Multimodal collaborative hplc intelligent diagnostic method, system, device and medium

Abstract

The application relates to a multi-mode cooperative HPLC intelligent diagnosis method, system, device and medium, the method comprising the following steps: acquiring and identifying communication data of a target test node to obtain adaptive configuration and standardized meter reading data; collecting communication parameters and uplink and downlink meter reading messages based on the adaptive configuration, analyzing the standardized meter reading data, the communication parameters and the uplink and downlink meter reading messages to obtain topology data; determining communication quality parameters according to the topology data and the uplink and downlink meter reading messages, and completing frequency band and channel adjustment according to the communication quality parameters to obtain frequency band channel configuration; controlling master devices and slave devices to respectively access corresponding target test nodes for cooperative testing according to the topology data and the frequency band channel configuration, and obtaining signal attenuation data; calculating sectional attenuation values based on the signal attenuation data and the topology data, generating an attenuation curve through the sectional attenuation values, and then determining a fault diagnosis result according to the attenuation curve and the topology data. The application has the effect of improving positioning accuracy.

Description

Technical Field

[0001] This invention belongs to the technical field of power line carrier communication fault diagnosis, and in particular relates to a multimodal collaborative HPLC intelligent diagnostic method, system, equipment and medium. Background Technology

[0002] Currently, HPLC communication technology has been widely used in scenarios such as electricity information collection, electricity meter communication, and low-voltage power distribution network data interaction. In practical applications, there are a large number of related communication nodes, a wide distribution range, and dynamic changes in the field network topology, link transmission status, and external interference environment. There are also differences in communication protocols and carrier schemes between different devices. The field diagnostic process is constrained by the complexity of the network structure and the volatility of the communication environment.

[0003] Existing HPLC fault diagnosis methods typically judge the field communication situation based on single-point communication data or local message status. The results often only reflect the communication phenomena of local nodes or local links, making it difficult to accurately characterize the overall network status, link transmission quality, and the correlation of abnormal attenuation sections. This can easily lead to large errors in fault judgment and low location efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a multimodal collaborative HPLC intelligent diagnostic method, system, device, and medium to solve the technical problem that existing HPLC fault diagnosis methods, which rely on local communication phenomena to judge the overall network status, result in a large deviation between the fault diagnosis results and the actual abnormal location of the link.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multimodal collaborative HPLC intelligent diagnostic method, the method comprising: Acquire the communication data of the target test node, identify the communication data, and obtain the adapted configuration and standardized meter reading data; Based on the adaptation configuration, communication parameters and uplink / downlink meter reading messages are collected, and topology data is obtained by parsing the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages. Based on the topology data and the uplink and downlink meter reading messages, the communication quality parameters are determined, and the frequency band and channel are adjusted according to the communication quality parameters to obtain the frequency band and channel configuration. Based on the topology data and the frequency band channel configuration, the host device and slave device are respectively connected to the corresponding target test node to perform collaborative testing and obtain signal attenuation data. Based on the signal attenuation data and the topology data, segmented attenuation values ​​are calculated, and attenuation curves are generated using the segmented attenuation values. Then, the fault diagnosis results are determined based on the attenuation curves and the topology data.

[0006] By adopting the above technical solution, and acquiring and identifying the communication data of the target test node to obtain adapted configuration and standardized meter reading data, it is possible to uniformly identify and standardize different communication protocol information, carrier scheme information, and meter reading data. This provides a consistent data foundation for subsequent communication status analysis and diagnostic processing, reducing the impact of different access environments on diagnostic accuracy. Furthermore, by collecting communication parameters and uplink / downlink meter reading messages based on the adapted configuration, and parsing the standardized meter reading data, communication parameters, and uplink / downlink meter reading messages to obtain topology data, it is possible to characterize the network topology, hierarchical relationships, and communication status within the network where the target test node is located. This provides a basis for link partitioning, anomaly location, and test node selection. It provides valid evidence; by determining communication quality parameters based on topology data and uplink / downlink meter reading messages, and completing frequency band and channel adjustments based on these parameters, it obtains frequency band and channel configurations. This allows for targeted adjustments to communication resources based on the current link transmission status, thereby improving communication adaptability and link stability during testing. By controlling the host and slave devices to connect to the corresponding target test nodes for collaborative testing based on topology data and frequency band / channel configurations, and calculating segmented attenuation values ​​based on signal attenuation data and topology data, it determines fault diagnosis results through attenuation curves and topology data. This allows for the correlation between link attenuation and actual node connection relationships, thereby improving the diagnostic accuracy of abnormal attenuation sections and fault locations.

[0007] In one example, the present invention can be further configured as follows: acquiring the communication data of the target test node and identifying the communication data to obtain the adapted configuration and standardized meter reading data includes: The communication protocol information, carrier scheme information, and meter reading data of the target test node are obtained to obtain the communication data; The communication protocol information and the carrier scheme information in the communication data are matched to obtain the adaptation configuration; The meter reading data is converted and formatted according to the adaptation configuration to obtain the standardized meter reading data.

[0008] By adopting the above technical solution, the communication protocol information, carrier scheme information, and meter reading data of the target test node are obtained. The communication protocol information and carrier scheme information are matched and processed to obtain the adaptation configuration. Then, the meter reading data is converted and formatted according to the adaptation configuration to obtain standardized meter reading data. This can transform raw communication data from different sources and with different formats into a unified data expression form, thereby improving the compatibility and processing efficiency of the subsequent data parsing process.

[0009] In one example, the present invention can be further configured as follows: the step of collecting communication parameters and uplink / downlink meter reading messages based on the adaptation configuration, and parsing the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages to obtain topology data, including: Based on the adaptation configuration, a data acquisition method corresponding to the target test node is established; The network status of the target test node is collected according to the data collection method described above, and the communication parameters and the uplink and downlink meter reading messages are obtained. The standardized meter reading data is correlated with the communication parameters to obtain the network relationship between each node; The uplink and downlink meter reading messages are hierarchically parsed and their status is marked based on the network relationship to obtain the topology data.

[0010] By adopting the above technical solution, a data acquisition method corresponding to the target test node is established based on the adaptive configuration. The network status of the target test node is collected according to the data acquisition method to obtain communication parameters and uplink and downlink meter reading messages. The standardized meter reading data is then associated with the communication parameters, and the uplink and downlink meter reading messages are hierarchically parsed and status labeled in combination with the network relationship to obtain topology data. This allows the network structure and node status of the target test node to be restored from both the communication data and message interaction process, thereby enhancing the ability to identify network topology relationships and link status.

[0011] In one example, the present invention can be further configured as follows: determining communication quality parameters based on the topology data and the uplink and downlink meter reading messages, and completing frequency band and channel adjustment based on the communication quality parameters to obtain frequency band and channel configuration, includes: The communication links corresponding to each node are divided according to the topology data; By combining the uplink and downlink meter reading messages corresponding to each of the communication links, the transmission status analysis is performed to obtain the communication quality parameters; The communication status of different frequency bands and channels is compared and processed based on the communication quality parameters to obtain the comparison results; Based on the comparison processing results, the frequency band and channel are switched and adjusted to obtain the frequency band and channel configuration.

[0012] By adopting the above technical solution, the communication links corresponding to each node are divided according to the topology data, and the transmission status is analyzed in conjunction with the uplink and downlink meter reading messages corresponding to each communication link to obtain communication quality parameters. This allows communication anomalies to be refined to the specific link range, thereby improving the accuracy of identifying abnormal links. By comparing the communication status of different frequency bands and channels based on the communication quality parameters, the comparison results are obtained, and the frequency bands and channels are switched and adjusted according to the comparison results to obtain the frequency band and channel configuration. This ensures that the selection of frequency bands and channels matches the current communication status, thereby improving the communication reliability and testing effectiveness of subsequent collaborative testing.

[0013] In one example, the present invention can be further configured as follows: the step of configuring the host device and slave device to access the corresponding target test node for collaborative testing based on the topology data and the frequency band channel configuration to obtain signal attenuation data includes: Select the target test nodes corresponding to the host device and the slave device based on the topology data; The host device and the slave device are tested and configured according to the frequency band channel configuration; The host device and the slave device are controlled to connect to the selected target test node and establish a collaborative test relationship. Under the aforementioned collaborative testing relationship, signal transmission and reception tests are performed to obtain the signal attenuation data.

[0014] By adopting the above technical solution, and selecting the target test nodes corresponding to the host and slave devices based on the topology data, and configuring the host and slave devices for testing according to the frequency band channel configuration, the collaborative testing can be established on the basis of a clear link range and consistent communication configuration, thereby improving the relevance and comparability of the testing process. By controlling the host and slave devices to access the selected target test nodes respectively and establishing a collaborative testing relationship, signal transmission testing and reception testing are performed under the collaborative testing relationship to obtain signal attenuation data. This can form a collaborative testing mechanism at both ends of the target link, thereby effectively obtaining link data reflecting the actual transmission attenuation status.

[0015] In one example, the present invention can be further configured as follows: performing signal transmission and reception tests under the cooperative testing relationship to obtain the signal attenuation data includes: The test parameters of the two devices are set synchronously according to the frequency band channel configuration. The device acting as the host sends test signals to the corresponding target test node. The device acting as a slave controls the receiving and processing of the test signal to obtain the received signal data; The signal transmission state is characterized by attenuation based on the received signal data to obtain the signal attenuation data.

[0016] By adopting the above technical solution, the test parameters of the two devices are synchronously set according to the frequency band channel configuration, and the device acting as the master sends test signals to the corresponding target test node. The device acting as the slave receives and processes the test signals to obtain received signal data. Then, the signal transmission status is characterized by attenuation based on the received signal data to obtain signal attenuation data. This ensures that the sending conditions on the master side are consistent with the receiving conditions on the slave side, and transforms the signal changes during the link transmission process into quantifiable attenuation information, thereby improving the authenticity and analyzability of the signal attenuation data.

[0017] In one example, the present invention can be further configured as follows: calculating segmented attenuation values ​​based on the signal attenuation data and the topology data, generating an attenuation curve using the segmented attenuation values, and then determining a fault diagnosis result based on the attenuation curve and the topology data, including: The signal attenuation data is mapped to the node connection relationship in the topology data to obtain the attenuation relationship corresponding to each segment. Calculate the segmented attenuation value based on the attenuation relationship corresponding to each segment; The attenuation curves are obtained by performing curve construction processing on each of the segmented attenuation values. By combining the attenuation curve and the topology data, the abnormal attenuation section is located and analyzed to obtain the fault diagnosis result.

[0018] By adopting the above technical solution, the signal attenuation data is mapped to the node connection relationships in the topology data to obtain the attenuation relationship for each segment. Based on the attenuation relationship for each segment, the segmented attenuation value is calculated, which refines the overall link attenuation situation to specific segments, thereby improving the ability to identify local abnormal links. By constructing curves from the attenuation values ​​of each segment, an attenuation curve is obtained. Combining the attenuation curve and topology data, the abnormal attenuation segment is located and analyzed to obtain the fault diagnosis result. This can intuitively represent the attenuation differences of different segments and correspond the abnormal segment to the actual topology location, thereby improving the location accuracy of the fault diagnosis result.

[0019] In a second aspect, the present invention provides a multimodal collaborative HPLC intelligent diagnostic method system, the system comprising: The adaptation identification module is used to acquire the communication data of the target test node and identify the communication data to obtain the adaptation configuration and standardized meter reading data. The topology parsing module is used to collect communication parameters and uplink / downlink meter reading messages based on the adaptation configuration, and to parse the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages to obtain topology data. The adjustment and configuration module is used to determine communication quality parameters based on the topology data and the uplink and downlink meter reading messages, and to complete frequency band and channel adjustment based on the communication quality parameters to obtain frequency band and channel configuration; The collaborative testing module is used to control the host device and slave device to access the corresponding target test node to conduct collaborative testing based on the topology data and the frequency band channel configuration, so as to obtain signal attenuation data. The diagnostic output module is used to calculate segmented attenuation values ​​based on the signal attenuation data and the topology data, generate an attenuation curve based on the segmented attenuation values, and then determine the fault diagnosis result based on the attenuation curve and the topology data.

[0020] By adopting the above technical solution, and acquiring and identifying the communication data of the target test node to obtain adapted configuration and standardized meter reading data, it is possible to uniformly identify and standardize different communication protocol information, carrier scheme information, and meter reading data. This provides a consistent data foundation for subsequent communication status analysis and diagnostic processing, reducing the impact of different access environments on diagnostic accuracy. Furthermore, by collecting communication parameters and uplink / downlink meter reading messages based on the adapted configuration, and parsing the standardized meter reading data, communication parameters, and uplink / downlink meter reading messages to obtain topology data, it is possible to characterize the network topology, hierarchical relationships, and communication status within the network where the target test node is located. This provides a basis for link partitioning, anomaly location, and test node selection. It provides valid evidence; by determining communication quality parameters based on topology data and uplink / downlink meter reading messages, and completing frequency band and channel adjustments based on these parameters, it obtains frequency band and channel configurations. This allows for targeted adjustments to communication resources based on the current link transmission status, thereby improving communication adaptability and link stability during testing. By controlling the host and slave devices to connect to the corresponding target test nodes for collaborative testing based on topology data and frequency band / channel configurations, and calculating segmented attenuation values ​​based on signal attenuation data and topology data, it determines fault diagnosis results through attenuation curves and topology data. This allows for the correlation between link attenuation and actual node connection relationships, thereby improving the diagnostic accuracy of abnormal attenuation sections and fault locations.

[0021] In a third aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the multimodal collaborative HPLC intelligent diagnostic method.

[0022] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the multimodal collaborative HPLC intelligent diagnostic method. Attached Figure Description

[0023] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a multimodal collaborative HPLC intelligent diagnostic method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the multimodal collaborative HPLC intelligent diagnostic device according to an embodiment of the present invention; Figure 3 This is a front view of the multimodal collaborative HPLC intelligent diagnostic device according to an embodiment of the present invention; Figure 4 This is a left view of the multimodal collaborative HPLC intelligent diagnostic device according to an embodiment of the present invention; Figure 5 This is a right view of the multimodal collaborative HPLC intelligent diagnostic device according to an embodiment of the present invention; Figure 6 This is a top view of the multimodal collaborative HPLC intelligent diagnostic device according to an embodiment of the present invention; Figure 7 This is a bottom view of the multimodal collaborative HPLC intelligent diagnostic device according to an embodiment of the present invention; Figure 8 This is a structural block diagram of the multimodal collaborative HPLC intelligent diagnostic method system according to an embodiment of the present invention; Figure 9 This is a structural block diagram of an electronic device according to an embodiment of the present invention.

[0024] In the diagram: 1-Single-phase energy meter communication module interface; 2-Three-phase energy meter communication module interface; 3-Concentrator communication module interface; 4-Concentrator 4G module communication interface; 5-LCD display screen; 6-Network port; 7-Power supply; 8-USB interface; 9-220V high-voltage interface; 10-Infrared interface; 11-RS485 interface; 12-USB charging interface; 13-Neck strap. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0026] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0027] Example 1 like Figure 1 As shown, this invention discloses a multimodal collaborative HPLC intelligent diagnostic method, which specifically includes the following steps: S10: Acquire the communication data of the target test node, identify the communication data, and obtain the adapted configuration and standardized meter reading data.

[0028] Specifically, such as Figures 2-7 The schematic diagram and six-view drawing of the multimodal collaborative HPLC intelligent diagnostic system shown depict the main body of the device, which includes a single-phase energy meter communication module interface 1, a three-phase energy meter communication module interface 2, a concentrator communication module interface 3, a concentrator 4G module communication interface 4, an LCD screen 5, a network port 6, a power supply 7, a USB interface 8, a 220V high-voltage power interface 9, an infrared interface 10, an RS485 interface 11, a USB charging interface 12, and a neck strap 13. The LCD screen 5 is used for function selection, parameter setting, data visualization, and human-machine interaction. The various module interfaces are used for high-voltage power supply and low-voltage data interaction detection of the single-phase STA module, three-phase STA module, and CCO module. The infrared interface 10 and RS485 interface 11 are used for meter reading and equipment detection via infrared and wired communication. The 220V high-voltage power interface 9 and the power supply 7 are used for equipment power supply control. During the target test node access process, communication data is acquired through the above interfaces and subsequent identification processing is completed to obtain adapted configuration and standardized meter reading data.

[0029] S20: Collect communication parameters and uplink / downlink meter reading messages based on the adaptive configuration, and parse the standardized meter reading data, communication parameters, and uplink / downlink meter reading messages to obtain topology data.

[0030] Specifically, the communication monitoring process corresponding to the target test node is initiated according to the adapted configuration. With the cooperation of carrier communication monitoring and wireless monitoring, node communication status information and meter reading interaction information are continuously collected. The standardized meter reading data is jointly analyzed with the collected communication parameters and uplink and downlink meter reading messages to extract the connection layer, network relationship and network access status between nodes. The device network structure, signal status and communication level are displayed on the display interface, thereby generating topology data that can be used for subsequent link analysis and collaborative testing.

[0031] S30: Determine communication quality parameters based on topology data and uplink / downlink meter reading messages, and complete frequency band and channel adjustment based on communication quality parameters to obtain frequency band and channel configuration.

[0032] Specifically, based on the connection relationships and link levels of each node in the topology data, the message interaction of the communication path where the target test node is located is continuously analyzed. Combined with the response status, transmission integrity and abnormal status identification of communication interruption, data packet loss and signal attenuation in the uplink and downlink meter reading messages, the communication quality changes are identified. Then, the communication performance under different frequency bands and channels is compared, and automatic switching or manual confirmation switching is performed to form a frequency band and channel configuration that is compatible with the current interference environment.

[0033] S40: Based on the topology data and frequency band channel configuration, control the host device and slave device to connect to the corresponding target test node to conduct collaborative testing and obtain signal attenuation data.

[0034] Specifically, the target link and corresponding node location to be tested for attenuation are determined based on the topology data. The host and slave devices are connected to the corresponding test nodes under the same frequency band and channel configuration. By establishing a collaborative test relationship between the master and slave, the link transmission status information is obtained during the process of continuously initiating test signal transmission at one end and receiving and parsing at the other end, thereby forming signal attenuation data that can reflect the attenuation of the target link.

[0035] S50: Calculates segmented attenuation values ​​based on signal attenuation data and topology data, generates attenuation curves based on segmented attenuation values, and then determines fault diagnosis results based on attenuation curves and topology data.

[0036] Specifically, the signal attenuation data obtained from collaborative testing is correlated with the node connection relationships represented by the topology data to determine the attenuation distribution of each segment in the target link. Based on this, the segmental attenuation value of each segment is calculated, attenuation curves are constructed according to the segment order, abnormal attenuation segments are identified and located, and the link location and interference range corresponding to the abnormal segments are determined by combining the topology hierarchy relationship to obtain the fault diagnosis results.

[0037] In one embodiment, step S10 involves acquiring the communication data of the target test node and identifying the communication data to obtain the adapted configuration and standardized meter reading data, including: S11: Obtain the communication protocol information, carrier scheme information, and meter reading data of the target test node to obtain communication data.

[0038] Specifically, such as Figures 2-7The schematic diagram and six-view drawing of the multimodal collaborative HPLC intelligent diagnostic system shown indicate that the main body of the device is equipped with a single-phase energy meter communication module interface 1, a three-phase energy meter communication module interface 2, a concentrator communication module interface 3, a concentrator 4G module communication interface 4, an infrared interface 10, an RS485 interface 11, and a 220V high-voltage interface 9. When acquiring communication data from the target test node, the corresponding interface access path can be selected according to the type of the object under test to connect to the single-phase energy meter communication module, the three-phase energy meter communication module, the concentrator communication module, or the concentrator 4G module. Meter reading data and equipment detection data are acquired through the infrared interface 10 or the RS485 interface 11. At the same time, function selection and parameter settings are completed through the LCD screen 5, thereby obtaining communication protocol information, carrier scheme information, and meter reading data to obtain communication data.

[0039] S12: Match the communication protocol information and carrier scheme information in the communication data to obtain the adaptation configuration.

[0040] Specifically, the communication protocol information and carrier scheme information in the communication data are compared and identified by calling the preset parsing rules to determine the communication protocol type and carrier access method used by the current target test node. Based on the identification results, the corresponding data transmission and reception rules, copying and interaction rules, and access and monitoring methods required for subsequent monitoring processes are determined, so that the device can complete the adaptation process to match the target test node in a hybrid network environment and obtain the adaptation configuration.

[0041] S13: Perform field conversion and format standardization on the meter reading data according to the adaptation configuration to obtain standardized meter reading data.

[0042] Specifically, based on the data organization and parsing rules determined in the adaptation configuration, the data items in the original meter reading data are subjected to field extraction, field mapping, format conversion, and content regularization. The data content returned under different communication protocols and different carrier schemes is uniformly transformed into a standardized data expression form with consistent structure and semantics, so that subsequent topology parsing, status monitoring, and anomaly analysis can be processed based on a unified data foundation to obtain standardized meter reading data.

[0043] In one embodiment, in step S20, communication parameters and uplink / downlink meter reading messages are collected based on the adaptation configuration, and the standardized meter reading data, communication parameters, and uplink / downlink meter reading messages are parsed to obtain topology data, including: S21: Establish a data acquisition method corresponding to the target test node based on the adaptation configuration.

[0044] Specifically, based on the communication access rules, monitoring rules, and data interaction rules determined in the adaptation configuration, the acquisition entry point, acquisition timing, and acquisition content corresponding to the target test node are determined, so that carrier communication monitoring and wireless monitoring can collaboratively acquire data according to the current node's communication environment, thereby establishing a data acquisition method corresponding to the target test node.

[0045] S22: Collect the network status of the target test node according to the data acquisition method to obtain communication parameters and uplink and downlink meter reading messages.

[0046] Specifically, the network operation status of the target test node and its associated nodes is continuously collected according to the established data collection method. Communication parameters such as meter number, source TEI, network NID, and RSSI signal strength are extracted from the collection results to characterize the node's identity, network affiliation, and signal status. At the same time, uplink and downlink meter reading messages generated during the meter reading process are captured synchronously, so that the communication parameters and uplink and downlink meter reading messages can jointly reflect the current network structure, link status, and interaction process.

[0047] S23: Correlate standardized meter reading data with communication parameters to obtain the network relationship between each node.

[0048] Specifically, based on the device identifier and reading object information in the standardized meter reading data, it is associated with the meter number, source TEI, network NID and signal status information in the communication parameters to determine the affiliation, interconnection and hierarchical relationship of each node in the same network environment, and the node connection logic obtained therefrom is organized into the network relationship between each node.

[0049] S24: Combine the network topology relationship to perform hierarchical parsing and status labeling on the uplink and downlink meter reading messages to obtain topology data.

[0050] Specifically, based on the obtained network topology, the sending direction, response direction, interaction sequence, and arrival status of uplink and downlink meter reading messages are analyzed hierarchically to determine the hierarchical position of each node in the communication link and whether it is in a normal network access state, an abnormal interaction state, or an interruption state. The node hierarchy, link direction, network structure, and network access status are uniformly labeled to obtain topology data.

[0051] In one embodiment, step S30, namely determining communication quality parameters based on topology data and uplink / downlink meter reading messages, and completing frequency band and channel adjustment based on the communication quality parameters to obtain frequency band and channel configuration, includes: S31: Divide the communication links corresponding to each node according to the topology data.

[0052] Specifically, based on the node hierarchy, link connection direction, and network structure determined in the topology data, the communication path where the target test node is located is divided into corresponding communication link segments, so that each communication link can correspond to a clear node connection range and message transmission range, thus establishing a link division basis for subsequent communication quality analysis.

[0053] S32: Combine the uplink and downlink meter reading messages corresponding to each communication link to perform transmission status analysis and obtain communication quality parameters.

[0054] Specifically, the uplink and downlink meter reading messages corresponding to each communication link are collected according to the link segment. The integrity, continuity and abnormal situations in the message sending, forwarding, response and return process are analyzed to identify state changes such as communication interruption, data packet loss, response abnormality and attenuation. Based on this, communication quality parameters that can characterize the communication performance of each link are extracted.

[0055] S33: Compare and process the communication status of different frequency bands and channels based on communication quality parameters to obtain the comparison results.

[0056] Specifically, based on the communication quality parameters of each link, the communication status of links under different frequency bands and channels is compared horizontally. The transmission stability, the degree of anomaly occurrence, and the degree of interference under different configuration conditions are analyzed to determine the frequency band and channel combination that is more suitable for maintaining stable communication in the current power grid environment, and the comparison processing results are obtained.

[0057] S34: Adjust the frequency band and channel according to the comparison processing results to obtain the frequency band and channel configuration.

[0058] Specifically, based on the comparison processing results, frequency band and channel switching adjustments are performed to ensure that the working configuration used by the host and slave devices during subsequent collaborative testing matches the current link environment. After automatic switching or manual confirmation of switching, the new working frequency band and working channel are unified into a frequency band and channel configuration for subsequent testing.

[0059] In one embodiment, in step S40, the host device and slave device are configured to access the corresponding target test node for collaborative testing based on topology data and frequency band channel configuration to obtain signal attenuation data, including: S41: Select the target test nodes corresponding to the host and slave devices based on the topology data.

[0060] Specifically, based on the node hierarchy, link connection, and abnormal segment distribution reflected in the topology data, two target test nodes suitable for collaborative attenuation testing are selected from the target link. The selected nodes are located at different positions on the link under test or at relevant positions in abnormal segments, thereby establishing a meaningful access node correspondence between the host device and the slave device.

[0061] S42: Perform test configuration on the host and slave devices according to the frequency band and channel configuration.

[0062] Specifically, the operating frequency band, operating channel, and communication configuration required for the test process of the host and slave devices are uniformly set according to the frequency band and channel configuration, so that the two devices can carry out collaborative testing on the same configuration basis, and the test scenario and mode are confirmed through the touch screen to ensure that the subsequent test results are comparable and corresponding.

[0063] S43: Control the host device and slave device to connect to the selected target test node and establish a collaborative test relationship.

[0064] Specifically, the host device and the slave device are connected to the selected target test node respectively, and the two devices are established in a master-slave cooperative test relationship through mode settings. The host device plays the role of initiating the test signal, and the slave device plays the role of receiving the test signal, so that the same target link can enter the attenuation test state with the cooperation of both ends.

[0065] S44: Perform signal transmission and reception tests under a cooperative testing relationship to obtain signal attenuation data.

[0066] Specifically, after the host device and the slave device have established a collaborative testing relationship, the host device initiates the test signal transmission process along the target link, the slave device receives and parses the arriving test signal, and converts the changes of the test signal during the link transmission process into data content that can be used to characterize the transmission attenuation state, thereby obtaining signal attenuation data.

[0067] In one embodiment, step S44, i.e., performing signal transmission and reception tests under a cooperative testing relationship to obtain signal attenuation data, includes: S441: Synchronize the test parameters of the two devices according to the frequency band channel configuration.

[0068] Specifically, before the collaborative test begins, the test parameters of the host and slave devices are synchronously set according to the frequency band and channel configuration, so that the two devices can be tested on the same working frequency band and working channel, and the parameter benchmarks are kept consistent during the test process to avoid attenuation data distortion due to configuration differences.

[0069] S442: Controls the device acting as the host to send test signals to the corresponding target test node.

[0070] Specifically, in host mode, test signals are continuously sent to the target test node. The sent test signals adopt an encrypted form used for link testing and are continuously output according to the synchronized test parameters to ensure that the target link has continuous and stable excitation input during the test.

[0071] S443: Controls the device acting as a slave to receive and process the test signal to obtain the received signal data.

[0072] Specifically, in slave mode, the test signal received via the target link is processed, and the arrival status, content integrity, and reception status of the test signal are analyzed. The analysis results are then organized into received signal data that reflects the signal performance of the receiving end.

[0073] S444: Based on the received signal data, the signal transmission state is characterized by attenuation to obtain signal attenuation data.

[0074] Specifically, the transmission status of the test signal in the target link is characterized by attenuation based on the signal changes reflected in the received signal data. The attenuation degree, abnormal change trend and link transmission degradation after the signal arrives are transformed into an attenuation data expression form that can be used for subsequent segment analysis, thus obtaining signal attenuation data.

[0075] In one embodiment, step S50 involves calculating segmented attenuation values ​​based on signal attenuation data and topology data, generating an attenuation curve using the segmented attenuation values, and then determining the fault diagnosis result based on the attenuation curve and topology data, including: S51: The signal attenuation data is matched with the node connection relationship in the topology data to obtain the attenuation relationship for each segment.

[0076] Specifically, the signal attenuation data is processed according to the node connection relationship, link connection direction and segment division order in the topology data, so that each segment of signal attenuation data can be mapped to a specific node connection interval, and the attenuation relationship corresponding to each segment in the target link can be determined accordingly.

[0077] S52: Calculate the segmented attenuation value based on the attenuation relationship corresponding to each segment.

[0078] Specifically, the target link is analyzed segment by segment based on the attenuation relationship of each segment, and the signal attenuation degree corresponding to different segments is converted into comparable segmented attenuation values, so that the transmission loss of each segment can be expressed in a unified way, and a segmented data basis is provided for subsequent curve construction.

[0079] S53: Perform curve construction processing on the attenuation values ​​of each segment to obtain the attenuation curve.

[0080] Specifically, the attenuation values ​​of each segment are arranged and processed into curves according to the segment order of the target link, so that the attenuation degree of different segments is displayed in a continuous variation form, and the attenuation difference of each segment is intuitively reflected in the curve display results, thus obtaining the attenuation curve.

[0081] S54: Combine attenuation curves and topology data to locate and analyze abnormal attenuation sections, and obtain fault diagnosis results.

[0082] Specifically, the segments in the attenuation curve that show sudden changes, abnormal increases, or continuous abnormalities are matched with the node connection positions in the topology data to locate the link positions where the abnormal attenuation segments are located. Then, the distribution of possible interference sources or abnormal link positions is determined by combining the node hierarchy, link direction, and segment distribution, and finally, the fault diagnosis results are obtained.

[0083] Example 2 like Figure 3 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a multimodal collaborative HPLC intelligent diagnostic method system, comprising: The adaptation identification module is used to acquire the communication data of the target test node, identify the communication data, and obtain the adaptation configuration and standardized meter reading data. The topology parsing module is used to collect communication parameters and uplink / downlink meter reading messages based on the adaptive configuration, and to parse the standardized meter reading data, communication parameters, and uplink / downlink meter reading messages to obtain topology data. The adjustment and configuration module is used to determine communication quality parameters based on topology data and uplink and downlink meter reading messages, and to complete frequency band and channel adjustment based on the communication quality parameters to obtain frequency band and channel configuration; The collaborative testing module is used to control the host device and slave device to connect to the corresponding target test node to conduct collaborative testing based on topology data and frequency band channel configuration, and to obtain signal attenuation data. The diagnostic output module is used to calculate segmented attenuation values ​​based on signal attenuation data and topology data, generate attenuation curves based on the segmented attenuation values, and then determine the fault diagnosis results based on the attenuation curves and topology data.

[0084] Optional, the adaptation and recognition module includes: The data acquisition submodule is used to acquire the communication protocol information, carrier scheme information, and meter reading data of the target test node to obtain communication data; The matching processing submodule is used to match the communication protocol information and carrier scheme information in the communication data to obtain the adaptation configuration; The format normalization submodule is used to perform field conversion and format normalization on the meter reading data according to the adaptation configuration to obtain standardized meter reading data.

[0085] Optionally, the topology resolution module includes: The data acquisition and setup submodule is used to establish a data acquisition method corresponding to the target test node based on the adaptation configuration. The status acquisition submodule is used to collect the network status of the target test node according to the data acquisition method, and obtain communication parameters and uplink and downlink meter reading messages; The association processing submodule is used to associate standardized meter reading data with communication parameters to obtain the network relationship between each node; The hierarchical calibration submodule is used to perform hierarchical parsing and status calibration on uplink and downlink meter reading messages in combination with the network topology to obtain topology data.

[0086] Optionally, the adjustment configuration module includes: The link segmentation submodule is used to segment the communication links corresponding to each node based on the topology data; The status analysis submodule is used to perform transmission status analysis by combining the uplink and downlink meter reading messages corresponding to each communication link, and to obtain communication quality parameters. The comparison processing submodule is used to compare the communication status of different frequency bands and channels based on communication quality parameters and obtain the comparison processing results. The switching and adjustment submodule is used to switch and adjust the frequency band and channel according to the comparison processing results to obtain the frequency band and channel configuration.

[0087] Optionally, the collaborative testing module includes: The node selection submodule is used to select the target test nodes that the host and slave devices should connect to based on the topology data. The test configuration submodule is used to test and configure the host and slave devices according to the frequency band and channel configuration. The collaborative setup submodule is used to control the host device and slave device to connect to the selected target test node and establish a collaborative test relationship; The attenuation test submodule is used to perform signal transmission and reception tests under a collaborative testing relationship to obtain signal attenuation data.

[0088] Optionally, the attenuation test submodule includes: The parameter synchronization unit is used to synchronize the test parameters of the two devices according to the frequency band channel configuration. The signal transmitting unit is used to control the host device to send test signals to the corresponding target test node. The signal receiving unit is used to control the slave device to receive and process the test signal to obtain the received signal data; The attenuation characterization unit is used to characterize the signal transmission state based on the received signal data to obtain signal attenuation data.

[0089] Optionally, the diagnostic output module includes: The relationship mapping submodule is used to map the signal attenuation data to the node connection relationships in the topology data to obtain the attenuation relationship corresponding to each segment. The segmented calculation submodule is used to calculate the segmented attenuation value based on the attenuation relationship corresponding to each segment. The curve construction submodule is used to perform curve construction processing on the attenuation values ​​of each segment to obtain the attenuation curve; The location analysis submodule is used to locate and analyze abnormal attenuation sections by combining attenuation curves and topology data, and obtain fault diagnosis results.

[0090] Example 3 like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a multimodal collaborative HPLC intelligent diagnostic method; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0091] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the multimodal collaborative HPLC intelligent diagnostic method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0092] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0093] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0094] The memory 101 in the electronic device 100 stores multiple instructions to implement a multimodal collaborative HPLC intelligent diagnostic method, and the processor 102 can execute multiple instructions to achieve the following: Acquire the communication data of the target test node, identify the communication data, and obtain the adapted configuration and standardized meter reading data; Based on the adaptation configuration, communication parameters and uplink / downlink meter reading messages are collected, and topology data is obtained by parsing the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages. Based on the topology data and the uplink and downlink meter reading messages, the communication quality parameters are determined, and the frequency band and channel are adjusted according to the communication quality parameters to obtain the frequency band and channel configuration. Based on the topology data and the frequency band channel configuration, the host device and slave device are respectively connected to the corresponding target test node to perform collaborative testing and obtain signal attenuation data. Based on the signal attenuation data and the topology data, segmented attenuation values ​​are calculated, and attenuation curves are generated using the segmented attenuation values. Then, the fault diagnosis results are determined based on the attenuation curves and the topology data.

[0095] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multimodal synergistic HPLC intelligent diagnostic method, characterized in that, The method includes: Acquire the communication data of the target test node, identify the communication data, and obtain the adapted configuration and standardized meter reading data; Based on the adaptation configuration, communication parameters and uplink / downlink meter reading messages are collected, and topology data is obtained by parsing the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages. Based on the topology data and the uplink and downlink meter reading messages, the communication quality parameters are determined, and the frequency band and channel are adjusted according to the communication quality parameters to obtain the frequency band and channel configuration. Based on the topology data and the frequency band channel configuration, the host device and slave device are respectively connected to the corresponding target test node to perform collaborative testing and obtain signal attenuation data. Based on the signal attenuation data and the topology data, segmented attenuation values ​​are calculated, and attenuation curves are generated using the segmented attenuation values. Then, the fault diagnosis results are determined based on the attenuation curves and the topology data.

2. The multimodal synergistic HPLC intelligent diagnostic method according to claim 1, characterized in that, The process of acquiring communication data from the target test node and identifying the communication data to obtain adapted configuration and standardized meter reading data includes: The communication protocol information, carrier scheme information, and meter reading data of the target test node are obtained to obtain the communication data; The communication protocol information and the carrier scheme information in the communication data are matched to obtain the adaptation configuration; The meter reading data is converted and formatted according to the adaptation configuration to obtain the standardized meter reading data.

3. The multimodal synergistic HPLC intelligent diagnostic method according to claim 1, characterized in that, The process involves collecting communication parameters and uplink / downlink meter reading messages based on the adapted configuration, and parsing the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages to obtain topology data, including: Based on the adaptation configuration, a data acquisition method corresponding to the target test node is established; The network status of the target test node is collected according to the data collection method described above, and the communication parameters and the uplink and downlink meter reading messages are obtained. The standardized meter reading data is correlated with the communication parameters to obtain the network relationship between each node; The uplink and downlink meter reading messages are hierarchically parsed and their status is marked based on the network relationship to obtain the topology data.

4. The multimodal synergistic HPLC intelligent diagnostic method according to claim 1, characterized in that, The step of determining communication quality parameters based on the topology data and the uplink and downlink meter reading messages, and completing frequency band and channel adjustment based on the communication quality parameters to obtain frequency band and channel configuration, includes: The communication links corresponding to each node are divided according to the topology data; By combining the uplink and downlink meter reading messages corresponding to each of the communication links, the transmission status analysis is performed to obtain the communication quality parameters; The communication status of different frequency bands and channels is compared and processed based on the communication quality parameters to obtain the comparison results; Based on the comparison processing results, the frequency band and channel are switched and adjusted to obtain the frequency band and channel configuration.

5. The multimodal synergistic HPLC intelligent diagnostic method according to claim 1, characterized in that, The step of configuring the host and slave devices to access the corresponding target test nodes for collaborative testing based on the topology data and the frequency band channel configuration, and obtaining signal attenuation data, includes: Select the target test nodes corresponding to the host device and the slave device based on the topology data; The host device and the slave device are tested and configured according to the frequency band channel configuration; The host device and the slave device are controlled to connect to the selected target test node and establish a collaborative test relationship. Under the aforementioned collaborative testing relationship, signal transmission and reception tests are performed to obtain the signal attenuation data.

6. The multimodal synergistic HPLC intelligent diagnostic method according to claim 5, characterized in that, The signal transmission test and reception test are performed under the cooperative test relationship to obtain the signal attenuation data, including: The test parameters of the two devices are set synchronously according to the frequency band channel configuration. The device acting as the host sends test signals to the corresponding target test node. The device acting as a slave controls the receiving and processing of the test signal to obtain the received signal data; The signal transmission state is characterized by attenuation based on the received signal data to obtain the signal attenuation data.

7. The multimodal synergistic HPLC intelligent diagnostic method according to claim 1, characterized in that, The process of calculating segmented attenuation values ​​based on the signal attenuation data and the topology data, generating an attenuation curve using the segmented attenuation values, and then determining the fault diagnosis result based on the attenuation curve and the topology data includes: The signal attenuation data is mapped to the node connection relationship in the topology data to obtain the attenuation relationship corresponding to each segment. Calculate the segmented attenuation value based on the attenuation relationship corresponding to each segment; The attenuation curves are obtained by performing curve construction processing on each of the segmented attenuation values. By combining the attenuation curve and the topology data, the abnormal attenuation section is located and analyzed to obtain the fault diagnosis result.

8. A multimodal collaborative HPLC intelligent diagnostic method system, characterized in that, The system includes: The adaptation identification module is used to acquire the communication data of the target test node and identify the communication data to obtain the adaptation configuration and standardized meter reading data. The topology parsing module is used to collect communication parameters and uplink / downlink meter reading messages based on the adaptation configuration, and to parse the standardized meter reading data, the communication parameters, and the uplink / downlink meter reading messages to obtain topology data. The adjustment and configuration module is used to determine communication quality parameters based on the topology data and the uplink and downlink meter reading messages, and to complete frequency band and channel adjustment based on the communication quality parameters to obtain frequency band and channel configuration; The collaborative testing module is used to control the host device and slave device to access the corresponding target test node to conduct collaborative testing based on the topology data and the frequency band channel configuration, so as to obtain signal attenuation data. The diagnostic output module is used to calculate segmented attenuation values ​​based on the signal attenuation data and the topology data, generate an attenuation curve based on the segmented attenuation values, and then determine the fault diagnosis result based on the attenuation curve and the topology data.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the steps of the multimodal collaborative HPLC intelligent diagnostic method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the steps of the multimodal collaborative HPLC intelligent diagnostic method as described in any one of claims 1 to 7.

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