Remote data monitoring method and system based on charging cabinet

Abstract

The embodiment of the invention discloses a remote data monitoring method and system based on a charging cabinet, and the method comprises the steps: firstly building an encrypted communication link between the charging cabinet and a remote monitoring server, and collecting a real-time communication log set of the charging cabinet in a preset monitoring period through the encrypted communication link, the record comprises a communication session establishment record and a data transmission interaction record; performing network state parameter extraction on the real-time communication log set to obtain a link quality feature sequence and a delay fluctuation feature sequence, and then executing a routing node data packet tracking operation to generate a routing tracking result set; and identifying network fault point position information according to the route tracking result set, generating a network optimization suggestion report containing a fault repair priority and a path optimization strategy in combination with the link quality feature sequence and the delay fluctuation feature sequence, and pushing the network optimization suggestion report to an operation and maintenance management terminal of the remote monitoring server. And intelligent management and efficient operation and maintenance of the network are realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data analysis technology, and specifically to a remote data monitoring method and system based on a charging cabinet. Background Art

[0002] Currently, monitoring the network performance between charging cabinets and remote monitoring servers is key to ensuring the safe and normal operation of charging cabinets. However, in the actual process of network performance fault diagnosis and optimization, existing methods lack systematicity and specificity. They cannot quickly and accurately locate network fault points, nor can they provide effective fault repair priority and path optimization strategies, resulting in delayed network fault repair. Therefore, a more comprehensive technical solution is needed to address these issues. Summary of the Invention

[0003] The embodiments of the present invention provide a remote data monitoring method and system based on a charging cabinet.

[0004] In a first aspect, an embodiment of the present invention provides a remote data monitoring method based on a charging cabinet, which is applied to a remote data monitoring system based on a charging cabinet. The method includes: Establishing an encrypted communication link between the charging cabinet and the remote monitoring server, and collecting a set of real-time communication logs of the charging cabinet within a preset monitoring period through the encrypted communication link, wherein the real-time communication logs include communication session establishment records and data transmission interaction records; Performing network status parameter extraction processing on the real-time communication log set to obtain a link quality feature sequence characterizing network connectivity stability and a delay fluctuation feature sequence characterizing data transmission efficiency; Perform routing node data packet tracking operations based on the link quality feature sequence and the delay fluctuation feature sequence to generate a routing node data packet tracking result set including node transmission path information and data packet state change information; The network fault point location information is identified based on the route tracking result set, and a network optimization recommendation report including fault repair priority and path optimization strategy is generated in combination with the link quality feature sequence and delay fluctuation feature sequence, and the network optimization recommendation report is pushed to the operation and maintenance management terminal of the remote monitoring server.

[0005] In a second aspect, an embodiment of the present invention provides a remote data monitoring system based on a charging cabinet, comprising: processor; a storage device having a computer program stored thereon, When the computer program is executed by the processor, the processor implements any of the remote data monitoring methods based on the charging cabinet.

[0006] An embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the remote data monitoring method based on the charging cabinet are implemented.

[0007] The embodiment of the present invention first establishes an encrypted communication link between the charging cabinet and the remote monitoring server and collects a set of real-time communication logs within a preset monitoring period, which includes communication session establishment records and data transmission interaction records; secondly, the real-time communication log set is processed to extract network status parameters to obtain link quality feature sequences and delay fluctuation feature sequences, which can accurately characterize network connectivity stability and data transmission efficiency; then, based on the link quality feature sequence and delay fluctuation feature sequence, a routing node packet tracking operation is performed to generate a routing tracking result set containing node transmission path information and packet state change information, so that the transmission path and state change of the packet in the network are more complete and accurate; finally, the network fault point location information is identified based on the routing tracking result set, and a network optimization recommendation report is generated in combination with the link quality feature sequence and delay fluctuation feature sequence. The report contains fault repair priority and path optimization strategy, which can solve network faults in a targeted manner, optimize network paths, and improve network reliability and transmission efficiency. The network optimization recommendation report is further pushed to the operation and maintenance management terminal of the remote monitoring server, which facilitates the operation and maintenance personnel to obtain information in a timely manner and take corresponding measures, thereby realizing intelligent management and efficient operation and maintenance of the network. In summary, the embodiment of the present invention improves the performance and stability of the charging cabinet remote data monitoring network. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of a remote data monitoring method based on a charging cabinet provided in an embodiment of the present invention.

[0009] Figure 2 A schematic diagram of the basic structure of a remote data monitoring system based on a charging cabinet provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0011] See also Figure 1 As shown in FIG, this figure is a flow chart of a remote data monitoring method based on a charging cabinet provided by an embodiment of the present invention. This method can be applied to a remote data monitoring system based on a charging cabinet. Figure 1 As shown, the method includes steps 110 to 140.

[0012] Step 110: Establish an encrypted communication link between the charging cabinet and the remote monitoring server, and collect a set of real-time communication logs of the charging cabinet within a preset monitoring period through the encrypted communication link. The real-time communication log set includes communication session establishment records and data transmission interaction records.

[0013] In an embodiment of the present invention, an encrypted communication link can be established between the charging cabinet and the remote monitoring server, such as by combining symmetric and asymmetric encryption. First, the remote monitoring server generates a pair of public and private keys, with the public key being public and the private key being confidential. When the charging cabinet establishes a connection request with the remote monitoring server, it obtains the server's public key. The charging cabinet then uses the public key to encrypt a temporarily generated symmetric encryption key and sends the encrypted symmetric key to the server. The server uses the private key to decrypt the symmetric key, thereby establishing an encrypted communication link between the two parties based on the same symmetric key.

[0014] During the preset monitoring period, the charging cabinet's real-time communication logs are collected via this encrypted communication link. For communication session establishment records, each step of each session establishment attempt is recorded in detail. For example, when the charging cabinet initiates a session establishment request, the request timestamp, request type (such as authentication request, data transmission request, etc.), and related parameters carried (such as device identification, version information, etc.) are recorded. If the session is successfully established, the success timestamp, established connection type (such as TCP connection, UDP connection, etc.), and assigned port number are recorded. If the session establishment fails, the reason for the failure is recorded, such as server rejection, network timeout, etc.

[0015] For data transmission interaction records, detailed information about each data packet is recorded. When the charging cabinet sends a data packet, the sending time, size, type (such as request packet, response packet, etc.), and a summary of the data content are recorded. When the corresponding confirmation packet is received, the reception time and confirmation status (such as successful confirmation, partial confirmation, etc.) are recorded. If retransmission occurs during the transmission process, the number of retransmissions and the retransmission time are recorded. By collecting these detailed information, a complete real-time communication log collection is formed.

[0016] Step 120: Perform network status parameter extraction processing on the real-time communication log set to obtain a link quality feature sequence characterizing network connectivity stability and a delay fluctuation feature sequence characterizing data transmission efficiency.

[0017] After obtaining a collection of real-time communication logs, they need to be deeply analyzed to extract key network status parameters. This process involves multiple steps and various processing methods, the purpose of which is to convert the raw communication log data into a feature sequence that can intuitively reflect the network status.

[0018] In one embodiment, the network status parameter extraction process is performed on the real-time communication log set to obtain a link quality feature sequence characterizing network connectivity stability and a delay fluctuation feature sequence characterizing data transmission efficiency, including: Step 121: Perform session success rate statistics on the communication session establishment records in the real-time communication log set, calculate the ratio of the number of communication sessions successfully established to the total number of communication sessions attempted to be established per unit time, and generate a link connection success rate time series.

[0019] When processing communication session establishment records, the records are first sorted chronologically, dividing the entire preset monitoring period into multiple unit time intervals. Within each unit time interval, the total number of communication session establishment attempts and the number of successful communication session establishments are counted. For example, within a short unit time interval, a charging cabinet may initiate multiple different types of session establishment requests, including authentication sessions and data synchronization sessions. Each request is categorized and counted based on the success or failure indicator in the record.

[0020] During the statistical process, records must be rigorously screened and verified. Abnormal records, such as incomplete records or those with unusual timestamps, must be corrected or eliminated to ensure statistical accuracy. Next, the ratio of the number of successfully established communication sessions to the total number of attempted communication sessions within each unit time interval is calculated. Over time, these ratios are arranged in chronological order to form a time series of link connection success rates. This series can intuitively reflect the stability of network link connections at different points in time.

[0021] Step 122: performing transmission delay measurement processing on the data transmission interaction records in the real-time communication log set, extracting the time interval from the sending moment to the receiving confirmation moment of each data packet, and generating a transmission delay time series.

[0022] When processing data transmission interaction records, focus on the time each data packet is sent and the time it is acknowledged. For each sent data packet, accurately locate its send timestamp in the record. When the corresponding acknowledgement packet is received, record its receive timestamp. By calculating the difference between these two timestamps, you can determine the transmission delay of the packet.

[0023] In practice, some complex situations may arise. For example, due to network jitter or packet loss, multiple retransmissions may occur. For retransmitted packets, the transmission delay must be calculated based on the time they were first sent and the time when the final acknowledgment was received. Furthermore, for concurrently transmitted packets, these packets must be accurately distinguished and correlated to ensure that the calculated transmission delay is for each individual packet.

[0024] As time goes by, the transmission delay of each data packet is arranged in the order of sending time to generate a transmission delay time series, which can reflect the delay of data transmission at different time points.

[0025] Step 123: Perform sliding window smoothing processing on the link connection success rate time series to generate a link quality feature sequence with time continuity.

[0026] To make the link connection success rate time series more continuous and stable, a sliding window smoothing process is required. First, determine an appropriate sliding window size. The size of the sliding window determines the data range involved in the smoothing calculation.

[0027] During smoothing, starting from the beginning of the link connection success rate time series, aggregate the data covered by the sliding window. For example, you can use an averaging method to calculate the average of all data points within the sliding window. This average value is used as the value of the data point corresponding to the center of the sliding window. Then, shift the sliding window backward by one unit and repeat the aggregation process until the entire link connection success rate time series is covered.

[0028] This sliding window smoothing process can effectively reduce data fluctuations and noise, making the generated link quality feature sequence smoother and more continuous. This sequence can more accurately reflect the connectivity stability of the network link.

[0029] Step 124: performing statistical feature extraction processing on the transmission delay time series, calculating delay distribution feature values ​​at different quantiles, and generating a delay fluctuation feature sequence including the delay distribution feature values.

[0030] For transmission delay time series, we need to further explore their inherent statistical characteristics. By calculating the delay distribution characteristic values ​​at different quantiles, we can gain a more comprehensive understanding of the distribution of data transmission delays.

[0031] First, sort the transmission delay time series, arranging the data from smallest to largest. Then, calculate the corresponding delay distribution characteristic values ​​based on different quantile requirements. For example, calculate the 25th percentile, 50th percentile (median), and 75th percentile. These quantiles can reflect the distribution of data transmission delay at different levels.

[0032] The 25th percentile indicates that 25% of data transmission delays are less than this value, reflecting the lower limit of data transmission delay. The 50th percentile (median) indicates that half of the data transmission delays are less than this value, reflecting the median level of data transmission delay. The 75th percentile indicates that 75% of the data transmission delays are less than this value, reflecting the upper limit of data transmission delay.

[0033] The delay distribution characteristic values ​​at different quantiles are arranged in chronological order to generate a delay fluctuation characteristic sequence containing the delay distribution characteristic values. This sequence can intuitively reflect the fluctuation of data transmission delay.

[0034] Step 130: Perform routing node data packet tracing operations based on the link quality characteristic sequence and the delay fluctuation characteristic sequence to generate a routing tracing result set including node transmission path information and data packet status change information.

[0035] After obtaining the link quality feature sequence and delay fluctuation feature sequence, we use this information to perform routing node packet tracking operations. The purpose is to understand the transmission path of the data packet in the network and the status changes at each routing node.

[0036] In another embodiment, performing a routing node data packet tracking operation based on the link quality characteristic sequence and the delay fluctuation characteristic sequence to generate a routing tracking result set including node transmission path information and data packet state change information includes: Step 131: Identify a network connectivity abnormality period based on the link quality feature sequence, and determine a target monitoring period during which a routing node data packet tracing operation needs to be performed.

[0037] First, the link quality signature sequence is analyzed to identify periods of abnormal network connectivity. A threshold can be set to determine whether network connectivity is abnormal. When a data point in the link quality signature sequence falls below the threshold, the network connectivity is considered abnormal during that period.

[0038] When determining an abnormal period, it is necessary to consider the continuity and trend of the data. For example, if multiple consecutive data points are below the threshold and show a downward trend, the continuous period can be determined to be a period of abnormal network connectivity.

[0039] These periods of abnormal network connectivity are identified as target monitoring periods for performing packet tracing on routing nodes. During these periods, the network may be experiencing faults or instability, and packet tracing can help pinpoint the problem more accurately.

[0040] Step 132: During the target monitoring period, a path detection data packet set is sent to the encrypted communication link, where the path detection data packet set includes detection data packets with different lifetime values.

[0041] During the target monitoring period, in order to obtain the transmission path information of the data packet, a set of path detection data packets is sent to the encrypted communication link. The detection data packets in the set have different time-to-live values ​​(TTL).

[0042] The Time to Live value determines the maximum number of routing nodes a packet can traverse on the network. When a packet's Time to Live value reaches 0, it is discarded and an error message is returned. By sending probe packets with different Time to Live values, you can gradually determine the packet's transmission path.

[0043] For example, a probe packet with a time-to-live value of 1 is first sent. If the packet is discarded and an error message is returned, the location of the first routing node can be determined. Then a probe packet with a time-to-live value of 2 is sent, and so on, gradually expanding the detection range until the complete path to the remote monitoring server is found.

[0044] Step 133: Receive a data packet response message returned from the routing node, parse the node identification information and response time information in the data packet response message, and generate a routing node hop count sequence.

[0045] After sending the path detection data packet, a data packet response message will be received from the routing node. These response messages contain important node identification information and response time information.

[0046] First, the received packet response message is parsed. By parsing specific fields in the message, node identification information, such as the routing node's IP address and device name, is extracted. The time the response message was received is recorded. The time difference between the time the probe packet was sent and the time the response message was received is used to determine the response time.

[0047] Based on this information, a routing node hop count sequence is generated. When generating the sequence, the node identifiers and corresponding hop count positions are recorded in the order that the packet passes through the routing nodes. For example, the first routing node passed through has a hop count of 1, the second has a hop count of 2, and so on. This sequence can intuitively reflect the transmission path of the packet in the network.

[0048] As a preferred embodiment, the receiving of a data packet response message returned from a routing node, parsing the node identification information and response time information in the data packet response message, and generating a routing node hop count sequence includes: Step 1331: Optimize the received data packet response message and filter out abnormal response messages with data loss or damage.

[0049] After receiving the data packet response message, the first step is to optimize the processing of these messages. Due to the complexity of the network environment, some abnormal response messages with data loss or damage may be received. These abnormal messages will affect the subsequent analysis and processing results, so abnormal messages need to be filtered out.

[0050] Furthermore, the integrity and legitimacy of the response message can be checked to determine whether it is an abnormal message. For example, the message length can be checked to see if it meets expectations, and whether key fields in the message are missing or incorrect. Response messages that do not meet these requirements are removed from the received message set.

[0051] Step 1332: Extract source node address information and target node address information from the data packet response message optimized by the response message, and establish an inter-node communication relationship mapping table.

[0052] After filtering out abnormal response messages, the remaining valid response messages are further processed. The source and destination node addresses are extracted from these messages. The source node address indicates the node that sent the probe packet, while the destination node address indicates the node that received the response message. By extracting this information, an inter-node communication relationship mapping table is established. This mapping table records the communication relationship between each source and destination node, including information such as the communication time and type.

[0053] Step 1333: Parse the lifetime field value in the data packet response message to determine the number of routing nodes that each data packet passes through.

[0054] The packet response message contains a time-to-live field value. By parsing this field value, the number of routing nodes that each packet passes through can be determined.

[0055] When a packet is discarded and a response message is returned, the number of routing nodes the packet traversed can be inferred based on the changes in the Time to Live field value. For example, if a probe packet with a Time to Live value of 3 is discarded after passing through two routing nodes, it can be determined that the probe packet traversed two routing nodes. By parsing the Time to Live field values ​​in all response messages, the transmission path and number of routing nodes traversed by each packet can be accurately determined.

[0056] Step 1334: Construct a routing node hop count sequence based on the data packet sending order and receiving order and the lifetime field value. Each element in the routing node hop count sequence includes a routing node identifier and a corresponding hop count position. The routing nodes in the routing node hop count sequence are arranged in the actual order of the data packet transmission path.

[0057] When constructing the sequence, the routing node identifiers and their corresponding hop counts are recorded sequentially, following the packet's actual transmission path within the network. For example, the first routing node passed through has a hop count of 1, and its corresponding node identifier is its IP address; the second routing node passed through has a hop count of 2, and its corresponding node identifier is its IP address, and so on. The routing node hop count sequence constructed in this way accurately reflects the packet's transmission path within the network.

[0058] Step 134: constructing an inter-node transmission delay matrix according to the routing node hop count sequence and the delay fluctuation characteristic sequence, wherein the inter-node transmission delay matrix is ​​used to represent the data packet transmission time interval between adjacent routing nodes.

[0059] In order to have a more comprehensive understanding of the transmission conditions between various routing nodes in the network, it is necessary to construct an inter-node transmission delay matrix based on the routing node hop count sequence and delay fluctuation characteristic sequence.

[0060] In another preferred embodiment, constructing an inter-node transmission delay matrix according to the routing node hop count sequence and the delay fluctuation characteristic sequence includes: Step 1341: extract adjacent routing node pairs from the routing node hop count sequence and determine all potential node connection combinations.

[0061] First, the routing node hop count sequence is analyzed to extract adjacent routing node pairs. Adjacent routing node pairs represent two routing nodes that are directly connected in the data packet transmission path.

[0062] By traversing the routing node hop count sequence, all adjacent routing node pairs are found. For example, if the routing node hop count sequence is [Node A, 1; Node B, 2; Node C, 3], then the adjacent routing node pairs are (Node A, Node B) and (Node B, Node C). Based on this, all adjacent routing node pairs are sorted to determine all potential node connection combinations.

[0063] Step 1342: Based on the delay fluctuation characteristic sequence, calculate the average transmission delay of data packets between each pair of adjacent routing nodes.

[0064] For each pair of adjacent routing nodes, the average transmission delay of packets between them needs to be calculated. Based on the data in the delay fluctuation signature sequence and the routing node hop count sequence, the transmission delay of each packet between adjacent routing nodes can be determined.

[0065] For a pair of adjacent routing nodes, the transmission delays of all packets passing through that node pair are counted and then averaged. For example, if there are 10 packets passing through (node ​​A, node B), with transmission delays of t1, t2, ..., t10, the average transmission delay between the packets in this node pair is the sum of these transmission delays divided by the number of packets. By performing this calculation for all adjacent routing node pairs, the average transmission delay between each node pair is obtained.

[0066] Step 1343: Establish a matrix structure with routing node identifiers as row and column indices, where matrix element values ​​represent average transmission delays between corresponding routing node pairs.

[0067] After calculating the average transmission delay of packets between each pair of adjacent routing nodes, a matrix structure is constructed. The row and column indices of the matrix are both based on the routing node identifiers. Each element in the matrix represents the average transmission delay between the corresponding pair of routing nodes. For example, the element in the i-th row and j-th column of the matrix represents the average transmission delay between routing nodes i and j. If there is no direct connection between two routing nodes, the corresponding matrix element value can be set to a special value, such as infinity or 0. This matrix structure can intuitively display the transmission delay between each routing node in the network.

[0068] Step 1344: Perform sparse processing on the matrix structure, and remove matrix elements corresponding to routing node pairs whose transmission delay exceeds a preset threshold.

[0069] To reduce matrix complexity and storage space, the matrix structure needs to be sparsified. First, a preset threshold is set to determine which pairs of routing nodes have excessively long transmission delays. Matrix elements corresponding to routing node pairs whose transmission delays exceed the preset threshold are removed. For example, if the average transmission delay between a pair of routing nodes exceeds the preset threshold, the corresponding element in the matrix is ​​deleted. This sparsification process makes the matrix more concise while retaining essential network transmission information.

[0070] Step 1345: perform symmetry adjustment processing on the matrix structure after the sparsification processing, so that the element value of the i-th row and j-th column in the matrix structure after the symmetry check processing remains consistent with the element value of the j-th row and i-th column.

[0071] After sparsification, the matrix structure needs to be symmetric. Since network transmission delays are theoretically symmetrical, meaning the transmission delay from node A to node B should be similar to the transmission delay from node B to node A, the values ​​of the element in row i, column j and the element in row j, column i in the matrix are compared and adjusted. If they are unequal, their average is used as the value of the element in these two positions. This symmetric adjustment makes the matrix more consistent with actual network transmission conditions.

[0072] Step 135: Generate a routing tracking result set based on the node identification information, the routing node hop count sequence, and the inter-node transmission delay matrix. The node transmission path information in the routing tracking result set includes the topological connection relationship of the routing nodes, and the data packet state change information includes the data packet forwarding state and delay change at each routing node.

[0073] After completing the above steps, a routing trace result set is generated by combining the node identification information, the routing node hop count sequence, and the inter-node transmission delay matrix. For the node transmission path information, the topological connection relationship of the routing nodes is determined based on the routing node hop count sequence and the node identification information. For example, if the routing node hop count sequence is [node A, 1; node B, 2; node C, 3], it can be determined that node A is connected to node B, and node B is connected to node C.

[0074] For packet status change information, we record the packet forwarding status and delay changes at each routing node based on the inter-node transmission delay matrix and the routing node hop count sequence. For example, at a particular routing node, we record information such as the packet forwarding success rate and the changing trend of forwarding delay. By generating this set of route tracking results, we can fully understand the transmission status of packets in the network.

[0075] Step 140: Identify the location information of the network fault point based on the route tracking result set, and generate a network optimization recommendation report including fault repair priority and path optimization strategy in combination with the link quality feature sequence and delay fluctuation feature sequence, and push the network optimization recommendation report to the operation and maintenance management terminal of the remote monitoring server.

[0076] After obtaining the route tracking results, the location of the network fault point needs to be identified. Simultaneously, a network optimization recommendation report containing fault repair priorities and path optimization strategies is generated by combining the link quality and delay fluctuation feature sequences. This report is then pushed to the operation and maintenance management terminal of the remote monitoring server, allowing operators to take timely measures to optimize the network.

[0077] In one design concept, the network fault point location information is identified based on the route tracing result set, and a network optimization recommendation report including fault repair priority and path optimization strategy is generated in combination with the link quality feature sequence and delay fluctuation feature sequence, including: Step 141: Perform a topological structure analysis on the node transmission path information in the routing tracking result set to construct a network routing topology graph, where nodes in the network routing topology graph represent routing devices and edges represent communication links between nodes.

[0078] In order to better analyze the network structure and identify the fault points, it is necessary to perform topological structure analysis on the node transmission path information in the route tracking result set and construct a network routing topology map.

[0079] In one implementation, performing topological structure analysis on the node transmission path information in the route tracing result set to construct a network routing topology graph includes: Step 1411: extract all routing node identifiers that have appeared from the node transmission path information, and establish a unique node identifier set.

[0080] First, carefully comb through the node transmission path information and extract all routing node identifiers that appear. For example, the node transmission path information may contain multiple identifiers for routing nodes such as Node A, Node B, and Node C. These identifiers are sorted, and duplicate identifiers are removed to create a unique set of node identifiers. This set contains the identifiers of all routing nodes involved in packet transmission in the network.

[0081] Step 1412: Determine the direct connection relationship between nodes according to the order of appearance of routing nodes in the node transmission path information.

[0082] After obtaining a unique set of node identifiers, the direct connections between nodes are determined based on the order in which the routing nodes appear in the node transmission path information. For example, if the node transmission path information shows that a data packet is transmitted from node A to node B, and then from node B to node C, then a direct connection relationship can be determined between nodes A and B, and between nodes B and C. By performing this analysis on all node transmission path information, the direct connections between all nodes in the network can be determined.

[0083] Step 1413: Count the frequency of each direct connection relationship in all transmission paths and generate a connection frequency weight value.

[0084] For each direct connection, count its frequency across all transmission paths. For example, if the direct connection (node ​​A, node B) appears 20 times in 100 transmission paths, the frequency of this direct connection is 20%. This frequency is used as the connection frequency weight for this direct connection. The connection frequency weight reflects the importance of the connection in the network. A higher frequency indicates a more common connection and a greater impact on the network.

[0085] Step 1414: Construct a weighted directed graph as an initial network routing topology graph, using node identifiers as vertices, direct connections between nodes as edges, and connection frequency weight values ​​as edge weights.

[0086] Based on the previously obtained set of node identifiers, direct connections between nodes, and connection frequency weights, a weighted directed graph is constructed as the initial network routing topology. The node identifiers are used as the graph's vertices, the direct connections between nodes are used as the graph's edges, and the connection frequency weights are used as the edge weights. For example, if there is a direct connection between nodes A and B, and the connection frequency weight is 0.2, then the edge from node A to node B in the graph is labeled with a weight of 0.2. This weighted directed graph, constructed in this way, can intuitively display the network's topology and the importance of each connection.

[0087] Step 1415: performing redundant edge removal processing on the initial network routing topology graph, removing edges whose connection frequency weight values ​​are lower than a preset threshold, and generating the network routing topology graph.

[0088] To make the network routing topology more concise and effective, the initial network routing topology needs to be subjected to redundant edge removal. A preset threshold is set, and edges with a connection frequency weight below this threshold are removed from the graph. For example, if the preset threshold is 0.1 and an edge has a connection frequency weight of 0.05, this edge is deleted from the graph. This redundant edge removal process can remove connections that have little impact on the network, making the resulting network routing topology more clearly reflect the network's main structure.

[0089] Step 142: Based on the network routing topology map and the data packet status change information in the routing tracking result set, identify the routing nodes or communication links where abnormalities occur during data packet transmission, and determine a preliminary set of candidate network fault points.

[0090] After obtaining the network routing topology, the abnormal routing nodes or communication links in the network are identified by combining the data packet status change information in the routing tracking result set.

[0091] As an implementation method, the method of identifying abnormal routing nodes or communication links during data packet transmission based on the network routing topology map and the data packet state change information in the routing tracking result set, and determining a preliminary set of candidate network fault points includes: Step 1421: extracting the forwarding state index of the data packet at each routing node from the data packet state change information, wherein the forwarding state index includes the data packet forwarding success rate and the forwarding delay change rate.

[0092] Detailed analysis is performed on packet state change information to extract forwarding state indicators for each routing node. For each routing node, its packet forwarding success rate and forwarding delay change rate are calculated. The packet forwarding success rate refers to the ratio of the number of packets successfully forwarded at that routing node to the total number of packets received. The forwarding delay change rate refers to the change in packet forwarding delay at that routing node. For example, this can be calculated by calculating the ratio of the difference in forwarding delays between adjacent time periods to the forwarding delay of the previous time period. By extracting these forwarding state indicators for each routing node, we can understand the forwarding status of packets at each routing node.

[0093] Step 1422: Set a normal range threshold for the forwarding state indicator, and mark the forwarding state indicator that exceeds the normal range threshold as an abnormal state indicator.

[0094] To determine whether packet forwarding status is abnormal, set a threshold within the normal range for the forwarding status metric. For example, for the packet forwarding success rate, set the normal range to 90%-100%; for the forwarding delay change rate, set the normal range to -10%-10%. For each routing node's forwarding status metric, compare it to the normal range threshold. If a metric exceeds the normal range threshold, it is marked as abnormal.

[0095] Step 1423: According to the marked abnormal status indicator, determine that the corresponding routing node is a potential fault node.

[0096] When a routing node's forwarding state metric is marked as abnormal, the node is identified as a potential faulty node. For example, if node A's packet forwarding success rate is less than 90% and its forwarding delay variation rate exceeds 10%, node A is identified as a potential faulty node. This method allows for the initial screening of potentially faulty routing nodes.

[0097] Step 1424: Based on the communication links connected to the potential fault nodes in the network routing topology graph, calculate the data packet transmission error rate on the communication links.

[0098] For each potential fault node, identify the communication links connecting to it based on the network routing topology. Then, calculate the packet transmission error rate on these communication links. Count all packets passing through the communication link and the number of packets with errors. Divide the number of packets with errors by the total number of packets to obtain the packet transmission error rate for the communication link.

[0099] Step 1425: Mark the communication link whose data packet transmission error rate exceeds the preset error rate threshold as a potential fault link.

[0100] Set a preset error rate threshold. If the packet transmission error rate exceeds this threshold, the communication link is marked as a potential failure link. For example, if the preset error rate threshold is 5%, and the packet transmission error rate of a communication link is 8%, the link is marked as a potential failure link.

[0101] Step 1426: Generate a preliminary set of network failure point candidates based on the potential failure nodes and potential failure links.

[0102] The previously identified potential fault nodes and potential fault links are sorted out to generate a preliminary network fault point candidate set, which includes all routing nodes and communication links that may have faults.

[0103] Step 143: evaluating the fault impact range of the network fault point candidate set in combination with the link quality feature sequence and the delay fluctuation feature sequence, and calculating an index of the impact degree of each candidate fault point on the overall network performance.

[0104] After obtaining a preliminary set of candidate network fault points, it is necessary to combine the link quality feature sequence and the delay fluctuation feature sequence to evaluate the fault impact range of each candidate fault point and calculate its impact on the overall network performance.

[0105] As another implementation, the step of evaluating the fault impact range of the network fault point candidate set by combining the link quality feature sequence and the delay fluctuation feature sequence, and calculating an index of the degree of impact of each candidate fault point on the overall network performance, includes: Step 1431: construct a fault propagation model based on the network routing topology graph to simulate the impact range of each candidate fault point on the entire communication network when a fault occurs.

[0106] According to the network routing topology, a fault propagation model is constructed. This model can simulate the propagation of the fault in the network and its impact on other nodes and links when a candidate fault point fails.

[0107] For example, when a routing node fails, it may affect directly connected nodes and links, and then affect more distant nodes and links. The scope and extent of this impact can be predicted through the fault propagation model.

[0108] Step 1432: Calculate the link quality baseline value of the network in a normal state based on the link quality feature sequence as a first reference standard for evaluating the impact of the fault.

[0109] Extract data from the link quality signature sequence and calculate a baseline link quality value for the network under normal conditions. Methods such as averaging or median can be used to obtain a value that represents the normal link quality of the network. This baseline link quality value serves as the first reference for assessing the impact of a fault and is used to compare changes in network link quality after a fault occurs.

[0110] Step 1433: Compare the simulation result of the fault propagation model with the link quality reference value, and calculate the link quality degradation percentage.

[0111] Compare the network link quality at each candidate fault point simulated by the fault propagation model with the link quality baseline. Calculate the percentage drop in link quality relative to the baseline after the fault occurs. For example, if the link quality baseline is 90%, and the simulated link quality after a candidate fault point fails is 80%, the link quality drop is (90% - 80%) / 90%.

[0112] Step 1434: Determine the delay distribution characteristics of the network in a normal state according to the delay fluctuation characteristic sequence as a second reference standard for evaluating the impact of the fault.

[0113] Analyze data from the delay fluctuation feature sequence to determine the delay distribution characteristics of the network under normal conditions. For example, statistical characteristics such as the average delay and standard deviation of the delay under normal conditions can be calculated. These delay distribution characteristics serve as a secondary reference for assessing the impact of a fault, comparing changes in network data transmission delay after a fault occurs.

[0114] Step 1435: Calculate the delay increase percentage based on the difference between the delay fluctuation characteristics when the candidate fault point exists and the delay distribution characteristics under the normal state.

[0115] For each candidate fault point, we simulate the delay fluctuation characteristics when that fault point exists using the fault propagation model. These characteristics are compared with the delay distribution characteristics under normal conditions to calculate the delay increase percentage. For example, if the average delay under normal conditions is 100ms, and the average delay simulated when a candidate fault point exists is 120ms, the delay increase percentage is (120-100) / 100.

[0116] Step 1436: Combine the link quality degradation percentage and the delay increase percentage, and determine the impact index of each candidate fault point through weighted summation.

[0117] To comprehensively consider the impact of link quality and latency on network performance, the link quality degradation percentage and latency increase percentage are weighted and summed. Different weights can be set, for example, a weight of 0.6 for the link quality degradation percentage and a weight of 0.4 for the latency increase percentage.

[0118] Multiply the link quality degradation percentage by its weight, and the delay increase percentage by its weight, and then add the two results to obtain the impact index of each candidate fault point. This index can more comprehensively reflect the impact of each candidate fault point on the overall network performance.

[0119] Step 144: Sort the network fault point candidate set according to the impact degree index to determine the final network fault point location information and the corresponding fault repair priority.

[0120] Based on the impact index calculated for each candidate fault point, the set of candidate network fault points is sorted. The impact index is ranked from highest to lowest, with candidates with higher impact ranked higher. Candidate fault points ranked higher have a greater impact on network performance and require priority repair. Based on the sorting results, the final network fault point location information and corresponding repair priority are determined. For example, the candidate fault point ranked first is the highest priority for repair, and its corresponding location information is the network fault point location information.

[0121] Step 145: Based on the network fault point location information and the network routing topology, search for alternative routing paths and generate a path optimization strategy including a path switching suggestion and a bandwidth allocation solution.

[0122] After determining the location information of the network fault point, in order to avoid the serious impact of the network failure on data transmission, it is necessary to search for alternative routing paths based on the network routing topology map and generate a path optimization strategy.

[0123] As another optional embodiment, searching for alternative routing paths based on the network fault point location information and the network routing topology map, and generating a path optimization strategy including a path switching suggestion and a bandwidth allocation plan, includes: Step 1451: With the charging cabinet and the remote monitoring server as the starting point and the end point, remove the node or link corresponding to the location information of the network fault point in the network routing topology diagram.

[0124] Based on the network fault point location information, the corresponding node or link is found in the network routing topology and removed. A subsequent path search is performed in the network routing topology after removing the faulty node or link, using the charging cabinet and remote monitoring server as the starting and ending points. For example, if the network fault point location information indicates that node A is faulty, node A and its associated edges are removed from the network routing topology.

[0125] Step 1452: Use the shortest path search algorithm to find multiple candidate alternative paths from the charging cabinet to the remote monitoring server in the remaining network routing topology graph.

[0126] In the network routing topology after removing faulty nodes or links, a shortest path search algorithm (such as Dijkstra's algorithm) is used to find multiple candidate alternative paths from the charging cabinet to the remote monitoring server. This algorithm traverses all nodes and edges in the graph to find the shortest path from the starting point to the end point. Because multiple shortest paths or near-shortest paths may exist, multiple candidate alternative paths are generated.

[0127] Step 1453: Calculate a path quality evaluation value of each candidate replacement path based on the link quality feature sequence and the delay fluctuation feature sequence. The path quality evaluation value comprehensively considers factors such as path length, link stability, and transmission delay.

[0128] For each candidate alternative path, its path quality evaluation value is calculated based on the link quality feature sequence and delay fluctuation feature sequence. The path quality evaluation value comprehensively considers path length, link stability, and transmission delay factors.

[0129] For example, you can set the weight of path length to 0.3, the weight of link stability (which can be calculated from the link quality feature sequence) to 0.4, and the weight of transmission delay (which can be calculated from the delay fluctuation feature sequence) to 0.3. Multiply the evaluation values ​​of path length, link stability, and transmission delay by their weights respectively, and then add them together to obtain the path quality evaluation value of the candidate replacement path.

[0130] Step 1454: Sort the multiple candidate replacement paths according to the path quality evaluation values, and select a preset number of candidate replacement paths with the highest evaluation values ​​as recommended replacement routing paths.

[0131] Sort all candidate alternative paths by their path quality evaluation values ​​from highest to lowest. Select a preset number of candidate alternative paths with the highest evaluation values ​​as recommended alternative routing paths. For example, if the preset number is 3, then the three candidate alternative paths with the highest path quality evaluation values ​​are selected.

[0132] Step 1455: Based on the available bandwidth and historical data transmission volume of the recommended alternative routing paths, a bandwidth allocation plan is formulated to determine the bandwidth allocation ratio of each recommended alternative routing path.

[0133] For each recommended alternative routing path, understand its available bandwidth and historical data volume. Based on this information, develop a bandwidth allocation plan. Bandwidth can be allocated based on the ratio of historical data volume. For example, if the historical data volume ratio of recommended alternative routing paths A, B, and C is 2:3:1, the available bandwidth can be allocated according to this ratio to determine the bandwidth allocation ratio for each path.

[0134] Step 1456: Generate a path optimization strategy based on the recommended alternative routing path and the bandwidth allocation solution. The path optimization strategy includes triggering conditions for path switching and a dynamic bandwidth adjustment mechanism.

[0135] The recommended alternative routing paths and bandwidth allocation plan are integrated to generate a path optimization strategy, which includes path switching trigger conditions and a dynamic bandwidth adjustment mechanism. The path switching trigger conditions can be set based on network status changes. For example, path switching is triggered when the link quality of the current path deteriorates to a certain level or when the transmission delay exceeds a preset threshold. The dynamic bandwidth adjustment mechanism dynamically adjusts the bandwidth allocation ratio of each recommended alternative routing path based on real-time network traffic and link status.

[0136] Step 146: Generate a network optimization recommendation report based on the network fault point location information, fault repair priority and path optimization strategy.

[0137] Based on the previously determined network fault location information, fault repair priority, and path optimization strategy, a network optimization recommendation report is generated. This report details the location of the network fault, the repair priority for each fault point, recommended alternative routing paths, and bandwidth allocation solutions. This report provides comprehensive network optimization guidance to operations personnel, helping them take timely measures to repair faults, optimize network paths, and improve network performance and stability.

[0138] In an independently implementable embodiment, after the network optimization recommendation report is pushed to the operation and maintenance management terminal of the remote monitoring server, it also includes: receiving the path optimization strategy execution status fed back by the operation and maintenance management terminal, and collecting the target real-time communication log set after execution through the encrypted communication link; performing network status parameter extraction processing on the target real-time communication log set after execution to obtain the target link quality feature sequence after execution and the target delay fluctuation feature sequence after execution; comparing and analyzing the target link quality feature sequence after execution with the link quality feature sequence, and calculating the link quality improvement index, comparing and analyzing the target delay fluctuation feature sequence after execution with the delay fluctuation feature sequence, and calculating the delay fluctuation optimization rate index; adjusting the parameters of the path optimization strategy in the network optimization recommendation report based on the link quality improvement index and the delay fluctuation optimization rate index to generate an updated path optimization strategy; and storing the updated path optimization strategy in the policy database of the remote monitoring server.

[0139] In this embodiment, after the network optimization recommendation report is pushed to the operation and maintenance management terminal of the remote monitoring server, feedback on the path optimization policy execution status is received from the operation and maintenance management terminal. Based on this feedback information, a set of target real-time communication logs after execution is collected via an encrypted communication link. Similar network status parameter extraction processing is performed on the target real-time communication logs after execution to obtain a target link quality feature sequence and a target delay fluctuation feature sequence after execution. The target link quality feature sequence after execution is compared and analyzed with the previous link quality feature sequence. A link quality improvement index is calculated, for example, by calculating the difference between corresponding data points in the two sequences and then averaging them. Similarly, the target delay fluctuation feature sequence after execution is compared and analyzed with the previous delay fluctuation feature sequence to calculate a delay fluctuation optimization rate index. Based on the link quality improvement index and the delay fluctuation optimization rate index, the path optimization policy parameters in the network optimization recommendation report are adjusted. For example, if the link quality improvement index is unsatisfactory, the path switching trigger conditions or bandwidth allocation scheme can be adjusted. An updated path optimization policy is generated and stored in the policy database of the remote monitoring server for subsequent use.

[0140] In an independently implementable embodiment, after the network optimization recommendation report is pushed to the operation and maintenance management terminal of the remote monitoring server, it also includes: extracting the routing node identifier corresponding to the fault point and the routing node identifier involved in the optimized path based on the network fault point location information and path optimization strategy in the network optimization recommendation report; matching the extracted routing node identifier with the node transmission path information in the routing tracking result set to determine the target routing node set that needs to be continuously monitored; analyzing the packet state change information of the target routing node set in the routing tracking result set, and counting the packet forwarding abnormality frequency and delay fluctuation amplitude of the target routing node; adjusting the detection parameters of the routing node packet tracking operation based on the packet forwarding abnormality frequency and delay fluctuation amplitude of the target routing node, the detection parameters including the lifetime value range and sending interval of the path detection packet set; sending the adjusted detection parameters to the charging cabinet as configuration parameters for executing the routing node packet tracking operation in the next cycle.

[0141] Based on this embodiment, the routing node identifier corresponding to the fault point and the routing node identifiers involved in the optimized path are extracted based on the network fault point location information and path optimization strategy in the network optimization recommendation report. These identifiers are then matched with the node transmission path information in the route tracing result set to identify the target routing node set that requires continuous monitoring. The packet state change information for the target routing node set in the route tracing result set is analyzed, and the packet forwarding anomaly frequency and delay fluctuation amplitude of these nodes are calculated. For example, the packet forwarding anomaly frequency is calculated by calculating the ratio of the number of packet forwarding failures to the total number of forwardings for a target routing node within a certain period of time. The delay fluctuation amplitude is calculated by calculating the difference between the maximum and minimum transmission delays for this node. Based on the packet forwarding anomaly frequency and delay fluctuation amplitude of the target routing node, the detection parameters of the routing node packet tracking operation are adjusted. If the packet forwarding anomaly frequency or delay fluctuation amplitude is large, the lifetime value range of the path detection packet set can be appropriately increased, and the transmission interval can be shortened to enable more detailed monitoring of the network status of these nodes. The adjusted detection parameters are sent to the charging cabinet and used as configuration parameters for the next routing node packet tracking operation, ensuring timely detection of potential network failures.

[0142] In an independently implementable embodiment, after the network optimization recommendation report is pushed to the operation and maintenance management terminal of the remote monitoring server, it also includes: extracting network fault point location information, fault repair priority and path optimization strategy from the network optimization recommendation report, extracting pre-optimization network performance indicators from the link quality feature sequence and the delay fluctuation feature sequence, and extracting post-optimization network performance indicators from the real-time communication log set after executing the network optimization recommendation report; matching the corresponding data points of the pre-optimization network performance indicators and the post-optimization network performance indicators within the same preset monitoring period to obtain matching results; constructing a network performance comparison feature sequence based on the matching results, the network performance comparison feature sequence including the changing trend of the link quality improvement degree over time and the changing trend of the delay fluctuation optimization rate over time; generating a visual display interface including the fault point topology location label, performance trend curve and path optimization strategy effect heat map based on the network fault point location information and the network performance comparison feature sequence; and pushing the visual display interface to the operation and maintenance management terminal of the remote monitoring server for display.

[0143] In an embodiment of the present invention, network fault point location information, fault repair priority, and path optimization strategy are extracted from the network optimization recommendation report. At the same time, the network performance indicators before optimization are extracted from the link quality feature sequence and the delay fluctuation feature sequence, and the network performance indicators after optimization are extracted from the real-time communication log set after the execution of the network optimization recommendation report. The corresponding data points of the network performance indicators before optimization and the network performance indicators after optimization within the same preset monitoring period are matched. For example, the link quality value and the delay fluctuation value at the same time point before and after optimization are matched. After obtaining the matching results, a network performance comparison feature sequence is constructed based on this. The network performance comparison feature sequence includes the trend of link quality improvement over time and the trend of delay fluctuation optimization rate over time. For example, the difference between the link quality after optimization and the link quality before optimization at each time point can be calculated to obtain the link quality improvement degree; the ratio of the delay fluctuation after optimization to the delay fluctuation before optimization can be calculated to obtain the delay fluctuation optimization rate. Based on the network fault point location information and network performance comparison feature sequences, a visualization interface is generated. This interface includes topological location annotations for the fault point, visually displaying its location within the topology map; performance trend curves, showing how link quality improvements and latency fluctuation optimization rates change over time; and path optimization strategy effectiveness heat maps, using color depth to indicate the effectiveness of path optimization strategies in different areas. This visualization interface is then pushed to the remote monitoring server's operation and maintenance management terminal for display, enabling operators to more intuitively understand the effectiveness of network optimization and network performance changes.

[0144] Therefore, the embodiment of the present invention first establishes an encrypted communication link between the charging cabinet and the remote monitoring server and collects a set of real-time communication logs within a preset monitoring period, which includes communication session establishment records and data transmission interaction records; secondly, the real-time communication log set is processed to extract network status parameters to obtain link quality feature sequences and delay fluctuation feature sequences, which can accurately characterize network connectivity stability and data transmission efficiency; then, based on the link quality feature sequence and delay fluctuation feature sequence, a routing node packet tracking operation is performed to generate a routing tracking result set containing node transmission path information and packet state change information, so that the transmission path and state change of the packet in the network are more complete and accurate; finally, the network fault point location information is identified based on the routing tracking result set, and a network optimization recommendation report is generated in combination with the link quality feature sequence and delay fluctuation feature sequence. The report contains fault repair priority and path optimization strategy, which can solve network faults in a targeted manner, optimize network paths, and improve network reliability and transmission efficiency. The network optimization recommendation report is further pushed to the operation and maintenance management terminal of the remote monitoring server, which facilitates operation and maintenance personnel to obtain information in a timely manner and take corresponding measures, thereby realizing intelligent management and efficient operation and maintenance of the network. In summary, the embodiment of the present invention improves the performance and stability of the charging cabinet remote data monitoring network.

[0145] See also Figure 2 As shown in FIG. 1 , this figure is a schematic diagram of the basic structure of a remote data monitoring system 200 based on a charging cabinet provided by an embodiment of the present invention. The remote data monitoring system 200 based on a charging cabinet includes: Processor 201; a storage device 202 having a computer program 2020 stored thereon; When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the remote data monitoring methods based on the charging cabinet.

[0146] Based on the above, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above method are implemented.

[0147] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

Claims

1. A remote data monitoring method based on a charging cabinet, characterized in that: The method comprises: Establishing an encrypted communication link between the charging cabinet and the remote monitoring server, and collecting a set of real-time communication logs of the charging cabinet within a preset monitoring period through the encrypted communication link, wherein the real-time communication logs include communication session establishment records and data transmission interaction records; Performing network status parameter extraction processing on the real-time communication log set to obtain a link quality feature sequence characterizing network connectivity stability and a delay fluctuation feature sequence characterizing data transmission efficiency; Perform routing node data packet tracking operations based on the link quality feature sequence and the delay fluctuation feature sequence to generate a routing node data packet tracking result set including node transmission path information and data packet state change information; The network fault point location information is identified based on the route tracking result set, and a network optimization recommendation report including fault repair priority and path optimization strategy is generated in combination with the link quality feature sequence and delay fluctuation feature sequence, and the network optimization recommendation report is pushed to the operation and maintenance management terminal of the remote monitoring server.

2. The remote data monitoring method based on the charging cabinet according to claim 1 is characterized in that: The network status parameter extraction process is performed on the real-time communication log set to obtain a link quality feature sequence characterizing network connectivity stability and a delay fluctuation feature sequence characterizing data transmission efficiency, including: Performing session success rate statistics processing on the communication session establishment records in the real-time communication log set, calculating the ratio of the number of communication sessions successfully established to the total number of communication sessions attempted to be established per unit time, and generating a link connection success rate time series; Performing transmission delay measurement processing on the data transmission interaction records in the real-time communication log set, extracting the time interval from the sending time to the receiving confirmation time of each data packet, and generating a transmission delay time series; Performing sliding window smoothing processing on the link connection success rate time series to generate a link quality feature sequence with time continuity; Statistical feature extraction processing is performed on the transmission delay time series, delay distribution feature values ​​at different quantiles are calculated, and a delay fluctuation feature sequence including the delay distribution feature values ​​is generated.

3. The remote data monitoring method based on the charging cabinet according to claim 1 is characterized in that: The performing of a routing node data packet tracking operation based on the link quality characteristic sequence and the delay fluctuation characteristic sequence to generate a routing tracking result set including node transmission path information and data packet state change information includes: Identifying a network connectivity abnormality period based on the link quality feature sequence, and determining a target monitoring period during which a routing node data packet tracking operation needs to be performed; sending a path detection data packet set to the encrypted communication link during the target monitoring period, wherein the path detection data packet set includes detection data packets with different time-to-live values; Receiving a data packet response message returned from a routing node, parsing the node identification information and response time information in the data packet response message, and generating a routing node hop count sequence; Constructing an inter-node transmission delay matrix based on the routing node hop count sequence and the delay fluctuation characteristic sequence, wherein the inter-node transmission delay matrix is ​​used to represent the data packet transmission time interval between adjacent routing nodes; A routing tracking result set is generated by combining the node identification information, the routing node hop count sequence and the inter-node transmission delay matrix. The node transmission path information in the routing tracking result set includes the topological connection relationship of the routing nodes, and the data packet state change information includes the data packet forwarding state and delay change at each routing node.

4. The remote data monitoring method based on the charging cabinet according to claim 3 is characterized in that: The receiving of a data packet response message returned from a routing node, parsing the node identification information and response time information in the data packet response message, and generating a routing node hop count sequence includes: Optimize the response messages of received data packets and filter out abnormal response messages with data loss or damage; Extracting source node address information and destination node address information from a data packet response message optimized by the response message, and establishing an inter-node communication relationship mapping table; Parsing the time-to-live field value in the data packet response message to determine the number of routing nodes passed by each data packet; Constructing a routing node hop count sequence based on the data packet sending and receiving order in combination with the lifetime field value, wherein each element in the routing node hop count sequence includes a routing node identifier and a corresponding hop count position, and the routing nodes in the routing node hop count sequence are arranged in the actual order of the data packet transmission path; The constructing an inter-node transmission delay matrix according to the routing node hop count sequence and the delay fluctuation characteristic sequence includes: Extracting adjacent routing node pairs from the routing node hop count sequence to determine all potential node connection combinations; Calculating the average transmission delay of data packets between each pair of adjacent routing nodes based on the delay fluctuation characteristic sequence; Establish a matrix structure with routing node identifiers as row and column indices, where the matrix element values ​​represent the average transmission delay between corresponding routing node pairs; Performing a sparse processing on the matrix structure, removing matrix elements corresponding to routing node pairs whose transmission delay exceeds a preset threshold; A symmetry adjustment process is performed on the matrix structure after the sparsification process, so that the element value of the i-th row and j-th column in the matrix structure after the symmetry check process is consistent with the element value of the j-th row and i-th column.

5. The remote data monitoring method based on the charging cabinet according to claim 1 is characterized in that: The identifying of the network fault point location information based on the route tracing result set and generating a network optimization recommendation report including a fault repair priority and a path optimization strategy in combination with the link quality feature sequence and the delay fluctuation feature sequence include: Performing a topological structure analysis on the node transmission path information in the routing tracking result set to construct a network routing topology graph, wherein nodes in the network routing topology graph represent routing devices and edges represent communication links between nodes; Based on the network routing topology map and the data packet state change information in the routing tracking result set, identifying routing nodes or communication links where abnormalities occur during data packet transmission, and determining a preliminary set of candidate network fault points; Combine the link quality feature sequence and the delay fluctuation feature sequence to evaluate the fault impact range of the network fault point candidate set, and calculate the impact degree index of each candidate fault point on the overall network performance; Sorting the candidate set of network fault points according to the impact index to determine the final network fault point location information and the corresponding fault repair priority; Based on the network fault point location information and the network routing topology, searching for alternative routing paths, and generating a path optimization strategy including path switching suggestions and bandwidth allocation solutions; Based on the network fault point location information, fault repair priority and path optimization strategy, a network optimization recommendation report is generated.

6. The remote data monitoring method based on the charging cabinet according to claim 5 is characterized in that: The performing topological structure analysis on the node transmission path information in the routing tracking result set to construct a network routing topology graph includes: Extract all the routing node identifiers that have appeared from the node transmission path information and establish a unique node identifier set; Determining a direct connection relationship between nodes based on an appearance order of routing nodes in the node transmission path information; Count the frequency of each direct connection relationship in all transmission paths and generate a connection frequency weight value; With node identifiers as vertices, direct connections between nodes as edges, and connection frequency weights as edge weights, a weighted directed graph is constructed as the initial network routing topology. Redundant edge removal processing is performed on the initial network routing topology graph, edges with connection frequency weight values ​​lower than a preset threshold are removed, and the network routing topology graph is generated.

7. The remote data monitoring method based on the charging cabinet according to claim 5 is characterized in that: The step of identifying abnormal routing nodes or communication links during data packet transmission based on the network routing topology map and the data packet status change information in the routing tracking result set, and determining a preliminary set of candidate network fault points, includes: Extracting a forwarding state indicator of the data packet at each routing node from the data packet state change information, wherein the forwarding state indicator includes a data packet forwarding success rate and a forwarding delay change rate; Set a normal range threshold for forwarding status indicators, and mark forwarding status indicators that exceed the normal range threshold as abnormal status indicators; According to the marked abnormal status indicator, the corresponding routing node is determined to be a potential fault node; Calculating a data packet transmission error rate on a communication link connected to a potential fault node in the network routing topology graph; Marking a communication link whose data packet transmission error rate exceeds a preset error rate threshold as a potential fault link; Based on potential fault nodes and potential fault links, a preliminary set of network fault point candidates is generated.

8. The remote data monitoring method based on the charging cabinet according to claim 5 is characterized in that: The step of evaluating the fault impact range of the network fault point candidate set by combining the link quality feature sequence and the delay fluctuation feature sequence, and calculating an index of the impact degree of each candidate fault point on the overall network performance, includes: Building a fault propagation model based on the network routing topology to simulate the impact of each candidate fault point on the entire communication network when a fault occurs; Calculating a link quality baseline value of the network in a normal state according to the link quality feature sequence as a first reference standard for evaluating the impact of the fault; Comparing the simulation result of the fault propagation model with the link quality reference value to calculate the link quality degradation percentage; Determining the delay distribution characteristics of the network in a normal state according to the delay fluctuation characteristic sequence as a second reference standard for evaluating the impact of the fault; Calculate the delay increase percentage based on the difference between the delay fluctuation characteristics when the candidate fault point exists and the delay distribution characteristics under normal conditions; The impact index of each candidate fault point is determined by combining the link quality degradation percentage and the delay increase percentage through weighted summation.

9. The remote data monitoring method based on the charging cabinet according to claim 5 is characterized in that: The searching for alternative routing paths based on the network fault point location information and the network routing topology map, and generating a path optimization strategy including a path switching suggestion and a bandwidth allocation solution, includes: Taking the charging cabinet and the remote monitoring server as the starting point and the end point, remove the node or link corresponding to the location information of the network fault point in the network routing topology map; Use the shortest path search algorithm to find multiple candidate alternative paths from the charging cabinet to the remote monitoring server in the remaining network routing topology graph; Calculating a path quality evaluation value for each candidate alternative path based on the link quality feature sequence and the delay fluctuation feature sequence, wherein the path quality evaluation value comprehensively considers factors such as path length, link stability, and transmission delay; sorting multiple candidate alternative paths according to path quality evaluation values, and selecting a preset number of candidate alternative paths with the highest evaluation values ​​as recommended alternative routing paths; Based on the available bandwidth and historical data transmission volume of the recommended alternative routing paths, a bandwidth allocation plan is formulated to determine the bandwidth allocation ratio for each recommended alternative routing path; A path optimization strategy is generated by combining the recommended alternative routing path and the bandwidth allocation solution. The path optimization strategy includes triggering conditions for path switching and a dynamic bandwidth adjustment mechanism.

10. A remote data monitoring system based on a charging cabinet, characterized in that: include: processor; A storage device having a computer program stored thereon, wherein when the computer program is executed by the processor, the processor implements the remote data monitoring method based on the charging cabinet as described in any one of claims 1-9.

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