Fire-fighting multi-machine dual-priority queue scheduling transmission method and system

Abstract

The application discloses a fire-fighting multi-machine double-priority queue scheduling transmission method and system, and relates to the technical field of fire fighting. The method constructs a task priority dynamic evaluation module, calculates the basic priority score of each task according to real-time fire situation information; constructs a double-priority queue management module, distributes tasks to a high-priority queue or a low-priority queue according to the basic priority score, and performs priority promotion processing on tasks waiting for timeout; constructs a transmission resource allocation module, dynamically adjusts the data transmission bandwidth proportion based on the task quantity weight factor and the priority correction coefficient of each queue; constructs an execution task scheduling module, extracts tasks from each queue in priority order. The application solves the problems of scheduling decision and bandwidth allocation fragmentation, priority evaluation unable to dynamically evolve and long-term task queue retention, and realizes the collaborative optimization of task scheduling and transmission resources.

Description

Technical Field

[0001] This invention relates to the field of fire protection technology, specifically to a fire protection multi-machine dual-priority queue scheduling and transmission method and system. Background Technology

[0002] Multi-machine collaborative dispatch technology for fire fighting is a key technology branch in the field of fire information technology. Its goal is to complete the parallel execution of multiple tasks such as fire detection, fire fighting operations, material delivery, and personnel search and rescue through the coordinated cooperation of multiple drones, multiple fire fighting robots or other types of fire fighting equipment.

[0003] The invention patent with publication number CN121961068A proposes a method and system for scheduling firefighting drones based on multi-drone, multi-nest intelligent matching. This scheme establishes a nest matching priority evaluation model using the analytic hierarchy process (AHP) and completes multi-task pre-allocation and dynamic reallocation using a cluster collaborative scheduling optimization algorithm that integrates multiple constraints. The design focuses on the matching and scheduling optimization between drones and nests, without specifically addressing data transmission queue management and transmission resource allocation during multi-drone parallel task execution.

[0004] The invention patent with publication number CN119623976A provides a task scheduling method for a smart fire management service platform. This scheme constructs a support vector machine model to classify tasks within a monitoring area, forming a task priority list, and then establishes a two-way selection queue for personnel matching. However, the task classification relies on a fixed task priority list mechanism, making it impossible to dynamically adjust task priorities according to the fire scene situation. The design scenario of this scheme is mainly for firefighter dispatch; it also fails to specifically consider the collaborative dispatch of multiple firefighting robots or drones and the corresponding data transmission queue management.

[0005] Existing fire-fighting multi-machine dispatching technology has shortcomings on two levels. First, the generation of dispatching instructions and the allocation of bandwidth in the transmission channels are disconnected. Under conditions of concurrent multi-task execution, competition for transmission resources directly affects the efficiency of dispatching instructions and the timeliness of task execution status feedback. Task dispatching decisions and data transmission resource allocation fail to achieve coordinated optimization. Second, the priority assessment mechanism struggles to keep pace with the dynamic evolution of the fire scene and does not consider the cumulative effect of task waiting time, potentially causing some tasks to remain in the queue for extended periods without receiving a dispatching opportunity. Summary of the Invention

[0006] The purpose of this invention is to provide a fire-fighting multi-machine dual-priority queue scheduling and transmission method and system, which solves the technical problems in fire-fighting multi-machine scheduling scenarios where task scheduling decisions and data transmission bandwidth allocation are disconnected and lack collaborative optimization, and where the priority evaluation mechanism cannot dynamically evolve with the fire scene situation and does not consider the cumulative effect of task waiting time, resulting in tasks being stuck in the queue for a long time.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for scheduling and transmitting data across multiple fire-fighting units using a dual-priority queue includes the following steps: Step 1: Construct a task priority dynamic evaluation module to calculate the basic priority score of each task based on real-time fire situation information. Real-time fire situation information includes temperature data, smoke concentration data, fire spread rate data, building structure damage assessment data, and trapped personnel location information. Step 2: Construct a dual-priority queue management module, establish a high-priority queue and a low-priority queue, and allocate the fire-fighting tasks to be executed to the high-priority queue or the low-priority queue according to the basic priority score; when the cumulative waiting time of any task in the high-priority queue exceeds the preset first threshold, construct a priority dynamic adjustment module to increase the priority score of the task. Step 3: Construct a transmission resource allocation module to dynamically adjust the proportion of data transmission bandwidth allocated to high-priority queues and low-priority queues based on the task quantity weight factor and priority correction coefficient of each queue. Step 4: Construct the task scheduling module. Extract tasks from each queue in descending order of priority score. Assign tasks to the idle fire execution unit with the highest matching score based on the matching score between the task and the fire execution unit. When the fault status indicator of a certain fire-fighting execution unit is activated, the execution task scheduling module removes the fire-fighting execution unit from the suitability calculation candidate set and places the tasks that have been assigned to the fire-fighting execution unit but have not yet been executed back at the head of their original queue.

[0008] Furthermore, in step 1, the task priority dynamic evaluation module calculates the basic priority score for each task based on the priority evaluation model. The mathematical expression of the priority evaluation model is: in: For the first The basic priority score for each task; , , , , These are weighting coefficients for temperature, smoke concentration, fire spread rate, building structural damage, and number of trapped personnel, respectively, and they satisfy the following conditions: Normalization constraints; , , , , These are the characteristic values ​​of temperature, smoke concentration, fire spread rate, building structural damage, and number of trapped personnel after standardized preprocessing.

[0009] Furthermore, the task priority dynamic evaluation module also uses the gradient descent algorithm to update the weight coefficients of temperature, smoke concentration, fire spread rate, building structure damage, and number of trapped personnel online based on the feedback data of the execution effect of the completed scheduled tasks, and ensures that the updated weight coefficients meet the normalization constraint conditions.

[0010] Furthermore, in step 2, when the dual-priority queue management module obtains a new task, it determines whether the basic priority score of the new task is greater than or equal to the preset queue division threshold. If the basic priority score of the new task is greater than or equal to the queue division threshold, the new task is inserted into the tail of the high-priority queue; otherwise, the new task is inserted into the tail of the low-priority queue.

[0011] Furthermore, when the current length of the high-priority queue exceeds the preset first queue capacity limit, the task with the lowest basic priority score is selected from the high-priority queue and moved to the tail of the low-priority queue; when the current length of the low-priority queue exceeds the preset second queue capacity limit, newly arriving tasks are rejected, and if the length of the low-priority queue does not decrease to below the second queue capacity limit within a preset time window, the task with the lowest basic priority score in the low-priority queue is forcibly terminated.

[0012] Furthermore, when a degraded task transferred from a high-priority queue to a low-priority queue is waiting for scheduling in the low-priority queue, if the task priority dynamic evaluation module recalculates the basic priority score of the degraded task based on the updated fire situation information and it is greater than or equal to the queue division threshold, and the current length of the high-priority queue does not exceed the upper limit of the first queue capacity, then the degraded task will be extracted from the low-priority queue and migrated back to the high-priority queue.

[0013] Furthermore, in step 2, the priority dynamic adjustment module adjusts the priority score of the task that triggers the priority upgrade operation according to the following mathematical expression: in: For the first The adjusted priority scores for each task; For the first The basic priority score for each task; For the first The cumulative waiting time for each task, in milliseconds (ms). The first threshold is expressed in milliseconds (ms). This is a preset waiting time weighting coefficient; The preset retry count weighting coefficient; For the first The cumulative number of times a task has failed to be scheduled.

[0014] Furthermore, when the adjusted priority score in the high-priority queue exceeds a preset second threshold... When a task is assigned, the priority dynamic adjustment module marks the task as an urgent task and inserts it into a separate urgent task buffer queue; the priority dynamic adjustment module notifies the transmission resource allocation module to allocate dedicated data transmission channel resources to the urgent task buffer queue.

[0015] Furthermore, in step 3, the transmission resource allocation module allocates the currently available total data transmission bandwidth capacity according to the following mathematical expression. Assigned to high-priority queues, low-priority queues, and urgent task buffer queues: in: , , These are the data transmission bandwidths allocated to the high-priority queue, low-priority queue, and urgent task buffer queue, respectively, in bits per second. , , These are the task quantity weighting factors for the high-priority queue, low-priority queue, and urgent task buffer queue, respectively, with the task quantity weighting factor being the square root of the current task quantity in the corresponding queue. , , These are the priority correction coefficients for the high-priority queue, low-priority queue, and urgent task buffer queue, respectively, and they satisfy the following conditions: The constraints.

[0016] Furthermore, the transmission resource allocation module also sets a minimum guaranteed bandwidth limit for low-priority queues; when allocating data transmission bandwidth, the data transmission bandwidth allocated to low-priority queues shall not be lower than the minimum guaranteed bandwidth limit.

[0017] Furthermore, after completing bandwidth allocation, the transmission resource allocation module continuously monitors the average waiting time of tasks in the low-priority queue. When the average waiting time of tasks in the low-priority queue exceeds a preset third threshold, the transmission resource allocation module temporarily allocates a preset compensation increment from the bandwidth allocated to the high-priority queue and the emergency task buffer queue in the next adjustment cycle, so that the data transmission bandwidth allocated to the low-priority queue is temporarily increased, and the sum of the allocated bandwidth of all queues after the adjustment is not greater than the total data transmission bandwidth capacity. When the average waiting time of tasks in the low-priority queue falls back below the third threshold, the transmission resource allocation module cancels the compensation increment and restores the normal bandwidth allocation ratio.

[0018] Furthermore, the transmission resource allocation module is also equipped with a historical inertia smoothing mechanism. After completing the bandwidth allocation, the bandwidth allocated to each queue is smoothed according to the following mathematical expression: in: Represents any queue; For the first Periodic queue The final allocated bandwidth after smoothing is expressed in bits per second. For the first Periodic queue The actual allocated bandwidth, in bits per second; For the first Periodic queue The theoretical allocated bandwidth, in bits per second; Let be the inertial smoothing factor, and let its value satisfy . .

[0019] Furthermore, before calculating the basic priority score, the task priority dynamic evaluation module also performs real-time perturbation on the weight coefficients in the priority evaluation model based on the fire entropy growth rate; fire entropy growth rate The calculation is based on the following mathematical expression: in: for The standard deviation of all temperature detection data points at the fire scene at any given time, in K; The preset time window is in seconds; when When the entropy increase threshold is not less than the preset threshold, temporarily increase the values ​​of the fire spread speed weight coefficient and the number of trapped people weight coefficient, and correspondingly compress the values ​​of other weight coefficients.

[0020] Furthermore, in step 4, the task scheduling module extracts tasks in the following order: tasks are extracted from the emergency task buffer queue in descending order of adjusted priority scores; after the emergency task buffer queue is empty, tasks are extracted from the high priority queue in descending order of adjusted priority scores; after the high priority queue is empty, tasks are extracted from the low priority queue in descending order of basic priority scores.

[0021] Furthermore, in step 4, the task scheduling module calculates the compatibility score between the task and the fire-fighting execution unit based on the working status information of each fire-fighting execution unit and the priority score of the task to be scheduled. The compatibility score is calculated according to the following mathematical expression: in: For the first The first task and the first The compatibility score between individual fire protection execution units; The distance index is the distance between the task execution location and the current location of the fire-fighting execution unit. The closer the distance, the higher the score. Each index has been standardized and preprocessed before being mapped to... interval; The remaining power level of the fire-fighting unit; For the communication link quality indicators of the fire protection execution unit; This refers to the current task execution progress indicators for the fire protection execution unit; This is a fault status indicator for the fire protection actuator; the maximum value is taken when there is no fault, and zero is taken when there is a fault. , , , , Let be the weighting coefficients for each indicator, and satisfy . The normalization constraints.

[0022] In addition, this invention also discloses a fire-fighting multi-machine dual-priority queue scheduling and transmission system for implementing the fire-fighting multi-machine dual-priority queue scheduling and transmission method described above, characterized in that it includes: The task priority dynamic evaluation module is used to calculate the basic priority score of each task based on real-time fire situation information; The dual-priority queue management module is used to establish high-priority queues and low-priority queues, and to allocate fire-fighting tasks to be executed to high-priority queues or low-priority queues according to basic priority scores; The priority dynamic adjustment module is used to monitor the waiting time of each task in the high priority queue, and to increase the priority score when the waiting time of any task exceeds a preset first threshold. The transmission resource allocation module is used to dynamically adjust the proportion of data transmission bandwidth allocated to high-priority queues and low-priority queues based on the current load status of the data transmission channel. The task scheduling module is used to extract tasks from each queue in descending order of priority score and assign them to idle fire-fighting execution units.

[0023] Compared with the prior art, the present invention has the following beneficial effects: This invention elevates task management in multi-machine fire dispatching from a static, single-dimensional model to a dynamic, multi-dimensional collaborative model by constructing a dual-priority queue hierarchical triage structure and a waiting time-driven progressive priority growth mechanism. The task priority dynamic evaluation module calculates basic priority scores in real time based on temperature data, smoke concentration data, fire spread rate data, building structural damage assessment data, and trapped personnel location information, overcoming the deficiency in existing technologies where priority lists cannot be dynamically adjusted according to the fire situation.

[0024] The dual-priority queue management module distributes tasks of varying urgency into high-priority and low-priority queues. Combined with overflow handling and degraded task rollback mechanisms, it maintains the controllability of the queue structure under high concurrency, ensuring that high-priority tasks receive scheduling opportunities first. The dynamic priority adjustment module linearly increases the priority score of tasks whose waiting time exceeds a preset first threshold and introduces a compensation term for the cumulative number of scheduling failures to prevent tasks from starving due to long waiting times and to suppress drastic priority oscillations.

[0025] At the coordination level of task scheduling and data transmission bandwidth, this invention dynamically allocates available data transmission bandwidth capacity according to the weighting factor of the number of tasks in each queue and the priority correction coefficient, and sets a minimum guaranteed bandwidth limit to ensure the transmission efficiency of critical tasks while maintaining the basic instruction transmission capability of low-priority tasks. A closed-loop feedback adjustment mechanism temporarily allocates compensation increments from the currently allocated bandwidth of high-priority queues and emergency task buffer queues when the average waiting time of tasks in the low-priority queue exceeds a preset third threshold, accelerating the digestion of low-priority queues and preventing low-priority tasks from starving under continuous high-load scenarios.

[0026] The historical inertia smoothing mechanism of this invention, through weighted smoothing calculation, avoids drastic changes in bandwidth allocation ratios caused by rapid changes in the fire situation, thus improving the stability of the data transmission channel. The task scheduling module extracts tasks in the order of emergency task buffer queue, high-priority queue, and low-priority queue, and allocates them based on the compatibility score between the task and the fire-fighting execution unit. Fire-fighting execution units activated by fault status indicators are immediately removed from the candidate set, and assigned but not yet executed tasks are placed back at the head of their original queues, preventing the cascading propagation of single-point equipment failures in the multi-machine collaborative system. The adaptive optimization submodule of weight coefficients updates the weight coefficients of the priority evaluation model online using gradient descent algorithm based on the execution effect feedback data of completed scheduled tasks, ensuring that the priority evaluation results continuously approximate actual rescue needs. The weighted real-time perturbation mechanism based on the fire entropy growth rate temporarily increases the weight coefficients of the fire spread speed and the number of trapped personnel when the fire situation deteriorates rapidly, enabling the system to respond instantly to the deteriorating situation and improving the scheduling timeliness of the first batch of targeted rescue tasks. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the overall process of the method of the present invention.

[0029] Figure 2 This is a flowchart of the downgrade task migration process of this invention.

[0030] Figure 3 This is a flowchart of the dynamic priority adjustment and emergency task handling process of the present invention.

[0031] Figure 4 This is a flowchart of the adaptive optimization process for weight coefficients in this invention. Detailed Implementation

[0032] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0033] The following is in conjunction with the appendix Figures 1-4 The embodiments of the present invention will be described in detail below.

[0034] Example 1: This example provides a fire-fighting multi-machine dual-priority queue scheduling and transmission system. The scheduling and transmission system includes a task priority dynamic evaluation module, a dual-priority queue management module, a priority dynamic adjustment module, a transmission resource allocation module, and an execution task scheduling module.

[0035] Among them, the task priority dynamic evaluation module is used to calculate the basic priority score of each task based on real-time fire situation information. The dual-priority queue management module is used to establish high-priority queues and low-priority queues, and to allocate fire-fighting tasks to be executed to high-priority queues or low-priority queues according to basic priority scores; The priority dynamic adjustment module is used to monitor the waiting time of each task in the high priority queue, and to increase the priority score when the waiting time of any task exceeds a preset first threshold. The transmission resource allocation module is used to dynamically adjust the proportion of data transmission bandwidth allocated to high-priority queues and low-priority queues based on the current load status of the data transmission channel. The task scheduling module is used to extract tasks from each queue in descending order of priority score and assign them to idle fire-fighting execution units.

[0036] The scheduling transmission method includes the following steps: Step S1: The task priority dynamic evaluation module receives multi-source heterogeneous data streams from fire detection equipment and calculates the basic priority score for each task based on real-time fire situation information. Real-time fire situation information includes temperature data, smoke concentration data, fire spread rate data, building structure damage assessment data, and trapped personnel location information. The task priority dynamic evaluation module performs standardized preprocessing on the above fire situation information, mapping data features with different dimensions and value ranges to a unified numerical interval. Then, based on a preset priority evaluation model, it outputs the basic priority score for each task. Each feature value is mapped to an interval. Internally, the standardization preprocessing uses the max-min normalization method for any original eigenvalues. The value after standardization is ,in and These are the preset minimum and maximum values ​​for the feature in a fire-fighting scenario; if the real-time collected raw feature value exceeds the preset range of maximum and minimum values... The current measured extreme value temporarily replaces the original extreme value to complete the normalization process. The system can be periodically updated based on long-term accumulated fire data. and The value of .

[0037] The mathematical expression for the priority evaluation model is: in: Indicates the first The basic priority score for each task; , , , , Let these represent the weighting coefficients for temperature, smoke concentration, fire spread rate, building structural damage, and number of trapped personnel, respectively, and satisfy the following conditions: Normalization constraints; , , , , These represent the characteristic values ​​of temperature, smoke concentration, fire spread rate, building structural damage, and number of trapped personnel, respectively, after standardized preprocessing.

[0038] Priority assessment models are not limited to the linear weighted form mentioned above; as an alternative implementation, the task priority dynamic assessment module can adopt a fuzzy inference model; in this alternative, the temperature feature value, smoke concentration feature value, fire spread rate feature value, building structure damage feature value, and number of trapped personnel feature value are first fuzzified and mapped to four fuzzy sets: low, medium, high, and extremely high. The membership degree of each feature value to each fuzzy set is then calculated based on a preset trapezoidal membership function.

[0039] The general expression for the trapezoidal membership function is: when the input value Falling into the upper base of the trapezoid When the membership degree is 1, the membership degree is 1; when Falling into the lower bottom range At that time, the degree of membership is taken ;when Falling into the lower bottom range At that time, the degree of membership is taken ;when exist When the membership degree is outside of this range, the membership degree is 0. The position parameters of the four vertices of the trapezoid on the universe of discourse; the fuzzy rule base contains the following typical rules: IF temperature feature value is high AND fire spread rate feature value is high THEN output priority level is extremely high; the task priority dynamic evaluation module performs fuzzy inference according to the preset fuzzy rule base, and each rule points the fuzzy combination of several input features to an output priority level; defuzzification is completed using the centroid method or the maximum membership method, and the basic priority score output after defuzzification is mapped to... The interval is consistent with the output scope of the linear weighted model.

[0040] The initial values ​​of each weight coefficient in the above priority evaluation model are determined based on historical rescue case statistics in the fire protection field.

[0041] In this embodiment, the temperature weighting coefficient Set the weighting factor to 0.20 for smoke concentration. The weighting coefficient for fire spread speed is set to 0.20. Take 0.25 as the building structure damage weighting coefficient. The weighting coefficient for the number of trapped personnel is set to 0.15. The weight coefficient for the number of trapped personnel is 0.20, which is relatively high among the five parameters, reflecting the primary consideration of personnel safety in the priority assessment of fire rescue missions. The weight coefficient for the speed of fire spread is the highest, reflecting the strong correlation between the fire development trend and the urgency of the mission. The above values ​​of each weight coefficient are only illustrative examples and can be adjusted according to the actual needs of the fire scenario in specific applications.

[0042] Step S2: The dual-priority queue management module is responsible for creating the high-priority queue. and low priority queues Two physically independent data structures are used, and the fire-fighting tasks to be executed are assigned to the corresponding queues according to their basic priority scores.

[0043] When a new task arrives, the dual-priority queue management module determines whether the new task's base priority score is greater than or equal to a preset queue partitioning threshold. If this condition is met, the new task will be inserted into the high-priority queue. Otherwise, insert it into the tail of the queue; otherwise, insert it into a lower priority queue. The tail; queue partitioning threshold The value is determined based on the statistical distribution of historical task priority scores, and is typically set to ensure that approximately 60% to 70% of tasks enter the high-priority queue. The remaining tasks are placed in a low-priority queue. In this embodiment, the queue partitioning threshold Take 50.

[0044] The dual-priority queue management module is configured with an overflow handling mechanism; when the high-priority queue... When the current length exceeds the preset first queue capacity limit, the dual-priority queue management module traverses the high-priority queue. Of all the tasks, select the one with the lowest base priority score, remove it, and insert it into the low-priority queue. At the tail of the queue, the task counters and index pointers of both queues are updated simultaneously; the setting of the upper limit of the first queue capacity needs to take into account both the timeliness of high-priority task scheduling and the computational overhead of queue management. In this embodiment, the upper limit of the first queue capacity is set to 500; when the low-priority queue... When the current length exceeds the preset second queue capacity limit, the dual-priority queue management module rejects newly arriving tasks, returns a queue full status indicator to the task initiator, and simultaneously starts a queue clearing countdown mechanism, continuously clearing low-priority queues during the countdown. Tasks in the queue are scheduled normally to accelerate queue consumption; within a preset time window, if low-priority queues... If the length of the queue does not decrease below the capacity limit of the second queue, the low-priority queue will be forcibly terminated. The task with the lowest basic priority score is selected, and the resources occupied by that task are released. In this embodiment, the upper limit of the second queue capacity is 1000, and the time window is 30 seconds.

[0045] As a preferred implementation, the dual-priority queue management module also features a degraded task rollback mechanism; a degraded task refers to a task that has been downgraded due to a high-priority queue. Overflow is transferred to a low-priority queue Tasks of this type are in a low-priority queue. During the waiting period for dispatch, if the basic priority score recalculated by the task priority dynamic evaluation module based on the updated fire situation information is again greater than or equal to the queue division threshold... And high-priority queue If the current length does not exceed the capacity limit of the first queue, the dual-priority queue management module will move the downgraded task from the low-priority queue. Extract from the queue and move back to the high-priority queue. The purpose of the degraded task rollback mechanism is to enable tasks that have been temporarily downgraded due to overflow to regain a scheduling level that matches their urgency when the fire situation worsens, thus avoiding irreversible impacts on the timeliness of emergency task response caused by overflow handling.

[0046] Step S3: The priority dynamic adjustment module is set to a high-priority queue. Each task to be scheduled is configured with an independent waiting time counter, and the cumulative waiting time of each task is periodically checked at a preset detection period; when the cumulative waiting time of a task reaches a preset first threshold... When this occurs, a priority promotion operation is triggered, and the mathematical expression on which the adjustment is based is: in: Indicates the first The adjusted priority scores for each task; This indicates the basic priority score for the task; This indicates the cumulative waiting time for the task, in milliseconds (ms). This represents the first threshold value, expressed in milliseconds (ms). This represents the preset waiting time weighting coefficient; This represents the preset retry count weighting coefficient; This indicates the cumulative number of times the task has failed to be scheduled.

[0047] In the above expression, the waiting time weighting coefficient This determines the rate at which task priority increases with the length of the waiting time; A value that is too large can cause long-waiting tasks to quickly occupy high-priority positions, while a value that is too small may not effectively prevent tasks from starving. In this embodiment, The value is set to 0.0005, meaning that for every 1000ms wait time after the first threshold, the task priority score increases by 0.5; the retry count weighting coefficient... In this embodiment, it is used to compensate for the time loss accumulated due to scheduling failures. Set to 2.0; first threshold A value of 5000ms is used, which is approximately five times the typical round-trip time from the issuance of a dispatch command to the commencement of equipment execution in a fire scenario, providing a reasonable normal waiting window for the task; the adjusted priority score can be greater than 100, and is only used as a basis for queue sorting; the waiting time counter is used to move the task into the high-priority queue. The timer starts at the specified moment and ends when the task is extracted from the queue and assigned to a fire execution unit; if a task is downgraded to a lower priority queue due to overflow, the timer will stop. Its waiting time counter is reset to zero during the transfer, and it enters the low-priority queue. Then the timer restarts.

[0048] When high priority queue The adjusted priority score appears in the middle. Exceeding the preset second threshold When a task is assigned, the priority dynamic adjustment module marks it as an urgent task and inserts it into a separate urgent task buffer queue. Middle; second threshold The priority score should be set higher than the upper limit of the base priority score for any task under normal circumstances, to ensure that only tasks that have accumulated sufficient adjustment due to long waiting times are marked as urgent tasks; in this embodiment, the second threshold... Take 90; Emergency Task Buffer Queue A priority queue data structure is adopted, internally sorted from high to low according to the adjusted priority score; the priority dynamic adjustment module also notifies the transmission resource allocation module to create a buffer queue for urgent tasks. Allocate dedicated data transmission channel resources.

[0049] Step S4: The transmission resource allocation module collects the current load parameters of the data transmission channel in real time, including at least the current transmission rate, channel utilization, and average transmission delay; based on the collected current load parameters, the transmission resource allocation module calculates the currently available data transmission bandwidth capacity. The unit is bit / s, and the available data transmission bandwidth capacity is allocated to the high-priority queue according to the following expression. Low-priority queues and emergency task buffer queue : in: , , These represent allocation to the high-priority queue. Low-priority queues and emergency task buffer queue The data transmission bandwidth, measured in bits per second; , , These represent the task quantity weighting factors for each queue; , , Let represent the priority correction coefficients for each queue, and satisfy . The constraints.

[0050] The task quantity weighting factor reflects the bandwidth requirement of the current load of each queue; in this embodiment, The function value that retrieves the number of tasks in each queue: in: , , High priority queues Low-priority queues and emergency task buffer queue The current number of tasks; the reason for using a square root function instead of a non-linear function is that when the queue length is large, the marginal benefit of further increasing bandwidth decreases, and the square root form can ensure that bandwidth allocation is balanced with the response queue load while avoiding excessive skewness towards long queues; priority correction coefficient. Take 1.0, Take 0.8, Take 1.5 to satisfy The constraints reflect the priority differences of each queue.

[0051] The transmission resource allocation module also provides low-priority queues. Set minimum guaranteed bandwidth limit During the allocation of data transmission bandwidth, it is allocated to low-priority queues. Data transmission bandwidth It must always be no less than the minimum guaranteed bandwidth limit. In this embodiment, the minimum guaranteed bandwidth limit is... Taking 10% of the total bandwidth ensures that low-priority tasks can maintain basic command transmission capabilities even when the data transmission channel is severely congested.

[0052] The transmission resource allocation module is further configured with a closed-loop feedback adjustment mechanism; after completing the bandwidth allocation according to the above expression, the transmission resource allocation module continuously monitors the low-priority queues. Average waiting time for medium-priority tasks; when low-priority queues The average waiting time for medium-duration tasks exceeds the preset third threshold. When this occurs, it indicates that low-priority tasks are backlogged. In the next adjustment cycle, the transmission resource allocation module will temporarily allocate a preset compensation increment from the currently allocated bandwidth of the high-priority queue and the urgent task buffer queue. The bandwidth allocated to lower priority queues is added to the bandwidth allocated to lower priority queues, and the sum of the allocated bandwidth for all queues after adjustment does not exceed the currently available total data transmission bandwidth capacity. When low-priority queues The average waiting time for medium-duration tasks has fallen back to the third threshold. After this, the transmission resource allocation module cancels the compensation increment and restores the normal bandwidth allocation ratio; in this embodiment, the third threshold... Take 10000ms and compensate for the increment. Pick The closed-loop feedback adjustment mechanism aims to compensate for the shortcomings of the pure proportional allocation model in protecting low-priority queues under continuous high load scenarios, provide temporary bandwidth compensation for low-priority tasks to prevent them from starving, and at the same time not affect the bandwidth allocation efficiency under normal load conditions.

[0053] Step S5: The task scheduling module extracts tasks from each queue in a preset order; the extraction order is: first from the urgent task buffer queue. Tasks are extracted from the queue according to their adjusted priority scores, from highest to lowest; an emergency task buffer queue. If the queue is empty, then switch to the higher priority queue. Tasks are extracted from the queue according to their adjusted priority scores, from highest to lowest; high-priority queue If it is also empty, then start from the low-priority queue. Tasks are extracted from high to low priority scores.

[0054] The task scheduling module collects real-time operational status information from each fire-fighting execution unit. This information includes at least the unit's current location, remaining battery power, communication link quality, current task execution progress, and fault status indicators. Based on the operational status information of the fire-fighting execution units and the priority score of the tasks to be scheduled, the task scheduling module calculates a suitability score between the task and the fire-fighting execution unit, using the following formula: in: Indicates the first The first task and the first The compatibility score between individual fire protection execution units; This represents the distance index between the task execution location and the current location of the fire-fighting execution unit. The closer the distance, the higher the score. After standardized preprocessing, it is mapped to... interval; The remaining power index of the fire-fighting execution unit is mapped to [a specific value] after standardized preprocessing. interval; The communication link quality index of the fire-fighting execution unit is mapped to after standardization preprocessing. interval; This indicates the current task execution progress indicators of the fire protection execution unit, which are mapped to [the target data] after standardized preprocessing. interval; This indicates the fault status index of the fire-fighting actuator. This index is a binary index, which is set to 100 when the fire-fighting actuator is fault-free and 0 when the fire-fighting actuator malfunctions. , , , , This represents the weight coefficient corresponding to each indicator, and satisfies... The normalization constraint conditions; in this embodiment, the weight coefficients are respectively taken as: , , , , The task scheduling module selects the fire-fighting execution unit with the highest adaptability score as the target execution unit, generates scheduling instructions, and sends them to the target fire-fighting execution unit through the data transmission channel.

[0055] The task scheduling module is also equipped with equipment fault propagation blocking logic. When the fault status flag of a certain fire-fighting execution unit is activated, the task scheduling module removes the fire-fighting execution unit from the suitability calculation candidate set, so that it will no longer participate in the suitability calculation of any subsequent tasks. At the same time, the task scheduling module traverses all tasks that have been assigned to the fire-fighting execution unit but have not yet started execution, resets the task status flag of such tasks to pending scheduling, and places them at the head of their original queue, so that they are extracted with priority over other tasks in the queue in the next scheduling cycle. The role of the equipment fault propagation blocking logic is to limit the impact of a single point of equipment failure to the faulty equipment itself, prevent the task from being delayed indefinitely due to the inability of the faulty equipment to execute the task, and avoid the cascading spread of the consequences of the failure in the multi-machine collaborative task chain.

[0056] The fire-fighting multi-machine dual-priority queue scheduling and transmission system in this embodiment is also equipped with a system monitoring and alarm module, a clock synchronization module, a log recording module, a data backup and recovery module, and a security protection module. The system monitoring and alarm module is used to monitor the operating status of each functional module of the system in real time, including processor utilization, memory usage, queue backlog, data transmission channel bandwidth utilization, and task scheduling success rate. When any monitoring indicator exceeds a preset alarm threshold, an alarm notification is triggered. The clock synchronization module is used to provide a unified time base for each functional module of the system and uses a precise time protocol to achieve clock synchronization between modules. The log recording module is used to record task scheduling events, transmission resource allocation events, and abnormal events during system operation. The data backup and recovery module is used to periodically back up the system's operating data and perform status recovery based on the backup data in the event of a system failure. The security protection module is used to encrypt the communication content of the data transmission channel and perform identity authentication and access control for data interaction between the system and external devices.

[0057] Example 2: This example, based on Example 1, further defines the adaptive optimization method for weight coefficients in the priority evaluation model. In Example 2, the task priority dynamic evaluation module is also equipped with a weight coefficient adaptive optimization submodule. Based on the execution effect feedback data of completed scheduling tasks, the weight coefficient adaptive optimization submodule uses a gradient descent algorithm to update the weight coefficients for temperature, smoke concentration, fire spread rate, building structure damage, and number of trapped personnel online. The execution effect feedback data includes a binary indicator of whether the task was successful and the temperature change rate of the relevant fire area within a preset time period after the task execution. The temperature change rate is calculated as follows: after the task execution is completed, the fire detection equipment continuously collects temperature data within a preset time period within a circular area with a preset radius centered on the task execution location, calculates the temperature change rate over time, and uses this as a quantitative evaluation value of the fire control effect.

[0058] The adaptive optimization submodule for weight coefficients compares the actual execution performance feedback data of the completed scheduled tasks with the basic priority score calculated during scheduling, and constructs a loss function with weight coefficients as independent variables. The mathematical expression of the loss function is as follows: in: This represents the number of samples for which scheduling tasks have been completed. For the first The score for the actual fire control effect after the dispatching task has been completed; For the first The basic priority score calculated during scheduling for each completed task.

[0059] The iterative update formula for the weight coefficients is: in: This is the weight coefficient vector before the update; This is the updated weight coefficient vector; Set the learning rate; The gradient of the loss function with respect to the weight coefficients is used; along the negative gradient direction of the loss function with respect to each weight coefficient, the values ​​of each weight coefficient are gradually adjusted with a preset learning rate; in this embodiment, the preset learning rate is 0.01; the updated weight coefficients must satisfy... The normalization constraint condition is applied; if the update result does not meet the normalization constraint, the weight coefficient adaptive optimization submodule performs normalization correction on the updated weight coefficients. Specifically, each weight coefficient is divided by the sum of all weight coefficients.

[0060] The adaptive optimization submodule for weight coefficients initiates the first update after the cumulative number of completed tasks processed by the system reaches a preset update trigger threshold. Subsequently, it performs periodic online updates of weight coefficients at a preset update cycle. In this embodiment, the update trigger threshold is set to 200 completed tasks, and the update cycle is set to update once every 500 completed tasks. The above threshold and cycle settings take into account both the statistical reliability of weight coefficient updates and the computational resource overhead.

[0061] The remaining steps and parameter configurations in this embodiment are the same as in Embodiment 1.

[0062] Example 3: This example verifies the technical effect of the present invention by setting up a comparative example. Both the example and the comparative example are based on a simulated fire-fighting multi-machine collaborative scheduling scenario. The unified scenario configuration parameters are: 100 fire detection devices, 200 fire-fighting execution units (including fire-fighting drones and fire-fighting robots), a total network bandwidth of 1Gbps in the test environment, and a test duration of 30 minutes. During the test duration, the system receives and processes a total of 1,000 tasks to be scheduled. At the 5th minute, 15th minute, and 25th minute of the test, a sudden emergency task scenario is simulated once, and 50 new tasks with an urgency level higher than all current tasks to be processed are injected each time.

[0063] Comparative Example 1: Comparative Example 1 adopts a single-queue scheduling scheme based on static priority; this scheme sets up only one task queue, and all tasks to be scheduled enter the single queue in order of arrival time; the priority score of each task is calculated once when the task enters the queue according to the same priority evaluation model as the present invention, and is not updated in subsequent scheduling processes; during scheduling, the system starts scanning from the head of the queue and selects the task with the highest priority score in the queue for scheduling; the data transmission bandwidth adopts a fixed allocation method and is not dynamically adjusted according to the queue status.

[0064] The remaining hardware environment, network conditions, and task generation model of Comparative Example 1 are completely consistent with those of Example 3-1 to ensure the objectivity of the comparison results.

[0065] Comparative Example 2: Comparative Example 2 adopts a dual-priority queue scheme, but does not include a closed-loop feedback adjustment mechanism for data transmission bandwidth or a dynamic priority adjustment mechanism; in Comparative Example 2, tasks are allocated to higher-priority queues according to their basic priority scores. and low priority queues Queue partitioning threshold Also set to 50; data transmission bandwidth is allocated according to the high-priority queue. and low priority queues Tasks are allocated proportionally based on their quantity, but no minimum guaranteed bandwidth is set, and no closed-loop feedback adjustment is introduced; the priority score of each task remains unchanged after being enqueued and is not dynamically adjusted based on waiting time or number of retries; the task scheduling order is to prioritize tasks from the highest priority queue. Extracting data from the middle based on basic priority scores, from highest to lowest, with a high-priority queue. If it is empty, then start from the low priority queue. Extract from.

[0066] Comparative Example 2 is used to separately verify the technical effectiveness of the priority dynamic adjustment mechanism and the data transmission bandwidth closed-loop feedback adjustment mechanism.

[0067] Example 3-1: Example 3-1 adopts the complete technical solution of the present invention, including all the features of Example 1 and Example 2; the system configuration parameters of Example 3-1 are as follows: queue partitioning threshold Set the value to 50, the maximum capacity of the first queue to 500, the maximum capacity of the second queue to 1000, and the first threshold. Take 5000ms as the waiting time weighting coefficient. Set the weighting factor to 0.0005 for the number of retries. Set it to 2.0, the second threshold. Take 90, the third threshold Take 10000ms and compensate for the increment. Pick 5%, priority correction factor Take 1.0, Take 0.8, Take 1.5 as the minimum guaranteed bandwidth. Take 10% of the total bandwidth; Fit weighting coefficient Take 0.30, Take 0.20, Take 0.20, Take 0.15, The learning rate for adaptive optimization of weight coefficients is set to 0.15; the update trigger threshold is set to 200 completed tasks; and the update cycle is set to update once every 500 completed tasks.

[0068] Table 1: Comparison of test data between Example 3-1 and Comparative Example 1 and Comparative Example 2.

[0069] The comparative data in Table 1 shows that Example 3-1 outperforms Comparative Example 1 and Comparative Example 2 in all evaluation indicators; the average scheduling delay for emergency tasks decreased from 387ms in Comparative Example 1 and 201ms in Comparative Example 2 to 98ms, reflecting the synergistic effect of the dual-priority queue tiered distribution and the gradual priority growth mechanism; the timeout rate for low-priority tasks decreased from 16.73% in Comparative Example 1 and 5.88% in Comparative Example 2 to 1.52%, indicating that the data transmission bandwidth closed-loop feedback adjustment mechanism and the minimum guaranteed bandwidth lower limit prevented the backlog and starvation of low-priority tasks; although Comparative Example 2 improved scheduling performance through the dual-queue structure, it still had shortcomings in emergency task response and low-priority task protection due to the lack of dynamic priority adjustment and bandwidth feedback mechanisms.

[0070] Example 3-2: Example 3-2 further verifies the solution of the present invention under high load scenarios; the system configuration parameters of Example 3-2 are the same as those of Example 3-1, the difference is that the load intensity of the test scenario is increased: the number of fire detection devices is increased to 250, the cumulative number of received tasks during the test duration is increased to 3000, and the number of sudden emergency task scenarios is increased to 5.

[0071] Table 2: Test data of Example 3-2 under high load scenarios.

[0072] Test data under high load scenarios show that even with an increase in the number of tasks and the number of sudden events, the timeout rate of low-priority tasks is still controlled at a low level of 3.47%, which verifies the protection effect of the closed-loop feedback adjustment mechanism and the minimum guaranteed bandwidth limit under extreme load conditions.

[0073] Example 3-3: Example 3-3 is used to verify the effect of the dynamic priority adjustment mechanism separately; in this example, 100 tasks are selected and placed into the high-priority queue. Afterwards, continue waiting without scheduling, and observe the change in score over time after priority adjustment; when the cumulative waiting time exceeds the first threshold... After 5000ms, the priority score begins to increase linearly; when the cumulative waiting time reaches 30000ms, the priority score increases by 12.5%; when the cumulative waiting time reaches 60000ms, the priority score increases by 27.5%; for example, if a task has a base priority score of 80, the waiting time weighting coefficient... The priority score is set to 0.0005, and adjusted when the cumulative waiting time reaches 60000ms. If the score exceeds the base priority score limit of 100 for any newly arrived high-priority task, the task will be given priority in the queue and scheduled for execution.

[0074] Example 4: Based on Example 1, this example further limits the bandwidth allocation method of the transmission resource allocation module, providing an alternative to the instantaneous bandwidth allocation based solely on the current periodic queue status. The core difference lies in the introduction of a historical inertia smoothing mechanism for transmission bandwidth allocation, in order to avoid drastic changes in the bandwidth allocation ratio caused by rapid changes in the fire situation, thereby improving the stability of the data transmission channel.

[0075] In this embodiment, the transmission resource allocation module calculates the currently available total data transmission bandwidth capacity. Next, perform the following steps: Step S401: Calculate the theoretical allocated bandwidth values ​​for the high-priority queue, low-priority queue, and urgent task buffer queue in the current allocation cycle according to the modified expression in Step S4 of Embodiment 1, denoted as... , and The unit is bit / s.

[0076] Step S402: Obtain the actual allocated bandwidth value finally distributed to each queue in the previous allocation cycle, denoted as... , and The unit is bit / s.

[0077] Step S403: According to the preset inertia smoothing factor The final allocated bandwidth for the current period is weighted and smoothed to generate the smoothed allocated bandwidth to be distributed to each queue. , and The unit is bit / s. Its mathematical expression is: in: , represents any queue; For the first Periodic queue The final allocated bandwidth after smoothing is expressed in bits per second. For the first Periodic queue The actual allocated bandwidth, in bits per second; For the first Periodic queue The theoretical allocated bandwidth, in bits per second; Let be the inertial smoothing factor, and let its value satisfy . .

[0078] Inertial smoothing factor Its function is to adjust the dependence of historical allocation values. The larger the value, the stronger the dependence on historical states, the smoother the bandwidth changes, and the higher the system stability, but the slower the response speed to sudden traffic. The smaller the value, the closer it is to directly using the theoretical value, and the faster the response, but it may introduce jitter; in this embodiment, Setting it to 0.4 effectively smooths out instantaneous bandwidth fluctuations while ensuring response speed; the transmission resource allocation module ultimately uses the calculated value... , and The actual bandwidth allocation command for the current cycle is issued.

[0079] The technical advantage of this embodiment is that when the sudden change in the fire situation causes a sudden surge in the workload, the bandwidth will not jump from one steady state to another instantly, but will transition in a smooth and gradual manner, avoiding the impact of bandwidth changes on the transmission channel and ensuring the reliability of scheduling instructions and status data interaction. Simulation results show that in the simulated scenario of sudden high-concurrency tasks, the packet loss rate of the transmission channel is reduced by about 15% compared with the solution without this mechanism.

[0080] Example 5: Based on Example 1, this example further defines the priority evaluation method of the task priority dynamic evaluation module, providing an alternative to setting the weight coefficients of the priority evaluation model based solely on offline statistics or long-term feedback. The core difference lies in the introduction of a weight real-time perturbation mechanism based on the fire entropy growth rate, so as to realize online and instantaneous adjustment of the weights of the priority evaluation model, enabling it to respond instantly to the rapid deterioration of the fire situation.

[0081] In this embodiment, the task priority dynamic evaluation module calculates the basic priority score for each task. Previously, the following steps were also performed: Step S101: Receive real-time data from fire detection equipment and calculate the current rate of increase in fire entropy. The rate of increase of fire entropy is defined as the percentage increase within a preset time window. The mathematical expression for the rate of increase of the standard deviation of temperature data at all detection points within the fire area is: in: for The standard deviation of all temperature detection data points at the fire scene at any given time, in K; The preset time window is 30 seconds in this embodiment; the temperature standard deviation reflects the unevenness and disorder of the temperature distribution in the fire area, and the growth rate directly characterizes the speed at which the fire gets out of control and spreads.

[0082] Step S102: Calculate the fire entropy growth rate. Compared with the preset entropy increase start threshold Compare; if This indicates that the fire situation is deteriorating rapidly, triggering the immediate weight perturbation mechanism; otherwise, the original weight coefficients remain unchanged. In this embodiment, Take 1.5 (standard deviation unit / min).

[0083] Step S103: When the weighted instantaneous perturbation mechanism is triggered, temporarily adjust the weight coefficient of fire spread rate in the priority assessment model. Weighting coefficient for the number of trapped people The value of is determined, and other weighting coefficients are compressed accordingly to ensure that the sum of all weighting coefficients still satisfies . The normalization constraints are adjusted as follows: in: and These are the weighting coefficients for the rate of fire spread after disturbance and the weighting coefficients for the number of trapped people, respectively. and In this embodiment, the preset disturbance increment is used. Take 0.15, Set to 0.10; correspondingly, the temperature weighting coefficient Smoke concentration weighting coefficient And building structure damage weighting coefficient Temporarily compressed, adjusted new weighting coefficients , and Should meet .

[0084] The task priority dynamic evaluation module uses adjusted weight coefficients during the period when the weight real-time perturbation mechanism is activated. Calculate the base priority score for all newly generated tasks; when the rate of increase in fire entropy is detected in subsequent monitoring cycles. Persistently below the entropy increase threshold If the time exceeds the preset pullback confirmation period, the weighting coefficient will be restored to its initial value before the disturbance.

[0085] The technical advantage of this embodiment lies in mimicking the thought process of a fire commander who, upon sensing a fire spiraling out of control, instinctively shifts their focus to preventing its spread and rescuing lives. By introducing the physical quantity of fire entropy growth rate, the system possesses an intuitive and rapid response capability, enabling it to prioritize tasks strongly related to fire deterioration and personnel rescue in the first instance, thus gaining valuable time for rescue efforts. Simulations show that in simulated scenarios of sudden fire acceleration, this mechanism can reduce the scheduling delay of the first batch of targeted rescue tasks by an average of 22%.

[0086] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for scheduling and transmitting data across multiple fire-fighting machines using a dual-priority queue, characterized in that: Includes the following steps: Step 1: Construct a task priority dynamic evaluation module to calculate the basic priority score of each task based on real-time fire situation information. Real-time fire situation information includes temperature data, smoke concentration data, fire spread rate data, building structure damage assessment data, and trapped personnel location information. Step 2: Construct a dual-priority queue management module, establish a high-priority queue and a low-priority queue, and allocate the fire-fighting tasks to be executed to the high-priority queue or the low-priority queue according to the basic priority score; When the cumulative waiting time of any task in the high-priority queue exceeds the preset first threshold, the priority dynamic adjustment module is constructed to increase the priority score of the task. Step 3: Construct a transmission resource allocation module to dynamically adjust the proportion of data transmission bandwidth allocated to high-priority queues and low-priority queues based on the task quantity weight factor and priority correction coefficient of each queue. Step 4: Construct the task scheduling module. Extract tasks from each queue in descending order of priority score. Assign tasks to the idle fire execution unit with the highest matching score based on the matching score between the task and the fire execution unit. When the fault status indicator of a certain fire-fighting execution unit is activated, the execution task scheduling module removes the fire-fighting execution unit from the suitability calculation candidate set and places the tasks that have been assigned to the fire-fighting execution unit but have not yet been executed back at the head of their original queue.

2. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 1, characterized in that, In step 1, the task priority dynamic evaluation module calculates the basic priority score for each task based on the priority evaluation model. The mathematical expression of the priority evaluation model is: in: For the first The basic priority score for each task; , , , , These are weighting coefficients for temperature, smoke concentration, fire spread rate, building structural damage, and number of trapped personnel, respectively, and they satisfy the following conditions: Normalization constraints; , , , , These are the characteristic values ​​of temperature, smoke concentration, fire spread rate, building structural damage, and number of trapped personnel after standardized preprocessing.

3. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 2, characterized in that, The task priority dynamic evaluation module also uses the gradient descent algorithm to update the weight coefficients of temperature, smoke concentration, fire spread rate, building structure damage, and number of trapped people online based on the feedback data of the execution effect of completed scheduled tasks, and ensures that the updated weight coefficients meet the normalization constraint conditions.

4. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 1, characterized in that, In step 2, when the dual-priority queue management module obtains a new task, it determines whether the basic priority score of the new task is greater than or equal to the preset queue division threshold. If the basic priority score of the new task is greater than or equal to the queue division threshold, the new task is inserted into the tail of the high-priority queue; otherwise, the new task is inserted into the tail of the low-priority queue.

5. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 4, characterized in that, When the current length of the high-priority queue exceeds the preset first queue capacity limit, the task with the lowest basic priority score is selected from the high-priority queue and moved to the tail of the low-priority queue; when the current length of the low-priority queue exceeds the preset second queue capacity limit, newly arriving tasks are rejected, and if the length of the low-priority queue does not decrease to below the second queue capacity limit within the preset time window, the task with the lowest basic priority score in the low-priority queue is forcibly terminated.

6. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 5, characterized in that, When a degraded task transferred from a high-priority queue to a low-priority queue is waiting for scheduling in the low-priority queue, if the task priority dynamic evaluation module recalculates the basic priority score of the degraded task based on the updated fire situation information and it is greater than or equal to the queue division threshold, and the current length of the high-priority queue does not exceed the upper limit of the first queue capacity, then the degraded task will be extracted from the low-priority queue and migrated back to the high-priority queue.

7. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 1, characterized in that, In step 2, the priority dynamic adjustment module adjusts the priority score of the task that triggers the priority boosting operation according to the following mathematical expression: in: For the first The adjusted priority scores for each task; For the first The basic priority score for each task; For the first The cumulative waiting time for each task, in milliseconds (ms). The first threshold is expressed in milliseconds (ms). This is a preset waiting time weighting coefficient; The preset retry count weighting coefficient; For the first The cumulative number of times a task has failed to be scheduled.

8. The fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 7, characterized in that, When there are high-priority queue members whose adjusted priority scores exceed a preset second threshold. When a task is assigned, the priority dynamic adjustment module marks the task as an urgent task and inserts it into a separate urgent task buffer queue. The priority dynamic adjustment module notifies the transmission resource allocation module to allocate dedicated data transmission channel resources to the emergency task buffer queue.

9. A fire-fighting multi-machine dual-priority queue scheduling and transmission method according to claim 8, characterized in that, In step 3, the transmission resource allocation module allocates the currently available total data transmission bandwidth capacity according to the following mathematical expression. Assign to high-priority queues, low-priority queues, and urgent task buffer queues: in: , , These are the data transmission bandwidths allocated to the high-priority queue, low-priority queue, and urgent task buffer queue, respectively, in bits per second. , , These are the task quantity weighting factors for the high-priority queue, low-priority queue, and urgent task buffer queue, respectively. The task quantity weighting factor is the square root of the current task quantity in the corresponding queue. , , These are the priority correction coefficients for the high-priority queue, low-priority queue, and urgent task buffer queue, respectively, and they satisfy the following conditions: The constraints.

10. A fire-fighting multi-machine dual-priority queue scheduling and transmission system, used to implement the fire-fighting multi-machine dual-priority queue scheduling and transmission method according to any one of claims 1 to 9, characterized in that, include: The task priority dynamic evaluation module is used to calculate the basic priority score of each task based on real-time fire situation information; The dual-priority queue management module is used to establish high-priority queues and low-priority queues, and to allocate fire-fighting tasks to be executed to high-priority queues or low-priority queues according to basic priority scores; The priority dynamic adjustment module is used to monitor the waiting time of each task in the high priority queue, and to increase the priority score when the waiting time of any task exceeds a preset first threshold. The transmission resource allocation module is used to dynamically adjust the proportion of data transmission bandwidth allocated to high-priority queues and low-priority queues based on the current load status of the data transmission channel. The task scheduling module is used to extract tasks from each queue in descending order of priority score and assign them to idle fire-fighting execution units.

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