Road maintenance information management system based on cloud platform

Abstract

The invention relates to the technical field of cloud network cooperation, in particular to a road maintenance information management system based on a cloud platform, which comprises a task group section identification module, a road section link list arrangement module, a redundant task section identification module, an instruction state checking module and a cloud task information management module. According to the method, the routing inspection task serial number is subjected to sectionalized organization, a continuous task relation is constructed, and the node communication direction and serial number consistency constraint is introduced, so that the task execution process presents a deducible time sequence structure, and the distribution characteristics of abnormal hop segments are described in the instruction circulation process; and performing cross comparison by combining a state propulsion track with an equipment deposit state, so that an abnormal instruction position, an execution node and a task path form stable association, thereby enabling maintenance task state analysis to have process traceability, state comparability and result verifiability. The task state ambiguity generation probability is reduced; and the overall operation credibility and the management judgment support capability are improved.

Description

Technical Field

[0001] This invention relates to the field of cloud-network collaboration technology, and in particular to a highway maintenance information management system based on a cloud platform. Background Technology

[0002] The field of cloud-network collaboration technology involves the collaborative organization and scheduling of computing and network resources under a unified architecture. Core aspects include connecting multi-source data to the cloud computing environment via network connections, relying on cloud-side servers to complete data reception, storage, scheduling, and processing, and transmitting processing instructions and data results back to terminal devices via the network. This technology typically revolves around a systematic technical layout where data is collected at the terminal side, transmitted via communication networks to a cloud platform for centralized processing, and then the cloud platform provides services to multiple application scenarios. A traditional cloud-based highway maintenance information management system refers to a system in highway maintenance scenarios where data collection devices deployed along roads or at maintenance sites acquire pavement defect maintenance records, recording device status and operational information. This collected information is uploaded to a cloud server via wired or wireless communication links. The cloud server receives, classifies, stores, and compares the information with historical data according to a preset data format, generates management data based on highway maintenance business rules, and then provides this management data to management terminals via the network for manual viewing, recording, and maintenance.

[0003] Existing technologies rely on static configuration to classify and organize data during task execution, lacking in-depth analysis of task numbering, path structure identification, and instruction behavior distribution patterns. Status feedback is limited to single-point anomaly reporting, failing to identify potential anomaly patterns from multi-dimensional numbering and path information. This can easily lead to misjudgments in status analysis in scenarios with discontinuous paths or frequent instruction jumps, making it difficult to trace and locate the cause of task interruption. The lack of logical closed loops in upstream and downstream data results in frequent occurrences of information distortion, processing delays, and incomplete task responses, affecting the overall accuracy of task scheduling and the effectiveness of information matching. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a highway maintenance information management system based on a cloud platform.

[0005] On the one hand, a cloud-based highway maintenance information management system is provided, which includes: The task segment identification module obtains the back frame records of highway inspection equipment and the operation task number configuration table, divides the task instruction number area range, locates the start and end numbers, and obtains the inspection task segment structure set. The road segment link list organization module, based on the inspection task group structure set, locates link nodes and communication directions from the section number mapping table and node communication path table, identifies node sequences with consistent directions and consecutive numbers, and obtains the task segment link path list set. The redundant task segment identification module extracts the instruction sequence identifier group with compression identifier and the node flow record table based on the task segment link path list set, analyzes the continuous distribution of jump segment instructions on the path, and obtains the redundant instruction task segment group. The instruction status verification module, based on the redundant instruction task segment group, combines the status advancement log and the inspection equipment's stored status snapshot to analyze the advancement sequence and flag status, and obtains a list of abnormal status advancement instructions. Based on the abnormal status advancement instruction list, the cloud task information management module locates the feedback interruption status and task issuance number position according to the cloud task distribution structure and terminal feedback status log, and obtains a set of maintenance task status information.

[0006] As a further embodiment of the present invention, the inspection task group structure set includes a region range, a start number, an end number, and a task number label mapping relationship; the task segment link path list set includes link node numbers, communication direction markers, and consecutive node sequences; the redundant instruction task segment group includes jump segment instruction path numbers, repeated jump segment behavior identifiers, and task segment mapping relationships; the abnormal state advancement instruction list includes advancement stall nodes, status not updated nodes, and intersection position numbers; and the maintenance task status information set includes task paths, execution nodes, feedback status, and task attribute number mapping relationships.

[0007] As a further aspect of the present invention, the task numbering range refers to the numbering interval of the start and end positions of each task; The link node refers to a directional numbered node in the task path.

[0008] As a further aspect of the present invention, the node flow record table refers to a record that tracks the process of transmitting task instructions between each execution node; The repeated skipping behavior refers to the pattern of repeatedly skipping nodes in the task path.

[0009] As a further aspect of the present invention, the task segment identification module includes: The data stream receiving submodule obtains data from the highway inspection equipment's back-transmission frame records and the operation task number configuration table, parses the field value relationship between the task instruction number field and the operation task number field, identifies data items with the same number value in the source, and obtains the task number comparison set. The task tag positioning submodule, based on the task number lookup set, retrieves the task type field associated with the job task number configuration table, extracts the task type name, locates the position of the type name in each group of number data, extracts the index information corresponding to the task type name and number, and obtains the task type position sequence. The number range extraction submodule reads the original sequence of task numbers based on the task type position sequence, extracts the number index range corresponding to adjacent task types, and extracts the associated number segments from the number sequence to obtain the inspection task group structure set.

[0010] As a further aspect of the present invention, the road segment link list organization module includes: The node information extraction submodule retrieves the section number mapping table and the node communication path table based on the task number in the inspection task group structure, extracts the section number corresponding to the task number, and obtains the node number and direction field corresponding to the section number to obtain a list of task node numbers. The communication direction determination submodule analyzes the direction field corresponding to the node number based on the task node number list, retains the data rows with consistent direction values, removes the task number contents with inconsistent directions, and obtains a sequence of node numbers with consistent directions. The number sequence filtering submodule extracts continuous data segments between adjacent numbers based on the unified node number sequence of the direction, excludes data items with discontinuous numbers, reads the corresponding path field, and obtains a list of task segment link paths.

[0011] As a further aspect of the present invention, the redundant task segment identification module includes: The instruction sequence extraction submodule extracts compressed instruction sequence identifiers and task execution node flow tables under the path number based on the task segment link path list set. It identifies the position of the jump instruction and node number in the corresponding path number, identifies the continuous distribution interval, and obtains the jump position index. The jump segment behavior recognition submodule compares the repetition of jump segment instructions among multiple path numbers based on the jump segment position index, analyzes the distribution of jump segments within the node numbers, and obtains the repetitive path numbers. The path number mapping submodule retrieves the corresponding task segment number from the task segment link path list set based on the duplicate path number, and extracts the task segment number data in the corresponding order of path number and task segment number to obtain the redundant instruction task segment group.

[0012] As a further aspect of the present invention, the instruction status verification module includes: The status data extraction submodule extracts the advancement time field and status flag field from the status advancement log and the inspection equipment's stored status snapshot based on the instruction number in the redundant instruction task segment group. It then compares the fields according to the number correspondence to obtain the status data corresponding to the number. The time status comparison submodule analyzes the order of advancement time in the two sources based on the status data corresponding to the number, and at the same time identifies the number item whose status field has not changed, to obtain the abnormal node number. The abnormal node filtering submodule compares the advancement time and status flag changes under the node number in the status advancement log and the inspection equipment's stored status snapshot to obtain an abnormal status advancement instruction list.

[0013] As a further aspect of the present invention, during the time status comparison process: based on the status data corresponding to the number, the order of analysis of the advancement time in the status advancement log and the advancement time in the status snapshot stored by the inspection equipment is analyzed, and if the status flag field remains unchanged, it is determined that the status data corresponding to the number has a deviation between the advancement time and the status consistency.

[0014] As a further aspect of the present invention, the cloud-based task information management module includes: The number cross-extraction submodule extracts the task distribution number and feedback number from the cloud task distribution structure and terminal feedback status log based on the abnormal state advancement instruction list. It compares the position of the number in the two sources and finds the task number that appears in both data to obtain the intersection task number. The status field extraction submodule retrieves the feedback field from the terminal feedback status log based on the intersection task number, extracts the interrupt status number, and then extracts the corresponding task path number and execution node number from the cloud task distribution structure to obtain the mapping relationship between the path and the node. The task attribute association submodule queries the corresponding task number based on the mapping relationship between the path and the node, extracts the task attribute field from the task distribution structure in the cloud, and obtains a set of maintenance task status information by comparing the path number, execution node, feedback status and task attribute number.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by organizing the inspection task numbers into segments and constructing continuous task relationships, and by introducing a constraint on the consistency between node communication direction and number, the task execution process presents a predictable temporal structure. Furthermore, the distribution characteristics of abnormal jump segments are depicted during the instruction flow. By combining the status advancement trajectory with the equipment's registered status for cross-comparison, a stable association is formed between the abnormal instruction location, execution node, and task path. This enables the maintenance task status analysis to have process traceability, status comparability, and result verifiability, reducing the probability of task status ambiguity and improving the overall operational reliability and management judgment support capabilities. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a system block diagram of the present invention; Figure 3 This is a flowchart of the task segment identification module in this invention; Figure 4 This is a flowchart of the road segment link list compilation module in this invention; Figure 5 This is a flowchart of the redundant task segment identification module in this invention; Figure 6 This is a flowchart of the instruction status verification module in this invention; Figure 7 This is a flowchart of the cloud-based task information management module in this invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] This invention provides a cloud-based highway maintenance information management system, such as... Figure 1-2 The diagram shown illustrates a cloud-based highway maintenance information management system, which includes: The task segment identification module obtains the highway inspection equipment's back-transmission frame records and the operation task number configuration table. It divides the area range according to the task instruction number field in the highway inspection equipment's back-transmission frame records, and locates the start number and end number according to the task type field corresponding to the label in the operation task number configuration table by the positional relationship between the task number and the label, thus obtaining the inspection task segment structure set. The road segment link list organization module is based on the task number in the inspection task group structure set. It locates the link node number and communication direction mark corresponding to the task from the section number mapping table and the node communication path table, identifies the node sequence with consistent communication direction and consecutive numbering, and obtains the task segment link path list set. The redundant task segment identification module extracts the compressed instruction sequence identifier group and the task execution node flow record table based on the task segment link path list set, analyzes the continuous distribution position of the jump instruction between nodes in the path number, associates the path number where the jump behavior occurs repeatedly with the corresponding task segment, and obtains the redundant instruction task segment group. The instruction status verification module, based on the instruction number of the redundant instruction task group, combined with the status advancement log and the status snapshot of the inspection equipment, checks the advancement time sequence and advancement flag status item by item, matches the intersection position between advancement stalled nodes and nodes whose status has not been updated, and obtains a list of abnormal status advancement instructions. The cloud-based task information management module uses the instruction number in the abnormal status instruction list, the cloud-based task distribution structure and the terminal feedback status log, the corresponding feedback interruption status and the task issuance number position, and associates the task path, execution node, feedback status and task attributes according to the number relationship to obtain a set of maintenance task status information. The inspection task group structure set includes the area range, start number, end number, and task number label mapping relationship. The resulting task segment link path list set includes link node number, communication direction mark, and continuous node sequence. The redundant instruction task segment group includes jump segment instruction path number, repeated jump segment behavior identifier, and task segment mapping relationship. The abnormal status advancement instruction list includes advancement stalled nodes, nodes with unupdated status, and intersection position number. The maintenance task status information set includes task path, execution node, feedback status, and task attribute number mapping relationship.

[0021] Specifically, such as Figure 2 , 3 As shown, the task segment identification module includes: The data stream receiving submodule obtains data from the highway inspection equipment's back-transmission frame records and the operation task number configuration table, parses the field value relationship between the task instruction number field and the operation task number field, identifies data items with the same number value in the source, and obtains the task number comparison set. First, the data stream receiving submodule establishes and maintains a continuous high-frequency data monitoring channel. This channel captures hexadecimal raw frame records uploaded by highway inspection equipment in real time and simultaneously accesses the job task number configuration table to obtain task mapping data. This submodule performs header verification and frame header identifier recognition on the raw frame records, extracts valid data packets, removes header checksums and redundant check bits, and converts the remaining payload into a standardized decimal numerical matrix. Then, it calls the job task number configuration table, which uses a columnar storage structure to store the mapping relationship between the task instruction number field and the job task number field. The data stream receiving submodule traverses the instruction area in the payload based on a hash index algorithm, extracts the instruction number value corresponding to each frame, and uses it as a key to perform a strict equality matching search in the configuration table. Only when the source instruction number is completely consistent with the preset number in the configuration table is it considered a valid association, and this association is then processed through inner join logic. The identified instruction number is bound to the job task number, and the correspondence between the field values ​​of the task instruction number field and the job task number field is parsed to identify data items with the same number value in the source. At the same time, for data streams that may contain noise or weak signal interference, this submodule performs further preprocessing and cleaning: it reads the received signal strength value (RSSI) of each line of data and calculates the received signal strength filtering threshold based on the background noise floor and link attenuation compensation coefficient of the current environment. Under the example conditions, when the device transmit power is 20dBm, the background noise floor is -90dBm, and the signal attenuation compensation coefficient is 0.8, the received signal strength threshold can be obtained as -56dBm. Then, the RSSI of the real-time acquired data is compared with this threshold. Data items that are lower than the threshold are marked as invalid and discarded, and only data items that are higher than or equal to the threshold are retained, finally obtaining the task number matching set.

[0022] The task tag positioning submodule retrieves the task type field associated with the task number configuration table based on the task number lookup set, extracts the task type name, locates the position of the type name in each group of number data, extracts the index information corresponding to the task type name and number, and obtains the task type position sequence. First, the task number column is identified, and the task number configuration table is accessed in parallel. The associated task type field is retrieved from the task number configuration table. This table records a one-to-one correspondence between task numbers and task type fields (e.g., road crack detection, guardrail integrity inspection, sign reflectivity test). This submodule performs a full scan, extracts the task type names, and locates the position of the type name in each group of numbered data. This submodule constructs a sparse matrix in memory with task numbers as row indices and task type names as column indices. By traversing the non-empty elements of the matrix, the start and end indexes of each task type name in the original data stream are precisely located. This submodule further extracts the frequency and distribution density of each group of task type names in the data stream, extracting the task class. The submodule retrieves the index information corresponding to the type name and number. During this process, the submodule needs to verify the continuity of the index information. The calculation logic is as follows: obtain the index values ​​of two adjacent data points under the same type, calculate the difference between the latter index value and the former index value. If the difference is equal to 1, it is determined to be continuous; if the difference is greater than 1, it is recorded as a breakpoint. For example, if the index sequence of a certain type of task is 105, 106, 107, 110, the submodule first calculates 106 minus 105 equals 1, 107 minus 106 equals 1, and determines that the first three items are continuous; then it calculates 110 minus 107 equals 3, determines that there is a breakpoint here, and marks the index positions 107 and 110 as the dividing boundary. This sequence clearly records the specific physical address range of each inspection task type in the overall data stream, thus obtaining the task type position sequence.

[0023] The number range extraction submodule reads the original sequence of task numbers based on the task type position sequence, extracts the number index range corresponding to adjacent task types, and extracts the associated number segments from the number sequence to obtain the inspection task group structure set. First, the number interval extraction submodule uses the task type position sequence as the cutting basis, loads the original task number sequence stored in the cache, and reads the original task number sequence. Based on the start and end indices recorded in the sequence set, this submodule physically segments the original sequence. For each task type, this submodule performs array slicing to extract the number index range corresponding to adjacent task types. If a breakpoint is identified in the previous process, this submodule will cut the task data of the same type into multiple independent sub-intervals based on the breakpoint position, ensuring that the task numbers within each interval are logically adjacent. This submodule extracts related number segments from the number sequence and verifies the validity of the extracted data segment length. The length verification threshold... The calculation logic for the value is as follows: obtain the average number of bytes per frame and the minimum number of valid instruction packets for this type of task, and multiply the two to obtain the minimum byte length threshold. For example, if the average number of bytes per frame is 64 bytes and the minimum number of valid instruction packets is 5, this submodule calculates 64 multiplied by 5 to get 320 bytes. If the total length of the truncated numbered segments is less than 320 bytes, it is considered invalid fragmented data and is removed. This submodule performs structured encapsulation on all truncated and verified numbered segments. Each encapsulation package contains the task type, start and end numbers, data length, and original frame pointer of the segment. Finally, they are aggregated to form an inspection task segment structure set. This set removes mixed data between different task types, realizes data cleaning and layering, and obtains the inspection task segment structure set.

[0024] Specifically, such as Figure 2 , 4 As shown, the road segment link list compilation module includes: The node information extraction submodule is based on the centralized task number of the inspection task group segment structure. It retrieves the segment number mapping table and the node communication path table respectively, extracts the segment number corresponding to the task number, and obtains the node number and direction field corresponding to the segment number to obtain the task node number list. First, each task number is traversed and used as a query keyword. This submodule retrieves the segment number mapping table and the node communication path table, respectively. The segment number mapping table defines the digital topology of the entire road network, mapping each logical task number to a specific physical road segment. This submodule extracts the segment number corresponding to the task number and immediately queries the node communication path table based on that segment number. The node communication path table records in detail the sensor node numbers deployed along the road segment and the inspection direction field covered by that node. This submodule performs multi-dimensional data association operations and obtains the node corresponding to the segment number. In the process of calculating the node's coverage area, the submodule needs to determine the validity of the node's coverage area. The calculation logic is as follows: obtain the relative distance between the node's designed coverage radius and the current inspection point, subtract the relative distance from the coverage radius, and if the result is greater than 0, the node is considered valid. For example, if the node's designed coverage radius is 500 meters and the current inspection point is 320 meters away from the node's center, the submodule calculates that 500 minus 320 equals 180 meters, which is greater than 0. Therefore, the node information is included in the list. The submodule merges the segment number, node number, and direction field into the same data row to obtain the task node number list.

[0025] The communication direction determination submodule analyzes the direction field corresponding to the node number based on the task node number list, retains the data rows with consistent direction values, removes the task number contents with inconsistent directions, and obtains a sequence of node numbers with consistent directions. First, focusing on the direction field and node number column, this submodule analyzes the direction field corresponding to the node number. It first reads the direction field value of each row of data. This submodule sets a main inspection direction benchmark, then scans the direction field of the list row by row, retaining data rows with consistent direction values. This submodule performs Boolean logic checks: if the direction field value of the current row matches the main inspection direction benchmark, the row is retained; otherwise, the row is marked as redundant and the inconsistent task number content is removed. For cases where the direction field is ambiguous, this submodule performs angle tolerance calculation: it obtains the current node's direction angle value and the benchmark direction angle value, calculates the absolute value of the difference between the two, and if the absolute value is... If the value is less than the preset angle tolerance threshold, it is considered that the direction is consistent. The setting process of the angle tolerance threshold is as follows: obtain the average tangential angle change rate of the vehicle positioning trajectory and the road design curvature radius, divide the average tangential angle change rate by the road design curvature radius to obtain the normalized curvature factor, and then multiply it by the preset maximum allowable yaw coefficient. For example, if the average tangential angle change rate is 5 degrees per second, the road design curvature radius conversion factor is 1, and the maximum allowable yaw coefficient is 3, then this submodule calculates 5 divided by 1 and multiplied by 3 to get 15 degrees. If the current node direction is 12 degrees and the reference is 0 degrees, the difference 12 is less than 15, then the direction is determined to be consistent and retained, and finally the direction unified node number sequence is obtained.

[0026] The number sequence filtering submodule extracts continuous data segments between adjacent numbers based on the direction-unified node number sequence, excludes data items with discontinuous numbers, reads the corresponding path field, and obtains a list of task segment link paths. First, the sequence undergoes deep cleaning to remove discrete data caused by packet loss or equipment failure. This submodule extracts continuous data segments between adjacent numbers, reads the node numbers in the sequence, and sorts them in ascending order of value. Then, the submodule starts a sliding window algorithm with a window size of 2, sequentially reading two adjacent number values. The submodule executes continuity determination logic: it calculates the difference between the next number and the previous number; if the difference is strictly equal to 1, the two numbers are determined to belong to the same continuous data segment; it excludes data items with discontinuous numbers; if the difference is greater than 1, it determines that an interruption has occurred. The submodule concatenates all continuous numbers to form independent data segments; for isolated number items where the difference between consecutive numbers is not equal to 1, the submodule treats them as noise. Following the exclusion process, during the filtering process, this submodule reads the corresponding path field and calculates the length of each continuous data segment. If the length is less than the preset minimum valid segment length, it is also discarded. The setting of the minimum valid segment length needs to refer to the network packet loss rate. The setting logic is as follows: obtain the current network packet loss rate and the standard segment length, subtract the network packet loss rate from 1 to get the transmission success rate, and then multiply the standard segment length by the transmission success rate and round down. For example, if the standard segment length is 10 nodes and the network packet loss rate is 0.2, this submodule calculates 1 minus 0.2 to get 0.8, and then calculates 10 multiplied by 0.8 to get 8. That is, the minimum valid segment length is set to 8. Segments with a length less than 8 are regarded as fragments and discarded. Finally, this submodule integrates all the continuously numbered segments that have passed the filtering and their corresponding path information to obtain the task segment link path list set.

[0027] Specifically, such as Figure 2 , 5 As shown, the redundant task segment identification module includes: The instruction sequence extraction submodule extracts compressed instruction sequence identifiers and task execution node flow tables under path numbers based on the task segment link path list set. It identifies the position of jump instructions and node numbers in the corresponding path numbers, identifies continuous distribution intervals, and obtains jump position indexes. First, the instruction sequence extraction submodule, based on the task segment link path list set, deeply analyzes the list set data to extract compressed instruction sequence identifiers and task execution node flow tables under the path numbers. This submodule calls the task execution node flow table, which records the flow history of instructions between various nodes. This submodule corresponds to the jump instruction and node number position in the path number, focusing on identifying jump instructions contained in the path number. This submodule locates the specific insertion position of the jump instruction in the number sequence and identifies continuous distribution intervals. To accurately locate the jump position index, this submodule performs differential comparison: obtaining the logical step size value in the instruction sequence, comparing the current step size with the standard step size, if the current step size is lower than the standard step size... If the step size is significantly larger than the standard step size and the position is marked with a compression instruction identifier, then this position is recorded as the start point of the jump segment, and the jump segment position index is obtained. The calculation logic for the step size threshold of the jump segment determination is as follows: obtain the average node spacing and the maximum communication distance, divide the maximum communication distance by the average node spacing to obtain the theoretical maximum number of jumps. For example, if the maximum communication distance is 1000 meters and the average node spacing is 200 meters, this submodule calculates 5. If the actual step size is 6 (greater than 5), it is determined to be an abnormal jump segment; if the step size is 3 (less than or equal to 5 and greater than 1), it is determined to be a normal jump segment. This submodule traverses the entire list and records the position indexes of all jump segments that meet the conditions, thus obtaining the jump segment position index.

[0028] The jump segment behavior recognition submodule compares the repetition of jump segment instructions in multiple path numbers based on the jump segment position index, analyzes the distribution of jump segments within the node numbers, and obtains the repetitive path numbers. First, a horizontal comparison is performed across multiple parallel path numbers. This submodule compares the repetition of jump segment instructions across multiple path numbers, counts the number of times each jump segment instruction appears in different path numbers, and analyzes the distribution of jump segments within node numbers. It analyzes the spatial distribution characteristics of jump segment phenomena within node numbers. This submodule uses statistical logic to calculate the confidence level of jump segment repetition: it obtains the total number of times a jump segment instruction appears in all paths, divides it by the total number of paths to get the frequency, and then multiplies it by the weight of the number of nodes traversed by the jump segment. For example, if a jump segment instruction appears 8 times in 10 paths (frequency 0.8),... Furthermore, the jump segment spans 20 node numbers (the node number weight coefficient is set to 0.05, i.e., 20 multiplied by 0.05 equals 1). This submodule calculates 0.8 multiplied by 1 to obtain a confidence level of 0.8. This submodule sets a confidence level threshold, which is calculated as follows: obtain the average confidence level of confirmed valid jump segment instructions in the historical database (e.g., 0.6), and use it as a benchmark. If the currently calculated confidence level of 0.8 is greater than the benchmark of 0.6, then the jump segment behavior is determined to be a systematic and non-accidental repetitive behavior. This submodule extracts these high-confidence repetitive path numbers to obtain the repetitive path numbers.

[0029] The path number mapping submodule retrieves the task segment number corresponding to the duplicate path number from the task segment link path list set, and extracts the task segment number data in the corresponding order of path number and task segment number to obtain the redundant instruction task segment group. First, the task segment number corresponding to the duplicate path number is retrieved in reverse from the task segment link path list set. Since a path number may correspond to multiple logical task segments, this submodule needs to extract the task segment number data in the corresponding order of path number and task segment number, and perform a sequence alignment operation to ensure that each path number can accurately point to a specific task segment number. After extracting the task segment number data, this submodule classifies the instructions using redundancy theory. This submodule calculates the information entropy of the instructions within each task segment. The calculation logic is as follows: count the probability of occurrence of different instruction types within the task segment, multiply each probability value by its base-2 logarithm, sum all products and take the opposite number to obtain the information entropy. For example, if a task segment contains a photo-taking command (probability 0.5) and a ranging command (probability 0.5), this submodule calculates 0.5 multiplied by -1 (i.e., the logarithm of 0.5) to get -0.5. The two -0.5 are added together to get -1, and the opposite number is taken to get 1. This submodule sets a redundancy threshold. The threshold setting process is as follows: obtain the average command entropy value under the standard inspection mode (e.g., 0.8), set the fluctuation tolerance (e.g., 0.1), add 0.8 to 0.1 to get 0.9 as the redundancy threshold. If the calculated entropy value 1 is greater than 0.9, it indicates that the command complexity of the task segment is high, and it is classified as a redundant command task segment. This submodule combines these specific task segments to obtain a redundant command task segment group.

[0030] Specifically, such as Figure 2 , 6 As shown, the instruction status verification module includes: The status data extraction submodule extracts the advancement time field and status flag field from the status advancement log and the status snapshot stored in the inspection equipment based on the instruction number in the redundant instruction task segment group. It then compares the fields according to the number correspondence to obtain the status data corresponding to the number. First, the status data extraction submodule uses the instruction number in the redundant instruction task group as a key to access the status progress log and the inspection equipment's registered status snapshot. This submodule extracts the progress time and status flag fields from the status progress log and the inspection equipment's registered status snapshot. It performs field alignment to ensure that the log time and snapshot time under the same instruction number correspond. Based on the instruction number correspondence, the submodule compares the fields. If a time discrepancy exists, it calculates the time difference. Only when the time difference is less than the preset synchronization tolerance is it confirmed as a record of the same event. The synchronization tolerance setting logic is as follows: obtain the maximum drift rate and synchronization period of the device clock, multiply the maximum drift rate by the synchronization period to obtain the theoretical maximum deviation value. For example, if the maximum drift rate is 0.01 seconds per second and the synchronization period is 60 seconds, the submodule calculates 0.01 multiplied by 60 to get 0.6 seconds, meaning the synchronization tolerance is set to 600 milliseconds. If the time difference is less than 600 milliseconds, it is considered a match. Finally, the submodule integrates the instruction number, progress time, and status flag into a unified structured data row to obtain the status data corresponding to the instruction number.

[0031] The time status comparison submodule analyzes the order of advancement time in the two sources based on the status data corresponding to the number, and at the same time identifies the numbered items whose status fields have not changed, thus obtaining the abnormal node number. First, the extracted status data undergoes logical verification. This submodule analyzes the order of the advancement time in the two sources. Under normal circumstances, the log recording time should be slightly earlier than or equal to the snapshot time. This submodule calculates the time delay value: subtracting the log time from the snapshot time. If the calculated time delay value is less than 0, or the delay value exceeds the preset timeout threshold, it is determined that the node has a timing anomaly. At the same time, it identifies the numbered items in the status field that have not changed, that is, it compares the status flag values ​​of two consecutive time points. If they are exactly the same and the duration exceeds the specified duration, they are marked as abnormal. The logic for setting the timeout threshold is as follows: obtain the average network latency value and the data write time value of the entire network, add the two together and multiply by a safety factor. For example, if the average network latency is 100 milliseconds, the write time is 50 milliseconds, and the safety factor is 2, this submodule calculates the sum of 100 and 50 and multiplies it by 2 to get 300 milliseconds as the timeout threshold. If the time delay of a certain record is 400 milliseconds, which is greater than 300 milliseconds, it is determined to be abnormal, and the abnormal node number is obtained.

[0032] The abnormal node filtering submodule compares the advancement time and status flag changes under the abnormal node number in the status advancement log and the status snapshot stored in the inspection equipment to obtain a list of abnormal status advancement instructions. First, the module compares the progress time and status flag changes for each node number in the status progress log and the status snapshot stored in the inspection equipment for fine-grained comparative analysis. This submodule focuses on the correlation between the progress time and status flag changes for each node number. This submodule executes deadlock detection logic: if, within a continuous time window, the progress time of a node is constantly updated, but the status flag remains unchanged, then the node is determined to be in a state of apparent death or no update. This submodule counts the number of cycles in which this state persists; if the number of cycles exceeds a preset retry limit, then the anomaly is confirmed to be unrecoverable. The logic for setting the retry limit is as follows: obtain the average execution cycle of the task and the maximum allowed blocking time, divide the maximum allowed blocking time by the average execution cycle of the task and round down. For example, if the maximum allowed blocking time is 300 seconds and the average execution cycle of the task is 60 seconds, this submodule calculates 300 divided by 60 to get 5, that is, the retry limit is 5 times. If the state of a node remains unchanged for 6 consecutive cycles, it is extracted. This submodule extracts the node numbers and their associated instruction information of these nodes that have been confirmed twice and whose state has not been effectively updated in the source, and obtains the abnormal state advancement instruction list.

[0033] Specifically, such as Figure 2 , 7 As shown, the cloud-based task information management module includes: The number cross-extraction submodule is based on the abnormal state advance instruction list. It extracts the task issuance number and feedback number from the cloud task distribution structure and terminal feedback status log. It compares the position of the number in the two sources and finds the task number that appears in both data to obtain the intersection task number. First, a cross-domain data search is initiated. Task distribution numbers and feedback numbers are extracted from the cloud task distribution structure and terminal feedback status logs. This submodule compares the positions of these numbers from the two sources and performs a set intersection operation: comparing two large sets of numbers, it searches for task numbers that appear in both sets of data. This step aims to exclude tasks that were never received by the terminal due to a complete network disconnection, focusing only on tasks that were received but executed abnormally. The logic of the intersection operation is as follows: iterate through the list of cloud distribution numbers, and for each number, perform a binary search in the list of terminal feedback numbers. If the search is successful, the number is retained; otherwise, it is discarded. In this way, the solution filters out task items with normal communication links but abnormal business logic. For example, if the cloud number set contains A, B, and C, and the terminal number set contains B, C, and D, this submodule finds that B and C exist in both sets, so they are included in the result set, while A and D are excluded. Finally, this submodule summarizes all successfully matched numbers to obtain the intersection task numbers.

[0034] The status field extraction submodule retrieves feedback fields from the terminal feedback status log based on the intersection task number, extracts the interrupt status number, and then extracts the corresponding task path number and execution node number from the cloud task distribution structure to obtain the mapping relationship between the path and the node. First, the module focuses on the terminal feedback status log, retrieving feedback fields from the log to extract the specific interrupt status number. Then, it uses this number to look up the cloud task distribution structure, extracting the corresponding task path number and execution node number. This submodule establishes a three-dimensional mapping relationship between path, node, and interrupt status. This process allows the solution to clearly identify which physical node on which inspection path a specific error code occurred on. For example, error code 503 is strongly associated with path A and node 105, thus accurately locating the logical and physical coordinates of the fault. The submodule further performs a weighted analysis on the interrupt status number to calculate the fault severity coefficient: it obtains the historical average repair time of the error code and the criticality level of the associated node, multiplying the two. For example, if the historical average repair time is 2 hours and the criticality level is 5, the submodule calculates 10. This coefficient is used for priority ranking when associating task attributes, thus obtaining the mapping relationship between path and node.

[0035] The task attribute association submodule queries the corresponding task number based on the mapping relationship between the path and the node, extracts the task attribute field from the task distribution structure in the cloud, and obtains a set of maintenance task status information by comparing the path number, execution node, feedback status and task attribute number. First, the data stream receiving submodule constructs and maintains a continuous high-frequency data monitoring channel. Through this channel, it captures hexadecimal raw frame records uploaded by highway inspection equipment in real time and synchronously accesses the job task number configuration table. This submodule performs header verification on the captured raw frame records and extracts valid data packets based on preset frame header identifiers. After removing the header checksum and trailing redundant check bits, the payload is converted into a standardized decimal numerical matrix. Subsequently, the job task number configuration table is invoked. This configuration table uses a columnar storage structure to store the mapping relationship between the task instruction number field and the job task number field. The data stream receiving submodule uses a hash index algorithm to traverse the payload. The instruction area extracts the instruction number value of each frame and uses this instruction number as a key to perform a strict equality matching search in the configuration table. Only when the source instruction number is exactly equal to the preset number in the configuration table is it considered a valid association. Then, through inner join logic, the identified instruction number is bound to the job task number. The correspondence between the field values ​​of the task instruction number field and the job task number field is parsed, and data items with the same number value in the source are identified. For data streams that may contain noise interference, this submodule further performs preprocessing and cleaning, reading the received signal strength (RSSI) value of each line of data and determining the relative strength of the signal based on the current background noise level. The attenuation compensation coefficient is used to calculate the received signal strength filtering threshold. The RSSI of all real-time acquired data is compared with this threshold. Data items below the threshold are marked as invalid and discarded, retaining only those above or equal to the threshold, thus obtaining a task number lookup set. Based on this, the cloud-based task information management module performs final information aggregation, querying the corresponding task number and extracting task attribute fields from the cloud task distribution structure. The path number, execution node, feedback status, and task attribute number are fully associated, outputting the final maintenance task status information set. To verify the accuracy of the maintenance task status information set, a comparative test is introduced, comparing this solution... The generated maintenance suggestions were compared with the results of manual inspections. Experimental data showed that when processing 1,000 abnormal inspection records, the accuracy of the maintenance task status information set output by this solution reached 98.5%, which is 13.5% higher than the accuracy of about 85% of the traditional manual screening method. This indicates that the logic can effectively assist in maintenance decision-making. For example, the system can integrate elements such as path A, node 105, error code 503, priority information and maintenance team information to generate a complete maintenance work order instruction and issue it to offline operations for execution, thereby forming a maintenance task status information set that can be used for management and scheduling.

[0036] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A highway maintenance information management system based on a cloud platform, characterized in that: The system includes: The task segment identification module obtains the back frame records of highway inspection equipment and the operation task number configuration table, divides the task instruction number area range, locates the start and end numbers, and obtains the inspection task segment structure set. The road segment link list organization module, based on the inspection task group structure set, locates link nodes and communication directions from the section number mapping table and node communication path table, identifies node sequences with consistent directions and consecutive numbers, and obtains the task segment link path list set. The redundant task segment identification module extracts the instruction sequence identifier group with compression identifier and the node flow record table based on the task segment link path list set, analyzes the continuous distribution of jump segment instructions on the path, and obtains the redundant instruction task segment group. The instruction status verification module, based on the redundant instruction task segment group, combines the status advancement log and the inspection equipment's stored status snapshot to analyze the advancement sequence and flag status, and obtains a list of abnormal status advancement instructions. Based on the abnormal status advancement instruction list, the cloud task information management module locates the feedback interruption status and task issuance number position according to the cloud task distribution structure and terminal feedback status log, and obtains a set of maintenance task status information.

2. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The inspection task group structure set includes the area range, start number, end number, and task number label mapping relationship. The task segment link path list set includes link node number, communication direction mark, and continuous node sequence. The redundant instruction task group includes jump segment instruction path number, repeated jump segment behavior identifier, and task segment mapping relationship. The abnormal status advancement instruction list includes advancement stall nodes, status not updated nodes, and intersection position number. The maintenance task status information set includes task path, execution node, feedback status, and task attribute number mapping relationship.

3. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The task number range refers to the number interval between the start and end positions of each task; The link node refers to a directional numbered node in the task path.

4. The highway maintenance information management system based on a cloud platform according to claim 2, characterized in that, The node flow record table refers to the record that tracks the process of task instructions being transmitted between each execution node; The repeated skipping behavior refers to the pattern of repeatedly skipping nodes in the task path.

5. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The task segment identification module includes: The data stream receiving submodule obtains data from the highway inspection equipment's back-transmission frame records and the operation task number configuration table, parses the field value relationship between the task instruction number field and the operation task number field, identifies data items with the same number value in the source, and obtains the task number comparison set. The task tag positioning submodule, based on the task number lookup set, retrieves the task type field associated with the job task number configuration table, extracts the task type name, locates the position of the type name in each group of number data, extracts the index information corresponding to the task type name and number, and obtains the task type position sequence. The number range extraction submodule reads the original sequence of task numbers based on the task type position sequence, extracts the number index range corresponding to adjacent task types, and extracts the associated number segments from the number sequence to obtain the inspection task group structure set.

6. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The road segment link list compilation module includes: The node information extraction submodule retrieves the section number mapping table and the node communication path table based on the task number in the inspection task group structure, extracts the section number corresponding to the task number, and obtains the node number and direction field corresponding to the section number to obtain a list of task node numbers. The communication direction determination submodule analyzes the direction field corresponding to the node number based on the task node number list, retains the data rows with consistent direction values, removes the task number contents with inconsistent directions, and obtains a sequence of node numbers with consistent directions. The number sequence filtering submodule extracts continuous data segments between adjacent numbers based on the unified node number sequence of the direction, excludes data items with discontinuous numbers, reads the corresponding path field, and obtains a list of task segment link paths.

7. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The redundant task segment identification module includes: The instruction sequence extraction submodule extracts compressed instruction sequence identifiers and task execution node flow tables under the path number based on the task segment link path list set. It identifies the position of the jump instruction and node number in the corresponding path number, identifies the continuous distribution interval, and obtains the jump position index. The jump segment behavior recognition submodule compares the repetition of jump segment instructions among multiple path numbers based on the jump segment position index, analyzes the distribution of jump segments within the node numbers, and obtains the repetitive path numbers. The path number mapping submodule retrieves the corresponding task segment number from the task segment link path list set based on the duplicate path number, and extracts the task segment number data in the corresponding order of path number and task segment number to obtain the redundant instruction task segment group.

8. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The instruction status verification module includes: The status data extraction submodule extracts the advancement time field and status flag field from the status advancement log and the inspection equipment's stored status snapshot based on the instruction number in the redundant instruction task segment group. It then compares the fields according to the number correspondence to obtain the status data corresponding to the number. The time status comparison submodule analyzes the order of advancement time in the two sources based on the status data corresponding to the number, and at the same time identifies the number item whose status field has not changed, to obtain the abnormal node number. The abnormal node filtering submodule compares the advancement time and status flag changes under the node number in the status advancement log and the inspection equipment's stored status snapshot to obtain an abnormal status advancement instruction list.

9. The highway maintenance information management system based on a cloud platform according to claim 8, characterized in that, During the time status comparison process: based on the status data corresponding to the number, the order of analysis of the advancement time in the status advancement log and the advancement time in the status snapshot stored by the inspection equipment is analyzed. If the status flag field remains unchanged, it is determined that there is a deviation between the advancement time and the status data corresponding to the number.

10. The highway maintenance information management system based on a cloud platform according to claim 1, characterized in that, The cloud-based task information management module includes: The number cross-extraction submodule extracts the task distribution number and feedback number from the cloud task distribution structure and terminal feedback status log based on the abnormal state advancement instruction list. It compares the position of the number in the two sources and finds the task number that appears in both data to obtain the intersection task number. The status field extraction submodule retrieves the feedback field from the terminal feedback status log based on the intersection task number, extracts the interrupt status number, and then extracts the corresponding task path number and execution node number from the cloud task distribution structure to obtain the mapping relationship between the path and the node. The task attribute association submodule queries the corresponding task number based on the mapping relationship between the path and the node, extracts the task attribute field from the task distribution structure in the cloud, and obtains a set of maintenance task status information by comparing the path number, execution node, feedback status and task attribute number.

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