An examination information management system and method based on the Internet of Things

Abstract

The application provides an examination information management system and method based on the Internet of Things. The method comprises the following steps: collecting examination plan data in a management system of a unified examination center to construct an examination information unit set; collecting the running state of IoT examination equipment in real time through an edge collection agent deployed at an examination point to form a resource state score vector; combining the resource state score vector and an examination task graph, and constructing a task scheduling priority queue according to the scheduling priority index; calculating the corresponding scheduling score for each task to obtain a scheduling sequence; and issuing the scheduling sequence and the corresponding scheduling score to each examination point to drive the IoT examination equipment to execute corresponding action instructions, and collecting the execution state feedback in real time to form a task execution state feedback closed loop for subsequent dynamic scheduling adjustment or abnormal early warning. The application effectively improves the stability of task execution, the accuracy of scheduling and the controllability of faults in the unified examination scene.

Description

Technical Field

[0001] This invention belongs to the field of examination information management, and in particular relates to an examination information management system and method based on the Internet of Things. Background Technology

[0002] Most current examination information management systems still employ modular and static scheduling methods, failing to meet the actual needs of unified examination centers for global task controllability, dynamic resource scheduling, and high-stability examination assurance. In large-scale unified examination scenarios, tens of thousands of task nodes are involved, each strongly bound to multiple devices, examination sites, personnel, and time windows. Traditional systems generally rely on preset templates for fixed task allocation, lacking the ability to express complex dependencies between tasks and to perceive real-time status changes. Resource anomalies (such as equipment failure, network fluctuations, and personnel absences) cannot be identified and handled by the system in a timely manner, resulting in rigid scheduling and insufficient fault tolerance. Once scheduling fails, manual intervention is required, hindering truly intelligent examination operation management.

[0003] Meanwhile, most current system task scheduling strategies ignore factors such as device conflicts, execution order dependencies, and device status fluctuations, often relying solely on task time, number, or preset priority for simple sorting. This scheduling method cannot flexibly respond to sudden exam changes, task adjustments, or resource failures, limiting the informatization level of exam organization and the robustness of the system. Especially in some important exams, there are extremely high requirements for system stability, response speed, resource matching accuracy, and task completion rate, and current technologies are insufficient to meet the multi-objective scheduling and closed-loop control needs of this complex scenario.

[0004] Therefore, there is an urgent need to build an integrated examination information management system with task graph modeling capabilities, real-time IoT resource sensing capabilities, dynamic task scheduling and optimization mechanisms, and closed-loop execution control functions, so as to improve the intelligence level and overall reliability of the unified examination organization from the system structure. Summary of the Invention

[0005] The purpose of this invention is to propose an examination information management system and method based on the Internet of Things (IoT). By proposing a unified examination information management system and method based on task graph modeling, using IoT device status scoring as a bridge, resource perception-driven scheduling as the core, and feedback closed-loop control as a guarantee, the system effectively improves the stability of task execution, the accuracy of scheduling, and the controllability of faults in unified examination scenarios.

[0006] To achieve the above objectives, a first aspect of the present invention provides an examination information management method based on the Internet of Things, the method comprising the following steps:

[0007] Exam plan data is collected from the management system of the unified examination center to construct an exam information unit set. Graph nodes are constructed and weights are assigned based on the exam information unit set to obtain an exam task graph. The exam information unit set includes several exam information units, each of which includes exam site number, exam paper number, time period, device ID list, and task.

[0008] The operational status of IoT examination devices is collected in real time by an edge acquisition agent deployed at the test site to calculate the device score of all devices. The device scores are aggregated by task to obtain the resource adaptation score of each task and form a resource status score vector.

[0009] By combining the resource status scoring vector and the examination task graph, a scheduling priority index for each task is constructed through weighted combination, and a task scheduling priority queue is constructed based on the scheduling priority index.

[0010] Preliminary task scheduling is performed based on the task scheduling priority queue, and the device conflicts between all tasks in the already scheduled task set and the currently scheduled task are evaluated to obtain the current resource conflict cost.

[0011] The current scheduling score is generated based on the task scheduling priority queue and the current resource conflict cost. This score is used to determine whether the current task should enter the scheduling sequence. The corresponding scheduling score is calculated for each task and then filtered to obtain the scheduling sequence.

[0012] The scheduling sequence and corresponding scheduling scores are sent to each examination site to drive the IoT examination device to execute the corresponding action instructions and collect execution status feedback in real time, forming a task execution status feedback closed loop for subsequent dynamic scheduling adjustment or abnormal warning.

[0013] Furthermore, the step of constructing graph nodes and assigning weights based on the set of examination information units to obtain the examination task graph specifically includes:

[0014] Obtain a set of examination information units including the test center number, test paper number, time period, device ID list and task, and use each examination information unit as a node to obtain a set of nodes;

[0015] The dependencies between tasks come from a dependency table exported by the exam scheduling system. The `depends_on` field records the sequential dependencies between tasks, which are then converted into a set of edges. elements in , indicating task It is a task Prerequisite tasks;

[0016] The edge weights corresponding to the current task are obtained by weighted summation based on the number of devices required for the task, the task duration, and the task complexity coefficient.

[0017] By combining the set of nodes, the set of edges, and the corresponding edge weights, the examination task graph is obtained.

[0018] Furthermore, the operation status of the IoT examination device is collected in real time by an edge acquisition agent deployed at the examination site. The collected data includes: CPU utilization, memory usage, network latency, device working status, error flags, and the current number of concurrent tasks.

[0019] Furthermore, the device score for all devices is obtained by combining the CPU utilization, memory usage, and network latency, and by introducing a constraint based on the current number of concurrent tasks on the device combined with error flags.

[0020] Furthermore, the CPU utilization is monitored by a performance monitoring program running locally on the device; the memory utilization is collected through local interface calls; the network latency is estimated using ICMP or MQTT response time; the device operating status indicates whether the device is online; the error flag indicates whether a physical layer error exists; and the current number of concurrent tasks is counted by the edge agent to determine the number of active connections.

[0021] Furthermore, the step of combining the resource status scoring vector and the examination task graph to construct a scheduling priority index for each task through weighted combination, and constructing a task scheduling priority queue based on the scheduling priority index, specifically includes:

[0022] Introducing the resource status scoring vector and the examination task graph structure depth A weighted combination is used to construct a scheduling priority index. :

[0023] ;

[0024] in, Rate the resource status; Indicates task The corresponding topological level depth of node v in the exam task graph; Score the task specificity based on the task. Specifies whether the task type is a critical control task; the value is 0 or 1. These are the resource preference coefficient and the critical task priority coefficient, respectively.

[0025] Based on the scheduling priority index from high to low, a task scheduling priority queue is obtained.

[0026] Furthermore, the current resource conflict cost The calculation is as follows:

[0027] ;

[0028] in, For the task List of required equipment; For the task The exam time period; For indicator functions, if the device If the task has already been scheduled and the times overlap, the value is 1; otherwise, the value is 0. A value of 0 indicates that there is no conflict on the device; the closer to 1, the more the resources required by the task are almost entirely occupied. For the task List of required equipment; For the task The exam period.

[0029] Furthermore, in each round of scheduling, the system selects tasks in descending order of scheduling scores and executes the following constraint check process:

[0030] A. All precursor mission nodes Satisfy the edge All tasks have been scheduled. It is an edge set;

[0031] B. Resource status score at the current time ,in The minimum schedulable score threshold;

[0032] C. Costs of Resource Conflicts ,in The maximum conflict rate that the system can tolerate;

[0033] Tasks that satisfy A, B, and C will be added to the scheduling sequence, and the scheduled device table and time interval set will be updated.

[0034] Furthermore, the process of distributing the scheduling sequence and corresponding scheduling scores to each examination site, driving the IoT examination device to execute the corresponding action instructions, and collecting execution status feedback in real time to form a task execution status feedback closed loop for subsequent dynamic scheduling adjustments or anomaly warnings, specifically includes:

[0035] Each task in the scheduling sequence is converted into a specific instruction to be issued;

[0036] The system pushes instructions to the edge control unit of the test center corresponding to the test center number based on the test center number field in the task. The edge control unit is an embedded computing module deployed in the test center's local area network and controls the device through MQTT or local protocol.

[0037] The issuance of each task instruction will trigger the test center control unit to initiate equipment actions. All equipment must report feedback information after completion, including whether the equipment successfully executed the task; the time consumed by the equipment to complete the task; if the task fails, record the failure type number; and the number of times the task was retried to identify unstable equipment states.

[0038] Aggregate device feedback information and score the overall execution effectiveness of the corresponding task; if the effectiveness score... This indicates that the task was completely successful; if the validity score is... This indicates that some devices failed to execute the command.

[0039] The feedback information is analyzed in conjunction with the task scheduling structure to determine whether the task needs to be repaired or rescheduled: if the effectiveness score is... , If the fault tolerance threshold is reached, the scheduling and repair process will be triggered to generate the repaired task;

[0040] A dynamically updated task execution status table is generated based on the repaired tasks, and the scheduling sequence is adjusted accordingly to ensure that all tasks are eventually completed effectively.

[0041] In a second aspect, the present invention provides an examination information management system based on the Internet of Things, the system comprising:

[0042] The information structure modeling unit is used to collect examination plan data from the management system of the unified examination center to construct a set of examination information units. Based on the set of examination information units, graph nodes are constructed and weights are assigned to obtain an examination task graph. The set of examination information units includes several examination information units, and each examination information unit includes examination site number, examination paper number, time period, device ID list, and task.

[0043] The test site IoT device status sensing unit is used to collect the real-time operating status of IoT examination devices through an edge acquisition agent deployed at the test site to calculate the device score of all devices. The device scores are aggregated by task to obtain the resource adaptation score of each task and form a resource status score vector. Combining the resource status score vector and the examination task map, a scheduling priority index for each task is constructed through weighted combination, and a task scheduling priority queue is constructed according to the scheduling priority index. Preliminary task scheduling is performed according to the task scheduling priority queue, and the device conflicts between all tasks in the already scheduled task set and the currently scheduled task are evaluated to obtain the current resource conflict cost.

[0044] The task graph scheduling optimization unit is used to generate a current scheduling score based on the task scheduling priority queue and the current resource conflict cost, which is used to determine whether the current task enters the scheduling sequence. The unit calculates the corresponding scheduling score for each task and filters it to obtain the scheduling sequence.

[0045] The scheduling and feedback control closed-loop unit is used to send the scheduling sequence and the corresponding scheduling score to each examination site, drive the IoT examination device to execute the corresponding action instructions, and collect execution status feedback in real time to form a task execution status feedback closed loop for subsequent dynamic scheduling adjustment or abnormal warning.

[0046] The beneficial technical effects of the present invention are at least as follows:

[0047] This invention structures examination tasks as a graph, enabling each task node to possess not only its own information attributes but also dependencies on preceding and subsequent tasks, providing a topological foundation for subsequent scheduling optimization. Building upon this structure, the system further introduces an edge IoT device state awareness mechanism, constructing a resource adaptation scoring model for each task. This scoring model considers not only dynamic indicators such as device load and response latency but also factors like device conflict degree and error state constraints, significantly improving the ability to judge the actual availability of the scores. At the task scheduling layer, the system combines the graph structure and scoring data to propose a resource-aware-driven task sorting and conflict control mechanism, avoiding the erroneous allocation logic of "resources reachable but unusable" in traditional scheduling. Finally, a closed-loop task execution mechanism is constructed through task instruction generation, issuance, and IoT feedback collection, supporting abnormal device replacement, task backoff, and scheduling reordering, enabling the entire examination process to have dynamic adaptive scheduling capabilities. This system effectively improves the stability of task execution, the accuracy of scheduling, and the controllability of faults in unified examination scenarios, representing an innovative and engineering-practical overall solution for large-scale examination resource scheduling. Attached Figure Description

[0048] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0049] Figure 1 This is a flowchart of an examination information management method based on the Internet of Things disclosed in an embodiment of the present invention.

[0050] Figure 2 This is a framework diagram of an examination information management system based on the Internet of Things disclosed in an embodiment of the present invention. Detailed Implementation

[0051] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0052] Example 1

[0053] like Figure 1 As shown in the figure, an embodiment of the present invention provides an examination information management method based on the Internet of Things, the method comprising:

[0054] S1. Collect examination plan data from the management system of the unified examination center to construct an examination information unit set. Construct graph nodes and assign weights based on the examination information unit set to obtain an examination task graph. The examination information unit set includes several examination information units, and each examination information unit includes examination site number, examination paper number, time period, device ID list, and task.

[0055] Specifically, the goal of this step is to transform the existing examination plan data from the unified examination center into a graph structure that the system can recognize and compute. This graph structure should not only reflect the execution dependencies between tasks but also structurally model examination information related to the tasks (such as test center numbers, equipment, time periods, and test paper numbers). This provides a computable task graph and task data units for subsequent scheduling optimization. Compared to static tasks recorded in data tables in traditional systems, this solution constructs a task graph with structural expressive capabilities and a dynamic scheduling interface, which forms the logical foundation of the entire system.

[0056] The system first collects exam plan data from the unified examination center's management system. This data is typically stored in the exam administration database in structured CSV or JSON format, with fields including exam task number, task type (e.g., terminal deployment, personnel binding, exam paper activation), target exam site number, associated equipment list, task start and end times, and the corresponding exam subject number. This data is transmitted to the system from the exam information center system via standard interfaces (e.g., REST API or direct database connection). For example, the following is a sample data structure for a "terminal deployment task" (not JSON syntax, only for field illustration):

[0057] task_id: "task_01492" # Task number;

[0058] task_type: "terminal_setup" # Task type, corresponding action type ;

[0059] location_id: "A312" # Exam site number ;

[0060] device_list: ["dev_01", "dev_02", "dev_03"] # List of device IDs ;

[0061] time_start: "2025-07-12 08:00" # Start time;

[0062] time_end: "2025-07-12 09:00" # End time, combined with other times to form the end date. ;

[0063] paper_id: "paper_04" # Exam paper number ;

[0064] depends_on: ["task_01387"] # Dependency task used to generate E;

[0065] After these fields are structured, each exam task is abstracted into a graph node. Its associated attribute set constitutes the exam information unit:

[0066]

[0067] in, This indicates the test center number (directly extracted from the location_id field). This indicates the exam paper number (field paper_id); This represents a time period, calculated as the difference between the start and end times. It is a list of device IDs; The task type is derived from the task_type field.

[0068] Furthermore, the dependencies between tasks are derived from a dependency table exported by the exam scheduling system, with the `depends_on` field recording the sequential dependencies between tasks. The system then transforms this into a set of edges. elements in , indicating task It is a task Prerequisite tasks. Edge types include: sequential dependency, resource conflict, overlapping time windows, etc., which are stored in the graph using labels.

[0069] Each task node is also assigned a weight. This weight is used to measure task priority and resource consumption in subsequent scheduling optimization. The weight calculation formula is as follows:

[0070]

[0071] in, This is the number of devices required for the task, calculated as follows: If the device list has 3 items, then ; It is the duration of the task, in minutes, determined by... The result is obtained by subtracting the start time from the end time. It is the task complexity coefficient, based on The type is assigned by a lookup table, for example, "terminal_setup" corresponds to... The "identity binding" task may be set to 2; Weighting coefficients set for the center, for example The parameters are set according to the actual task scheduling strategy; all parameters involved in the calculation have been normalized before being substituted to ensure that they are within the same numerical range, thus ensuring the rationality of the combined weighting.

[0072] Furthermore, the completed task graph structure ,in It is a collection of all task nodes, each Carrying Information Unit ; It is a set of inter-task dependency edges. This graph-structured data will be passed to subsequent scheduling steps for task ordering and resource binding matching. (Information unit) This serves as the information carrier required for subsequent task execution control.

[0073] This step outputs two parts of data: the exam task graph. It includes task structure and dependencies; a set of exam information units. The information data bound to each task node.

[0074] S2. The operating status of IoT examination devices is collected in real time by an edge acquisition agent deployed at the examination site to calculate the device score of all devices. The device scores are aggregated by task to obtain the resource adaptation score of each task and form a resource status score vector. Combining the resource status score vector and the examination task map, a scheduling priority index for each task is constructed by weighted combination, and a task scheduling priority queue is constructed according to the scheduling priority index. Preliminary task scheduling is performed according to the task scheduling priority queue, and the device conflicts between all tasks in the already scheduled task set and the currently scheduled task are evaluated to obtain the current resource conflict cost.

[0075] Specifically, the goal of this step is to analyze the exam task graph generated in step one. With task information unit set Based on the task's requirements for equipment, establish connections with task nodes. A strongly bound, time-sensitive resource awareness scoring mechanism. By deploying edge data acquisition agents at examination sites, the operational status of IoT devices is collected and analyzed in real time. The task-resource matching degree is calculated, and a resource availability scoring vector is ultimately generated for task scheduling optimization. Unlike traditional resource state "pre-registration" mechanisms, this step emphasizes task-aware, state-driven computation, with each scoring result... All for the mission It is tailor-made to reflect the real-time possibility of task execution and the intensity of resource dependence.

[0076] This step uses the two inputs output from the previous step: (1) Task graph Used to obtain all task nodes (2) Task Information Unit Used to extract the list of devices required for each task. and its test center number The output of this step The scheduling algorithm, which directly affects step three, is a key variable used for weighted node sorting and path selection during the scheduling process. Therefore, its computational logic and physical rationality play a decisive role in the stability and scheduling accuracy of the entire system.

[0077] Furthermore, to achieve precise task adaptation to devices, the system needs to obtain the device operating status from the IoT device gateway deployed at the examination site, specifically including the following types of data fields:

[0078] CPU utilization ( ): Reported every 10 seconds by a performance monitoring program (such as an embedded agent) running locally on the device, in units of 0 to 1;

[0079] Memory utilization ( ): Data is collected via a local API call;

[0080] Network latency ( ): Use ICMP or MQTT response time as an estimate, with a 1-minute moving average as the data collection window;

[0081] Equipment operating status ( Is the device online?

[0082] Error flags ( ): Is there a physical layer error?

[0083] Current number of concurrent tasks ( ): The number of active connections is counted through the edge agent.

[0084] Furthermore, regarding the task Required equipment set To improve the relevance and robustness of the scoring mechanism, this solution introduces a fusion scoring mechanism that comprehensively considers factors such as equipment availability, load balancing, and scheduling preferences, and designs the following scoring formula:

[0085]

[0086] in, Rate the equipment; These are the normalized CPU utilization, memory utilization, and average communication latency, respectively. The scoring weights are set statically, for example, 0.3, 0.3, 0.2, and the values ​​are adjustable; It is a constraint, and its core innovation lies in introducing a combined assessment of task conflict degree and equipment health index:

[0087] ;

[0088] in, This represents the current number of concurrent tasks on the device. The maximum capacity configured in the system; This is a hardware error flag, with a value of 0 or 1. Enhancement coefficients for constraint terms (e.g.) This significantly increases the penalty weight for faulty devices.

[0089] It is important to note that the introduction of this constraint is a significant improvement in this invention. Traditional systems often overlook the risks of "task overload" or "soft failure" of IoT devices, which can easily lead scheduling algorithms to mistakenly allocate "idle but abnormal" devices as high-availability resources, causing devices to disconnect or experience sudden performance drops during the examination. This method significantly improves the robustness of the scoring system and the practical feasibility of scheduling through embedded load state and error state identification.

[0090] When for all devices Calculate the score Then, the system aggregates scores by task to obtain a resource suitability score for each task. :

[0091]

[0092] in, For the task The number of devices it depends on The resource status score for this task is incorporated into the subsequent task scheduling model.

[0093] Ultimately, the system combines the scores of all tasks into a resource score vector. This vector is the same as the one in step one. and It is also passed into the scheduling step to provide a state basis for graph optimization.

[0094] This step outputs: Resource Status Score Vector Each element Indicates task The current matching score of the required resources.

[0095] S3. Generate a current scheduling score based on the task scheduling priority queue and the current resource conflict cost. This score is used to determine whether the current task should enter the scheduling sequence. Calculate the corresponding scheduling score for each task and filter them to obtain the scheduling sequence.

[0096] Specifically, the core objective of this step is to build upon the examination task graph constructed in step one. With information unit set and the resource score vector output in step two. This paper proposes a dynamic scheduling and path generation method for unified examination tasks. Unlike traditional methods that simply sort examination tasks by number, time period, or static priority, this method integrates multiple dimensions of information, including resource awareness status, structural dependencies, and device conflicts, to propose a scheduling optimization method that combines graph structure and resource feedback. This method not only improves the resource rationality and feasibility of scheduling but also dynamically addresses the execution risks caused by fluctuations in the state of IoT resources.

[0097] The input for this step includes the following three items:

[0098] Exam Task Graph Its nodes Indicates the task, edge Indicates dependency relationships;

[0099] Information unit set ,in Indicates task The test sites, test papers, time periods, equipment requirements, and types of actions;

[0100] Resource Status Scoring Vector Each element is a task The quantitative score for resource adaptability at the current moment has taken into account information such as IoT device load and health status.

[0101] The first step in the scheduling process is to construct a task scheduling priority queue based on the task graph structure and resource scores. Traditional scheduling methods typically rely solely on topological sorting or static priorities (such as exam site priority, device number order, etc.), which are insufficient in response to sudden resource anomalies, task congestion, and device conflicts in real exam environments. This invention proposes a solution that incorporates a resource-sensitive node priority scoring mechanism, introducing resource scores... and structural depth A weighted combination is used to construct a scheduling priority index. :

[0102]

[0103] in, The resource adaptation score is calculated in step two; Indicates task The topological hierarchy depth in the task graph; Scoring based on task specificity, according to Specifies whether the task type is a critical control task (such as identity binding, terminal verification, etc.), with a value of 0 or 1; These are the resource preference coefficient and the critical task priority coefficient, respectively (e.g. , (It can be dynamically set).

[0104] The formula embodies the combination of three scheduling priorities: real-time resource status, task structure position, and business priority, making the scheduling logic no longer static, but oriented towards the execution feasibility of the actual examination task.

[0105] Furthermore, after obtaining the initial scheduling order, the system attempts to schedule tasks one by one and performs resource feasibility verification and equipment conflict assessment. To this end, the patent introduces a resource conflict cost term. For the set of scheduled tasks All tasks and currently scheduled tasks Evaluate the device conflicts between them:

[0106]

[0107] in, For the task List of required equipment; For the task The exam time period; For indicator functions, if the device If the task has already been scheduled and the times overlap, the value is 1; otherwise, the value is 0. A value of 0 indicates that there is no conflict on the device; the closer to 1, the more the resources required by the task are almost entirely occupied. For the task List of required equipment; For the task The exam period.

[0108] This conflict metric not only considers whether device IDs conflict, but also incorporates the intersection of time windows. This refined resource time cross-identification mechanism is one of the key engineering innovations of this step, allowing for a more accurate assessment of the risk of overlapping timeframes for different tasks on the same equipment in actual exams.

[0109] Based on this, the system constructs the final scheduling scoring function. This is used to determine whether a task enters the scheduling sequence:

[0110]

[0111] in This is the conflict constraint factor, and its recommended value range is 1.0 to 3.0. The larger the value, the more sensitive the system is to resource conflicts.

[0112] It is important to note that during each round of scheduling, the system... Select tasks in descending order of priority and perform the following constraint check process:

[0113] All precursor missions satisfy All tasks have been scheduled.

[0114] Current time resource rating ,in The minimum schedulable score threshold (e.g., 0.4);

[0115] Conflict items ,in The maximum conflict rate that the system can tolerate (e.g., 0.3).

[0116] Tasks that meet the above three conditions will be added to the scheduling sequence. And update the scheduled device table and time interval set.

[0117] This step outputs the following two items: scheduling sequence This is used to generate execution control instructions subsequently; each task scheduling score A list used for task scheduling visualization and operation and maintenance analysis.

[0118] In summary, this step creatively introduces resource scoring into the sorting and selection process of the task scheduling graph. By integrating controllable weighting methods based on dimensions such as topology, resource scoring, and device conflict awareness, a highly adaptable examination task scheduling mechanism in a real-world environment is constructed.

[0119] S4. The scheduling sequence and corresponding scheduling scores are sent to each examination site to drive the IoT examination device to execute the corresponding action instructions and collect execution status feedback in real time to form a task execution status feedback closed loop for subsequent dynamic scheduling adjustment or abnormal warning.

[0120] Specifically, the main objective of this step is to process the task scheduling sequence generated in step three. and corresponding scheduling scores The commands are actually distributed to each examination site, driving the IoT examination devices to execute the corresponding action instructions and collecting real-time execution status feedback to form a closed loop for subsequent dynamic scheduling adjustments or anomaly warnings. This step completes the transition from "computational layer decision-making" to "physical world execution," and is the core link in this invention's transition from scheduling modeling to engineering implementation. Unlike traditional examination systems that only issue commands in a one-way communication manner, this step strengthens the device-level feedback mechanism and constructs a feedback quantification model through the task-device correspondence, realizing a closed-loop control and rescheduling structure.

[0121] The input to this step is the two results from the previous step: the scheduling sequence. Each element This corresponds to a scheduled exam task; scheduling and scoring list. Each task Priority scores are used to help determine the recovery priority when dealing with conflicts or failures.

[0122] Furthermore, the operational procedure for this step is as follows:

[0123] First, the system will schedule the sequence. Each task in Each instruction is then converted into a specific directive to be issued. This instruction structure originates from the task information unit. This includes the exam time period for the task. Action type Required equipment Test Center Location and the upstream tasks they depend on. .For example:

[0124] Task v_17:

[0125] Time: 2025-09-01 08:00–10:00

[0126] Action: Verify_Student_ID

[0127] Device: [dev_4, dev_9]

[0128] Location: site_A05

[0129] Depend_On: [v_12]

[0130] The fields are explained as follows:

[0131] Task v_17: This is the task in the task graph. The node number in the graph. Each task has a unique number in the graph. This indicates the 17th task node identified by the system, corresponding to the task information unit. .

[0132] Time: 2025-09-01 08:00–10:00: This indicates the scheduled execution time of the task. This field is derived from the exam schedule and is pre-set by the exam administration system. All tasks must be scheduled and executed within this time window.

[0133] Action: Verify_Student_ID: Indicates the action type for this task. In this example, it's "student identity verification." This action triggers the system to call a preset device instruction template to capture and verify the candidate's image. Each action type is defined by system standards and corresponds to a set of IoT device driver commands.

[0134] Device: [dev_4, dev_9]: This field is a list of IoT devices that the task depends on. ,For example:

[0135] dev_4: This refers to the image acquisition terminal, such as a high-definition camera;

[0136] dev_9: This is an authentication and comparison module, such as an embedded face recognition engine in an edge processing node;

[0137] These device IDs come from the system's device registry and are used to calculate resource scores in step two. Status rating Its availability has been verified.

[0138] Location: site_A05: Indicates the exam location where the task is situated. For example, "site_A05" represents the 5th test site in city A. The system uses this field to distribute tasks to the corresponding test site's edge control node, which then redistributes them to local devices.

[0139] Depend_On: [v_12]: Indicates the task. Task-dependent The completion result, that is, in the task graph There are edges in The scheduling system must... Execution can only be scheduled after successful execution and confirmation of status. This logic is automatically controlled by the topology scheduling mechanism in step S3.

[0140] Furthermore, the system based on the task... The field will push the instructions to the test center. The edge control unit, an embedded computing module, is deployed on the examination center's local area network and controls devices via MQTT or a local protocol. (Equipment required for the task) It must have been marked as good in the device rating in step two and must not conflict with other tasks in the schedule.

[0141] Each task instruction triggers the test center control unit to initiate device actions, such as camera activation, authentication module initialization, and terminal network connectivity testing. All devices must report the following feedback information upon completion:

[0142] : Indicates task medium equipment Whether the execution was successful (Boolean value, 1 indicates success, 0 indicates failure);

[0143] The time taken for the device to complete the task, in seconds;

[0144] If the task fails, record the failure type number (such as hardware failure, communication failure, etc.).

[0145] The number of times the task is retried, used to identify unstable device states.

[0146] The system aggregates device feedback results and defines tasks. Overall Implementation Effectiveness Score The formula is as follows:

[0147]

[0148] in, The number of devices required for the mission. .like This indicates that the task was completely successful; if If this occurs, it indicates that some devices have failed to execute the commands. The system will then determine whether to adjust the closed-loop control based on the following strategy.

[0149] It's important to note that the core of feedback control is to jointly analyze device-level feedback information with the task scheduling structure to determine whether task repair or rescheduling is necessary. The system sets fault tolerance thresholds. (like ),like If this occurs, the scheduling and repair process will be triggered. The repair process includes:

[0150] For failed equipment Check the error codes it reported If the error is a temporary communication failure, and The system will attempt to resend the original device command.

[0151] If the error is due to a hardware failure (such as a sensor not responding), the system will select a backup device from the pool of available devices at the current test center. The substitution is subject to the following conditions:

[0152]

[0153] in This represents the set of all scheduled tasks within that time period. This is the score given by the device in step two. This is the scoring threshold.

[0154] If the device is not replaceable or the task dependency is blocked, the system rolls back the task. It also includes all its subtasks, which are scheduled with delays and exception logs are recorded.

[0155] Ultimately, the system will generate a dynamically updated task execution status table. And adjust the scheduling sequence accordingly. This ensures that all tasks are ultimately completed effectively and that the process is traceable.

[0156] The output of this step is: Execution Validity Score List Used to analyze the overall task completion rate; the closed-loop adjusted scheduling sequence It is used for subsequent task execution or visualization.

[0157] Example 2

[0158] like Figure 2 As shown in the figure, this embodiment of the invention also provides an examination information management system based on the Internet of Things, the system comprising:

[0159] Information structure modeling unit 301 is used to collect examination plan data from the management system of the unified examination center to construct a set of examination information units, construct graph nodes and assign weights based on the set of examination information units to obtain an examination task graph; the set of examination information units includes several examination information units, and the examination information units include examination site number, examination paper number, time period, device ID list and task;

[0160] The test site IoT device status sensing unit 302 is used to collect the real-time operating status of IoT examination devices through an edge acquisition agent deployed on the test site side, to calculate the device score of all devices, aggregate the device scores by task, obtain the resource adaptation score of each task, and form a resource status score vector; combine the resource status score vector and the examination task map, construct the scheduling priority index of each task through weighted combination, and construct a task scheduling priority queue according to the scheduling priority index; perform preliminary task scheduling according to the task scheduling priority queue, and evaluate the device conflicts between all tasks in the already scheduled task set and the currently scheduled task to obtain the current resource conflict cost.

[0161] The task graph scheduling optimization unit 303 is used to generate a current scheduling score based on the task scheduling priority queue and the current resource conflict cost, which is used to determine whether the current task enters the scheduling sequence. The corresponding scheduling score is calculated for each task and filtered to obtain the scheduling sequence.

[0162] The scheduling and feedback control closed-loop unit 304 is used to send the scheduling sequence and the corresponding scheduling score to each examination site, drive the IoT examination device to execute the corresponding action instructions, and collect execution status feedback in real time to form a task execution status feedback closed loop for subsequent dynamic scheduling adjustment or abnormal warning.

[0163] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended specification. In some cases, the actions or steps described in the specification may be performed in a different order than those shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0164] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0165] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0166] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0171] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0172] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0173] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0174] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0175] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0176] Finally, it should be noted that the lithium battery pack chip equalization control platform disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is 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. Such 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 method for managing examination information based on the Internet of Things, characterized in that, The method includes the following steps: Exam plan data is collected from the management system of the unified examination center to construct an exam information unit set. Graph nodes are constructed and weights are assigned based on the exam information unit set to obtain an exam task graph. The exam information unit set includes several exam information units, each of which includes exam site number, exam paper number, time period, device ID list, and task. The operational status of IoT examination devices is collected in real time by an edge acquisition agent deployed at the test site to calculate the device score of all devices. The device scores are aggregated by task to obtain the resource adaptation score of each task and form a resource status score vector. By combining the resource status scoring vector and the examination task graph, a scheduling priority index for each task is constructed through weighted combination, and a task scheduling priority queue is constructed based on the scheduling priority index. Preliminary task scheduling is performed based on the task scheduling priority queue, and the device conflicts between all tasks in the already scheduled task set and the currently scheduled task are evaluated to obtain the current resource conflict cost. The current scheduling score is generated based on the task scheduling priority queue and the current resource conflict cost. This score is used to determine whether the current task should enter the scheduling sequence. The corresponding scheduling score is calculated for each task and then filtered to obtain the scheduling sequence. The scheduling sequence and corresponding scheduling scores are sent to each examination site to drive the IoT examination device to execute the corresponding action instructions and collect execution status feedback in real time, forming a task execution status feedback closed loop for subsequent dynamic scheduling adjustment or abnormal warning. The step of constructing graph nodes and assigning weights based on the set of examination information units to obtain the examination task graph specifically includes: Obtain a set of examination information units including the test center number, test paper number, time period, device ID list and task, and use each examination information unit as a node to obtain a set of nodes; The dependencies between tasks come from a dependency table exported by the exam scheduling system. The `depends_on` field records the sequential dependencies between tasks, which are then converted into a set of edges. elements in ,element This indicates that task i is a prerequisite task for task j; The edge weights corresponding to the current task are obtained by weighted summation based on the number of devices required for the task, the task duration, and the task complexity coefficient. The examination task graph is obtained by combining the node set, edge set, and corresponding edge weights; the operating status of the IoT examination device is collected in real time by the edge acquisition agent deployed at the examination site, and the collected data includes: CPU utilization, memory usage, network latency, device working status, error flags, and current number of concurrent tasks; The process of combining the resource status scoring vector and the examination task graph to construct scheduling priority indicators for each task through weighted combination, and then constructing a task scheduling priority queue based on the scheduling priority indicators, specifically includes: Introducing the resource status scoring vector and the depth of the examination task graph structure A weighted combination is used to construct a scheduling priority index. : ； in, Rate the resource status; Indicates task The corresponding topological level depth of node v in the exam task graph; Score the task specificity based on the task. Specifies whether the task type is a critical control task; the value is 0 or 1. These are resource preference coefficient and critical task priority, respectively. Based on the scheduling priority index from high to low, a task scheduling priority queue is obtained.

2. The examination information management method based on the Internet of Things according to claim 1, characterized in that, The device score for all devices is calculated by combining the CPU utilization, memory usage, network latency, constraints based on the current number of concurrent tasks on the device, and error flags.

3. The examination information management method based on the Internet of Things according to claim 1, characterized in that, The CPU utilization is measured by a performance monitoring program running locally on the device; the memory utilization is collected through local interface calls; and the network latency is estimated using ICMP or MQTT response times. The device working status indicates whether the device is online; the error flag indicates whether a physical layer error exists; the current concurrent task count is calculated by the edge agent to count the number of active connections.

4. The examination information management method based on the Internet of Things according to claim 1, characterized in that, The current resource conflict cost The calculation is as follows: ； in, For the task List of required equipment; For the task The exam time period; For indicator functions, if the device If the task has already been scheduled and the times overlap, the value is 1; otherwise, the value is 0. A value of 0 indicates that there is no conflict on the device; the closer to 1, the more the resources required by the task are almost entirely occupied. For the task List of required equipment; For the task The exam period.

5. A method for managing examination information based on the Internet of Things according to any one of claims 3 or 4, characterized in that, In each round of scheduling, the system selects tasks in descending order of scheduling score and performs the following constraint check process: A. All precursor mission nodes Satisfy the edge All tasks have been scheduled. It is an edge set; B. Resource status score at the current time ,in The minimum schedulable score threshold; C. Costs of Resource Conflicts ,in The maximum conflict rate that the system can tolerate; Tasks that satisfy A, B, and C will be added to the scheduling sequence, and the scheduled device table and time interval set will be updated.

6. The examination information management method based on the Internet of Things according to claim 1, characterized in that, The process of distributing the scheduling sequence and corresponding scheduling scores to each examination site, driving the IoT examination device to execute the corresponding action instructions, and collecting execution status feedback in real time to form a task execution status feedback closed loop for subsequent dynamic scheduling adjustments or anomaly warnings, specifically includes: Each task in the scheduling sequence is converted into a specific instruction to be issued; The system pushes instructions to the edge control unit of the test center corresponding to the test center number based on the test center number field in the task. The edge control unit is an embedded computing module deployed in the test center's local area network and controls the device through MQTT or local protocol. The issuance of each task instruction will trigger the test center control unit to initiate equipment actions. All equipment must report feedback information after completion, including whether the equipment successfully executed the task; the time consumed by the equipment to complete the task; if the task fails, record the failure type number; and the number of times the task was retried to identify unstable equipment states. Aggregate device feedback information and score the overall execution effectiveness of the corresponding task; if the effectiveness score... This indicates that the task was completely successful; if the validity score is... This indicates that some devices failed to execute the command. The feedback information is analyzed in conjunction with the task scheduling structure to determine whether the task needs to be repaired or rescheduled: if the effectiveness score is... , If the fault tolerance threshold is reached, the scheduling and repair process will be triggered to generate the repaired task; A dynamically updated task execution status table is generated based on the repaired tasks, and the scheduling sequence is adjusted accordingly to ensure that all tasks are eventually completed effectively.

7. A system for implementing the examination information management method based on the Internet of Things as described in claim 1, characterized in that, The system includes: The information structure modeling unit is used to collect examination plan data from the management system of the unified examination center to construct a set of examination information units. Based on the set of examination information units, graph nodes are constructed and weights are assigned to obtain an examination task graph. The set of examination information units includes several examination information units, and each examination information unit includes examination site number, examination paper number, time period, device ID list, and task. The test site IoT device status sensing unit is used to collect the real-time operating status of IoT examination devices through an edge acquisition agent deployed at the test site to calculate the device score of all devices. The device scores are aggregated by task to obtain the resource adaptation score of each task and form a resource status score vector. Combining the resource status score vector and the examination task map, a scheduling priority index for each task is constructed through weighted combination, and a task scheduling priority queue is constructed according to the scheduling priority index. Preliminary task scheduling is performed according to the task scheduling priority queue, and the device conflicts between all tasks in the already scheduled task set and the currently scheduled task are evaluated to obtain the current resource conflict cost. The task graph scheduling optimization unit is used to generate a current scheduling score based on the task scheduling priority queue and the current resource conflict cost, which is used to determine whether the current task enters the scheduling sequence. The unit calculates the corresponding scheduling score for each task and filters it to obtain the scheduling sequence. The scheduling and feedback control closed-loop unit is used to send the scheduling sequence and the corresponding scheduling score to each examination site, drive the IoT examination device to execute the corresponding action instructions, and collect execution status feedback in real time to form a task execution status feedback closed loop for subsequent dynamic scheduling adjustment or abnormal warning.

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