SCADA data transmission verification method and system based on 3D spatial position perception

Abstract

The present application relates to the field of data processing technology and discloses a SCADA data transmission verification method and system based on 3D spatial position perception. The method comprises: establishing a spatial mapping table of the three-dimensional coordinates and adjacency relationships of the equipment through spatial coordinate scanning, generating a spatial identification data packet containing the current position through a spatial coordinate encapsulation algorithm for the sensor data, calculating the Euclidean distance between the data sending position and the registered position using the KNN algorithm for position verification, comparing the verified temperature data with the historical data of the adjacent equipment to generate a consistency detection report, and sending correction instructions to the abnormal AGV equipment to achieve two-way error correction. The present application solves the technical problem that the existing SCADA data transmission verification method cannot identify 3D spatial position errors, resulting in device ID confusion, position drift, and regional authority conflicts.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a SCADA data transmission verification method and system based on 3D spatial position perception. Background Art

[0002] Existing SCADA (Supervisory Control and Data Acquisition) systems are widely used for data collection, equipment monitoring, and process control in industrial production processes. Traditional SCADA systems primarily employ data transmission verification methods based on the TCP / IP protocol. These systems verify the integrity of data packets through CRC checks, the MD5 hash algorithm, or digital signatures, and employ sequence number mechanisms and acknowledgement (ACK) mechanisms to ensure data transmission reliability. Existing verification methods focus on integrity verification and timestamp synchronization during network transmission. They utilize a two-way handshake verification protocol and heartbeat packet mechanisms to detect connection validity, while also implementing data caching and rollback mechanisms to handle transmission anomalies.

[0003] However, existing technologies have significant shortcomings. Traditional verification methods treat all SCADA data as two-dimensional data, completely ignoring the need to verify 3D spatial information. This makes it impossible to identify and address data transmission errors in the spatial dimension. Existing technologies only verify the numerical correctness of data, not the spatial rationality. This leads to the inability to detect spatial logic errors such as device ID confusion, mobile device positioning errors, pipeline flow errors, and regional security conflicts.

[0004] Based on the analysis of the limitations of existing technologies, 3D SCADA systems urgently need to solve the problem of data transmission verification based on spatial location awareness, including how to establish the three-dimensional spatial topological relationship of devices and perform spatial identity authentication, how to realize position deviation detection through spatial coordinate encapsulation and KNN algorithm, how to establish a neighborhood consistency verification model to detect spatial logical conflicts, and how to implement a bidirectional spatial correction mechanism to correct position anomalies. Solving these problems is of great significance to improving the spatial data reliability of 3D SCADA systems and preventing spatial dimension security risks. Summary of the Invention

[0005] The present application provides a SCADA data transmission verification method and system based on 3D spatial position perception, which is used to solve the technical problem that the existing SCADA data transmission verification method cannot identify 3D spatial position errors, resulting in device ID confusion, position drift and regional authority conflicts.

[0006] In the first aspect, the present application provides a SCADA data transmission verification method based on 3D spatial position perception, and the SCADA data transmission verification method based on 3D spatial position perception includes: recording the device positions of temperature sensors, pressure sensors and AGV equipment in the factory workshop through spatial coordinate scanning, and establishing a spatial mapping table containing the three-dimensional coordinates and adjacency relationships of the equipment; processing the temperature data and pressure data collected by the sensor through a spatial coordinate encapsulation algorithm according to the spatial mapping table, and generating a spatial identification data packet containing the current three-dimensional position coordinates of the sending device; verifying the position of the spatial identification data packet through a KNN spatial distance calculation algorithm, calculating the Euclidean distance between the data sending position and the device registration position in the spatial mapping table, and outputting the position verification result; comparing the numerical difference between the verified temperature data and the historical temperature data of the adjacent equipment in the spatial mapping table according to the position verification result, and generating a neighborhood consistency detection report; sending a correction instruction containing correct coordinate information to the AGV device that detects abnormal position deviation based on the neighborhood consistency detection report for two-way error correction.

[0007] In a second aspect, the present application provides a SCADA data transmission verification system based on 3D spatial position perception, the SCADA data transmission verification system based on 3D spatial position perception comprising:

[0008] The recording module is used to record the location of temperature sensors, pressure sensors, and AGV equipment in the factory workshop through spatial coordinate scanning, and establish a spatial mapping table containing the three-dimensional coordinates and adjacency relationships of the equipment;

[0009] An encapsulation module is used to process the temperature data and pressure data collected by the sensor through a spatial coordinate encapsulation algorithm according to the spatial mapping table to generate a spatial identification data packet containing the current three-dimensional position coordinates of the sending device;

[0010] A verification module is used to verify the position of the spatial identification data packet using the KNN spatial distance calculation algorithm, calculate the Euclidean distance between the data sending location and the device registration location in the spatial mapping table, and output the position verification result;

[0011] A comparison module is configured to compare the verified temperature data with the historical temperature data of adjacent devices in the spatial mapping table according to the position verification result, and generate a neighborhood consistency detection report;

[0012] The error correction module is used to send a correction instruction containing correct coordinate information to the AGV device detected with abnormal position deviation based on the neighborhood consistency detection report to perform bidirectional error correction.

[0013] In a third aspect, a SCADA data transmission verification device based on 3D spatial position perception is provided, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the SCADA data transmission verification device based on 3D spatial position perception executes the above-mentioned SCADA data transmission verification method based on 3D spatial position perception.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned SCADA data transmission verification method based on 3D spatial position perception.

[0015] The technical solution provided by this application incorporates 3D spatial topology information into SCADA data transmission verification for the first time by establishing a spatial mapping table containing device 3D coordinates and adjacency relationships. This fundamentally addresses the technical flaw of traditional verification methods that treat data as "flat information" and ignore the spatial dimension. A spatial coordinate encapsulation algorithm embeds the current 3D position coordinates of the device into the data packet, ensuring that each packet carries a clear spatial identity, effectively preventing device ID confusion and cross-regional data ambiguity. The KNN spatial distance calculation algorithm, using Euclidean distance calculation and nearest neighbor search, accurately identifies device position offset anomalies and mobile device positioning errors. Compared to existing approaches that rely solely on numerical verification, this application's spatial verification mechanism expands anomaly detection from one dimension to three, significantly improving the accuracy and comprehensiveness of anomaly identification. By comparing the numerical differences between verified temperature data and historical data from adjacent devices, a neighborhood consistency detection model based on the principle of physical continuity is established. This model leverages the physical law that temperature distributions between adjacent devices should follow spatial continuity. This model effectively detects anomalous data transmissions that violate physical logic, filling a gap in existing spatial logic verification. The two-way error correction mechanism achieves closed-loop control of verification and error correction by sending correction instructions to the abnormal device and receiving confirmation feedback. Compared with the traditional one-way verification method, the two-way mechanism of this application ensures the integrity and effectiveness of exception handling.

[0016] In 3D SCADA applications for automated container terminals, the KNN algorithm's spatial neighbor search characteristics are highly compatible with the spatial distribution of terminal equipment. The algorithm can set differentiated neighbor thresholds and verification strategies based on the diverse mobility characteristics of AGVs, temperature sensors, and pressure sensors, effectively adapting to the complex mixed deployment of fixed and mobile equipment in terminal operations. The AES encryption mechanism within the spatial coordinate encapsulation algorithm ensures the secure transmission of location information over wireless networks, mitigating the security risk of location data being tampered with or forged. The neighborhood consistency detection algorithm leverages the physical correlation of temperature sensor data through a physical constraint verification model to accurately identify abnormal data caused by equipment failure, environmental interference, or cyberattacks in complex terminal environments. The algorithm's threshold determination mechanism is optimized based on the actual environmental conditions of terminal operations, effectively reducing false positives while ensuring detection accuracy. The technical solution of this application is particularly suitable for applications requiring precise spatial positioning and security monitoring in large industrial facilities. Through comprehensive evaluation and processing using a multi-dimensional anomaly determination algorithm, it can uniformly manage and address issues across multiple dimensions, including spatial anomalies, permission violations, and security risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a schematic diagram of an embodiment of a SCADA data transmission verification method based on 3D spatial position perception in an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of an embodiment of a SCADA data transmission verification system based on 3D spatial position perception in an embodiment of the present application;

[0020] Figure 3 It is a schematic block diagram of the structure of a SCADA data transmission verification device based on 3D spatial position perception in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present application provide a SCADA data transmission verification method and system based on 3D spatial position perception. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0022] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the SCADA data transmission verification method based on 3D spatial position perception includes:

[0023] Step S101: Scan the spatial coordinates of the temperature sensors, pressure sensors, and AGV equipment in the factory workshop to record and process the equipment locations, and establish a spatial mapping table containing the three-dimensional coordinates and adjacency relationships of the equipment;

[0024] Step S102: Processing the temperature data and pressure data collected by the sensor using a spatial coordinate encapsulation algorithm according to the spatial mapping table to generate a spatial identification data packet containing the current three-dimensional position coordinates of the sending device;

[0025] Step S103: Perform location verification on the spatial identification data packet using the KNN spatial distance calculation algorithm, calculate the Euclidean distance between the data sending location and the device registration location in the spatial mapping table, and output the location verification result;

[0026] Step S104: Compare the verified temperature data with the historical temperature data of the adjacent devices in the spatial mapping table based on the position verification result to generate a neighborhood consistency detection report;

[0027] Step S105: Based on the neighborhood consistency detection report, a correction instruction containing correct coordinate information is sent to the AGV device where the position deviation abnormality is detected to perform two-way error correction.

[0028] It is understandable that the execution subject of this application can be a SCADA data transmission verification system based on 3D spatial position perception, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, in an automated container terminal, three-dimensional spatial coordinates are measured for temperature sensors, pressure sensors, and automated guided vehicle (AGV) equipment distributed throughout the loading and unloading area. This method obtains precise location information for each device, including X-coordinates representing east-west position, Y-coordinates representing north-south position, and Z-coordinates representing height. Based on these measured coordinate data, the three-dimensional Euclidean distance between each device is calculated. Devices whose distance is less than a set adjacency threshold are marked as adjacent. A device distance matrix is ​​constructed by calculating the spatial distances between all pairs of devices, while an adjacent device list records the information of adjacent devices around each device. A hash algorithm uniquely identifies each device's coordinate information, forming a device spatial hash identifier. Ultimately, a complete spatial mapping table is constructed, including the device identifier, three-dimensional coordinates, adjacent device list, and spatial hash identifier. A spatial coordinate encapsulation algorithm processes the real-time data collected by the sensors by extracting the registered coordinates of the sending device from the spatial mapping table to obtain a device identifier and reference position coordinates. The temperature values ​​in the loading and unloading area monitored by the temperature sensor and the hydraulic system pressure values ​​collected by the pressure sensor are associated and bound to their corresponding device identifiers to form raw data tuples. The sending device's current actual location coordinates are differentially calculated with the reference location coordinates to generate a 3D spatial offset vector, which reflects the device's spatial offset relative to its registered location. The AES encryption algorithm encrypts the offset vector to ensure the security of location information during transmission. The packet encapsulation protocol combines the original data tuple, the encrypted offset data, and the current timestamp into a fixed-length data packet. Finally, the device's current 3D location coordinates are added to complete the spatial identification packet.

[0030] When verifying location information, the KNN spatial distance calculation algorithm parses the device's current location coordinates and device identifier in the spatial identification data packet to obtain the device's real-time location information and device identity information. The three-dimensional Euclidean distance calculation formula is used to calculate the deviation between the device's real-time location and the registered location in the spatial mapping table, resulting in the device's spatial position offset. The improved KNN algorithm searches for the K nearest neighbor devices in the spatial mapping table and determines the rationality of the position offset by comparing the device type's movement threshold. Fixed devices have a lower movement threshold, while AGV devices have a relatively higher movement threshold. A weighted spatial distance matrix is ​​obtained by multiplying the spatial distance with the device type correlation factor. The KNN nearest neighbor search algorithm selects the K devices with the smallest distance from the matrix as the set of valid neighbor device candidates. Cross-validation processing detects device identifier conflicts and identifies anomalies such as multiple device IDs at the same location or device IDs in different locations.

[0031] Neighborhood consistency detection filters temperature data records that have passed location verification, queries the corresponding list of adjacent devices from the spatial mapping table, and extracts the historical temperature data sequence of each adjacent temperature sensor. The physical constraint verification model calculates the gradient rationality of the current temperature data and the historical temperature data of the neighboring devices, and performs physical continuity verification according to the linear relationship between the distance between devices and the temperature difference. The temperature data consistency score reflects the degree of match between the current device temperature value and the temperature values ​​of the surrounding adjacent devices. The threshold judgment algorithm marks data with a score below the preset threshold as a neighborhood inconsistency anomaly. The two-way error correction mechanism extracts abnormal device information from the neighborhood consistency detection report, queries the spatial mapping table to obtain the correct standard position coordinates, generates a correction instruction data packet containing the device identification, correct coordinates, correction type and timestamp, sends it to the corresponding AGV device through the priority transmission protocol, and receives confirmation information on the completion of the position correction.

[0032] In actual application at an automated container terminal, when an AGV-001 truck in the loading and unloading area reports its location as coordinate point A, the system first searches the spatial mapping table for the device's registered location, coordinate point B. Calculating the three-dimensional Euclidean distance between the two points reveals that the offset exceeds the AGV's normal movement threshold. A KNN algorithm searches the five nearest neighboring devices around AGV-001 and discovers that their historical location data indicates that AGV-001 should be at coordinate point C, not A. Neighborhood consistency testing reveals that the temperature data reported by AGV-001 differs significantly from the temperature readings of sensors near its claimed location, while being highly consistent with the temperature data from sensors near coordinate point C. The system determines that AGV-001 has a position error and generates a correction instruction containing the correct coordinate point C, which is sent to the device. After receiving the instruction, AGV-001 repositions to the correct location and sends back a confirmation message, completing two-way error correction.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] Perform three-dimensional spatial coordinate measurement on all equipment in the factory workshop to obtain the position coordinate values ​​of each equipment;

[0035] Based on the location coordinates of each device, the distance between devices is calculated using the Euclidean distance calculation formula to obtain the distance matrix between devices;

[0036] According to the distance matrix between devices, the adjacency threshold is set to determine the device adjacency relationship and obtain the adjacent device list of each device;

[0037] The location coordinate value and the adjacent device list are processed by a hash algorithm to generate a spatial index to obtain a device spatial hash identifier;

[0038] A data structure is constructed based on the device space hash identifier to organize the mapping table and obtain a space mapping table including the device identifier, three-dimensional coordinates, a list of adjacent devices and the space hash identifier.

[0039] Specifically, in automated container terminals, 3D coordinate measurement and processing are required to accurately locate various types of equipment distributed throughout the loading and unloading area, including fixed temperature and pressure sensors and mobile automated guided vehicles (AGVs). The measurement process uses a laser scanner or GPS positioning device to acquire the spatial position of each device. The X coordinate represents the device's east-west position within the terminal, the Y coordinate represents its north-south position, and the Z coordinate represents the device's height above the ground. These coordinates are directly read and recorded by the measuring device, forming a unique 3D spatial location identifier for each device.

[0040] When calculating inter-device distance, the Euclidean distance formula calculates the spatial distance between all pairs of devices. This method extracts the three-dimensional coordinates of the two devices, calculates the difference in the X, Y, and Z coordinates, then squares each of these three differences. The squared results are added together and the square root is taken to obtain the straight-line distance between the two devices. The inter-device distance matrix is ​​formed by traversing the relationships between all device pairs. Each element in the matrix represents the spatial distance between two devices. The rows and columns of the matrix correspond to different device identifiers, and the matrix is ​​symmetrical.

[0041] The adjacency threshold is determined based on the device type and communication range. The adjacency threshold for temperature sensors is set at 10 meters, for pressure sensors at 15 meters, and for AGVs at 50 meters. Device adjacency determination is performed by comparing the distance values ​​in the inter-device distance matrix with the adjacency threshold for the corresponding device type. When the distance value is less than the threshold, the two devices are marked as adjacent. The adjacent device list records the identifiers of all devices around each device that meet the adjacency criteria. The list data structure is stored in a linked list or array format.

[0042] When processing location coordinates and neighbor lists, the hash algorithm converts the device's 3D coordinate data and neighbor information into a fixed-length hash value. Spatial index generation is performed by concatenating the X, Y, and Z coordinates with the string representation of the neighbor list, then inputting them into a hash function to calculate the device's spatial hash identifier. The hash function uses the MD5 or SHA algorithm to ensure unique hash values ​​for different devices. The uniqueness of device identifiers is ensured by the fact that identical inputs produce identical hash values, while different inputs produce different hash values.

[0043] The data structure is constructed using a hash table or tree structure to organize the spatial mapping table. The primary key of the mapping table is the device identifier, and the corresponding value contains four fields: the device's three-dimensional coordinates, a list of adjacent devices, and a spatial hash identifier. The mapping table organization process traverses the basic information of all devices, inserting each device's complete information into the data structure in turn, and establishing a mapping relationship between the device identifier and the device's detailed information. The spatial mapping table structure design supports fast query operations, directly obtaining the corresponding device's location information and adjacency relationships based on the device identifier, and also supports device verification and conflict detection based on the spatial hash identifier.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] Extract the registered coordinate information of the sending device from the spatial mapping table to perform device identification processing to obtain the device identifier and reference position coordinates;

[0046] The real-time monitoring values ​​of the temperature sensor and pressure sensor are associated with the device identifier to obtain the original data tuple with the device identity;

[0047] The current actual position coordinates of the sending device and the reference position coordinates are calculated by coordinate difference calculation to calculate the position offset and obtain the three-dimensional space position offset vector;

[0048] Based on the three-dimensional space position offset vector, the position information is encrypted using the AES encryption algorithm to obtain encrypted position offset data;

[0049] The original data tuple, the encrypted position offset data and the current timestamp are combined and packaged through the data packet encapsulation protocol to obtain a fixed-length data packet structure;

[0050] The device's current three-dimensional position coordinate field is added to the data packet structure for encapsulation processing to obtain a space identification data packet containing the sending device's current three-dimensional position coordinates.

[0051] Specifically, device identification processing in the automated container terminal retrieves the complete registration information of the sending device by querying the spatial mapping table. The device identifier serves as a unique key value to index the corresponding device record, and the reference position coordinates represent the three-dimensional spatial position information measured during the device's initial installation. The spatial mapping table is stored using a hash table data structure, allowing direct access to the corresponding device record via the device identifier. The record contains complete information such as the device type, installation location, and adjacent device relationships. The extraction process uses the device identifier as a query condition to read the corresponding coordinate data and device attribute information from the mapping table, ensuring the accuracy and consistency of the device identity during subsequent data processing. Data association and binding processing establishes a direct mapping relationship between the real-time values ​​collected by the sensor and the corresponding device identifier. The ambient temperature value monitored by the temperature sensor and the hydraulic system pressure value collected by the pressure sensor are used as the raw monitoring data, and the device identifier serves as a unique identifier of the data source. The raw data tuple structure contains four basic fields: device identifier, data type, value size, and acquisition time. The data type field distinguishes the different attributes of temperature data and pressure data, and the value size field records the specific measurement results of the sensor. The association binding process traverses the real-time data streams of all sensor devices, pairs the numerical information in each data packet with the identifier of the sending device, and forms a complete data record with the device identity.

[0052] The coordinate difference calculation process calculates the position offset of the device in the three directions of X, Y, and Z by comparing the difference between the current actual position of the sending device and the reference position coordinates. The current actual position coordinates are obtained in real time through the device's built-in GPS module or laser positioning device, and the reference position coordinates read the device's registered installation position from the spatial mapping table. The position offset calculation is calculated by subtracting the reference X coordinate from the current X coordinate, the reference Y coordinate from the current Y coordinate, and the reference Z coordinate from the current Z coordinate to obtain the offset values ​​in the three directions. The three-dimensional spatial position offset vector contains three components: X-direction offset, Y-direction offset, and Z-direction offset. The modulus of the vector represents the overall offset distance of the device relative to the registered position, and the direction of the vector represents the spatial direction of the device offset.

[0053] The AES encryption algorithm securely encrypts position offset vectors. The AES algorithm uses symmetric encryption and encrypts data in groups using a 128-bit or 256-bit key. Position information encryption first converts the three-dimensional position offset vector into a byte sequence, which is then encrypted using an AES encryption algorithm using a preset encryption key. The encryption process consists of four main stages: key expansion, initial round transformation, multiple rounds of iterative transformation, and final round transformation. Each stage performs a different mathematical transformation on the input data. The encrypted position offset data has a fixed length format. Even if the original offset vector value changes, the encrypted data length remains consistent, ensuring the uniformity of the data packet structure and transmission efficiency.

[0054] The packet encapsulation protocol combines the original data tuple, the encrypted position offset data, and the current timestamp into a packet. The timestamp records the precise time the packet was generated, using the UNIX timestamp format to represent the number of seconds from the start of standard time. The packetization process arranges the various data types in a fixed field order. The packet header contains control information such as the version number, packet length, and checksum. The packet body contains core content such as the device identifier, original monitoring data, encrypted position information, and a timestamp. The fixed-length packet structure uses padding or truncation to ensure that all packets have the same byte size. The packet length is set based on a balance between network transmission efficiency and storage space. The encapsulation process adds the device's current three-dimensional position coordinate field to the fixed-length packet structure. The current position coordinates are stored in plaintext format for easy reading and verification by the receiver. The final structure of the spatial identification packet consists of six main fields: control field, device identity field, monitoring data field, encrypted offset field, timestamp field, and current coordinate field. The field order of the packet is designed to follow the logical sequence of data processing, and the receiver parses the packet content according to this field order.

[0055] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0056] Parse the device's current location coordinates and device identifier from the spatial identification data packet to perform spatial identity authentication processing to obtain the device's real-time location information and device identity information;

[0057] The real-time location information of the device and the registered location coordinates of the same device identifier in the spatial mapping table are used to calculate the spatial deviation using the three-dimensional Euclidean distance formula to obtain the device spatial position offset;

[0058] Based on the device's spatial position offset, an improved KNN algorithm is used to search for K nearest neighbor devices in the spatial mapping table for spatial topology verification. This identifies abnormal transmissions with device ID confusion and position drift, and obtains a list of spatial neighbor devices.

[0059] The spatial coordinates of each device in the spatial neighborhood device list and the current device position are verified using a spatial consistency constraint model to verify regional permissions, detect cross-regional data transmission and illegal access to dangerous areas, and obtain regional compliance detection results;

[0060] Based on the device's spatial position offset and regional compliance detection results, a comprehensive evaluation is performed through a multi-dimensional anomaly judgment algorithm to obtain a position verification result that includes spatial anomaly type, deviation level, and security risk level.

[0061] Specifically, the spatial identity authentication process obtains the key identity information of the device by parsing the received spatial identification data packet. The data packet parsing extracts the content of each data segment according to the predefined field order. The current position coordinates of the device are directly read from the position field of the data packet, which contains the real-time X-axis, Y-axis, and Z-axis three-dimensional spatial coordinate values ​​of the device. The device identifier is extracted from the identity field of the data packet as the unique identification code of the device. The real-time position information of the device reflects the actual spatial position of the device at the time the data packet is sent, and the device identity information determines the source device of the data packet. The combination of the two constitutes the basic data for spatial identity authentication. The verification process checks the validity of the identity information by comparing the format and encoding rules of the device identifier, and verifies the rationality and range limit of the position coordinate value.

[0062] The three-dimensional Euclidean distance formula calculates the spatial deviation between a device's real-time location and its registered location. This spatial deviation calculation process first queries the corresponding registered location coordinates from the spatial mapping table based on the device identifier. The real-time and registered location coordinates are then substituted into the distance formula for calculation. The device's spatial position offset is calculated using the Euclidean distance formula. The offset value reflects the degree of spatial movement of the device relative to its initial installation position. A larger offset indicates a more significant change in the device's position.

[0063] When searching for nearest neighbor devices in a spatial mapping table, the improved KNN algorithm first establishes a search range centered on the current device's location. The algorithm then identifies neighbors by calculating the spatial distances between the current device and all other devices in the mapping table. K nearest neighbors are selected based on distance sorting, with the K devices with the smallest distances considered candidate neighbors. Spatial topology verification detects anomalies by analyzing the distribution patterns and relative positions of neighboring devices. A device ID mismatch is identified when a neighboring device with the same identifier as the current device but a different location is found. Position drift anomalies are identified by comparing the device's current location with its historical location trajectory. Position changes outside the normal range are flagged as abnormal transmissions. The spatial neighbor device list records information on all valid neighbor devices that have passed topology verification, including detailed attributes such as neighbor identifiers, location coordinates, and device type. A spatial consistency constraint model verifies the regional permissions of each device in the neighbor device list. The model establishes a permission matrix based on device type and the region in which it resides. Regional permission verification checks the functional area and security level to which the current device belongs to determine whether the device has permission to transmit data in that area. Cross-region data transmission detection compares a device's registered region with its current region, triggering an anomaly alert when the device crosses the boundary between regions with different security levels. Dangerous area illegal access detection queries the regional security configuration table to determine whether the current location belongs to a restricted-access dangerous area. Unauthorized device entry into a dangerous area is flagged as illegal access. Regional compliance detection results contain key information such as region type, permission level, and access status, serving as an important basis for subsequent anomaly determination.

[0064] The multi-dimensional anomaly determination algorithm comprehensively evaluates the spatial position offset of the device and the regional compliance detection results. The algorithm establishes an evaluation system that includes multiple dimensions such as position deviation, permission violation, and security risk. The comprehensive evaluation process calculates the overall score of the anomaly indicators in each dimension by weighted calculation. The situation where the position offset exceeds the allowable range of the device type is assigned a higher anomaly weight, and the situation where the regional permission violation is assigned the highest security risk weight. The spatial anomaly type classification includes different categories such as position offset anomaly, device misuse anomaly, and regional unauthorized anomaly. Each anomaly type corresponds to specific detection rules and processing strategies. The deviation level is divided into three levels according to the severity of the anomaly: slight deviation, medium deviation, and severe deviation. The security risk level is divided into three levels: low risk, medium risk, and high risk. The location verification result integrates all anomaly determination information to form a verification report.

[0065] In a specific application scenario at an automated container terminal, an AGV-007, an external automated guided vehicle (AGV), transmits a spatial identification data packet containing its temperature sensor data. Spatial authentication processing parses the data packet to obtain the device identifier AGV-007 and its current location coordinates as a point within the terminal's loading and unloading area. A three-dimensional Euclidean distance calculation reveals that AGV-007's real-time location is significantly offset from its registered parking position in the spatial mapping table, exceeding the AGV's normal operating range. An improved KNN algorithm, searching for neighboring devices around AGV-007, discovers another device record identified as AGV-007 in the vicinity, but the two devices have significantly different location coordinates. Spatial topology verification identifies a device ID confusion anomaly. A spatial consistency constraint model check reveals that AGV-007 is currently located in a hazardous materials storage area, while its permissions only allow it to operate in the general loading and unloading area. Regional permission verification detects an illegal cross-region access anomaly. A multi-dimensional anomaly determination algorithm comprehensively analyzes the position offset anomaly and regional permission violations, flagging the transmission as a high-risk, severe anomaly event.

[0066] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0067] Compare the device's spatial position offset with the preset device type movement threshold, set a 0.5-meter threshold for fixed devices and a 10-meter threshold for AGV devices to determine the rationality of device movement and obtain the device movement status identifier;

[0068] Based on the device mobility status identification, the distance of all devices in the spatial mapping table is recalculated by improving the Euclidean distance weight function, and the spatial distance is multiplied by the device type correlation factor to obtain a weighted spatial distance matrix;

[0069] According to the weighted spatial distance matrix, the KNN nearest neighbor search algorithm is used to select the K devices with the smallest distance for neighbor screening, excluding device nodes that are beyond the effective communication range to obtain a valid neighbor device candidate set;

[0070] Cross-verify the device identifier of each device in the valid neighbor device candidate set with the device identifier in the current data packet to detect whether there are multiple device IDs at the same location or conflicts between device IDs in different locations, and obtain the device ID consistency verification result;

[0071] Based on the device ID consistency verification results, anomaly identification and processing are performed through the spatial logic conflict detection algorithm, marking the data transmission with device ID confusion and position drift, and obtaining a list of spatial neighborhood devices containing abnormal device identification and anomaly type.

[0072] Specifically, the rationality of equipment movement is determined by comparing the spatial position offset of the equipment with a preset movement threshold to achieve anomaly detection. The comparison process sets different threshold standards based on the physical characteristics and operating characteristics of the equipment type. Fixed equipment includes temperature sensors and pressure sensors installed in fixed positions in container automated terminals. Under normal circumstances, this type of equipment does not change position. The movement threshold is set to 0.5 meters to take into account measurement errors and slight vibrations of the equipment. AGV equipment, as a mobile device in terminal operations, has a large range of movement within the loading and unloading area. The movement threshold is set to 10 meters to accommodate its normal operational movement needs. The equipment movement status identification is generated by determining whether the offset exceeds the threshold range of the corresponding equipment type. Equipment with an offset less than the threshold is marked as normal, and equipment with an offset exceeding the threshold is marked as abnormal movement. The determination process obtains the corresponding threshold parameters by querying the equipment type information, and then compares the calculated position offset with the threshold numerically, and generates a Boolean status identification based on the comparison result.

[0073] The improved Euclidean distance weight function optimizes and adjusts the spatial distance calculation based on the device movement status identification. The mathematical expression of the weight function is:

[0074]

[0075] in represents the weighted distance between device i and device j, represents the original Euclidean distance between two devices, represents the relevance factor based on device type, Represents an adjustment factor based on mobility status. The device type correlation factor is set based on device capabilities and communication requirements. The correlation factor between devices of the same type is smaller, while the correlation factor between devices of different types is larger. The mobility status adjustment factor is determined based on the device's mobility status identifier. The adjustment factor for devices in a normal state is the standard value, while the adjustment factor for devices in an abnormal state is increased to reduce their priority in neighbor selection. The distance recalculation process calculates weighted distance values ​​by traversing all device pairs in the spatial mapping table and applying the improved weighting function to each pair. The weighted spatial distance matrix is ​​formed by updating all element values ​​in the original distance matrix. The matrix structure remains unchanged, but the values ​​are weighted.

[0076] The KNN nearest neighbor search algorithm uses a weighted spatial distance matrix to screen and sort neighbor devices. The algorithm first extracts the weighted distance between the current device and all other devices, then sorts them in ascending distance order. K devices are selected by taking the top K devices with the smallest distances from the sorted results. The value of K is determined based on a balance between neighborhood analysis requirements and computational efficiency. Neighbor screening adds communication range constraints to the distance sorting process. The effective communication range is determined based on device type and network environment. Devices outside the communication range are excluded from neighbor candidate selection, even if they are closer. Device node exclusion is achieved by checking the communication reachability between each candidate device and the current device. This is based on technical parameters such as signal strength, network topology, and transmission protocol. The valid neighbor candidate set contains all device records that meet the distance and communication requirements. Device information in the candidate set includes complete attributes such as device identifier, location coordinates, device type, and weighted distance. Cross-validation detects identity conflicts by comparing the device identifier in the valid neighbor candidate set with the device identifier in the current data packet. This verification process uses both string matching and hash comparison to ensure detection accuracy. Detecting multiple device IDs at the same location is accomplished by analyzing the candidate set for records with identical location coordinates but different device identifiers. The determination of location coordinate identity is made within a certain tolerance range to account for measurement errors. Detecting inter-location device ID conflicts is accomplished by searching the candidate set for device records with the same identifier as the current device but significantly different location coordinates. The determination of location differences is based on the normal range of movement for the device type. Device ID consistency verification results are generated by statistically analyzing the results of conflict detection and include detailed information such as the conflict type, number of conflicting devices, and conflict severity. The data structure of the verification results is stored in a structured format to facilitate subsequent anomaly analysis and processing decisions.

[0077] The spatial logic conflict detection algorithm performs in-depth anomaly pattern recognition based on the device ID consistency verification results. The algorithm identifies the root cause of abnormal transmission by analyzing the spatial distribution pattern and temporal correlation of conflicting devices. Anomaly identification processing includes the detection of two main types of anomalies: device ID confusion and position drift. Device ID confusion is identified by discovering that the same device identifier appears in multiple different locations, and position drift is identified by detecting abnormal changes in the device position relative to the historical trajectory. Marking processing is achieved by assigning a specific anomaly label and severity level to each abnormal device. The anomaly label distinguishes different types of spatial logic conflicts, and the severity level reflects the impact of the anomaly on overall monitoring and security. The spatial neighborhood device list integrates all detection results to form a final device status report. Each device record in the list contains status descriptions such as basic device information, anomaly type, anomaly degree, and detection time.

[0078] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0079] Filter the temperature data records with a passed verification status from the position verification results, perform valid data extraction processing, and obtain a temperature data set that has passed the position verification;

[0080] Based on the device identifier in the temperature data set, the corresponding adjacent device list is searched from the spatial mapping table to perform neighboring device identification processing, and the spatial adjacent device information of each temperature sensor is obtained;

[0081] Perform time series extraction on the historical temperature data of each adjacent temperature sensor in the spatial adjacent device information to obtain a historical temperature data sequence of the adjacent device;

[0082] The temperature gradient rationality calculation is performed on the current temperature data and the historical temperature data series of neighboring devices using a physical constraint verification model. Physical continuity verification is performed based on the linear relationship between the distance between devices and the temperature difference to obtain a temperature data consistency score.

[0083] Anomaly detection is performed based on the temperature data consistency score using a threshold judgment algorithm. Data with a consistency score lower than the preset threshold is marked as a neighborhood inconsistency anomaly, and a neighborhood consistency detection report containing the abnormal device identification, consistency score, and cause of the anomaly is obtained.

[0084] Specifically, the valid data extraction process purifies the data set by screening the temperature data records whose verification status is marked as passed in the location verification results. The screening process traverses all temperature data records by matching the status field. The location verification result contains the verification status code of each temperature sensor data packet. Records with normal status codes are retained, and records with abnormal status codes are excluded. The temperature data set that has passed the location verification contains all temperature sensor records that have passed the spatial location verification. Each record contains complete information such as device identifier, temperature value, acquisition time, location coordinates, etc. The organizational structure of the data set is stored in the form of an array or linked list, which supports fast traversal and query operations. The data records are sorted by device identifier or acquisition time to optimize the efficiency of subsequent processing. The extraction process loops through the original verification results, checks the verification status field of each record, copies the records with a passed status to the new data set, and counts the number and distribution of valid records.

[0085] The neighboring device identification process retrieves the corresponding neighboring device information from the spatial mapping table based on the device identifier in the temperature dataset. The query process achieves rapid positioning through a hash table or index structure. Each device record in the spatial mapping table contains an adjacent device list field, which stores the identifiers of all devices that are spatially adjacent to the current device. The generation of the adjacent device list is based on the physical distance and communication range between devices. Devices with a distance less than the preset adjacent threshold are marked as adjacent. Spatial adjacent device information includes detailed attributes such as the identifier of the adjacent device, device type, location coordinates, and spatial distance from the current device. The identification process traverses each device identifier in the temperature dataset and queries the corresponding adjacent device list from the spatial mapping table in turn. The query results are associated and bound with the current device information to form a complete neighborhood relationship mapping. During the processing, it is necessary to filter out non-temperature sensor devices in the adjacent device list and only retain adjacent device information of the temperature sensor type.

[0086] Time series extraction processing obtains historical temperature data for each adjacent temperature sensor from spatially adjacent device information. The extraction range is based on a set time window, typically selecting continuous data records within a certain period of time in the past. Historical temperature data is stored in a time series database, with data records sorted by timestamp, supporting fast queries based on time ranges. The historical temperature data sequence of a neighboring device contains all temperature measurements of each neighboring device within a specified time window. The structure of the data sequence includes basic fields such as timestamp, temperature value, and device identifier. The extraction process traverses the list of neighboring devices, performs a time range query on each adjacent temperature sensor, obtains its historical temperature data record, and then merges the historical data of multiple devices in chronological order to form a comprehensive neighborhood historical data sequence. The time alignment processing of the data sequence solves the problem of inconsistent sampling frequencies of different devices through interpolation or averaging calculations, ensuring the time synchronization of neighborhood data.

[0087] The physical constraint verification model models the relationship between inter-device temperature differences and spatial distance based on the physical laws of temperature conduction. The model assumes that temperature changes in adjacent areas follow the principle of continuity. The temperature gradient rationality calculation compares the current temperature data with the historical temperature data of neighboring devices to determine whether the temperature distribution meets physical continuity requirements. The linear relationship between inter-device distance and temperature difference is expressed as a mathematical relationship in which the temperature difference is proportional to the distance. Closer devices should have smaller temperature differences, while more distant devices should have larger temperature differences. Physical continuity verification calculates the difference between the current device temperature and the historical temperature of each neighboring device, then compares this difference with the corresponding inter-device distance. The temperature data consistency score is generated by statistically analyzing the degree of match between the current temperature data and the historical data of the neighboring device. Data with a high degree of match receives a higher consistency score, while data with a low degree of match receives a lower score. The score is calculated using a weighted average, with closer neighboring devices receiving a higher weight in the score and more distant devices receiving a lower weight.

[0088] The threshold judgment algorithm detects anomalies based on the consistency score of temperature data. The algorithm uses a preset threshold to divide the consistency score into normal and abnormal ranges. Anomaly detection is performed by comparing the consistency score of each temperature data point against a preset threshold. Data with a score below the threshold is marked as a neighborhood inconsistency anomaly. The preset threshold is determined based on statistical analysis of historical data and expert experience. A threshold that is too high results in insufficient anomaly detection sensitivity, while a threshold that is too low results in an increased false alarm rate. The neighborhood inconsistency anomaly tag contains basic information such as the identifier of the abnormal device, the anomaly type, and the detection time. The anomaly type distinguishes between different anomaly patterns such as sudden temperature changes, temperature offsets, and temperature fluctuations. The neighborhood consistency detection report integrates all anomaly detection results to form a comprehensive report. The report contains detailed information such as the abnormal device identification, consistency score, anomaly cause, and recommended remedial measures. The report data structure uses a structured format and also records the timestamp of the detection process and the detection parameter settings.

[0089] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0090] Extract abnormal device identification and abnormal type information from the neighborhood consistency detection report to perform abnormal data analysis and processing, and obtain a list of devices that need to be corrected and the corresponding abnormal causes;

[0091] Based on the list of devices that need to be corrected, the correct registered coordinates of each abnormal device are queried from the spatial mapping table to perform reference position acquisition processing to obtain the standard position coordinate information of each device;

[0092] The standard position coordinate information and the cause of the abnormality are packaged and processed through the correction instruction generation algorithm to generate a correction instruction data packet containing the device identification, correct coordinates, correction type and timestamp;

[0093] The correction instruction data packet is sent and processed through a priority transmission protocol, the transmission priority is set according to the severity of the abnormality, and the correction instruction is sent to the corresponding AGV device to obtain instruction sending confirmation information;

[0094] The position correction completion confirmation information returned by the AGV device is received and bidirectional verification is performed, the device position information in the space mapping table is updated, and the correction operation log is recorded to obtain the bidirectional error correction completion status.

[0095] Specifically, the abnormal data analysis and processing achieves accurate identification of problem devices by parsing the structured abnormal information in the neighborhood consistency detection report. The analysis process extracts the device identifier and abnormality type label from the abnormal record field of the report. The abnormal device identifier serves as the unique identification code of the device, and the abnormality type information distinguishes different spatial logical error categories such as location offset abnormalities, neighborhood inconsistency abnormalities, and device ID disorder abnormalities. The list of devices that need to be corrected is generated by traversing all abnormal records in the report. The list structure contains key attributes such as device identifier, abnormality severity, and detection time. The abnormality cause analysis is attributed based on the abnormality type and the specific data differences during the detection process. The causes of location offset abnormalities include physical movement of the device, GPS positioning errors, network transmission errors, etc. The causes of neighborhood inconsistency abnormalities include sensor failure, environmental interference, data transmission delays, etc. The analysis and processing identifies the time pattern and impact range of the abnormality by correlating the historical records and current status data of the abnormal device, forming a complete analysis result that includes the root cause of the abnormality and recommended treatment solutions.

[0096] The reference position acquisition process retrieves the original registration information of each abnormal device from the spatial mapping table based on the list of devices that need to be corrected. The query process uses the device identifier as the index key to access the corresponding record in the mapping table. The correct registration coordinates represent the standard spatial position of the device at the time of initial installation or the last correct calibration. The coordinate information contains the precise values ​​of the three dimensions of the X-axis, Y-axis, and Z-axis. The standard position coordinate information is read directly from the position field of the spatial mapping table, and the relevant attributes such as the installation time, calibration history, and device type of the device are obtained at the same time. The acquisition process processes the coordinate information of multiple abnormal devices at the same time through batch query operations. The query results are sorted by device identifiers and correlated with the abnormal cause information. The validity and timeliness of the registered coordinates need to be verified during the processing. Expired or invalid coordinate data needs to be updated through backup records or re-measurement to ensure that the correction instructions are based on accurate and reliable reference position information.

[0097] The correction instruction generation algorithm combines the standard position coordinate information with the cause of the anomaly. The algorithm formulates corresponding correction strategies and instruction formats based on different types of anomalies. The instruction encapsulation process uses a structured data format to organize the correction information. The data packet header contains control fields such as the instruction type, priority, and checksum. The device identification field records the unique identifier of the target device that needs to be corrected. The correct coordinate field contains the standard position information obtained from the spatial mapping table. The correction type field distinguishes between different correction operations such as position relocation, data retransmission, and device restart. The timestamp field records the precise time of instruction generation, which is used for subsequent instruction validity verification and execution status tracking. The correction instruction data packet uses a fixed-length format to ensure the consistency and reliability of network transmission. The checksum of the data packet is calculated and generated by the CRC algorithm. The receiving end verifies the integrity and correctness of the data packet through the checksum.

[0098] The priority transmission protocol processes correction instructions at different levels based on the severity of the anomaly, defining three transmission levels: high, medium, and low. The transmission process determines the transmission priority based on the anomaly type and impact scope. Safety-related position anomalies are assigned the highest priority, functional anomalies are assigned medium priority, and minor deviations are assigned low priority. The transmission protocol utilizes a queue management mechanism to handle instructions of varying priorities, with high-priority instructions being sent first and low-priority instructions being sent when the network is idle. After receiving the correction instruction, the AGV device performs the corresponding correction actions, including GPS repositioning, updating position coordinates, and adjusting motion trajectories. Command transmission confirmation is implemented through an ACK (Acknowledgement) mechanism. Upon receiving a command, the AGV device immediately returns an acknowledgment message containing feedback information such as command reception status, estimated execution time, and current device status. The transmission confirmation process monitors the receipt of ACK messages to determine the success of the command transmission. Instructions that do not receive an acknowledgment message trigger a retransmission mechanism.

[0099] The bidirectional verification process verifies the effectiveness of the correction operation by receiving and analyzing the position correction completion confirmation information returned by the AGV. This confirmation information includes detailed data such as the new position coordinates after correction, the correction execution time, and the correction result status. The verification process determines the correction effect by comparing the deviation between the corrected position and the standard position. Corrections that are within the allowable range are marked as successful, while corrections that exceed the range are marked as failed and trigger a recalibration process. The spatial mapping table update process writes the new position information of the successfully corrected device to the corresponding record in the mapping table. The update operation includes multiple data items such as the position coordinate field, the last update time field, and the correction history field. The correction operation log records detailed information about the entire bidirectional error correction process. The log content includes a complete operation trajectory, including the time of anomaly detection, the time when the correction command was sent, the AGV response time, the correction completion time, and the position comparison before and after correction. The bidirectional error correction completion status is generated by combining the correction results and log records. The status information reflects the final result of the correction operation and the current status of the device.

[0100] The above describes the SCADA data transmission verification method based on 3D spatial position perception in the embodiment of the present application. The following describes the SCADA data transmission verification system based on 3D spatial position perception in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a SCADA data transmission verification system based on 3D spatial position perception includes:

[0101] The recording module is used to record the location of temperature sensors, pressure sensors, and AGV equipment in the factory workshop through spatial coordinate scanning, and establish a spatial mapping table containing the three-dimensional coordinates and adjacency relationships of the equipment;

[0102] An encapsulation module is used to process the temperature data and pressure data collected by the sensor through a spatial coordinate encapsulation algorithm according to the spatial mapping table to generate a spatial identification data packet containing the current three-dimensional position coordinates of the sending device;

[0103] A verification module is used to verify the position of the spatial identification data packet using the KNN spatial distance calculation algorithm, calculate the Euclidean distance between the data sending location and the device registration location in the spatial mapping table, and output the position verification result;

[0104] A comparison module is configured to compare the verified temperature data with the historical temperature data of adjacent devices in the spatial mapping table according to the position verification result, and generate a neighborhood consistency detection report;

[0105] The error correction module is used to send a correction instruction containing correct coordinate information to the AGV device detected with abnormal position deviation based on the neighborhood consistency detection report to perform bidirectional error correction.

[0106] above Figure 2 The SCADA data transmission verification system based on 3D spatial position perception in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The SCADA data transmission verification device based on 3D spatial position perception in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0107] Reference Figure 3 In an embodiment of the present invention, a SCADA data transmission verification device based on 3D spatial position perception is further provided. The SCADA data transmission verification device based on 3D spatial position perception can be a server, and its internal structure can be as follows: Figure 3 As shown. The SCADA data transmission verification device based on 3D spatial position perception includes a processor, a memory, a display screen, an input device, a network interface and a database connected via a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the SCADA data transmission verification device based on 3D spatial position perception includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the SCADA data transmission verification device based on 3D spatial position perception is used to store the corresponding data in this embodiment. The network interface of the SCADA data transmission verification device based on 3D spatial position perception is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0108] Those skilled in the art will understand that Figure 3 The structure shown in is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the SCADA data transmission verification device based on 3D spatial position perception to which the solution of the present invention is applied.

[0109] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the SCADA data transmission verification method based on 3D spatial position perception.

[0110] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a SCADA data transmission verification device based on 3D spatial location perception (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0112] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A SCADA data transmission verification method based on 3D spatial position perception, characterized in that: The method comprises: The temperature sensors, pressure sensors, and AGV equipment in the factory workshop are scanned through spatial coordinates to record the equipment locations and establish a spatial mapping table containing the equipment's three-dimensional coordinates and adjacency relationships; According to the spatial mapping table, the temperature data and pressure data collected by the sensor are processed by a spatial coordinate encapsulation algorithm to generate a spatial identification data packet containing the current three-dimensional position coordinates of the sending device; Performing location verification on the spatial identification data packet using the KNN spatial distance calculation algorithm, calculating the Euclidean distance between the data sending location and the device registration location in the spatial mapping table, and outputting the location verification result; Comparing the verified temperature data with the historical temperature data of adjacent devices in the spatial mapping table according to the position verification result to generate a neighborhood consistency detection report; Based on the neighborhood consistency detection report, a correction instruction containing correct coordinate information is sent to the AGV device where the position deviation abnormality is detected to perform bidirectional error correction.

2. The SCADA data transmission verification method based on 3D spatial position perception according to claim 1, characterized in that, The temperature sensors, pressure sensors and AGV equipment in the factory workshop are scanned by spatial coordinates to record the equipment positions and establish a spatial mapping table containing the three-dimensional coordinates and adjacency relationships of the equipment, including: Perform three-dimensional spatial coordinate measurement on all equipment in the factory workshop to obtain the position coordinate values ​​of each equipment; Based on the position coordinate values ​​of each device, the distance between the devices is calculated using the Euclidean distance calculation formula to obtain the distance matrix between the devices; Setting an adjacency threshold according to the inter-device distance matrix to perform device adjacency determination processing to obtain a list of adjacent devices for each device; Performing spatial index generation processing on the location coordinate value and the adjacent device list through a hash algorithm to obtain a device space hash identifier; A data structure is constructed based on the device space hash identifier to perform mapping table organization processing, thereby obtaining a space mapping table including the device identifier, three-dimensional coordinates, a list of adjacent devices and the space hash identifier.

3. The SCADA data transmission verification method based on 3D spatial position perception according to claim 1 is characterized in that, The temperature data and pressure data collected by the sensor are processed by a spatial coordinate encapsulation algorithm according to the spatial mapping table to generate a spatial identification data packet containing the current three-dimensional position coordinates of the sending device, including: Extracting the registered coordinate information of the sending device from the spatial mapping table to perform device identification processing to obtain a device identifier and reference position coordinates; Performing data association and binding processing on the real-time monitoring values ​​of the temperature sensor and the pressure sensor and the device identifier to obtain an original data tuple with the device identity; Calculating the position offset of the current actual position coordinates of the sending device and the reference position coordinates by coordinate difference calculation to obtain a three-dimensional space position offset vector; Performing position information encryption processing based on the three-dimensional space position offset vector using an AES encryption algorithm to obtain encrypted position offset data; The original data tuple, the encrypted position offset data and the current timestamp are combined and packaged by a data packet encapsulation protocol to obtain a data packet structure of fixed length; The device current three-dimensional position coordinate field is added to the data packet structure for encapsulation processing to obtain a space identification data packet containing the current three-dimensional position coordinates of the sending device.

4. The SCADA data transmission verification method based on 3D spatial position perception according to claim 1 is characterized in that, The position verification of the spatial identification data packet using the KNN spatial distance calculation algorithm, calculating the Euclidean distance between the data sending location and the device registration location in the spatial mapping table, and outputting the position verification result includes: Parsing the device's current location coordinates and device identifier from the spatial identification data packet to perform spatial identity authentication processing to obtain the device's real-time location information and device identity information; The real-time location information of the device and the registered location coordinates of the same device identifier in the spatial mapping table are used to calculate the spatial deviation using a three-dimensional Euclidean distance formula to obtain the device spatial location offset; Based on the spatial position offset of the device, an improved KNN algorithm is used to search for K nearest neighbor devices in the spatial mapping table to perform spatial topology verification processing, identify abnormal transmissions with device ID confusion and position drift, and obtain a list of spatial neighbor devices; Performing regional authority verification on the spatial coordinates of each device in the spatial neighborhood device list and the current device position using a spatial consistency constraint model, detecting cross-regional data transmission and illegal access to dangerous areas, and obtaining regional compliance detection results; A comprehensive evaluation is performed based on the device spatial position offset and regional compliance detection results through a multi-dimensional anomaly determination algorithm to obtain a position verification result including the spatial anomaly type, deviation level and security risk level.

5. The SCADA data transmission verification method based on 3D spatial position perception according to claim 4 is characterized in that, The spatial topology verification process is performed by searching for K nearest neighbor devices in the spatial mapping table based on the device spatial position offset using an improved KNN algorithm, identifying abnormal transmissions with device ID confusion and position drift, and obtaining a list of spatial neighbor devices, including: Compare the device spatial position offset with the preset device type movement threshold, set a 0.5-meter threshold for fixed devices and a 10-meter threshold for AGV devices to determine the rationality of device movement, and obtain a device movement status identifier; Recalculating the distances of all devices in the spatial mapping table using an improved Euclidean distance weight function based on the device mobility status identifier, and multiplying the spatial distances by the device type correlation factor to obtain a weighted spatial distance matrix; Selecting K devices with the smallest distances for neighbor screening based on the weighted spatial distance matrix using a KNN nearest neighbor search algorithm, excluding device nodes that are beyond the effective communication range, and obtaining a valid neighbor device candidate set; Cross-verify the device identifier of each device in the valid neighbor device candidate set with the device identifier in the current data packet to detect whether there are multiple device IDs at the same location or conflicts between device IDs in different locations, and obtain a device ID consistency verification result; Based on the device ID consistency verification result, anomaly identification processing is performed through a spatial logic conflict detection algorithm, and data transmission with device ID confusion and position drift is marked to obtain a spatial neighborhood device list containing abnormal device identification and abnormal type.

6. The SCADA data transmission verification method based on 3D spatial position perception according to claim 1 is characterized in that, The step of comparing the verified temperature data with the historical temperature data of adjacent devices in the spatial mapping table based on the position verification result to generate a neighborhood consistency detection report includes: Filter the temperature data records with a passed verification status from the position verification results, perform valid data extraction processing, and obtain a temperature data set that passes the position verification; Based on the device identifier in the temperature data set, query the corresponding adjacent device list from the spatial mapping table to perform neighboring device identification processing to obtain spatial adjacent device information of each temperature sensor; Performing time series extraction processing on the historical temperature data of each adjacent temperature sensor in the spatial adjacent device information to obtain a historical temperature data sequence of the adjacent device; The temperature gradient rationality calculation is performed on the current temperature data and the historical temperature data series of the neighboring devices using a physical constraint verification model. Physical continuity verification is performed based on the linear relationship between the distance between devices and the temperature difference to obtain a temperature data consistency score. Anomaly detection is performed based on the temperature data consistency score using a threshold judgment algorithm, and data with a consistency score lower than a preset threshold is marked as a neighborhood inconsistency anomaly, obtaining a neighborhood consistency detection report including the abnormal device identifier, consistency score, and cause of the anomaly.

7. The SCADA data transmission verification method based on 3D spatial position perception according to claim 1 is characterized in that, The step of sending a correction instruction containing correct coordinate information to the AGV device detected with abnormal position deviation based on the neighborhood consistency detection report to perform bidirectional error correction includes: Extract abnormal device identification and abnormal type information from the neighborhood consistency detection report to perform abnormal data analysis and processing to obtain a list of devices that need to be corrected and the corresponding abnormal causes; Based on the list of devices that need to be corrected, the correct registered coordinates of each abnormal device are searched from the spatial mapping table to perform reference position acquisition processing to obtain the standard position coordinate information of each device; The standard position coordinate information and the cause of the abnormality are packaged into instructions through a correction instruction generation algorithm to generate a correction instruction data packet containing a device identifier, correct coordinates, correction type, and a timestamp; The correction instruction data packet is sent and processed through a priority transmission protocol, the transmission priority is set according to the severity of the abnormality, and the correction instruction is sent to the corresponding AGV device to obtain instruction sending confirmation information; The position correction completion confirmation information returned by the AGV device is received and bidirectional verification is performed, the device position information in the space mapping table is updated, and the correction operation log is recorded to obtain the bidirectional error correction completion status.

8. A SCADA data transmission verification system based on 3D spatial position perception, characterized in that: For implementing the SCADA data transmission verification method based on 3D spatial position perception according to any one of claims 1 to 7, the SCADA data transmission verification system based on 3D spatial position perception comprises: The recording module is used to record the location of temperature sensors, pressure sensors, and AGV equipment in the factory workshop through spatial coordinate scanning, and establish a spatial mapping table containing the three-dimensional coordinates and adjacency relationships of the equipment; An encapsulation module is used to process the temperature data and pressure data collected by the sensor through a spatial coordinate encapsulation algorithm according to the spatial mapping table to generate a spatial identification data packet containing the current three-dimensional position coordinates of the sending device; A verification module is used to verify the position of the spatial identification data packet using the KNN spatial distance calculation algorithm, calculate the Euclidean distance between the data sending location and the device registration location in the spatial mapping table, and output the position verification result; A comparison module is configured to compare the verified temperature data with the historical temperature data of adjacent devices in the spatial mapping table according to the position verification result, and generate a neighborhood consistency detection report; The error correction module is used to send a correction instruction containing correct coordinate information to the AGV device detected with abnormal position deviation based on the neighborhood consistency detection report to perform bidirectional error correction.

9. A SCADA data transmission verification device based on 3D spatial position perception, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the SCADA data transmission verification method based on 3D spatial position perception according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is caused to execute the SCADA data transmission verification method based on 3D spatial position perception according to any one of claims 1 to 7.

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