Harbor area dust removal control method and system, equipment and medium

Abstract

The invention provides a harbor area dust removal control method and system, equipment and a medium, and belongs to the technical field of environmental protection, the method comprises the steps that multi-source data of a harbor area is acquired, and the multi-source data comprises production operation information, weather data and TSP concentration data; based on the multi-source data and the dust fall strategy knowledge base, generating an initial dust fall scheme of the harbor district; wherein the initial dust falling scheme comprises dust falling modes of a plurality of positions in the harbor district; and based on the initial dust fall scheme and the real-time position information of each environmental sanitation vehicle, selecting and distributing each environmental sanitation vehicle to obtain a final dust fall scheme of the wharf and harbor district. According to the harbor area dust removal control method and system, the equipment and the medium, the resource utilization efficiency can be improved, the dust removal effect is improved, and the cost is effectively reduced.

Description

Technical Field

[0001] This application belongs to the field of environmental protection technology, and more specifically, it relates to a method, system, equipment, and medium for dust control in port areas. Background Technology

[0002] As a major bulk cargo loading and unloading base, the port faces the challenge of dust pollution control. The port area has a long shoreline, a large storage area, a complex cargo structure, and many dust-prone cargoes (such as coal and bauxite). In addition, it adopts traditional ship loading and unloading and truck transfer processes, so it is characterized by a wide distribution of operating areas and a large traffic volume.

[0003] To solve the problem of dust pollution and ensure that the comparative data of the port city continues to improve, the common methods are to regularly spray and water the entire area, or to manually dispatch according to experience. These methods can effectively control dust pollution. However, these methods are too costly, and existing environmental protection facilities have problems such as data silos, slow response, and low resource dispatch efficiency, which ultimately makes dust control extremely difficult. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, equipment, and medium for dust control in port areas, in order to solve the technical problems of high cost and difficulty in dust control in port areas.

[0005] A first aspect of this application provides a port area dust control method, comprising: Acquire multi-source data from the terminal and port area, including production operation information, weather data, and TSP concentration data; Based on multi-source data and a dust suppression strategy knowledge base, an initial dust suppression plan for the port area is generated; the initial dust suppression plan includes dust suppression methods at multiple locations within the port area. Based on the initial dust suppression plan and the real-time location information of each sanitation vehicle, the sanitation vehicles are selected and allocated to obtain the final dust suppression plan for the port area.

[0006] In one possible implementation, an initial dust suppression plan for the port area is generated based on multi-source data and a dust suppression strategy knowledge base, including: Based on production operation information, the port area is divided into multiple sub-areas; each sub-area corresponds to a type of cargo and a dusty period. For each sub-region, the dust risk level of that sub-region is calculated based on the type of cargo, weather data, and TSP concentration data. Based on the various dust risk levels, the dust suppression methods for each sub-region are matched in the dust suppression strategy knowledge base to obtain the initial dust suppression plan for the port area.

[0007] In one possible implementation, production operation information includes pallet information, port clearance plans, ship loading and unloading plans, and cargo types; based on this production operation information, the terminal port area is divided into multiple sub-areas, including: Spatial division is based on berth operation area, yard storage area and road transport area, and the geographical boundaries of each sub-area are determined; By spatially mapping the stacking information, port clearance plan, ship loading and unloading plan, and cargo type in the production information to each sub-region, the corresponding cargo type for each sub-region is obtained.

[0008] In one possible implementation, before calculating the dust risk level of each sub-region based on the corresponding cargo type, weather data, and TSP concentration data, the method further includes: In a standard environment, loading and unloading operations were simulated for each type of cargo to determine the dust coefficient corresponding to each type of cargo. Accordingly, for each sub-region, the dust risk level of that sub-region is calculated based on the type of cargo, weather data, and TSP concentration data, including: For each sub-region, the dust risk index value of the sub-region is calculated by weighted summation based on the dust coefficient of the corresponding cargo type, wind speed and humidity in the weather data, and TSP concentration. Based on the preset range, the dust risk index values ​​of each sub-region are divided into high dust risk level, medium dust risk level and low dust risk level.

[0009] In one possible implementation, before generating the initial dust suppression plan for the port area based on multi-source data and a dust suppression strategy knowledge base, the following is also included: Based on the dust suppression effect and cost in historical dust suppression operation data, the dust suppression methods corresponding to each dust risk level are determined, and a dust suppression strategy knowledge base is formed.

[0010] In one possible implementation, sanitation vehicles include water sprinkler trucks, mist cannon trucks, and sweeper trucks; based on the initial dust suppression plan and the real-time location information of each sanitation vehicle, the vehicles are selected and allocated to obtain the final dust suppression plan for the port area, including: Based on the initial dust suppression plan and the correspondence between dust risk level, dust suppression method and sanitation vehicle type, the sanitation vehicle type corresponding to each sub-area is determined; the correspondence between dust risk level, dust suppression method and sanitation vehicle type includes: high dust risk level - fog cannon dust suppression - fog cannon truck, medium dust risk level or low dust risk level - water spraying and sweeping - water spraying truck or sweeping truck. Based on the real-time location information of each sanitation vehicle, the distance between each sanitation vehicle and each sub-area is calculated; For each sub-region, assign the sanitation vehicle of the corresponding type that is closest to that sub-region to that sub-region.

[0011] In one possible implementation, for each sub-region, the sanitation vehicle of the corresponding type and closest to that sub-region is assigned to that sub-region, including: If the number of fog cannons that can be allocated to the first sub-area is insufficient, the number of water sprinkler trucks or sweeper trucks to be allocated to the first sub-area is calculated based on the substitution efficiency coefficient, and the nearest water sprinkler trucks or sweeper trucks of the corresponding number are allocated to the first sub-area.

[0012] A second aspect of this application provides a port area dust removal control system, comprising: The acquisition module is used to acquire multi-source data from the port area, including production operation information, weather data, and TSP concentration data. The initial scheme generation module is used to generate an initial dust suppression scheme for the port area based on multi-source data and a dust suppression strategy knowledge base; the initial dust suppression scheme includes dust suppression methods for multiple locations within the port area; The final scheme generation module is used to select and allocate each sanitation vehicle based on the initial dust suppression scheme and the real-time location information of each sanitation vehicle, so as to obtain the final dust suppression scheme for the port area.

[0013] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described port area dust removal control method.

[0014] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described port area dust removal control method.

[0015] The beneficial effects of the port area dust control method, system, equipment, and medium provided in this application embodiment are as follows: By integrating multi-source data such as production operation information, weather data, and TSP concentration data, a comprehensive understanding of the factors affecting dust pollution in the port area (operational activities, environmental conditions, and current pollution status) is ensured. Based on production operation information, dust sources are identified (such as loading and unloading areas for specific types of cargo). By combining the correspondence between dust scenarios and dust suppression methods in the dust suppression strategy knowledge base, suitable dust suppression methods are matched for each area to form a preliminary plan and avoid indiscriminate operations. Finally, by combining the real-time location of sanitation vehicles, vehicles are allocated to ensure that high-priority areas can be quickly covered, while reducing empty runs of vehicles, improving resource utilization efficiency, enhancing dust removal effects, and effectively reducing costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of a port area dust control method provided in an embodiment of this application; Figure 2 This is a structural block diagram of a port area dust removal control system provided in one embodiment of the present application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0020] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a port area dust control method according to an embodiment of this application. The method includes: S101: Obtain multi-source data from the port area, including production operation information, weather data, and TSP concentration data.

[0021] In this embodiment, production operation information refers to the planning and status data related to cargo handling in the port area, including stacking information (the specific location and quantity of cargo stacked), port clearance plan (the time, route, and quantity of cargo transferred from the storage yard to outside the port), loading and unloading ship plan (ship berthing time, type and quantity of cargo loaded and unloaded), and cargo type (such as coal, ore, grain, etc.). Weather data affects dust dispersion, including wind speed, wind direction, humidity, and rainfall. Total Suspended Particulates (TSP) concentration is the core indicator for measuring the real-time pollution level of dust.

[0022] The hardware layer implemented in this application includes the following parts: Vehicle positioning: Beidou dual-mode high-precision positioning terminal: accuracy ≤1 meter, supports RTK dynamic calibration, covering all sanitation vehicles (sprinkler trucks, fog cannon trucks, sweeper trucks).

[0023] Sprinkler system control equipment: deployed from the pump rooms of Phase I, II, and III to the central control center on the 5th floor of the joint operation building, supporting image signal access to achieve system control.

[0024] Wireless network bridging: Pumping station to Substation #1: Industrial-grade wireless bridge with bandwidth ≥300Mbps, supporting dual-band redundancy. Substation #1 to Intermodal Building: Low-latency interconnection achieved through existing wired network.

[0025] Car wash station upgrade: A high-definition camera was installed at the exit of the weighbridge to view the car wash operation.

[0026] Based on the aforementioned facilities, production operation information is obtained through the application programming interface (API) of the port area's production management system, with data updates maintained at intervals of 1 to 5 minutes. Weather data originates from the meteorological bureau's data interface or the port area's self-built meteorological monitoring network, including both real-time monitoring data and weather forecasts for the next 3 hours. TSP concentration data is collected through dust monitoring equipment distributed at key locations within the port area, with monitoring points spaced 200 to 500 meters apart. By constructing a multi-source data acquisition network system and utilizing IoT technology, real-time acquisition and stable transmission of various monitoring data are achieved, providing a complete and reliable data foundation for subsequent analysis and decision-making processes.

[0027] S102: Based on multi-source data and a dust suppression strategy knowledge base, generate an initial dust suppression plan for the port area; wherein, the initial dust suppression plan includes dust suppression methods at multiple locations within the port area.

[0028] In this embodiment, the dust suppression strategy knowledge base stores a set of standard schemes and operation parameters for dust suppression operations in different work scenarios. By deeply analyzing the spatiotemporal distribution characteristics of production operations and combining the operation experience accumulated in the professional knowledge base, the theoretically optimal dust suppression operation implementation scheme is generated, which can quickly determine the most suitable dust suppression scheme during actual dust suppression operations.

[0029] The initial dust suppression plan is a preliminary work plan generated based on theoretical analysis and historical data. It involves multiple locations within the port area and can be combined with production operation information to adopt different dust suppression methods and operating parameters according to the differences in dust conditions at different locations, so as to ensure the dust suppression effect while avoiding excessive costs.

[0030] S103: Based on the initial dust suppression plan and the real-time location information of each sanitation vehicle, select and allocate each sanitation vehicle to obtain the final dust suppression plan for the port area.

[0031] In this embodiment, sanitation vehicles include mist cannons (long-range spray dust suppression), water sprinklers (road surface watering and wetting), and sweepers (integrated sweeping and watering). Since the functions of various sanitation vehicles differ, the dust suppression effects they produce are different, and they are usually in a mobile state with no fixed location. Therefore, based on the theoretical solution, it is necessary to optimize the configuration and allocation of operational resources by combining the availability status and real-time location information of sanitation vehicles, so as to improve the feasibility of the operational plan and the overall operational efficiency.

[0032] This application embodiment integrates multi-source data, including production operation information, weather data, and TSP concentration data, to ensure comprehensive perception of factors affecting dust pollution in the port area (operational activities, environmental conditions, and current pollution status). It identifies dust sources (such as loading and unloading areas for specific cargo types) based on production operation information, and combines this with the correspondence between dust scenarios and dust suppression methods in the dust suppression strategy knowledge base to match suitable dust suppression methods for each area, forming a preliminary plan and avoiding indiscriminate operations. Finally, it allocates vehicles based on the real-time location of sanitation vehicles to ensure rapid coverage of high-priority areas, while reducing empty vehicle runs, improving resource utilization efficiency, enhancing dust removal effectiveness, and effectively reducing costs.

[0033] In one possible implementation, an initial dust suppression plan for the port area is generated based on multi-source data and a dust suppression strategy knowledge base, including: Based on production operation information, the port area is divided into multiple sub-areas; each sub-area corresponds to a type of cargo and a dusty period. For each sub-region, the dust risk level of that sub-region is calculated based on the type of cargo, weather data, and TSP concentration data. Based on the various dust risk levels, the dust suppression methods for each sub-region are matched in the dust suppression strategy knowledge base to obtain the initial dust suppression plan for the port area.

[0034] In this embodiment, based on the management concept of "zoning management, hierarchical control, and classified disposal", the complex port operation environment is decomposed into multiple easily manageable sub-units. Each sub-area corresponds to only a single type of cargo and time period, ensuring the pertinence of subsequent risk assessment and dust reduction strategies.

[0035] Dust risk level is used to quantify the degree of dust pollution in a region. It takes into account three dimensions: the inherent dust-generating properties of the cargo type (e.g., coal is more prone to dust generation than steel), environmental diffusion conditions (e.g., strong winds accelerate dust diffusion), and the current pollution level (e.g., TSP concentration has exceeded the standard). It quantifies the dust risk of a sub-region and provides a basis for matching subsequent dust reduction strategies.

[0036] Finally, the dust suppression strategy knowledge base pre-stores the corresponding rules of risk level and dust suppression method. By matching the rules, the dust suppression method can be directly determined for each sub-area. After integration, an initial plan covering the entire port area is formed, avoiding the problem of extensive uniform operation across the entire area and improving the targeting of dust suppression.

[0037] In one possible implementation, production operation information includes pallet information, port clearance plans, ship loading and unloading plans, and cargo types; based on this production operation information, the terminal port area is divided into multiple sub-areas, including: Spatial division is based on berth operation area, yard storage area and road transport area, and the geographical boundaries of each sub-area are determined; By spatially mapping the stacking information, port clearance plan, ship loading and unloading plan, and cargo type in the production information to each sub-region, the corresponding cargo type for each sub-region is obtained.

[0038] In this embodiment, the specific content of the production operation information includes: Stacking location information: Records structured data of cargo stacking location (latitude and longitude coordinates), stacking height, and floor area (e.g., "Stacking location No. 3 in Block B of the storage yard, coordinates (X1,Y1) to (X2,Y2), 5000 tons of coal stored").

[0039] Port clearance plan: This includes the entry and exit times of port clearance vehicles, transportation routes, and the types and quantities of cargo loaded (e.g., "10:00-16:00, 20 trucks transport coal from storage yard B to the factory outside the port, with each truck carrying 50 tons").

[0040] Ship loading and unloading plan: berth of the vessel, type of cargo to be loaded and unloaded, start / end time of operation, and loading / unloading volume (e.g., "berth No. 2, unloading iron ore from 08:00 to 14:00, with an estimated unloading volume of 10,000 tons").

[0041] Cargo type: Cargo can be classified according to dust generation into high dust (coal, ore), medium dust (grain), and low dust (containers, steel).

[0042] The specific steps for dividing a region into sub-regions based on production operation information include: First, the entire port area is divided into three functional zones: berth operation area (ship loading and unloading area), storage yard area (cargo storage area), and road transport area (vehicle passage area). Each functional zone is further subdivided by physical location (e.g., the berth operation area is divided by berth numbers 1-5, and the storage yard area is divided by AE zone). Geographical boundaries are defined using GIS map coordinates (e.g., the boundary of berth 1 is a polygon of (X1,Y1)-(X2,Y2)-(X3,Y3)-(X4,Y4)). Then, the port area GIS system links production operation information with geographical boundaries: stacking information is directly mapped to the corresponding sub-area of ​​the storage yard area; ship loading and unloading plans are mapped to the corresponding sub-area of ​​the berth operation area; "loading points" in the port clearance plan are mapped to the sub-area of ​​the storage yard area, and "transport routes" are mapped to the sub-area of ​​the road transport area. Each sub-area is ultimately associated with a unique cargo type and dusty period, i.e., the main operating time period within that sub-area. When scheduling sanitation vehicles, staggered scheduling can be implemented based on the dusty period corresponding to each sub-area.

[0043] In one possible implementation, before calculating the dust risk level of each sub-region based on the corresponding cargo type, weather data, and TSP concentration data, the method further includes: In a standard environment, loading and unloading operations were simulated for each type of cargo to determine the dust coefficient corresponding to each type of cargo. Accordingly, for each sub-region, the dust risk level of that sub-region is calculated based on the type of cargo, weather data, and TSP concentration data, including: For each sub-region, the dust risk index value of the sub-region is calculated by weighted summation based on the dust coefficient of the corresponding cargo type, wind speed and humidity in the weather data, and TSP concentration. Based on the preset range, the dust risk index values ​​of each sub-region are divided into high dust risk level, medium dust risk level and low dust risk level.

[0044] In this embodiment, the dust coefficient is a quantitative index (0-1) characterizing the inherent dust-generating properties of a type of cargo; a higher value indicates that the cargo generates more dust under the same operating conditions. The specific steps for determining the dust coefficient include: First, a standard experimental environment is constructed, including a 50m x 50m enclosed test area, equipped with professional equipment such as laser dust monitors, anemometers, and temperature and humidity sensors. Test conditions are strictly controlled within the range of wind speed 2-8 m / s and humidity 30%-80%.

[0045] The specific experimental procedure includes: preparing test samples of standard volume, simulating loading and unloading operations according to standard operating procedures, collecting dust concentration data for 30 minutes, and finally normalizing the collected dust concentration data and mapping it to the 0-1 range as the dust coefficient for various types of goods.

[0046] The risk index value was calculated using a weighted summation method, with the weights of each factor determined based on expert experience and big data training. The weights for dust coefficient, wind speed, humidity, and TSP concentration were set at 0.4, 0.3, 0.2, and 0.1, respectively. The calculation process first normalized each factor: dust concentration was normalized to a range of 0-1, wind speed to 0-15 m / s, humidity to the negative normalized range of 20%-90%, and TSP concentration to 0-500 μg / m³. Finally, the weighted summation of all factors yielded the comprehensive risk index value.

[0047] Risk indicator values ​​are divided into three levels: 0-0.3 is a low-risk level, corresponding to the routine monitoring operation mode; 0.3-0.7 is a medium-risk level, requiring preventive operation measures; and 0.7-1.0 is a high-risk level, requiring the activation of emergency response procedures.

[0048] In one possible implementation, before generating the initial dust suppression plan for the port area based on multi-source data and a dust suppression strategy knowledge base, the following is also included: Based on the dust suppression effect and cost in historical dust suppression operation data, the dust suppression methods corresponding to each dust risk level are determined, and a dust suppression strategy knowledge base is formed.

[0049] In this embodiment, historical data collection includes three dimensions: the operation record dimension collects dust suppression methods, operation parameters, and effect evaluation data; the cost data dimension counts water consumption, fuel consumption, and labor costs; and the environmental data dimension records changes in TSP concentration before and after the operation.

[0050] The knowledge extraction process employs a multi-stage screening mechanism: first, historical operation cases are screened according to risk level; then, they are ranked and evaluated based on operation effectiveness; finally, the best operation strategy is extracted from the top 10 cases with the best results. Each risk level corresponds to a strategy that includes key parameters such as dust suppression method, operation duration, water flow rate, effectiveness index, and unit cost. The knowledge base also establishes a continuous optimization mechanism, including verifying strategy effectiveness through A / B testing, continuously optimizing operation parameters using machine learning models, and ensuring the rationality and feasibility of the strategies through expert review.

[0051] The knowledge base is centered on dust reduction strategies and is categorized and stored according to risk levels. Each strategy contains a complete system of operational parameters and effect evaluation data.

[0052] In one possible implementation, sanitation vehicles include water sprinkler trucks, mist cannon trucks, and sweeper trucks; based on the initial dust suppression plan and the real-time location information of each sanitation vehicle, the vehicles are selected and allocated to obtain the final dust suppression plan for the port area, including: Based on the initial dust suppression plan and the correspondence between dust risk level, dust suppression method and sanitation vehicle type, the sanitation vehicle type corresponding to each sub-area is determined; the correspondence between dust risk level, dust suppression method and sanitation vehicle type includes: high dust risk level - fog cannon dust suppression - fog cannon truck, medium dust risk level or low dust risk level - water spraying and sweeping - water spraying truck or sweeping truck. Based on the real-time location information of each sanitation vehicle, the distance between each sanitation vehicle and each sub-area is calculated; For each sub-region, assign the sanitation vehicle of the corresponding type that is closest to that sub-region to that sub-region.

[0053] In this embodiment, the operational characteristics of various sanitation vehicles are as follows: Fog cannon truck: Long range (10-30 meters) and fine mist particles (50-100μm), suitable for rapid dust suppression in areas with high concentrations of dust; Water sprinkler truck: Wide coverage (road surface and surrounding area 1-3 meters), suitable for suppressing low to medium concentration dust; Sweeper truck: It has both sweeping (removing accumulated dust) and watering functions, and is suitable for routine dust removal in road transport areas.

[0054] Therefore, for high-risk sub-areas, mist cannons are prioritized (due to their highest dust suppression efficiency). The straight-line distance (or navigation path distance) between the vehicle and the sub-area is calculated using the vehicle's onboard positioning terminal, and the nearest available mist cannon is selected. For medium / low-risk sub-areas, water trucks or sweeper trucks are allocated, selected based on the area type. For example, sweeper trucks are prioritized in road areas, also allocated according to the principle of proximity. Vehicle status information is updated in real time during the allocation process to ensure the accuracy and timeliness of resource allocation.

[0055] In one possible implementation, for each sub-region, the sanitation vehicle of the corresponding type and closest to that sub-region is assigned to that sub-region, including: If the number of fog cannons that can be allocated to the first sub-area is insufficient, the number of water sprinkler trucks or sweeper trucks to be allocated to the first sub-area is calculated based on the substitution efficiency coefficient, and the nearest water sprinkler trucks or sweeper trucks of the corresponding number are allocated to the first sub-area.

[0056] In this embodiment, the substitution efficiency coefficient is derived through in-depth analysis of historical operation data. First, historical substitution operation records are loaded, and for combinations of fog cannon trucks with alternative vehicle types such as water sprinkler trucks and sweeper trucks, the operation effect ratio and quantity conversion coefficient are calculated respectively. The effect ratio reflects the difference in operation effect between the alternative vehicle type and the original vehicle type, while the quantity conversion coefficient determines the proportion of vehicles required to achieve the same effect.

[0057] The alternative solution generation process employs a tiered decision-making mechanism: first, the required number of fog cannon vehicles is calculated; then, the required number of alternative vehicle types is calculated based on the substitution efficiency coefficient. The system checks whether available vehicles meet the quantity requirements according to vehicle type priority; once the requirements are met, an alternative solution is immediately generated. Simultaneously, operational parameters are automatically adjusted, including extending operational time based on the effectiveness ratio, increasing operational intensity based on quantity conversion coefficients, and dynamically adjusting operational frequency based on actual results.

[0058] The operation time is extended according to the inverse relationship between the base time and the effect ratio, the operation intensity is adjusted according to the product of the base intensity and the quantity conversion coefficient, and the operation frequency is dynamically optimized based on the real-time monitoring effect.

[0059] In one specific embodiment, dust control in the port area can be achieved through the following steps: Port Area GIS Platform Integration: Obtain high-precision maps (including coordinates of roads, storage yards, and dust-prone areas) via API.

[0060] Real-time data stream: integrates vehicle positioning, dust monitoring, production operations, weather data, and monitoring data around the positioning point.

[0061] TSP concentration data: Data such as alarms from environmental monitoring stations exceeding standards are connected to the platform.

[0062] Dustfall data model: Production operation system interface: Real-time acquisition of stack location information (such as H4-2), port loading / unloading plans, and cargo type (PB powder / FB powder).

[0063] Dust characteristics database for different cargo types: Preset parameters for different cargo types (dust coefficient, dust suppression method, operation time), as shown in Table 1: Table 1

[0064] Intelligent scheduling module: Based on dust monitoring data, production plans and other data, dynamically generate dust suppression tasks and intelligently push them to relevant environmental protection vehicles.

[0065] Manual dispatch module: Users can manually add dust suppression tasks at the dust suppression demand points on the map or manually select spray facilities or environmental protection vehicles, and specify the type of dust suppression equipment (spraying, watering, mist cannon, sweeping). Environmental protection vehicles can check the online status of the vehicles (whether they are faulty).

[0066] Map visualization enhancement module: Device dynamic layer management: The icons for elements such as sprinkler point status, pump room, car wash station, weather station, and environmental protection vehicles are dynamically updated (showing their availability). TSP concentration heat map: Real-time display of the pollution level around the stack; when the level exceeds the standard, the stack is highlighted (e.g., with a red border).

[0067] Task video linkage: Click on the spray point, water pump station, car wash station and environmental protection vehicle to pop up the surrounding related monitoring video window and view the operation status in real time.

[0068] Task trajectory playback: Filter vehicle trajectories by time period, overlay map paths and timelines, and view surveillance video playback simultaneously.

[0069] Centralized Sprinkler Control Module: Displays real-time images and operating parameters of sprinkler equipment, and supports manual / automatic mode switching.

[0070] Car wash station management module: Video displays the working status of the car wash station.

[0071] As can be seen from the above, this solution can achieve precise, intelligent, and efficient dust control, improve the efficiency of environmental resource utilization, optimize air quality, and realize digital management of environmental protection operations.

[0072] Corresponding to the port area dust control method in the above embodiments, Figure 2 This is a structural block diagram of a port area dust removal control system provided according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The port area dust removal control system 2 includes: Module 21 is used to acquire multi-source data from the port area, including production operation information, weather data, and TSP concentration data. The initial scheme generation module 22 is used to generate an initial dust suppression scheme for the port area based on multi-source data and a dust suppression strategy knowledge base; wherein, the initial dust suppression scheme includes dust suppression methods at multiple locations within the port area; The final scheme generation module 23 is used to select and allocate each sanitation vehicle based on the initial dust suppression scheme and the real-time location information of each sanitation vehicle, so as to obtain the final dust suppression scheme for the port area.

[0073] In one possible implementation, the initial scheme generation module 22 is specifically used for: Based on production operation information, the port area is divided into multiple sub-areas; each sub-area corresponds to a type of cargo and a dusty period. For each sub-region, the dust risk level of that sub-region is calculated based on the type of cargo, weather data, and TSP concentration data. Based on the various dust risk levels, the dust suppression methods for each sub-region are matched in the dust suppression strategy knowledge base to obtain the initial dust suppression plan for the port area.

[0074] In one possible implementation, production operation information includes stacking information, port clearance plan, loading and unloading plan, and cargo type; the initial scheme generation module 22 is specifically used for: Spatial division is based on berth operation area, yard storage area and road transport area, and the geographical boundaries of each sub-area are determined; By spatially mapping the stacking information, port clearance plan, ship loading and unloading plan, and cargo type in the production information to each sub-region, the corresponding cargo type for each sub-region is obtained.

[0075] In one possible implementation, the initial scheme generation module 22 is further used for: Before calculating the dust risk level of each sub-region based on the corresponding cargo type, weather data, and TSP concentration data, loading and unloading operations are simulated for each cargo type in a standard environment to determine the dust coefficient corresponding to each cargo type. For each sub-region, the dust risk index value of the sub-region is calculated by weighted summation based on the dust coefficient of the corresponding cargo type, wind speed and humidity in the weather data, and TSP concentration. Based on the preset range, the dust risk index values ​​of each sub-region are divided into high dust risk level, medium dust risk level and low dust risk level.

[0076] In one possible implementation, the initial scheme generation module 22 is further used for: Before generating the initial dust suppression plan for the port area based on multi-source data and a dust suppression strategy knowledge base, the dust suppression methods corresponding to each dust risk level are determined based on the dust suppression effect and cost in historical dust suppression operation data, and a dust suppression strategy knowledge base is formed.

[0077] In one possible implementation, sanitation vehicles include water sprinkler trucks, mist cannon trucks, and sweeper trucks; the final solution generation module 23 is specifically used for: Based on the initial dust suppression plan and the correspondence between dust risk level, dust suppression method and sanitation vehicle type, the sanitation vehicle type corresponding to each sub-area is determined; the correspondence between dust risk level, dust suppression method and sanitation vehicle type includes: high dust risk level - fog cannon dust suppression - fog cannon truck, medium dust risk level or low dust risk level - water spraying and sweeping - water spraying truck or sweeping truck. Based on the real-time location information of each sanitation vehicle, the distance between each sanitation vehicle and each sub-area is calculated; For each sub-region, assign the sanitation vehicle of the corresponding type that is closest to that sub-region to that sub-region.

[0078] In one possible implementation, the final solution generation module 23 is specifically used for: If the number of fog cannons that can be allocated to the first sub-area is insufficient, the number of water sprinkler trucks or sweeper trucks to be allocated to the first sub-area is calculated based on the substitution efficiency coefficient, and the nearest water sprinkler trucks or sweeper trucks of the corresponding number are allocated to the first sub-area.

[0079] This application embodiment integrates multi-source data, including production operation information, weather data, and TSP concentration data, to ensure comprehensive perception of factors affecting dust pollution in the port area (operational activities, environmental conditions, and current pollution status). It identifies dust sources (such as loading and unloading areas for specific cargo types) based on production operation information, and combines this with the correspondence between dust scenarios and dust suppression methods in the dust suppression strategy knowledge base to match suitable dust suppression methods for each area, forming a preliminary plan and avoiding indiscriminate operations. Finally, it allocates vehicles based on the real-time location of sanitation vehicles to ensure rapid coverage of high-priority areas, while reducing empty vehicle runs, improving resource utilization efficiency, enhancing dust removal effectiveness, and effectively reducing costs.

[0080] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the acquisition module 21 and the evaluation module 22 are shown.

[0081] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0082] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0083] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0084] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the embodiments of the port area dust removal control method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0085] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0086] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0087] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dust control in a port area, characterized in that, include: Acquire multi-source data from the port area, including production operation information, weather data, and TSP concentration data; Based on the multi-source data and dust suppression strategy knowledge base, an initial dust suppression plan is generated for the port area; wherein, the initial dust suppression plan includes dust suppression methods at multiple locations within the port area; Based on the initial dust suppression plan and the real-time location information of each sanitation vehicle, each sanitation vehicle is selected and allocated to obtain the final dust suppression plan for the port area.

2. The port area dust control method according to claim 1, characterized in that, The initial dust suppression plan for the port area is generated based on the multi-source data and the dust suppression strategy knowledge base, including: Based on the production operation information, the port area is divided into multiple sub-areas; each sub-area corresponds to a type of cargo. For each sub-region, the dust risk level of that sub-region is calculated based on the type of cargo, weather data, and TSP concentration data. Based on the various dust risk levels, the dust suppression methods for each sub-region are matched in the dust suppression strategy knowledge base to obtain the initial dust suppression plan for the port area.

3. The port area dust control method according to claim 2, characterized in that, The production operation information includes pallet information, port clearance plan, ship loading and unloading plan, and cargo type; based on the production operation information, the terminal port area is divided into multiple sub-areas, including: Spatial division is based on berth operation area, yard storage area and road transport area, and the geographical boundaries of each sub-area are determined; The stacking information, port clearance plan, loading and unloading plan, and cargo type in the production information are spatially mapped to each sub-region to obtain the cargo type corresponding to each sub-region.

4. The port area dust control method according to claim 2, characterized in that, Before calculating the dust risk level of each sub-region based on the corresponding cargo type, weather data, and TSP concentration data, the method further includes: In a standard environment, loading and unloading operations were simulated for each type of cargo to determine the dust coefficient corresponding to each type of cargo. Accordingly, for each sub-region, the dust risk level is calculated based on the type of cargo, weather data, and TSP concentration data corresponding to that sub-region, including: For each sub-region, the dust risk index value of the sub-region is calculated by weighted summation based on the dust coefficient of the corresponding cargo type, wind speed and humidity in the weather data, and TSP concentration. Based on the preset range, the dust risk index values ​​of each sub-region are divided into high dust risk level, medium dust risk level and low dust risk level.

5. The port area dust control method according to claim 2, characterized in that, Before generating the initial dust suppression plan for the port area based on the multi-source data and dust suppression strategy knowledge base, the following steps are also included: Based on the dust suppression effect and cost in historical dust suppression operation data, the dust suppression methods corresponding to each dust risk level are determined, and a dust suppression strategy knowledge base is formed.

6. The port area dust control method according to claim 2, characterized in that, The sanitation vehicles include water sprinkler trucks, mist cannon trucks, and sweeper trucks; the final dust suppression plan for the port area is obtained by selecting and allocating the sanitation vehicles based on the initial dust suppression plan and the real-time location information of each sanitation vehicle, including: Based on the initial dust suppression plan and the correspondence between dust risk level, dust suppression method and sanitation vehicle type, the sanitation vehicle type corresponding to each sub-area is determined; wherein, the correspondence between dust risk level, dust suppression method and sanitation vehicle type includes: high dust risk level - fog cannon dust suppression - fog cannon truck, medium dust risk level or low dust risk level - water spraying and sweeping - water spraying truck or sweeping truck. Based on the real-time location information of each sanitation vehicle, the distance between each sanitation vehicle and each sub-area is calculated; For each sub-region, assign the sanitation vehicle of the corresponding type that is closest to that sub-region to that sub-region.

7. The port area dust control method according to claim 6, characterized in that, The step of assigning the sanitation vehicle of the corresponding type and closest to each sub-region to that sub-region includes: If the number of fog cannons that can be allocated to the first sub-region is insufficient, the number of water sprinkler trucks or sweeper trucks to be allocated to the first sub-region is calculated based on the substitution efficiency coefficient, and the nearest water sprinkler trucks or sweeper trucks of the corresponding number are allocated to the first sub-region.

8. A dust removal control system for port areas, characterized in that, include: The acquisition module is used to acquire multi-source data from the port area, including production operation information, weather data, and TSP concentration data. The initial scheme generation module is used to generate an initial dust suppression scheme for the port area based on the multi-source data and the dust suppression strategy knowledge base; wherein, the initial dust suppression scheme includes dust suppression methods at multiple locations within the port area; The final scheme generation module is used to select and allocate each sanitation vehicle based on the initial dust suppression scheme and the real-time location information of each sanitation vehicle, so as to obtain the final dust suppression scheme of the port area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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