Automobile coal dynamic allocation and transportation method and device, electronic equipment and storage medium

Abstract

The invention provides an automobile coal dynamic allocation and transportation method and device, electronic equipment and a storage medium. Allocation and transportation related data of automobile coal and boiler operation data are collected in real time; a dynamic combustion demand prediction model is constructed based on the two types of data so as to generate an in-furnace coal quality demand curve in a future preset time period and determine a fire coal demand and a coal quality parameter range in each time period, and then a vehicle scheduling priority evaluation model is established in combination with the in-furnace coal quality demand curve and vehicle information so as to carry out priority ranking on vehicles; meanwhile, a vehicle entering sequence table and a dispatching instruction are automatically generated based on the sorting result and pushed to the vehicle-mounted terminal and the power plant unloading management system, and therefore the problem that in the prior art, dispatching is based on a fixed coal plan, dynamic response to the real-time combustion requirement is lacked, and consequently the dispatching plan and the boiler combustion requirement are disjointed can be solved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of dynamic automobile coal scheduling in thermal power plants, and particularly relates to a dynamic automobile coal scheduling method and device, an electronic device and a storage medium. BACKGROUND

[0002] As a core energy infrastructure in the power system, the fuel scheduling efficiency of a thermal power plant directly affects the economic operation and environmental performance of the unit. With the intensification of power load fluctuations and the increasingly stringent environmental standards, the traditional automobile coal scheduling method based on fixed coal arrival plans has been difficult to meet the dynamic needs of the boiler combustion system. In related technologies, the whole process management of fuel from entering the plant to entering the furnace is usually constructed through the coordinated operation of the vehicle scheduling system, the coal quality detection system and the unloading equipment. Specifically, this system includes key links such as coal arrival plan formulation, vehicle entry arrangement, coal yard storage and blending, and in-furnace coal quality control, but the overall scheduling is still mainly static, lacking real-time response capability to the combustion demand.

[0003] However, in the existing automobile coal scheduling method, historical data and fixed plans are directly used for scheduling, and the real-time combustion parameters of the boiler and the coal quality demand are not fully integrated, which may lead to problems such as low coal quality matching degree, unreasonable unloading sequence, insufficient direct burning ratio, or frequent secondary blending in the coal yard, thereby affecting the fuel utilization rate, increasing the operation cost, and reducing the flexibility and economy of the unit operation. In addition, the queuing congestion of vehicles in the plant and the uneven allocation of unloading resources further restrict the improvement of scheduling efficiency, and intelligent means are urgently needed to realize the deep coordination between the scheduling and the combustion system. SUMMARY

[0004] The present disclosure provides a dynamic automobile coal scheduling method and device, an electronic device and a storage medium, aiming to at least solve one of the technical problems in the related art to some extent.

[0005] According to a first aspect of the present disclosure, a dynamic automobile coal scheduling method is provided, comprising:

[0006] real-time collection of automobile coal scheduling related data and boiler operation data;

[0007] based on the collected scheduling related data and boiler operation data, a dynamic combustion demand prediction model is constructed to generate an in-furnace coal quality demand curve in a future preset time period, and the demand amount and coal quality parameter range of different coal varieties in each period are determined;

[0008] According to the in-furnace coal quality demand curve and vehicle information, a vehicle scheduling priority evaluation model is established to prioritize the vehicles, preferentially schedule vehicles with coal quality parameters meeting the current period combustion demand and directly entering the furnace, and dynamically adjust the scheduling order according to the proportion of the coal variety in the mixed coal blending.

[0009] Based on the priority ranking result, the vehicle entry plant order table and the scheduling instruction are automatically generated, and the scheduling instruction is pushed to the vehicle on-board terminal and the power plant unloading management system, so as to realize automatic queuing and scheduling of the vehicle.

[0010] Optionally, the transportation related data includes position information and expected arrival time of the vehicle in transit, coal variety and quantity of the queuing vehicle, operation state and processing capacity of the unloading equipment, and the boiler operation data includes current load, combustion state, environmental protection requirement and target coal quality parameter;

[0011] The real-time collection of the transportation related data and the boiler operation data of the coal vehicle includes:

[0012] The position information of the vehicle in transit is obtained in real time through the vehicle-mounted GPS system, and the expected arrival time of the vehicle is predicted in combination with the traffic flow data;

[0013] The coal variety and quantity of the queuing vehicle in the parking lot are detected online by using the coal quality rapid detection equipment, and the coal quality parameters of the transported coal are obtained.

[0014] Optionally, based on the collected transportation related data and the boiler operation data, a dynamic combustion demand prediction model is constructed, including:

[0015] The time series analysis and machine learning algorithm are adopted, the current load of the boiler, the historical combustion data and the environmental protection index are combined, and the coal quality demand change trend in the future time length is predicted;

[0016] According to the prediction result, a plurality of coal quality parameter combination schemes are generated, and each coal quality parameter combination scheme is evaluated to generate a coal quality demand curve for entering the furnace.

[0017] Optionally, the vehicle scheduling priority evaluation model is established according to the coal quality demand curve for entering the furnace and the vehicle information, including:

[0018] The coal quality matching degree weight parameter is set, and the weight is dynamically adjusted according to the deviation degree of the vehicle coal quality and the target coal quality in the current period;

[0019] The real-time load rate of the unloading equipment is taken as the adjustment factor of the scheduling order, and the vehicle that can be distributed to the low load equipment is preferentially scheduled, so as to balance the equipment use efficiency.

[0020] Optionally, it further includes:

[0021] When it is detected that there is a deviation between the entry plant coal quality and the predicted demand, the edge computing node is started for local optimization calculation, and the update and push of the scheduling instruction are completed within a preset time length.

[0022] According to a second aspect of the present disclosure, a dynamic dispatching device for automobile coal is provided, comprising:

[0023] a data acquisition module configured to acquire real-time dispatching related data of automobile coal and boiler operation data;

[0024] a demand prediction module configured to construct a dynamic combustion demand prediction model based on the acquired dispatching related data and boiler operation data, generate a coal quality demand curve for a future preset time period, and determine the demand amount and coal quality parameter range for different coal varieties in each time period;

[0025] a dispatch priority module configured to establish a vehicle dispatch priority evaluation model according to the coal quality demand curve and vehicle information, prioritize the vehicles, and preferentially dispatch vehicles with coal quality parameters meeting the current time period combustion demand and directly entering the furnace, and dynamically adjust the dispatch order according to the proportion of the coal variety in the mixed coal;

[0026] a dispatch instruction generation module configured to automatically generate a vehicle entry order list and dispatch instructions based on the priority sorting results, and push the dispatch instructions to vehicle-mounted terminals and power plant unloading management systems to realize automatic queuing and dispatching of vehicles.

[0027] Optionally, the dispatching related data includes location information and expected arrival time of in-transit vehicles, coal variety and quantity of queued vehicles, operation state and processing capacity of unloading equipment, and the boiler operation data includes current load, combustion state, environmental protection requirements and target coal quality parameters;

[0028] The data acquisition module is further configured to:

[0029] acquire the location information of in-transit vehicles in real time through a vehicle-mounted GPS system, and predict the expected arrival time of the vehicles in combination with traffic flow data;

[0030] detect the coal variety and quantity of queued vehicles in a parking lot in real time using coal quality rapid detection equipment, and acquire the coal quality parameters of the transported coal.

[0031] Optionally, the demand prediction module is further configured to:

[0032] adopt time series analysis and machine learning algorithms, and predict the coal quality demand change trend in a future time period in combination with the current load of the boiler, historical combustion data and environmental protection indicators;

[0033] generate multiple sets of coal quality parameter combination schemes according to the prediction results, and evaluate each set of coal quality parameter combination scheme to generate the coal quality demand curve.

[0034] Optionally, the dispatch priority module is further configured to:

[0035] A coal quality matching degree weight parameter is set, and the weight is dynamically adjusted according to the deviation degree of the vehicle coal quality from the target coal quality in the current period;

[0036] The real-time load rate of the unloading equipment is taken as an adjustment factor of the scheduling sequence, and the vehicle that can be distributed to the low-load equipment is preferentially scheduled to balance the equipment use efficiency.

[0037] Optionally, the method further comprises:

[0038] The edge computing module is configured to start the edge computing node to perform local optimization calculation when it is detected that the incoming coal quality deviates from the predicted demand, and to complete the update and push of the scheduling instruction within a preset time length.

[0039] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0040] at least one processor; and

[0041] a memory connected with the at least one processor in communication; wherein

[0042] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0043] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the method of the first aspect.

[0044] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of the first aspect.

[0045] The present disclosure provides a dynamic automobile coal transportation method and device, an electronic device and a storage medium. The dynamic combustion demand prediction model is constructed based on the two types of data to generate the coal quality demand curve of the furnace in the future preset time period and determine the coal demand and coal quality parameter range of each period, and then the vehicle scheduling priority evaluation model is established based on the coal quality demand curve and the vehicle information to prioritize the vehicles (the vehicles with coal quality parameters meeting the current period combustion demand can be directly put into the furnace, and the scheduling order is dynamically adjusted according to the proportion of the coal variety in the mixed coal), and the vehicle entry plant order table and the scheduling instruction are automatically generated based on the sorting result and pushed to the vehicle-mounted terminal and the power plant unloading management system. Therefore, the problems of the prior art, such as the disconnection between the transportation plan and the boiler combustion demand, the low matching degree between the coal quality and the furnace requirement, the unoptimized vehicle entry plant order and the congestion, and the low direct burning ratio, can be solved, the technical effects of accurately matching the automobile coal transportation and the boiler combustion demand, reducing the secondary coal blending link in the coal yard to reduce the fuel loss and the operation cost, improving the direct burning ratio, avoiding the unloading congestion to improve the transportation efficiency, and ensuring the safe and economic operation of the unit can be achieved.

[0046] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings are used to better understand the present scheme and do not limit the present disclosure. Among them:

[0048] Figure 1 A flowchart of an automobile coal dynamic transportation method provided by an embodiment of the present disclosure is shown in the figure;

[0049] Figure 2 A structural schematic diagram of an automobile coal dynamic transportation device provided by an embodiment of the present disclosure is shown in the figure;

[0050] Figure 3 A structural schematic diagram of another automobile coal dynamic transportation device provided by an embodiment of the present disclosure is shown in the figure;

[0051] Figure 4 A schematic block diagram of an example electronic device provided by an embodiment of the present disclosure is shown in the figure. DETAILED DESCRIPTION

[0052] Exemplary embodiments of the present disclosure are described herein with reference to the accompanying drawings, which are meant to be exemplary. Therefore, it should be recognized that various modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0053] The automobile coal dynamic dispatching method and device, the electronic device and the storage medium of the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0054] Figure 1 A flowchart of an automobile coal dynamic dispatching method provided by the embodiments of the present disclosure is shown.

[0055] As shown in the figure, the method comprises the following steps: Figure 1

[0056] Step 101, collecting real-time dispatching related data and boiler operation data of automobile coal.

[0057] ​In the embodiments of the present disclosure, two types of core data, i.e., coal transportation related data and boiler operation data, need to be collected in real time. The coal transportation related data includes the planned amount of each coal type (i.e., the total amount of different types of coal expected to arrive within a specific time period planned in advance, used to clarify the overall coal supply plan), the location of the vehicle in transit (obtained in real time through the vehicle positioning system, accurately reflecting the current transportation route node of the vehicle, facilitating the grasp of the transportation progress), the expected arrival time (calculated based on the current location of the vehicle, real-time traffic conditions and transportation distance, which can predict the time rhythm of coal entering the power plant), the coal type and quantity of the vehicles queuing in the parking lot (statistics of the specific types of coal carried by the vehicles waiting to unload in the parking lot of the power plant and the corresponding number of vehicles, clearly grasping the instant coal resource situation to be processed), and the operation status of the current unloading equipment (such as whether the equipment is in normal operation, fault shutdown or maintenance state, which directly relates to whether the unloading link can proceed smoothly) and the processing capacity (i.e., the total amount of unloaded coal that the unloading equipment can complete per unit time, reflecting the maximum processing efficiency of the unloading link). The boiler operation data includes the target calorific value of the coal entering the furnace (the unit mass calorific value that the coal required for boiler combustion should reach, which is a key indicator to ensure the thermal efficiency of the boiler), the sulfur content (the mass fraction of sulfur elements in coal, which determines whether the sulfur dioxide emission after combustion meets the environmental protection standard), the ash content (the mass fraction of residual ash after coal combustion, which affects the internal ash accumulation and heat exchange efficiency of the boiler), and other key coal quality parameters, as well as the ideal ratio of each coal type (the optimal ratio of mixed combustion of different coal types to meet the requirements of boiler combustion efficiency and environmental protection). The above data needs to be continuously collected through sensors, online monitoring instruments, vehicle positioning systems, and unloading equipment monitoring modules, etc., to ensure that the data can reflect the actual state of coal supply and boiler operation in real time and accurately. The beneficial effect of this step is to provide real and timely basic data support for subsequent combustion demand analysis and vehicle scheduling, avoiding the disconnection between the transportation plan and the actual demand due to data loss or lag, and laying a foundation for subsequent precise transportation.

[0058] In step 102, based on the collected coal transportation related data and boiler operation data, a dynamic combustion demand prediction model is constructed to generate the coal quality demand curve of the coal entering the furnace in the future preset time period, and to determine the demand amount of different coal types and the coal quality parameter range in each period.

[0059] In the embodiments of the present disclosure, the dynamic combustion demand prediction model is constructed based on the transportation-related data (including the planned amount of each coal type, in-transit vehicle information, queuing vehicle coal type and quantity in the parking lot, and unloading equipment operating status and processing capacity) collected in real time in step 101 and the boiler operating data (including the target calorific value, sulfur content, ash content, and other coal quality parameters of the coal fed into the boiler, and the ideal ratio of each coal type). When constructing the model, the current actual load of the boiler (reflecting the real-time work demand of the boiler, directly related to the coal consumption), the combustion state (such as the furnace temperature, flue gas composition, etc., reflecting whether the current combustion is stable and efficient), and the environmental protection requirements (such as the limitation of sulfur content and ash content of coal-fired in regional pollutant emission standards) need to be combined, and historical operating data (including the coal-fired demand law under the same or similar load in the past, the coal quality adaptation effect, etc.) are introduced as the basis for model training and calibration to ensure that the model can accurately map the correlation between the boiler combustion demand and the data. Based on this model, the coal quality demand curve in the future preset time period (such as 1-4 hours, which can be flexibly adjusted according to the operation scheduling period of the power plant unit) is further generated. The curve takes time as the horizontal axis and the demand value of the coal quality parameter (calorific value, sulfur content, ash content) as the vertical axis, dynamically presenting the changing requirements of the boiler on coal quality in different periods. At the same time, through model calculation and curve analysis, the specific demand amount of different coal types in each period (such as X tons of high-calorific value coal and Y tons of low-calorific value coal in a certain period) and the coal quality parameter range that each coal type needs to meet (such as the sulfur content of a certain coal type needs to be controlled in 0.5%-0.8% and the calorific value needs to be maintained at 22-24 MJ / kg in this period) are determined to ensure that the coal fed into the boiler can adapt to the current load and environmental protection requirements of the boiler. The beneficial effect of this step is to convert the abstract boiler combustion demand into specific and executable coal type and coal quality parameter requirements, providing accurate demand guidance for subsequent vehicle scheduling, avoiding the blindness of transportation due to the lack of clear demand prediction, and ensuring the preliminary matching of the coal fed into the boiler and the boiler demand.

[0060] In step 103, a vehicle scheduling priority evaluation model is established according to the coal quality demand curve and vehicle information, and the vehicles are prioritized. The vehicles with coal quality parameters meeting the current period combustion demand and directly fed into the boiler are preferentially scheduled, and the scheduling order is dynamically adjusted according to the proportion of the coal type in the mixed coal blending.

[0061] In the embodiments of the present disclosure, the coal quality demand curve generated in step 102 (which specifies the demand for different coal varieties in each time period in the future preset time period and the coal quality parameter range, and is the core basis for judging whether the coal is suitable for the current combustion demand of the boiler) and the vehicle information (which includes the coal varieties carried by the vehicles on the way and the vehicles queuing in the parking lot, the actual coal quality parameters of the corresponding coal varieties, the estimated arrival time of the vehicles, the current order of the vehicles queuing in the parking lot, etc., which directly reflects the actual situation of the dispatchable coal resources) are input to construct a vehicle dispatch priority evaluation model. The core logic of the model revolves around "adaptation to combustion demand": first, compare the coal quality parameters of all vehicles to be dispatched with the coal quality parameter range of the current period in the coal quality demand curve, and select the vehicles whose coal quality parameters fully meet the requirements and can be directly fed into the furnace without secondary coal blending in the coal yard, and set them as the highest priority for dispatching, so as to reduce the intermediate storage and coal blending link; for vehicles whose coal quality parameters need to be mixed with other coal varieties to meet the feeding requirements, the model will first determine the preset proportion of the coal variety carried by the vehicle in the current period mixed coal blending scheme, and then prioritize the same variety of vehicles to be dispatched according to the proportion - the higher the proportion of the coal variety, the higher the dispatch priority of the corresponding vehicle, to ensure that different varieties of coal can enter the feeding link in the preset proportion after dispatching, and the mixed coal quality can accurately match the parameter requirements in the coal quality demand curve. At the same time, the model will combine the estimated arrival time (to avoid insufficient coal supply in the current period due to the late arrival of the vehicle) and the queuing order (to reduce the invalid waiting of the queuing vehicles) in the vehicle information in the sorting process, so that the priority sorting result not only meets the combustion demand, but also has practical execution feasibility. The beneficial effect of this step is that the priority rules can avoid the blindness of dispatching, improve the dispatching efficiency of direct-fired vehicles, help to improve the direct-fired proportion, ensure the accuracy of mixed coal blending, prevent the coal quality from not meeting the standard due to the imbalance of the coal variety proportion, and provide a scientific basis for subsequent dispatching instruction generation.

[0062] Step 104: Based on the priority sorting result, automatically generate a vehicle entry order table and a dispatching instruction, and push the dispatching instruction to the vehicle-mounted terminal of the vehicle and the power plant unloading management system, to realize automatic queuing and dispatching of the vehicle.

[0063] In the embodiments of the present disclosure, the vehicle priority ranking result obtained in step 103 (i.e., the order of dispatching the to-be-dispatched vehicles, including the parking lot queued vehicles and the on-the-way vehicles, which has been determined in combination with the core factors such as the furnace coal quality demand and the mixed coal blending ratio) is taken as the core basis to start the automatic processing flow to generate the vehicle entry plant order list and the dispatching instruction. The vehicle entry plant order list lists the key information of each to-be-dispatched vehicle in detail, including the vehicle number, the carried coal type, the corresponding coal quality parameter, the expected entry plant time, etc., and the arrangement order of all vehicles strictly follows the priority ranking result, so as to ensure that the vehicles with high priority (such as the vehicles with coal quality meeting the current combustion demand and directly entering the furnace) can enter the power plant first, and the sequence disorder caused by manual numbering is avoided. The dispatching instruction further refines the execution information on the basis of the entry plant order, and in addition to specifying the expected entry plant time of the vehicle, it also specifies the unloading position of each vehicle according to the real-time state of the power plant unloading equipment (such as the idle condition of each unloading point, the processing capacity, and the adaptability of the vehicle carried coal type), so as to ensure that the vehicle can be directly connected to the corresponding unloading point after entering the plant, and the invalid movement is reduced.

[0064] Subsequently, the system pushes the dispatching instruction to the vehicle-mounted terminal of the corresponding vehicle and the power plant unloading management system through the preset communication link: when pushing to the vehicle-mounted terminal, the driver can obtain the entry plant time and unloading position of the vehicle in real time, plan the entry plant route in advance, and avoid disordered parking after arrival due to lack of information; when pushing to the unloading management system, the unloading staff can know the information of the vehicle entering the plant in advance, and make preparations such as equipment debugging and personnel arrangement in advance, so as to realize seamless connection between the unloading link and the vehicle entering the plant. At the same time, the vehicle entry plant order list is updated in the unloading management system at the same time, so as to facilitate the staff to monitor the vehicle entry plant progress in real time, and the automatic numbering and dispatching of the vehicle are realized as a whole, without manual intervention in the numbering and instruction conveying process.

[0065] The present disclosure provides a dynamic scheduling method for automobile coal, which comprises the following steps: collecting real-time scheduling related data and boiler operation data of automobile coal, constructing a dynamic combustion demand prediction model based on the two types of data to generate a coal quality demand curve for the future preset time period and determine the coal demand and coal quality parameter range of each period, combining the coal quality demand curve with vehicle information to establish a vehicle scheduling priority evaluation model to prioritize the vehicles (prioritize scheduling vehicles whose coal quality parameters meet the current period's combustion demand and can be directly fed into the furnace, and dynamically adjust the scheduling order according to the proportion of the coal variety in the mixed coal), and automatically generate a vehicle entry sequence table and scheduling instructions based on the sorting results and push them to the vehicle-mounted terminal and the power plant unloading management system. Therefore, the problems of the prior art, such as the disconnection between the scheduling plan and the boiler combustion demand due to the fixed coal arrival plan, the lack of dynamic response to real-time combustion demand, the low matching degree of coal quality and furnace requirements, the unoptimized vehicle entry sequence leading to congestion, and the low direct burning ratio, can be solved, and the technical effects of accurately matching the automobile coal scheduling with the boiler combustion demand, reducing the secondary coal blending link in the coal yard to reduce fuel loss and operating cost, improving the direct burning ratio, avoiding unloading congestion to improve the scheduling efficiency, and ensuring the safe and economic operation of the unit can be achieved.

[0066] In the embodiments of the present disclosure, for the operation of "collecting real-time scheduling related data and boiler operation data of automobile coal", there are multiple feasible specific implementations. For the sake of clarity, the following enumerated embodiments are only exemplary and do not constitute a limitation on the protection scope of the present disclosure. The following specific embodiments are described in detail: the position information of the in-transit vehicle is obtained in real time by the vehicle-mounted GPS system, and the predicted arrival time of the vehicle is predicted in combination with the traffic flow data; the coal variety and quantity of the queued vehicles in the parking lot are detected online by using the coal quality rapid detection equipment, and the coal quality parameters of the transported coal are obtained. The scheduling related data includes the position information and predicted arrival time of the in-transit vehicle, the coal variety and quantity of the queued vehicle, the operation state and processing capacity of the unloading equipment, and the boiler operation data includes the current load, combustion state, environmental protection requirements, and target coal quality parameters.

[0067] Specifically, for the collection of the position information of the on-the-way vehicle, the vehicle's geographic position coordinates can be captured in real time by relying on the vehicle-mounted GPS system, the real-time traffic flow data of the regional traffic management platform can be accessed synchronously, the remaining driving distance between the vehicle's current position and the power plant and the real-time traffic efficiency of the road can be combined, the estimated arrival time of the vehicle can be accurately predicted through the conventional travel time estimation method (such as the matching calculation based on the historical travel time consumption of the same road section and the current flow), and the coal transportation process can be dynamically mastered; for the collection of the coal information of the queuing vehicles in the parking lot, the coal quality rapid detection equipment (such as the X-ray fluorescence analyzer and the portable calorific value tester) can be used for online detection, the equipment can determine the coal variety carried by the vehicle and the corresponding coal quality parameters (such as the calorific value, sulfur content, and ash content) through the rapid analysis of the elemental composition and calorific value of the coal, and the number of queuing vehicles of each coal variety can be counted by combining the associated records of the vehicle counting device at the entrance of the parking lot and the detection equipment. In addition, the running state and processing capacity of the unloading equipment in the transportation related data can be obtained by the state monitoring sensor (such as the speed sensor and the pressure sensor) of the equipment, to determine whether the equipment is in normal operation, fault shutdown or maintenance state, and the processing capacity per unit time can be calculated by the historical coal unloading amount statistics and the real-time running parameters (such as the unloading conveyor belt speed); the boiler operation data include the current load of the boiler (reflecting the real-time work demand of the boiler, such as the steam amount generated per hour), the combustion state (such as the furnace temperature, the oxygen concentration and the pollutant concentration in the flue gas), the environmental protection requirements (such as the sulfur dioxide and nitrogen oxide emission limits specified by the local environmental protection department), and the target coal quality parameters (the target calorific value, sulfur content, and ash content range that the coal entering the furnace needs to reach), which can be collected in real time by the online monitoring module of the boiler control system and transmitted to the data aggregation end.

[0068] In the embodiments involved in the present disclosure, for the operation of “constructing a dynamic combustion demand prediction model based on the collected transportation related data and the boiler operation data”, there are multiple feasible specific implementations. For the sake of clear description and to make the technical solutions of the present disclosure be clearly and completely described, the following listed embodiments are only exemplary and do not constitute a limitation on the protection scope of the present disclosure, and the following specific introduction part lists some exemplary embodiments: time series analysis and machine learning algorithm are used to combine the current load of the boiler, the historical combustion data and the environmental protection indicators to predict the coal quality demand change trend in the future time period; a plurality of coal quality parameter combination schemes are generated according to the prediction results, and each coal quality parameter combination scheme is evaluated to generate the coal quality demand curve entering the furnace.

[0069] Specifically, when constructing the model, the core adopts a combination of time series analysis and machine learning algorithms: the time series analysis is used to mine the time sequence rules in the historical combustion data, and the historical operation data of the boiler (such as the demand records of the coal quality entering the furnace under different load intervals and different environmental protection requirements in the past) will be extracted, the internal law of the change of the coal quality demand with time will be identified through trend decomposition (such as separating long-term trend, periodic fluctuation and random fluctuation), and the real-time collected current load of the boiler (reflecting the immediate work demand) and environmental protection indicators (such as the upper limit of sulfur dioxide and nitrogen oxide emissions specified by the local environmental protection department) are integrated to preliminarily predict the change direction of the coal quality demand. The machine learning algorithm further improves the prediction accuracy, and a regression model (such as a gradient boosting regression model) or a lightweight neural network model can be selected, the coal plan quantity, the unloading equipment processing capacity (indirectly affecting the coal supply rhythm, which needs to match the demand rhythm) and the current combustion state (such as the furnace temperature, the flue gas composition) in the boiler operation data are taken as the model input features, the model is trained through the historical data, the model learns the nonlinear relationship between each feature and the coal quality demand (calorific value, sulfur content, ash content), and thus the coal quality demand change trend in the future preset time (such as 1-4 hours) is more accurately predicted. After obtaining the preliminary prediction result, a plurality of coal quality parameter combination schemes (for example, for a certain period of time, a scheme of calorific value 22-23 MJ / kg, sulfur content 0.5-0.6%, and a scheme of calorific value 23-24 MJ / kg, sulfur content 0.6-0.7% that meet the environmental protection and load requirements are generated) are generated based on the predicted demand trend, and each group of schemes is evaluated from the three dimensions of “environmental compliance” (whether it meets the emission limit value), “load adaptability” (whether it can support the current and future load), and “supply feasibility” (combined with the coal plan quantity to determine whether the scheme can be implemented), and the optimal scheme is selected, and finally a continuous coal quality demand curve entering the furnace is generated based on the optimal scheme.

[0070] In the embodiments of the present disclosure, for the operation of “establishing a vehicle scheduling priority evaluation model according to the coal quality demand curve entering the furnace and vehicle information”, there are multiple feasible specific implementation manners. For the sake of clear description, so that the technical scheme of the present disclosure can be clearly and completely described, the following enumerated embodiments are only exemplary and do not constitute a limitation on the protection scope of the present disclosure, and the following specific introduction part will specifically introduce some exemplary embodiments: a coal quality matching degree weight parameter is set, and the weight is dynamically adjusted according to the deviation degree of the vehicle coal quality and the target coal quality in the current period; the real-time load rate of the unloading equipment is taken as an adjustment factor of the scheduling sequence, and the vehicles that can be distributed to the low-load equipment are preferentially scheduled to balance the equipment use efficiency.

[0071] Specifically, in the model construction process, first, set the coal quality matching degree weight parameter: the benchmark value of the parameter can be preset combined with the statistical data of "coal quality adaptation combustion efficiency" in the historical operation of the power plant (such as initially set to 0.6, which can be dynamically calibrated according to the actual combustion effect), and in the specific calculation, first extract the target coal quality parameters (including the specific numerical range of the target calorific value, sulfur content, and ash content) of the current period from the coal quality demand curve, then call the actual coal quality detection data of the coal transported by the vehicle to be dispatched in the vehicle information (such as obtained through the coal quality rapid detection equipment in the parking lot), calculate the deviation degree of a single coal quality parameter through the relative deviation formula (such as the absolute value of (actual coal quality parameter-target coal quality parameter) / target coal quality parameter), and then obtain the comprehensive deviation degree of the vehicle coal quality and the target coal quality through weighted summation; then dynamically adjust the coal quality matching degree weight parameter according to the comprehensive deviation degree - if the comprehensive deviation degree ≤ 5% (meeting the direct burning requirements), the weight parameter is increased to 0.8-0.9, so that the vehicle has a higher proportion in the priority ranking; if the comprehensive deviation degree > 10% (needs to participate in coal blending), the weight parameter is lowered to 0.3-0.5, reducing its dispatching priority, and ensuring that vehicles with high coal quality adaptation are preferentially dispatched. At the same time, the real-time load rate of the unloading equipment is used as an adjustment factor for the dispatching sequence: the real-time load rate of the unloading equipment is determined by the ratio of the current actual processing capacity of the equipment (obtained through the unloading equipment state monitoring sensor, such as the unloading tonnage per unit time) to the rated processing capacity of the equipment (the maximum unloading efficiency designed by the equipment), for example, if the current actual processing capacity of a certain unloading equipment is 80 tons / hour and the rated processing capacity is 100 tons / hour, then the real-time load rate is 80%; in the model, the value range of the adjustment factor is set to 0.1-0.4, when the real-time load rate of a certain unloading equipment ≤ 60% (in a low load state), the value of the adjustment factor corresponding to the vehicle to be dispatched allocated to the equipment is increased to 0.3-0.4, and the priority score after superimposing the coal quality matching degree weight parameter is improved, and such vehicles are preferentially dispatched; when the real-time load rate of the equipment ≥ 90% (close to full load), the corresponding adjustment factor value is reduced to 0.1-0.2, reducing the priority of the vehicles allocated to the equipment, to avoid equipment overload and congestion.

[0072] The vehicle dispatching priority evaluation model realizes the scientific ranking of vehicle priority through the comprehensive calculation of "coal quality matching degree weight parameter + unloading equipment load adjustment factor" (such as priority score = coal quality matching degree weight parameter x coal quality adaptation score + unloading equipment load adjustment factor x equipment adaptation score). The beneficial effects of this embodiment are that it not only guarantees the precise matching of the coal quality and the combustion demand, which helps to improve the direct burning ratio and reduce the cost of secondary coal blending, but also balances the use efficiency of the unloading equipment, avoids single equipment congestion, and further improves the overall operation efficiency.

[0073] Within the scope of the embodiments set forth in the present disclosure, in addition to the aforementioned, there are still several feasible specific implementation steps. In order to present these diversified implementation modes in a clear, accurate and orderly manner, the following is specifically explained for some exemplary embodiments: when it is detected that there is a deviation between the coal quality entering the plant and the predicted demand, the edge computing node is started for local optimization calculation, and the update and pushing of the scheduling instruction are completed within a preset time length.

[0074] Specifically, the real-time detection of the coal quality entering the plant is completed by relying on the plant sampling system. The system quickly samples and analyzes the parameters of the coal carried by each vehicle entering the plant, obtains the actual calorific value, sulfur content, ash content and other coal quality data, and then compares these actual data with the corresponding period predicted coal quality demand parameters (i.e. the target parameters in the coal quality demand curve) generated by the dynamic combustion demand prediction model in real time. When the deviation value of the key coal quality parameters exceeds the preset threshold (such as 5%, which can be flexibly adjusted according to the stability of boiler combustion and environmental protection requirements), the deviation response mechanism is automatically triggered. The edge computing node here is a computing module deployed near the data acquisition end of the plant sampling area, unloading equipment control unit, etc. Its core advantage lies in that it does not need to transmit a large amount of real-time data to the remote cloud for processing, which can greatly reduce the data transmission delay and improve the calculation response speed, and adapt to the requirements of time efficiency in the dispatching scene. After the edge computing node is started, only the local link affected by the deviation is optimized and calculated, and there is no need to re-execute the complete combustion demand prediction and full-quantity vehicle sorting process. For example, if it is detected that the sulfur content of the coal entering the plant is higher than the predicted demand, the node will quickly retrieve the information of low-sulfur coal in the vehicles queuing in the current in-transit and parking lot, and preferentially improve the dispatching priority of such vehicles, or adjust the proportion of vehicles that need to be mixed with low-sulfur coal and the dispatching order, to ensure that the sulfur content of the mixed coal entering the furnace returns to the target range. The preset time length is set to 5-10 minutes according to the dispatching rhythm of the plant, and after the edge computing node completes the optimization calculation within this time length, the updated scheduling instruction (including the adjusted vehicle entry sequence and specified unloading position) is automatically generated and pushed to the vehicle-mounted terminal of the related vehicle and the plant unloading management system through the original communication link, so as to realize the instant update of the scheduling instruction. The beneficial effect of this implementation mode is that it can quickly resolve the influence of the deviation of the coal quality entering the plant on the combustion, avoid the accumulation of the deviation to cause the decrease of the boiler efficiency or the exceeding of the environmental protection standard, and at the same time, reduce the consumption of calculation resources through local optimization, and improve the anti-interference ability and response efficiency of the dispatching system.

[0075] It should be noted that the embodiments of the present disclosure can include multiple steps, which are numbered for the purpose of description, but these numbers do not limit the execution time slots and execution order between the steps; the steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0076] Corresponding to the above-described method for dynamic transportation of coal by truck, this disclosure also proposes a device for dynamic transportation of coal by truck. Since the device embodiments of this disclosure correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to the method embodiments described above, and will not be repeated here.

[0077] Figure 2 This is a schematic diagram of the structure of a dynamic coal transportation device provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes:

[0078] Data acquisition module 21 is used to collect real-time data related to the transportation of coal by truck and boiler operation data;

[0079] Demand forecasting module 22 is used to construct a dynamic combustion demand forecasting model based on the collected transportation-related data and boiler operation data, generate the coal quality demand curve for the furnace in the future preset time period, and determine the demand for different coal types and the range of coal quality parameters for each time period.

[0080] The scheduling priority module 23 is used to establish a vehicle scheduling priority evaluation model based on the coal quality demand curve and vehicle information, prioritize vehicles, prioritize vehicles whose coal quality parameters meet the current combustion demand and can be directly fed into the furnace, and dynamically adjust the scheduling order according to the proportion of coal type in the blended coal.

[0081] The scheduling instruction generation module 24 is used to automatically generate a vehicle entry sequence table and scheduling instructions based on the priority sorting results, and push the scheduling instructions to the vehicle on-board terminal and the power plant unloading management system to realize automatic vehicle numbering and scheduling.

[0082] Furthermore, in one possible implementation of this embodiment, such as Figure 3 As shown, the transportation-related data includes the location information and estimated arrival time of vehicles en route, the type and quantity of coal used by queuing vehicles, and the operating status and processing capacity of the unloading equipment. The boiler operation data includes the current load, combustion status, environmental protection requirements, and target coal quality parameters.

[0083] The data acquisition module 21 is also used for:

[0084] The vehicle GPS system obtains the real-time location information of vehicles on the road and combines it with traffic flow data to predict the estimated arrival time of the vehicles.

[0085] The coal quality rapid testing equipment is used to conduct online testing of the type and quantity of coal used by vehicles queuing in the parking lot, and to obtain the coal quality parameters of the coal being transported.

[0086] Furthermore, in one possible implementation of this embodiment, such as Figure 3As shown, the demand prediction module 22 is further configured to:

[0087] The time series analysis and machine learning algorithm are used to predict the change trend of coal quality demand in the future time period, combined with the current load of the boiler, historical combustion data and environmental protection indicators.

[0088] According to the prediction result, a plurality of coal quality parameter combination schemes are generated, and each coal quality parameter combination scheme is evaluated to generate a coal quality demand curve.

[0089] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 As shown, the scheduling priority module 23 is further configured to:

[0090] The coal quality matching degree weight parameter is set, and the weight is dynamically adjusted according to the deviation degree of the vehicle coal quality from the target coal quality in the current period;

[0091] The real-time load rate of the unloading equipment is used as an adjustment factor of the scheduling sequence, and the vehicle that can be distributed to the low-load equipment is preferentially scheduled to balance the equipment use efficiency.

[0092] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 As shown, the scheduling priority module 23 is further configured to:

[0093] The edge computing module 25 is configured to start the edge computing node to perform local optimization calculation when it is detected that the incoming plant coal quality deviates from the predicted demand, and to complete the update and push of the scheduling instruction within a preset time period.

[0094] It should be noted that the foregoing explanation and description of the method embodiment are also applicable to the device of the present embodiment, and the principle is the same, which is not limited in the present embodiment.

[0095] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0096] Figure 4 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0097] As Figure 4As shown, the electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 302 or a computer program loaded into a RAM (Random Access Memory) 303 from a storage unit 308. Various programs and data required for the operation of the electronic device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0098] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306 such as a keyboard, a mouse, and the like, an output unit 307 such as various types of displays, a speaker, and the like, a storage unit 308 such as a magnetic disk, an optical disk, and the like, and a communication unit 309 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0099] The computing unit 301 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, and the like. The computing unit 301 performs various methods and processes described above, such as the dynamic dispatching method for coal cars. For example, in some embodiments, the dynamic dispatching method for coal cars can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the aforementioned dynamic dispatching method for coal cars by any other appropriate means, such as by means of firmware.

[0100] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System on a Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0101] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general or special purpose computer, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0102] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include a linearly-programmed electrical connection, a portable computer diskette, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory), or flash memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0104] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0105] The computer system can include clients and servers. This relationship can be between a client and a server that are typically distant from each other and typically interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with a blockchain.

[0106] It should be noted that artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of people, and has both hardware and software technologies. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, etc. several major directions.

[0107] The first, second, and the like various numerical numbers involved in the present disclosure are only for the convenience of differentiation in the description, and do not limit the scope of the embodiments of the present disclosure, nor represent the order of precedence.

[0108] At least one of the present disclosure can also be described as one or more, and the plurality can be two, three, four or more, which is not limited by the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D". The technical features described by "first", "second", "third", "A", "B", "C" and "D" have no order or size order.

[0109] It should be understood that the steps shown above can be reordered, added or deleted. For example, the steps described in the present disclosure can be executed in parallel, sequentially or in different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which is not limited herein.

[0110] The above detailed description does not limit the scope of the disclosure. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the disclosure shall be included in the scope of the disclosure.

Claims

1. A method for dynamic transportation of coal by truck, characterized in that, include: Real-time collection of data related to coal transportation by truck and boiler operation; Based on the collected transportation-related data and boiler operation data, a dynamic combustion demand prediction model is constructed to generate the coal quality demand curve for the furnace in the future preset time period, and to determine the demand for different coal types and the range of coal quality parameters for each time period. Based on the coal quality demand curve and vehicle information, a vehicle scheduling priority evaluation model is established to prioritize vehicles, prioritizing those whose coal quality parameters meet the current combustion requirements and can be directly fed into the furnace. The scheduling order is also dynamically adjusted based on the proportion of different types of coal in the blended coal. Based on the priority ranking results, a vehicle entry sequence table and scheduling instructions are automatically generated, and the scheduling instructions are pushed to the vehicle on-board terminal and the power plant unloading management system to realize automatic vehicle numbering and scheduling.

2. The method according to claim 1, characterized in that, The transportation-related data includes the location information and estimated arrival time of vehicles en route, the type and quantity of coal in the queue of vehicles, and the operating status and processing capacity of the unloading equipment. The boiler operation data includes the current load, combustion status, environmental protection requirements, and target coal quality parameters. The real-time acquisition of data related to coal transportation by truck and boiler operation includes: The vehicle GPS system obtains the real-time location information of vehicles on the road and combines it with traffic flow data to predict the estimated arrival time of the vehicles. The coal quality rapid testing equipment is used to conduct online testing of the type and quantity of coal used by vehicles queuing in the parking lot, and to obtain the coal quality parameters of the coal being transported.

3. The method according to claim 1, characterized in that, The dynamic combustion demand prediction model is constructed based on the collected dispatch-related data and boiler operation data, including: By employing time series analysis and machine learning algorithms, combined with current boiler load, historical combustion data, and environmental indicators, we can predict the trend of coal quality demand changes over a future period. Based on the prediction results, multiple sets of coal quality parameter combination schemes are generated, and each set of coal quality parameter combination schemes is evaluated to generate the coal quality demand curve for furnace feed.

4. The method according to claim 1, characterized in that, The step of establishing a vehicle scheduling priority evaluation model based on the coal quality demand curve and vehicle information includes: Set a coal quality matching degree weight parameter, and dynamically adjust the weight according to the degree of deviation between the vehicle's coal quality and the target coal quality for the current period. The real-time load rate of the unloading equipment is used as an adjustment factor for the scheduling order, and vehicles that can be allocated to low-load equipment are given priority in scheduling to balance the efficiency of equipment use.

5. The method according to claim 1, characterized in that, Also includes: When a deviation is detected between the quality of incoming coal and the predicted demand, the edge computing node is activated to perform local optimization calculations and completes the update and push of scheduling instructions within a preset time period.

6. A dynamic coal transportation device for automobiles, characterized in that, include: The data acquisition module is used to collect real-time data related to the transportation of coal by truck and boiler operation data. The demand forecasting module is used to construct a dynamic combustion demand forecasting model based on the collected transportation-related data and boiler operation data, generate the coal quality demand curve for the furnace in the future preset time period, and determine the demand for different coal types and the range of coal quality parameters for each time period. The scheduling priority module is used to establish a vehicle scheduling priority evaluation model based on the coal quality demand curve and vehicle information, prioritize vehicles, prioritize vehicles whose coal quality parameters meet the current combustion demand and can be directly fed into the furnace, and dynamically adjust the scheduling order according to the proportion of coal type in the blended coal. The dispatch instruction generation module is used to automatically generate a vehicle entry sequence table and dispatch instructions based on the priority sorting results, and push the dispatch instructions to the vehicle on-board terminal and the power plant unloading management system to realize automatic vehicle numbering and dispatching.

7. The apparatus according to claim 6, characterized in that, The transportation-related data includes the location information and estimated arrival time of vehicles en route, the type and quantity of coal in the queue of vehicles, and the operating status and processing capacity of the unloading equipment. The boiler operation data includes the current load, combustion status, environmental protection requirements, and target coal quality parameters. The data acquisition module is also used for: The vehicle GPS system obtains the real-time location information of vehicles on the road and combines it with traffic flow data to predict the estimated arrival time of the vehicles. The coal quality rapid testing equipment is used to conduct online testing of the type and quantity of coal used by vehicles queuing in the parking lot, and to obtain the coal quality parameters of the coal being transported.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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