Simulation method, device, equipment, medium and program product for sandstone processing

Abstract

The application relates to the technical field of sandstone processing simulation, and discloses a sandstone processing simulation method, a sandstone processing simulation device, a sandstone processing simulation equipment, a sandstone processing simulation medium and a sandstone processing simulation program product.The sandstone processing simulation method comprises the following steps: obtaining sandstone processing information, constructing a structured knowledge base according to the sandstone processing information, and constructing an intelligent agent template library according to the sandstone processing information; obtaining sandstone processing demand information input by a user, structurally analyzing the sandstone processing demand information to obtain structured modeling demand information; generating a sandstone processing production simulation model and simulation configuration data according to the structured modeling demand information, the structured knowledge base and the intelligent agent template library; and performing simulation simulation of sandstone processing according to the simulation configuration data and the sandstone processing production simulation model.The sandstone processing production simulation model and the simulation configuration data are determined through the structured knowledge base and the intelligent agent template library, so that the simulation simulation of sandstone processing is performed, and the accuracy of the simulation simulation of sandstone processing is improved.

Description

Technical Field

[0001] This invention relates to the field of sand and gravel processing simulation technology, specifically to simulation methods, devices, equipment, media, and program products for sand and gravel processing. Background Technology

[0002] The sand and gravel processing process involves crushing and screening raw stones to produce crushed stone and manufactured sand for construction. This process involves multiple pieces of equipment operating in tandem. Relying on repeated on-site adjustments is not only costly and time-consuming, but also makes it difficult to quickly achieve ideal operating conditions. Therefore, simulation technology is needed to pre-run and optimize the production line parameters.

[0003] In related technologies, the method for simulating the sand and gravel processing process is mainly based on system simulation, which lacks support for natural language input modeling. This not only limits the modeling efficiency, but also requires modelers to have both process knowledge and simulation modeling skills. In addition, the training data of large language models contains little content related to sand and gravel processing technology and lacks interface constraints for specific simulation systems, resulting in inaccurate simulations of the sand and gravel processing process that do not conform to reality. Summary of the Invention

[0004] This invention provides a simulation method, apparatus, equipment, medium, and program product for sand and gravel processing, in order to solve the problem that the simulation of sand and gravel processing is not accurate enough and does not conform to the actual situation due to the methods of simulating sand and gravel processing in related technologies.

[0005] In a first aspect, the present invention provides a simulation method for sand and gravel processing, comprising: acquiring sand and gravel processing information; constructing a structured knowledge base based on the sand and gravel processing information; and constructing an intelligent agent template library based on the sand and gravel processing information. The sand and gravel processing information is used to characterize the entire sand and gravel processing process; the structured knowledge base stores information on sand and gravel processing equipment, sand and gravel processing procedures, and rock type influence rules; the intelligent agent template library characterizes the configurable information of various sand and gravel processing intelligent agents. The method also includes: acquiring user-inputted sand and gravel processing requirements; performing structured parsing of the sand and gravel processing requirements to obtain structured modeling requirements; generating a sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirements, the structured knowledge base, and the intelligent agent template library; the simulation configuration data being the operational configuration data for simulating the sand and gravel processing process; and performing a simulation of sand and gravel processing based on the simulation configuration data and the sand and gravel processing production simulation model.

[0006] This invention provides a simulation method for sand and gravel processing. It acquires sand and gravel processing information and constructs a structured knowledge base based on this information to store information on sand and gravel processing equipment, processing procedures, and rock type influence rules. It also constructs a configurable agent template library to represent various sand and gravel processing agents, achieving standardized management of the information and providing a data foundation for subsequent sand and gravel processing simulation. This invention acquires user-inputted sand and gravel processing requirements and performs structured parsing to obtain structured modeling requirements. This transforms colloquial sand and gravel processing requirements into clear, quantifiable, and process-aligned structured modeling requirements, eliminating ambiguity and bias in user understanding. Based on the structured modeling requirements, the structured knowledge base, and the agent template library, this invention generates a sand and gravel processing production simulation model and simulation configuration data. This facilitates the automated generation of the simulation model and its associated configuration data. The standardized information from the structured knowledge base and agent template library ensures consistency between the simulation model parameters, process logic, and the actual sand and gravel processing production process. This invention simulates sand and gravel processing based on simulation configuration data and a sand and gravel processing production simulation model. It simulates the actual production process in a virtual environment, completing production testing simulations under different working conditions, equipment combinations, and raw material conditions without the need to build a physical production line. Compared with related technologies, this invention only requires the user to input sand and gravel processing requirements to automatically perform high-precision sand and gravel processing simulations, improving the accuracy and efficiency of sand and gravel processing simulation.

[0007] In one optional implementation, a structured knowledge base and an intelligent agent template library are constructed based on sand and gravel processing information. This includes: structurally storing sand and gravel processing equipment information, sand and gravel processing flow information, and rock type influence rule information from the sand and gravel processing information to obtain the structured knowledge base; the sand and gravel processing equipment information is used to characterize the specification parameter range and applicable conditions of each type of equipment in the sand and gravel processing production line; the sand and gravel processing flow information is used to characterize the typical process configuration modes of various sand and gravel processing production lines; and the rock type influence rule information is used to characterize the influence rules of different rock types on equipment process parameters; based on the sand and gravel processing information, various sand and gravel processing intelligent agents are defined, configurable information of these agents is extracted, and an intelligent agent template library is constructed based on the configurable information of these agents.

[0008] In one optional implementation, the sand and gravel processing demand information is structured and parsed to obtain structured modeling demand information, including: extracting related element information from the sand and gravel processing demand information; determining implicit constraints based on the related element information and a structured knowledge base; supplementing missing parameters in the sand and gravel processing demand information to obtain target sand and gravel processing demand information; and converting the related element information, implicit constraints, and target sand and gravel processing demand information into a standard structured format to obtain structured modeling demand information.

[0009] In one optional implementation, a sand and gravel processing production simulation model and simulation configuration data are generated based on structured modeling requirement information, a structured knowledge base, and an intelligent agent template library. This includes: matching the structured modeling requirement information in the structured knowledge base to obtain equipment specifications and material feeding plan data; matching the structured modeling requirement information in the intelligent agent template library to obtain routing configuration data; the routing configuration data is used to characterize the routing rules of the sand and gravel processing process corresponding to the structured modeling requirement information; and generating the sand and gravel processing production simulation model and simulation configuration data based on the equipment specifications, material feeding plan data, and routing configuration data.

[0010] In one optional implementation, the simulation of sand and gravel processing is performed based on the simulation configuration data and the sand and gravel processing production simulation model, including: inputting the simulation configuration data into the sand and gravel processing production simulation model, and performing the sand and gravel processing simulation.

[0011] In one optional implementation, after generating a sand and gravel processing production simulation model and simulation configuration data based on structured modeling requirements information, a structured knowledge base, and an intelligent agent template library, the sand and gravel processing simulation method further includes: sequentially performing parameter range verification, topology structure verification, and capacity matching verification on the simulation configuration data, and modifying the sand and gravel processing production simulation model based on the verification results.

[0012] This invention improves the accuracy of sand and gravel processing simulation models by sequentially verifying parameter range, topology structure, and capacity matching of simulation configuration data, and modifying the simulation model based on the verification results. It filters out unreasonable configuration data, avoids unnecessary simulations that consume computing resources, and improves the accuracy of the simulation model.

[0013] In one optional implementation, after simulating sand and gravel processing based on simulation configuration data and a sand and gravel processing production simulation model, the sand and gravel processing simulation method further includes: extracting simulation evaluation indicators based on the simulation results, performing structured transformation on the simulation evaluation indicators, and generating a simulation report; performing capacity achievement analysis, product quality analysis, energy consumption analysis, and equipment utilization balance analysis on the sand and gravel processing production simulation model in sequence based on the simulation report, and obtaining analysis results; generating a configuration modification plan based on the analysis results, correcting the simulation configuration data using the configuration modification plan, returning to perform parameter range verification, topology structure verification, and capacity matching verification on the simulation configuration data in sequence, and iterating the steps of modifying the sand and gravel processing production simulation model based on the verification results until a preset number of iterations is reached to obtain the optimal simulation configuration data.

[0014] After simulation, this invention extracts simulation evaluation indicators and generates a structured simulation report. Based on the report, it sequentially performs analyses on capacity achievement, product quality, energy consumption, and equipment utilization balance to obtain analysis results. It then generates configuration modification schemes and corrects the simulation configuration data. After parameter range verification, topology structure verification, and capacity matching verification, it modifies the model and iterates to a preset number of times to obtain the optimal simulation configuration data. This constructs a closed-loop feedback mechanism for simulation results, multi-dimensional analysis, configuration correction, model verification, and iterative optimization, transforming the static results of a single simulation into a dynamic process of continuous optimization, and efficiently determining the optimal simulation configuration data.

[0015] In one optional implementation, the simulation method for sand and gravel processing further includes storing and displaying simulation configuration data, simulation reports, configuration modification schemes, and optimal simulation configuration data.

[0016] Secondly, the present invention provides a simulation device for sand and gravel processing, comprising: a database construction module for acquiring sand and gravel processing information, constructing a structured knowledge base based on the sand and gravel processing information, and constructing an intelligent agent template library based on the sand and gravel processing information; the sand and gravel processing information is information used to characterize the entire sand and gravel processing process, the structured knowledge base is used to store information on sand and gravel processing equipment, sand and gravel processing flow information, and rock type influence rules information, and the intelligent agent template library is used to characterize the configurable information of various sand and gravel processing intelligent agents; a structured parsing module for acquiring sand and gravel processing requirement information input by the user, performing structured parsing on the sand and gravel processing requirement information to obtain structured modeling requirement information; a simulation data determination module for generating a sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirement information, the structured knowledge base, and the intelligent agent template library; the simulation configuration data is the operation configuration data for simulating the sand and gravel processing process; and a simulation module for performing sand and gravel processing simulation based on the simulation configuration data and the sand and gravel processing production simulation model.

[0017] 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 simulation method for sand and gravel processing described in the first aspect or any corresponding embodiment thereof.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the simulation method for sand and gravel processing described in the first aspect or any corresponding embodiment thereof.

[0019] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the simulation method for sand and gravel processing according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0020] 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.

[0021] 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 simulation method for sand and gravel processing according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for simulating sand and gravel processing according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the simulation method for sand and gravel processing according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a sand and gravel processing simulation device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] As an optional application scenario of this invention, such as Figure 1 As shown, the sand and gravel processing simulation 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.

[0025] 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.

[0026] Establishing a simulation model for a sand and gravel processing production line is a highly specialized and labor-intensive engineering task. A typical sand and gravel processing production line includes dozens of different types of equipment, each involving the configuration of more than ten process parameters. The material connections and routing rules between the equipment need to be set one by one according to the process plan, and the material delivery plan needs to be formulated based on the capacity target and raw material supply. Completing these configuration tasks requires not only simulation expertise, such as the meaning of simulation model parameters, but also data structure and process expertise, such as equipment selection, process flow, and product requirements for sand and gravel processing. The modeling threshold is relatively high.

[0027] The construction of simulation models for sand and gravel processing production lines requires modelers to possess both knowledge of sand and gravel processing technology and the ability to apply simulation platforms. The solutions proposed by process designers are typically described in natural language text and tabular form, such as "using three-stage crushing and two-stage screening," "using a jaw crusher for coarse crushing," "using two cone crushers in a closed circuit for medium crushing," and "screen aperture 5 / 25 / 40mm," etc. These need to be manually translated by professional modelers into parameter configurations and topological connections for the simulation model. This process presents three problems: first, it is inefficient; second, modelers need to possess both knowledge of sand and gravel processing technology and the ability to simulate and model sand and gravel processing; and finally, misunderstandings can arise, as the manual translation process from process description to model code can easily introduce parameter setting errors or missing connections. Current general-purpose language models do not understand the process constraints of sand and gravel processing, and the accuracy of simulation cannot be guaranteed. General-purpose language models may generate grammatically correct models but with flawed process logic.

[0028] This invention provides a simulation method for sand and gravel processing. By using a structured knowledge base and an intelligent agent template library to determine the simulation model and configuration data for sand and gravel processing, the simulation of sand and gravel processing can be performed, thereby improving the accuracy of the simulation.

[0029] According to an embodiment of the present invention, a simulation method for sand and gravel processing 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.

[0030] This embodiment provides a simulation method for sand and gravel processing, which can be used with computer equipment. Figure 2 This is a first flowchart of a simulation method for sand and gravel processing according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain sand and gravel processing information, construct a structured knowledge base based on the sand and gravel processing information, and construct an intelligent agent template library based on the sand and gravel processing information.

[0031] Among them, the sand and gravel processing information is used to characterize the entire sand and gravel processing process; the structured knowledge base is used to store information on sand and gravel processing equipment, sand and gravel processing procedures, and rock type influence rules; and the intelligent agent template library is used to characterize the configurable information of various sand and gravel processing intelligent agents.

[0032] In some optional implementations, sand and gravel processing information refers to all information about the entire sand and gravel processing process. It is a basic data set describing the entire process of sand and gravel processing from raw materials to finished products, including raw rock type, equipment model, equipment parameters, equipment performance, process flow, operating procedures, historical operating data, fault records, etc.

[0033] In some alternative implementations, sand and gravel processing information can be obtained by reading data from the existing sand and gravel processing system through a data acquisition interface, importing industry standards, equipment manuals, or manually entering historical project experience data.

[0034] In some alternative implementations, the structured knowledge base includes information on sand and gravel processing equipment, sand and gravel processing procedures, and rock type influence rules.

[0035] The information on sand and gravel processing equipment includes performance-level details, specifically the specifications and applicable conditions for each model of equipment in the sand and gravel processing production line. For example, regarding crushing equipment, it covers jaw crushers, cone crushers, impact crushers, and vertical shaft impact crushers, recording the typical range of rated capacity, rated power range, maximum feed particle size range, adjustable discharge port range, and recommended overload threshold range for each type. For screening equipment, it records the range of screen layers for each model of vibrating screen, recommended range of screen aperture for each layer, empirical values ​​of screening efficiency, and rated capacity range. For conveying equipment, it records the length, speed, and maximum load capacity of belt conveyors. For storage facilities, it records the capacity specifications for each type of silo, such as receiving silos, semi-finished product silos, and finished product silos. For wastewater treatment facilities, it records the volume of sedimentation tanks and the duration parameters for each process stage. This information on sand and gravel processing equipment is derived from equipment manufacturers' product manuals and accumulated industry engineering practices.

[0036] In some optional implementations, the sand and gravel processing flow information is information at the process flow level, including typical process configuration modes of sand and gravel processing production lines. Depending on product specifications and capacity requirements, production line configurations can be categorized into the following typical modes: a simple configuration of single-stage crushing and single-stage screening, suitable for low-capacity scenarios requiring only coarse aggregate; a standard configuration of two-stage crushing and two-stage screening, suitable for medium-capacity scenarios requiring multiple aggregate sizes; and a full configuration of three-stage crushing, three-stage screening, and sand making, suitable for high-capacity scenarios requiring manufactured sand and multiple aggregate sizes. Each configuration mode records its typical equipment combination scheme, recommended topology connection template, and routing rule template. The equipment combination scheme includes equipment type, number of units, etc. The topology connection template characterizes the material transfer path between equipment, and the routing rule template characterizes the material diversion rules for each node.

[0037] In some optional implementations, the rock type influence rule information is lithological parameter correlation dimension information. The rock type influence rule information includes the influence rules of different rock types on equipment process parameters; the differences in physical properties of different rocks will affect the crushing effect and equipment selection. Different rocks can include granite, limestone, basalt, sandstone, etc. The lithological parameter correlation dimension information records the following correlation rules: the influence of each rock type on the crushing transformation model parameters, such as the recommended values ​​of lithological correction coefficients corresponding to different rocks; the correction of each rock type on the screening efficiency, such as rocks with high mud content may lead to a decrease in screening efficiency; the influence of each rock type on the equipment wear rate; the recommended number of crushing stages and equipment type combination for each rock type, such as granite with high hardness usually requires at least two crushing stages, with jaw crusher recommended for the first stage and cone crusher recommended for the second stage.

[0038] In some alternative implementations, the structured knowledge base is organized in the form of structured documents, with each knowledge item containing applicable conditions, parameter values ​​or recommended ranges, and source descriptions.

[0039] In some optional implementations, the agent template library adopts a three-layer agent architecture, including equipment-layer agent templates, workshop-layer templates, and system-layer templates, defining a total of fifteen agent types. The equipment-layer agent templates cover seven equipment-layer agent types: crushing equipment, screening equipment, feeders, belt conveyors, hoppers, bins, and sedimentation tanks. Each template includes a parameter definition table and state quantity information. The parameter definition table lists all configurable parameters for that type of agent, with each parameter recording its name, data type, physical unit, default value, and value constraints. Taking crushing equipment as an example, its parameter definition table may include the following parameters: crushing equipment type, rated capacity, rated power, maximum feed particle size, idling energy consumption rate, overload threshold, initial discharge port value, discharge port adjustment time, cooling time, failure rate, maintenance response time, planned maintenance time, and planned maintenance interval. The state quantity information records the number of states and a list of state names for each type of agent, used for reference when generating operating scenarios or analyzing simulation results. For example, crushing equipment can have nine states: shutdown, standby, running, overload, cooling, discharge port adjustment, pause, fault, and maintenance; screening equipment has seven states; belt conveyors have six states, etc.

[0040] In some optional implementations, the workshop-level templates target five types of workshop-level agents: crushing workshop, screening workshop, sand making workshop, wastewater treatment workshop, and receiving station. Each template lists the types of equipment it manages, supported scheduling strategy options, and suggested application scenarios for each strategy. The scheduling strategy options include five types: round-robin allocation, minimum load priority, shortest queue priority, random allocation, and specification priority allocation. Each template provides recommended use cases for each strategy; for example, the minimum load priority strategy is recommended for the crushing workshop, the shortest queue priority strategy for the screening workshop, and the round-robin strategy for the wastewater treatment workshop.

[0041] In some optional implementations, the system layer template records configurable parameters for three system layer agents, including a topology management agent, a routing management agent, and an indicator collection agent. For example, the evaluation period of the routing management agent and the collection period of the indicator collection agent are configurable parameters.

[0042] In some alternative implementations, the agent template library provides a complete list of optional agents—when generating simulation configurations, there is no need to design agent structures from scratch or guess parameter names and types; simply select the appropriate agent type from the agent template library and fill in the specific parameter values ​​for each instance. Parameter constraint information can be self-verified during the generation phase, reducing the number of violations in subsequent constraint verifications.

[0043] Step S202: Obtain the sand and gravel processing requirements information input by the user, perform structured parsing on the sand and gravel processing requirements information, and obtain structured modeling requirements information.

[0044] Among them, the sand and gravel processing demand information is natural language description demand information. For example, the sand and gravel processing demand information can be "a granite production line with an hourly output of 100 tons, and finished product specifications of three aggregates: 0mm-5mm, 5mm-10mm, and 10mm-20mm". The natural language description demand information contains the core information required for modeling, but the format is not uniform and the information granularity is uncertain. By designing prompt word engineering method, the natural language sand and gravel processing demand information is parsed into standardized structured modeling demand information.

[0045] Step S203: Based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library, generate a sand and gravel processing production simulation model and simulation configuration data; the simulation configuration data is the operation configuration data for simulating the sand and gravel processing process.

[0046] Specifically, based on the structured modeling requirements, the simulation configuration data is obtained by matching the structured knowledge base and the intelligent agent template library. Then, based on the structured modeling requirements, the corresponding sand and gravel processing intelligent agent is selected from the intelligent agent template library to generate a sand and gravel processing production simulation model.

[0047] Step S204: Simulate sand and gravel processing based on simulation configuration data and sand and gravel processing production simulation model.

[0048] This embodiment provides a simulation method for sand and gravel processing. It acquires sand and gravel processing information and constructs a structured knowledge base based on this information to store information on sand and gravel processing equipment, processing procedures, and rock type influence rules. It also constructs a smart agent template library to represent various sand and gravel processing smart agents, enabling standardized management of the information and providing a data foundation for subsequent sand and gravel processing simulations. This embodiment acquires user-inputted sand and gravel processing requirements and performs structured parsing to obtain structured modeling requirements. This transforms colloquial sand and gravel processing requirements into clear, quantifiable, and process-aligned structured modeling requirements, eliminating ambiguity and bias in user understanding. Based on the structured modeling requirements, the structured knowledge base, and the smart agent template library, this embodiment generates a sand and gravel processing production simulation model and simulation configuration data. This facilitates the automated generation of the simulation model and its associated configuration data. The standardized information from the structured knowledge base and smart agent template library ensures consistency between the simulation model parameters, process logic, and the actual sand and gravel processing production process. This invention simulates sand and gravel processing based on simulation configuration data and a sand and gravel processing production simulation model. It simulates the actual production process in a virtual environment, completing production testing simulations under different working conditions, equipment combinations, and raw material conditions without the need to build a physical production line. Compared with related technologies, this invention only requires the user to input sand and gravel processing requirements to automatically perform high-precision sand and gravel processing simulations, improving the accuracy and efficiency of sand and gravel processing simulations.

[0049] This embodiment provides a simulation method for sand and gravel processing, which can be used with computer equipment. Figure 3 This is a second flowchart of a simulation method for sand and gravel processing according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain sand and gravel processing information, construct a structured knowledge base based on the sand and gravel processing information, and construct an intelligent agent template library based on the sand and gravel processing information.

[0050] Specifically, step S301 includes: Step S3011: The sand and gravel processing equipment information, sand and gravel processing process information, and rock type influence rule information in the sand and gravel processing information are stored in a structured manner to obtain a structured knowledge base.

[0051] Among them, the sand and gravel processing equipment information is used to characterize the specification parameter range and applicable conditions of each model of equipment in the sand and gravel processing production line; the sand and gravel processing process information is used to characterize the typical process configuration mode of various sand and gravel processing production lines; and the rock type influence rule information is used to characterize the influence rules of different rock types on equipment process parameters.

[0052] Step S3012: Based on the sand and gravel processing information, define a variety of sand and gravel processing intelligent agents, extract the configurable information of the various sand and gravel processing intelligent agents, and construct an intelligent agent template library based on the configurable information of the various sand and gravel processing intelligent agents.

[0053] Among them, the various sand and gravel processing intelligent agents can include fifteen types of intelligent agents, namely, crushing equipment intelligent agents, screening equipment intelligent agents, feeder intelligent agents, belt conveyor intelligent agents, hopper intelligent agents, silo intelligent agents, sedimentation tank intelligent agents, crushing workshop intelligent agents, screening workshop intelligent agents, sand making workshop intelligent agents, wastewater treatment workshop intelligent agents, receiving station intelligent agents, topology management intelligent agents, routing management intelligent agents, and index collection intelligent agents.

[0054] In some optional implementations, each sand and gravel processing agent has corresponding configurable information, and each configurable information records parameter name, data type, physical unit, default value and value constraints.

[0055] Step S302: Obtain the sand and gravel processing requirements information input by the user, perform structured parsing on the sand and gravel processing requirements information, and obtain structured modeling requirements information.

[0056] Specifically, step S302 includes: Step S3021: Extract related element information from the sand and gravel processing demand information, and determine implicit constraints based on the related element information and the structured knowledge base.

[0057] Among them, the related element information is the explicit information of clearly described process elements. For example, the related element information includes: production capacity target, lithology type and product specification list. The production capacity target includes a value and unit, such as "100 tons per hour". The lithology type is a textual description that maps to the lithology enumeration value in the simulation system. For example, "granite" maps to the granite type in the system enumeration. The product specification list includes the particle size range of each product. For example, three product specifications are parsed: 0mm-5mm, 5mm-10mm and 10mm-20mm.

[0058] In some optional implementations, implicit constraints are constraints that are not explicitly stated by the user but are necessary for the process. For example, the maximum particle size limit in the product is 20mm, which means that the maximum screen opening of the screening equipment is not less than 20mm. Granite is a hard rock, and according to the lithological parameter association rules in the structured knowledge base, at least two stages of crushing are usually required to crush the raw material to below 20mm. A jaw crusher is recommended for the first stage, which is suitable for coarse crushing of hard rock, and a cone crusher is recommended for the second stage, which is suitable for medium and fine crushing of hard rock. A capacity of 100 tons per hour requires matching the equipment model with the corresponding capacity range.

[0059] Step S3022: Supplement the missing parameters in the sand and gravel processing demand information to obtain the target sand and gravel processing demand information.

[0060] Among these, reasonable default values ​​should be filled in for process details not mentioned by the user in the sand and gravel processing requirements information. For example: whether sand making is required, if the sand and gravel processing requirements information only lists aggregate specifications and does not mention manufactured sand, then no sand making workshop will be configured; moisture content, if not mentioned, will take the default value; closed-loop mode, if not mentioned, will start in open-loop mode by default, and the adaptive control strategy will decide whether to switch to closed-loop mode during operation.

[0061] Step S3023: Convert the associated element information, implicit constraints, and target sand and gravel processing requirements into a standard structured format to obtain structured modeling requirements information.

[0062] The structured modeling requirements include the following fields: production capacity target, lithology type, product specification array, recommended number of crushing stages, recommended crusher type for each stage, number of screening layers and screen hole configuration for each layer, whether a sand making workshop is configured, whether wastewater treatment is configured, and supplementary parameters such as initial moisture content and maximum particle size of raw materials.

[0063] Step S303: Based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library, generate a sand and gravel processing production simulation model and simulation configuration data; the simulation configuration data is the operation configuration data for simulating the sand and gravel processing process.

[0064] Specifically, step S303 includes: Step S3031: Match the structured knowledge base with the structured modeling requirements information to obtain equipment specifications and material feeding plan data.

[0065] The equipment specifications are a set of technical parameters for sand and gravel processing equipment that meet the user's modeling needs and are matched from a structured knowledge base. For example, for crushing equipment, the equipment model matching the production capacity target is searched from the structured knowledge base. For instance, for a 100-ton-per-hour granite production line, a jaw crusher with an optional rated capacity of 200 tons per hour is selected. All parameter fields for this equipment are filled in. For example, the crusher subtype is set to jaw, the rated capacity is set to 200 tons per hour, the rated power is set according to the power parameters of this model in the knowledge base, the maximum feed particle size is set according to the feed inlet size of this model, the initial value of the discharge port is calculated based on the subsequent screening screen openings and process requirements, ensuring that the discharge port value is slightly smaller than the maximum screen opening of the next screening stage to reduce closed-loop load, the overload threshold is taken as the recommended default value, and the failure rate and maintenance parameters are taken as typical values ​​for this type of equipment in the knowledge base. For screening equipment, the number of screen layers and the screen opening diameter of each layer are determined according to the product specification list, the screening efficiency is taken as the typical value recommended by the knowledge base, and the rated capacity is set according to the parameters of this type of vibrating screen in the structured knowledge base. For conveying equipment, such as belt conveyors, the belt length and speed parameters are estimated based on the transmission distance and material flow rate of each segment in the production line topology. For silos, capacity parameters are estimated based on the production capacity target. The capacity of the receiving silo is usually set to the amount of material corresponding to one to two hours of production capacity to cope with fluctuations in material supply; the capacity of the finished product silo is set according to the material supply rate.

[0066] In some optional implementations, after the device specification parameters for each device are generated, the results are organized into a field format that conforms to the specification parameter class defined in the data layer of the simulation system. The name, type, and unit of each field strictly follow the definition in the agent template library.

[0067] In some optional implementations, feeding plan data is generated based on the production capacity target, simulation duration, and raw material supply information from the structured modeling requirements. The feeding plan data is organized in time-series format, with each record containing five fields: the simulation start time of the planned feeding, the total feeding mass of this plan, lithology type, initial gradation template name, and moisture content. The initial gradation template name references a pre-set gradation curve template and describes the particle size distribution of the raw material. Based on the production capacity target, the material within the total simulation duration is evenly distributed into several batches. The mass of each batch is set according to the simulation's time resolution requirements; smaller batches result in higher time resolution but more events.

[0068] Step S3032: Match the structured modeling requirement information in the agent template library to obtain routing configuration data; the routing configuration data is used to characterize the routing rules of the sand and gravel processing process corresponding to the structured modeling requirement information.

[0069] In this process, a sand and gravel processing agent is selected from the agent template library based on the structured modeling requirements information, and the routing configuration data is determined. The core of the routing configuration data is the routing rule list, in which each rule specifies the source node, target node, applicable particle size range, and priority.

[0070] In some optional implementations, for the output of each crushing unit, a routing rule is generated to send its product to the downstream belt conveyor or screening workshop; for the output of each screening unit, multiple routing rules are generated based on the number of screen layers and product specifications. The product from each screening layer is routed to the corresponding finished product bin, the next-level crushing workshop, or the sand making workshop according to its characteristic particle size range. The priority of each rule is set in descending order of particle size range to ensure the determinism of the matching order.

[0071] In some optional implementations, if the structured modeling requirements include a closed-loop mode, an additional closed-loop routing configuration is generated, which includes a rule to return oversized material to the upstream crushing plant. Open-loop and closed-loop configurations are stored with different routing configuration names and process modes for switching during runtime.

[0072] Step S3033: Generate a sand and gravel processing production simulation model and simulation configuration data based on equipment specifications, material feeding plan data, and routing configuration data.

[0073] Step S304: Simulate sand and gravel processing based on simulation configuration data and sand and gravel processing production simulation model.

[0074] Specifically, step S304 includes: Step S3041: Input the simulation configuration data into the sand and gravel processing production simulation model to perform sand and gravel processing simulation.

[0075] In some optional implementations, after generating the sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library, the sand and gravel processing simulation method further includes: sequentially performing parameter range verification, topology structure verification, and capacity matching verification on the simulation configuration data, and modifying the sand and gravel processing production simulation model based on the verification results.

[0076] Among them, parameter range verification involves checking each equipment specification parameter in the simulation configuration data to see if it is within the physically reasonable range. The verification rules are directly derived from the value constraints of each parameter in the intelligent agent template library, including but not limited to: whether the rated capacity is positive; whether the overload threshold is between [0,1]; whether the failure rate is non-negative; whether the sieve aperture array is strictly arranged in descending order; whether the number of sieve layers is consistent with the length of the sieve aperture array; whether the initial value of the discharge port is positive and does not exceed the maximum feed particle size; and whether the overload threshold of the belt conveyor is between [0,1].

[0077] In some alternative implementations, parameter range validation also includes cross-parameter consistency checks: the idling energy consumption rate of the same equipment should not exceed its rated power; the initial current inventory value of the silo should not exceed its capacity limit.

[0078] In some optional implementations, topology structure verification involves using the topology management agent of the simulation system to perform structural verification on the routing configuration data. This verification confirms that all receiving bins can reach all finished product bins. Suspended node detection confirms that each non-finished product bin node has at least one downstream outlet. Loop structure detection distinguishes between legitimate process loops and illegitimate closed loops; in a legitimate process loop, at least one node has an outgoing edge pointing to the outside of the loop, while in an illegitimate closed loop, material entering can never flow out. At the routing rule level, rule coverage integrity checks are performed to confirm that each processing node that may produce material has a corresponding discharge rule in the rule list; target node existence checks confirm that the target node for each rule exists in the topology registry; and particle size interval consistency checks confirm that there is no overlap in particle size intervals of the same priority between rules from the same source node.

[0079] In some optional implementations, capacity matching verification involves estimating the material flow rate of each node during steady-state operation based on the rated capacity and topological connections of the equipment, without actually running a simulation, and checking for any significant capacity mismatches. For example, starting from the receiving station, the infeed and discharge flow rates of each node are estimated sequentially according to the topology. The infeed flow rate equals the sum of the material quantities routed to that node from all upstream nodes; the discharge flow rate is limited by the rated capacity of that node. If the infeed flow rate of a node significantly exceeds its rated capacity, that node is marked as a potential capacity bottleneck and reported. If the total feed rate of a segment exceeds the rated capacity of the first crusher in that segment, it is marked as a flow mismatch.

[0080] In some optional implementations, parameter range verification, topology structure verification, and capacity matching verification are performed sequentially, proceeding to the next level only after the previous level passes. Verification results include both passing and failing. If a verification passes, sand and gravel processing is simulated based on the simulation configuration data and the sand and gravel processing production simulation model. If a verification fails, the sand and gravel processing production simulation model is modified based on the verification results. Specifically, for items that fail verification, a detailed problem description is generated, including the problem parameter name, current value, constraints, and suggested correction direction, which is then fed back to the sand and gravel processing production simulation model for correction. The configuration is modified in a targeted manner based on the problem information, such as adjusting out-of-range parameters to within the range or adding routing rules for nodes lacking discharge rules. The corrected routing configuration is then resubmitted for verification. If, after three consecutive rounds of correction, there are still items that fail verification, the system reports the remaining verification problem items to the user for manual intervention.

[0081] This invention improves the accuracy of sand and gravel processing simulation models by sequentially verifying parameter range, topology structure, and capacity matching of simulation configuration data, and modifying the simulation model based on the verification results. This filters out unreasonable configuration data, avoids unnecessary simulations that consume computing resources, and improves the accuracy of the simulation model.

[0082] In some optional implementations, after simulating sand and gravel processing based on simulation configuration data and a sand and gravel processing production simulation model, the sand and gravel processing simulation method further includes: extracting simulation evaluation indicators based on the simulation results, performing structured transformation on the simulation evaluation indicators, and generating a simulation report; performing capacity achievement analysis, product quality analysis, energy consumption analysis, and equipment utilization balance analysis on the sand and gravel processing production simulation model in sequence based on the simulation report, and obtaining analysis results; generating a configuration modification plan based on the analysis results, correcting the simulation configuration data using the configuration modification plan, returning to perform parameter range verification, topology structure verification, and capacity matching verification on the simulation configuration data in sequence, and iterating the steps of modifying the sand and gravel processing production simulation model based on the verification results until a preset number of iterations is reached to obtain the optimal simulation configuration data.

[0083] The simulation evaluation metrics include system-level metrics and equipment-level metrics. System-level metrics include: total output of the entire production line, comprehensive energy consumption per ton of product, output of each product size, and final storage level of each silo. Total output of the entire production line is calculated by summing the amount of finished products entering the silo. Comprehensive energy consumption per ton of product equals total energy consumption of the entire production line divided by total output. Output of each product size is the cumulative output corresponding to that size. Equipment-level metrics include the utilization rate, availability rate, and average load rate of each piece of equipment. Equipment utilization rate is the proportion of time the equipment agent is in core processing state to the total simulation time. Availability rate is the proportion of non-fault and non-maintenance time of the equipment to the total simulation time. Workshop-level metrics include: total processing volume of each workshop, number of scheduling failures, average equipment utilization rate within the workshop, and average equipment availability rate within the workshop. The number of scheduling failures is the number of times the candidate pool is empty, reflecting insufficient equipment or uneven load.

[0084] After simulation, the present invention extracts simulation evaluation indicators and generates a structured simulation report. Based on the report, it sequentially performs analyses on capacity achievement, product quality, energy consumption, and equipment utilization balance to obtain analysis results. It then generates a configuration modification scheme and corrects the simulation configuration data. After parameter range verification, topology structure verification, and capacity matching verification, it modifies the model and iterates to a preset number of times to obtain the optimal simulation configuration data. This constructs a closed-loop feedback mechanism for simulation results, multi-dimensional analysis, configuration correction, model verification, and iterative optimization, transforming the static results of a single simulation into a dynamic process of continuous optimization, and efficiently determining the optimal simulation configuration data.

[0085] In some optional implementations, the simulation evaluation indicators are structured and transformed to generate a simulation report. Based on the simulation report, the sand and gravel processing production simulation model is sequentially analyzed for capacity achievement, product quality, energy consumption, and equipment utilization balance, yielding the analysis results. Specifically, the capacity achievement analysis involves dividing the total output of the entire production line by the simulation duration to obtain the actual capacity, expressed in tons per hour, and comparing it with the user-specified capacity target. If the actual capacity is less than 80% of the target value, the reasons are analyzed and improvement suggestions are proposed: check whether the workshop with the highest equipment utilization rate is the capacity bottleneck, and suggest increasing the feeding rate of that workshop or adding more equipment; check whether workshops with frequent scheduling failures have insufficient equipment; check whether there is a situation where a certain piece of equipment is continuously overloaded, leading to a decrease in effective capacity. The product quality analysis involves dividing the output of each particle size class by the total output to obtain the actual proportion of each particle size class, and comparing it with the user's expected product gradation ratio. If the output of a certain particle size is insufficient, the following parameter adjustment solutions are proposed: Insufficient output of this particle size may be due to an excessively large discharge port of the upstream crushing equipment, resulting in a coarser product. It is recommended to reduce the discharge port size to increase the product proportion in this range. Alternatively, it may be due to improper screen aperture configuration, causing material of this particle size to be mistakenly diverted to other particle sizes. It is recommended to adjust the particle size boundary point of the routing rules. Energy consumption analysis involves comparing the energy consumption per ton of product with industry benchmarks. If energy consumption is high, analyze the energy consumption composition: statistically analyze the proportion of total energy consumption by crushing equipment, screening equipment, and conveying equipment, and identify equipment with a high energy consumption proportion. Suggested optimization solutions may include: adjusting the discharge port size to reduce ineffective processing in the crushing section; enabling closed-loop mode to improve the effective output rate of a single crushing operation, provided the proportion of oversized particles is controllable; or switching the scheduling strategy to balance the load of each piece of equipment and reduce cooling downtime caused by overload. Equipment utilization balance analysis involves checking the variance of the utilization rate of each piece of equipment in the same workshop. If the variance is too large, it indicates that the scheduling strategy has not effectively balanced the load, and it is recommended to switch to a more suitable scheduling strategy.

[0086] In some optional implementations, the configuration modification scheme includes three elements: the object to be modified, the content to be modified, and the reason for modification. The object to be modified is a specific device or a specific routing rule, the content to be modified is the parameter name and the new value. After the configuration modification scheme is submitted for constraint verification, the modified scheme that passes the verification is applied to the simulation configuration, and the simulation is rerun to obtain a new round of running results.

[0087] In some optional implementations, after a new round of simulation is completed, the results indicators are analyzed again and compared with the previous round to evaluate the improvement effect. Iteration stops when certain conditions are met, such as: the deviation between the actual production capacity and the target production capacity is less than 2%, the energy consumption per ton of product deviates from the previous round by less than 5%, and the deviations in the proportion of each product size are within acceptable ranges. If the main indicators of two consecutive iterations meet the convergence conditions, the optimization process is considered to have converged, and the final configuration scheme and corresponding simulation results are output. If convergence is not achieved after a preset maximum number of iterations, the system outputs the current optimal configuration scheme and its indicator values, and prompts the user that adjustments to the production capacity target or equipment configuration scheme may be necessary.

[0088] In some optional implementations, the simulation method for sand and gravel processing also includes storing and displaying simulation configuration data, simulation reports, configuration modification schemes, and optimal simulation configuration data.

[0089] Each generated simulation configuration data and parameter tuning suggestion is presented in a structured manner. For the initial modeling stage, a complete list of equipment and its parameters, topology connections, and routing rules are displayed. For the iterative optimization stage, a list of differences from the previous configuration is shown, with each difference marked with its original value, modified value, and reason for modification. Each suggestion is accompanied by a reasoning explanation in natural language. For example, "It is recommended to reduce the discharge opening of the second cone crusher in the secondary crushing workshop from 50 mm to 35 mm. Reason: Current simulation results show that the output of particles in the 10 to 20 mm range only reaches 72% of the target, and this particle size is mainly produced by the secondary crushing. After reducing the discharge opening, the median particle size of the crushed products is expected to decrease from about 40 mm to about 28 mm, and the proportion of products falling into the 10 to 20 mm range will increase significantly."

[0090] In some optional implementations, users can perform three actions on each suggestion: accept, modify, or reject. Acceptance means the suggestion is directly incorporated into the next round of configuration. Modification means the user adjusts the parameter values ​​in the suggestion before incorporating it into the configuration. For example, if the user believes that reducing the discharge port to 35 mm is too large and changes it to 40 mm, this suggestion is accepted and incorporated into the configuration. The user's actions, along with the reasons for them, are recorded in the decision log. The configuration scheme, after user review, proceeds to the next round of constraint verification and simulation.

[0091] In some optional implementations, complete information for each iteration of the entire modeling and optimization process is recorded, including: the input configuration for that round, simulation results, analysis comments, user review decisions, and the final list of adopted configuration changes. The input configuration includes a complete snapshot of equipment parameters, routing rules, and material feeding plans. The simulation results include complete data on system-level, workshop-level, and equipment-level indicators. The analysis comments include the original text and a structured list of discrepancies suggestions. The user review decisions record the acceptance / modification / rejection of each suggestion and the reasons. Decision logs have a dual value: first, they ensure the traceability of the modeling process—allowing users to trace back at any time why a parameter was set to a certain value, what adjustments were made, and what the basis for those adjustments was; second, the sand and gravel processing simulation method provided in this embodiment records complete information for each iteration of the entire modeling and optimization process, ensuring the traceability of the modeling process, allowing users to trace back at any time the reasons for parameter settings, the adjustment process, and the reasons for adjustments. It can also serve as an incremental update source for the structured knowledge base. Effective parameter tuning schemes verified through actual simulations, such as "setting the discharge port of the second-stage cone crusher between 35 and 40 mm in a granite production line can optimize the proportion of 10-20 mm particle size products," can be summarized as a new knowledge item added to the lithological parameter association dimension of the structured knowledge base, continuously improving the accuracy of recommended parameters in subsequent modeling tasks.

[0092] This embodiment provides a simulation method for sand and gravel processing, which can be used with computer equipment. Figure 4 This is a third flowchart of a simulation method for sand and gravel processing according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: The project includes: knowledge base construction in the field of sand and gravel processing; intelligent agent template library construction for large language models; structured parsing of natural language process descriptions; automatic generation of simulation model configuration data; multi-level constraint verification of generated results; automatic analysis and iterative optimization of simulation results; structured suggestion display and decision log recording.

[0093] The construction of the domain knowledge base (i.e., structured knowledge base) and intelligent agent template library in this embodiment of the invention subjectes the output of the sand and gravel processing simulation process to dual constraints of physical rationality and system interface specifications, thereby improving the reliability of the generated configuration. The structured organization of the knowledge base automates the knowledge retrieval and injection process, eliminating the need for manual writing of targeted prompts each time. The automatic conversion from natural language to structured configuration lowers the professional threshold for simulation modeling from "simulation technology and process knowledge" to "being able to describe basic process requirements in natural language." Users only need to specify the production capacity target, lithology type, and product specifications to obtain a complete simulation configuration scheme, without needing to understand the data structure definition and parameter naming specifications of the simulation system. The three-layer constraint verification system in this embodiment of the invention filters unreasonable configuration data during the generation stage, avoiding invalid simulations that consume computing resources. The mechanism of automatic feedback of verification results for targeted correction allows most verification problems to be automatically repaired without manual intervention. This invention's closed-loop iterative process, encompassing configuration generation, simulation execution, result analysis, and parameter optimization, uses quantified simulation metrics to drive parameter tuning decisions. The automatic analysis of simulation results covers multiple dimensions, including production capacity achievement, product quality, energy efficiency, and equipment utilization balance. The reasoning process and parameter tuning suggestions are presented in natural language, facilitating user understanding and decision-making. This invention's human-machine collaborative workflow balances automation efficiency with human controllability. Users can review and propose modifications in each iteration, retaining final decision-making power over key parameters. A complete decision log ensures the modeling process is auditable and traceable, while also providing empirical data for the continuous updating of the domain knowledge base.

[0094] This embodiment also provides a simulation device for sand and gravel processing, which is used to implement the above embodiments and preferred embodiments; 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.

[0095] This embodiment provides a simulation device for sand and gravel processing, such as... Figure 5 As shown, it includes: The database construction module 501 is used to acquire sand and gravel processing information, construct a structured knowledge base based on the sand and gravel processing information, and construct an intelligent agent template library based on the sand and gravel processing information. The sand and gravel processing information is used to characterize the entire sand and gravel processing process. The structured knowledge base is used to store information on sand and gravel processing equipment, sand and gravel processing procedures, and rock type influence rules. The intelligent agent template library is used to characterize the configurable information of various sand and gravel processing intelligent agents.

[0096] The structured parsing module 502 is used to obtain the sand and gravel processing requirements information input by the user, and to perform structured parsing on the sand and gravel processing requirements information to obtain structured modeling requirements information.

[0097] The simulation data determination module 503 is used to generate a sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library; the simulation configuration data is the operation configuration data for simulating the sand and gravel processing process.

[0098] The simulation module 504 is used to simulate sand and gravel processing based on simulation configuration data and sand and gravel processing production simulation model.

[0099] In some alternative implementations, the database construction module 501 includes: The knowledge base construction unit is used to structurally store information on sand and gravel processing equipment, sand and gravel processing process, and rock type influence rules in the sand and gravel processing information, resulting in a structured knowledge base. The sand and gravel processing equipment information is used to characterize the specification parameter range and applicable conditions of each type of equipment in the sand and gravel processing production line. The sand and gravel processing process information is used to characterize the typical process configuration mode of various sand and gravel processing production lines. The rock type influence rule information is used to characterize the influence rules of different rock types on equipment process parameters.

[0100] The template library construction unit is used to define various sand and gravel processing intelligent agents based on sand and gravel processing information, extract the configurable information of various sand and gravel processing intelligent agents, and construct an intelligent agent template library based on the configurable information of various sand and gravel processing intelligent agents.

[0101] In some alternative implementations, the structure parsing module 502 includes: The element extraction unit is used to extract related element information from sand and gravel processing demand information, and determine implicit constraints based on the related element information and the structured knowledge base.

[0102] The missing parameter supplementation unit is used to supplement the missing parameters in the sand and gravel processing requirement information to obtain the target sand and gravel processing requirement information.

[0103] The formatting processing unit is used to convert associated feature information, implicit constraints, and target sand and gravel processing requirements into a standard structured format to obtain structured modeling requirements information.

[0104] In some alternative implementations, the simulation data determination module 503 includes: The parameter determination unit is used to match the structured knowledge base with the structured modeling requirements information to obtain equipment specification parameters and material feeding plan data.

[0105] The routing data determination unit is used to match the intelligent agent template library according to the structured modeling requirement information to obtain routing configuration data; the routing configuration data is used to characterize the routing rules of the sand and gravel processing process corresponding to the structured modeling requirement information.

[0106] The simulation data determination unit is used to generate a sand and gravel processing production simulation model and simulation configuration data based on equipment specifications, material feeding plan data, and routing configuration data.

[0107] In some alternative implementations, the simulation module 504 includes: The simulation unit is used to input simulation configuration data into the sand and gravel processing production simulation model to simulate sand and gravel processing.

[0108] In some alternative implementations, the simulation apparatus for sand and gravel processing further includes: The data verification module is used to sequentially verify the parameter range, topology structure, and capacity matching of the simulation configuration data, and modify the sand and gravel processing production simulation model based on the verification results.

[0109] In some alternative implementations, the simulation apparatus for sand and gravel processing further includes: The simulation report generation module is used to extract simulation evaluation indicators based on simulation results, perform structured transformation of simulation evaluation indicators, and generate simulation reports.

[0110] The model analysis module is used to perform capacity achievement analysis, product quality analysis, energy consumption analysis, and equipment utilization balance analysis on the sand and gravel processing production simulation model in sequence according to the simulation report, and obtain the analysis results.

[0111] The simulation data correction module is used to generate a configuration modification plan based on the analysis results, correct the simulation configuration data using the configuration modification plan, and return the simulation configuration data to perform parameter range verification, topology structure verification, and capacity matching verification in sequence. The steps of modifying the sand and gravel processing production simulation model based on the verification results are iterated until the preset number of iterations is reached to obtain the optimal simulation configuration data.

[0112] In some alternative implementations, the simulation apparatus for sand and gravel processing further includes: The data display module is used to store and display simulation configuration data, simulation reports, configuration modification schemes, and optimal simulation configuration data.

[0113] The sand and gravel processing simulation device provided in this embodiment of the invention can execute the sand and gravel processing simulation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing 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.

[0114] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0115] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design 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.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0116] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 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.

[0117] 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 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the sand and gravel processing simulation method of the embodiments of the present invention.

[0118] Figure 6The 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.

[0119] 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 simulation method for sand and gravel processing shown in the above embodiments is implemented.

[0120] 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.

[0121] 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 simulation method for sand and gravel processing, characterized in that, The method includes: Acquire sand and gravel processing information, construct a structured knowledge base based on the sand and gravel processing information, and construct an intelligent agent template library based on the sand and gravel processing information; the sand and gravel processing information is used to characterize the entire sand and gravel processing process, the structured knowledge base is used to store sand and gravel processing equipment information, sand and gravel processing flow information, and rock type influence rule information, and the intelligent agent template library is used to characterize the configurable information of various sand and gravel processing intelligent agents; Obtain the sand and gravel processing requirements information input by the user, and perform structured parsing on the sand and gravel processing requirements information to obtain structured modeling requirements information; Based on the structured modeling requirements, the structured knowledge base, and the intelligent agent template library, a sand and gravel processing production simulation model and simulation configuration data are generated; the simulation configuration data is the operation configuration data for simulating the sand and gravel processing process. The sand and gravel processing is simulated based on the simulation configuration data and the sand and gravel processing production simulation model.

2. The method according to claim 1, characterized in that, The step of constructing a structured knowledge base based on the sand and gravel processing information and constructing an intelligent agent template library based on the sand and gravel processing information includes: The sand and gravel processing equipment information, sand and gravel processing process information, and rock type influence rule information in the sand and gravel processing information are structured and stored to obtain the structured knowledge base; the sand and gravel processing equipment information is used to characterize the specification parameter range and applicable conditions of each type of equipment in the sand and gravel processing production line; the sand and gravel processing process information is used to characterize the typical process configuration mode of various sand and gravel processing production lines; and the rock type influence rule information is used to characterize the influence rules of different rock types on equipment process parameters. Based on the sand and gravel processing information, a variety of sand and gravel processing intelligent agents are defined, the configurable information of the various sand and gravel processing intelligent agents is extracted, and the intelligent agent template library is constructed based on the configurable information of the various sand and gravel processing intelligent agents.

3. The method according to claim 1 or 2, characterized in that, The step of performing structured parsing of the sand and gravel processing demand information to obtain structured modeling demand information includes: Extract related element information from the sand and gravel processing demand information, and determine implicit constraints based on the related element information and the structured knowledge base; The missing parameters in the sand and gravel processing demand information are supplemented to obtain the target sand and gravel processing demand information; The associated element information, the implicit constraints, and the target sand and gravel processing requirements information are converted into a standard structured format to obtain the structured modeling requirements information.

4. The method according to claim 1 or 2, characterized in that, The step of generating a sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library includes: Based on the structured modeling requirements information, the equipment specifications and material feeding plan data are matched in the structured knowledge base to obtain the equipment specifications and material feeding plan data. The structured modeling requirements are matched against the agent template library to obtain routing configuration data; the routing configuration data is used to characterize the routing rules of the sand and gravel processing process corresponding to the structured modeling requirements. Based on the equipment specifications, the material feeding plan data, and the routing configuration data, the sand and gravel processing production simulation model and the simulation configuration data are generated.

5. The method according to claim 1 or 2, characterized in that, The simulation of sand and gravel processing based on the simulation configuration data and the sand and gravel processing production simulation model includes: The simulation configuration data is input into the sand and gravel processing production simulation model to simulate sand and gravel processing.

6. The method according to claim 1 or 2, characterized in that, After generating the sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library, the method further includes: The simulation configuration data is sequentially verified for parameter range, topology structure, and production capacity matching. Based on the verification results, the sand and gravel processing production simulation model is modified.

7. The method according to claim 6, characterized in that, After simulating sand and gravel processing based on the simulation configuration data and the sand and gravel processing production simulation model, the method further includes: Simulation evaluation indicators are extracted based on the simulation results, and the simulation evaluation indicators are then structurally transformed to generate a simulation report. Based on the simulation report, the sand and gravel processing production simulation model is sequentially analyzed for capacity achievement, product quality, energy consumption, and equipment utilization balance, and the analysis results are obtained. Based on the analysis results, a configuration modification scheme is generated. The simulation configuration data is then corrected using the configuration modification scheme. The process of sequentially verifying the parameter range, topology structure, and production capacity matching of the simulation configuration data, and modifying the sand and gravel processing production simulation model based on the verification results, is iterated until a preset number of iterations is reached to obtain the optimal simulation configuration data.

8. The method according to claim 7, characterized in that, The method further includes: The simulation configuration data, the simulation report, the configuration modification scheme, and the optimal simulation configuration data are stored and displayed.

9. A simulation device for sand and gravel processing, characterized in that, The device includes: The database construction module is used to acquire sand and gravel processing information, construct a structured knowledge base based on the sand and gravel processing information, and construct an intelligent agent template library based on the sand and gravel processing information. The sand and gravel processing information is used to characterize the entire sand and gravel processing process. The structured knowledge base is used to store sand and gravel processing equipment information, sand and gravel processing flow information, and rock type influence rule information. The intelligent agent template library is used to characterize the configurable information of various sand and gravel processing intelligent agents. The structured parsing module is used to obtain the sand and gravel processing requirements information input by the user, and to perform structured parsing on the sand and gravel processing requirements information to obtain structured modeling requirements information; The simulation data determination module is used to generate a sand and gravel processing production simulation model and simulation configuration data based on the structured modeling requirements information, the structured knowledge base, and the intelligent agent template library; the simulation configuration data is the operation configuration data for simulating the sand and gravel processing process. The simulation module is used to simulate sand and gravel processing based on the simulation configuration data and the sand and gravel processing production simulation model.

10. 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 simulation method for sand and gravel processing as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the simulation method for sand and gravel processing as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the simulation method for sand and gravel processing as described in any one of claims 1 to 8.

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