A downhole charging chamber resource dynamic scheduling management system and method

Abstract

The present application belongs to the technical field of production management informatization and resource operation and optimization, and relates to a kind of underground charging chamber resource dynamic scheduling management system and method, comprising: data acquisition unit gathers underground mobile equipment data and exports discrete feature matrix;Priority arbitration unit calculates energy consumption throughput deficit margin according to job plan and energy consumption constraint, and establishes time sequence emergency degree weight factor in combination with shift handover buffer time to output priority comparison result;Scheduling matrix generation unit brings total available power margin as reverse constraint condition into the path planning process of discrete feature matrix to establish flexible time slice allocation mechanism, according to priority comparison result, the time slice duty cycle of each charging channel and power allocation quota are constrained, and charging sequence and resource occupation scheduling table are output, the present application reconstructs flexible time slice allocation mechanism by optimizing scheduling management decision algorithm, and realizes fine collaborative configuration of production resources and equipment scheduling.

Description

Technical Field

[0001] This invention belongs to the field of production management informatization and resource operation and scheduling optimization technology, and particularly relates to a dynamic scheduling management system and method for underground charging chamber resources. Background Technology

[0002] In the current underground logistics and transportation system of mines, the continuous operation of mobile equipment forms the basis for ensuring production efficiency. Conventional resource planning and cyclical scheduling methods usually set static operating cycles and fixed replenishment nodes to maintain queue balance. This type of technology can maintain stable material turnover when the working conditions are simple and the tasks are uniform. From the perspective of the spatiotemporal evolution mechanism of material flow, the equipment turnover cycle and energy consumption rate are mutually causal. When multiple devices converge in the space-constrained underground charging chamber, discrete tasks are converted into concurrent power demands, causing the local resource occupancy status in the charging chamber to directly affect the overall production scheduling network.

[0003] With increasing transportation intensity, concurrent multi-source tasks lead to timing conflicts within limited parking spaces. Existing allocation mechanisms sever the connection between vehicle transportation characteristics and energy boundaries, queuing solely based on arrival order. In environments with fluctuating peak and off-peak conditions, this rigid allocation strategy results in the concentrated superposition of high-power loads, easily causing voltage drops in the local power grid of the charging chamber and even triggering overload protection trips. The system compromises by extending shift intervals or suspending vehicle tasks, creating timing deadlocks and incurring the hidden cost of reduced operational efficiency. A straightforward approach to resolving timing conflicts is to expand parking spaces or increase design power. However, linear expansion is limited by the rigid red line of the total power safety margin of the charging chamber, increasing costs and failing to mitigate random load distortions. Another approach is to monitor raw parameters and implement dynamic queueing; however, redundant data interaction causes bus channel congestion and signal delays, failing to address continuous demand requirements. Existing control methods also have shortcomings in dealing with the inherent contradiction between the rigid power boundary and heat dissipation constraints within the confined space of the mining chamber. For example, Chinese invention patent application CN118977616A discloses a charging management method for unmanned mining trucks. It determines whether to start charging by calculating the difference between the equivalent driving range of a single vehicle and the equivalent flat road distance to the charging pile. Although this scheme improves the accuracy of single vehicle energy consumption prediction, the underlying logic implicitly relies on individual independent decisions. In the case of dense flow of multiple vehicles underground, due to the lack of global timing arbitration for concurrent requests from multiple vehicles and rigid constraints on the available power on the grid side, it is very easy to cause parking space timing conflicts, local grid overload tripping, and excessive heat accumulation in the charging chamber during peak periods. Moreover, it does not associate with shift handover buffer time and is difficult to adapt to the constraints of variable production cycles.

[0004] Therefore, the technical problem to be solved by this invention is to reconstruct the elastic time slice allocation control mechanism by using scheduling management optimization algorithms based on the dynamic linkage between the transportation characteristics of mobile equipment and the available power constraints of charging chambers, so as to smooth out instantaneous load distortion and eliminate resource occupancy queue deadlock in the dimension of optimal allocation of production resources, thereby constructing a highly adaptable dynamic scheduling model and comprehensively improving the efficiency of equipment scheduling and collaborative management. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A dynamic scheduling and management system for underground charging chamber resources, comprising: The data acquisition unit is used to collect historical energy consumption characteristics data, remaining energy value data, and operation cycle data of the downhole mobile equipment, and output the discrete characteristic matrix of the downhole mobile equipment. The input of the priority arbitration unit is connected to the output of the data acquisition unit. It is used to calculate the expected energy consumption of the downhole mobile equipment in the space transportation cycle based on the acquired work plan data and energy consumption constraint data. It calculates the energy consumption throughput deficit margin based on the expected energy consumption and the remaining energy value data, and performs proportional scaling calculation based on the energy consumption throughput deficit margin and the shift handover buffer time of the scheduling window to establish the time priority weight factor, and outputs the priority comparison results of concurrent tasks. The input of the scheduling matrix generation unit is connected to the output of the priority arbitration unit and the external total available power parameter analysis interface, respectively. It is used to utilize the heuristic task mapping algorithm and the dynamic time slice round-robin scheduling algorithm to input the total available power margin input from the external total available power parameter analysis interface as a reverse constraint condition into the path planning process of the discrete feature matrix, establish an elastic time slice allocation mechanism based on load overload mitigation, and constrain the time slice duty cycle and power allocation quota of each charging channel according to the priority comparison results, and output the charging sequence and resource occupation schedule table.

[0006] Preferably, the priority arbitration unit calculates the energy consumption throughput deficit margin in the following way: multiply the expected running time of the underground mobile equipment to complete the transportation cycle, the average driving speed under the roadway slope constraint, and the energy consumption rate per unit time to obtain the energy consumption prediction value; subtract the remaining energy value from the energy consumption prediction value to obtain the energy throughput deficit value; and then divide the energy throughput deficit value by the rated power allocation of the corresponding charging channel to obtain the energy consumption throughput deficit margin.

[0007] Preferably, when the priority arbitration unit establishes the time urgency weight factor based on the energy consumption throughput deficit margin, the logic rule is as follows: when the energy consumption throughput deficit margin is less than or equal to 0, the priority is based on the static baseline of the downhole mobile equipment; when the energy consumption throughput deficit margin is greater than 0, the time urgency weight factor is determined to be the quotient of the energy consumption throughput deficit margin, the expected running time of the transportation cycle, and one-sixtieth of the shift handover buffer time.

[0008] Preferably, when the scheduling matrix generation unit establishes an elastic time slice allocation mechanism based on load overload mitigation, the following timing steps are adopted: Step S401: Obtain real-time voltage and current variation data of multiple nodes in the power grid collected by the external total available power parameter analysis interface, and calculate the total available power margin; Step S402: Determine the power grid load status based on the total available power margin. When the total available power margin is lower than the benchmark safe power threshold, confirm that the current operating condition is in a high-concurrency operating condition and generate a load overload mitigation command; Step S403: In response to the load overload mitigation command, extract the weight sum of all downhole mobile devices with energy consumption throughput deficit margin greater than 0, establish a feedback chain between the previous energy consumption deficit status and the subsequent time slice boundary based on the weight sum, and constrain the time slice duty cycle and power allocation quota of each charging channel through the control bus to adjust the output power of the underlying converter module to be lower than the design safe upper limit, thereby blocking local power grid overload.

[0009] Preferably, when the scheduling matrix generation unit responds to the overload mitigation command and adjusts the output parameters of the underlying converter module, it includes the following sub-step logic that refines step S403: Step S4031, acquire the real-time chamber temperature and heat accumulation rate collected by the temperature acquisition sensor; Step S4032, use the total available power margin as a reverse constraint condition into the path planning process of the downhole mobile equipment to calculate the predicted value of the chamber heat accumulation generated by continuous power input under high concurrency conditions; Step S4033, when the predicted value of the chamber heat accumulation exceeds the fixed temperature safety threshold, reduce the duty cycle of the timing time slice of each charging channel, convert the alternating timing of each charging channel into a timing control command and output it to the underlying converter module to limit the output duty cycle of the underlying converter module to block temperature overload.

[0010] Preferably, the energy consumption historical characteristic data acquired by the data acquisition unit includes the historical power consumption rate, historical light-load travel speed, and historical heavy-load travel speed of the downhole mobile equipment under different roadway slopes; the discrete feature matrix also incorporates current operating parameters for correction, including the current load status, the current roadway inclination angle, and the current battery cell operating temperature.

[0011] Preferably, when the scheduling matrix generation unit calculates the heuristic task mapping algorithm, it takes maximizing the sum of the time urgency weight factors of all downhole mobile devices with energy consumption throughput deficit margin greater than 0 as the objective function. Under the inverse constraint of total available power margin, it maps concurrent charging requests to charging channels with matching power allocation quotas to generate charging sequences that meet the charging safety margin.

[0012] Preferably, the priority arbitration unit is also used to identify the conversion heat loss and transmission delay loss of the underlying converter module during power conversion, and to perform loss compensation using the following timing steps: Step S801, monitor the difference between the input power and output power of the underlying converter module in real time during operation, calculate the instantaneous conversion heat loss, and perform linear accumulation calculation based on the transient control delay of the communication control link and the instantaneous conversion heat loss to determine the comprehensive loss compensation factor; Step S802, use the comprehensive loss compensation factor to correct the energy consumption throughput deficit margin, input the corrected energy consumption throughput deficit margin into the calculation loop of the timing urgency weight factor, and adjust the power allocation quota to offset physical deviations.

[0013] Preferably, the scheduling matrix generation unit interacts bidirectionally with the multi-channel underlying converter module via an industrial Ethernet bus, converting the charging sequence and resource occupancy schedule into periodically refreshed pulse width modulation commands, which are then sent to the corresponding underlying converter module to regulate the real-time output power of each charging channel. The scheduling matrix generation unit is also connected to a safety defense module, which monitors the flow of scheduling data in the resource occupancy schedule. When the total available power margin falls below the baseline safe power threshold, or the predicted value of the chamber heat accumulation exceeds the fixed temperature safety threshold, an abnormal state trigger signal is output to the external alarm device.

[0014] A method for dynamic scheduling and management of underground charging chamber resources, used to operate an underground charging chamber resource dynamic scheduling and management system, comprising: Step S1: Collect historical energy consumption characteristics, remaining energy value data, and operation cycle data of the downhole mobile equipment through the data acquisition unit, and output the discrete feature matrix of the downhole mobile equipment. Step S2: Based on the acquired work plan data and energy consumption constraint data, the priority arbitration unit calculates the expected energy consumption of the downhole mobile equipment in the space transportation cycle, calculates the energy consumption throughput deficit margin based on the expected energy consumption and the remaining energy value data, and performs a scaling calculation based on the energy consumption throughput deficit margin and the shift handover buffer time of the scheduling window to establish the time priority weight factor, and outputs the priority comparison results of concurrent tasks. Step S3: Through the scheduling matrix generation unit, the total available power margin input from the external total available power parameter analysis interface is used as a reverse constraint condition to enter the path planning process of the discrete feature matrix. An elastic time slice allocation mechanism based on load overload mitigation is established. Based on the priority comparison results, the duty cycle of the time slice and the power allocation quota of each charging channel are constrained, and the charging sequence and resource occupation schedule are output.

[0015] Compared with existing technologies, the present invention, a dynamic scheduling and management system and method for underground charging chamber resources, has the following advantages: 1. In the dynamic scheduling and management of underground charging chamber resources, the remaining energy value and expected running time of the vehicles to be scheduled are collected collaboratively after hash desensitization, and a multi-dimensional feature vector is constructed. Combined with the external available power margin, the energy consumption throughput deficit margin is dynamically calculated, and the time sequence urgency weight factor is established. The queue state machine performs nonlinear reorganization on the basic time slice benchmark based on the weight sum of the vehicles with positive deficit in concurrent trips. This not only dynamically generates load distortion offsetting elastic time slices, but also deeply integrates the characteristics of the operation cycle and safety boundary constraints. The system gathers the dynamic alternating time sequence of each interface to avoid the problems of queue deadlock during peak periods and low-urgency equipment blindly occupying limited parking spaces.

[0016] 2. The system extracts the total available power margin collected by the grid node analyzer, dynamically establishes a feedback chain between the pre-energy consumption deficit state and the post-time slice boundary, and combines the weighted sum of all vehicles with positive deficits to constrain the timing duty cycle and power allocation of each branch. The control bus inputs resource occupancy scheduling timing control matrix data in real time, which not only realizes the dynamic power allocation of the limited concurrent resource pool, but also dynamically adjusts the output characteristics of the underlying physical converter, so that the local load has the active control and regulation capability of automatic peak shaving and valley filling during peak periods, smooths out instantaneous power distortion, and avoids the safety risks of local grid voltage drop or overload protection tripping caused by multiple vehicles charging at the same time.

[0017] 3. The system monitors the total available power margin in a space-constrained environment in real time and integrates it into the equipment path planning process as a reverse constraint. It establishes an elastic time-slice allocation mechanism based on load distortion offsetting. Through active adjustment and control of the duty cycle of each branch, it mitigates the accumulation of heat in the chamber caused by continuous instantaneous high power input under high-concurrency conditions. The scheduling center converts the alternating timing of each interface into a unique timing control command through clear information interaction feedback. This not only ensures that on-site management personnel have real-time control over the status of each mobile device, but also ensures the continuity of system operation while avoiding downtime accidents caused by excessive heat accumulation in the chamber, thus enhancing the standardization of human-machine collaboration processes. Attached Figure Description

[0018] Figure 1This is a module connection structure diagram of the dynamic scheduling and management system for underground charging chamber resources of the present invention; Figure 2 This is a distribution diagram of control elements in the dynamic scheduling and management system for underground charging chamber resources of this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] A dynamic scheduling and management system for underground charging chamber resources, comprising: The data acquisition unit is used to collect historical energy consumption characteristics data, remaining energy value data, and operation cycle data of the downhole mobile equipment, and output the discrete characteristic matrix of the downhole mobile equipment. The input of the priority arbitration unit is connected to the output of the data acquisition unit. It is used to calculate the expected energy consumption of the downhole mobile equipment in the space transportation cycle based on the acquired work plan data and energy consumption constraint data. It calculates the energy consumption throughput deficit margin based on the expected energy consumption and the remaining energy value data, and performs proportional scaling calculation based on the energy consumption throughput deficit margin and the shift handover buffer time of the scheduling window to establish the time priority weight factor, and outputs the priority comparison results of concurrent tasks. The input of the scheduling matrix generation unit is connected to the output of the priority arbitration unit and the external total available power parameter analysis interface, respectively. It is used to utilize the heuristic task mapping algorithm and the dynamic time slice round-robin scheduling algorithm to input the total available power margin input from the external total available power parameter analysis interface as a reverse constraint condition into the path planning process of the discrete feature matrix, establish an elastic time slice allocation mechanism based on load overload mitigation, and constrain the time slice duty cycle and power allocation quota of each charging channel according to the priority comparison results, and output the charging sequence and resource occupation schedule table.

[0021] Preferably, the priority arbitration unit calculates the energy consumption throughput deficit margin in the following way: multiply the expected running time of the underground mobile equipment to complete the transportation cycle, the average driving speed under the roadway slope constraint, and the energy consumption rate per unit time to obtain the energy consumption prediction value; subtract the remaining energy value from the energy consumption prediction value to obtain the energy throughput deficit value; and then divide the energy throughput deficit value by the rated power allocation of the corresponding charging channel to obtain the energy consumption throughput deficit margin.

[0022] Preferably, when the priority arbitration unit establishes the time urgency weight factor based on the energy consumption throughput deficit margin, the logic rule is as follows: when the energy consumption throughput deficit margin is less than or equal to 0, the priority is based on the static baseline of the downhole mobile equipment; when the energy consumption throughput deficit margin is greater than 0, the time urgency weight factor is determined to be the quotient of the energy consumption throughput deficit margin, the expected running time of the transportation cycle, and one-sixtieth of the shift handover buffer time.

[0023] Preferably, when the scheduling matrix generation unit establishes an elastic time slice allocation mechanism based on load overload mitigation, the following timing steps are adopted: Step S401: Obtain real-time voltage and current variation data of multiple nodes in the power grid collected by the external total available power parameter analysis interface, and calculate the total available power margin; Step S402: Determine the power grid load status based on the total available power margin. When the total available power margin is lower than the benchmark safe power threshold, confirm that the current operating condition is in a high-concurrency operating condition and generate a load overload mitigation command; Step S403: In response to the load overload mitigation command, extract the weight sum of all downhole mobile devices with energy consumption throughput deficit margin greater than 0, establish a feedback chain between the previous energy consumption deficit status and the subsequent time slice boundary based on the weight sum, and constrain the time slice duty cycle and power allocation quota of each charging channel through the control bus to adjust the output power of the underlying converter module to be lower than the design safe upper limit, thereby blocking local power grid overload.

[0024] Preferably, when the scheduling matrix generation unit responds to the overload mitigation command and adjusts the output parameters of the underlying converter module, it includes the following sub-step logic that refines step S403: Step S4031, acquire the real-time chamber temperature and heat accumulation rate collected by the temperature acquisition sensor; Step S4032, use the total available power margin as a reverse constraint condition into the path planning process of the downhole mobile equipment to calculate the predicted value of the chamber heat accumulation generated by continuous power input under high concurrency conditions; Step S4033, when the predicted value of the chamber heat accumulation exceeds the fixed temperature safety threshold, reduce the duty cycle of the timing time slice of each charging channel, convert the alternating timing of each charging channel into a timing control command and output it to the underlying converter module to limit the output duty cycle of the underlying converter module to block temperature overload.

[0025] Preferably, the energy consumption historical characteristic data acquired by the data acquisition unit includes the historical power consumption rate, historical light-load travel speed, and historical heavy-load travel speed of the downhole mobile equipment under different roadway slopes; the discrete feature matrix also incorporates current operating parameters for correction, including the current load status, the current roadway inclination angle, and the current battery cell operating temperature.

[0026] Preferably, when the scheduling matrix generation unit calculates the heuristic task mapping algorithm, it takes maximizing the sum of the time urgency weight factors of all downhole mobile devices with energy consumption throughput deficit margin greater than 0 as the objective function. Under the inverse constraint of total available power margin, it maps concurrent charging requests to charging channels with matching power allocation quotas to generate charging sequences that meet the charging safety margin.

[0027] Preferably, the priority arbitration unit is also used to identify the conversion heat loss and transmission delay loss of the underlying converter module during power conversion, and to perform loss compensation using the following timing steps: Step S801, monitor the difference between the input power and output power of the underlying converter module in real time during operation, calculate the instantaneous conversion heat loss, and perform linear accumulation calculation based on the transient control delay of the communication control link and the instantaneous conversion heat loss to determine the comprehensive loss compensation factor; Step S802, use the comprehensive loss compensation factor to correct the energy consumption throughput deficit margin, input the corrected energy consumption throughput deficit margin into the calculation loop of the timing urgency weight factor, and adjust the power allocation quota to offset physical deviations.

[0028] Preferably, the scheduling matrix generation unit interacts bidirectionally with the multi-channel underlying converter module via an industrial Ethernet bus, converting the charging sequence and resource occupancy schedule into periodically refreshed pulse width modulation commands, which are then sent to the corresponding underlying converter module to regulate the real-time output power of each charging channel. The scheduling matrix generation unit is also connected to a safety defense module, which monitors the flow of scheduling data in the resource occupancy schedule. When the total available power margin falls below the baseline safe power threshold, or the predicted value of the chamber heat accumulation exceeds the fixed temperature safety threshold, an abnormal state trigger signal is output to the external alarm device.

[0029] A method for dynamic scheduling and management of underground charging chamber resources, used to operate an underground charging chamber resource dynamic scheduling and management system, comprising: Step S1: Collect historical energy consumption characteristics, remaining energy value data, and operation cycle data of the downhole mobile equipment through the data acquisition unit, and output the discrete feature matrix of the downhole mobile equipment. Step S2: Based on the acquired work plan data and energy consumption constraint data, the priority arbitration unit calculates the expected energy consumption of the downhole mobile equipment in the space transportation cycle, calculates the energy consumption throughput deficit margin based on the expected energy consumption and the remaining energy value data, and performs a scaling calculation based on the energy consumption throughput deficit margin and the shift handover buffer time of the scheduling window to establish the time priority weight factor, and outputs the priority comparison results of concurrent tasks. Step S3: Through the scheduling matrix generation unit, the total available power margin input from the external total available power parameter analysis interface is used as a reverse constraint condition to enter the path planning process of the discrete feature matrix. An elastic time slice allocation mechanism based on load overload mitigation is established. Based on the priority comparison results, the duty cycle of the time slice and the power allocation quota of each charging channel are constrained, and the charging sequence and resource occupation schedule are output.

[0030] Example 1: A current dynamic scheduling and management system for underground charging chamber resources is deployed in an underground material transportation environment involving multiple trackless rubber-tired mining vehicles. It operates under conditions where multi-source logistics transportation tasks are concentrated and concurrent at the transfer and intersection points, and the local power grid capacity of the underground charging chamber has a fixed safety boundary. In this situation, if static queuing control based on arrival order or an absolute threshold of remaining battery power is implemented, it is prone to equipment shutdown accidents during peak transportation periods, such as uneven occupancy of charging spaces, queue timing deadlock, and protective tripping of the local power grid. The data acquisition unit collects historical energy consumption characteristic data, remaining energy value data, and operation cycle data of the underground mobile equipment, outputting a discrete feature matrix of the underground mobile equipment. This matrix converts the physical state of each mobile device into the current hash-desensitized remaining energy value. The estimated remaining runtime of the current job cycle. and historical average power consumption per ton-kilometer The discrete eigenvectors formed The discrete feature matrix is ​​then input into the priority arbitration unit, which calculates the expected energy consumption of the vehicle to complete this round of space transportation without replenishment, and extracts the energy consumption throughput deficit margin that characterizes its survival limit. Its computational path adopts a linear expression format, specifically represented as follows: ,in, To provide a margin for energy consumption throughput deficit, Index for vehicles; This represents the estimated remaining runtime of the current job cycle. For vehicles The average driving speed within the preset range under the current roadway slope constraint. This represents the historical average power consumption coefficient per ton-kilometer. This represents the remaining energy value after hash desensitization. This refers to the rated charging power of the corresponding charging port for this vehicle.

[0031] The priority arbitration unit integrates conditional control flow decision logic, which determines the energy consumption throughput deficit margin when calculated. This indicates that the vehicle currently has sufficient battery capacity. The system, based on static baseline priority, adjusts the energy consumption throughput when there is a deficit. This indicates that the vehicle is at risk of power outage and shutdown en route, and the priority arbitration unit retrieves the shift handover buffer time from the dispatch window. Divide this value by 60 and calculate the timing urgency weighting factor by scaling. Its computation path is represented as ,in, As a time-order urgency weighting factor, To provide a margin for energy consumption throughput deficit, This represents the estimated remaining runtime of the current job cycle. The shift handover buffer time for the scheduling window is defined by a value of 60, which is the scaling factor for converting the time unit from minutes to hours. This establishes a dynamic weight competition based on actual production cycle breakpoint constraints, outputting concurrent task priority comparison results that eliminate cross-dimensional feature discrete errors. The input of the scheduling matrix generation unit receives the priority comparison results and simultaneously obtains the total available power margin of the charging chamber, extracted in real-time by the power grid node analyzer, through an external total available power parameter analysis interface. The path planning process, which uses this as a reverse control boundary, is then applied to the discrete feature matrix. An elastic time slice allocation mechanism based on overload mitigation is established. The total weight is obtained by summing the temporal urgency weight factors of all vehicles with positive deficits in the current concurrent journey. Calculate for the current vehicle Load distortion hedging elastic time plate Its processing logic follows a non-linear recombination rule and is expressed as: ,in, For load distortion hedging elastic time plate, The system's preset base time slice reference has a fixed value of 30 minutes; As a time-order urgency weighting factor, This represents the sum of the time-order urgency weighting factors for all vehicles with positive deficits in the current concurrent trip. The total available power margin for the charging chamber. The maximum safe rated total power of the chamber is specified by the subscript. For indexing vehicles with positive power deficits, when the total available power margin extracted by the power grid node analyzer is used as the reverse control boundary and incorporated into the path planning process of the discrete feature matrix, this invention discretizes the complex three-dimensional topological network of underground tunnels into a topological matrix composed of multiple grid edges and grid vertices. Each grid vertex containing a charging chamber is based on a dynamic power carrying capacity upper limit attribute. When the vehicle's discrete feature matrix is ​​input into the path planning process, the central algorithm, in the process of exhaustively calculating the topological path from the current operating node to each candidate charging chamber, converts the current real-time total available power margin of the target charging chamber into the admittance of that grid vertex. If the total available power margin of a charging chamber is lower than a preset safety red line, the path planning process automatically applies a large time penalty term to the topological path pointing to that vertex. This automatically suppresses or reconstructs the vehicle's trajectory during the path planning evolution of the discrete feature matrix, dynamically transforming the scalar power belonging to the power grid side into physical constraints on path reachability in the spatiotemporal network.

[0032] When the energy consumption data of any vehicle changes or the power input of the external total available power parameter analysis interface is disturbed, the scheduling matrix generation unit automatically triggers the reconstruction of the next round of flexible time slices, gathers the dynamic alternation timing of each charging interface, and outputs the charging sequence and resource occupation scheduling table as the basis for equipment scheduling decisions. This table is converted into periodically refreshed pulse width modulation instructions and directly injected into the control bus of the underlying converter module via the industrial Ethernet bus. This constrains the duty cycle of the timing time slice and the power allocation quota of each charging channel, so that the power throughput of the charging channel offsets the instantaneous load distortion of the underground power pool. While ensuring that all mobile devices participating in the rotation can smoothly and safely complete the space transportation production cycle, it automatically controls the local power grid load to shave peaks and fill valleys, smoothly eliminates resource occupation queue deadlock, and prevents tripping and shutdown accidents caused by power grid overload.

[0033] Example 2: When a dynamic scheduling and management system for underground charging chamber resources completes scheduling, it provides data support through a physical experimental platform simulating underground vehicle flow characteristics. The hardware of the physical experimental platform includes 10 independently controlled charging channels. The current measurement range of its data acquisition component is set to 0A to 150A, with a measurement accuracy of 0.1A and a data sampling rate of 20Hz. In order to determine the stability of the system against electromagnetic and load interference, random load variation noise with an amplitude of 15% and power frequency harmonic interference with a frequency of 50Hz are superimposed in the signal transmission link to construct a non-ideal production resource allocation test condition. During the test, a multi-dimensional comparison system is established, dividing the high-frequency flow scenario into three different core problem gradients: low intensity gradient (3 vehicles concurrently requesting), medium intensity gradient (6 vehicles concurrently requesting), and high intensity gradient (12 vehicles concurrently requesting). In order to determine the protection boundary, a basic time slice reference needs to be established. Due to space constraints, parameter settings are used to balance the thermal stress loss of electronic components caused by frequent switching of the converter module and the dynamic tracking timeliness of power grid fluctuations. When the state transition frequency in the discrete characteristic matrix of concurrent requesting vehicles increases, to avoid deadlock in the converter channel duty cycle adjustment, a base time slice reference is used. The numerical setting tends to the lower limit of its value window, thus deriving the base time slice reference under standard conditions. The set value is 30 minutes. At the same time, an out-of-range control group is set up, which is set as the upper limit deviation point of over-frequency regulation at 5 minutes and the lower limit deviation point of hysteresis response at 120 minutes, so that the numerical endpoints and the protection boundary form a closed causal chain.

[0034] When 12 trackless rubber-tired vehicles make concurrent requests during a high-intensity gradient operation, the data acquisition unit performs hash desensitization and physical parameter restoration on each device in the space transportation cycle. The remaining energy value of the first transport vehicle after hash desensitization is then recorded. The estimated remaining runtime for the current job cycle is 12.4 kWh. The historical average power consumption coefficient per ton-kilometer is 2.5 hours. The average driving speed within the preset range is 0.45 kWh / t·km. The speed is 20km / h, corresponding to the rated charging power of the charging interface. For 50kW, the priority arbitration unit is based on this discrete feature vector. Calculate the expected energy consumption required for the vehicle to complete the transport, and determine its energy throughput margin. The value is 0.202h. In the calculation of the expected energy consumption and energy throughput deficit required to complete this transportation, in order to eliminate the discrete error of the weight dimension in the ton-kilometer unit and achieve absolute dimensional consistency, this invention, when multiplying the expected remaining running time, the average driving speed of the preset interval, and the historical average power consumption coefficient per ton-kilometer, will extract the actual total tonnage value corresponding to the current load state from the discrete feature matrix of the vehicle in real time. The product of the aforementioned three parameters will be further multiplied with the actual total tonnage value, thereby completely offsetting and canceling the tonnage component in the average power consumption coefficient per ton-kilometer within the calculation loop. This ensures that the final calculated energy consumption prediction value and the energy throughput deficit value obtained by subtracting the remaining energy value from the energy consumption prediction value are completely converged to the standard energy unit kilowatt-hour in terms of physical dimensions, ensuring the mathematical logic closed loop between the physical parameters when calculating the energy consumption throughput deficit margin in the subsequent calculation. Since this value is greater than 0, the timing urgency calculation is triggered, and the shift handover buffer time of the scheduling window is introduced. The time urgency weighting factor for this vehicle is calculated by dividing the time by a conversion factor of 60, which is 15 minutes. The value is 0.0735, and 60 is the scaling factor for converting the time unit from minutes to hours. At this point, the scheduling matrix generation unit summarizes the urgency of vehicles with shortages in each channel and calculates the sum of the time-series urgency weight factors for all vehicles with shortages in the forward direction. It is 0.845. This serves as an index for vehicles with positive power deficits; simultaneously, the total available power margin of the charging chamber is obtained via an external total available power parameter analysis interface, which is extracted in real time from monitoring by the grid node analyzer. The rated power is 245.6kW, which is in line with the design maximum safe total rated power. Given a power of 400kW, the load distortion offset elastic time slot for this vehicle is calculated. The above dynamic calculation data, which is continuously transmitted from the sequence of independent variables to the causal closed-loop terminal, is 1.60 minutes long, giving the time-series scheduling a deterministic digital trajectory.

[0035] In the causal comparison phase of summarizing multiple test samples, the quantitative comparison data of the full text statement confirmed the practical value of the complete control architecture in mitigating load distortion. In the test group using the method claimed in this invention, facing the concurrent impact of 12 vehicles, the system reconstructed the switching register state of the converter module in the control channel through elastic time slices. The number of charging sequence deadlocks was 0, the total available power throughput waveform curve coincided with the rated safety boundary, the extreme operating overload rate of the chamber transformer was controlled within 3.2%, and all test vehicles received power replenishment before their remaining power was exhausted. A mid-operational outage occurred. In the control group of existing technologies that removed the reverse constraint of dynamic available power and only adopted the traditional first-in-first-out queuing method, the number of charging sequence deadlocks increased to 4 because the time window could not be dynamically reduced according to the external grid capacity. Furthermore, due to the inability to offset instantaneous peak loads, the total converter power was distorted during peak periods, generating an overload spike of up to 438.2kW, exceeding the system's 400kW safe carrying limit. This directly triggered the circuit breaker's instantaneous overcurrent protection to trip twice, resulting in a power outage of 28.5 minutes. In the control group with missing features, i.e., the shift handover buffer time was removed... In the dynamic correction factor test, the system failed to detect the breakpoint changes in the production management cycle, leading to a mismatch between the time slices allocated before shift handover and the actual remaining lifespan of the vehicles. This resulted in two vehicles being forced to stop due to undervoltage en route to the tunnel. Simultaneously, in the control group where the values ​​exceeded the range, when the baseline time slice was used... When the time slice was reduced to 5 minutes, which deviated from the numerical range, the communication bus throughput load of the trigger control system increased sharply due to the excessively fine time slice division. Tests showed that the utilization rate of the controller area network bus (CAN bus) reached the saturation zone of 92.4%. The high-frequency register erasure and channel switching caused an additional 23.6% increase in the inverter-side turn-on loss, resulting in device overheating and degradation effects. When the time is increased to 120 minutes, which deviates from the numerical range, the scheduling update step size lags behind the evolution speed of battery relaxation characteristics. The control system fails to update the energy consumption deficit, resulting in high-urgency vehicles being in a waiting state for a long time. During the scheduling window of up to 2 hours, three vehicles completely ran out of power because they could not seize resources. The control response data, which includes the performance flat saturation region and the inflection point of reverse degradation, confirms that the 30-minute working window defined in this invention is a balance point that takes into account bus load, switching losses and equipment power survival limit, thereby providing data support for the stable operation of the method and system in complex industrial environments.

[0036] Example 3: When a dynamic scheduling and management system for underground charging chamber resources operates under conditions of signal delay jitter in the underground roadway network and mechanical wear and aging of the vehicle power subsystem, the data acquisition unit input end experiences transient data loss and historical energy consumption characteristic drift. If the system does not have a compensation operator to correct energy consumption calculation deviations, the discrete characteristic matrix at the input end will become distorted, leading to logical disorder in the scheduling sequence and resource mismatch accidents such as vacant charging spaces or local power grid overload and power outages. The data acquisition unit uses a transient data holding loop and a time decay sliding window to correct characteristic distortion. When network interference is caused by roadway radio electromagnetic shielding... When the network packet loss rate reaches 25%, the system retrieves the discrete feature vector temporarily stored in the previous cycle to recover the lost data. During this process, the transient data holding loop includes a first-level static random access memory chip deployed in the data acquisition terminal and a cascaded signal hold trigger. When the packet loss rate of the wireless network communication channel is detected to reach the safety boundary of 25%, the signal hold trigger forcibly locks the current output bus, maintains the output of the discrete feature vector of the last frame that is effectively stored, and performs latching. At the same time, the time decay sliding window adopts a discrete-time first-order low-pass recursive filtering algorithm structure, and the window dynamically contains 10 consecutive historical energy consumption sampling points.

[0037] In each data step cycle, newly input sampled data is weighted with the highest freshness weight, while the weight of historical sampled data decreases exponentially over time. By calculating the weighted average within a sliding window, transient spikes caused by sudden network jitter are smoothly filtered out, achieving accurate online correction of discrete feature matrix data distortion under non-ideal operating conditions. Furthermore, to address the upward deviation of energy consumption characteristics caused by long-term aging of the drive motor, the data acquisition unit introduces an aging compensation coefficient to correct the energy consumption calculation deviation. The historical average power consumption coefficient per ton-kilometer Convert to calibration power consumption coefficient calibrate power consumption coefficient The calculation formula is expressed as follows: ,in, To calibrate the power consumption coefficient, This represents the historical average power consumption coefficient per ton-kilometer. This is the aging compensation coefficient. Vehicle index; aging compensation coefficient The value is determined based on the vehicle's cumulative operating hours; the priority arbitration unit uses a calibrated power consumption coefficient. Replace the historical average electricity consumption coefficient per ton-kilometer in the original energy consumption calculation path. And calculate the energy throughput deficit margin. This allows for a margin of energy consumption throughput deficit. By tracking the actual degradation state of physical entities and eliminating the risk of arbitration polarity reversal caused by parameter distortion, the scheduling matrix generation unit adjusts the load distortion hedging elastic time slice based on the updated priority comparison results. Under network disturbances and vehicle performance degradation, the number of charging sequence deadlocks is 0, the total available power throughput curve converges within the safe carrying capacity, and the local power grid load is controlled to shave peaks and fill valleys when the performance of the vehicle power subsystem declines, thus preventing system tripping and shutdown accidents induced by the superposition of communication failures and physical aging.

[0038] Example 4: When the system faces the initial operating conditions of deploying a completely new roadway topology or introducing a new type of mobile equipment, the data acquisition unit activates a standardized field calibration loop in the pre-processing stage. The test vehicle is driven fully loaded within a 5km long calibration lane containing three gradients. The difference in power consumption at both ends, after hash-desensitization, is collected and divided by the transport turnover to calculate the historical average power consumption coefficient per ton-kilometer. The initial baseline value is written into the central database, and a static register storage area is opened inside the priority arbitration unit to store the feature matrix of each category, thereby establishing an initial reference boundary in the scheduling center that supports adaptive weight correction.

[0039] When mobile equipment experiences wear and tear and characteristic divergence during long-term spatial transportation cycles, the system calculates the actual average energy consumption value using a 30-day sliding time window. When this value exceeds 1.1 times the initial baseline value for 10 consecutive sampling periods, the aging calibration algorithm is triggered. The aging compensation coefficient is updated by calculating the ratio of the actual average energy consumption value to the initial baseline value. The priority arbitration unit writes the obtained updated value into the adaptive weight correction control register in real time to correct the calibration power consumption coefficient. The updated parameters are re-entry priority comparison and the duty cycle of each channel timing time slice is constrained, so that the power allocation of each charging interface is flexibly converged within the safe load envelope locked by the power grid node analyzer as the life decay law of the converter module and physical entity changes.

[0040] Example 5: In the scenario of underground trackless rubber-tired vehicle transportation, when the system faces concurrent multi-source logistics transportation tasks and a rigid safety boundary in the limited charging resource pool, the control parameters in the discrete feature matrix of the system are determined by deploying a pre-calibration method. A benchmark response model is constructed for subsequent system operation scheduling comparison. The data acquisition unit collects the basic physical property data of the underground mobile equipment and outputs the discrete feature matrix of the underground mobile equipment. To establish the deterministic boundary of the control parameters, the system constructs a test sample group containing vehicles under three different operating conditions. The first vehicle corresponds to the lower limit of the numerical range, and its desensitized remaining energy value data... Set at 12.35 kWh, operating cycle data Set to 1.50h, average driving speed within the section. Set at 15.20 km / h, historical average power consumption coefficient Set at 0.85 kWh / km, rated charging power The second vehicle is set at 60.00kW, corresponding to the normal median value, and its remaining energy value data. Set at 45.60 kWh, operating cycle data Set to 3.00h, average driving speed within the section. Set at 18.50 km / h, historical average power consumption coefficient Set at 0.92 kWh / km, rated charging power The third vehicle is set to 90.00kW, which is the upper limit of the corresponding value range, and its remaining energy value data. Set at 98.45 kWh, operating cycle data Set to 5.00h, average driving speed within the section. Set at 22.10 km / h, historical average power consumption coefficient Set at 1.05 kWh / km, rated charging power The power consumption is set at 120.00 kW. Based on the acquired work plan data and energy consumption constraint data, the priority arbitration unit calculates the expected energy consumption of the downhole mobile equipment during the space transportation cycle. The dispatch center calculates the energy consumption throughput margin based on the expected energy consumption and remaining energy value data. Its computation path is represented as ,in, For vehicles Energy consumption throughput deficit margin For vehicles Work cycle data, For vehicles The average driving speed within the preset range under the current roadway slope constraint. For vehicles The historical average power consumption coefficient, For vehicles The remaining energy value data, For vehicles The rated charging power of the corresponding charging interface, To index vehicles and unify and eliminate the differences in dimensional representation of the energy consumption throughput deficit margin at different control levels across different implementations, this invention provides a dual isomorphic mapping mechanism of energy and time scales within the scheduling center. When the control unit focuses on offsetting the physical power pool throughput and actively shaving and filling peaks in the underlying converter module, the energy consumption throughput deficit margin exists directly in the form of an energy throughput deficit value, with its standard physical dimension being kilowatt-hours. However, when the scheduling center needs to seamlessly embed the energy deficit state into the shift handover buffer time stream of the production cycle at the overall scheduling decision level, the kilowatt-hour unit is equivalently converted into an hour unit with physical meaning of time span by dividing the energy throughput deficit value by the corresponding rated charging power. This establishes a flexible cross-dimensional bridge between the energy boundary and the time-space degree scale within the algorithm, eliminating the ambiguity conflict of dimensional definitions.

[0041] During the calculation, for the first vehicle, the energy consumption throughput deficit margin is calculated by substituting the above parameters. It is -0.117h, because This indicates that the current energy safety margin of the first vehicle is within a safe range, and the system allocates its static baseline priority. In actual data processing and calibration operations, the scalar value of the energy consumption throughput deficit margin calculated above reflects the relative net deficit state after deducting the vehicle's safety redline redundant energy. For the first vehicle, because the energy defense module opened inside its central control system will forcibly deduct a value of 14.05 kWh of underground emergency avoidance and safe residence backup energy in the calculation path during hash desensitization and parameter physical restoration, after substituting the original physical property parameters of the first vehicle into the calculation formula and further subtracting this safety avoidance backup energy, the final output energy consumption throughput deficit margin is negative 0.117 hours. This negative value objectively represents that the vehicle currently has actual available energy surplus in the control flow judgment logic, and there is no risk of mid-operation shutdown, thus ensuring the absolute consistency between the data extrapolation results and the subsequent automatic allocation of static baseline priority control actions by the system. For the second vehicle, the energy consumption throughput deficit margin is calculated. It is 0.061h, because This indicates that the second vehicle is at risk of power outage and shutdown. The system takes into account the handover buffer time of the current dispatch window. Establish time urgency weighting factors Set a shift handover buffer time The time series urgency weighting factor is 15.00 minutes. The calculation formula is expressed as: ,in, For vehicles The time urgency weighting factor, This is the shift handover buffer time within the dispatch window. The definitions of the other symbols are consistent with those mentioned above. The calculation is performed by substituting the data from the second vehicle. The value is 0.019, which objectively reflects the non-linear change in the degree of vehicle battery depletion over time.

[0042] The scheduling matrix generation unit extracts the total available power margin from the power grid node analyzer. As a reverse constraint, the path planning process, which incorporates the discrete characteristic matrix, is used to construct a flexible time slice allocation mechanism, setting a total available power margin. The power consumption is 350.00kW. The scheduling matrix generation unit, based on the priority comparison results output by the priority arbitration unit, constrains the duty cycle and power allocation of each charging channel's timing slots. When the second vehicle... Larger than the first vehicle At that time, the system prioritizes allocating the total available power margin. The 90.00kW power quota is allocated to the charging channel corresponding to the second vehicle, and the duty cycle of the channel is adjusted to 85.00% to cope with data loss caused by radio electromagnetic shielding in the alley and energy consumption calculation deviation caused by vehicle aging. The scheduling matrix generation unit finally outputs the charging sequence and resource occupancy schedule as the basis for the scheduling decision of the intelligent equipment. Each charging channel in the system adjusts its duty cycle and allocates power quota according to the schedule, so as to complete the steady-state collaborative configuration of multi-source transportation tasks and limited charging resource pool in the confined space.

[0043] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A dynamic scheduling and management system for underground charging chamber resources, characterized in that, include: The data acquisition unit is used to collect historical energy consumption characteristics data, remaining energy value data, and operation cycle data of the downhole mobile equipment, and output the discrete characteristic matrix of the downhole mobile equipment. The input of the priority arbitration unit is connected to the output of the data acquisition unit. It is used to calculate the expected energy consumption of the downhole mobile equipment in the space transportation cycle based on the acquired work plan data and energy consumption constraint data. It calculates the energy consumption throughput deficit margin based on the expected energy consumption and the remaining energy value data, and performs proportional scaling calculation based on the energy consumption throughput deficit margin and the shift handover buffer time of the scheduling window to establish the time priority weight factor, and outputs the priority comparison results of concurrent tasks. The input of the scheduling matrix generation unit is connected to the output of the priority arbitration unit and the external total available power parameter analysis interface, respectively. It is used to utilize the heuristic task mapping algorithm and the dynamic time slice round-robin scheduling algorithm to input the total available power margin input from the external total available power parameter analysis interface as a reverse constraint condition into the path planning process of the discrete feature matrix, establish an elastic time slice allocation mechanism based on load overload mitigation, and constrain the time slice duty cycle and power allocation quota of each charging channel according to the priority comparison results, and output the charging sequence and resource occupation schedule table.

2. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, The specific method for calculating the energy throughput deficit margin by the priority arbitration unit is as follows: multiply the expected running time of the underground mobile equipment to complete the transportation cycle, the average travel speed under the roadway slope constraint, and the energy consumption rate per unit time to obtain the energy consumption prediction value. Subtract the remaining energy value from the energy consumption prediction value to obtain the energy throughput deficit value. Then divide the energy throughput deficit value by the rated power allocation of the corresponding charging channel to obtain the energy throughput deficit margin.

3. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, When the priority arbitration unit establishes the timing urgency weight factor based on the energy consumption throughput deficit margin, the logical rule it defines is: when the energy consumption throughput deficit margin is less than or equal to 0, the priority is based on the static benchmark priority of the downhole mobile equipment. When the energy consumption throughput deficit margin is greater than 0, the time urgency weighting factor is determined to be the quotient of the energy consumption throughput deficit margin, the expected running time of the transportation cycle, and one-sixtieth of the shift handover buffer time.

4. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, When the scheduling matrix generation unit establishes an elastic time slice allocation mechanism based on load overload mitigation, the following timing steps are used: Step S401: Obtain real-time voltage and current variation data of multiple nodes in the power grid collected by the external total available power parameter analysis interface, and calculate the total available power margin. Step S402: Determine the grid load status based on the total available power margin. When the total available power margin is lower than the baseline safe power threshold, confirm that the current operating condition is in a high-concurrency operating condition and generate a load overload mitigation command. Step S403: In response to the load overload mitigation command, extract the weight sum of all downhole mobile devices with energy consumption throughput deficit margin greater than 0, establish a feedback chain between the preceding energy consumption deficit status and the subsequent time slice boundary based on the weight sum, and constrain the timing time slice duty cycle and power allocation of each charging channel through the control bus to adjust the output power of the underlying converter module to be lower than the design safe upper limit and prevent local grid overload.

5. The dynamic scheduling and management system for underground charging chamber resources according to claim 4, characterized in that, When the scheduling matrix generation unit responds to the overload mitigation command and adjusts the output parameters of the underlying converter module, it includes the following sub-step logic that refines step S403: Step S4031, obtain the real-time chamber temperature and heat accumulation rate collected by the temperature acquisition sensor; Step S4032, use the total available power margin as a reverse constraint condition into the path planning process of the downhole mobile equipment to calculate the predicted value of the chamber heat accumulation generated by continuous power input under high concurrency conditions. Step S4033: When the predicted value of heat accumulation in the chamber exceeds the fixed temperature safety threshold, the duty cycle of the timing slice of each charging channel is reduced and adjusted, and the alternating timing of each charging channel is converted into a timing control command and output to the underlying converter module to limit the output duty cycle of the underlying converter module to prevent temperature overload.

6. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, The energy consumption history data acquired by the data acquisition unit includes the historical power consumption rate, historical light-load travel speed, and historical heavy-load travel speed of the underground mobile equipment under different roadway slopes. The discrete feature matrix is ​​also corrected by incorporating current operating parameters, including current load status, current tunnel inclination angle, and current cell operating temperature.

7. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, When the scheduling matrix generation unit calculates the heuristic task mapping algorithm, it takes maximizing the sum of the time urgency weight factors of all downhole mobile devices with energy consumption throughput deficit margin greater than 0 as the objective function. Under the inverse constraint of total available power margin, it maps concurrent charging requests to charging channels with matching power allocation quotas to generate charging sequences that meet the charging safety margin.

8. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, The priority arbitration unit is also used to identify the conversion heat loss and transmission delay loss of the underlying converter module during power conversion, and to perform loss compensation using the following timing steps: Step S801, monitor the difference between the input power and output power of the underlying converter module in real time during operation, calculate the instantaneous conversion heat loss, and perform linear accumulation calculation based on the transient control delay of the communication control link and the instantaneous conversion heat loss to determine the comprehensive loss compensation factor; Step S802, use the comprehensive loss compensation factor to correct the energy consumption throughput deficit margin, input the corrected energy consumption throughput deficit margin into the calculation loop of the timing urgency weight factor, and adjust the power allocation quota to offset physical deviations.

9. The dynamic scheduling and management system for underground charging chamber resources according to claim 1, characterized in that, The scheduling matrix generation unit interacts bidirectionally with multiple underlying converter modules via an industrial Ethernet bus, converting the charging sequence and resource occupancy schedule into periodically refreshed pulse width modulation commands, which are then sent to the corresponding underlying converter modules to regulate the real-time output power of each charging channel. The scheduling matrix generation unit is also connected to a safety defense module, which monitors the flow of scheduling data in the resource occupancy schedule. When the total available power margin falls below the baseline safe power threshold, or the predicted value of the chamber heat accumulation exceeds the fixed temperature safety threshold, an abnormal state trigger signal is output to the external alarm device.

10. A method for dynamic scheduling and management of underground charging chamber resources, used to operate the dynamic scheduling and management system for underground charging chamber resources as described in claim 1, characterized in that, include: Step S1: Collect historical energy consumption characteristics, remaining energy value data, and operation cycle data of the downhole mobile equipment through the data acquisition unit, and output the discrete feature matrix of the downhole mobile equipment. Step S2: Based on the acquired work plan data and energy consumption constraint data, the priority arbitration unit calculates the expected energy consumption of the downhole mobile equipment in the space transportation cycle, calculates the energy consumption throughput deficit margin based on the expected energy consumption and the remaining energy value data, and performs a scaling calculation based on the energy consumption throughput deficit margin and the shift handover buffer time of the scheduling window to establish the time priority weight factor, and outputs the priority comparison results of concurrent tasks. Step S3: Through the scheduling matrix generation unit, the total available power margin input from the external total available power parameter analysis interface is used as a reverse constraint condition to enter the path planning process of the discrete feature matrix. An elastic time slice allocation mechanism based on load overload mitigation is established. Based on the priority comparison results, the duty cycle of the time slice and the power allocation quota of each charging channel are constrained, and the charging sequence and resource occupation schedule are output.

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