Adaptive Cooperative Control Method for Multi-Type Road De-icing Equipment Operation

Abstract

This invention relates to an adaptive collaborative control method for various types of road de-icing equipment, belonging to the field of intelligent control technology. This method designs differentiated control strategies and collaborative mechanisms for mechanical de-icing equipment used for heavy ice and thermal or de-icing agent de-icing equipment used for light ice. It adopts a multi-level control architecture to achieve layered collaboration in task allocation, coordination control, and execution control, ensuring the system's scalability and robustness. It establishes intelligent functions such as weather adaptation and performance self-optimization, improving the system's environmental adaptability. Employing a multi-objective global optimal algorithm, it considers three dimensions: maximizing de-icing efficiency, minimizing operating costs, and maximizing inter-equipment coordination. Through precise control of traction, steering angle, and de-icing power, it reduces de-icing agent waste and energy consumption, achieving a balance between social and economic benefits.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology and relates to an adaptive collaborative control method for the operation of multiple types of road de-icing equipment. Background Technology

[0002] Road de-icing is a crucial engineering issue for ensuring traffic safety and maintaining smooth traffic flow on trunk highways and urban roads during winter. With the continuous expansion of transportation networks and the frequent occurrence of extreme weather events, traditional manual de-icing methods are no longer sufficient to meet the needs of modern transportation systems.

[0003] Current road de-icing operations mainly rely on specialized equipment with different functions. Based on the thickness and physical properties of the ice layer, de-icing equipment is mainly divided into two categories: for thicker (usually greater than 20mm) and hard-structured heavy ice, mechanical de-icing equipment is mainly used, which cuts and breaks the ice layer with blades; for thinner (usually less than 10mm) light ice, thermal de-icing equipment (using the principle of heat exchange) or de-icing agent spreading equipment (using the principle of lowering the freezing point) is mainly used.

[0004] However, in practical engineering applications and automated operations, the operational efficiency and collaborative operation strategies of road de-icing equipment directly impact road traffic. Existing de-icing methods still suffer from the following prominent problems:

[0005] (1) The three mechanisms of mechanical ice breaking, thermal ice melting and chemical ice melting are completely different. Their corresponding operation efficiency calculation models, power characteristics and energy consumption are very different. The traditional integrated control logic is difficult to take into account the performance of multiple types of equipment.

[0006] (2) Weather factors such as ice strength, ambient temperature, humidity and wind speed have a great impact on the de-icing effect. Existing operation strategies often use fixed parameters, making it difficult to dynamically adjust the operation speed, cutting force or heat supply according to real-time weather conditions, resulting in excessive energy consumption or incomplete de-icing.

[0007] (3) In a work queue consisting of multiple pieces of equipment of different types, how to reasonably allocate tasks and maintain a safe working distance is the core problem. Different equipment have different dynamic response speeds. Traditional manual scheduling or simple formation control can easily cause driving conflicts between equipment, and it is difficult to achieve the global optimal balance between work efficiency, economic cost and queue coordination.

[0008] In summary, traditional single-machine or single-type de-icing modes are no longer sufficient to meet the requirements of modern intelligent transportation for efficient, precise, and safe de-icing. Therefore, developing an adaptive collaborative control method for multi-type road de-icing equipment has become a critical issue that urgently needs to be addressed in the field of road traffic operation and intelligent control. Summary of the Invention

[0009] In view of this, the purpose of this invention is to establish an adaptive and cooperative control method for the operation of various types of road de-icing and snow removal equipment from a systems engineering perspective, which covers physical mechanisms, mathematical modeling, adaptive control and cooperative optimization.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] An adaptive cooperative control method for multiple types of road de-icing equipment operations includes the following steps:

[0012] S1: Establish road de-icing efficiency calculation models by category: For mechanical ice-breaking equipment (H-type equipment) for heavy ice formation and ice-melting equipment (L-type equipment) for light ice formation, establish corresponding de-icing efficiency calculation functions according to their de-icing mechanisms.

[0013] S2: Construct the vehicle dynamics model of the equipment: comprehensively consider air resistance, rolling resistance, slope resistance and different types of de-icing resistance, and establish the longitudinal dynamic equation and speed influence function of the equipment.

[0014] S3: Single Equipment Adaptive Control Strategy: Establish a state-space model of the de-icing system and construct model predictive control objective functions for different types of de-icing equipment. Adaptively adjust the parameters of different types of de-icing equipment according to real-time weather influencing factors to achieve optimal control input solution for a single piece of equipment under constraints.

[0015] S4: Multi-equipment collaborative control strategy: A hierarchical distributed control architecture is adopted to realize the collaborative operation of multiple de-icing equipment of various types; the hierarchical distributed control architecture includes a task layer, a coordination layer, and an execution layer; the task layer is used for task allocation and path planning for multiple equipment of various types; the coordination layer is used for consistency collaborative control and conflict detection between equipment; the execution layer realizes single equipment control and local optimization by dynamically adjusting the operation priority;

[0016] S5: Multi-objective system overall optimization; with the goals of maximizing efficiency, minimizing cost, and maximizing coordination, the global optimization solution is performed on the operational status of multiple equipment queues.

[0017] Furthermore, in step S1, the de-icing efficiency calculation function for the H-type equipment is:

[0018]

[0019] in, H-type equipment At any moment De-icing efficiency, H-type equipment At any moment Mechanical de-icing efficiency, ; The baseline efficiency for H-type equipment; Let velocity be the influence function; Weather influencing factors; For equipment At any moment Ice-breaking power; The compressive strength of the ice layer is a function of air temperature; For equipment The contact area between the blade and the ice layer, For equipment The angle of attack of the blade; For a moment Minimum ice-breaking force required The minimum contact area between the equipment and the ice layer;

[0020] The de-icing efficiency calculation function for L-type equipment is:

[0021]

[0022] in, For L-type equipment De-icing efficiency, For thermal ice melting equipment At any moment The ice melting efficiency ; Equipment for applying de-icing agent At any moment The ice melting efficiency ;

[0023] Thermal melting efficiency:

[0024]

[0025] in, For equipment At any moment heat transfer efficiency, ; ,in For L-type equipment at all times The actual heat provided Provides reference heat supply power for L-type equipment; For a moment The heat power required per unit time For a moment The total heat required to melt ice, The duration of a unit of time;

[0026] De-icing agent de-icing efficiency:

[0027]

[0028] in, For equipment At any moment The freezing point depression value; For a moment The melting threshold temperature; For equipment At any moment The amount of de-icing agent used; For a moment The optimal dosage of de-icing agent.

[0029] Furthermore, in step S2, the longitudinal dynamic equation of the equipment is:

[0030]

[0031] in, For equipment At any moment The quality; For equipment At any moment speed; For equipment At any moment traction force; For equipment At any moment The driving resistance; For equipment At any moment De-icing resistance; For equipment indexing, For all equipment;

[0032]

[0033] in, For equipment The drag coefficient depends on the vehicle's shape; air density, For equipment Work speed; For equipment Frontal area, i.e., the projected area of ​​the front of the vehicle; For equipment Rolling resistance coefficient, It is the acceleration due to gravity. The road slope angle;

[0034] Classification of de-icing resistance models:

[0035]

[0036] in, The coefficient of friction on the ice surface for H-type equipment; The coefficient of friction for L-shaped equipment on ice surface; For H-type equipment cutting resistance; For L-type equipment, the resistance to de-icing agent spraying or the resistance to thermal blowing are... To provide support for the ice surface; For equipment Type identifier.

[0037] The velocity influence function is:

[0038]

[0039] in, Let velocity be the influence function. For work speed.

[0040] Furthermore, in step S3, the state-space model of the de-icing system is established as follows:

[0041]

[0042] in, For equipment At any moment The acceleration; For equipment At any moment The state vector includes position, velocity, and de-icing efficiency, etc. For equipment At any moment The control input vectors include traction force, steering angle, and de-icing power; For equipment At any moment The output vector; This is the process noise vector; To measure the noise vector; , , , , , The system matrix is ​​determined by the parameters of the equipment itself;

[0043] H-type equipment status matrix for:

[0044]

[0045] L-shaped equipment status matrix for:

[0046]

[0047] in, The time constant for the efficiency of H-type equipment; The time constant is the L-shaped efficiency.

[0048] Furthermore, in step S3, the categorized model prediction control objective function is constructed, specifically including:

[0049] The model predictive control objective function for H-type equipment (focusing on icebreaking force control) is:

[0050]

[0051] The model predictive control objective function for the L-type equipment (focusing on hot air temperature and chemical agent control) is:

[0052]

[0053] in, The model predicts the control objective function for the H-type equipment. The model predicts the control objective function for L-type equipment; To predict the length of the time domain; To control the length of the time domain; For H-type equipment ice-breaking capability, For reference ice-breaking force (H type); The actual heat provided to the L-type equipment For reference heat supply (L type); and For H-type equipment weight matrix; and For L-shaped equipment weight matrix; The control input vectors (traction force, steering angle, de-icing power) for H-type equipment. For L-type equipment, the control input vectors are (traction force, steering angle, and de-icing power).

[0054] The constraints include:

[0055] The categorized control constraints are: ,in, For equipment At any moment The control input vector, , Equipment type The lower and upper limits of the control input vector;

[0056] The categorized state constraints are as follows: ,in, For equipment At any moment The output vector, , For equipment type The lower and upper limits of the control output vector;

[0057] The control increment constraint is: ,in, For equipment type The maximum increment of the input vector.

[0058] Furthermore, in step S3, the equipment's type parameters are adaptively updated as follows:

[0059] H-type equipment parameter adjustment:

[0060]

[0061] L-type equipment parameter adjustments:

[0062]

[0063] in, For H-type equipment at all times The cutting force, The reference cutting force for H-type equipment, For H-type equipment at any time The optimal operating speed, For H-type equipment at any time De-icing power, The standard de-icing power for H-type equipment; For L-type equipment at all times Heat supply, For L-type equipment at all times The amount of de-icing agent used, Provide the reference heat supply power for the L-shaped equipment. The standard de-icing agent application rate for L-type equipment. For L-type equipment at all times The optimal operating speed, The reference operating speed for L-shaped equipment; Influencing factors of ice layer;

[0064] when In less severe weather conditions: H-type equipment increases cutting force. and de-icing power Reduce operating speed; L-shaped equipment increases heat supply. and the amount of de-icing agent used This reduces the speed of operations;

[0065] when When approaching level 1 (good weather): all equipment parameters are close to the baseline value, and the operating speed can be appropriately increased.

[0066] Furthermore, in step S4, the optimization model for task allocation across multiple equipment queues of various types is as follows:

[0067]

[0068]

[0069] in, Let the objective function be the de-icing cost. , These are the operating cost coefficients for H-type and L-type equipment, respectively. , These are the operating times for H-type and L-type equipment, respectively. , These refer to the working widths of H-type and L-type equipment, respectively. For equipment i The speed of operation; This represents the total de-icing requirements for the roads.

[0070] The consensus collaborative control algorithm is as follows:

[0071]

[0072] This algorithm ensures that multiple pieces of equipment of different types can operate in a coordinated and consistent manner. Among them, For equipment The state vector; For equipment Control input; For equipment The set of neighbors; These are elements of the adjacency matrix; The equipment type coupling term is calculated by the following formula.

[0073]

[0074] in, , To provide coordination benefits between different types of equipment.

[0075] Furthermore, in step S4, the inter-equipment conflict detection conditions... for:

[0076]

[0077] in, , For equipment and The position vector; For a safe buffer distance; This is the safe distance matrix.

[0078] Furthermore, in step S4, the formula for calculating job priority is:

[0079]

[0080] in, As a task priority, For equipment Based on basic priority, H-type is higher than L-type; This is the urgency weighting coefficient; The delay penalty coefficient; Efficiency weighting coefficient; For equipment The task urgency at any given moment (dimensionless) reflects the importance and time sensitivity of the task. For equipment At any moment The degree of delay (dimensionless) reflects the lag in task execution; For equipment The overall de-icing efficiency.

[0081] Furthermore, in step S5, the multi-objective optimization problem is formulated as follows:

[0082]

[0083] in,

[0084]

[0085] The constraints are:

[0086]

[0087] in, Let the de-icing efficiency be the objective function. Let the objective function be the total cost of de-icing. To equip the coordination objective function, For equipment The state vector, For the safety distance matrix, For equipment At any moment The control input vector, , Equipment type The lower and upper limits of the control input vector, For equipment type At any moment Overall de-icing efficiency, For equipment type The lowest overall de-icing efficiency.

[0088] The beneficial effects of this invention are as follows:

[0089] 1) Coordination and precise matching of multiple equipment queues of different types: Differentiated control strategies and coordination mechanisms were designed for the different characteristics of mechanical de-icing equipment for heavy ice and thermal or de-icing agent de-icing equipment for light ice. By establishing a task allocation optimization model, the optimal task assignment of multiple equipment queues of different types was achieved under the premise of meeting the total road de-icing demand, ensuring that each type of equipment can perform effectively in the most suitable scenario.

[0090] 2) Multi-level control architecture: A layered distributed control architecture (task layer, coordination layer, execution layer) is adopted. The consistency algorithm ensures the synchronization of multi-machine formation operations, realizes the layered collaboration of task allocation, coordination control and execution control, and ensures the scalability and robustness of the system.

[0091] 3) Adaptive optimization mechanism: Intelligent functions such as weather adaptation (introducing weather influencing factors, which can dynamically adjust cutting force, heat supply and spreading amount according to real-time temperature, humidity and wind speed) and performance self-optimization have been established to improve the system's environmental adaptability.

[0092] 4) Balanced optimization of overall cost and operational efficiency: The multi-objective global optimal algorithm takes into account three dimensions: "maximizing de-icing efficiency", "minimizing operational costs", and "maximizing coordination among equipment". By precisely controlling traction, steering angle and de-icing power, it reduces the waste of de-icing agents and energy consumption, achieving a balance between social and economic benefits.

[0093] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0094] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0095] Figure 1 Flowchart of an adaptive collaborative control method for various types of road de-icing equipment operations. Detailed Implementation

[0096] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0097] Please see Figure 1 This invention provides an adaptive collaborative control method for multiple types of road de-icing equipment. This embodiment focuses on the collaborative control of two types of road de-icing equipment.

[0098] 1. Two types of road de-icing equipment

[0099] (1) Mechanical de-icing equipment for refreezing (Type H)

[0100] Equipment set definition:

[0101] Applicable scenarios: Mechanical breaking of thick ice layers (>20mm), directly breaking the ice structure through the impact and cutting action of mechanical blades.

[0102] (2) Ice melting equipment for light ice (L-type)

[0103] Equipment set definition:

[0104] Applicable scenarios: Melting and removing thin ice layers (<10mm), by using heat or spreading de-icing agents.

[0105] (3) Relevant symbols

[0106] Equipment markings: For equipment indexing, For all equipment; For equipment Type identifier.

[0107] State variables: For equipment The state vector, For position coordinates, For work speed, The driving direction angle, This represents the current de-icing efficiency.

[0108] Control input: For equipment The control vector, For traction force, For steering angle, This refers to the de-icing power.

[0109] 2. Calculation of road de-icing efficiency

[0110] (1) Mechanical icebreaking mechanism (H-type equipment)

[0111] Mechanical ice breaking uses blades to apply impact and shear forces to the ice layer, causing it to break when the stress exceeds the compressive strength of the ice layer.

[0112] ① Ice-breaking force calculation model:

[0113]

[0114] This formula calculates the minimum force required for the de-icing truck blades to break the ice. When the force applied by the blades exceeds this value, the ice will break. For equipment At any moment Ice-breaking power; The compressive strength of the ice layer is a function of air temperature; For equipment The contact area between the blade and the ice layer; For equipment The angle of attack of the blade.

[0115] ② The effect of weather temperature on ice strength:

[0116]

[0117] This formula describes how the compressive strength of ice changes with temperature. The lower the temperature, the harder the ice and the more difficult it is to break. The baseline strength is the compressive strength of the ice layer at 0°C. Temperature coefficient; For exponential parameters; The ambient temperature.

[0118] (2) Thermal melting mechanism (L-type equipment)

[0119] Thermal melting causes a phase change in the ice layer by providing heat, transforming it from a solid to a liquid state.

[0120] Heat required to melt ice:

[0121]

[0122] This formula calculates the total heat required to completely melt ice into water. Among them, For a moment The total heat required to melt ice; For a moment The mass of ice; The latent heat of melting ice; The specific heat capacity of ice; For a moment Temperature difference.

[0123] Heat transfer efficiency model:

[0124]

[0125] This type of computing equipment The heat transfer efficiency. Among them, For equipment At any moment heat transfer efficiency, ; For equipment The Olympiad Math; For equipment Stanton's number.

[0126] (3) Mechanism of de-icing agent (L-type equipment)

[0127] Chemical de-icing agents promote melting by lowering the freezing point, primarily based on the principle of freezing point depression in solutions. Freezing point depression formula:

[0128]

[0129] This type of computing equipment By how many degrees did the melting temperature of the ice decrease after the addition of de-icing agent? For equipment At any moment The freezing point depression value; This lowers the freezing point constant of water; For equipment At any moment The molar concentration of the solute; It is the van der Hoff factor.

[0130] (4) Comprehensive de-icing efficiency models for various types

[0131] ① H-type de-icing efficiency :

[0132]

[0133] This formula calculates the mechanical de-icing efficiency of the H-type equipment. Among them, H-type equipment At any moment Mechanical de-icing efficiency, ; The baseline efficiency for H-type equipment; For a moment Minimum ice-breaking force required; Let velocity be the influence function; These are weather-related factors.

[0134] ② L-shaped de-icing efficiency

[0135]

[0136] This formula calculates the de-icing efficiency of the L-type equipment. Among them, For thermal ice melting equipment At any moment The ice melting efficiency ; Equipment for applying de-icing agent At any moment The ice melting efficiency ;

[0137] Thermal melting efficiency:

[0138]

[0139] in, , For L-type equipment at all times The actual heat provided Provides reference heat supply power for L-type equipment; For a moment The heat power required per unit time.

[0140] De-icing agent de-icing efficiency:

[0141]

[0142] in, For a moment The melting threshold temperature; For equipment At any moment The amount of de-icing agent used; For a moment The optimal dosage of de-icing agent.

[0143] 3. Vehicle dynamics modeling of the equipment

[0144] De-icing equipment needs to overcome various resistances during operation, including air resistance, rolling resistance, slope resistance, and de-icing resistance.

[0145] (1) Longitudinal dynamic equation

[0146]

[0147] This formula describes the motion state of a de-icing truck during de-icing operations. Among them, For equipment At any moment The weight of the L-shaped equipment will decrease as the de-icing agent is sprayed. For equipment At any moment speed; For equipment At any moment traction force; For equipment At any moment The driving resistance; For equipment At any moment De-icing resistance.

[0148]

[0149] in, For equipment The drag coefficient depends on the vehicle's shape; air density, For equipment Work speed; For equipment Frontal area, i.e., the projected area of ​​the front of the vehicle; For equipment Rolling resistance coefficient, It is the acceleration due to gravity. This refers to the road slope angle.

[0150] Classification of de-icing resistance models:

[0151]

[0152] in, The coefficient of friction on the ice surface for H-type equipment; The coefficient of friction for L-shaped equipment on ice surface; For H-type equipment cutting resistance; For L-type equipment, the resistance to de-icing agent spraying or the resistance to hot air blowing; The support force required for the equipment to withstand ice is related to the vehicle's weight and the road's gradient. This can be obtained in real time through vehicle sensors, or it can be... Estimate.

[0153] (2) Uniform velocity influence function

[0154]

[0155] This formula describes the impact of operating speed on de-icing efficiency and is applicable to all equipment types.

[0156] 4. Single-equipment adaptive control strategy

[0157] (1) System state-space model

[0158] System state equations:

[0159]

[0160] This equation is a continuous-time state-space model that describes the dynamic behavior of the de-icing system. Wherein, For equipment At any moment The acceleration; For equipment At any moment The state vector includes position, velocity, de-icing efficiency, etc. For equipment At any moment The control input vectors (traction force, steering angle, de-icing power); For equipment At any moment The output vector; This is the process noise vector; To measure the noise vector; , , , , , It is a system matrix, determined by the parameters of the equipment itself.

[0161] H-type equipment status matrix:

[0162]

[0163] L-type equipment status matrix:

[0164]

[0165] in, The time constant for the efficiency of H-type equipment; The time constant is the L-shaped efficiency.

[0166] (2) Type-based rolling optimization objective function

[0167] H-type equipment objective function (focusing on icebreaking force control):

[0168]

[0169] Objective function for L-type equipment (focusing on hot air temperature and chemical agent control):

[0170]

[0171] This formula represents the model predictive control objective function categorized by type, designing different control priorities based on the characteristics of different equipment types. Among them, To predict the length of the time domain; To control the length of the time domain; For reference ice-breaking force (H type); For reference heat supply (L type); and For H-type equipment weight matrix; and This is the weight matrix for L-shaped equipment.

[0172] (3) Constraint handling

[0173] Category-based control constraints:

[0174]

[0175] Classified state constraints:

[0176]

[0177] Control Incremental Constraints:

[0178]

[0179] in, , For equipment type The upper and lower limits of the control input vector; , For equipment type The upper and lower limits of the control output vector. For equipment type The maximum increment of the input vector.

[0180] (4) Adaptive control strategy

[0181] The control parameters are dynamically adjusted according to real-time weather conditions to improve the robustness of the de-icing effect.

[0182] Weather Influencing Factors for: This factor comprehensively considers the impact of temperature, humidity, and wind speed on de-icing operations. Among them, Ambient temperature; Relative humidity; This refers to wind speed.

[0183] The equipment's type-specific parameters are updated adaptively as follows:

[0184] H-type equipment parameter adjustment:

[0185]

[0186] L-type equipment parameter adjustments:

[0187]

[0188] This set of formulas describes how the parameters of different types of de-icing equipment adaptively adjust according to weather conditions. Among them, For H-type equipment at any time The cutting force; For L-type equipment at all times Heat supply; For L-type equipment at all times The amount of de-icing agent used; This refers to factors affecting the ice layer.

[0189] when In less severe weather conditions: H-type equipment increases cutting force. and de-icing power Reduce operating speed; L-shaped equipment increases heat supply. and the amount of de-icing agent used This reduces the speed of operations.

[0190] when When approaching level 1 (good weather): all equipment parameters are close to the baseline value, and the operating speed can be appropriately increased.

[0191] 5. Multi-equipment collaborative control strategy

[0192] (1) Hierarchical distributed control architecture

[0193] A hierarchical distributed control architecture is adopted to enable the collaborative operation of multiple de-icing equipment of various types.

[0194] Control hierarchy: ① Task layer: Task allocation and path planning for multiple equipment queues of various types; ② Coordination layer: Coordination and conflict resolution between equipment; ③ Execution layer: Single equipment control and local optimization.

[0195] 1) Task allocation for multiple equipment of different types:

[0196] Task allocation optimization model:

[0197]

[0198]

[0199] This model is used for optimal task allocation among multiple, multi-type equipment queues, minimizing total cost while meeting de-icing requirements. , For H-type and L-type equipment, the operating cost coefficient is used. , The operating time for H-type and L-type equipment; , For H-type and L-type equipment, the operating width is specified. This represents the total de-icing requirements for the roads.

[0200] 2) Consistency Cooperative Control Algorithm

[0201]

[0202] This algorithm ensures that multiple pieces of equipment of different types can operate in a coordinated and consistent manner. Among them, For equipment The state vector; For equipment Control input; For equipment The set of neighbors; These are elements of the adjacency matrix; The equipment type coupling term is calculated by the following formula.

[0203]

[0204] in, , To provide coordination benefits between different types of equipment.

[0205] (2) Equipment collision detection and avoidance

[0206] 1) Safety distance design for different types of equipment

[0207] Safe distance matrix:

[0208]

[0209] in, To maintain a safe distance between H-type equipment, To maintain a safe distance between H-type and L-type equipment. To maintain a safe distance between L-type and H-type equipment. The safe distance between L-shaped equipment and L-shaped equipment.

[0210] Collision detection conditions:

[0211]

[0212] This condition is used to detect whether two de-icing devices are too close together, and it takes into account the safety requirements of different types of equipment. Among them, , For equipment and The position vector; For a safe buffer distance.

[0213] 2) Priority dynamically adjusted

[0214] Overall priority calculation:

[0215]

[0216] This formula is used to dynamically adjust the operational priority of equipment, taking into account factors such as equipment type and efficiency. Among them, For equipment Based on basic priority, H-type is higher than L-type; This is the urgency weighting coefficient; The delay penalty coefficient; Efficiency weighting coefficient; For equipment The task urgency at any given moment (dimensionless) reflects the importance and time sensitivity of the task. For equipment At any moment The degree of delay (dimensionless) reflects the lag in task execution; For equipment The overall de-icing efficiency.

[0217] 6. Multi-objective system optimization

[0218] Multi-objective optimization problem:

[0219]

[0220] in:

[0221]

[0222] Constraints:

[0223]

[0224] in, For equipment The state vector, For the safety distance matrix, For equipment At any moment The control input vector, , Equipment type The lower and upper limits of the control input vector, For equipment type At any moment Overall de-icing efficiency, For equipment type The lowest overall de-icing efficiency.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adaptive cooperative control method for multi-type road de-icing equipment operations, characterized in that, The method includes the following steps: S1: Establish road de-icing efficiency calculation models by category: For mechanical ice-breaking equipment (H-type equipment) for heavy ice formation and ice-melting equipment (L-type equipment) for light ice formation, establish corresponding de-icing efficiency calculation functions according to their de-icing mechanisms. S2: Construct the vehicle dynamics model of the equipment: comprehensively consider air resistance, rolling resistance, slope resistance and different types of de-icing resistance, and establish the longitudinal dynamic equation and speed influence function of the equipment. S3: Single Equipment Adaptive Control Strategy: Establish a state-space model of the de-icing system and construct model predictive control objective functions for different types of de-icing equipment. Adaptively adjust the parameters of different types of de-icing equipment according to real-time weather influencing factors to achieve optimal control input solution for a single piece of equipment under constraints. S4: Multi-equipment collaborative control strategy: A hierarchical distributed control architecture is adopted to realize the collaborative operation of multiple de-icing equipment of various types; the hierarchical distributed control architecture includes a task layer, a coordination layer, and an execution layer; the task layer is used for task allocation and path planning for multiple equipment of various types; the coordination layer is used for consistency collaborative control and conflict detection between equipment; the execution layer realizes single equipment control and local optimization by dynamically adjusting the operation priority; S5: Multi-objective system overall optimization; with the goals of maximizing efficiency, minimizing cost, and maximizing coordination, the global optimization solution is performed on the operational status of multiple equipment queues; In step S1, the de-icing efficiency calculation function for the H-type equipment is: in, H-type equipment At any moment De-icing efficiency, H-type equipment At any moment Mechanical de-icing efficiency, ; The baseline efficiency for H-type equipment; Let this be the velocity effect function; Weather influencing factors; For equipment At any moment ice-breaking power The compressive strength of the ice layer, For equipment The contact area between the blade and the ice layer, For equipment The angle of attack of the blade; For a moment Minimum ice-breaking force required The minimum contact area between the equipment and the ice layer; The de-icing efficiency calculation function for L-type equipment is: in, For L-type equipment De-icing efficiency, For thermal ice melting equipment At any moment The ice melting efficiency ; Equipment for applying de-icing agent At any moment The ice melting efficiency ; Thermal melting efficiency: in, For equipment At any moment heat transfer efficiency, ; , For L-type equipment at all times The actual heat provided Provides reference heat supply power for L-type equipment; For a moment The heat power required per unit time For a moment The total heat required to melt ice, The duration of a unit of time; De-icing agent de-icing efficiency: in, For equipment At any moment The freezing point depression value; For a moment The melting threshold temperature; For equipment At any moment The amount of de-icing agent used; For a moment The optimal dosage of de-icing agent; In step S3, the equipment's type parameters are adaptively updated as follows: H-type equipment parameter adjustment: L-type equipment parameter adjustments: in, For H-type equipment at all times The cutting force, The reference cutting force for H-type equipment, For H-type equipment at all times The optimal operating speed, For H-type equipment at all times De-icing power, The standard de-icing power for H-type equipment; For L-type equipment at all times The amount of de-icing agent used, The standard de-icing agent application rate for L-type equipment. For L-type equipment at all times The optimal operating speed, This is the reference operating speed for L-shaped equipment; Factors affecting ice layer; In severe weather: H-type equipment increases cutting force and de-icing power Reduce operating speed; L-shaped equipment increases heat supply. and the amount of de-icing agent used This reduces the speed of operations; When the weather is good: all equipment parameters are close to the baseline values, and the operating speed increases.

2. The adaptive cooperative control method for multi-type road de-icing equipment operation according to claim 1, characterized in that, In step S2, the longitudinal dynamic equation of the equipment is: in, For equipment At any moment The quality; For equipment At any moment speed; For equipment At any moment traction force; For equipment At any moment The driving resistance; For equipment At any moment De-icing resistance; For equipment index, For all equipment sets, the H-type equipment set is defined as: L-shaped equipment set is defined as ; in, For equipment The drag coefficient depends on the vehicle's shape; air density, For equipment Work speed; For equipment Frontal area, i.e., the projected area of ​​the front of the vehicle; For equipment Rolling resistance coefficient, It is the acceleration due to gravity. The road slope angle; Classification of de-icing resistance models: in, The coefficient of friction on the ice surface for H-type equipment; The coefficient of friction for L-shaped equipment on ice surface; For H-type equipment cutting resistance; For L-type equipment, the resistance to de-icing agent spraying or the resistance to hot air blowing; To provide support for the ice surface; For equipment Type identifier.

3. The adaptive cooperative control method for multi-type road de-icing equipment operation according to claim 2, characterized in that, In step S3, the categorized model prediction control objective function is constructed, specifically including: The model predictive control objective function for the H-type equipment is: The model predictive control objective function for the L-type equipment is: in, The model predicts the control objective function for the H-type equipment. The model predicts the control objective function for L-type equipment; To predict the length of the time domain; To control the length of the time domain; For H-type equipment ice-breaking power, For reference ice-breaking force; for L The actual heat provided by the equipment For reference heat supply; and For H-type equipment weight matrix; and For L-shaped equipment weight matrix; For the control input vector of the H-type equipment, For L-type equipment, the control input vector is... The constraints include: The categorized control constraints are: ,in, For equipment At any moment The control input vector, , Equipment type The lower and upper limits of the control input vector; The categorized state constraints are as follows: ,in, For equipment At any moment The output vector, , For equipment type The lower and upper limits of the control output vector; The control increment constraint is: ,in, For equipment type The maximum increment of the input vector.

4. The adaptive cooperative control method for multi-type road de-icing equipment operation according to claim 3, characterized in that, In step S4, the optimization model for task allocation across multiple equipment queues of various types is as follows: in, Let the objective function be the cost of de-icing. , These are the operating cost coefficients for H-type and L-type equipment, respectively. , These are the operating times for H-type and L-type equipment, respectively. , These refer to the working widths of H-type and L-type equipment, respectively. For equipment i The speed of operation; This represents the total de-icing requirements for the roads.

5. The adaptive cooperative control method for multi-type road de-icing equipment operation according to claim 1, characterized in that, In step S4, the conflict detection conditions between equipment are... for: in, , For equipment and The position vector; For a safe buffer distance; This is the safety distance matrix.

6. The adaptive cooperative control method for multi-type road de-icing equipment operation according to claim 1, characterized in that, In step S4, the formula for calculating job priority is: in, As a task priority, For equipment Based on basic priority, H-type is higher than L-type; This is the urgency weighting coefficient; The delay penalty coefficient; Efficiency weighting coefficient; For equipment In terms of the urgency of the task at any given moment For equipment At any moment The degree of delay, For equipment The overall de-icing efficiency.

7. The adaptive cooperative control method for multi-type road de-icing equipment operation according to claim 4, characterized in that, In step S5, the multi-objective optimization problem is formulated as follows: in, The constraints are: in, Let the de-icing efficiency be the objective function. Let the objective function be the cost of de-icing. To equip the coordination objective function, For equipment The state vector, For the safety distance matrix, For equipment At any moment The control input vector, , Equipment type The lower and upper limits of the control input vector, For equipment type At any moment Overall de-icing efficiency, For equipment type The lowest overall de-icing efficiency.

Images