A resource scheduling method and system for information management of highway maintenance

Abstract

The present application relates to the technical field of information management using blockchain, and discloses a resource scheduling method and system for highway maintenance information management, which realizes real, reliable and tamper-proof record management of task execution history based on blockchain technology, and provides a data basis for accurate construction of resource history portrait. On this basis, a quantifiable history portrait is further established for various resources, realizing the refined expression of resource capacity and adaptation degree. And the subsequent scheduling priority rules are optimized through the resource history portrait, so that resource allocation no longer depends on static rules, but is based on intelligent evaluation and dynamic adjustment of historical performance, effectively improving the execution efficiency, response stability and resource matching accuracy of highway maintenance tasks, thereby significantly enhancing the informationization and intelligent scheduling level of the entire highway maintenance system.

Description

Technical Field

[0001] This invention relates to the technical field of information management using blockchain, and more specifically to a resource scheduling method and system for information management of highway maintenance. Background Technology

[0002] In existing technologies, with the extension of the service life of transportation infrastructure and the increasing complexity of maintenance tasks, current highway maintenance resource scheduling systems face problems such as information lag, low resource utilization, and inaccurate scheduling responses. Most mainstream solutions currently rely on manual judgment or rule-driven static scheduling strategies, lacking in-depth analysis of historical resource performance and failing to guarantee the reliability and traceability of data during the scheduling process. Furthermore, traditional task execution data is often stored in a decentralized and tamper-proof manner, making it difficult to serve as a reliable basis for subsequent decision-making and optimization. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a resource scheduling method and system for information management of highway maintenance, so as to solve the technical problems such as low resource utilization and inaccurate scheduling response caused by the inability to guarantee the reliability and traceability of data during the scheduling process when scheduling resources for highway maintenance.

[0004] The first aspect of this invention discloses a resource scheduling method for information management of highway maintenance, the method comprising the following steps:

[0005] S1. Collect road segment status data for the first road segment to be maintained, and generate the first maintenance task list based on the road segment status data;

[0006] S2. Based on the preset first task scheduling rules, scheduling resource status information and geographical location matching degree, perform task resource matching operation to generate the first scheduling scheme;

[0007] S3. During task execution, the execution history data of the task execution entity is recorded in real time, and the execution history data is synchronously uploaded to the resource history chain; the resource history chain is used to store the execution history data through blockchain technology and generate an immutable and trustworthy record of resource scheduling.

[0008] S4. Based on the trusted records of resource scheduling, establish resource history profiles for various types of job resources; if resource history profiles exist, proceed to step S5;

[0009] S5. Collect road segment status data for the second road segment to be maintained, and perform resource scheduling operations on the second road segment to be maintained based on the road segment status data and resource history profile.

[0010] Furthermore, the execution history data includes task identifier, task start and end time, task type identifier, executor identifier, job completion feedback information, abnormal event records, and task execution score.

[0011] Furthermore, the process of synchronously uploading execution history data to the resource history chain includes:

[0012] Based on the task identifier contained in the execution history data, the task urgency and scheduling impact parameters recorded in the maintenance task list for the corresponding task are retrieved.

[0013] The execution history data to be uploaded is prioritized based on the urgency and scheduling impact parameters of each task, and high-priority history data is packaged and uploaded to the resource history chain first; wherein, the scheduling impact is used to measure the intensity of the chain scheduling impact caused by each task in the resource scheduling network.

[0014] Furthermore, the resource history chain adopts a consortium blockchain architecture;

[0015] After obtaining the execution history data, the process of storing the execution history data using blockchain technology includes:

[0016] After hashing and signing the execution history data, a history block is generated according to the task batch and broadcast to multiple on-chain relay nodes to ensure the execution history data is immutably stored. The task batch is a set of tasks collected within a preset synchronization period, and the data in the block is encapsulated according to the task priority order. Each block of the resource history chain includes hash digest information of the execution history data, task execution digest index, on-chain timestamp, and relay node signature field used to verify the trustworthiness of the upload.

[0017] Furthermore, the process of constructing a resource history profile based on the trusted resource scheduling record in step S4 includes:

[0018] Based on the trusted records of resource scheduling, an aggregation analysis operation is performed on each type of job resource, and a set of resource feature indicators is generated based on the aggregation analysis results.

[0019] Based on the set of resource feature indicators, a resource profile feature vector is constructed, and a multi-dimensional attribute weighted clustering algorithm is used to group and label resources of the same type, thereby obtaining a resource history profile set with tagged attributes.

[0020] Furthermore, the aggregated analysis results include historical task participation frequency, task type distribution, mean and standard deviation of task completion scores, average operation time, and abnormal event occurrence rate.

[0021] Furthermore, the weights of each attribute in the multidimensional attribute weighted clustering algorithm are set according to the scheduling sensitivity of different resource types; the scheduling sensitivity represents the degree of influence of each feature index on the quality of resource scheduling.

[0022] Further, step S5 includes:

[0023] S501. Construct a task requirement feature vector based on road segment status data;

[0024] S502. Determine the task adaptability distribution indicators corresponding to the resources based on the resource history profile;

[0025] S503. Determine the compatibility score between the task to be generated and the existing schedulable resources based on the task requirement feature vector and the task adaptability distribution index of resources;

[0026] S504. Filter and prioritize tasks based on their fit scores to generate a second maintenance task list.

[0027] Furthermore, step S5 also includes:

[0028] S505. Based on the preset second task scheduling rules, scheduling resource status information and geographical location matching degree, perform task resource matching operation to generate a second scheduling scheme;

[0029] S506. Execute the road section maintenance task based on the second scheduling scheme, and continue to execute steps S3~S5.

[0030] A second aspect of this invention discloses a resource scheduling system for information management of highway maintenance, the system comprising:

[0031] The generation module is used to collect the road segment status data of the first road segment to be maintained and generate the first maintenance task list based on the road segment status data.

[0032] The matching module is used to perform task resource matching operations and generate a first scheduling scheme based on the preset first task scheduling rules, scheduling resource status information and geographical location matching degree.

[0033] The data recording and synchronization module is used to record the execution history data of the task execution subject in real time during task execution, and synchronously upload the execution history data to the resource history chain; the resource history chain is used to store the execution history data through blockchain technology to generate an immutable and trustworthy record of resource scheduling.

[0034] The profile building module is used to build resource history profiles for various types of operational resources based on trusted resource scheduling records;

[0035] The scheduling module is used to collect road segment status data of the second road segment to be maintained when a resource history profile exists, and to perform resource scheduling operations on the second road segment to be maintained based on the road segment status data and the resource history profile.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] This invention utilizes blockchain technology to achieve authentic, reliable, and tamper-proof evidence management of task execution history, thus providing a data foundation for the accurate construction of resource history profiles. By uploading execution history data to the blockchain and establishing a trusted record for resource scheduling, the historical task behavior, performance feedback, and abnormal response of each operational resource can be effectively recorded. Based on this, quantifiable history profiles are further established for various resources, enabling a detailed expression of resource capabilities and suitability. Furthermore, the resource history profiles are used to optimize subsequent scheduling priority rules, ensuring that resource allocation no longer relies solely on static rules but is based on intelligent evaluation and dynamic adjustment according to historical performance. This effectively improves the execution efficiency, response stability, and resource matching accuracy of highway maintenance tasks, thereby significantly enhancing the informatization and intelligent scheduling level of the entire highway maintenance system. Attached Figure Description

[0038] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 This is a flowchart illustrating a resource scheduling method for information management of highway maintenance, as disclosed in an embodiment of the present invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example

[0041] The first aspect of this invention discloses a resource scheduling method for information management of highway maintenance. This resource scheduling method relates to the technical field of information management using blockchain technology. Please refer to [link / reference needed]. Figure 1 , Figure 1 This is a flowchart illustrating a resource scheduling method for information management of highway maintenance disclosed in an embodiment of the present invention. The method includes the following steps:

[0042] S1. Collect road segment status data for the first road segment to be maintained, and generate the first maintenance task list based on the road segment status data;

[0043] S2. Based on the preset first task scheduling rules, scheduling resource status information and geographical location matching degree, perform task resource matching operation to generate the first scheduling scheme;

[0044] S3. During task execution, the execution history data of the task execution entity is recorded in real time, and the execution history data is synchronously uploaded to the resource history chain; the resource history chain is used to store the execution history data through blockchain technology and generate an immutable and trustworthy record of resource scheduling.

[0045] S4. Based on the trusted records of resource scheduling, establish resource history profiles for various types of job resources; if resource history profiles exist, proceed to step S5;

[0046] S5. Collect road segment status data for the second road segment to be maintained, and perform resource scheduling operations on the second road segment to be maintained based on the road segment status data and resource history profile.

[0047] Furthermore, the execution history data includes, but is not limited to, task identifier, task start and end time, task type identifier, executor identifier, job completion feedback information, abnormal event records, and task execution score.

[0048] Specifically, in this embodiment of the invention, the task type identifier is used to mark the specific work category of the maintenance task, and is usually generated based on the fields in the task work order. For example, different task types may include: crack repair, road marking repainting, pothole filling, bridge and culvert inspection, etc. This field is automatically generated by the work code set when the task is issued, ensuring that the resource-task suitability assessment in the subsequent profile construction has a clear category reference.

[0049] The task completion feedback information is a summary of completion status data reported by the personnel or equipment after the task execution is terminated. It mainly includes the task completion time, description of remaining problems, amount of materials used, and feedback on the status of the equipment. When it can be reported by the equipment, this information is automatically collected and uploaded through the maintenance operation terminal, serving as an important evaluation parameter for the resource task completion capability.

[0050] The anomaly event log is used to record interference, malfunctions, or deviations from the expected process that occur during task execution. Specific details include, but are not limited to, the time of occurrence, the type of anomaly (e.g., equipment failure, construction interruption, weather impact), the handling method, and the processing time. Data is sourced from on-site monitoring logs and manually tagged reports. This field helps assess the stability and emergency response capabilities of operational resources under non-ideal conditions.

[0051] Task execution score is a comprehensive indicator for evaluating the quality of a single task execution. The scoring criteria include deviations from the expected project duration, the pass rate of random quality checks, the occurrence of any abnormal events, and the consistency of feedback. The score is a standardized numerical value, facilitating horizontal comparisons with results from other tasks.

[0052] Furthermore, the process of synchronously uploading execution history data to the resource history chain includes:

[0053] Based on the task identifier contained in the execution history data, the task urgency and scheduling impact parameters recorded in the maintenance task list for the corresponding task are retrieved.

[0054] The execution history data to be uploaded is prioritized based on the urgency and scheduling impact parameters of each task, and high-priority history data is packaged and uploaded to the resource history chain first; wherein, the scheduling impact is used to measure the intensity of the chain scheduling impact caused by each task in the resource scheduling network.

[0055] First, it should be noted that in this embodiment of the invention, the generation process of the first maintenance task list is based on the road segment status data of the road section to be maintained, with the aim of achieving task identification, priority scoring, and basic scheduling preparation. Specifically, by collecting road segment status data covering the target road segment, such as structural health assessment data, service life data, sensor detection information, traffic load data, and historical maintenance record data, and combining them with a preset task type judgment rule set, the maintenance tasks to be generated are automatically identified, including but not limited to crack repair, pavement cleaning, slope repair, and road marking repainting.

[0056] Based on the identified maintenance tasks, a basic field set is constructed for each task. These fields include, but are not limited to, task identifier, road segment location label, task type identifier, estimated duration, task urgency, and scheduling impact parameter. The task urgency is calculated based on a weighted average of structural damage score, historical repair frequency, aging coefficient, and on-site risk level, and is used to measure the urgency of the task. The estimated duration is initially estimated by matching the task type to a standard procedure library and considering the available schedulable resources.

[0057] Furthermore, in this embodiment of the invention, the scheduling task impact parameter is used to quantify the intensity of the chain scheduling impact of each task in the resource scheduling network, reflecting the resource coupling and priority processing value between tasks. The scheduling task impact parameter can be derived from the dependency graph topology of the resource scheduling network.

[0058] In a preferred embodiment, specifically, a resource scheduling dependency graph is first constructed based on historical scheduling data and the current list of adjustable resources. The nodes of the dependency graph represent task units, and the edges represent resource competition relationships or temporal dependencies.

[0059] For any task to be scheduled, its scheduling influence is defined as follows:

[0060] in, Indicates task The set of adjacent tasks; Indicates task The quantity of currently available resources (i.e., the inverse indicator of resource scarcity). Indicates task and The strength of resource coupling between two entities can be determined by their respective needs for the same resource.

[0061] As another preferred implementation, to ensure consistency in impact assessment across different task types, a task type adjustment factor is introduced and multiplied by the aforementioned task impact. This adjustment factor is a standardized value representing the average number of scheduling chain reactions triggered by the corresponding task type in historical scheduling, used to reflect the scheduling disturbance potential commonly possessed by a certain type of task.

[0062] Therefore, to ensure priority storage of critical task records during the upload of execution history data to the resource history chain, this invention prioritizes the execution history data to be uploaded according to the importance of the tasks. Specifically, it first extracts the task identifier associated with each execution history data entry, and then retrieves the corresponding task urgency and scheduling impact parameters from the maintenance task list at the time the task was generated. Task urgency is used to measure the timeliness requirements and risk level of the current task.

[0063] After obtaining the two parameters mentioned above, corresponding weighting coefficients are set based on the actual business strategy, and a comprehensive priority score is given to each execution history data. History data with higher scores indicates greater criticality in the scheduling network and will therefore be processed first during the upload process. Subsequently, data to be uploaded is selected sequentially according to priority scores from highest to lowest until the current block capacity limit is reached.

[0064] Through the aforementioned priority sorting mechanism, this invention can ensure the priority of the execution data of critical tasks in the evidence storage process, avoid the omission of important scheduling information due to network congestion or storage limitations, and effectively enhance the real-time perception and traceability of key nodes in resource scheduling and high-risk tasks.

[0065] Furthermore, the resource history chain adopts a consortium blockchain architecture;

[0066] After obtaining the execution history data, the process of storing the execution history data using blockchain technology includes:

[0067] After hashing and signing the execution history data, a history block is generated according to the task batch and broadcast to multiple on-chain relay nodes to ensure the execution history data is immutably stored. The task batch is a set of tasks collected within a preset synchronization period, and the data in the block is encapsulated according to the task priority order. Each block of the resource history chain includes hash digest information of the execution history data, task execution digest index, on-chain timestamp, and relay node signature field used to verify the trustworthiness of the upload.

[0068] Specifically, in this embodiment of the invention, the resource history chain adopts a consortium blockchain architecture that combines the immutability of blockchain with the high efficiency of multi-node collaboration within the consortium. Each relay node in the consortium blockchain is jointly operated by traffic management units, maintenance units, and third-party regulatory agencies to ensure the multi-party verifiability of the execution history data and the neutrality of on-chain evidence storage.

[0069] During task execution, generated execution history data is collected in real time and temporarily cached in a history cache pool. A history packaging operation is triggered periodically according to a set synchronization cycle; the accumulated execution history data set within a cycle is identified as a task batch. During the packaging phase, a hash signature operation is first performed on each execution history data entry to ensure data integrity and tamper-proofing. Subsequently, based on the previously determined priority order of the execution history data, the batch of execution history data is sorted sequentially and packaged into history blocks in this order.

[0070] The generated history block includes several core fields: a hash digest of each execution history data entry, the corresponding task execution digest index, the on-chain timestamp, and a signature field from at least one relay node (used to verify the on-chain credibility of this upload). Immediately after generation, the history block is broadcast to multiple on-chain relay nodes via the consortium blockchain. These nodes jointly reach a consensus and confirm the data, which is then recorded in the latest block of the resource history chain.

[0071] By adopting a consortium blockchain architecture and a task batch packaging mechanism, this invention effectively balances the efficiency of data upload to the blockchain with the credibility of on-chain evidence storage, ensuring the immutability of execution history data.

[0072] Furthermore, the process of constructing a resource history profile based on the trusted resource scheduling record in step S4 includes:

[0073] Based on the trusted records of resource scheduling, an aggregation analysis operation is performed on each type of job resource, and a set of resource feature indicators is generated based on the aggregation analysis results.

[0074] Based on the set of resource feature indicators, a resource profile feature vector is constructed, and a multi-dimensional attribute weighted clustering algorithm is used to group and label resources of the same type, thereby obtaining a resource history profile set with tagged attributes.

[0075] Furthermore, the aggregated analysis results include historical task participation frequency, task type distribution, mean and standard deviation of task completion scores, average task time, and abnormal event occurrence rate.

[0076] Furthermore, the weights of each attribute in the multidimensional attribute weighted clustering algorithm are set according to the scheduling sensitivity of different resource types; scheduling sensitivity represents the degree of influence of each feature index on the quality of resource scheduling.

[0077] In this embodiment of the invention, the trusted resource scheduling record is used to support the subsequent construction of resource history profiles and cluster analysis. Its generation process is based on the mapping relationship between the task execution summary index stored in the resource history chain and the task execution logs in the chain.

[0078] Specifically, when synchronizing execution history data onto the blockchain, this invention, in addition to writing hash digest information, task execution digest index, relay signature, and blockchain timestamp into the resource history chain, also records the original content of the executed task completely in the off-chain log database, and maintains a one-to-one correspondence between this record and the digest index of the corresponding block in the resource history chain. By reading the task execution digest index in the resource history chain, the corresponding original history data in the off-chain log database is located, and a structured scheduling information entry is constructed using fields such as the resource identifier, task score, anomaly record, and task participation time corresponding to the task. Finally, a trusted resource scheduling record set is aggregated and constructed on a resource-by-resource basis, providing a trusted original basis for subsequent profile construction, cluster analysis, and sensitivity assessment.

[0079] In this embodiment of the invention, to achieve differentiated resource history profiling and clustering feasibility, the reliable records of all similar operational resources are first aggregated and analyzed. Specifically, after classifying resources by type (such as road repair workers, equipment operators, bridge maintenance equipment, etc.), historical task data of each type of resource are collected, and statistical indicators including but not limited to: historical task participation frequency, task type distribution ratio vector, mean and standard deviation of task completion scores, average operation time, and abnormal event occurrence rate per unit time are calculated. Based on the above statistical indicators, a set of resource characteristic indicators is generated to characterize the scheduling reliability, execution capability, and risk level of resources.

[0080] Next, after standardizing the set of resource feature indicators, a resource profile feature vector is constructed. Furthermore, this invention employs a multi-dimensional attribute weighted clustering algorithm to group and label resources of the same type, thereby improving the tagged expressive ability of the profile. The core of this clustering algorithm lies in assigning a set of adjustable weight parameters to each feature indicator. These weights are initialized based on the performance differences of different resource types in task scheduling, primarily derived from historical scheduling feedback data in the training set, and in this embodiment, preferably depending on scheduling sensitivity.

[0081] Specifically, scheduling sensitivity refers to the degree to which a change in a certain indicator affects the effectiveness of task scheduling (such as scheduling success rate or task response time). For example, if a task's completion score has a smaller standard deviation, it is more likely to be prioritized for task allocation, and thus its scheduling sensitivity is higher. In practice, sensitivity is obtained through regression residual analysis of scheduling results and is used in weight updates.

[0082] Furthermore, to enhance the adaptability of clustering, this invention introduces a scheduling feedback optimization mechanism during the clustering iteration process, that is, to fine-tune the attribute weights based on the scheduling success rate, resource responsiveness and user evaluation after the previous round of clustering.

[0083] By introducing scheduling sensitivity and feedback optimization mechanisms to achieve dynamic adjustment of the clustering process, the coupling between resource history profiles and actual scheduling needs is effectively enhanced. This enables the resource history profiles to be invoked in multiple stages such as subsequent task generation, priority adjustment, and scheduling strategy selection, thereby forming a profile-driven intelligent scheduling closed-loop system.

[0084] As another preferred implementation, to further improve the dynamic adaptability of resource history profiles in practical scheduling applications, the weight assignment method for each feature dimension during the construction of resource history profiles has been optimized. Specifically, for each type of job resource resource's resource feature indicators, a scheduling sensitivity update function is introduced to quantify the responsiveness of each feature to the scheduling results and achieve adaptive weight adjustment. The scheduling sensitivity update function is constructed using an exponential smoothing mechanism, and its expression is as follows:

[0085]

[0086] in, The updated scheduling sensitivity value for the i-th resource profile feature. The current scheduling sensitivity value for the i-th resource profile feature; This represents the feature representation value of the i-th resource profile in the current sample. This represents the mean of the feature within the cluster group it belongs to; This is a smoothing coefficient used to control the fusion ratio of new and old weights.

[0087] This function dynamically adjusts the weights of resource profile features in clustering, giving higher weights to features exhibiting abnormal behavior (i.e., high outlier rates) in subsequent scheduling optimization. This enhances the responsiveness and practicality of resource profiles for real-world scheduling scenarios. By introducing this function, the sensitivity of the profile clustering structure to capturing actual resource performance is effectively improved, thereby increasing the accuracy of scheduling recommendations.

[0088] As another preferred implementation, in order to improve the clustering accuracy of resource history profiles and avoid resource clustering bias caused by excessive weights of certain feature dimensions, this invention constructs a weighted clustering loss function with a regularization term during the resource clustering process. Its expression is as follows:

[0089]

[0090] in, Let be the value of the resource in the k-th feature dimension, where k ranges from 1 to n, and n is the number of feature dimensions; The value for this dimension is assigned to the corresponding cluster center; The scheduling sensitivity weight for the k-th dimension; This is a regularization coefficient used to suppress extreme weight biases and ensure that the clustering results have overall balance.

[0091] By setting this function, the resource clustering process is no longer based solely on the original feature distance, but can simultaneously consider the importance of features to the scheduling results and the rationality of weight configuration, and improve the quality of clustering labels in the resource history profile, making subsequent scheduling priority judgment and resource recommendation strategies more stable, interpretable and convergent.

[0092] Further, step S5 includes:

[0093] S501. Construct a task requirement feature vector based on road segment status data;

[0094] S502. Determine the task adaptability distribution indicators corresponding to the resources based on the resource history profile;

[0095] S503. Determine the compatibility score between the task to be generated and the existing schedulable resources based on the task requirement feature vector and the task adaptability distribution index of resources;

[0096] S504. Filter and prioritize tasks based on their fit scores to generate a second maintenance task list.

[0097] Furthermore, step S5 also includes:

[0098] S505. Based on the preset second task scheduling rules, scheduling resource status information and geographical location matching degree, perform task resource matching operation to generate a second scheduling scheme;

[0099] S506. Execute the road section maintenance task based on the second scheduling scheme, and continue to execute steps S3~S5.

[0100] In this embodiment of the invention, a resource history profile refers to a multi-dimensional set of labeled vectors constructed for each type of scheduled resource based on historical task execution data, resource attribute characteristics, and scheduling behavior trajectories. This set is used to characterize the performance tendencies and capability features of the resource in different task scenarios. Specifically, the resource history profile may include, but is not limited to, resource identification information, a set of resource feature indicators, clustering labels (reflecting the resource's group affiliation), task adaptability distribution indicators (i.e., the probability distribution of the resource's matching capability under different task demand vectors), resource scheduling sensitivity vectors, timeliness labels, and profile update timestamps (used to control the lifecycle management of the profile).

[0101] Understandably, in the initial stage where no resource history profile data is available, this invention performs basic scheduling decisions based on a first maintenance task list. The first maintenance task list is generated based on road segment status data, task urgency, task level, and road segment priority collected during road maintenance, and a task requirement list is generated by a rule engine. Subsequently, according to preset first task scheduling rules, combined with information such as the basic availability status of each operational resource, current load level, and geographical location matching, preliminary resource filtering and task binding operations are performed to generate a first scheduling scheme.

[0102] The first task scheduling rule focuses more on the principle of matching static resource capabilities and prioritizing task urgency, and has not yet considered the adaptation history of resources in multiple types of tasks. Therefore, task allocation has a certain degree of uncertainty and low adaptation risk.

[0103] After establishing a relatively complete resource history profile, the second stage of scheduling logic begins. First, a task requirement feature vector is constructed based on the new road segment status data. Then, vector matching calculations are performed between this vector and the task adaptability distribution index in the resource history profile to determine the compatibility score between the task and the resource.

[0104] During this process, tasks are filtered and prioritized based on their adaptation scores, generating a second maintenance task list that includes not only the first maintenance task list but also resource matching information. Subsequently, according to the second task scheduling rules, and combined with information such as the current resource status and geographical location matching, resource matching is performed to generate a second scheduling plan. Unlike the first scheduling rules, the second task scheduling rules introduce an adaptive discrimination mechanism based on resource profiles. During the scheduling process, it pays more attention to the predictive improvement of task success rate based on historical experience and actual performance, exhibiting stronger personalization, refinement, and scenario coupling.

[0105] This invention adopts a two-stage scheduling strategy. The first scheduling stage focuses more on response speed and basic capability screening, which is suitable for cold start or profile unavailability scenarios. The second scheduling stage introduces a profile-driven precise matching strategy after the resource profile matures, which greatly improves the accuracy of task allocation and the overall system operating efficiency.

[0106] The resource matching dimension introduced in the second stage greatly enhances the interpretability and controllability of the scheduling scheme for resource-task matching logic, promotes the transition from rule-driven to profile-driven, and reflects the intelligent evolution path of experience learning-rule simplification-effect closed loop.

[0107] Furthermore, it can be understood that the role of resource history profiling in this invention is not limited to the aforementioned processes. The resource history profiling can also provide data support and optimization basis in multiple scheduling and management stages. For example, during the dynamic adjustment of resource scheduling priorities, the multidimensional characteristic trends reflected in the resource history profiling can be compared and analyzed with the current scheduling behavior to help identify resource performance fluctuations and dynamically adjust weight coefficients. In addition, in the evaluation and feedback optimization of scheduling execution effects, the resource history profiling can also serve as a traceability basis, establishing a causal chain between task execution effects and resource characteristics, thereby continuously iteratively optimizing resource scheduling strategies. In this invention, resource history profiling can be integrated into multiple key stages such as task generation, resource allocation, and scheduling feedback, possessing significant data accumulation and decision-making support value. Example

[0108] A second aspect of this invention discloses a resource scheduling system for information management of highway maintenance, the system comprising:

[0109] The generation module is used to collect the road segment status data of the first road segment to be maintained and generate the first maintenance task list based on the road segment status data.

[0110] The matching module is used to perform task resource matching operations and generate a first scheduling scheme based on the preset first task scheduling rules, scheduling resource status information and geographical location matching degree.

[0111] The data recording and synchronization module is used to record the execution history data of the task execution subject in real time during task execution, and synchronously upload the execution history data to the resource history chain; the resource history chain is used to store the execution history data through blockchain technology to generate an immutable and trustworthy record of resource scheduling.

[0112] The profile building module is used to build resource history profiles for various types of operational resources based on trusted resource scheduling records;

[0113] The scheduling module is used to collect road segment status data of the second road segment to be maintained when a resource history profile exists, and to perform resource scheduling operations on the second road segment to be maintained based on the road segment status data and the resource history profile.

[0114] It should be noted that the specific implementation process of Example 2 is similar to that of Example 1, and will not be repeated in Example 2.

[0115] Finally, it should be noted that the above-described embodiments include multiple parallel implementations of the present invention. Deleting or otherwise adjusting one or more implementations will not affect the implementation of the solution. Furthermore, the resource scheduling method and system for information management of highway maintenance disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A resource scheduling method for information management of highway maintenance, characterized in that, The method includes the following steps: S1. Collect road segment status data for the first road segment to be maintained, and generate the first maintenance task list based on the road segment status data; S2. Based on the preset first task scheduling rules, scheduling resource status information and geographical location matching degree, perform task resource matching operation to generate the first scheduling scheme; S3. During task execution, the execution history data of the task execution entity is recorded in real time, and the execution history data is synchronously uploaded to the resource history chain; the resource history chain is used to store the execution history data through blockchain technology, aggregate and generate an immutable resource scheduling trusted record on a resource-by-resource basis, and the generation process is completed based on the mapping relationship between the task execution summary index stored in the resource history chain and the off-chain task execution log, and provides a trusted original basis for resource profile construction, cluster analysis operation and scheduling sensitivity assessment; S4. Perform aggregation analysis on each type of job resource based on the trusted resource scheduling record, and generate a set of resource feature indicators based on the aggregation analysis results; construct a resource profile feature vector based on the resource feature indicator set, and use a multi-dimensional attribute weighted clustering algorithm to group and label resources of the same type, thereby obtaining a resource history profile set with tagged attributes; The aggregation analysis results include historical task participation frequency, task type distribution, mean and standard deviation of task completion scores, average operation time, and abnormal event occurrence rate; the weights of each attribute in the multidimensional attribute weighted clustering algorithm are set according to the scheduling sensitivity of different resource types; the scheduling sensitivity represents the degree of influence of each feature index on the quality of resource scheduling; the resource history profile includes resource identification information, a set of resource feature indicators, clustering labels reflecting the affiliation of resource groups, task adaptability distribution indicators representing the probability distribution of resource matching ability under different task demand vectors, resource scheduling sensitivity vector, timeliness label, and profile update timestamp; If a resource history profile exists, proceed to step S5; S5. Collect road segment status data for the second road segment to be maintained, and perform resource scheduling operations on the second road segment to be maintained based on the road segment status data and resource history profile.

2. The resource scheduling method for information management of highway maintenance according to claim 1, characterized in that, The execution history data includes task identifier, task start and end time, task type identifier, executor identifier, job completion feedback information, abnormal event records, and task execution score.

3. The resource scheduling method for information management of highway maintenance according to claim 2, characterized in that, The process of synchronously uploading execution history data to the resource history chain includes: Based on the task identifier contained in the execution history data, the task urgency and scheduling impact parameters recorded in the maintenance task list for the corresponding task are retrieved. The execution history data to be uploaded is prioritized based on the urgency and scheduling impact parameters of each task, and high-priority history data is packaged and uploaded to the resource history chain first; wherein, the scheduling impact is used to measure the intensity of the chain scheduling impact caused by each task in the resource scheduling network.

4. The resource scheduling method for information management of highway maintenance according to claim 3, characterized in that, The resource history chain adopts a consortium blockchain architecture; After obtaining the execution history data, the process of storing the execution history data using blockchain technology includes: After hashing and signing the execution history data, a history block is generated according to the task batch and broadcast to multiple on-chain relay nodes to ensure the execution history data is immutably stored. The task batch is a set of tasks collected within a preset synchronization period, and the data in the block is encapsulated according to the task priority order. Each block of the resource history chain includes hash digest information of the execution history data, task execution digest index, on-chain timestamp, and relay node signature field used to verify the trustworthiness of the upload.

5. The resource scheduling method for information management of highway maintenance according to claim 1, characterized in that, Step S5 includes: S501. Construct a task requirement feature vector based on road segment status data; S502. Determine the task adaptability distribution indicators corresponding to the resources based on the resource history profile; S503. Determine the compatibility score between the task to be generated and the existing schedulable resources based on the task requirement feature vector and the task adaptability distribution index of resources; S504. Filter and prioritize tasks based on their fit scores to generate a second maintenance task list.

6. The resource scheduling method for information management of highway maintenance according to claim 5, characterized in that, Step S5 further includes: S505. Based on the preset second task scheduling rules, scheduling resource status information and geographical location matching degree, perform task resource matching operation to generate a second scheduling scheme; S506. Execute the road section maintenance task based on the second scheduling scheme, and continue to execute steps S3~S5.

7. A resource scheduling system for information management of highway maintenance, the system being implemented based on the method described in any one of claims 1-6, characterized in that, The system includes: The generation module is used to collect the road segment status data of the first road segment to be maintained and generate the first maintenance task list based on the road segment status data. The matching module is used to perform task resource matching operations and generate a first scheduling scheme based on the preset first task scheduling rules, scheduling resource status information and geographical location matching degree. The data recording and synchronization module is used to record the execution history data of the task execution subject in real time during task execution, and synchronously upload the execution history data to the resource history chain; the resource history chain is used to store the execution history data through blockchain technology to generate an immutable and trustworthy record of resource scheduling. The profile building module performs aggregate analysis on each type of job resource based on trusted resource scheduling records. It generates a set of resource feature indicators based on the aggregate analysis results, constructs a resource profile feature vector based on the set of resource feature indicators, and uses a multi-dimensional attribute weighted clustering algorithm to group and label resources of the same type, resulting in a resource history profile set with tagged attributes. The aggregate analysis results include historical task participation frequency, task type distribution, mean and standard deviation of task completion scores, average job time, and abnormal event occurrence rate. The weights of each attribute in the multi-dimensional attribute weighted clustering algorithm are set according to the scheduling sensitivity of different resource types. The scheduling sensitivity represents the degree of influence of each feature indicator on the quality of resource scheduling. The resource history profile includes resource identification information, a set of resource feature indicators, clustering labels reflecting the resource group affiliation, task adaptability distribution indicators representing the probability distribution of resource matching ability under different task demand vectors, resource scheduling sensitivity vector, timeliness labels, and profile update timestamps. The scheduling module is used to collect road segment status data of the second road segment to be maintained when a resource history profile exists, and to perform resource scheduling operations on the second road segment to be maintained based on the road segment status data and the resource history profile.

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