k(t) from the static device K, the data verification service applies cross-validation rules for evaluation. These cross-validation rules may include: persistence comparison. That is, analyzing the data change amplitude ΔV_k of device K in the recent period (e.g., 20 minutes). This change amplitude is then compared with a preset persistence threshold vector. and Compare them. If the dynamic data V_m is abnormal, and the static data change magnitude meets the requirements... or If the dynamic data V_m is abnormal, but the change in static data does not exceed the threshold, then the reading of the mobile data acquisition device is determined to be abnormal.

[0059] 1b4. Generate Judgment Information. Specifically, based on the comparison and verification results, generate corresponding anomaly judgment information. If the anomaly is determined to be environmental, generate the message "Local environmental anomaly, requiring emergency handling." If the anomaly is determined to be device reading, generate the message "Mobile data acquisition device reading abnormal, requiring attention."

[0060] As described above, through the implementation of S1210 and S1220, the system links the originally isolated dynamic and static data, and uses the proximity of space and time to perform mutual verification, which greatly improves the accuracy of anomaly judgment, effectively distinguishes between real environmental events and equipment failures, and realizes intelligent operation and maintenance and monitoring.

[0061] As an optional example, the first environmental data and the second environmental data include at least: temperature data, humidity data, wind data, gas concentration data, and image information.

[0062] S130. Based on the location information of the plurality of static data acquisition devices, the dynamic location information of the plurality of mobile data acquisition devices, and the anomaly judgment information, update the three-dimensional digital map of the target port area.

[0063] As an optional implementation, the anomaly judgment information includes at least one of the following: a conclusive judgment indicating the authenticity of an anomaly event; location information for locating the spatial location where the anomaly occurred; and confidence information characterizing the reliability of the judgment.

[0064] As an optional implementation method, refer to Figure 2 Step S130, "updating the 3D digital map of the target port area," may include: dynamically displaying the real-time location of the mobile data acquisition device on the 3D digital map using a first visual identifier, statically displaying the location of the static data acquisition device using a second visual identifier, and marking the location corresponding to the anomaly judgment information using a third visual identifier. This update process is initiated by the application layer's business logic, calling the support layer's 3D modeling and data services to integrate, render, and ultimately present various types of information in the data layer—an automated process. Its core lies in transforming abstract database records into intuitive visual elements within a 3D scene.

[0065] The specific implementation includes the following steps: St1, Service Invocation and Data Integration.

[0066] Specifically, when the 3D map needs to be updated (for example, when new location data or anomaly detection information is received), the port monitoring application in the application layer will call the 3D modeling service in the support layer.

[0067] The service then invokes the data retrieval service to retrieve the following latest information in parallel from the unified database of the data layer: static location information, such as the preset geographic coordinates (latitude and longitude) and model information of all static data acquisition devices obtained from the "Equipment Asset Library"; dynamic location information, such as the real-time latitude and longitude coordinates with timestamps reported by the positioning module of all Class A and Class B mobile data acquisition devices in the "Real-time Location Database"; and anomaly judgment information, such as the conclusions generated by the data verification service obtained from the "Anomaly Event Library", including anomaly type (environmental anomaly / equipment failure), precise location (static device ID or dynamic location coordinates), and confidence level.

[0068] St2, 3D scene mapping and rendering.

[0069] Specifically, the 3D modeling service maps the acquired data into a 3D map engine, and the steps may include: For static data acquisition devices, a second visual identifier (e.g., a gray, fixed sensor icon) can be rendered at their corresponding fixed coordinate location. Clicking this icon allows you to view device details and the real-time data stream.

[0070] For mobile data acquisition devices, a first visual identifier (e.g., a green, movable truck or forklift model) can be rendered at their real-time reported coordinates. This identifier will move smoothly on the map as the location information is updated, forming a motion trajectory.

[0071] For anomaly assessment information, a third visual identifier can be used to highlight the location where the anomaly occurred (whether it's a static device location or an anomaly reported by a dynamic device). The shape and color of this identifier change dynamically according to the anomaly content. Specifically, for example, for a conclusive assessment of "environmental anomaly," a flashing red warning icon (such as a flame icon indicating high temperature, or a gas leak icon indicating abnormal oxygen concentration) can be used; for a conclusive assessment of "equipment malfunction," a static yellow exclamation mark icon can be used; for confidence level information, the transparency or size of the visual identifier can be used to represent it. For example, high-confidence anomalies use opaque, large-sized icons; low-confidence anomalies use semi-transparent, small-sized icons to prompt management personnel to pay close attention.

[0072] Through the above updates, the 3D digital map provided to users by the application layer has rich interactive functions. The 3D map set in the platform can display ship models in the port in real time. Users can click on the ship model to pop up a detailed information window, which includes ship name, estimated arrival time, arrival time, planned departure time, port, wharf, anchorage, berth, ship dynamics, and cargo type and volume information. Corresponding application terminal devices include PCs, laptops, large screens, etc.

[0073] S140. The three-dimensional digital map is pushed to the user terminal for monitoring and display.

[0074] As an optional implementation, the rendered 3D map scene is pushed to various user terminals (such as PCs, monitoring screens, and mobile laptops) in the application layer via the network of the transport layer in the form of a data stream. This update process is near real-time, ensuring that users see a true mapping of the current port status on any terminal, realizing the visualization of the port environment and equipment as a "digital twin".

[0075] As an optional implementation method, refer to Figure 5 The port monitoring method further includes a monitoring step of the quiet zone: S510. In response to receiving a motion trigger signal from a static sensing device in the stationary area used for monitoring regional motion, broadcast a network request to the stationary area.

[0076] As an alternative example, static sensing devices (which may include infrared beam sensors, lidar, vibration sensors, or motion-detection cameras) are deployed at the perimeter or inside the static area to monitor movement within the area. When a device (such as a Class C flow machine) moves, triggering such a sensor, the sensor generates a motion trigger signal.

[0077] The signal is transmitted to the server via the transport layer (possibly a dedicated local area network within the quiet zone). The server then broadcasts a network request to the entire quiet zone via a communication base station deployed there. This request carries a specific device type code to identify that it is intended for a temporary external positioning device on a Class C streamer.

[0078] S520. In response to receiving response information from the positioning device, obtain the identifier of the positioning device.

[0079] As an optional example, a simple positioning device on a Class C stream machine in the static area continuously monitors the network. Upon receiving a network request, it executes a self-check logic: querying whether the data in its built-in positioning module is changing.

[0080] The device will only respond to a network request when its location data changes (indicating that it is moving). The response message contains the device's unique identification information (such as a MAC address or device number). This response message is then transmitted back to the server via the transport layer.

[0081] S530. In response to verifying, based on the identifier, that the second type of device to which the positioning device belongs has not obtained departure permission, generate and issue an alarm message.

[0082] As an optional example, after the server receives the response, the license query service in the support layer is invoked. This service uses the received identifier as the key information to query the "Port Equipment Management Database" in the data layer and performs the following verification: First, locate the corresponding Category II device (Category C flow machine) based on the identifier. Second, check the device's business process status to confirm whether it has applied for and obtained a departure permit from the platform. If the verification result is "Departure permit not obtained," it indicates that this movement is an unauthorized violation. The system will generate a high-level alarm message, which will be pushed to security personnel through the application-layer monitoring interface and may trigger on-site audible and visual alarms, indicating a security risk. If the verification result is "Departure permit obtained," the movement is considered legal, the system will not generate an alarm, and the device's status can be updated to "Departing." This implementation method achieves low-cost, high-efficiency, and precise security monitoring of non-cooperative devices (Category II devices) through an automated chain of "motion sensing - device response - permission verification," effectively filling management blind spots and ensuring order and security in quiet areas.

[0083] As described above, the port monitoring method based on data cross-validation provided in this disclosure effectively solves problems such as data silos, redundant computing power, and extensive management in port monitoring by constructing a hierarchical system architecture and establishing a dynamic and static data cross-validation mechanism. It ensures the reliability of data sources through equipment classification and control, intelligently identifies real-world environmental anomalies and equipment failures using spatiotemporal correlation rules, and finally achieves accurate visualization in a three-dimensional digital map. This significantly improves the accuracy, operational efficiency, and intelligent management level of port monitoring, providing an efficient technical path for realizing port digital twins.

[0084] Example 2 It should be understood that the port monitoring method based on data cross-validation described in the foregoing embodiments herein can also be similarly applied to the following port monitoring device based on data cross-validation for similar extensions. For simplicity, it is not described in detail.

[0085] Figure 6 This is a schematic diagram of a port monitoring device based on data cross-validation provided in an exemplary embodiment of this disclosure. (Refer to...) Figure 6The port monitoring device includes: Data storage unit 610 is configured to: in response to acquiring first environmental data and second environmental data about the target port area, store the first environmental data and second environmental data in a database; The target port area is the port area that needs to be monitored. The first environmental data is collected by multiple static data acquisition devices in the target port area, and the second environmental data is collected by multiple mobile data acquisition devices in the target port area. The mobile data acquisition devices are deployed on mobile carriers in the target port area. The cross-validation judgment unit 620 is configured to: in response to determining that there is abnormal data in the first environmental data or the second environmental data, based on the spatiotemporal information of the abnormal data and the type of data acquisition device, retrieve the same type of normal data collected by another type of data acquisition device in the same spatiotemporal context from the database, and use preset data cross-validation rules to compare and verify the abnormal data to obtain abnormal judgment information of the matching verification result. The map update unit 630 is configured to update the three-dimensional digital map of the target port area based on the location information of the plurality of static data acquisition devices, the dynamic location information of the plurality of mobile data acquisition devices, and the anomaly judgment information. The data push unit 640 is configured to push the three-dimensional digital map to the user terminal for monitoring and display.

[0086] As described above, the port monitoring device based on cross-validation provided in this disclosure effectively solves problems such as data silos, redundant computing power, and extensive management in port monitoring by constructing a hierarchical system architecture and establishing a dynamic and static data cross-validation mechanism. It ensures the reliability of data sources through equipment classification and control, intelligently identifies real-world environmental anomalies and equipment failures using spatiotemporal correlation rules, and ultimately achieves precise visualization in a three-dimensional digital map. This significantly improves the accuracy, operational efficiency, and intelligent management level of port monitoring, providing an efficient technical path for realizing port digital twins.

[0087] Example 3 In addition, this disclosure also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, wherein when the computer program is executed, it implements the port monitoring method based on data cross-validation as described in any of the above embodiments of this disclosure.

[0088] Figure 7 This is a schematic diagram of an application embodiment of the electronic device disclosed herein. Below, reference is made to… Figure 7This describes an electronic device according to embodiments of the present disclosure. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0089] like Figure 7 As shown, the electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the port monitoring method based on data cross-validation of the various embodiments of this disclosure described above, and / or other desired functions.

[0090] In one example, the electronic device may further include input and output devices, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). Furthermore, the input device may include, for example, a keyboard, a mouse, etc. The output device can output various information to the outside, including determined distance information, direction information, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0091] Of course, for the sake of simplicity, Figure 7 Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0092] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the port monitoring method based on data cross-validation according to various embodiments of this disclosure as described in the foregoing portion of this specification.

[0093] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0094] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps in the port monitoring method based on data cross-validation according to various embodiments of this disclosure as described in the foregoing portion of this specification.

[0095] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0096] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0097] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0099] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0100] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0101] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.

[0102] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0103] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A port monitoring method based on data cross-validation, characterized in that, The port monitoring method includes: In response to obtaining first and second environmental data about the target port area, the first and second environmental data are stored in the database; The target port area is the port area that needs to be monitored. The first environmental data is collected by multiple static data acquisition devices in the target port area, and the second environmental data is collected by multiple mobile data acquisition devices in the target port area. The mobile data acquisition devices are deployed on mobile carriers in the target port area. In response to determining that there is abnormal data in the first environmental data or the second environmental data, based on the spatiotemporal information of the abnormal data and the type of data acquisition device, the same type of normal data collected by another type of data acquisition device in the same spatiotemporal context is retrieved from the database, and the abnormal data is compared and verified using a preset data cross-validation rule to obtain abnormal judgment information of the matching verification result. Based on the location information of the multiple static data acquisition devices, the dynamic location information of the multiple mobile data acquisition devices, and the anomaly judgment information, update the three-dimensional digital map of the target port area; The three-dimensional digital map is pushed to the user terminal for monitoring and display.

2. The port monitoring method according to claim 1, characterized in that, The mobile carrier is a flow machine that enters the target port area; Before performing the step of storing the first environmental data and the second environmental data in a database in response to obtaining the first environmental data and the second environmental data about the target port area, the port monitoring method further includes: In response to receiving the port entry registration information of the flow machine equipment, the flow machine equipment is classified into Class I equipment or Class II equipment using the equipment classification service; The first type of equipment is authorized to operate within the target port area and is equipped with the mobile data acquisition device; the second type of equipment is restricted to operating in a designated quiet area within the target port area and is equipped with a positioning device.

3. The port monitoring method according to claim 2, characterized in that, Using equipment classification services, the flow machine equipment is classified into either Category I or Category II equipment, including: In response to determining from the port entry registration information that the flow machine equipment is port-owned equipment, the flow machine equipment is classified as the first type of equipment; In response to determining, based on the port entry registration information, that the flow machine equipment is not owned by the port and that the non-owned equipment has agreed to be registered, the non-owned equipment is classified as the first type of equipment; In response to determining, based on the port entry registration information, that the flow machine equipment is not owned by the port and that the non-owned equipment does not agree to registration, the non-owned equipment is classified as the second type of equipment.

4. The port monitoring method according to claim 1, characterized in that, Based on the spatiotemporal information and data acquisition device type of the matched abnormal data, similar normal data collected by another type of data acquisition device in the same spatiotemporal context is retrieved from the database, and the abnormal data is compared and verified using preset data cross-validation rules, including: In response to determining that the abnormal data comes from the static data acquisition device, the system retrieves similar data reported by the mobile data acquisition device that passed through the area surrounding the static data acquisition device within a preset historical time period from the database as the normal data, and determines the consistency between the normal data and the abnormal data. In response to determining that the abnormal data comes from the mobile data acquisition device, the system retrieves similar data reported by the static data acquisition device within a preset time period from the database within a preset geographical range of the real-time location where the mobile data acquisition device reported the abnormal data, and determines the consistency between the normal data and the abnormal data.

5. The port monitoring method according to claim 2, characterized in that, The port monitoring method also includes a monitoring step for the quiet zone: In response to receiving a motion trigger signal from a static sensing device within the static area used for monitoring regional motion, a network request is broadcast to the static area. In response to receiving response information from the positioning device, the identifier of the positioning device is obtained; In response to the verification based on the identifier that the second type of device to which the positioning device belongs has not obtained departure permission, an alarm message is generated and issued.

6. The port monitoring method according to claim 1, characterized in that, The updated 3D digital map of the target port area includes: On the three-dimensional digital map, the real-time location of the mobile data acquisition device is dynamically displayed using a first visual identifier, the location of the static data acquisition device is statically displayed using a second visual identifier, and the location corresponding to the anomaly judgment information is marked using a third visual identifier.

7. The port monitoring method according to claim 1, characterized in that, The anomaly detection information includes at least one of the following: A conclusive judgment used to indicate the authenticity of an abnormal event; Location information used to pinpoint the spatial location of the anomaly; Confidence information used to characterize the reliability of a judgment.

8. The port monitoring method according to claim 1, characterized in that, The first environmental data and the second environmental data include at least: temperature data, humidity data, wind data, gas concentration data, and image information.

9. A port monitoring device based on data cross-validation, characterized in that, The port monitoring device includes: The data storage unit is configured to: in response to acquiring first environmental data and second environmental data about the target port area, store the first environmental data and second environmental data in a database; The target port area is the port area that needs to be monitored. The first environmental data is collected by multiple static data acquisition devices in the target port area, and the second environmental data is collected by multiple mobile data acquisition devices in the target port area. The mobile data acquisition devices are deployed on mobile carriers in the target port area. The cross-validation judgment unit is configured to: in response to determining that there is abnormal data in the first environmental data or the second environmental data, based on the spatiotemporal information of the abnormal data and the type of data acquisition device, retrieve the same type of normal data collected by another type of data acquisition device in the same spatiotemporal environment from the database, and use preset data cross-validation rules to compare and verify the abnormal data to obtain abnormal judgment information of matching verification results. The map update unit is configured to update the three-dimensional digital map of the target port area based on the location information of the plurality of static data acquisition devices, the dynamic location information of the plurality of mobile data acquisition devices, and the anomaly judgment information. The data push unit is configured to push the three-dimensional digital map to the user terminal for monitoring and display.

10. An electronic device, the electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the port monitoring method based on data cross-validation as described in claims 1 to 8.

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