Machining unmanned workshop visualization method, device and equipment based on twin technology

Abstract

The invention discloses a machining unmanned workshop visualization method, device and equipment based on a twin technology, and relates to the field of intelligent manufacturing. The dynamic equipment depreciation rate calculation method based on the performance attenuation model is particularly suitable for machining equipment unmanned workshops including numerical control machine tools, machining centers, turning centers and the like, and the dynamic equipment depreciation rate calculation method based on the performance attenuation model is provided according to the characteristics of prop loss, cutting parameter numbers, workpiece quality fluctuation and the like in the machining production process. A digital twinning technology and cost mapping are fused, a digital twinning system dynamically interacting with a physical workshop is constructed, real-time sensing and dynamic mapping of cost data such as equipment depreciation, energy consumption and material consumption are achieved, data fusion and visual display are carried out through a mechanical and unmanned workshop digital twinning model, and the real-time sensing and dynamic mapping of cost data such as equipment depreciation, energy consumption and material consumption are achieved. And benefit comparative analysis of a manned production mode and an unmanned production mode is realized. According to the method, the cost mapping dynamically matched with the physical production process of the machining unmanned workshop is established based on the twin technology, and real-time and accurate sensing of the production cost is achieved.

Description

Technical Field

[0001] This invention belongs to the technical field of intelligent manufacturing, particularly the field of machining, and specifically relates to a visualization method, device and equipment for unmanned machining workshops based on twin technology. Background Technology

[0002] With the rapid development of Industry 4.0 and intelligent manufacturing technologies, traditional manned machining workshops are gradually transforming towards unmanned and automated operations. Unmanned machining workshops are automated workshops that include CNC machine tools, machining centers, turning centers, and other machining equipment. By integrating advanced automated equipment, robotics, and intelligent control systems, unmanned machining workshops can achieve 24-hour continuous production, significantly improving production efficiency.

[0003] However, existing technologies still present challenges in the management and monitoring of unmanned machining workshops. Compared to traditional manned workshops, the cost structure of unmanned machining workshops undergoes a fundamental change: while labor costs decrease significantly, the proportion of equipment investment, maintenance costs, and energy consumption increases substantially. The question remains whether the efficiency of unmanned machining workshops has actually improved compared to traditional manned workshops. This shift in cost structure places higher demands on the transparency, traceability, and accurate assessment of resource utilization efficiency in the production process. Traditional monitoring systems based on static data cannot accurately and in real-time map the actual resource consumption status and cost-effectiveness of unmanned machining workshops. Especially under multi-variety, small-batch production models, they lack dynamic integration, real-time computing capabilities, and visualization capabilities for complex production scenarios, severely hindering the improvement of intelligent management levels in unmanned machining workshops.

[0004] For example, existing cost accounting methods are mainly based on traditional manual recording and periodic statistics, making it difficult to achieve real-time and accurate calculation of production costs in unmanned machining workshops. In particular, there is a lack of dynamic calculation mechanisms based on the actual production process for aspects such as equipment depreciation, energy consumption, and material losses. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a visualization method, device, and equipment for unmanned machining workshops based on twin technology. This method enables the establishment of a cost image that dynamically matches the physical production process of the unmanned machining workshop, achieving real-time and accurate perception of production resource consumption. It also enables real-time monitoring and data visualization of the unmanned machining workshop, providing data support for production scheduling and process parameter optimization, as well as data support for unmanned transformation decisions.

[0006] In a first aspect, the present invention provides a visualization method for unmanned machining workshops based on twin technology, comprising: Acquire status data of the unmanned machining workshop; the status data includes equipment data, logistics data, quality data, environmental data, and personnel management data; The state data is mapped onto a pre-established digital twin model of an unmanned machining workshop using a virtual-real mapping mechanism to obtain a state mirror of the digital twin model; Cost mapping is performed based on the state mirror of the digital twin model and the preset cost rule base to obtain the cost image of the unmanned machining workshop; wherein, the cost mapping includes the calculation of equipment depreciation cost; the preset cost rule base is used to determine the dynamic depreciation rate of the equipment based on the equipment data and the environmental data, and using a pre-established performance degradation model, so as to determine the equipment depreciation cost; The cost image of the unmanned machining workshop is visualized using the digital twin model of the aforementioned unmanned machining workshop.

[0007] In an optional implementation, the equipment data includes multiple equipment operating condition factors, and the environmental data includes environmental coupling factors; the environmental coupling factors characterize the coupling factors of the environment's impact on equipment damage; the preset cost rule base is specifically used for: The basic depreciation rate is determined according to the following formula (1). : (1) In equation (1), This represents the original cost of the equipment. Indicates the residual value of the equipment. Indicates the estimated number of hours the equipment will be used; The performance degradation rate is determined according to the following formula (2). : (2) In equation (2), The cumulative running time weighting coefficient, The load factor is the weighting factor. For environmental coupling weight coefficients, To maintain the quality weighting coefficient; satisfy ; Indicates the equipment's reference operating time; Indicates the cumulative operating time of the equipment. Indicates the equipment load rate. Indicates the environmental coupling factor. Indicates maintenance quality; The dynamic depreciation rate of the equipment is determined according to the following formula (3). : (3) Determine the equipment depreciation cost according to the following formula (4). ; (4) In equation (4), This indicates the actual number of hours the equipment has been running.

[0008] In an optional implementation, the cost mapping further includes energy cost calculation, and the preset cost rule base is also used for: Energy costs are determined based on the following formula (5). : (5) In equation (5), Indicates the first The current power of each device Indicates the number of devices. It is a positive integer greater than 0; Indicates time interval, Indicates the current electricity price; The cost mapping also includes material cost calculation, and the preset cost rule base is further used for: Material costs are determined based on the following formula. : (6) In equation (6), Indicates the first k The consumption of this type of material. Indicates the first k The unit price of the material Indicates the material loss coefficient. This indicates the cost of tool wear; Among them, the tool wear cost Dynamic calculation based on the following formula (7): (7) In equation (7), Indicates the first j The lifespan of each cutting tool. Indicates the first j Total design life of each tool Indicates the first j The purchase price of each cutting tool; The material loss coefficient Based on the following formula (8), dynamic adjustment is performed: (8) In equation (8), Indicates the first n The basic loss coefficient of a certain material. α n Represents the quality sensitivity coefficient. Qdefect_ rate This indicates the real-time quality non-compliance rate.

[0009] In an optional implementation, mapping the state data to a pre-established digital twin model of an unmanned machining workshop to obtain a state mirror of the digital twin model includes: Add a timestamp to the status data to obtain the status data with the timestamp added; The status data after adding timestamps is preprocessed to obtain preprocessed status data; wherein, the preprocessing includes one or more of the following: format conversion, outlier handling, missing value handling, timestamp alignment, and data fusion; By using a virtual-real mapping mechanism, the preprocessed state data is updated to the corresponding object attributes in the pre-established digital twin model of the unmanned machining workshop, thus obtaining the state mirror of the digital twin model.

[0010] In an optional implementation, the step of using a virtual-real mapping mechanism to update the preprocessed state data onto the corresponding object attributes in the pre-established digital twin model of the unmanned machining workshop to obtain a state mirror of the digital twin model includes: The preprocessed state data is stored in a pre-established data cache pool to obtain cached data. The cached data is filtered based on the differential update algorithm to identify data to be updated that has a change exceeding a preset threshold. The data to be updated is allocated based on a load balancing algorithm to obtain the allocated data; Based on the semantic mapping mechanism, according to the physical entity identifier in the allocated data, a preset mapping relationship table is queried, and the virtual object corresponding to the physical entity identifier in the digital twin model of the unmanned machining workshop is located. The attribute values ​​in the allocated data are then written into the attribute fields of the corresponding virtual object to obtain the state mirror of the digital twin model.

[0011] In an optional implementation, the method further includes: Based on the state mirror of the digital twin model, efficiency is evaluated using a pre-established efficiency evaluation model to obtain an efficiency image; the efficiency evaluation model includes a production efficiency evaluation submodule, a quality efficiency evaluation submodule, an equipment efficiency evaluation submodule, and a cost efficiency evaluation submodule. The efficiency image is then visualized.

[0012] In an optional implementation, the method further includes: The state data is input into a pre-established manned production mode cost model to obtain a manned production mode cost image. The cost image of the unmanned machining workshop is compared with the cost image of the manned production model to obtain the cost-benefit comparison results; The cost-benefit comparison results are visualized using the digital twin model of the unmanned machining workshop. The visualization includes one or more of the following: 3D scene display, data dashboard display, and report display.

[0013] In an optional implementation, the equipment data is obtained from the production equipment via the OPCUA protocol, the logistics data is obtained via RFID tags, QR code identification technology and / or a logistics data acquisition module installed on the logistics equipment, and the quality data is obtained from the quality inspection equipment.

[0014] Secondly, the present invention provides a visualization device for an unmanned machining workshop based on twin technology, comprising: The data acquisition module is used to acquire status data of the unmanned machining workshop; the status data includes equipment data, logistics data, quality data, environmental data, and personnel management data. The mapping module is used to map the state data to a pre-established digital twin model of an unmanned machining workshop using a virtual-real mapping mechanism, so as to obtain a state mirror of the digital twin model. The cost mapping module is used to perform cost mapping based on the state mirror of the digital twin model and the preset cost rule base to obtain the cost image of the unmanned machining workshop; wherein, the cost mapping includes the calculation of equipment depreciation costs; the preset cost rule base is used to determine the dynamic depreciation rate of the equipment based on the equipment data and the environmental data, and using a pre-established performance degradation model, so as to determine the equipment depreciation cost; The display module is used to visualize the cost image of the unmanned machining workshop using the digital twin model of the unmanned machining workshop.

[0015] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the foregoing embodiments.

[0016] Fourthly, the present invention provides a computer-readable medium having processor-executable non-volatile program code, the program code causing the processor to perform the method described in any of the foregoing embodiments.

[0017] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: This invention provides a visualization method, device, and equipment for unmanned machining workshops based on twin technology. It acquires the state data of the unmanned machining workshop and uses a virtual-real mapping mechanism to map the state data onto a pre-established digital twin model of the unmanned machining workshop, obtaining a state mirror of the digital twin model. The state data includes equipment data, logistics data, quality data, environmental data, and personnel management data. This invention breaks down the barriers between heterogeneous data from multiple sources such as equipment, logistics, quality, and environment. It utilizes twin technology to construct a cost image that dynamically matches the physical production process of the unmanned machining workshop, thereby building a digital mapping that maintains a high degree of consistency with the real-time state of the physical workshop. This lays a unified, reliable, and comprehensive data foundation for subsequent cost data generation, fundamentally solving the pain points of traditional cost calculation data sources being singular, partial, and lagging. Furthermore, this invention performs cost mapping based on the state mirror of the digital twin model and a preset cost rule base to obtain a cost image of the unmanned machining workshop. The cost mapping includes the calculation of equipment depreciation costs. The preset cost rule base is used to determine the dynamic depreciation rate of the equipment based on equipment data and environmental data, and by using a pre-established performance degradation model, so as to determine the equipment depreciation cost. It can be seen that this invention can drive the construction algorithm of dynamic cost image based on high-fidelity data, such as the performance degradation model, so that the cost image can accurately reflect the real value loss of equipment caused by factors such as real-time load and environmental fluctuations, thereby significantly improving the accuracy of cost mapping. This transforms cost data generation from the original static accounting tool into a dynamic and accurate quantitative tool that can truly reflect the physical production process. This invention enables real-time and accurate perception of production resource consumption, transforming traditional static cost reports into dynamic quantitative monitoring tools that can interact with the physical production process. It achieves real-time monitoring and data visualization of unmanned machining workshops, providing data support for production scheduling and process parameter optimization, as well as data support for unmanned transformation and upgrading decisions. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the visualization method for an unmanned machining workshop based on twin technology provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system principle of the visualization device for an unmanned machining workshop based on twin technology provided in an embodiment of the present invention; Figure 3 A schematic diagram of the system principle of an electronic device provided in an embodiment of the present invention.

[0019] In the diagram: 100 - Data acquisition module; 200 - Mapping module; 300 - Cost mapping module; 400 - Display module; 1000 - Electronic device; 1001 - Communication interface; 1002 - Processor; 1003 - Memory; 1004 - Bus. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] Reference Figure 1 A visualization method for unmanned machining workshops based on twin technology includes the following steps S100 to S400.

[0022] Step S100: Obtain the status data of the unmanned machining workshop; the status data includes equipment data, logistics data, quality data, environmental data, and personnel management data.

[0023] In practical implementation, the application corresponding to the method of this embodiment can be installed on a server. The server is connected to equipment data acquisition modules, logistics data acquisition modules, quality data acquisition modules, environmental data acquisition modules, and personnel management data acquisition modules. Specifically, the equipment data acquisition module, integrated with the production line control system, acquires data such as the operating status, processing parameters, and fault information of each processing equipment in the unmanned machining workshop, and sends this data as equipment data to the server. The logistics data acquisition module, integrated with logistics equipment, acquires data such as the flow trajectory of materials, inventory changes, and delivery time, and sends this data as logistics data to the server. The quality data acquisition module, integrated with quality inspection equipment, acquires data such as product quality parameters, pass rate, and defective product information, and sends this data as quality data to the server. The environmental data acquisition module, through a sensor network, acquires environmental parameters such as temperature and humidity in the workshop, and sends this data as environmental data to the server. The personnel management data acquisition module, integrated with the attendance system, acquires the on-duty status of management personnel, and sends this data as personnel management data to the server.

[0024] Preferably, the equipment data is obtained from the production equipment via the OPCUA protocol. That is, the aforementioned equipment data acquisition module includes an OPCUA-based acquisition module, which is connected to both the production equipment (such as the control systems of various CNC machine tools, machining centers, and automated production lines) and the server to collect the equipment's operating parameters, such as spindle speed, feed rate, cutting force, tool wear, equipment temperature, and vibration parameters. The data acquisition frequency can be configured according to the equipment type and monitoring requirements, typically set to once every 1-10 seconds.

[0025] The logistics data acquisition module includes RFID tags, QR code recognition technology, and / or modules installed on logistics equipment. This equipment can be Automated Guided Vehicles (AGVs), robotic arms, automated warehouses, etc. The logistics data acquisition module is integrated with the equipment to collect logistics data. The collected logistics data is sent to a server via TCP / IP protocol, enabling the server to obtain information such as the real-time location, movement trajectory, and inventory changes of materials. RFID tags and QR code recognition technology enable end-to-end tracking of materials and products, resulting in high data collection efficiency and accuracy.

[0026] Quality data is obtained from quality inspection equipment. The quality data acquisition module is integrated with quality inspection equipment such as coordinate measuring machines, online inspection equipment, and vision inspection systems to acquire quality parameters such as product dimensional accuracy, surface quality, and geometric tolerances in real time, and automatically determine whether the product is qualified.

[0027] Existing technologies have shortcomings in the fusion and processing of multi-source heterogeneous data. Unmanned machining workshops involve multiple subsystems, including equipment control systems, logistics systems, quality inspection systems, and environmental monitoring systems. These systems generate data with different formats and communication protocols, making the effective integration and utilization of this data a significant technical challenge. This embodiment effectively solves the technical problem of data integration among different subsystems such as equipment control systems, logistics systems, and quality systems through a unified data acquisition and processing architecture.

[0028] This embodiment achieves the acquisition of status data in an unmanned machining workshop based on sensors and an automation system. During data acquisition, the completeness and accuracy of the data are challenged because this status data cannot be directly obtained from traditional workshop management systems. Traditional workshop management systems are primarily designed for manned operating environments and lack consideration for the specific needs of unmanned machining workshops. In manned workshops, operators can directly observe equipment status, product quality, and production progress, while unmanned machining workshops rely on sensors and automation systems to obtain relevant information. This embodiment accurately and completely achieves data acquisition, laying a solid data foundation for subsequent steps.

[0029] Step S200: The state data is mapped to the pre-established digital twin model of the unmanned machining workshop using a virtual-real mapping mechanism to obtain the state mirror of the digital twin model.

[0030] For example, a digital twin model of an unmanned machining workshop is established through the following steps (201) to (203).

[0031] Step (201): Based on the actual layout and equipment configuration of the unmanned machining workshop, construct a three-dimensional geometric model.

[0032] Step (202): Based on the working principles and process flow of each piece of equipment in the unmanned machining workshop, construct the equipment behavior model and the process flow model.

[0033] Step (203): Based on production management rules and quality standards, construct a constraint rule model.

[0034] For example, the virtual-real mapping mechanism includes semantic mapping mechanism, geometric coordinate mapping mechanism, feature point matching mechanism, image recognition-based mapping, etc. Preferably, this embodiment adopts the semantic mapping mechanism.

[0035] A 3D geometric model is the visual appearance and spatial digital mapping of an unmanned machining workshop. It can reproduce the geometric shape, size, material, and spatial relationships of all physical elements, including the layout, equipment appearance, equipment configuration, and material stacking locations of the workshop, making visual monitoring possible and serving as the spatial carrier for other models. This embodiment uses CAD software and 3D modeling technology to construct an accurate 3D model of the unmanned machining workshop. The 3D model includes all physical entities such as the factory structure, equipment layout, tooling fixtures, and material storage areas. The model's accuracy is required to reach the millimeter level to ensure the accuracy of the virtual-real mapping.

[0036] The equipment behavior model is the dynamic operational logic of an unmanned machining workshop (equipment), used to simulate the real behavior of physical equipment. This embodiment uses finite state machines and event-driven modeling methods to describe the equipment's working state transitions and the execution process of the technological flow. For example, a equipment behavior model is established for a CNC machine, including states such as standby, machining, pause, acceleration, deceleration, fault, and maintenance. The equipment behavior model defines the transition conditions and triggering events between these behavioral states. For example, upon receiving a machining instruction, the machine transitions from the standby state to the start state, and the transitions between states are triggered by corresponding events.

[0037] A process flow model defines the overall production process logic of multiple devices, used to simulate, optimize, and verify the process logic of the entire production system. For example, the production of a certain product may involve processes such as machine tool processing, robotic arm handling, and quality inspection at a testing station; the process flow model defines the process parameters between these processes.

[0038] The constraint rule model digitizes production management knowledge, quality standards, and safety regulations into specific rules that the server can recognize and execute, ensuring that the operation of the digital twin model of the unmanned machining workshop is subject to the actual constraints of the physical world. For example, the constraint rule model defines that two robotic arms cannot enter the same area to prevent collisions; another example is that a temperature exceeding a certain temperature threshold indicates an equipment malfunction, and a distance exceeding a preset distance threshold indicates a defective product.

[0039] This embodiment constructs a digital twin model of an unmanned machining workshop to achieve real-time monitoring of the unmanned machining workshop, laying a data foundation for subsequent cost and efficiency mapping.

[0040] Virtual-real mapping is a core function of digital twin technology. This embodiment employs a timestamp-based data synchronization mechanism and a state-change-based event-driven mechanism. The virtual-real mapping mechanism will be described in detail below through an embodiment.

[0041] In an optional embodiment, the state data is mapped onto the digital twin model of the unmanned machining workshop to obtain a state mirror of the digital twin model, including the following steps S204 to S206.

[0042] Step S204: Add timestamps to the status data to obtain the timestamped status data. Here, the server acquires status data at a preset frequency and adds a timestamp to each data point so that the timestamps can be used for subsequent data alignment and fusion.

[0043] Step S205: Preprocess the status data after adding timestamps to obtain preprocessed status data; wherein, the preprocessing includes one or more of the following: format conversion, outlier handling, missing value handling, timestamp alignment and data fusion.

[0044] Data with timestamps undergoes format conversion, such as converting data from different communication protocols into a standardized data format recognizable by the server, or unifying data from different bases into a single format. This format conversion addresses data heterogeneity, breaks down data silos, and lays the foundation for subsequent data fusion. Outliers deviating from the normal data range can easily interfere with subsequent cost and efficiency mappings; identifying and filtering these outliers effectively prevents data contamination. Missing values ​​may be missing during state data acquisition and transmission; filling these gaps by adding null values, using previous valid values, or replacing with the average value ensures the integrity of the state data. Timestamp alignment uses time as a reference, adjusting data from different devices to the same time base. Data fusion involves associating and fusing processed multi-dimensional data to achieve a complete state description of physical entities, providing accurate input for the digital twin model of an unmanned machining workshop.

[0045] Step S206 involves using a virtual-real mapping mechanism to update the preprocessed state data onto the corresponding object attributes in the pre-established digital twin model of the unmanned machining workshop, thereby obtaining a state mirror of the digital twin model. This step specifically includes steps S2061 to S2064.

[0046] Step S2061: The preprocessed state data is stored in a pre-established data cache pool to obtain cached data. Since the core of digital twins is real-time mapping, this requires numerous real-time data accesses to the database. If every data point accesses the database, the significant latency will cause the state of the digital twin model of the unmanned machining workshop to lag far behind the unmanned workshop state. Therefore, this embodiment establishes a data cache pool in the server's memory and stores the preprocessed state data in the data cache pool to reduce the frequency of database access and avoid system crashes that may result from a large number of data read / write operations.

[0047] Step S2062 filters the cached data based on the differential update algorithm to identify data whose changes exceed a preset threshold. In most cases, only a portion of the data changes per second. Transmitting all data to the server would result in significant data transmission pressure and a waste of computing resources. Therefore, this embodiment uses the differential update algorithm to filter the cached data, sending only the changed data to the server used by the method in this embodiment, avoiding full data transmission, reducing network pressure, and improving data processing efficiency. For example, the differential update algorithm may compare and filter the current cached state data with the state data of the previous period, filtering out data whose changes exceed a preset threshold, and then sending the filtered data to the server.

[0048] Step S2063 allocates the data to be updated based on a load balancing algorithm to obtain the allocated data. In high-concurrency scenarios, the server's processing capacity may decrease or even reach its limit, leading to slower response times or even crashes. Therefore, this embodiment employs a load balancing algorithm to rationally allocate computing resources, ensuring server stability under high concurrency conditions, improving server processing capacity, and enhancing reliability. For example, a complex load balancing algorithm may, after obtaining all data requests and processing tasks, dynamically allocate different data or tasks to one of the server's multiple computing nodes for processing according to preset rules, such as round-robin or minimum CPU load rules.

[0049] Step S2064 is based on a semantic mapping mechanism. According to the physical entity identifier in the allocated data, a preset mapping relationship table is queried, and the virtual object corresponding to the physical entity identifier in the digital twin model of the unmanned machining workshop is located. The attribute values ​​in the allocated data are written into the attribute fields of the corresponding virtual object to obtain the state mirror of the digital twin model.

[0050] The allocated data is preprocessed data with structured information, including a key field: the physical entity representation. This representation is the unique identifier of an object in the physical world (e.g., CNC machine tool 01), typically injected by sensors or the control system during data acquisition (e.g., injecting CNC_Machine_01). This embodiment employs a semantic-based mapping mechanism to establish a semantic association between physical entities and the digital twin model of the unmanned machining workshop. First, a pre-defined mapping table is maintained on the server. This table is defined at the initial stage of building the digital twin model of the unmanned machining workshop. Its function is to establish an unambiguous correspondence. By querying this table, the access path or memory address of the virtual object uniquely corresponding to the physical entity in the software system can be accurately located. The pre-defined mapping table maps physical entity identifiers to virtual objects. For example, "CNC machine tool 01" in the physical workshop corresponds to the "CNC_Machine_01" object in the digital model, with its operating status, machining parameters, fault information, and other attributes achieving a one-to-one correspondence. Once the virtual object is located, its operating status, processing parameters, fault information, and other attributes are written into its attribute fields. This updates the attributes of the virtual object in the unmanned machining workshop, thus determining the complete state of the entire unmanned machining workshop digital twin model at a given moment. This data set containing the latest states of all virtual objects constitutes a state mirror of the digital twin model that corresponds to the physical world in real time.

[0051] This embodiment accurately applies the preprocessed physical world data to the corresponding virtual entities in the digital twin model, thereby completing the state update of the virtual model and ensuring the correct mapping between physical data and the virtual model. This embodiment has high update efficiency and high accuracy.

[0052] Steps S204-S206 describe the specific process of mapping state data to the digital twin model of the unmanned machining workshop. In this embodiment, data synchronization is first performed by updating the real-time collected state data synchronously with the digital twin model of the unmanned machining workshop. Then, the real-time state of the physical workshop is mapped to the digital twin model. The constructed digital twin model can trigger analysis and early warning functions based on preset trigger conditions. The digital twin model of the unmanned machining workshop constructed in this embodiment is a three-dimensional model, capable of rich data display, allowing managers to intuitively understand the real-time status and production efficiency of the workshop, thus improving management efficiency.

[0053] Step S300: Cost mapping is performed based on the state mirror of the digital twin model and the preset cost rule base to obtain the cost image of the unmanned machining workshop; wherein, the cost mapping includes the calculation of equipment depreciation cost; the preset cost rule base is used to determine the dynamic depreciation rate of equipment based on equipment data and environmental data, and by using a pre-established performance degradation model, so as to determine the equipment depreciation cost.

[0054] In an optional embodiment, the equipment data includes multiple equipment operating condition factors, and the environmental data includes environmental coupling factors; the environmental coupling factors characterize the coupling factors of the environment's impact on equipment damage; the preset cost rule base is specifically used for: The basic depreciation rate is determined according to the following formula (1). : (1) In equation (1), This represents the original cost of the equipment. Indicates the residual value of the equipment. Indicates the estimated number of hours the equipment will be used; The performance degradation rate is determined according to the following formula (2). : (2) In equation (2), The cumulative running time weighting coefficient, The load factor is the weighting factor. For environmental coupling weight coefficients, To maintain the quality weighting coefficient; satisfy ; Indicates the equipment's reference operating time. Indicates the cumulative operating time of the equipment. Indicates the equipment load rate. Indicates the environmental coupling factor. Indicates maintenance quality; The dynamic depreciation rate of the equipment is determined according to the following formula (3). : (3) Determine the equipment depreciation cost according to the following formula (4). ; (4) In equation (4), This indicates the actual number of hours the equipment has been running.

[0055] The complete calculation process for dynamic depreciation cost of equipment is as follows: Step 1: Calculate the basic depreciation rate based on the equipment's basic parameters D bThis depreciation rate reflects the rate at which the value of the equipment diminishes under ideal operating conditions; Step 2: Based on real-time collected equipment operating data (cumulative running time) T 0 Load rate L Maintenance quality Q m ) and environmental data (environmental coupling factors) Ec The performance degradation rate is calculated using a performance degradation model. R d This parameter reflects the degree to which actual operating conditions affect the depreciation of equipment value; Step 3: Adjust the basic depreciation rate D b With performance degradation rate R d By combining these methods, a dynamic depreciation rate that truly reflects the actual decline in the value of the equipment can be calculated. Dd ; Step 4: Based on the actual operating time of the equipment H a and dynamic depreciation rate D d Accurately calculate equipment depreciation costs within a specific time period. .

[0056] Through the above four-step calculation, the transformation from traditional static depreciation to dynamic depreciation based on real-time data has been realized, which significantly improves the accuracy of equipment cost mapping in unmanned machining workshops.

[0057] Here, when performing cost mapping, the basic cost image is first constructed. The construction of the basic cost image refers to calculating the basic cost per unit time or for a single product based on basic data, such as equipment depreciation, energy consumption, and material costs. Then, the cost calculation parameters are dynamically adjusted in conjunction with real-time production data to achieve accurate cost mapping. For example, when calculating equipment depreciation costs, the basic depreciation rate is first determined using equation (1). Then, the basic depreciation rate is calculated using equations (2) and (3). Dynamic adjustments are made to obtain the dynamic depreciation rate. Finally, cost mapping is performed using equation (4).

[0058] Traditional equipment depreciation cost calculation As shown in equation (9), the parameters such as the original value of the equipment (i.e., the purchase cost of the equipment), the residual value of the equipment (the estimated value of the remaining value of the equipment at the end of its lifespan, determined based on experience), and the estimated number of hours of use of the equipment are all determined at the time of purchase and remain fixed throughout the entire lifespan. By comparing with equations (1) to (4), it can be seen that this embodiment introduces the performance degradation rate, which is determined by the real-time operating data of the equipment load rate. L Environmental coupling factors Ec Maintenance quality Q m Waiting through the function f (·) Calculations show that this variable is used to dynamically adjust the basic depreciation rate. This includes the cumulative operating time of the equipment. T 0 The load rate is obtained from the server in real time. L The environmental coupling factor is calculated based on the equipment's rated power and actual power. Ec Including parameters such as temperature, humidity, and vibration, maintenance quality Q m It is determined through maintenance records and equipment status assessment. As can be seen from equations (4) and (9), this embodiment retains the original equipment depreciation cost calculation framework, ensuring the connection and compliance with the traditional calculation system.

[0059] (9) In the above formula (9), This represents the original value of the equipment, and the remaining value of the equipment. Indicates the estimated number of hours the equipment will be used. This indicates the actual number of hours the equipment has been running.

[0060] When the equipment is running under ideal conditions, the performance degradation rate is close to 0, and the equipment depreciation cost is close to the depreciation cost calculated by traditional methods. However, when the equipment is running under harsh conditions, such as high load, poor environment, poor maintenance, etc., the performance degradation rate is greater than 0, which leads to the dynamic depreciation rate being greater than the benchmark depreciation rate, and the equipment depreciation cost increases. Equations (1) to (4) above can truly and accurately reflect the value depreciation of the equipment.

[0061] Traditional equipment depreciation uses a fixed-year method or a workload method. However, in unmanned machining workshops, the intensity of equipment use and working conditions may change. Therefore, this embodiment adopts a dynamic depreciation calculation method. This embodiment retains the existing equipment depreciation cost calculation framework and introduces a dynamic factor to dynamically correct the benchmark depreciation rate. It constructs a performance degradation model (Equations (2) to (3)) that can dynamically reflect the actual value decay process of the equipment, so as to reflect the value loss of the equipment in a true and accurate manner.

[0062] Furthermore, cost mapping includes not only equipment depreciation cost calculation, but also energy consumption cost calculation, labor cost calculation, and material cost calculation. The preset cost rule base is also used to determine energy costs based on the following formula (5). .

[0063] (5) In equation (5), Indicates the first The current power of each device Indicates the number of devices. It is a positive integer greater than 0; Indicates time interval, This indicates the current electricity price.

[0064] Specifically, existing energy costs are estimated based on the ratio of equipment rated power, operating time, and average electricity price. This method assumes that the equipment operates at its rated power and uses the average electricity price for calculation, which differs significantly from the actual situation. The improved formula (5) uses a more accurate calculation model, which determines the electricity consumption at different times by using real-time power, i.e., the current actual power consumption measured by the meter or sensor, and the current electricity price, i.e., the peak-valley-flat electricity price coefficient of the power grid. After the improvement, the original energy cost calculation is changed from static estimation to dynamic perception, which solves the problem of serious distortion of traditional cost calculation methods in the environment of unmanned machining workshops, and significantly improves the accuracy, timeliness and decision relevance of cost information.

[0065] In addition, labor cost = management hours × hourly wage / output, and material cost = raw material consumption × material unit price + tool wear cost. These two models are used to determine labor cost and material cost.

[0066] Therefore, in a possible embodiment, the preset cost rule base is also used to determine material costs based on the following formula. : (6) In equation (6), Indicates the first k The consumption of this type of material. Indicates the first k The unit price of the material Indicates the material loss coefficient. This indicates the cost of tool wear; Among them, tool wear cost Dynamic calculation based on the following formula (7): (7) In equation (7), Indicates the first j The lifespan of each cutting tool. Indicates the first j Total design life of each tool Indicates the first j The purchase price of each cutting tool; j For tool quantity index, and j positive integer Material loss coefficient Based on the following formula (8), dynamic adjustment is performed: (8) In equation (8), Indicates the first n The basic loss coefficient of a certain material. α n Represents the quality sensitivity coefficient. Qdefect_ rate This indicates the real-time quality non-conformity rate; n For material type index, and n It is a positive integer.

[0067] Material cost calculation is a crucial component of cost management in unmanned machining workshops. This embodiment builds upon the existing framework of "material cost = raw material consumption × material unit price + tool wear cost" to establish a more precise intelligent material cost calculation model. While retaining the basic structure of consumption × unit price, it introduces a dynamic wear coefficient Wᵢ, which adjusts the actual utilization rate of raw materials based on real-time quality inspection data, avoiding calculation errors caused by traditional fixed wear rates. This embodiment breaks through the traditional experience-based estimation method for tool cost calculation, establishing a precise calculation model based on actual tool life consumption. By monitoring data such as cumulative tool usage time, cutting parameters, and wear status, it calculates the tool life consumption ratio in real time, thereby accurately determining tool wear costs. This embodiment establishes a correlation model between quality inspection data and raw material wear coefficients. When the product defect rate increases, it automatically adjusts the corresponding raw material wear coefficient, achieving closed-loop optimization of cost mapping. This embodiment addresses the coexistence of multiple tool specifications in unmanned machining workshops by establishing a unified tool life management and cost allocation system, ensuring accurate allocation of tool costs for different processes and products.

[0068] This embodiment performs dynamic cost analysis using real-time collected data and dynamically adjusts traditional cost elements. For example, it adjusts depreciation calculations based on the actual operating efficiency of equipment and adjusts material loss rates based on quality inspection results, thereby improving the accuracy of cost mapping.

[0069] This embodiment transforms traditional offline cost calculation into dynamic cost mapping based on real-time data by constructing a digital twin model of an unmanned machining workshop, significantly improving the accuracy and timeliness of cost mapping.

[0070] This embodiment optimizes traditional cost calculation methods to address the characteristics of unmanned machining workshops, focusing on solving the cost allocation problems of dynamic cost calculation and multi-product mixed-line production. The cost allocation algorithm is described below.

[0071] In mixed-line production mode, common costs need to be reasonably allocated to different products. This embodiment adopts an allocation algorithm based on activity-based costing (ABC), as shown in steps (1) to (3) below.

[0072] Step (1) Identify cost drivers, including processing time, equipment utilization, material consumption, number of quality inspections, etc.

[0073] Step (2) Establish the relationship between cost drivers and products.

[0074] Step (3): Allocate common costs according to the proportion of cost drivers used.

[0075] To scientifically evaluate the effectiveness of unmanned transformation, in addition to cost mapping, this embodiment also conducts an efficiency evaluation of the unmanned machining workshop. In an optional embodiment, the method further includes the following steps S301 to S302.

[0076] Step S301: Based on the state mirror of the digital twin model, efficiency is evaluated using a pre-established efficiency evaluation model to obtain an efficiency image; the efficiency evaluation model includes a production efficiency evaluation sub-module, a quality efficiency evaluation sub-module, an equipment efficiency evaluation sub-module, and a cost efficiency evaluation sub-module. Step S302: Visualize the efficiency image.

[0077] In practice, step S302 can use the aforementioned digital twin model of the unmanned machining workshop to visualize the efficiency image.

[0078] The production efficiency assessment submodule is constructed based on the following formula: Production Efficiency = Actual Output / Theoretical Output × 100%. Actual output is calculated in real-time using a production counting system, while theoretical output is calculated based on the equipment's rated capacity and planned production time. The production efficiency assessment submodule also analyzes factors affecting production efficiency, such as equipment failure, material shortages, and process adjustments.

[0079] The quality efficiency assessment submodule is constructed using the following formula: Quality Efficiency = (Number of Qualified Products / Total Output) × 100%. In this formula, the product qualification rate is calculated in real time using quality inspection data, and the distribution and trends of quality problems are analyzed. Simultaneously, the quality efficiency assessment submodule also establishes a quality cost model to calculate the economic losses caused by quality problems.

[0080] The Equipment Efficiency Evaluation (OEE) submodule is constructed using the following formulas ① to ④: ① OEE = Time utilization rate × Performance utilization rate × Quality utilization rate; ② Time utilization rate = Actual operating time / Planned production time; ③ Performance utilization rate = ideal cycle time × total output / actual operating time; ④ Quality utilization rate = number of qualified products / total output.

[0081] By continuously monitoring and analyzing the OEE indicators ①~④, bottlenecks in equipment efficiency can be identified, providing a basis for production optimization.

[0082] In an optional embodiment, the method further includes the following steps S303 to S305.

[0083] Step S303: Input the status data into the pre-established manned production mode cost model to obtain the manned production mode cost image; Step S304: Compare the cost image of the unmanned machining workshop with the cost image of the manned production model to obtain the cost-benefit comparison results; Step S305: Visualize the cost-benefit comparison results using the digital twin model of the unmanned machining workshop.

[0084] Here, this embodiment also includes a step of historical data comparison and analysis, comparing various indicators before and after the unmanned transformation, quantitatively evaluating the transformation effect, and providing data support for subsequent continuous improvement.

[0085] This embodiment provides the above-mentioned benefit evaluation method. By establishing a comparative analysis algorithm for manned and unmanned production models, it is possible to quantitatively evaluate the economic benefits of unmanned transformation and provide a scientific basis for enterprise decision-making.

[0086] Step S400: Visualize the cost image of the unmanned machining workshop using a digital twin model of the unmanned machining workshop.

[0087] The aforementioned visualizations may include one or more of the following: 3D scene display, data dashboard display, and report display.

[0088] In this embodiment, a 3D digital twin scene of the unmanned machining workshop is obtained by mirroring the state of the digital twin model and rendering it using a visualization rendering engine. This scene is synchronized with the current state of the unmanned machining workshop, showcasing the workshop's real-time status and production process in a 3D format. The data dashboard displays key production indicators and costs in the form of charts and metrics, while the report display automatically generates various analytical reports based on management needs.

[0089] Although digital twin technology has developed rapidly in recent years, existing digital twin systems mainly focus on equipment monitoring and process optimization, rarely incorporating cost and efficiency mapping into the digital twin model. This makes it difficult for managers to accurately assess the economic benefits of unmanned transformation and to scientifically compare and analyze manned and unmanned production modes. This embodiment successfully solves the technical problems of difficult cost mapping and lack of scientific basis for efficiency assessment in unmanned machining workshops through the above-mentioned technical solution, providing a complete solution for the field of intelligent manufacturing. This embodiment achieves effective fusion of multi-source data through digital twin technology, establishes an accurate cost mapping model, and provides a scientific method for evaluating production efficiency, thereby providing data support for unmanned transformation decisions.

[0090] This embodiment can achieve comprehensive monitoring of unmanned machining workshops, accurately generate production cost images, and provide scientific efficiency evaluation functions.

[0091] This embodiment constructs a three-dimensional digital twin model of the workshop, collects multi-dimensional data such as equipment operating status, material flow, product quality, and environmental parameters in real time, establishes a virtual-real mapping relationship, and performs dynamic cost mapping and efficiency analysis based on real-time data. This enables real-time perception and visualization of production resource consumption, transforming traditional static cost reports into dynamic quantitative monitoring tools that can interact with the physical production process. This provides a direct basis for optimizing production technology decisions such as equipment scheduling and process parameters, and realizes intelligent management and scientific evaluation of unmanned machining workshops.

[0092] This embodiment is particularly applicable to unmanned machining workshops that include CNC machine tools, machining centers, turning centers, and other machining equipment. Addressing the characteristics of tool wear, cutting parameter numbering, and workpiece quality fluctuations in the machining production process, it proposes a dynamic equipment depreciation rate calculation method based on a performance degradation model. It integrates digital twin technology with cost mapping to construct a digital twin system that dynamically interacts with the physical workshop, enabling real-time perception and dynamic mapping of cost data such as equipment depreciation, energy consumption, and material consumption. The unmanned machining workshop's digital twin model is used to fuse and visualize the data, allowing for a comparative analysis of the benefits of manned and unmanned production modes. This embodiment establishes a cost image dynamically matched to the physical production process of the unmanned machining workshop based on twin technology, achieving real-time and accurate perception of production costs.

[0093] See Figure 2This invention provides a visualization device for an unmanned machining workshop based on digital twin technology, comprising a data acquisition module 100, a mapping module 200, a cost mapping module 300, and a display module 400. The data acquisition module 100 acquires the status data of the unmanned machining workshop; the status data includes equipment data, logistics data, quality data, environmental data, and personnel management data. The mapping module 200 uses a virtual-real mapping mechanism to map the status data onto a pre-established digital twin model of the unmanned machining workshop, obtaining a state image of the digital twin model. The cost mapping module 300 performs cost mapping based on the state image of the digital twin model and a preset cost rule base, obtaining a cost image of the unmanned machining workshop; wherein, the cost mapping includes equipment depreciation cost calculation; the preset cost rule base is used to determine the dynamic depreciation rate of the equipment based on equipment data and environmental data, and using a pre-established performance degradation model, to determine the equipment depreciation cost. The display module 400 uses the digital twin model of the unmanned machining workshop to visualize the cost image of the unmanned machining workshop.

[0094] In an optional embodiment, the equipment data includes multiple equipment operating condition factors, and the environmental data includes environmental coupling factors; the environmental coupling factors characterize the coupling factors of the environment's impact on equipment damage; the preset cost rule base is specifically used for: The basic depreciation rate is determined according to the following formula (1). : (1) In equation (1), This represents the original cost of the equipment. Indicates the residual value of the equipment. Indicates the estimated number of hours the equipment will be used; The performance degradation rate is determined according to the following formula (2). : (2) In equation (2), The cumulative running time weighting coefficient, The load factor is the weighting factor. For environmental coupling weight coefficients, To maintain the quality weighting coefficient; satisfy ; Indicates the equipment's reference operating time. Indicates the cumulative operating time of the equipment. Indicates the equipment load rate. Indicates the environmental coupling factor. Indicates maintenance quality; The dynamic depreciation rate of the equipment is determined according to the following formula (3). : (3) Determine the equipment depreciation cost according to the following formula (4). ; (4) In equation (4), This indicates the actual number of hours the equipment has been running.

[0095] In an optional embodiment, the cost mapping module 300 further includes energy cost calculation, and the preset cost rule base is also used for: Energy costs are determined based on the following formula (5). : (5) In equation (5), Indicates the first The current power of each device Indicates the number of devices. It is a positive integer greater than 0; Indicates time interval, Indicates the current electricity price; The cost mapping module 300 also includes material cost calculation, and the preset cost rule base is also used to determine material costs based on the following formula. : (6) In equation (6), Indicates the first k The consumption of this type of material. Indicates the first k The unit price of the material Indicates the material loss coefficient. This indicates the cost of tool wear; Among them, tool wear cost Dynamic calculation based on the following formula (7): (7) In equation (7), Indicates the first j The lifespan of each cutting tool. Indicates the first j Total design life of each tool Indicates the first j The purchase price of each cutting tool; j For tool quantity index, j It is a positive integer; Material loss coefficient Based on the following formula (8), dynamic adjustment is performed: (8) In equation (8), Indicates the first nThe basic loss coefficient of a certain material. α n Represents the quality sensitivity coefficient. Qdefect_ rate This indicates the real-time quality non-conformity rate; n For material type index, and n It is a positive integer.

[0096] In an optional embodiment, the mapping module 200 includes a timestamp module, a preprocessing module, and an update module. The timestamp module adds timestamps to the state data, resulting in timestamped state data. The preprocessing module preprocesses the timestamped state data, resulting in preprocessed state data; the preprocessing includes one or more of format conversion, outlier handling, missing value handling, timestamp alignment, and data fusion. The update module uses a virtual-real mapping mechanism to update the preprocessed state data onto the corresponding object attributes in the pre-established digital twin model of the unmanned machining workshop, resulting in a state mirror of the digital twin model.

[0097] In an optional embodiment, the update module includes a data caching module, an differential update module, a load balancing module, and a semantic mapping module. The data caching module stores the pre-processed state data in a pre-established data cache pool to obtain cached data. The differential update module filters the cached data based on a differential update algorithm to identify data whose changes exceed a preset threshold. The load balancing module allocates the data to be updated based on a load balancing algorithm to obtain allocated data. The semantic mapping module, based on a semantic mapping mechanism, queries a preset mapping table according to the physical entity identifiers in the allocated data and locates the virtual object corresponding to the physical entity identifier in the digital twin model of the unmanned machining workshop. It then writes the attribute values ​​from the allocated data into the attribute fields of the corresponding virtual object to obtain a state mirror of the digital twin model.

[0098] In an optional embodiment, the device further includes an efficiency evaluation module and an efficiency image display module. The efficiency evaluation module is used to evaluate efficiency based on the state mirror of the digital twin model using a pre-established efficiency evaluation model to obtain an efficiency image; the efficiency evaluation model includes a production efficiency evaluation sub-module, a quality efficiency evaluation sub-module, an equipment efficiency evaluation sub-module, and a cost efficiency evaluation sub-module. The efficiency image display module is used to visualize the efficiency image.

[0099] In an optional embodiment, the device further includes a manned cost mapping module, a comparison module, and a comparison result display module. The manned cost mapping module inputs status data into a pre-established manned production mode cost model to obtain a manned production model cost image. The comparison module compares the cost image of the unmanned machining workshop with the cost image of the manned production model to obtain a cost-benefit comparison result. The comparison result display module uses the digital twin model of the unmanned machining workshop to visualize the cost-benefit comparison result. The visualization includes one or more of the following: 3D scene display, data dashboard display, and report display.

[0100] In an optional embodiment, equipment data is obtained from production equipment via the OPCUA protocol, logistics data is obtained via RFID tags, QR code identification technology and / or logistics data acquisition modules installed on logistics equipment, and quality data is obtained from quality inspection equipment.

[0101] The apparatus provided in the embodiments of this application has the same inventive concept as the method provided in the embodiments of this application. As long as the method can solve the technical problem, the apparatus can also solve the technical problem. This will not be elaborated here.

[0102] Reference Figure 3 The present invention also provides an electronic device 1000, including a communication interface 1001, a processor 1002, a memory 1003, and a bus 1004. The processor 1002, the communication interface 1001, and the memory 1003 are connected via the bus 1004. The memory 1003 is used to store a computer program that supports the processor 1002 in executing the above-mentioned visualization method for unmanned machining workshops based on twin technology. The processor 1002 is configured to execute the program stored in the memory 1003.

[0103] Optionally, embodiments of the present invention also provide a computer-readable medium having non-volatile program code executable by a processor 1002, the program code causing the processor 1002 to execute the twin-based unmanned workshop visualization method as described in the above embodiments.

[0104] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. A visualization method for unmanned machining workshops based on twin technology, characterized in that, include: Acquire status data of the unmanned machining workshop; the status data includes equipment data, logistics data, quality data, environmental data, and personnel management data; Based on the virtual-real mapping mechanism, the state data is mapped onto a pre-established digital twin model of the unmanned machining workshop to obtain a state mirror of the digital twin model; Cost mapping is performed based on the state mirror of the digital twin model and the preset cost rule base to obtain the cost image of the unmanned machining workshop; wherein, the cost mapping includes the calculation of equipment depreciation cost; the preset cost rule base is used to determine the dynamic depreciation rate of the equipment based on the equipment data and the environmental data, and using a pre-established performance degradation model, so as to determine the equipment depreciation cost; The cost image of the unmanned machining workshop is visualized using the digital twin model of the aforementioned unmanned machining workshop.

2. The visualization method for unmanned machining workshops based on twin technology according to claim 1, characterized in that, The equipment data includes multiple equipment operating condition factors, and the environmental data includes environmental coupling factors; the environmental coupling factors characterize the coupling factors of the environment's impact on equipment damage; the preset cost rule base is specifically used for: The basic depreciation rate is determined according to the following formula (1). : (1) In equation (1), This represents the original cost of the equipment. Indicates the residual value of the equipment. Indicates the estimated number of hours the equipment will be used; The performance degradation rate is determined according to the following formula (2). : (2) In equation (2), The cumulative running time weighting coefficient, The load factor is the weighting factor. For environmental coupling weight coefficients, To maintain the quality weighting coefficient; satisfy ; Indicates the equipment's reference operating time; Indicates the cumulative operating time of the equipment. Indicates the equipment load rate. Indicates the environmental coupling factor. Indicates maintenance quality; The dynamic depreciation rate of the equipment is determined according to the following formula (3). : (3) Determine the equipment depreciation cost according to the following formula (4). ; (4) In equation (4), This indicates the actual number of hours the equipment has been running.

3. The visualization method for unmanned machining workshops based on twin technology according to claim 1, characterized in that, The cost mapping also includes energy cost calculation, and the preset cost rule base is further used for: Energy costs are determined based on the following formula (5). : (5) In equation (5), Indicates the first The current power of each device Indicates the number of devices. It is a positive integer greater than 0; Indicates time interval, Indicates the current electricity price; The cost mapping also includes material cost calculation, and the preset cost rule base is further used for: Material costs are determined based on the following formula. : (6) In equation (6), Indicates the first k The consumption of this type of material. Indicates the first k The unit price of the material Indicates the material loss coefficient. This indicates the cost of tool wear; Among them, the tool wear cost Dynamic calculation based on the following formula (7): (7) In equation (7), Indicates the first j The lifespan of each cutting tool. Indicates the first j Total design life of each tool Indicates the first j The purchase price of each cutting tool; The material loss coefficient Based on the following formula (8), dynamic adjustment is performed: (8) In equation (8), Indicates the first n The basic loss coefficient of a certain material. α n Represents the quality sensitivity coefficient. Qdefect_rate This indicates the real-time quality non-compliance rate.

4. The visualization method for unmanned machining workshops based on twin technology according to claim 1, characterized in that, The step of mapping the state data onto a pre-established digital twin model of an unmanned machining workshop to obtain a state mirror of the digital twin model includes: Add a timestamp to the status data to obtain the status data with the timestamp added; The status data after adding timestamps is preprocessed to obtain preprocessed status data; wherein, the preprocessing includes one or more of the following: format conversion, outlier handling, missing value handling, timestamp alignment, and data fusion; By using a virtual-real mapping mechanism, the preprocessed state data is updated to the corresponding object attributes in the pre-established digital twin model of the unmanned machining workshop, thus obtaining the state mirror of the digital twin model.

5. The visualization method for unmanned machining workshops based on twin technology according to claim 4, characterized in that, The process of using a virtual-real mapping mechanism to update the pre-processed state data onto the corresponding object attributes in the pre-established digital twin model of the unmanned machining workshop, thereby obtaining a state mirror of the digital twin model, includes: The preprocessed state data is stored in a pre-established data cache pool to obtain cached data. The cached data is filtered based on the differential update algorithm to identify data to be updated that has a change exceeding a preset threshold. The data to be updated is allocated based on a load balancing algorithm to obtain the allocated data; Based on the semantic mapping mechanism, according to the physical entity identifier in the allocated data, a preset mapping relationship table is queried, and the virtual object corresponding to the physical entity identifier in the digital twin model of the unmanned machining workshop is located. The attribute values ​​in the allocated data are then written into the attribute fields of the corresponding virtual object to obtain the state mirror of the digital twin model.

6. The visualization method for unmanned machining workshops based on twin technology according to claim 1, characterized in that, The method further includes: Based on the state mirror of the digital twin model, efficiency is evaluated using a pre-established efficiency evaluation model to obtain an efficiency image; the efficiency evaluation model includes a production efficiency evaluation submodule, a quality efficiency evaluation submodule, an equipment efficiency evaluation submodule, and a cost efficiency evaluation submodule. The efficiency image is then visualized.

7. The visualization method for unmanned machining workshops based on twin technology according to claim 1, characterized in that, The method further includes: The state data is input into a pre-established manned production mode cost model to obtain a manned production mode cost image. The cost image of the unmanned machining workshop is compared with the cost image of the manned production model to obtain the cost-benefit comparison results; The cost-benefit comparison results are visualized using the digital twin model of the unmanned machining workshop. The visualization includes one or more of the following: 3D scene display, data dashboard display, and report display.

8. The visualization method for unmanned machining workshops based on twin technology according to claim 1, characterized in that, The equipment data is obtained from the production equipment via the OPCUA protocol, the logistics data is obtained via RFID tags, QR code recognition technology and / or logistics data acquisition modules installed on the logistics equipment, and the quality data is obtained from the quality inspection equipment.

9. A visualization device for an unmanned machining workshop based on twin technology, characterized in that, include: The data acquisition module is used to acquire status data of the unmanned machining workshop; the status data includes equipment data, logistics data, quality data, environmental data, and personnel management data. The mapping module is used to map the state data to a pre-established digital twin model of an unmanned machining workshop using a virtual-real mapping mechanism, so as to obtain a state mirror of the digital twin model. The cost mapping module is used to perform cost mapping based on the state mirror of the digital twin model and the preset cost rule base to obtain the cost image of the unmanned machining workshop; wherein, the cost mapping includes the calculation of equipment depreciation costs; the preset cost rule base is used to determine the dynamic depreciation rate of the equipment based on the equipment data and the environmental data, and using a pre-established performance degradation model, so as to determine the equipment depreciation cost; The display module is used to visualize the cost image of the unmanned machining workshop using the digital twin model of the unmanned machining workshop.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1-8.

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