Cross-granularity simulation method, device and electronic equipment for sand and gravel processing and excavation backfill

Abstract

The present application relates to the field of water conservancy and hydropower engineering construction simulation technology, and discloses a sandstone processing and excavation backfill cross-granularity simulation method, device and electronic equipment, the method comprising: constructing a state diagram of a processing plant agent based on state variable information; based on the attribute and variable system, embedding a cross-granularity material conversion algorithm into the corresponding action of the state diagram, so that the processing plant agent performs cross-granularity processing on the whole process of sandstone processing micro-simulation and excavation backfill macro-simulation, and obtains simulation result data, the cross-granularity material conversion algorithm is used for data caliber adaptation and bidirectional information intercommunication between excavation backfill macro-simulation and sandstone processing micro-simulation, completes the conversion of incoming material data from macro to micro, the conversion of finished product data from micro to macro, the conversion of sandstone processing micro-simulation running state to macro running state recognizable by the processing plant agent, and the adaptation conversion of macro scheduling instruction to micro execution instruction.

Description

Technical Field

[0001] This invention relates to the field of simulation technology for construction of water conservancy and hydropower projects, specifically to cross-particle size simulation methods, devices, and electronic equipment for sand and gravel processing and excavation backfilling. Background Technology

[0002] Water conservancy and hydropower projects form a complete material flow chain from quarrying to material filling. Simulation technology can be used to model and analyze the construction process during the project planning stage. However, there are many difficulties in connecting the macroscopic simulation of the entire excavation and backfilling process with the microscopic simulation of sand and gravel processing and production due to differences in measurement units, material properties, and time scales.

[0003] Macroscopic simulations can only provide relatively coarse information about incoming materials, such as volume and lithology, and cannot match the fine parameters required for micro-processing, such as quality and particle size distribution. Furthermore, the naming of finished products at the micro-level is disconnected from the macroscopic material specification naming system, making it difficult to achieve unified conversion and integration when delivering finished materials. This makes it difficult to effectively integrate the production status of the micro-plant into information usable for macro-scheduling, and macro-scheduling instructions are also difficult to directly adapt to the plant's production control needs. Currently, related technologies generally use fixed empirical output coefficients to simplify the sand and gravel processing flow, treating the processing plant as a static conversion node. They only roughly calculate the finished product output based on the volume of incoming materials, completely ignoring real dynamic production characteristics such as particle size distribution evolution, equipment failure disturbances, process mode switching, closed-loop cycle adjustments, and changes in multi-aggregate ratios. This makes it difficult to accurately reflect limited feed supply, capacity fluctuations, and energy consumption changes, resulting in significant deviations in simulation results. Summary of the Invention

[0004] This invention provides a cross-particle size simulation method, device, and electronic equipment for sand and gravel processing and excavation backfilling, in order to solve the problem that the use of fixed empirical output coefficients to simplify the sand and gravel processing process in related technologies cannot accurately reflect the limited supply of materials, production capacity fluctuations and energy consumption changes, resulting in large deviations in simulation results.

[0005] In a first aspect, the present invention provides a cross-granularity simulation method for sand and gravel processing and excavation and backfilling. The method includes: acquiring the attribute and variable system of a processing plant intelligent agent, the attribute and variable system including state variable information, which characterizes the real-time operating state of the processing plant intelligent agent; constructing a state diagram of the processing plant intelligent agent based on the state variable information, the state diagram characterizing various operating states, operating state switching conditions, and corresponding execution actions of the processing plant intelligent agent; and embedding a cross-granularity material conversion algorithm into the corresponding actions of the state diagram based on the attribute and variable system, so that the processing plant intelligent agent can perform cross-granularity data conversion, state synchronization, and instruction coordination processing throughout the entire process of sand and gravel processing micro-simulation and excavation and backfilling macro-simulation to obtain simulation result data. The cross-granularity material conversion algorithm is used to adapt the data caliber and facilitate bidirectional information exchange between the excavation and backfilling macro-simulation and sand and gravel processing micro-simulation, completing the conversion of incoming material data from macro to micro, finished product data from micro to macro, the conversion of the sand and gravel processing micro-simulation operating state to the macro-operating state recognizable by the processing plant intelligent agent, and the adaptation and conversion of macro-scheduling instructions to micro-execution instructions.

[0006] The present invention provides a cross-particle size simulation method for sand and gravel processing and excavation / backfilling. First, it constructs an attribute and variable system for the processing plant's intelligent agent, then builds a state diagram based on this system, and embeds a cross-particle size material conversion algorithm into various operational actions. This solves the difficulty of connecting macroscopic simulation of excavation / backfilling with microscopic simulation of sand and gravel processing in terms of measurement units, material properties, and time scales. It achieves bidirectional conversion from macroscopic incoming material information to microscopic refined production data, and from microscopic finished material information to macroscopic engineering application data. It also enables the regularization and transformation of the processing plant's production operation status to a scheduling-available state, and the adaptation and transformation of macroscopic scheduling instructions to processing plant production execution instructions. Unlike simplifying the processing plant to a static conversion node, the method provided by this invention comprehensively incorporates actual dynamic production elements such as particle size distribution changes, equipment failure impacts, production process adjustments, and aggregate ratio changes. It realistically recreates the entire operational logic of sand and gravel processing, accurately reflects limited material supply, output fluctuations, and energy consumption, and effectively reduces simulation result deviations. Meanwhile, a two-way information exchange and status synchronization and command coordination mechanism between macro and micro simulations has been established, changing the previous situation where the two were disconnected and only supported one-way data transmission. This allows the overall construction planning to be formulated based on the real-time operating status of the processing plant, and the processing plant's production operations to respond promptly to the overall construction arrangements. This enables cross-granularity collaborative simulation of the entire process of excavation, transportation, processing, and filling, providing a realistic simulation basis for construction progress control, equipment resource allocation, and optimization of construction organization plans.

[0007] In one optional implementation, the simulation result data includes feed source data, finished product destination data, and full-process material flow data. The processing plant agent is also used to perform consistency verification on the full-process material flow data based on the feed source data and finished product destination data, with the material quality conservation relationship as a constraint, to obtain the verification result.

[0008] The method provided by this optional implementation method, based on the data of the source of the feed and the destination of the finished product, completes the consistency verification of the material flow data throughout the entire process with the material quality conservation constraint. It can promptly identify cross-particle size material conversion deviations and flow statistical errors, ensure the accurate and unified material data in the macro- and micro-simulation docking, improve the closed-loop management of material flow data, effectively enhance the authenticity and reliability of the overall simulation data, and accurately control the consumption and reserve of engineering materials, providing reliable data support for the overall allocation of materials on site.

[0009] In one alternative implementation, the attribute and variable system includes static parameters, reference variables, and internal timers. Static parameters are used to characterize the inherent features of the processing plant, reference variables are used to store access paths of other intelligent agents associated with the processing plant's intelligent agent, and internal timers are used to drive periodic actions.

[0010] The method provided by this optional implementation is based on static parameters to unify and solidify the inherent basic characteristics of the processing plant, which facilitates unified parameter configuration and call management; it quickly establishes association paths with other intelligent agents by referencing variables, and efficiently realizes data communication and business linkage among multiple intelligent agents; it combines internal timers to realize timing control, stably drives the orderly execution of various periodic operation actions, further improves the intelligent agent operation architecture of the processing plant, and enhances the standardization, linkage and timing stability of the overall cross-granularity simulation operation.

[0011] In one optional implementation, the state variables include multiple types of operating state information of the processing plant intelligent agent. The step of constructing a state diagram of the processing plant intelligent agent based on the state variables includes: determining the operating state switching conditions of multiple types of operating states and the entry actions, exit actions, and periodic dwell actions of each type of operating state; and constructing a state diagram of the processing plant intelligent agent based on the various types of operating states, the operating state switching conditions, and the entry actions, exit actions, and periodic dwell actions of each operating state.

[0012] The method provided in this optional implementation predefines the switching conditions and entry, exit, and periodic pause actions for various operating states, and then constructs a state diagram based on this. On the one hand, this makes the state flow logic of the processing plant's intelligent agent clear and its behavior standardized, avoiding the ambiguity and randomness of state switching and ensuring the consistency and predictability of the agent's operating state. On the other hand, it provides a standardized behavioral framework for subsequently embedding cross-granularity material conversion algorithms and realizing state synchronization and instruction coordination in macroscopic and microscopic simulations, effectively improving the stability and controllability of the simulation process.

[0013] In one alternative implementation, the various operating states include: initialization, standby, operation, limited material supply, reduced capacity, and shutdown.

[0014] The method provided by this optional implementation method divides the operation into multiple operating states, including initialization, standby, operation, limited material supply, reduced capacity, and shutdown. It comprehensively covers all actual working conditions of the processing plant from startup and normal production to insufficient material supply, reduced capacity, and shutdown. It can accurately distinguish different production operation scenarios, making it easy to match corresponding execution actions and control logic. It can promptly identify various production anomalies and adapt scheduling strategies, allowing the state flow of the intelligent agent to fit the real production rhythm, effectively improving the completeness and accuracy of the simulation's reproduction of the actual production conditions in the sand and gravel processing plant area.

[0015] In one optional implementation, the cross-granularity material conversion algorithm includes conversion calculations in incoming material receiving event response actions, conversion calculations in finished product delivery event response actions, conversion calculations of status information, and conversion calculations of control commands. The conversion calculations in incoming material receiving event response actions are used to convert macroscopic incoming material information in the excavation and backfilling macroscopic simulation into identifiable incoming batches and establish cross-granularity traceability material conversion. The conversion calculations in finished product delivery event response actions are used to convert material specifications and volume requirements in the excavation and backfilling macroscopic simulation into finished product material processing tasks executable in the microscopic sand and gravel processing simulation, and convert the actual delivered material results into macroscopically identifiable volume data, establishing cross-granularity supply and demand adaptation and material conversion with cross-granularity finished product destination association. The conversion calculations of status information are used to convert system-level and workshop-level operating indicators in the sand and gravel processing microscopic simulation into status information identifiable in the excavation and backfilling macroscopic simulation. The conversion calculations of control commands are used to convert scheduling control commands issued by the excavation and backfilling macroscopic simulation into production adjustment commands executable in the sand and gravel processing microscopic simulation.

[0016] The method provided in this optional implementation breaks down the cross-granularity material conversion algorithm into four types of scenario-based conversion calculations: incoming material receiving, finished product delivery, status information, and control commands. It achieves a closed-loop process for the entire lifecycle of materials from macro to micro and back to macro through bidirectional conversion between incoming materials and finished products, establishing cross-granularity traceability to ensure data traceability and improve the credibility of simulation results. Furthermore, it achieves the aggregation and synchronization of micro-operation indicators to macro-states and the precise implementation of macro-scheduling commands to micro-execution commands through the conversion of status information and control commands, opening up cross-level state flow and control flow. At the same time, the scenario-based subdivision logic avoids the accuracy loss of general conversion, making the data caliber more closely aligned with actual production needs. Ultimately, it provides a standardized and scalable technical framework for cross-granularity collaboration between micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, comprehensively improving the stability, controllability, and interaction efficiency of the simulation system.

[0017] Secondly, the present invention provides a cross-particle size simulation device for sand and gravel processing and excavation backfilling. The device includes: an acquisition module for acquiring the attribute and variable system of a processing plant intelligent agent, the attribute and variable system including state variable information, the state variable information being used to characterize the real-time operating state of the processing plant intelligent agent; a construction module for constructing a state diagram of the processing plant intelligent agent based on the state variable information, the state diagram characterizing various operating states of the processing plant intelligent agent, operating state switching conditions, and corresponding execution actions; and a processing module for embedding a cross-particle size material conversion algorithm into the state based on the attribute and variable system. In the corresponding actions shown in the diagram, the intelligent agent of the processing plant performs cross-granularity data conversion, state synchronization, and instruction coordination processing for the entire process of micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, and obtains simulation result data. The cross-granularity material conversion algorithm is used to adapt the data scope and facilitate two-way information exchange between macro-simulation of excavation and backfilling and micro-simulation of sand and gravel processing, and completes the conversion of incoming material data from macro to micro, finished product data from micro to macro, conversion of the running state of micro-simulation of sand and gravel processing to the running state of macro-intelligent agent that the processing plant can recognize, and adaptation and conversion of macro-scheduling instructions to micro-execution instructions.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the cross-particle size simulation method for sand and gravel processing and excavation backfilling described in the first aspect or any corresponding embodiment thereof.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the cross-particle size simulation method for sand and gravel processing and excavation backfilling according to the first aspect or any corresponding embodiment described above.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the cross-particle size simulation method for sand and gravel processing and excavation backfilling according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a cross-particle size simulation method for sand and gravel processing and excavation backfilling according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the second process of the cross-particle size simulation method for sand and gravel processing and excavation backfilling according to an embodiment of the present invention. Figure 4 This is a structural block diagram of a cross-particle size simulation device for sand and gravel processing and excavation backfilling according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the cross-grain size simulation method for sand and gravel processing and excavation backfilling depends is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0027] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0028] In related technologies, fixed empirical output coefficients are commonly used to simplify the sand and gravel processing process. The processing plant is treated as a static conversion node, and the output of finished products is roughly calculated based on the volume of incoming materials. This completely ignores the real dynamic production characteristics such as particle size distribution evolution, equipment failure disturbance, process mode switching, closed-loop cycle adjustment, and changes in the proportion of multiple aggregates. It cannot accurately reflect the limited supply of materials, capacity fluctuations and energy consumption changes, resulting in large deviations in simulation results.

[0029] Unlike the approach of simplifying the processing plant into a static conversion node, the method provided by this invention can comprehensively incorporate actual dynamic production factors such as changes in particle size distribution, the impact of equipment failure, adjustments to production processes, and changes in aggregate ratios. It can realistically restore the entire operation logic of sand and gravel processing, accurately reflect the limited supply of materials, fluctuations in output, and energy consumption, and effectively reduce the deviation of simulation results.

[0030] This application provides a cross-grain size simulation method for sand and gravel processing and excavation / backfilling, which can be applied to a single server to achieve cross-grain size simulation of sand and gravel processing and excavation / backfilling. The method provided in this application first constructs an attribute and variable system for the processing plant's intelligent agent, then constructs a state diagram based on the attribute and variable system, and simultaneously embeds a cross-grain size material conversion algorithm into various operational actions. This solves the difficulty of connecting macroscopic simulation of excavation / backfilling with microscopic simulation of sand and gravel processing in terms of measurement units, material properties, and time scales. It achieves bidirectional conversion from macroscopic incoming material information to microscopic refined production data, and from microscopic finished material information to macroscopic engineering application data, as well as the regularization and transformation of the processing plant's production operation status to a scheduling-available state, and the adaptation and transformation of macroscopic scheduling instructions to processing plant production execution instructions.

[0031] According to an embodiment of the present invention, a cross-particle size simulation method for sand and gravel processing and excavation backfilling is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This embodiment provides a cross-particle size simulation method for sand and gravel processing and excavation backfilling, which can be used in the aforementioned server. Figure 2 This is a flowchart of a cross-particle size simulation method for sand and gravel processing and excavation backfilling according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the attribute and variable system of the processing plant intelligent agent. The attribute and variable system includes state variable information, which is used to characterize the real-time operating status of the processing plant intelligent agent.

[0033] For example, the processing plant intelligent agent is a functional entity in the water conservancy and hydropower engineering simulation system, specifically designed to connect the sand and gravel processing stage and link the macroscopic simulation of excavation and backfilling with the microscopic simulation of sand and gravel processing. It is the core hub for realizing cross-granularity data interoperability and collaborative scheduling. The attribute and variable system is a pre-built standardized data architecture that integrates various data categories such as the intelligent agent's inherent characteristic parameters, dynamic operating data, and externally related call data. It serves as the unified data foundation for the intelligent agent to carry out simulation interaction and computation. The state variable information is a dynamic data category within the attribute and variable system, used to collect, record, and intuitively reflect the real-time operating conditions of the processing plant, such as production status, equipment operation, and capacity load, providing a basis for state judgment and process control.

[0034] In this embodiment, the agent modeling method uses agents as the basic modeling unit. An agent is an entity with autonomy, encapsulation, state, and behavior, capable of perceiving its environment and autonomously responding to external events according to its internal state diagram. Agents collaborate through clearly defined event interactions, and their internal implementation details are not visible to the outside world.

[0035] Within the framework of intelligent agent modeling, the sand and gravel processing plant is designed as an independent intelligent agent in the macroscopic simulation of excavation and backfilling in water conservancy and hydropower projects. This agent is called the processing plant intelligent agent, enabling the microscopic simulation of sand and gravel processing to be embedded as an independent unit into the macroscopic simulation of excavation and backfilling. This intelligent agent possesses the following three basic characteristics: Autonomy: The intelligent agent in the processing plant has its own state diagram and autonomously advances according to the operating logic defined in the state diagram. It can complete the evolution of its internal state and respond to external events without external command.

[0036] Encapsulation: The internal state, internal variables, and operational details of the micro-simulation of sand and gravel processing are not visible to other agents in the macro-simulation of excavation and backfilling. Other agents can only interact with the processing plant agent through events defined by the processing plant agent, which may include, but are not limited to, material receiving events, finished product delivery events, and control command events.

[0037] Relationship: The processing plant agent holds the sand and gravel processing micro-simulation through reference relationships. During state transitions, it invokes the functions of the sand and gravel processing micro-simulation to complete specific material handling. The referenced sand and gravel processing micro-simulation model exists independently within the simulation environment.

[0038] The relationship between the intelligent agent in the processing plant and the macroscopic simulation of excavation and backfilling is as follows: (1) Hierarchical Positioning. In the intelligent agent system of macroscopic simulation of excavation and backfilling in water conservancy and hydropower projects, the processing plant intelligent agent, the material yard intelligent agent, the transport vehicle intelligent agent, and the filling work area intelligent agent are at the same level. They are all material handling or logistics participation nodes in the macroscopic simulation of excavation and backfilling. These nodes are interconnected through material flow and information flow, and together they constitute the operating scenario of macroscopic simulation of excavation and backfilling. The processing plant intelligent agent is located in the middle of the material chain in this system and undertakes the function of converting the original rock after blasting into finished aggregate.

[0039] (2) Instantiation method. The processing plant agent is instantiated by the excavation and backfilling macro simulation during the simulation startup phase. During instantiation, the excavation and backfilling macro simulation creates a corresponding number of processing plant agent instances based on the actual number of processing plants in the project. When there are multiple processing plants in a project, such as a main processing plant and an auxiliary processing plant, each corresponds to an independent processing plant agent instance. Each instance independently holds its own attributes, state, and reference to the sand and gravel processing micro simulation, without interfering with each other.

[0040] (3) Interaction with other nodes in the entire process. The processing plant agent interacts with other nodes in the entire process through two types of events: The first category is material events. Upstream material receiving events are triggered by the material yard or transport vehicles, while downstream finished product delivery events are triggered by transport vehicles or filling work areas. Both types of events carry material information. For example, material receiving events carry the material volume, lithology, and source, while delivery events carry the finished product requirements, volume, and destination. The processing plant agent responds to these events in the state diagram.

[0041] The second category is information events. Status query events are initiated by the excavation and backfilling macro simulation system, and the processing plant intelligent agent sends back the current status information, such as production capacity and inventory status. Control command events are issued by the excavation and backfilling macro simulation system, and the processing plant intelligent agent responds to the command to adjust its internal behavior, such as switching process modes or adjusting the upper limit of the feeding rate.

[0042] (4) Time synchronization. The processing plant agent shares the same simulation clock with other nodes in the excavation and backfilling macro simulation. The state diagram transitions, timer triggers, and event responses of the processing plant agent all proceed according to the unified time schedule of the excavation and backfilling macro simulation, and there is no independent clock.

[0043] (5) Subordination to the micro-simulation of sand and gravel processing. In terms of engineering semantics, the processing plant agent is subordinate to the excavation and backfilling macro-simulation system. The excavation and backfilling macro-simulation system is responsible for formulating the material flow plan and resource allocation for the entire project. The processing plant agent, as a scheduled node, receives scheduling instructions and operates in accordance with the instructions. This subordinate relationship determines that certain state transitions of the processing plant agent, such as entering the stop operation state, must be actively triggered by the excavation and backfilling macro-simulation system, rather than being determined autonomously by the processing plant agent.

[0044] The relationship between the intelligent agent in the processing plant and the microscopic simulation of sand and gravel processing is as follows: (1) Association relationship rather than inclusion relationship. The relationship between the processing plant agent and the sand and gravel processing micro-simulation model is one of association. The processing plant agent holds reference variables pointing to the sand and gravel processing micro-simulation model, and calls the capabilities provided by the sand and gravel processing micro-simulation model through the reference. The sand and gravel processing micro-simulation model exists in the simulation environment as an independent set of agents. Each agent within it, such as the receiving station, crushing workshop, screening workshop, and various equipment, operates independently according to its own state machine mechanism. The core feature that distinguishes the association relationship from the inclusion relationship is that the sand and gravel processing micro-simulation model does not constitute an internal component of the processing plant agent; the attribute variables and state diagrams of the processing plant agent are independent of the attribute variables and state diagrams of each agent in the sand and gravel processing micro-simulation model; the state diagram transitions of the processing plant agent do not nest or summarize the state diagrams of the sand and gravel processing micro-simulation agent set.

[0045] (2) Multiple instances hold independent references. In a multi-processing plant scenario, each processing plant agent instance independently holds a reference to the sand and gravel processing micro-simulation model, meaning each processing plant corresponds to an independently instantiated sand and gravel processing micro-simulation. Each of the multiple sand and gravel processing micro-simulations has an independent equipment list, an independent set of material batches, an independent indicator acquisition agent, and an independent routing management agent, and they are not shared with each other. This independence is necessary because the equipment configurations, process schemes, and material handling statuses of different processing plants are different, and multiple independent instances are needed to correctly represent the differentiated operation of each processing plant.

[0046] (3) Lifecycle Management. The processing plant agent is responsible for the lifecycle management of the micro-simulation model of sand and gravel processing: During the instantiation phase, the processing plant agent triggers the instantiation of the sand and gravel processing micro-simulation model simultaneously with the creation of the macro-simulation of excavation and backfilling. Specifically, the processing plant agent invokes the startup process of the sand and gravel processing micro-simulation during its initialization state entry action, completing steps such as data loading, agent construction of each workshop and equipment, production line topology assembly, and starting various timers. After startup, the sand and gravel processing micro-simulation is in a running-ready state.

[0047] During the operation phase, the processing plant's intelligent agents complete specific material handling tasks by referencing and calling the micro-simulation of sand and gravel processing. Fine-grained events within the micro-simulation, such as a crusher malfunctioning or a material warehouse reaching its limit, are autonomously managed according to the state machine mechanism of each agent within the micro-simulation. The processing plant's intelligent agents do not directly intervene in these fine-grained events but instead perceive the macro-state of the micro-simulation through a periodic sampling mechanism.

[0048] During the termination phase, when the processing plant agent enters the stop-run state, it invokes the orderly termination process of the sand and gravel processing micro-simulation to complete the clearing of internal materials in transit and the stopping of each timer.

[0049] (1) Decoupling principle of call granularity. The call granularity of the processing plant agent to the micro-simulation of sand and gravel processing is limited to the macro interface, such as injecting new batches into the receiving station, issuing mode switching instructions to the routing management agent, and querying the indicator collection agent to obtain system-level indicators. The processing plant agent does not directly access the internal state variables of any equipment in the micro-simulation of sand and gravel processing, nor does it directly modify the attributes of any agent in the micro-simulation of sand and gravel processing. All calls are made through the methods exposed by the micro-simulation of sand and gravel processing. This decoupling of call granularity allows the internal modeling of the micro-simulation of sand and gravel processing to be independent of the macro-simulation evolution of excavation and backfilling.

[0050] Step S202: Construct a state diagram of the processing plant intelligent agent based on state variable information. The state diagram represents the various operating states of the processing plant intelligent agent, the conditions for switching operating states, and the corresponding execution actions.

[0051] For example, in the embodiments of this application, a state diagram is constructed based on multiple operating states, the switching conditions of different operating states, and the entry actions, exit actions, and dwell period cycle actions of various operating states.

[0052] Step S203: Based on the attribute and variable system, the cross-particle size material conversion algorithm is embedded into the corresponding action of the state diagram. This enables the processing plant agent to perform cross-particle size data conversion, state synchronization, and instruction coordination processing throughout the entire process of micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, thereby obtaining simulation result data. The cross-particle size material conversion algorithm is used to adapt the data scope and facilitate bidirectional information exchange between macro-simulation of excavation and backfilling and micro-simulation of sand and gravel processing. This completes the conversion of incoming material data from macro to micro, the conversion of finished product data from micro to macro, the conversion of the micro-simulation running state of sand and gravel processing to the macro-running state that the processing plant agent can recognize, and the adaptation and conversion of macro-scheduling instructions to micro-execution instructions.

[0053] For example, in this embodiment of the application, based on the constructed attribute and variable system and state diagram, the core computational logic of cross-granularity collaboration is deeply bound to the intelligent agent's behavioral process, achieving seamless integration of macroscopic and microscopic simulations. Specifically, the four core functions of the cross-granularity material conversion algorithm are embedded into the corresponding preset execution actions in the state diagram. Through this embedding method, the intelligent agent automatically completes the data caliber adaptation and two-way information exchange between the two types of simulations during the state transition process, and finally outputs simulation result data to support cross-granularity collaborative simulation of the entire process of water conservancy and hydropower engineering.

[0054] The cross-particle size simulation method for sand and gravel processing and excavation / backfilling provided in this embodiment first constructs an attribute and variable system for the processing plant's intelligent agent, then builds a state diagram based on this system, and embeds a cross-particle size material conversion algorithm into various operational actions. This solves the difficulty of connecting macroscopic simulation of excavation / backfilling with microscopic simulation of sand and gravel processing in terms of measurement units, material properties, and time scales. It achieves bidirectional conversion from macroscopic incoming material information to microscopic refined production data, and from microscopic finished material information to macroscopic engineering application data. It also enables the regularization and transformation of the processing plant's production operation status to a scheduling-available state, and the adaptation and transformation of macroscopic scheduling instructions to processing plant production execution instructions. Unlike approaches that simplify the processing plant to static conversion nodes, the method provided in this embodiment comprehensively incorporates actual dynamic production elements such as particle size distribution changes, equipment failure impacts, production process adjustments, and aggregate ratio changes. It realistically recreates the entire operational logic of sand and gravel processing, accurately reflects limited material supply, output fluctuations, and energy consumption, and effectively reduces simulation result deviations. Meanwhile, a two-way information exchange and status synchronization and command coordination mechanism between macro and micro simulations has been established, changing the previous situation where the two were disconnected and only supported one-way data transmission. This allows the overall construction planning to be formulated based on the real-time operating status of the processing plant, and the processing plant's production operations to respond promptly to the overall construction arrangements. This enables cross-granularity collaborative simulation of the entire process of excavation, transportation, processing, and filling, providing a realistic simulation basis for construction progress control, equipment resource allocation, and optimization of construction organization plans.

[0055] This embodiment provides a cross-particle size simulation method for sand and gravel processing and excavation backfilling, which can be used in the aforementioned server. Figure 3 This is a flowchart of a cross-particle size simulation method for sand and gravel processing and excavation backfilling according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain the attribute and variable system of the processing plant intelligent agent. The attribute and variable system includes state variable information, which is used to characterize the real-time operating status of the processing plant intelligent agent. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0056] In some alternative implementations, the attribute and variable system includes static parameters, reference variables, and internal timers. Static parameters are used to characterize the inherent features of the processing plant, reference variables are used to store access paths of other intelligent agents associated with the processing plant's intelligent agent, and internal timers are used to drive periodic actions.

[0057] For example, in this embodiment of the application, the static parameters are injected by the excavation and backfilling macroscopic simulation when the processing plant agent is instantiated, and remain unchanged during the simulation operation, describing the inherent characteristics of the processing plant, as follows: (1) Processing plant identifier: The unique identifier of the processing plant agent is used to distinguish multiple processing plant instances. In the multi-processing plant scenario, the excavation and backfilling macro simulation indexes each processing plant instance by the processing plant identifier; (2) Geographic coordinates: The location of the processing plant in the excavation and backfilling macroscopic simulation geographic coordinate system, including horizontal and vertical coordinate components. This parameter is used for transportation route calculation and transportation time estimation in the excavation and backfilling macroscopic simulation; (3) Processing capacity: The rated total capacity of the processing plant, in t / h, is equal to the sum of the rated capacity of all crushing equipment in the micro-simulation of sand and gravel processing. This parameter serves as the benchmark value of the processing plant's capacity in the whole process scheduling. (4) Sand and gravel processing micro-simulation configuration file path: points to the data source describing the complete configuration of the sand and gravel processing micro-simulation model, including equipment list, production line topology, routing rules, initial material feeding plan, etc. The processing plant agent reads the configuration from this path during the initialization phase to construct the sand and gravel processing micro-simulation model; (5) Density parameter table: a table of loose density values ​​organized by lithology and material state. The lithology dimension includes granite, limestone, basalt, sandstone, etc.; the material state dimension includes four categories: original rock state, post-blasting state, post-crushing state, and finished product stockpile state; each item in the table gives the loose density of the corresponding working condition. This table is used for volume-mass conversion in incoming material receiving and finished product delivery events. (6) Typical gradation template table: a set of typical gradation curves organized according to lithology and mining technology. Each typical gradation curve is an array of retained material mass percentages described by standard sieve aperture sequence. This table is used for attribute precision conversion in incoming material receiving events to convert single lithological attributes into gradation curves. (7) Finished Product Specification Mapping Table: A collection of finished product specification mapping records indexed by material specification name. Each mapping record may contain five items: allowable particle size range, list of constituent particle sizes, synthesis ratio, list of allowable lithologies, and finished product stock density. This table is used for specification mapping and multi-particle size synthesis in finished product delivery events.

[0058] For example, the state variables are dynamically updated during the simulation to reflect the real-time operation of the processing plant's intelligent agent, including: (1) Current state: The current state of the processing plant agent. It takes one of six states, including initialization, standby, running, limited material supply, reduced capacity, and stopped operation. This variable is the core of the state diagram mechanism. The state transition directly modifies its value. (2) Current idle capacity: The capacity of the processing plant that is not currently occupied and can immediately receive new materials, in t / h. This value is obtained by periodic sampling from the idle capacity of each workshop in the micro-simulation of sand and gravel processing. When its value is significantly lower than the installed capacity, the processing plant may be experiencing multiple equipment failures or capacity reduction. (3) Inventory mapping organized by material specifications: A mapping table with material specification name as key and current available quantity as value. The physical meaning of each key-value pair is: the maximum quantity of this material specification that can be delivered immediately at the processing plant, in meters. 3 This mapping is obtained by performing a reverse aggregation calculation on the finished product warehouse state of the microscopic simulation of sand and gravel processing through a periodic sampling action by traversing the finished product specification mapping table. (4) Material Request Queue: An ordered queue of material receiving requests that have not yet been processed. When the material supply is limited, newly arrived material requests are cached in this queue and wait for processing. When running, this queue is usually empty or contains only requests that are pending for a very short time. (5) Pending delivery request queue: An ordered queue of finished product delivery requests that have not yet been processed; when the inventory of a certain specification is temporarily insufficient, the delivery request can be cached in this queue to wait for the inventory to be replenished; (6) Recent cycle output rate: A mapping table with material specification name as the key and the actual output rate of that specification in the most recent feedback cycle as the value, in meters. 3 / h. This value is obtained by periodically sampling the cumulative grain size entering the warehouse in the current period from the index acquisition agent of the micro-simulation of sand and gravel processing, mapping and aggregating it according to specifications, and then converting it according to density. (7) The most recent state sampling time: the simulation clock reading of the last time the cycle sampling action was executed, in hours; (8) Material Source Record Table: A collection of records of all incoming materials received by this processing plant since the start of the simulation; (9) Finished Product Destination Record Table: A collection of records of all finished products shipped by this processing plant since the start of the simulation.

[0059] For example, reference variables store the access paths of other intelligent agents associated with the processing plant's intelligent agent. Each reference variable is defined according to four dimensions: variable name, variable type and meaning, assignment timing, and usage description, specifically including: (1) Reference of the main body of sand and gravel processing micro-simulation: The variable type is a reference to the main body of sand and gravel processing micro-simulation, that is, the intelligent body of processing production line management in the system layer; the assignment time is in the entry action of the processing plant intelligent body entering the initialization state. This action loads the configuration according to the sand and gravel processing micro-simulation configuration file path and instantiates the sand and gravel processing micro-simulation. After the instantiation is completed, the reference of the main intelligent body is assigned to the reference of the main body of sand and gravel processing micro-simulation; the purpose is to be the root entry point for the processing plant intelligent body to access all sub-intelligent bodies of sand and gravel processing micro-simulation in the future. (2) Reference of receiving station in micro-simulation of sand and gravel processing: The variable type is a reference to the receiving station in micro-simulation of sand and gravel processing. The assignment time is the same as the reference of the main body of micro-simulation of sand and gravel processing. The purpose is to call the batch injection method of the receiving station when injecting a new batch into micro-simulation of sand and gravel processing in the material receiving event response action. (3) Reference to the routing management agent in the micro-simulation of sand and gravel processing: The variable type is a reference to the routing management agent in the micro-simulation of sand and gravel processing. The assignment timing is the same as that of the main reference in the micro-simulation of sand and gravel processing. Its purpose is to switch the process mode (open circuit, closed circuit) in the control command response action and to call the corresponding method of the routing management agent when specifying a special routing tag for the incoming material event; (4) Reference of intelligent agent for collecting indicators in micro-simulation of sand and gravel processing: The variable type is a reference to the intelligent agent for collecting indicators in micro-simulation of sand and gravel processing. The assignment time is the same as the reference of the main body of micro-simulation of sand and gravel processing. The purpose is to query the system-level indicators and workshop-level indicators of micro-simulation of sand and gravel processing in the periodic sampling action. (5) Sand and gravel processing micro-simulation finished product warehouse reference mapping: The variable type is a mapping table with the sand and gravel processing micro-simulation particle level name as the key and the corresponding sand and gravel processing micro-simulation finished product warehouse reference as the value. The assignment timing is the same as the sand and gravel processing micro-simulation main reference. After the sand and gravel processing micro-simulation starts, it traverses all its finished product warehouses to establish this mapping. Its purpose is to query the corresponding sand and gravel processing micro-simulation finished product warehouse according to the particle level of the finished product specification mapping table and perform batch extraction in the finished product delivery event response action; (6) Upstream material source reference list: The variable type is a reference list of material yards or transport vehicle agents that supply materials to this processing plant in the excavation and backfilling macro simulation. The assignment time is when the excavation and backfilling macro simulation is injected according to the correspondence between material yards and processing plants in the engineering plan when the processing plant agent is instantiated. Its purpose is to confirm the reasonable source of the material receiving request in the material receiving event response. Only material receiving events initiated by sources listed in the upstream material source reference list are accepted. (7) Downstream delivery target reference list: The variable type is a reference list pointing to the transport vehicles or intelligent agents of the filling work area that receive the finished products of this processing plant in the macro simulation of excavation and backfilling; the assignment time is the same as the upstream material source reference list, and the purpose is to confirm the reasonable destination of the delivery request in the finished product delivery event response and to push the finished product delivery completion notification to the destination. (8) Reference to the macroscopic simulation system for excavation and backfilling: The variable type is a reference to the intelligent agent of the scheduling system responsible for scheduling decisions in the macroscopic simulation of excavation and backfilling. The assignment timing is the same as that of the upstream material source reference list. Its purpose is to actively send back the processing plant status information to the scheduling system during periodic sampling actions and to respond to the control commands issued by the scheduling system.

[0060] For example, the processing plant agent maintains the following three internal timers to drive periodic actions: (1) Periodic sampling timer: The trigger period is the sampling interval parameter, such as 1h; each time it is triggered, the periodic sampling action is executed, and the latest operating data is obtained by collecting the intelligent agent reference and the intelligent agent reference of each sand and gravel processing micro-simulation workshop through the sand and gravel processing micro-simulation index, and updating the available capacity, inventory classified by material specifications, recent production rate classified by material specifications and other state variables; whether to trigger state transition is determined according to the preset threshold conditions; (2) Status feedback timer: The trigger period is the feedback interval parameter, such as 1h, which can be the same as or different from the sampling interval; each time it is triggered, the status feedback action is executed, the status information feedback object is constructed and actively pushed to the excavation and backfilling macro simulation system through the scheduler reference.

[0061] (3) No-load timeout timer: This timer is only activated in the running state and is reset each time a new material request arrives. It is triggered when no material request is received for a continuous period of time exceeding the no-load threshold and the internal inventory is saturated, causing the running state to change to the standby state.

[0062] Step S302: Construct a state diagram of the processing plant intelligent agent based on state variable information. The state diagram represents the various operating states of the processing plant intelligent agent, the conditions for switching operating states, and the corresponding execution actions.

[0063] Specifically, the state variables include various types of operational state information of the processing plant intelligent agent, and step S302 above includes: Step S3021: Determine the switching conditions for multiple operating states, as well as the entry actions, exit actions, and periodic pause actions for each type of operating state.

[0064] For example, the various operating states include: initialization, standby, operation, limited material supply, reduced capacity, and shutdown. The specific descriptions of each operating state in this embodiment are as follows: (1) Initialization: The processing plant agent enters this state immediately after being instantiated by the macroscopic simulation of excavation and backfilling; this state is a transient state, and its only function is to start the microscopic simulation model of sand and gravel processing and assign values ​​to each reference variable. After the start is completed, it immediately transitions to the standby state; in this state, the processing plant does not yet have the ability to receive incoming materials or deliver finished products, and the response to incoming material receiving events and finished product delivery events is to buffer or reject them. (2) Standby: The intelligent agent of the processing plant has completed the startup of the micro-simulation of sand and gravel processing and has the ability to receive incoming materials, but there are currently no materials being processed or no new material activity for a long time; in this state, the processing plant is in a low-energy waiting operation mode. In this state, it responds to incoming material receiving events and transitions to the operating state; it can respond normally to finished product delivery events; and it sends back the current inventory and capacity information for status query events. (3) Operation: The processing plant is in a steady state of receiving incoming materials, processing them, and producing finished products. In this state, the micro-simulation of sand and gravel processing autonomously promotes the material handling of each piece of equipment according to its internal mechanism. The processing plant's intelligent agent periodically obtains the micro-simulation state of sand and gravel processing through a periodic sampling timer and judges whether it needs to transition to a state of limited material supply or reduced capacity according to threshold conditions. In this state, the processing plant simultaneously responds to incoming material receiving events, finished product delivery events, status query events, and control command events. (4) Limited Material Supply: This state is entered when the storage level of the receiving station's receiving silo in the micro-simulation of sand and gravel processing exceeds the preset upper limit threshold. The engineering meaning of this state is that the processing plant is temporarily unable to accept new material input due to material backlog. Continuing to receive material will cause the receiving silo to overflow or the crushing equipment to overload. In this state, the processing plant does not respond to material receiving events in a timely manner but adds the requests to the pending material request queue to wait for the inventory to be released. It continues to respond normally to finished product delivery events. When the inlet buffer drops below the preset lower limit threshold, it transitions back to the running state, and the requests cached in the pending queue are processed in the order of arrival. (5) Capacity Reduction: This state is entered when the sand and gravel processing micro-simulation shows a significant decrease in capacity. The typical trigger condition is that multiple key equipment simultaneously enters a fault or maintenance state, causing the workshop's idle capacity to drop below a certain threshold of the installed capacity. In the capacity reduction state, the processing plant can still receive incoming materials and deliver finished products, but its external capacity information shows that the current capacity is limited, prompting the excavation and backfilling macro-simulation system to adjust the transportation rhythm, such as delaying some non-urgent incoming materials or temporarily changing the transportation direction to other processing plants. When the sand and gravel processing micro-simulation equipment fault is recovered and the idle capacity rises above the recovery threshold, it transitions back to the operating state.

[0065] (6) Stop running: When the simulation is about to end, the excavation and backfilling macro simulation system will actively issue a stop running command to enter this state. The action of entering this state is to execute the orderly termination process of the sand and gravel processing micro simulation, stop receiving new materials, clear the internal materials in transit, including materials on the conveyor belt, in the equipment cavity, and in the silo, process them one by one until they are empty, stop all kinds of timers, and shut down the routing management agent and the index collection agent; after all the clearing actions are completed, it is considered that the life cycle of the processing plant agent has ended and no more state transitions will occur; this state can be entered from any state other than initialization.

[0066] For example, in this embodiment of the application, the triggering events and determination conditions for all running state switching are shown in Table 1 below: Table 1. Triggering events and judgment conditions for all operating state transitions

[0067] The state transitions for sequences 3, 4, 5, and 6 are all triggered by a periodic sampling timer. The internal state changes of the sand and gravel processing micro-simulation are not directly propagated to the state graph of the processing plant agent; instead, they are perceived through periodic sampling polling and a threshold determination mechanism. That is, the processing plant agent and the sand and gravel processing micro-simulation are associated; the processing plant agent does not subscribe to the state events of the sand and gravel processing micro-simulation agent but perceives the sand and gravel processing micro-simulation through periodic active queries.

[0068] External events take precedence over internal periodic events. The state transition corresponding to sequence number 8 (the shutdown command), as an external event, can be initiated from any state and has the highest priority. The state transition corresponding to sequence number 2 (the incoming material receiving event), as an external event, can be triggered immediately when the processing plant is in standby mode, without waiting for periodic sampling. This priority design enables the processing plant agent to quickly respond to commands from the excavation and backfilling macroscopic simulation system.

[0069] For example, in this embodiment of the application, an initialization operation upon entry, a calculation operation upon exit, and periodic execution actions during the state's dwell time are determined for each state. The initialization state's actions are designed as follows: (1) Entry Action: Call the startup process of sand and gravel processing micro-simulation, and complete the data loading, instantiation of each workshop intelligent agent, instantiation of each equipment intelligent agent, assembly of production line topology, startup of routing management intelligent agent and indicator collection intelligent agent, and startup of various internal timers in sequence; obtain the reference of the sand and gravel processing micro-simulation subject and assign it to the sand and gravel processing micro-simulation subject reference; traverse the sand and gravel processing micro-simulation model to obtain the references of receiving station, routing management intelligent agent, indicator collection intelligent agent, and each finished product warehouse and assign them to the corresponding reference variables; after completion, trigger the sand and gravel processing micro-simulation startup completion event; (2) Entry action: Start the idle timeout timer, update the current idle capacity to the rated capacity, and all capacity is idle when there is no material input; (3) Exit action: Stop the no-load timeout timer; (4) Stay period cycle action: When the cycle sampling timer is triggered, the inventory mapping is updated, that is, the current inventory is queried from the sand and gravel processing micro-simulation finished product warehouse; when the status feedback timer is triggered, the current standby status information is pushed to the scheduler.

[0070] For example, the action design for standby mode is as follows: (1) Entry action: Start the idle timeout timer; update the current idle capacity to the installed capacity, and all capacity is idle when there is no material input; (2) Exit action: Stop the no-load timeout timer; (3) Stay period cycle action: When the cycle sampling timer is triggered, the inventory mapping is updated, that is, the current inventory is queried from the sand and gravel processing micro-simulation finished product warehouse; when the status feedback timer is triggered, the current standby status information is pushed to the scheduler.

[0071] For example, the action design for the running state is as follows: (1) Entering action: Reset the idle timeout timer and record the start time of operation for subsequent statistics.

[0072] (2) Exit action: Stop the no-load timeout timer.

[0073] (3) Periodic actions during the dwell period: When the periodic sampling timer is triggered, the periodic sampling action is executed to update the available capacity, the inventory classified by material specifications, and the recent production rate classified by material specifications; it is determined whether the inlet receiving warehouse location exceeds the upper limit threshold, and if so, the state change of item 3 in Table 1 is triggered; it is determined whether the idle capacity ratio is lower than the reduction threshold, and if so, the state change of item 5 in Table 1 is triggered; when the status feedback timer is triggered, the status information is pushed to the scheduler; when the idle timeout timer is triggered, if it occurs, the state change of item 3 in Table 1 is triggered.

[0074] For example, the action design for a limited feed supply state is as follows: (1) Entering the action: By referencing the "pause to accept new batches" method of the receiving station in the micro-simulation of sand and gravel processing, the receiving switch of the receiving station is set to off; the time of entering this state is recorded for subsequent statistics on the duration of this state; (2) Exit action: The "Resume accepting new batch" method of the receiving station of sand and gravel processing micro-simulation is called through the receiving station reference; the pending material requests cached in the pending material queue are processed, and the material receiving event response actions are executed in the order of arrival; (3) Periodic action during the dwell period: When the periodic sampling timer is triggered, it checks whether the inlet receiving warehouse position has dropped below the lower limit threshold. If so, it triggers the state transition corresponding to serial number 4 in Table 1. When the state feedback timer is triggered, it pushes the state information, and specifically marks that the current state is the state of limited material supply.

[0075] For example, the action design for a capacity reduction state is as follows: Entry Action: Record the moment of entering this state; construct a capacity reduction alarm message and push it to the excavation and backfilling macro simulation system via the scheduler as an emergency notification, without waiting for the next state feedback cycle; Exit Action: Construct production capacity recovery information and push it to the excavation and backfilling macro simulation system via the scheduler; Periodic actions during the dwell period: When the periodic sampling timer is triggered, the idle capacity ratio is continuously monitored. If it is higher than the recovery threshold, the state transition corresponding to serial number 6 in the table is triggered. When the status feedback timer is triggered, status information is pushed, with special marking that the current state is a capacity reduction state and the current idle capacity ratio.

[0076] The action design for the stopped running state is as follows: Entry Actions: The "Stop Receiving" method of the receiving station in the sand and gravel processing micro-simulation is called via the reference variable of the receiving station agent; the "Terminate" method of the routing management agent is called via the reference variable of the routing management agent; the "Orderly Shutdown" method of each workshop and equipment in the sand and gravel processing micro-simulation is called; each silo and conveyor belt in the sand and gravel processing micro-simulation is traversed until its internal material is emptied, and its inventory or transit material quantity is continuously monitored until it drops to zero; all timers in the sand and gravel processing micro-simulation are stopped. Dwell periodic action: Periodically check whether the micro-simulated materials for sand and gravel processing have been completely emptied, and push a termination confirmation message to the scheduler after the emptying is completed.

[0077] Step S3022: Based on various operating states, operating state switching conditions, entry actions, exit actions, and periodic dwell actions of various operating states, a state diagram of the processing plant intelligent agent is constructed.

[0078] For example, various operating states are used as nodes in the state diagram, and the conditions for switching operating states are used as flow edges between nodes. Then, the entry actions, exit actions, periodic pause actions, and differential variable control rules corresponding to each state are bound to the corresponding state nodes, and finally integrated to form a complete state diagram.

[0079] Step S303: Based on the attribute and variable system, the cross-granularity material conversion algorithm is embedded into the corresponding actions of the state diagram. This enables the processing plant agent to perform cross-granularity data conversion, state synchronization, and instruction coordination throughout the entire process of micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, obtaining simulation result data. The cross-granularity material conversion algorithm is used to adapt the data scope and facilitate bidirectional information exchange between macro-simulation of excavation and backfilling and micro-simulation of sand and gravel processing. This completes the conversion of incoming material data from macro to micro, finished product data from micro to macro, the conversion of the micro-simulation running state of sand and gravel processing to the macro-running state that the processing plant agent can recognize, and the adaptation and conversion of macro-scheduling instructions to micro-execution instructions. For details, please refer to [link to details]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0080] In some optional implementations, the cross-granularity material conversion algorithm includes conversion calculations in incoming material receiving event response actions, conversion calculations in finished product delivery event response actions, conversion calculations of status information, and conversion calculations of control commands. An exemplary description of the cross-granularity material conversion algorithm is as follows: (a) Conversion calculation in the response action of incoming material receiving event.

[0081] The conversion calculation in the incoming material receiving event response action is used to convert the macroscopic incoming material information in the excavation and backfilling macroscopic simulation into identifiable incoming material batches and establish cross-granularity traceability material conversion.

[0082] In this embodiment, the incoming material receiving event is initiated by the intelligent agent of the material yard or transport vehicle in the macroscopic simulation of excavation and backfilling. The input information carried by the event includes: incoming material volume, lithology, mining technology, source identifier (pointing to an item in the upstream material source reference set), and delivery time. The conversion calculation in the incoming material receiving event response action is performed in the following five steps: (1) Reasonableness determination of incoming materials. Check whether the source of the event is in the upstream material source reference group list. If not, reject the event and return the rejection information to the source.

[0083] (2) Conversion from volume to mass. The loose density of rock material is equal to its total mass divided by the total volume occupied by the voids between particles. When the same rock is in its original state as a complete and continuous medium, the void ratio between particles is close to zero, and the density is at its maximum. After blasting, it is broken into blocks of varying sizes, and significant voids are formed between the blocks, causing the bulk density of the loose material after blasting to drop to 55% to 70% of the density of the original rock. This density reduction is described in engineering by the loose volume coefficient, which is defined as the ratio of the volume after blasting to the volume of the original rock.

[0084] When materials arrive at the processing plant entrance from the stockyard via transportation, their state is post-blasting loose material. The volume-to-mass conversion uses the post-blasting loose density. Let the incoming material volume be V, in meters. 3 The loose density after blasting was obtained by referring to the density comparison table, which uses lithology and stone type as row indexes and post-blasting state as column indexes. Unit: t / m 3 The converted mass is:

[0085] in, The converted material mass is expressed in tons (t). This step records the actual density value used. For use in recording the source of incoming materials.

[0086] (3) Attribute accuracy conversion from single lithology to complete gradation. The particle size distribution of the material after blasting is mainly determined by the mining process. Step blasting uses high-density charges and short-delay networks, and the rock mass undergoes large-volume fragmentation under the action of the detonation wave, resulting in a wide range of product particle sizes and a high proportion of large pieces. Smooth blasting uses peripheral hole decoupled charges, and the rock mass fractures smoothly along the designed contour line, resulting in relatively small product particle sizes and a narrow distribution. Mechanical crushing uses excavator bucket teeth or hydraulic breakers to directly act on the rock mass, resulting in smaller product particle sizes but with a high proportion of fine powder. These differences can be obtained in engineering by post-blasting screening measurements to obtain typical gradation data.

[0087] The conversion process is as follows: using "lithology + mining technology" as a combination, the corresponding typical gradation curve is retrieved from the gradation template table of static parameters, and denoted as G. template An array describing the percentage of retained material mass according to the standard sieve aperture sequence. If the incoming material event does not carry blasting method information, the default mining process for that lithology will be used for the query.

[0088] When the incoming material is a multi-source mixture (the volume of the same incoming material event includes materials from different stockyards or different processes mixed in a certain proportion), the conversion process supports weighted synthesis of gradations. Let the masses of the two mixing sources be m1 and m2, and the corresponding typical gradation curve retention material mass percentage arrays be G1=[g {1,1} , g {1,2} , ..., g {1,M} ] and G2=[g {2,1} , g {2,2} , ..., g {2,M} ], where M is the number of particle sizes in the standard sieve sequence, then the mass percentage of the i-th particle size retained in the synthesized gradation is:

[0089] in, This represents the percentage of retained material in the i-th particle size after synthesis. and These represent the percentage of retained material mass from the two mixed sources at the i-th particle size.

[0090] (4) Construct batches and input them into the sand and gravel processing micro-simulation via the receiving station. Assemble the outputs of the above steps into a material batch input object recognized by the sand and gravel processing micro-simulation, including start time, batch quality, lithology, gradation template name, moisture content, etc. After assembly, the processing plant agent calls the batch injection method of the receiving station of the sand and gravel processing micro-simulation via the receiving station reference, and injects the input item into the sand and gravel processing micro-simulation. After receiving the input, the receiving station constructs one or more material batches according to its internal mechanism and returns a list of batch identifiers for the constructed batches.

[0091] (5) Setting up and verifying the source of feed materials. The processing plant agent adds a source of feed material record to the state variable incoming material record table. The record includes: incoming material request identifier, receiving time, original incoming material volume, converted mass, blasted loose density, lithology, typical gradation template name, and batch identifier list. The core function of the source of feed material record is to establish a traceable relationship between "original volume, converted mass, and generated batch" for each batch of incoming material across the grain size boundary. It is the data basis for verifying the conservation of mass.

[0092] Then, a local conservation check is immediately performed: verifying whether the converted mass *m* matches the sum of the masses of the generated batches. Let the batch identifier list contain K batches, with masses *m1*, *m2*, ..., *m* respectively. K Let the sum be m. total Then it is required that m and m total The absolute value of the difference satisfies:

[0093] in, For unit conversion tolerance, take 10. -4 If the difference exceeds the tolerance, the processing flow for the incoming material should be stopped immediately and an anomaly reported to prevent boundary errors from propagating into the processing simulation.

[0094] (ii) Transformation calculation in the response action of finished product delivery event.

[0095] The conversion calculation in the finished product delivery event response action is used to convert the material specifications and volume requirements in the macroscopic simulation of excavation and backfilling into executable finished product material processing tasks in the microscopic sand and gravel processing simulation, and to convert the actual delivered material into macroscopically identifiable volume data, establishing cross-granularity supply and demand adaptation and material conversion for cross-granularity finished product destination association. In this embodiment, the finished product delivery event is initiated by the transport vehicle or filling area intelligent agent in the macroscopic simulation of excavation and backfilling. The input information carried by the event includes: the requested material specification name, denoted as material specification name, requested volume, and destination identifier, pointing to a certain item in the downstream target reference set. The conversion calculation in the finished product delivery event response action is executed in the following six steps: (1) Determination of reasonableness of delivery. Check whether the event destination is in the downstream target reference set list. If not, reject the event.

[0096] (2) Finished Product Specification Mapping Query. The corresponding mapping record is queried in the specification mapping table of static parameters using the specification name as the index. Each mapping record includes: allowable particle size range, a list of constituent particle sizes and their proportions (describing the target mass proportion of each internal particle size in this specification), and a list of allowable lithologies, etc.

[0097] (3) Conversion from volume to mass. The material at the inlet is loose material after blasting, and its density depends only on the lithology; while the density of the finished product stockpile is affected by both lithology and particle size width. When stockpiled in narrow particle size compartments, there is a lack of filling effect from particles of different sizes, and the stock density is lower than that of the crushed mixture. The narrower the particle size, the lower the density.

[0098] Let the request quantity be V. req Then the required delivery quality is m req The calculation is as follows:

[0099] in, This refers to the density of the finished material stockpiling.

[0100] (4) Quality allocation in different compartments and microscopic simulation of finished product extraction in sand and gravel processing. According to the synthesis ratio, m... req The components are allocated to their respective constituent granularities. Let the list of constituent granularities contain K elements, and the composition ratio of the i-th constituent granularity be r. i， Satisfying r1 + r2 + ... + r K = 1, then the target mass m to be extracted for this particle size is... i for: m i =m req · r i For each i ∈ {1, ..., K}, the processing plant agent queries the reference of the microscopic simulation finished product bin corresponding to the i-th particle size through the finished product bin reference set, calls the batch extraction method of that bin, and extracts batches sequentially from the bin according to the first-in-first-out principle. During the extraction process, lithology screening is performed. For each batch extracted, its lithology is first checked to see if it is in the list of allowed lithologies for that material specification: if it is, it is included in the delivery and the extraction quantity is accumulated; if not, the batch is skipped and remains in the original finished product bin to wait for extraction of other material specifications, and the next batch is extracted, until the accumulated extraction quantity reaches m. i .

[0101] If the total mass of materials constituting a particle size distribution bin that meet lithological constraints is less than m i The actual available quantity m of this particle size is delivered. i_actual The system records the shortage information for that item in the deliverables; it also adds the insufficient inventory status of that specification to the pending shipment queue, and notifies the scheduler to reference it during the next status feedback action.

[0102] (5) Assembly and mass-volume conversion of deliverables. The actual batches extracted from each constituent particle size are combined into one finished product deliverable, and the recorded information includes: actual delivered mass m. delivered It equals the sum of the actual mass removed from each particle size and the actual delivery mass m of each constituent particle size. i_actualThe lithological composition includes the weighted percentage of each lithology by quality, and a list of batch identifiers included in the deliverables. Finally, the quality is converted into downstream readable volume. V delivered = m delivered / ρ product Among them, V delivered This refers to the actual volume delivered. Downstream transportation is calculated in V. delivered Perform logistics scheduling.

[0103] (6) Setting up and partially validating finished product destination records. The processing plant agent adds a finished product destination record to the state variable delivery record table. The record content includes: delivery request identifier, delivery time, requested material specification name, and requested quantity V. req Actual delivery volume V delivered Actual delivery quality m delivered The finished product stockpiling density ρ used product And a list of batch identifiers included in the deliverables.

[0104] Two local checks are then performed: The first item is a mass conservation check, verifying the total mass of the extracted batch against the actual delivered mass m. delivered For consistency, the absolute value of the difference should not exceed .

[0105] The second item is the consistency verification of the composition ratio. For each component granularity i, the actual delivery ratio m is checked. i_actual / m delivered With the specified synthesis ratio r i For consistency, the absolute value of the difference should not exceed the composite ratio tolerance. When the ratio deviation exceeds the tolerance, a prompt message is recorded but delivery is not interrupted. The message indicates that the inventory ratio of a certain internal particle size bin has deviated from the expected value, and it may be necessary to adjust the process parameters of the processing plant, such as changing the screen configuration or adjusting the closed-loop control target, to restore the particle size ratio.

[0106] (iii) Calculation of state information conversion.

[0107] The state information conversion calculation is used to convert system-level and workshop-level operational indicators in the microscopic simulation of sand and gravel processing into state information identifiable in the macroscopic simulation of excavation and backfilling. The state information conversion calculation includes state acquisition and transmission in the periodic sampling action. The periodic sampling action is triggered by a sampling timer and is executed periodically during the dwell periods of four states: running, standby, limited material supply, and reduced capacity. The complete process of this action is as follows: (1) Obtain system-level indicators through the indicator collector. The processing plant agent uses the indicator collection agent to call the system-level indicator query method of the sand and gravel processing micro-simulation indicator collection agent to obtain the latest system-level operating data, including: cumulative energy consumption in this cycle, cumulative output in this cycle, cumulative warehousing volume mapping of each particle size, and final storage location level mapping of each silo, etc.

[0108] (2) Traverse each workshop to obtain workshop-level indicators. Through simulation examples, traverse all workshop agents in the micro-simulation of sand and gravel processing, including crushing workshop, screening workshop, sand making workshop, and wastewater treatment workshop, and call the workshop-level indicator query method of each workshop to obtain the cumulative processing volume, idle capacity of the workshop, average utilization rate of the workshop, and workshop scheduling failure count, etc.

[0109] (3) Cumulative idle capacity. The total idle capacity of the processing plant is obtained by summing the idle capacity field values ​​of each crushing workshop and assigning it to the available capacity in the status variable.

[0110] (4) Back-collect inventory information according to material specifications. Traverse all material specification records in the static parameter specification mapping table, and back-estimate the maximum quantity that can be synthesized for each specification according to its constituent particle size and synthesis ratio.

[0111] (5) Calculate the production rate of the most recent cycle. Map the cumulative amount of each particle size in the current cycle to the material specification according to the specification mapping table to obtain the cumulative production mass of each specification in the current cycle. Divide the cumulative production mass of each specification in the current cycle by the sampling interval to obtain the production mass rate of the current cycle. Divide the rate by the corresponding specification's ρ. product Obtain the recent production rate of materials of this specification.

[0112] (6) Construct a status information feedback object. Assemble a status information object containing five categories of content: capacity status, including the ratio of available capacity to rated capacity; inventory status organized by material specifications; fault status, i.e., traversing each workshop of the sand and gravel processing micro-simulation to obtain the list of equipment currently in fault or maintenance status and the expected recovery time of each equipment; output rate status, i.e., the recent production rate of each material; process mode status, which is queried by referencing the currently active process mode and the time of the most recent mode switch through the routing management agent.

[0113] (7) Active transmission of status information. The status information object is pushed to the scheduling system by calling the status feedback method of the excavation and backfilling macro simulation system through the scheduler reference.

[0114] (8) Threshold judgment and state transition triggering. The indicators obtained from this sampling are compared with the thresholds in the state transition judgment conditions: 1) If the inlet receiving warehouse position is higher than the upper threshold, the state transition to limited material supply is triggered; 2) If the inlet receiving warehouse position is lower than the lower threshold, the state transition from limited material supply to operation is triggered only when the current state is limited material supply; 3) If the available capacity or installed capacity is lower than the reduction threshold, the state transition to capacity reduction is triggered; 4) If the available capacity or installed capacity is higher than the recovery threshold, the state transition from capacity reduction to operation is triggered only when the current state is reduced capacity.

[0115] (iv) Control command conversion calculation.

[0116] The control command conversion calculation is used to convert the scheduling control commands issued by the macroscopic simulation of excavation and backfilling into executable production adjustment commands by the microscopic simulation of sand and gravel processing. For the control command conversion calculation, the control commands issued by the macroscopic simulation system of excavation and backfilling to the processing plant agent through the scheduler are divided into the following three categories, and the response actions for each category are as follows: Category 1: Process Mode Switching Instructions. The instruction content is the target process mode, including open circuit or closed circuit. In its response action, the processing plant agent calls the process mode switching method of the sand and gravel processing micro-simulation routing management agent through the routing management module, replacing its currently active routing configuration with the routing configuration corresponding to the target mode.

[0117] The second type: Feed rate upper limit adjustment command. The command content is the new feed rate upper limit value. In its response action, the processing plant agent calls the feed rate setting method of the sand and gravel processing micro-simulation receiving station through the receiving station, and sets the upper limit of the feeder's rate to the new value. This command is usually issued by the excavation and backfilling macro-simulation system when it detects a backlog trend in the buffer pile before the processing plant entrance, in order to preventively reduce the feed rate and avoid subsequent entry into a state of limited feed supply.

[0118] The third type: Finished product pre-extraction declaration instructions. The instructions specify the material specifications, volume, and scheduled extraction time for pre-extraction. The processing plant's intelligent agent adds this declaration to the scheduled delivery request queue in its response action. Simultaneously, based on the target output volume and scheduled time for this specification, it estimates the necessary process adjustments, such as whether to switch to a more efficient process mode or increase the priority of the corresponding granularity in routing. As needed, it issues internal adjustment instructions through the routing management intelligent agent. This pre-extraction declaration mechanism transmits the entire process's progress information to the processing plant in advance, enabling the plant to proactively adapt to rather than passively respond to changes in demand.

[0119] In some optional implementations, the simulation result data includes feed source data, finished product destination data, and full-process material flow data. The processing plant agent is also used to perform consistency verification on the full-process material flow data based on the feed source data and finished product destination data, using the material quality conservation relationship as a constraint benchmark, and obtain the verification result.

[0120] For example, in this embodiment of the application, the processing plant agent maintains two tables in the state variables: a material source record table and a finished product destination record table, as the data basis.

[0121] Each material source record includes the following: material request identifier, original material volume V (from the excavation and backfill macroscopic simulation), converted mass m, and the actual blasted loose density ρ used in this conversion. blast The lithology, the name of the typical gradation template used, and a list of batch identifiers for all material batches generated within the processing simulation based on the incoming material.

[0122] Each finished product destination record includes the following: delivery request identifier, delivery time, requested material specification name, and requested quantity (V). req Actual delivery volume V delivered Actual delivery quality m delivered The finished product stockpiling density ρ used product And a list of batch identifiers included in the deliverables.

[0123] The material source record and the finished product destination record together constitute a cross-granularity integrated dual-end traceability structure. The material source record describes how the incoming materials in the excavation and backfilling macroscopic simulation are transformed into batches in the processing simulation, while the finished product destination record describes how the batches in the processing simulation are transformed into finished product deliveries in the excavation and backfilling macroscopic simulation. Together, they serve as the analytical basis for the final destination of materials in a given incoming batch and the initial source of materials in a given delivery.

[0124] The consistency requirement for cross-granularity integration is material conservation: the total amount of material input into the processing plant during the excavation and backfilling macro-simulation must equal the processing plant's output plus the increase in internal inventory plus process losses plus waste discharge, with the difference within the engineering tolerance range. For a complete excavation and backfilling macro-simulation run, the mass conservation equation is as follows: Total incoming material mass = Total delivered mass + Remaining mass within the processing plant + Process loss mass + Waste discharge mass The total incoming material mass represents the total mass of all materials received at the processing plant entrance during the entire simulation period, which is obtained by summing the converted mass items m from all incoming material source records in the inbound record table; the total delivered mass represents the total mass of all finished products delivered at the processing plant exit during the entire simulation period, which is obtained by summing the actual delivered mass items m from all finished product destination records in the outbound record table. deliveredThe total mass of materials remaining within the processing plant at the end of the simulation is obtained by summing up the following: The remaining mass within the processing plant represents the total mass of materials still present at various locations within the plant, including the current inventory of all sand and gravel processing micro-simulation silos, the materials currently en route on all sand and gravel processing micro-simulation belt conveyors, and the materials awaiting processing in all sand and gravel processing micro-simulation crushers and screening machines. This mass is obtained by summing the current material holdings of each component in the sand and gravel processing micro-simulation through the processing plant agent. Process loss mass represents the mass of materials lost during processing in non-finished product form; waste discharge mass represents materials identified as non-compliant and actively discharged during processing, including materials whose lithology does not meet any material specifications, severely oversized materials that cannot be reduced in size through crushing, and materials requiring disposal under special conditions.

[0125] The engineering tolerance is the ratio of the absolute value of the difference between the left and right sides of the equation to the total mass of the incoming material, which does not exceed the global conservation tolerance.

[0126] The triggering times for mass conservation checks include: (1) Periodic verification. During the simulation, verification is performed according to the verification cycle, and only the difference between the cumulative total incoming material mass and the cumulative total delivered material mass plus the internal remaining mass is checked. Early warning is given when the difference continues to deviate, so that long-term accumulated deviations can be detected before the end of the simulation. This verification is triggered by a timer as part of the periodic action of the running state.

[0127] (2) Simulation End Verification. When the simulation ends, that is, after the processing plant's intelligent agent enters the shutdown state and the internal cleanup is completed, a complete quality conservation verification is performed, including the verification of all incoming material source records, all finished product destination records, internal remaining mass, process losses, and waste discharge. This verification is the final consistency determination.

[0128] The method provided in this application, based on the design principles of agent modeling, designs the sand and gravel processing plant as an independent encapsulated agent in the macroscopic simulation of excavation and backfilling. This agent participates in scheduling as an independent node in the macroscopic simulation, interacting equally with other nodes such as material yards, transport vehicles, and filling areas. It holds a microscopic simulation model of sand and gravel processing through reference relationships, ensuring that each instance in a multi-processing plant scenario independently holds its own microscopic simulation model without interference. This design allows the detailed simulation of the processing plant to be embedded as an independent unit in the macroscopic simulation of excavation and backfilling, preserving the complete accuracy of the microscopic simulation while eliminating the need for the macroscopic simulation to understand the internal mechanisms of the processing simulation. The state diagram of the processing plant agent includes six main states: initialization, standby, running, limited material supply, reduced capacity, and shutdown. State transitions are triggered by three sources: external events, periodic sampling timers, and idle timeout timers. The state diagram serves as the standard behavioral model for the processing plant agent, clearly representing the macroscopic operational stages of the processing plant from the perspective of the macroscopic simulation of excavation and backfilling. Loose density is categorized into four types based on material state: original rock, post-blasting, post-crushing, and finished product stockpile. An automatic volume-to-mass conversion method for lithology queries is defined. Post-blasting loose density is used, while finished product stockpile density, individually calibrated according to material specifications, is used at the outlet. This provides a clear physical basis for the conversion process and avoids density confusion between different states. Attribute precision conversion, based on joint queries of lithology and mining processes, expands a single lithological attribute into a complete gradation curve. The physical laws governing the impact of blasting processes on particle size distribution provide a physical basis for particle size enhancement. Based on a finished product specification mapping table, material specification requests are mapped to the synthesis and extraction of multiple internal particle sizes. Different material sources of different specifications are screened by allowing lithological constraints for differentiation. Periodic sampling serves as a unified mechanism for state perception in the micro-simulation of sand and gravel processing. Instead of directly subscribing to the state events of each device in the micro-simulation, the processing plant agent periodically and actively queries the micro-simulation and triggers its own state changes based on threshold conditions. This design aligns with the relationship between the processing plant agent and the micro-simulation, maintaining a loose coupling between the two. The proactive transmission of status information to the excavation and backfilling macro-simulation system and the issuance of full-process scheduling commands to the processing plant constitute a two-way information interaction, upgrading the two types of simulations from open-loop loose coupling to closed-loop tight coupling collaboration. Combined with the quality conservation verification of the material source record and the finished product destination record, the entire excavation and backfilling macro-simulation meets strict material conservation constraints.

[0129] This embodiment also provides a cross-particle size simulation device for sand and gravel processing and excavation backfilling. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0130] This embodiment provides a cross-particle size simulation device for sand and gravel processing and excavation backfilling, such as... Figure 4 As shown, it includes: The acquisition module 401 is used to acquire the attribute and variable system of the intelligent agent of the processing plant. The attribute and variable system includes state variable information, which is used to characterize the real-time operating status of the intelligent agent of the processing plant. Module 402 is used to construct a state diagram of the processing plant intelligent agent based on state variable information. The state diagram represents the various operating states, operating state switching conditions and corresponding execution actions of the processing plant intelligent agent. The processing module 403 is used to embed the cross-granularity material conversion algorithm into the corresponding actions of the state diagram based on the attribute and variable system. This enables the processing plant agent to perform cross-granularity data conversion, state synchronization, and instruction coordination processing throughout the entire process of micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, and obtain simulation result data. The cross-granularity material conversion algorithm is used to adapt the data scope and facilitate bidirectional information exchange between macro-simulation of excavation and backfilling and micro-simulation of sand and gravel processing. It completes the conversion of incoming material data from macro to micro, the conversion of finished product data from micro to macro, the conversion of the running state of micro-simulation of sand and gravel processing to the macro-running state that the processing plant agent can recognize, and the adaptation and conversion of macro-scheduling instructions to micro-execution instructions.

[0131] In some optional implementations, the simulation result data includes feed source data, finished product destination data, and full-process material flow data. The processing plant agent is also used to perform consistency verification on the full-process material flow data based on the feed source data and finished product destination data, using the material quality conservation relationship as a constraint benchmark, and obtain the verification result.

[0132] In some alternative implementations, the attribute and variable system includes static parameters, reference variables, and internal timers. Static parameters are used to characterize the inherent features of the processing plant, reference variables are used to store access paths of other intelligent agents associated with the processing plant's intelligent agent, and internal timers are used to drive periodic actions.

[0133] In some optional implementations, the state variables include various types of operational state information of the processing plant agent, and the construction module 402 includes: The first determination submodule is used for the switching conditions of various running states, as well as the entry actions, exit actions, and periodic pause actions of various running states. A submodule is constructed to build a state diagram of the processing plant agent based on the various operating states, operating state switching conditions, entry actions, exit actions, and periodic dwell actions of each operating state.

[0134] In some alternative implementations, the various operating states include: initialization, standby, operation, limited feed supply, reduced capacity, and shutdown.

[0135] In some optional implementations, the cross-granularity material conversion algorithm includes conversion calculations in incoming material receiving event response actions, conversion calculations in finished product delivery event response actions, conversion calculations of status information, and conversion calculations of control commands. The conversion calculations in incoming material receiving event response actions are used to convert macroscopic incoming material information in the excavation and backfilling macroscopic simulation into identifiable incoming batches and establish cross-granularity traceability material conversion. The conversion calculations in finished product delivery event response actions are used to convert material specifications and volume requirements in the excavation and backfilling macroscopic simulation into finished product material processing tasks executable in the microscopic sand and gravel processing simulation, and to convert actual delivered materials into macroscopically identifiable volume data, and establish cross-granularity supply and demand adaptation and material conversion with cross-granularity finished product destination association. The conversion calculations of status information are used to convert system-level and workshop-level operating indicators in the sand and gravel processing microscopic simulation into status information identifiable in the excavation and backfilling macroscopic simulation. The conversion calculations of control commands are used to convert scheduling control commands issued by the excavation and backfilling macroscopic simulation into production adjustment commands executable in the sand and gravel processing microscopic simulation.

[0136] The cross-particle size simulation device for sand and gravel processing and excavation backfilling provided in this embodiment of the invention can execute the cross-particle size simulation method for sand and gravel processing and excavation backfilling provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0137] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0138] The following is a detailed reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0139] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0140] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the cross-particle size simulation method for sand and gravel processing and excavation backfilling according to embodiments of the present invention.

[0141] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0142] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the cross-particle size simulation method for sand and gravel processing and excavation backfilling shown in the above embodiments is implemented.

[0143] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0144] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A cross-particle size simulation method for sand and gravel processing and excavation backfilling, characterized in that, The method includes: Acquire the attribute and variable system of the intelligent agent of the processing plant, wherein the attribute and variable system includes state variable information, and the state variable information is used to characterize the real-time operating status of the intelligent agent of the processing plant. Based on the state variable information, a state diagram of the processing plant intelligent agent is constructed. The state diagram represents the various operating states, operating state switching conditions, and corresponding execution actions of the processing plant intelligent agent. Based on the aforementioned attribute and variable system, a cross-grain size material conversion algorithm is embedded into the corresponding actions of the state diagram. This enables the processing plant agent to perform cross-grain size data conversion, state synchronization, and instruction coordination throughout the entire process of micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, thereby obtaining simulation result data. The cross-grain size material conversion algorithm is used to adapt the data scope and facilitate bidirectional information exchange between macro-simulation of excavation and backfilling and micro-simulation of sand and gravel processing. This completes the conversion of incoming material data from macro to micro, the conversion of finished product data from micro to macro, the conversion of the micro-simulation running state of sand and gravel processing to the macro-running state that the processing plant agent can recognize, and the adaptation and conversion of macro-scheduling instructions to micro-execution instructions.

2. The method according to claim 1, characterized in that, The simulation results data include feed source data, finished product destination data, and full-process material flow data. The processing plant agent is also used to perform consistency verification on the full-process material flow data based on the feed source data and finished product destination data, with the material quality conservation relationship as a constraint, and obtain the verification results.

3. The method according to claim 1, characterized in that, The attribute and variable system includes static parameters, reference variables, and internal timers. The static parameters are used to characterize the inherent features of the processing plant, the reference variables are used to store the access paths of other intelligent agents associated with the processing plant's intelligent agent, and the internal timers are used to drive periodic actions.

4. The method according to any one of claims 1 to 3, characterized in that, The state variables include various types of operational state information of the processing plant agent. The step of constructing a state diagram of the processing plant agent based on the state variables includes: Determine the switching conditions for multiple operating states, as well as the entry actions, exit actions, and periodic pause actions for each operating state; Based on the various operating states, operating state switching conditions, entry actions, exit actions, and periodic dwell actions of each operating state, a state diagram of the processing plant intelligent agent is constructed.

5. The method according to claim 4, characterized in that, The various operating states include: initialization, standby, operation, limited material supply, reduced capacity, and shutdown.

6. The method according to any one of claims 1 to 3, characterized in that, The cross-particle size material conversion algorithm includes conversion calculations in incoming material receiving event response actions, conversion calculations in finished product delivery event response actions, conversion calculations of status information, and conversion calculations of control commands. The conversion calculation in the incoming material receiving event response action is used to convert the macroscopic incoming material information in the macroscopic simulation of excavation and backfilling into identifiable incoming batches and establish cross-particle size traceability material conversion. The conversion calculation in the finished product delivery event response action is used to convert the material specifications and volume requirements in the macro simulation of excavation and backfilling into finished product material processing tasks that can be executed in the micro sand and gravel processing simulation, and to convert the actual delivered materials into macroscopically identifiable volume data, and to establish cross-particle size finished product destination association, cross-particle size supply and demand adaptation and material conversion. The conversion calculation of state information is used to convert the system-level and workshop-level operating indicators in the micro-simulation of sand and gravel processing into state information that can be identified in the macro-simulation of excavation and backfilling. The control command conversion calculation is used to convert the scheduling control commands issued by the macroscopic simulation of excavation and backfilling into production adjustment commands that can be executed by the microscopic simulation of sand and gravel processing.

7. A cross-particle size simulation device for sand and gravel processing and excavation backfilling, characterized in that, The device includes: The acquisition module is used to acquire the attribute and variable system of the intelligent agent of the processing plant. The attribute and variable system includes state variable information, which is used to characterize the real-time operating status of the intelligent agent of the processing plant. The construction module is used to construct a state diagram of the processing plant intelligent agent based on the state variable information. The state diagram represents the various operating states, operating state switching conditions and corresponding execution actions of the processing plant intelligent agent. The processing module is used to embed the cross-granularity material conversion algorithm into the corresponding actions of the state diagram based on the attribute and variable system. This enables the processing plant agent to perform cross-granularity data conversion, state synchronization, and instruction coordination processing throughout the entire process of micro-simulation of sand and gravel processing and macro-simulation of excavation and backfilling, thereby obtaining simulation result data. The cross-granularity material conversion algorithm is used to adapt the data scope and facilitate bidirectional information exchange between macro-simulation of excavation and backfilling and micro-simulation of sand and gravel processing. This completes the conversion of incoming material data from macro to micro, the conversion of finished product data from micro to macro, the conversion of the running state of sand and gravel processing micro-simulation to the macro-running state that the processing plant agent can recognize, and the adaptation and conversion of macro-scheduling instructions to micro-execution instructions.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the cross-particle size simulation method for sand and gravel processing and excavation backfilling as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the cross-particle size simulation method for sand and gravel processing and excavation backfilling as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the cross-particle size simulation method for sand and gravel processing and excavation backfilling as described in any one of claims 1 to 6.

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