Conveying control method and system for electric energy meter tray

Abstract

The invention provides a conveying control method for an electric energy meter tray. The conveying control method comprises the following steps: determining a path node and a target station which the electric energy meter tray needs to pass through, and a scheduling adaptation relationship among the three; according to the scheduling adaptation relation, constructing a three-dimensional state tensor so as to perform dynamic modeling on the scheduling intention of the electric energy meter tray, and constructing a task priority scoring function; acquiring a currently conveyed electric energy meter tray and a physical position, current to-be-processed quantity of each target station, current cache quantity of each path node and current congestion condition to the target station, and combining the maximum processing quantity of each target station, the physical position of each path node and the corresponding flow velocity to obtain a task priority score function; and obtaining a task priority score so as to perform task optimization control on each electric energy meter tray which is being conveyed at the current moment. By implementing the method and the system, the conditions of path congestion change, load of the target station and the like can be dynamically adapted, so that the problems of resource waste, task processing delay, production efficiency reduction and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of assembly line detection, and in particular to a conveying control method and system for an electric energy meter tray. Background Art

[0002] With the continuous development of electricity meter production processes and the promotion of smart grids, the scale of electricity meter production has gradually expanded, and the logistics scheduling tasks in the production process have become increasingly complex. In electricity meter calibration or production lines, the transportation of electricity meter pallets usually relies on fixed transportation routes and scheduling schemes. However, these schemes fail to meet the needs of modern production workshops for efficient, flexible, and precise scheduling, resulting in wasted resources, delayed task processing, and reduced production efficiency. The main reason is that traditional transportation scheduling schemes for electricity meter pallets have difficulty responding in real time to dynamically changing factors such as the path of the electricity meter pallet from one process to another and the load at the target workstation.

[0003] Therefore, there is an urgent need to improve the traditional transportation scheduling scheme for electricity meter pallets so that it can dynamically adapt to changes in path congestion and the load of the target workstation, thereby solving problems such as resource waste, task processing delays, and reduced production efficiency. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a method and system for controlling the transportation of electric meter pallets, which can dynamically adapt to changes in path congestion and the load of the target workstation, thereby solving problems such as resource waste, task processing delays, and reduced production efficiency.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a method for controlling the conveyance of an electric energy meter tray, the method comprising the following steps:

[0006] Determine the path nodes and target workstations that the energy meter pallet must pass through during transportation on a specified energy meter assembly line, and further obtain a scheduling adaptation relationship between the energy meter pallet, the path nodes, and the target workstations; wherein the scheduling adaptation relationship is that the energy meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node;

[0007] A three-dimensional state tensor is constructed based on the scheduling adaptation relationship, and the scheduling intention of the electric energy meter tray is dynamically modeled based on the three-dimensional state tensor to obtain a resource disturbance projection vector model, and a task priority scoring function of the electric energy meter tray is further constructed based on the resource disturbance projection vector model;

[0008] Obtaining, from the designated electricity meter production line, the current number of electricity meter pallets being transported and their corresponding physical locations, as well as the current number of pending electricity meter pallets at each target station, the cache capacity of each path node, and the current congestion status of each path node to the corresponding target station. Furthermore, combining the preset maximum number of processed electricity meter pallets at each target station, the preset physical location of each path node, and its corresponding preset maximum load capacity, in the task priority scoring function, obtain a task priority score for each electricity meter pallet being transported at the current moment;

[0009] According to the obtained task priority score, task optimization control is performed on each electric energy meter pallet being transported at the current moment.

[0010] Among them, the expression of the three-dimensional state tensor is in,

[0011] is the scheduling fitness tensor value of the energy meter tray i from the current position to the path node j and then to the target station k at time t; W k (t) is the number of electric energy meter pallets to be processed at the target station k at time t; W max D is the preset maximum number of electric energy meter pallets processed at the target station k; ij C is the Euclidean distance from the current position of the energy meter tray i to the path node j at time t, which is calculated based on the physical position of the energy meter tray i at time t and the preset physical position of the path node j; jk (t) is the congestion situation from path node j to target workstation k at time t, which is represented by the number of energy meter trays stranded at path node j at time t; C max is the preset maximum load capacity from path node j to target workstation k, which is represented by the maximum number of energy meter pallets that can be accommodated; μ is the distance attenuation factor, which is obtained by looking up the table based on the physical distance between energy meter pallet i and path node j at time t.

[0012] Among them, the expression of the resource disturbance projection vector model is: in,

[0013] is the resource disturbance projection vector of the energy meter tray i at time t; is the disturbance weight of path node j; is the three-dimensional state tensor at time t The gradient value in the direction of path node j; ξ is the preset capacity disturbance adjustment factor, which is a constant; is the acceleration of the cache capacity change of path node j at time t; L j(t) is the flow rate of the path node j through the electric energy meter tray at time t; σ is the preset adjustment sensitivity coefficient, which is a constant; Q j (t) is the cache capacity of path node j at time t; m is the total number of path nodes on the specified electricity meter pipeline.

[0014] Among them, the expression of the task priority scoring function is: in,

[0015] Φ i (t) is the task priority score of the energy meter tray i at time t;

[0016] is the initial phase score; λ1 is the weight coefficient of the preset dynamic disturbance component, which is a constant; for λ2 is the preset static tendency structure weight coefficient, which is a constant; The optimal path node for the electric energy meter tray i The fitness value to the target station k, where the optimal path node ρ k is the process importance weight of the preset target station k and is a constant;

[0017] is the mid-phase dynamic disturbance score; θ is the preset dynamic disturbance control coefficient, which is a constant;

[0018] is the final phase score; Φ i (t-1) is the task priority score at the previous cycle moment of time t.

[0019] The specific steps of performing task optimization control on each electric energy meter pallet currently being transported based on the obtained task priority score include:

[0020] The obtained task priority scores are sorted from high to low, and the transportation tasks of each electric energy meter tray at the current moment are controlled in sequence according to the sorted task priority scores.

[0021] The designated electricity meter production line is an electricity meter calibration production line or an electricity meter production production line.

[0022] The embodiment of the present invention further provides a conveying control system for an electric energy meter tray, comprising:

[0023] A conveyance relationship acquisition unit is used to determine the path nodes and target workstations that the energy meter pallet must pass through during the conveyance process on the specified energy meter production line, and further obtain a scheduling adaptation relationship between the energy meter pallet, the path nodes, and the target workstations; wherein the scheduling adaptation relationship is that the energy meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node;

[0024] a task priority scoring function construction unit, configured to construct a three-dimensional state tensor based on the scheduling adaptation relationship, and dynamically model the scheduling intention of the electric energy meter tray based on the three-dimensional state tensor to obtain a resource disturbance projection vector model, and further construct a task priority scoring function for the electric energy meter tray based on the resource disturbance projection vector model;

[0025] a task priority score calculation unit, configured to obtain, from the designated electric energy meter assembly line, the electric energy meter pallets currently being transported and their corresponding physical locations, as well as the current number of electric energy meter pallets to be processed at each target workstation, the cache capacity of each path node, and the current congestion status of each path node to the corresponding target workstation; and, in combination with a preset maximum number of processed electric energy meter pallets at each target workstation, the preset physical location of each path node, and its corresponding preset maximum load capacity, to obtain, in the task priority score function, a task priority score for each electric energy meter pallet currently being transported;

[0026] The transport task optimization control unit is used to perform task optimization control on each electric energy meter pallet being transported at the current moment according to the obtained task priority score.

[0027] Among them, the expression of the three-dimensional state tensor is in,

[0028] is the scheduling fitness tensor value of the energy meter tray i from the current position to the path node j and then to the target station k at time t; W k (t) is the number of electric energy meter pallets to be processed at the target station k at time t; W max D is the preset maximum number of electric energy meter pallets processed at the target station k; ij C is the Euclidean distance from the current position of the energy meter tray i to the path node j at time t, which is calculated based on the physical position of the energy meter tray i at time t and the preset physical position of the path node j; jk (t) is the congestion situation from path node j to target workstation k at time t, which is represented by the number of energy meter trays stranded at path node j at time t; C maxis the preset maximum load capacity from path node j to target workstation k, which is represented by the maximum number of energy meter pallets that can be accommodated; μ is the distance attenuation factor, which is obtained by looking up the table based on the physical distance between energy meter pallet i and path node j at time t.

[0029] Among them, the expression of the resource disturbance projection vector model is: in,

[0030] is the resource disturbance projection vector of the energy meter tray i at time t; is the disturbance weight of path node j; is the three-dimensional state tensor at time t The gradient value in the direction of path node j; ξ is the preset capacity disturbance adjustment factor, which is a constant; is the acceleration of the cache capacity change of path node j at time t; L j (t) is the flow rate of the path node j through the electric energy meter tray at time t; σ is the preset adjustment sensitivity coefficient, which is a constant; Q j (t) is the cache capacity of path node j at time t; m is the total number of path nodes on the specified electricity meter pipeline.

[0031] Among them, the expression of the task priority scoring function is: in,

[0032] Φ i (t) is the task priority score of the energy meter tray i at time t;

[0033] is the initial phase score; λ1 is the weight coefficient of the preset dynamic disturbance component, which is a constant; for λ2 is the preset static tendency structure weight coefficient, which is a constant; The optimal path node for the electric energy meter tray i The fitness value to the target station k, where the optimal path node ρ k is the process importance weight of the preset target station k and is a constant;

[0034] is the mid-phase dynamic disturbance score; θ is the preset dynamic disturbance control coefficient, which is a constant;

[0035] is the final phase score; Φ i (t-1) is the task priority score at the previous cycle moment of time t.

[0036] The implementation of the embodiments of the present invention has the following beneficial effects:

[0037] 1. This invention constructs a three-dimensional state tensor and introduces a resource disturbance projection vector and a task priority scoring function to achieve multi-dimensional dynamic adjustment of path conflicts and task priorities. This effectively solves the path competition problem between multiple electricity meter tray tasks and ensures that important tasks can be processed first during high-concurrency task execution, thus avoiding path resource conflicts and congestion.

[0038] 2. The present invention takes into account factors such as the load status of the target workstation, the congestion of the path nodes, and the buffer area capacity, thereby ensuring that existing resources are fully utilized during the production process and dynamically adapting to changes in path congestion and the load of the target workstation. This solves problems such as resource waste, task processing delays, and reduced production efficiency, avoids idle waste or overloaded operation of resources, and improves overall resource utilization.

[0039] 3. The task priority scoring function introduced in the present invention contains a smooth compensation for the final phase score, which can avoid drastic changes in priority scores due to sudden fluctuations, thereby reducing the volatility of the scheduling system. Even in complex environments, stable operation can be ensured by maintaining relatively smooth scheduling decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, other drawings obtained based on these drawings still fall within the scope of the present invention.

[0041] Figure 1 A flow chart of a method for controlling the transport of an electric energy meter tray provided in an embodiment of the present invention;

[0042] Figure 2 A schematic structural diagram of a conveying control system for an electric energy meter tray provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0044] like Figure 1 FIG. 1 is a diagram showing a method for controlling the transport of an electric energy meter tray according to an embodiment of the present invention, the method comprising the following steps:

[0045] Step S1: Determine the path nodes and target workstations that the energy meter pallet must pass through during transportation on a specified energy meter assembly line, and further obtain a scheduling adaptation relationship between the energy meter pallet, the path nodes, and the target workstations; wherein the scheduling adaptation relationship is that the energy meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node;

[0046] The specific process is as follows: First, by specifying the meter production line as a known work step on the meter calibration line or meter production line, the entire transportation process and path of the meter pallet from the source to the end of the line can be determined, including the path nodes and target workstations that need to be passed during transportation. Furthermore, the scheduling adaptation relationship between the meter pallet, the path nodes, and the target workstation is determined. Specifically, the meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node.

[0047] Secondly, laser ranging sensors and RFID reading and writing modules are deployed near each path node, and collection equipment is deployed at each target workstation to obtain the unique identification of the electricity meter pallet, the current position coordinates, and the process type information of the electricity meter carried in real time, as well as the congestion situation of each path node (such as the number of stranded electricity meter pallets) and cache capacity (such as the number of dispatched electricity meter pallets) and the real-time load status of each target workstation (such as the number of electricity meter pallets to be processed).

[0048] It is understandable that in order to facilitate the subsequent priority score calculation, the electricity meter assembly line needs to be three-dimensionally simulated and modeled, and the corresponding three-dimensional model parameters need to be configured, including the simulation position of each path node, the simulation position of each target workstation, the flow rate of each path node (such as the number of electricity meter pallets per unit time), the maximum load capacity of each path node (such as the maximum capacity of the processed electricity meter pallets), the maximum load state of each target workstation (such as the maximum number of processed electricity meter pallets) and other fixed parameters.

[0049] Step S2: constructing a three-dimensional state tensor based on the scheduling adaptation relationship, and dynamically modeling the scheduling intention of the electric energy meter tray based on the three-dimensional state tensor to obtain a resource disturbance projection vector model, and further constructing a task priority scoring function for the electric energy meter tray based on the resource disturbance projection vector model;

[0050] The specific process is as follows: First, to achieve dynamic adaptability modeling of the meter pallet, a three-dimensional state tensor is constructed, representing the structural data representation of the scheduling adaptation relationship between the meter pallet, the path nodes, and the target workstation. This tensor represents the degree of state adaptability of the scheduled object (i.e., the meter pallet) as it moves toward the path nodes and ultimately reaches the target workstation at the current moment. This three-dimensional state tensor not only reflects the spatial connection between the path nodes and the target workstation, but also embeds the congestion density along the path, the immediate load acceptance capacity of the workstation, and the impact of path distance on the pallet's scheduling behavior, forming the state input foundation for the entire scheduling decision.

[0051] At this time, the expression of the three-dimensional state tensor is shown in the following formula (1):

[0052]

[0053] in, W is the scheduling fitness tensor value of the energy meter pallet i from the current position to the path node j and then to the target workstation k at time t, reflecting the scheduling attraction of the path node and the target workstation to the energy meter pallet; k (t) is the number of electric energy meter pallets to be processed at the target station k at time t; W max D is the preset maximum number of electric energy meter pallets processed at the target station k; ij C is the Euclidean distance from the current position of the energy meter tray i to the path node j at time t, which is calculated based on the physical position of the energy meter tray i at time t and the preset physical position of the path node j; jk (t) is the congestion situation from path node j to target workstation k at time t, which is represented by the number of energy meter trays stranded at path node j at time t; C max is the preset maximum load capacity from path node j to target workstation k, which is represented by the maximum number of energy meter pallets that can be accommodated; μ is the distance attenuation factor, which is obtained by looking up the table based on the physical distance between energy meter pallet i and path node j at time t.

[0054] Secondly, after completing the construction of the three-dimensional state tensor, in order to realize the dynamic modeling of the scheduling intention of the electricity meter tray, the resource disturbance projection vector is further introduced to characterize the synthetic effect of the path node gradient influence and the dynamic change trend of the path cache on each scheduling object (i.e., the electricity meter tray) in the current state.

[0055] Specifically, the gradient of the three-dimensional state tensor along the path nodes is derived to capture the speed of state change of the scheduled object along the path, thereby forming an estimate of the scheduling attractiveness of the current path direction. The second-order derivative of cache capacity in the time dimension is also considered to reflect the instantaneous trend of path node resources and characterize the potential future congestion or release of the path. These two core indicators are weighted and superimposed to generate a high-dimensional perturbation driving vector, which provides directional judgment in multi-path competition.

[0056] At this time, the expression of the resource disturbance projection vector model is shown in the following formula (2):

[0057]

[0058] in, is the resource disturbance projection vector of the energy meter tray i at time t; is the disturbance weight of path node j, which is defined as the degree of influence of the current state of path node j on the overall scheduling priority; is the three-dimensional state tensor at time t The gradient value in the direction of path node j represents the rate of change in the degree of adaptation of energy meter tray i to select path node j as the transition path node and ultimately reach the target station k. It is used to simulate the guidance direction of the tensor state in the path space. ξ is a preset capacity disturbance adjustment factor and is a constant. Its empirical setting is between 0.5 and 2.0. It is used to represent the influence weight of the second-order derivative of the control cache capacity (acceleration) on the final disturbance vector. is the acceleration of the change of the cache capacity of path node j at time t, which is used to predict the future path smoothness; L j (t) is the flow rate of the path node j through the electric energy meter tray at time t; σ is the preset adjustment sensitivity coefficient and is a constant, with a value range of 0.5 to 3.0 and obtained through experiments; Q j (t) is the cache capacity of path node j at time t; m is the total number of path nodes on the specified electricity meter pipeline.

[0059] Finally, based on the resource disturbance projection vector, a three-phase priority scoring function is constructed. Through the initial phase scoring, middle phase dynamic disturbance adjustment and final phase scoring smoothing compensation, the task resource adaptability, path dynamic change and conflict adjustment of the electricity meter pallet are comprehensively considered to judge the task priority of the electricity meter pallet, ensuring that urgent and important tasks are handled first, thereby realizing efficient and flexible transportation scheduling of the electricity meter pallet.

[0060] At this time, the expression of the task priority scoring function is shown in the following formula (3):

[0061]

[0062] Among them, Φ i (t) is the task priority score of the energy meter tray i at time t;

[0063] is the initial phase score, which is used to represent the superposition of static resource adaptability and directional momentum; λ1 is the preset weight coefficient of the dynamic disturbance component and is a constant, which is used to represent the degree of attention paid to the dynamic change of the path direction of the electric energy meter tray i, and its value is between [0.3, 0.7]; for The modulus of λ2 is the preset static tendency structure weight coefficient and is a constant, which is used to express the degree of trust in the current state tensor and is obtained through experiments; The optimal path node for the electric energy meter tray i The fitness value to the target station k, where the optimal path node ρ k is the process importance weight of the preset target station k and is a constant, which is defined by expert experience;

[0064] is the mid-phase dynamic disturbance score, which is used to introduce a dynamic angle function to represent the nonlinear response of the disturbance path; θ is the preset dynamic disturbance control coefficient and is a constant. It is used to control the sensitivity of the scoring function to fluctuations in the disturbance trend and is set between 0.1 and 1.0. The larger the value, the more aggressive the scheduling;

[0065] is the final phase score, which is used to smooth the historical data of the priority change rate; Φ i (t-1) is the task priority score at the previous cycle moment of time t.

[0066] It should be noted that the initial-phase scoring component reflects the execution superiority of the current scheduling object among the available paths by weightedly superimposing the modulus of the resource perturbation projection vector and the fitness score of the selected path node and target workstation in the fitness tensor. The mid-phase dynamic perturbation scoring component uses a tangent function to construct the directional perturbation angle and superimposes a sinusoidal perturbation term to simulate the impact of perturbations on instantaneous priority fluctuations in the scheduling environment, enhancing the scoring's responsiveness to dynamic conflict changes. The final-phase scoring component introduces an inverse square function of the first-order derivative of the priority change rate to penalize drastic changes, prevent scheduling oscillations caused by short-term high fluctuations in the score, and establish the dynamic stability of the scheduling score. At this point, the initial-phase scoring, mid-phase dynamic perturbation scoring, and final-phase scoring smoothing compensation ensure that the task priority score is sensitive and responsive in both static and dynamic states.

[0067] Step S3: Obtain the currently transported energy meter pallets and their corresponding physical locations from the designated energy meter assembly line, as well as the current number of pending energy meter pallets at each target station, the cache capacity of each path node, and the current congestion status of each path node to the corresponding target station. Combined with the preset maximum number of processed energy meter pallets at each target station, the preset physical location of each path node, and its corresponding preset maximum load capacity, obtain a task priority score for each energy meter pallet currently being transported in the task priority scoring function.

[0068] The specific process involves deploying laser ranging sensors and RFID read / write modules near each path node in step S1, as well as data collection equipment at each target workstation. This process allows the identification of the currently transported energy meter pallets and their corresponding physical locations. Furthermore, the current number of pending energy meter pallets at each target workstation, the buffer capacity of each path node, and the current congestion situation from each path node to the corresponding target workstation are obtained. Furthermore, by integrating the 3D model parameters from the 3D simulation model of the energy meter production line into the task priority scoring function in step S2, the task priority score for each currently transported energy meter pallet can be quickly determined.

[0069] Step S4: Based on the obtained task priority scores, perform task optimization control on each electric energy meter pallet currently being transported.

[0070] The specific process is to sort the obtained task priority scores from high to low, and control the transportation tasks of each electricity meter tray at the current moment in sequence according to the sorted task priority scores.

[0071] It should be noted that available path resources are allocated sequentially to high-priority tasks based on the task priority of each meter pallet. In actual operation, the scheduling plan is dynamically adjusted based on factors such as the task priority of each meter pallet, the real-time status of path resources, and the status of the target workstation. For example, if congestion occurs at a certain path node, low-priority tasks may be postponed, while high-priority tasks are processed earlier. This enables flexible and dynamic allocation of path resources, prioritizing urgent and important tasks, and achieving efficient and intelligent transportation scheduling of meter pallets.

[0072] like Figure 2 FIG. 1 is a diagram showing a conveying control system for an electric energy meter tray according to an embodiment of the present invention, comprising:

[0073] The transport relationship acquisition unit 110 is used to determine the path nodes and target workstations that the energy meter pallet must pass through during its transport process on a specified energy meter production line, and further obtain a scheduling adaptation relationship between the energy meter pallet, the path nodes, and the target workstations; wherein the scheduling adaptation relationship is that the energy meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node;

[0074] a task priority scoring function construction unit 120 for constructing a three-dimensional state tensor based on the scheduling adaptation relationship, and dynamically modeling the scheduling intention of the electric energy meter tray based on the three-dimensional state tensor to obtain a resource disturbance projection vector model, and further constructing a task priority scoring function for the electric energy meter tray based on the resource disturbance projection vector model;

[0075] The task priority score calculation unit 130 is configured to obtain, from the designated electric energy meter production line, the electric energy meter pallets currently being transported and their corresponding physical locations, as well as the current number of electric energy meter pallets to be processed at each target workstation, the cache capacity of each path node, and the current congestion status of each path node to the corresponding target workstation. Furthermore, based on the preset maximum number of electric energy meter pallets to be processed at each target workstation, the preset physical location of each path node, and its corresponding preset maximum load capacity, the task priority score for each electric energy meter pallet currently being transported is obtained in the task priority score function.

[0076] The transport task optimization control unit 140 is used to perform task optimization control on each electric energy meter pallet being transported at the current moment according to the obtained task priority score.

[0077] Among them, the expression of the three-dimensional state tensor is in,

[0078] is the scheduling fitness tensor value of the energy meter tray i from the current position to the path node j and then to the target station k at time t; W k (t) is the number of electric energy meter pallets to be processed at the target station k at time t; W max D is the preset maximum number of electric energy meter pallets processed at the target station k; ij C is the Euclidean distance from the current position of the energy meter tray i to the path node j at time t, which is calculated based on the physical position of the energy meter tray i at time t and the preset physical position of the path node j; jk (t) is the congestion situation from path node j to target workstation k at time t, which is represented by the number of energy meter trays stranded at path node j at time t; C maxis the preset maximum load capacity from path node j to target workstation k, which is represented by the maximum number of energy meter pallets that can be accommodated; μ is the distance attenuation factor, which is obtained by looking up the table based on the physical distance between energy meter pallet i and path node j at time t.

[0079] Among them, the expression of the resource disturbance projection vector model is: in,

[0080] is the resource disturbance projection vector of the energy meter tray i at time t; is the disturbance weight of path node j; is the three-dimensional state tensor at time t The gradient value in the direction of path node j; ξ is the preset capacity disturbance adjustment factor, which is a constant; is the acceleration of the cache capacity change of path node j at time t; L j (t) is the flow rate of the path node j through the electric energy meter tray at time t; σ is the preset adjustment sensitivity coefficient, which is a constant; Q j (t) is the cache capacity of path node j at time t; m is the total number of path nodes on the specified electricity meter pipeline.

[0081] Among them, the expression of the task priority scoring function is: in,

[0082] Φ i (t) is the task priority score of the energy meter tray i at time t;

[0083] is the initial phase score; λ1 is the weight coefficient of the preset dynamic disturbance component, which is a constant; for λ2 is the preset static tendency structure weight coefficient, which is a constant; The optimal path node for the electric energy meter tray i The fitness value to the target station k, where the optimal path node ρ k is the process importance weight of the preset target station k and is a constant;

[0084] is the mid-phase dynamic disturbance score; θ is the preset dynamic disturbance control coefficient, which is a constant;

[0085] is the final phase score; Φ i (t-1) is the task priority score at the previous cycle moment of time t.

[0086] The implementation of the embodiments of the present invention has the following beneficial effects:

[0087] 1. This invention constructs a three-dimensional state tensor and introduces a resource disturbance projection vector and a task priority scoring function to achieve multi-dimensional dynamic adjustment of path conflicts and task priorities. This effectively solves the path competition problem between multiple electricity meter tray tasks and ensures that important tasks can be processed first during high-concurrency task execution, thus avoiding path resource conflicts and congestion.

[0088] 2. The present invention takes into account factors such as the load status of the target workstation, the congestion of the path nodes, and the buffer area capacity, thereby ensuring that existing resources are fully utilized during the production process and dynamically adapting to changes in path congestion and the load of the target workstation. This solves problems such as resource waste, task processing delays, and reduced production efficiency, avoids idle waste or overloaded operation of resources, and improves overall resource utilization.

[0089] 3. The task priority scoring function introduced in the present invention contains a smooth compensation for the final phase score, which can avoid drastic changes in priority scores due to sudden fluctuations, thereby reducing the volatility of the scheduling system. Even in complex environments, stable operation can be ensured by maintaining relatively smooth scheduling decisions.

[0090] It is worth noting that in the above system embodiment, the various system modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the various functional modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0091] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0092] The above disclosure is merely a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for controlling the transport of an electric energy meter tray, characterized in that: The method comprises the following steps: Determine the path nodes and target workstations that the energy meter pallet must pass through during transportation on a specified energy meter assembly line, and further obtain a scheduling adaptation relationship between the energy meter pallet, the path nodes, and the target workstations; wherein the scheduling adaptation relationship is that the energy meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node; A three-dimensional state tensor is constructed based on the scheduling adaptation relationship, and the scheduling intention of the electric energy meter tray is dynamically modeled based on the three-dimensional state tensor to obtain a resource disturbance projection vector model, and a task priority scoring function of the electric energy meter tray is further constructed based on the resource disturbance projection vector model; Obtaining, from the designated electricity meter production line, the current number of electricity meter pallets being transported and their corresponding physical locations, as well as the current number of pending electricity meter pallets at each target station, the cache capacity of each path node, and the current congestion status of each path node to the corresponding target station. Furthermore, combining the preset maximum number of processed electricity meter pallets at each target station, the preset physical location of each path node, and its corresponding preset maximum load capacity, in the task priority scoring function, obtain a task priority score for each electricity meter pallet being transported at the current moment; According to the obtained task priority score, task optimization control is performed on each electric energy meter pallet being transported at the current moment.

2. The method for controlling the conveyance of an electric energy meter tray according to claim 1, wherein: The expression of the three-dimensional state tensor is: in, is the scheduling fitness tensor value of the energy meter tray i from the current position to the path node j and then to the target station k at time t; W k (t) is the number of electric energy meter pallets to be processed at the target station k at time t; W max D is the preset maximum number of electric energy meter pallets processed at the target station k; ij C is the Euclidean distance from the current position of the energy meter tray i to the path node j at time t, which is calculated based on the physical position of the energy meter tray i at time t and the preset physical position of the path node j; jk (t) is the congestion situation from path node j to target workstation k at time t, which is represented by the number of energy meter trays stranded at path node j at time t; C max is the preset maximum load capacity from path node j to target workstation k, which is represented by the maximum number of energy meter pallets that can be accommodated; μ is the distance attenuation factor, which is obtained by looking up the table based on the physical distance between energy meter pallet i and path node j at time t.

3. The method for controlling the conveyance of an electric energy meter tray according to claim 2, wherein: The expression of the resource disturbance projection vector model is: in, is the resource disturbance projection vector of the energy meter tray i at time t; is the disturbance weight of path node j; is the three-dimensional state tensor at time t The gradient value in the direction of path node j; ξ is the preset capacity disturbance adjustment factor, which is a constant; is the acceleration of the cache capacity change of path node j at time t; L j (t) is the flow rate of the path node j through the electric energy meter tray at time t; σ is the preset adjustment sensitivity coefficient, which is a constant; Q j (t) is the cache capacity of path node j at time t; m is the total number of path nodes on the specified electricity meter pipeline.

4. The method for controlling the conveyance of an electric energy meter tray according to claim 3, wherein: The expression of the task priority scoring function is: in, Φ i (t) is the task priority score of the energy meter tray i at time t; is the initial phase score; λ1 is the weight coefficient of the preset dynamic disturbance component, which is a constant; for λ2 is the preset static tendency structure weight coefficient, which is a constant; The optimal path node for the electric energy meter tray i The fitness value to the target station k, where the optimal path node ρ k is the process importance weight of the preset target station k and is a constant; is the mid-phase dynamic disturbance score; θ is the preset dynamic disturbance control coefficient, which is a constant; is the final phase score; Φ i (t-1) is the task priority score at the previous cycle moment of time t.

5. The method for controlling the conveyance of an electric energy meter tray according to claim 1, wherein: The specific steps of performing task optimization control on each electric energy meter pallet currently being transported according to the obtained task priority score include: The obtained task priority scores are sorted from high to low, and the transportation tasks of each electric energy meter tray at the current moment are controlled in sequence according to the sorted task priority scores.

6. The method for controlling the conveyance of an electric energy meter tray according to claim 1, wherein: The designated electricity meter production line is an electricity meter calibration production line or an electricity meter production line.

7. A conveying control system for an electric energy meter tray, characterized in that: include: A conveyance relationship acquisition unit is used to determine the path nodes and target workstations that the energy meter pallet must pass through during the conveyance process on the specified energy meter production line, and further obtain a scheduling adaptation relationship between the energy meter pallet, the path nodes, and the target workstations; wherein the scheduling adaptation relationship is that the energy meter pallet first arrives at the nearest path node that it has not passed through, and then is dispatched to the target workstation corresponding to the nearest path node; a task priority scoring function construction unit, configured to construct a three-dimensional state tensor based on the scheduling adaptation relationship, and dynamically model the scheduling intention of the electric energy meter tray based on the three-dimensional state tensor to obtain a resource disturbance projection vector model, and further construct a task priority scoring function for the electric energy meter tray based on the resource disturbance projection vector model; a task priority score calculation unit, configured to obtain, from the designated electric energy meter assembly line, the electric energy meter pallets currently being transported and their corresponding physical locations, as well as the current number of electric energy meter pallets to be processed at each target workstation, the cache capacity of each path node, and the current congestion status of each path node to the corresponding target workstation; and, in combination with a preset maximum number of processed electric energy meter pallets at each target workstation, the preset physical location of each path node, and its corresponding preset maximum load capacity, to obtain, in the task priority score function, a task priority score for each electric energy meter pallet currently being transported; The transport task optimization control unit is used to perform task optimization control on each electric energy meter pallet being transported at the current moment according to the obtained task priority score.

8. The conveying control system for the electric energy meter tray according to claim 7, characterized in that: The expression of the three-dimensional state tensor is: in, is the scheduling fitness tensor value of the energy meter tray i from the current position to the path node j and then to the target station k at time t; W k (t) is the number of electric energy meter pallets to be processed at the target station k at time t; W max D is the preset maximum number of electric energy meter pallets processed at the target station k; ij C is the Euclidean distance from the current position of the energy meter tray i to the path node j at time t, which is calculated based on the physical position of the energy meter tray i at time t and the preset physical position of the path node j; jk (t) is the congestion situation from path node j to target workstation k at time t, which is represented by the number of energy meter trays stranded at path node j at time t; C max is the preset maximum load capacity from path node j to target workstation k, which is represented by the maximum number of energy meter pallets that can be accommodated; μ is the distance attenuation factor, which is obtained by looking up the table based on the physical distance between energy meter pallet i and path node j at time t.

9. The electric energy meter tray conveying control system according to claim 8, characterized in that: The expression of the resource disturbance projection vector model is: in, is the resource disturbance projection vector of the energy meter tray i at time t; is the disturbance weight of path node j; is the three-dimensional state tensor at time t The gradient value in the direction of path node j; ξ is the preset capacity disturbance adjustment factor, which is a constant; is the acceleration of the cache capacity change of path node j at time t; L j (t) is the flow rate of the path node j through the electric energy meter tray at time t; σ is the preset adjustment sensitivity coefficient, which is a constant; Q j (t) is the cache capacity of path node j at time t; m is the total number of path nodes on the specified electricity meter pipeline.

10. The electric energy meter tray conveying control system according to claim 9, characterized in that: The expression of the task priority scoring function is: in, Φ i (t) is the task priority score of the energy meter tray i at time t; is the initial phase score; λ1 is the weight coefficient of the preset dynamic disturbance component, which is a constant; for λ2 is the preset static tendency structure weight coefficient, which is a constant; The optimal path node for the electric energy meter tray i The fitness value to the target station k, where the optimal path node ρ k is the process importance weight of the preset target station k and is a constant; is the mid-phase dynamic disturbance score; θ is the preset dynamic disturbance control coefficient, which is a constant; is the final phase score; Φ i (t-1) is the task priority score at the previous cycle moment of time t.

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