Factory full-life-cycle management and control method, device and equipment and storage medium

Abstract

The invention provides a factory full-life-cycle management and control method, device and equipment and a storage medium, and belongs to the technical field of digitization. The method comprises the steps of performing dynamic mapping on a target factory through a pre-constructed three-dimensional twin model dynamically associated with an entity of the target factory, and obtaining factory dynamic mapping data in real time; on the basis of the obtained mapping data and a preset optimization model, the whole life cycle of the target factory is optimized, and an optimization strategy is obtained; according to the obtained optimization strategy, the whole life cycle of the target factory is managed and controlled, the real-time performance, the collaboration and the sustainability of the whole life cycle management and control of the factory are finally achieved, and it is ensured that the green manufacturing standard is met.

Description

Technical Field

[0001] The present invention relates to the field of digital technology, and in particular to a factory full life cycle management method, device, equipment and storage medium. Background Art

[0002] To accelerate the green transformation of the manufacturing industry, factories need to systematically integrate low-carbon goals and digital technologies in their full life cycle management to help build a green manufacturing system.

[0003] In related technologies, factory management systems mostly use independently deployed energy consumption monitoring modules and production control modules, and optimize energy efficiency through offline data collection and manual experience adjustment. However, there are problems such as poor data timeliness, difficulty in coordination, and lagging optimization strategies, making it difficult to meet green manufacturing standards. Summary of the Invention

[0004] The embodiments of the present invention provide a factory full life cycle management method, device, equipment and storage medium to solve the problems of poor data timeliness, difficulty in collaboration, lagging optimization strategies and difficulty in meeting green manufacturing standards in factory management.

[0005] In a first aspect, an embodiment of the present invention provides a factory lifecycle management and control method, which is applied to a digital twin platform and includes:

[0006] Dynamically mapping the target factory based on a three-dimensional twin model dynamically associated with the target factory entity to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target factory;

[0007] Based on the mapping data and a preset optimization model, the entire life cycle of the target factory is optimized to obtain an optimization strategy; wherein the preset optimization model is used to optimize the mapping data of the factory so that the energy efficiency, emission intensity, and resource recycling rate of the factory throughout its entire life cycle meet green manufacturing standards;

[0008] According to the optimization strategy, the entire life cycle of the target factory is managed and controlled.

[0009] In a possible implementation, the preset optimization model includes a preset evaluation unit and a preset optimization unit;

[0010] The entire life cycle of the target plant is optimized based on the mapping data and the preset optimization model to obtain an optimization strategy, including:

[0011] Using the mapping data as input data of the preset evaluation unit, and using the preset evaluation unit to evaluate the energy efficiency, emission intensity, and resource recycling rate of the target factory, thereby obtaining an evaluation result output by the preset evaluation unit; wherein the preset evaluation unit evaluates the energy efficiency, emission intensity, and resource recycling rate of the factory based on the green manufacturing standard;

[0012] If the evaluation result does not meet the green manufacturing standard, optimizing the mapping data using the preset optimization unit to obtain optimized mapping data, and re-performing the step of using the mapping data as input data of the preset evaluation unit based on the optimized mapping data, until the evaluation result corresponding to the optimized mapping data meets the green manufacturing standard, thereby determining the final mapping data;

[0013] The optimization strategy is obtained according to the final mapping data.

[0014] In a possible implementation, optimizing the mapping data using the preset optimization unit to obtain optimized mapping data includes:

[0015] determining decision variables in the mapping data;

[0016] Taking maximizing energy efficiency, minimizing emission intensity and maximizing resource recycling rate as optimization objectives, the decision variables are optimized to obtain the optimal decision variables;

[0017] According to the preferred decision variables, the three-dimensional twin model is dynamically updated, and based on the updated three-dimensional twin model, the target factory is dynamically mapped to obtain optimized mapping data.

[0018] In one possible implementation, the green manufacturing standards include energy efficiency standards, emission intensity standards, and resource recycling rate standards for the factory;

[0019] The mapping data is used as input data of the preset evaluation unit to evaluate the energy efficiency, emission intensity, and resource recycling rate of the target plant using the preset evaluation unit, and an evaluation result output by the preset evaluation unit is obtained, including:

[0020] using the mapped data as input data for the preset evaluation unit, determining the energy efficiency, emission intensity, and resource recycling rate of the target plant, and comparing the energy efficiency, emission intensity, and resource recycling rate of the target plant with energy efficiency standards, emission intensity standards, and resource recycling rate standards for the plant;

[0021] If the energy efficiency, emission intensity and resource recycling rate of the target factory all meet the energy efficiency standards, emission intensity standards and resource recycling rate standards of the factory, then the evaluation result is judged to meet the green manufacturing standards; otherwise, the evaluation result is judged not to meet the green manufacturing standards.

[0022] In one possible implementation, the full life cycle includes the planning and design phase of the target factory, and the corresponding mapping data includes energy consumption distribution data and carbon footprint flow data of the target factory. The decision variables in the energy consumption distribution data and the carbon footprint flow data include renewable energy installed capacity, energy storage system configuration ratio, waste heat recovery pipeline network layout parameters, process route carbon emission coefficient, raw material procurement and transportation route, and waste material recycling and treatment method;

[0023] The full life cycle includes the operation stage of the target factory, and the corresponding mapping data includes equipment scheduling data and process parameter data. The decision variables in the equipment scheduling data and the process parameter data include equipment start and stop timing, production scheduling priority, process parameter setting value, equipment maintenance cycle, energy allocation ratio and production line cycle time.

[0024] In one possible implementation, after dynamically mapping the target plant based on the three-dimensional twin model dynamically associated with the entity of the target plant to obtain mapping data, the method further includes:

[0025] Inputting the mapping data of the target plant's operating phase into a pre-trained fault prediction model to obtain a failure probability value of the corresponding equipment within a preset future time period output by the fault prediction model; wherein the fault prediction model is used to predict the failure probability of the target plant's operating equipment;

[0026] If the fault probability value is greater than a preset threshold, fault warning information is generated.

[0027] In one possible implementation, before dynamically mapping the target plant based on the three-dimensional twin model dynamically associated with the entity of the target plant to obtain mapping data, the method further includes:

[0028] Acquiring geometric modeling data, physical property data, and real-time sensor data of the target factory;

[0029] Based on the geometric modeling data, the physical property data and the real-time sensing data, the factory buildings, production line equipment, logistics system and energy consumption units of the target factory are digitally modeled to obtain the three-dimensional twin model.

[0030] In a second aspect, an embodiment of the present invention provides a factory lifecycle management and control device, which is applied to a digital twin platform. The device includes:

[0031] a mapping unit, configured to dynamically map the target plant based on a three-dimensional twin model dynamically associated with the target plant's entity to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target plant;

[0032] an optimization unit configured to optimize the entire life cycle of the target factory based on the mapping data and a preset optimization model to obtain an optimization strategy; wherein the preset optimization model is configured to optimize the mapping data of the factory so that the energy efficiency, emission intensity, and resource recycling rate of the factory throughout its entire life cycle meet green manufacturing standards;

[0033] The execution unit is used to manage and control the entire life cycle of the target factory according to the optimization strategy.

[0034] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method in the first aspect or any possible implementation of the first aspect is implemented.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method in the first aspect or any possible implementation of the first aspect.

[0036] The embodiments of the present invention provide a method, device, equipment and storage medium for factory full life cycle management and control. The target factory is dynamically mapped through a pre-built three-dimensional twin model that is dynamically associated with the entity of the target factory to obtain mapping data. By obtaining the factory dynamic mapping data in real time, the data lag problem in traditional management is solved; then, based on the obtained mapping data and the preset optimization model, the full life cycle of the target factory is optimized to obtain an optimization strategy to achieve accurate improvement of energy consumption, emissions, and resource recycling indicators to match green manufacturing requirements; and then, according to the obtained optimization strategy, the full life cycle of the target factory is managed and controlled, and finally the real-time, collaborative and sustainable management of the factory full life cycle is achieved, ensuring that the green manufacturing goals are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flow chart for implementing a factory life cycle management method provided by an embodiment of the present invention;

[0039] Figure 2 This is a flowchart of another method for controlling the entire factory life cycle provided by an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the structure of a factory life cycle management and control device provided by an embodiment of the present invention;

[0041] Figure 4 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail. SUMMARY OF THE INVENTION

[0044] After research, the inventors found that factory management systems mostly use independently deployed energy consumption monitoring modules and production control modules, and optimize energy efficiency through offline data collection and manual experience adjustment. However, there are problems such as poor data timeliness, data fragmentation throughout the entire life cycle, difficulty in collaboration, lagging optimization strategies, and low resource allocation efficiency, making it difficult to meet green manufacturing standards.

[0045] In order to achieve real-time, collaborative and sustainable management of the entire life cycle of the factory and make it comply with green manufacturing standards, in the implementation of the present invention, the target factory is dynamically mapped through a pre-built three-dimensional twin model that is dynamically associated with the entity of the target factory, and the factory dynamic mapping data is obtained in real time; then, based on the acquired mapping data and the preset optimization model, the entire life cycle of the target factory is optimized to obtain an optimization strategy; and then, according to the obtained optimization strategy, the entire life cycle of the target factory is managed and controlled, and finally the real-time, collaborative and sustainable management of the entire life cycle of the factory is achieved, ensuring compliance with green manufacturing standards.

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0047] Figure 1 The following is a flowchart of a factory lifecycle management and control method provided by an embodiment of the present invention, which is applied to a digital twin platform:

[0048] Step 101 , dynamically mapping the target factory based on a three-dimensional twin model dynamically associated with the entity of the target factory to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target factory.

[0049] Among them, in this embodiment, the three-dimensional twin model is a 1:1 virtual factory model constructed through digital technology, which includes the geometric structure (such as length, width, height, spatial position, etc.), physical properties (such as equipment material, load-bearing capacity) and dynamic behavior (such as equipment operating status, material flow trajectory) of physical entities such as factory buildings, equipment, logistics, and energy consumption units. It is a "digital mirror" of the factory.

[0050] This embodiment synchronizes the real-time data generated by the target factory, such as data collected by sensors and material locations in the logistics system, to the 3D twin model, so that the virtual model is consistent with the physical entity state.

[0051] In one example, the mapping data covers all the data elements of the factory, including: static data of the factory, such as the coordinates of the factory model, equipment design parameters and logistics path planning diagrams, etc.; it also includes dynamic data of the factory, such as real-time operating parameters of equipment, real-time location of materials, real-time data of energy consumption units, etc.

[0052] Step 102, based on the mapping data and the preset optimization model, the entire life cycle of the target factory is optimized to obtain an optimization strategy; wherein the preset optimization model is used to optimize the mapping data of the factory so that the energy consumption efficiency, emission intensity and resource recycling rate of the factory throughout its entire life cycle meet the green manufacturing standards.

[0053] Among them, optimization strategies refer to specific adjustment plans formulated for the entire life cycle of the factory (such as equipment layout in the design phase and production scheduling in the operation phase), such as "reducing the power of a certain production line equipment by 10% to reduce energy consumption" and "increasing the waste recycling ratio to 60% to improve the resource recycling rate".

[0054] For example, this embodiment first cleans and standardizes the mapped data to extract key decision variables, such as installed renewable energy capacity and equipment start-up and shutdown times. A multi-objective optimization algorithm is then used to input the decision variables and objective function, searching for the optimal solution within the feasible solution space. From the Pareto optimal solution set output by the algorithm, the solution with the best overall performance is selected, incorporating green manufacturing standards. This results in an executable optimization strategy that ensures that the energy efficiency, emissions intensity, and resource recycling rate of the factory throughout its lifecycle meet green manufacturing standards.

[0055] Step 103: Manage and control the entire life cycle of the target factory according to the optimization strategy.

[0056] Illustratively, this embodiment converts the optimization strategy into equipment control instructions, production plans, or management processes to manage and control the entire life cycle of the target factory.

[0057] In a feasible implementation, this embodiment also uses a three-dimensional twin model to display the execution effect of the optimization strategy in real time (such as whether the energy consumption, emission intensity, and resource recycling rate after optimization meet the standards). If the actual effect deviates from the expected effect, the optimization is performed again.

[0058] This embodiment adjusts the plant layout according to the optimization strategy corresponding to the planning and design stage, such as changing the number and location of waste heat recovery pipes and adjusting equipment selection; optimizes and adjusts process parameters according to the optimization strategy corresponding to the operation stage, such as lowering the temperature of a certain process to reduce carbon dioxide emissions; and optimizes the waste treatment process according to the optimization strategy corresponding to the operation and maintenance stage.

[0059] In summary, this embodiment dynamically maps the target factory through a pre-built three-dimensional twin model that is dynamically associated with the entity of the target factory to obtain mapping data. By obtaining the factory dynamic mapping data in real time, the data lag problem in traditional management is solved; then, based on the obtained mapping data and the preset optimization model, the entire life cycle of the target factory is optimized to obtain an optimization strategy to achieve accurate improvement of energy consumption, emissions, and resource recycling indicators to match green manufacturing requirements; and then, based on the obtained optimization strategy, the entire life cycle of the target factory is managed and controlled, ultimately achieving real-time, collaborative, and sustainable management of the entire life cycle of the factory to ensure that green manufacturing goals are achieved.

[0060] Figure 2 A flowchart for implementing another factory lifecycle management and control method provided by an embodiment of the present invention, which is applied to a digital twin platform, is detailed as follows:

[0061] Step 201: Acquire geometric modeling data, physical property data, and real-time sensor data of a target factory.

[0062] Among them, geometric modeling data includes computer-aided design (CAD) drawings, laser point cloud data, building information modeling (BIM) data, etc. of the target factory; physical property data includes material parameters, mechanical properties, energy consumption quotas, etc. of the target factory; real-time sensor data includes equipment operating status, logistics location information, energy consumption monitoring data, etc.

[0063] Step 202: Digitally model the factory buildings, production line equipment, logistics systems, and energy consumption units of the target factory based on the geometric modeling data, physical property data, and real-time sensor data to obtain a three-dimensional twin model.

[0064] For example, this embodiment first constructs the spatial skeleton of the target factory based on geometric modeling data to restore the precise geometric structure of the factory buildings and equipment; then binds the physical property data to the model components to define their behavior rules, such as energy consumption calculation logic; finally, through spatiotemporal alignment technology, the real-time sensor data is mapped one-to-one with the model coordinates to drive the dynamic update of the virtual model.

[0065] Step 203 , dynamically mapping the target factory based on the three-dimensional twin model dynamically associated with the entity of the target factory to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target factory.

[0066] The entire life cycle includes the planning, design, construction, operation, maintenance, transformation and decommissioning of the factory.

[0067] In one example, the entire life cycle includes the planning and design stages of the target factory. The corresponding mapping data includes the energy consumption distribution data and carbon footprint flow data of the target factory. The decision variables in the energy consumption distribution data and carbon footprint flow data include renewable energy installed capacity, energy storage system configuration ratio, waste heat recovery pipeline layout parameters, process route carbon emission coefficient, raw material procurement and transportation route, and waste recycling and treatment methods.

[0068] In another example, the entire life cycle includes the operation stage of the target factory, and the corresponding mapping data includes equipment scheduling data and process parameter data. The decision variables in the equipment scheduling data and process parameter data include equipment start and stop timing, production scheduling priority, process parameter setting value, equipment maintenance cycle, energy allocation ratio and production line cycle time.

[0069] For example, during the operation phase of a factory, this embodiment can perform predictive maintenance on the equipment based on the acquired mapping data of the operation phase, such as the operation data of the factory equipment. It can also issue fault warnings to equipment with potential faults, allowing for timely equipment maintenance management.

[0070] In one feasible implementation, this embodiment inputs the mapping data of the target factory's operation phase into a pre-trained fault prediction model, such as a long short-term memory (LSTM) network model, to obtain the fault probability value of the corresponding equipment within a preset future time output by the fault prediction model; wherein the fault prediction model is used to predict the failure probability of the target factory's operating equipment; if the failure probability value is greater than a preset threshold, fault warning information is generated.

[0071] In step 204, the mapping data is used as input data of a preset evaluation unit to evaluate the energy efficiency, emission intensity, and resource recycling rate of the target factory using the preset evaluation unit to obtain an evaluation result output by the preset evaluation unit; wherein the preset evaluation unit evaluates the energy efficiency, emission intensity, and resource recycling rate of the factory based on the green manufacturing standard.

[0072] In one example, the green manufacturing standard includes a factory's energy efficiency standard, emission intensity standard, and resource recycling rate standard; step 204 includes the following steps:

[0073] The mapped data is used as input data for a preset evaluation unit to determine the target plant's energy efficiency, emission intensity, and resource recycling rate, and then compared with the plant's energy efficiency standard, emission intensity standard, and resource recycling rate standard.

[0074] If the target factory's energy efficiency, emission intensity and resource recycling rate all meet the factory's energy efficiency standards, emission intensity standards and resource recycling rate standards, the assessment results are judged to meet the green manufacturing standards; otherwise, the assessment results are judged not to meet the green manufacturing standards.

[0075] In this embodiment, the mapping data may be mapping data corresponding to any stage in the entire life cycle of the target factory.

[0076] This embodiment converts green manufacturing standards into quantifiable evaluation rules, such as a factory's energy efficiency threshold, emission intensity threshold, and resource recycling rate threshold.

[0077] In another feasible implementation, this embodiment can also first extract key evaluation indicators from the mapping data, including energy efficiency indicators: such as comprehensive energy consumption per unit product, equipment load rate, and proportion of renewable energy; emission intensity indicators: such as carbon emissions per unit output value, volatile organic compound emission concentration, and wastewater compliance rate; resource recycling rate indicators: such as waste recycling rate, water resource recycling rate, and packaging material reuse rate.

[0078] The key evaluation indicators are then used as input data for the evaluation unit. Each sub-unit in the evaluation unit is used to evaluate each type of key evaluation indicators to obtain an evaluation score for each type of key evaluation indicator. Finally, a comprehensive score is calculated based on a preset weighted scoring mechanism, and the comprehensive score is compared with the preset standard to obtain the evaluation result.

[0079] In step 205, if the evaluation result does not meet the green manufacturing standard, the mapping data is optimized using the preset optimization unit to obtain the optimized mapping data. Based on the optimized mapping data, the step of using the mapping data as input data of the preset evaluation unit is re-executed until the evaluation result corresponding to the optimized mapping data meets the green manufacturing standard, and the final mapping data is determined.

[0080] In a feasible implementation, step 205 includes:

[0081] Determine the decision variables in the mapping data; optimize the decision variables with maximizing energy efficiency, minimizing emission intensity, and maximizing resource recycling rate as optimization goals to obtain the preferred decision variables; dynamically update the three-dimensional twin model based on the preferred decision variables, and dynamically map the target plant based on the updated three-dimensional twin model to obtain optimized mapping data.

[0082] In one example, this embodiment performs variable expansion on the decision variables in the determined mapping data to obtain an initial population. Based on the initial population, the decision variables are optimized with the optimization goals of maximizing energy efficiency, minimizing emission intensity, and maximizing resource recycling rate to obtain the preferred decision variables:

[0083] Obtain the energy efficiency, emission intensity and resource recycling rate corresponding to each decision variable in the initial population.

[0084] According to the energy efficiency, emission intensity and resource recycling rate corresponding to each decision variable, the decision variables in the initial population are non-dominated sorted to obtain the non-dominated sorting result, and the number of iterations corresponding to the current non-dominated sorting result is recorded; wherein, the decision variables in the non-dominated sorting result are divided into the same or different levels, each level includes one or more decision variables, the decision variables in the same level do not dominate each other, and the decision variables in the latter level in different levels are dominated by the decision variables in the previous level.

[0085] If the number of iterations reaches the preset number of iterations, the decision variables in the first level of the non-dominated sorting result are output as the preferred decision variables.

[0086] Otherwise, according to the preset screening conditions, the parent decision variables are screened and, under the constraints of the preset constraints, cross-mutation is performed based on the parent decision variables to obtain the child decision variables, and the child decision variables are merged into the initial population as new decision variables, and the merged initial population is used as the current initial population; wherein, the screening conditions are set based on maximizing energy efficiency, minimizing emission intensity, and maximizing resource recycling rate.

[0087] Re-execute the steps of performing non-dominated sorting on each decision variable in the current initial population according to the energy efficiency, emission intensity and resource recycling rate corresponding to each decision variable, obtain the non-dominated sorting result, and record the number of iterations corresponding to the current non-dominated sorting result until the optimal decision variable is output.

[0088] Step 206: Obtain an optimization strategy based on the final mapping data.

[0089] Illustratively, this embodiment determines an optimization object based on the obtained final mapping data, and obtains an optimization strategy for the optimization object based on the final mapping data.

[0090] Step 207: Manage and control the entire life cycle of the target factory according to the optimization strategy.

[0091] For example, this step refers to step 103 and will not be described in detail.

[0092] In summary, this embodiment obtains three types of data of geometric modeling, physical properties, and real-time sensing of the target factory to construct a three-dimensional twin model covering factory buildings, equipment, logistics, and energy consumption units, breaking the problem of data fragmentation in traditional factories, forming a unified data base for the entire life cycle, and realizing cross-stage collaboration. Furthermore, the factory status can be mapped in real time based on the constructed three-dimensional twin model, and equipment predictive maintenance and fault warning can be realized by combining models such as LSTM, thereby improving abnormal response speed and reducing the risk of unplanned downtime. This embodiment also converts green manufacturing standards into quantifiable evaluation rules, and performs multi-dimensional evaluations of indicators such as energy efficiency, emission intensity, and resource recycling rate through preset evaluation units, and uses optimization units to optimize decision variables, which can ensure that the factory's full life cycle indicators continue to meet green standards, achieve improved energy efficiency, reduced emission intensity, and optimized resource recycling rate.

[0093] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0094] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0095] Figure 3 The following is a schematic diagram of the structure of a factory life cycle control device provided by an embodiment of the present invention, which is applied to a digital twin platform. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0096] like Figure 3 As shown in the figure, the factory life cycle control device includes:

[0097] The mapping unit 31 is used to dynamically map the target factory based on a three-dimensional twin model dynamically associated with the entity of the target factory to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target factory.

[0098] The optimization unit 32 is used to optimize the entire life cycle of the target factory based on the mapping data and the preset optimization model to obtain an optimization strategy; wherein the preset optimization model is used to optimize the mapping data of the factory so that the energy consumption efficiency, emission intensity and resource recycling rate of the factory throughout its life cycle meet the green manufacturing standards.

[0099] The execution unit 33 is used to manage and control the entire life cycle of the target factory according to the optimization strategy.

[0100] In one possible implementation, the preset optimization model includes a preset evaluation unit and a preset optimization unit; the optimization unit 32 is specifically configured to:

[0101] The mapping data is used as input data of a preset evaluation unit, so as to utilize the preset evaluation unit to evaluate the energy efficiency, emission intensity and resource recycling rate of the target factory, and obtain an evaluation result output by the preset evaluation unit; wherein, the preset evaluation unit evaluates the energy efficiency, emission intensity and resource recycling rate of the factory based on the green manufacturing standard.

[0102] If the evaluation result does not meet the green manufacturing standard, the mapping data is optimized using the preset optimization unit to obtain the optimized mapping data. Based on the optimized mapping data, the step of using the mapping data as the input data of the preset evaluation unit is re-executed until the evaluation result corresponding to the optimized mapping data meets the green manufacturing standard and the final mapping data is determined.

[0103] According to the final mapping data, the optimization strategy is obtained.

[0104] In one possible implementation, the optimization unit 32 is specifically configured to:

[0105] Identify decision variables in the mapped data.

[0106] Taking maximizing energy efficiency, minimizing emission intensity and maximizing resource recycling rate as optimization objectives, the decision variables are optimized to obtain the optimal decision variables.

[0107] According to the preferred decision variables, the three-dimensional twin model is dynamically updated, and based on the updated three-dimensional twin model, the target factory is dynamically mapped to obtain optimized mapping data.

[0108] In one possible implementation, the green manufacturing standard includes the factory's energy efficiency standard, emission intensity standard, and resource recycling rate standard; the optimization unit 32 is specifically used to: use the mapping data as input data of the preset evaluation unit, determine the energy efficiency, emission intensity, and resource recycling rate of the target factory, and compare the energy efficiency, emission intensity, and resource recycling rate of the target factory with the factory's energy efficiency standard, emission intensity standard, and resource recycling rate standard; if the energy efficiency, emission intensity, and resource recycling rate of the target factory all meet the factory's energy efficiency standard, emission intensity standard, and resource recycling rate standard, then the evaluation result is determined to meet the green manufacturing standard; otherwise, the evaluation result is determined to not meet the green manufacturing standard.

[0109] In one possible implementation, the entire life cycle includes the planning and design stages of the target factory. The corresponding mapping data includes the energy consumption distribution data and carbon footprint flow data of the target factory. The decision variables in the energy consumption distribution data and carbon footprint flow data include the installed capacity of renewable energy, the configuration ratio of the energy storage system, the layout parameters of the waste heat recovery pipeline network, the carbon emission coefficient of the process route, the raw material procurement and transportation route, and the waste recycling and treatment method.

[0110] The entire life cycle includes the operation stage of the target factory. The corresponding mapping data includes equipment scheduling data and process parameter data. The decision variables in the equipment scheduling data and process parameter data include equipment start and stop timing, production scheduling priority, process parameter setting value, equipment maintenance cycle, energy allocation ratio and production line cycle time.

[0111] In one possible implementation, after the mapping unit 31, the device also includes an early warning unit, which is used to: input the mapping data of the operation phase of the target factory into a pre-trained fault prediction model, and obtain the failure probability value of the corresponding equipment within a preset time in the future output by the fault prediction model; wherein the fault prediction model is used to predict the failure probability of the operating equipment of the target factory; if the failure probability value is greater than a preset threshold, fault early warning information is generated.

[0112] In one possible implementation, before the mapping unit 31, the device also includes a construction unit for: obtaining the geometric modeling data, physical property data and real-time sensor data of the target factory; based on the geometric modeling data, physical property data and real-time sensor data, digitally modeling the factory buildings, production line equipment, logistics systems and energy consumption units of the target factory to obtain a three-dimensional twin model.

[0113] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0114] Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, the steps of the above-described method embodiments are implemented. Alternatively, when the processor 40 executes the computer program 42, the functions of the modules / units in the above-described device embodiments are implemented.

[0115] For example, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4.

[0116] The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation on the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0117] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0118] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device 4. Furthermore, the memory 41 can also include both an internal storage unit of the electronic device 4 and an external storage device. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or is about to be output.

[0119] For the sake of convenience and brevity, the division of the above functional modules / units is only used as an example. In actual applications, the above functions can be assigned to different functional modules / units as needed. The above modules / units can be implemented in the form of hardware, software, or a combination of hardware and software.

[0120] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in the above-mentioned method embodiments.

[0121] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the methods in the above-mentioned method embodiments.

[0122] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. Computer-readable media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.

[0123] In the above embodiments, the descriptions of each embodiment have their own focus. For parts not described or recorded in detail in one embodiment, please refer to the relevant descriptions of other embodiments. Unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features of different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0124] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A factory life cycle management method, characterized in that: Applied to a digital twin platform, the method includes: Dynamically mapping the target factory based on a three-dimensional twin model dynamically associated with the target factory entity to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target factory; Based on the mapping data and a preset optimization model, the entire life cycle of the target factory is optimized to obtain an optimization strategy; wherein the preset optimization model is used to optimize the mapping data of the factory so that the energy efficiency, emission intensity, and resource recycling rate of the factory throughout its entire life cycle meet green manufacturing standards; According to the optimization strategy, the entire life cycle of the target factory is managed and controlled.

2. The factory life cycle management method according to claim 1, characterized in that: The preset optimization model includes a preset evaluation unit and a preset optimization unit; The entire life cycle of the target plant is optimized based on the mapping data and the preset optimization model to obtain an optimization strategy, including: Using the mapping data as input data of the preset evaluation unit, and using the preset evaluation unit to evaluate the energy efficiency, emission intensity, and resource recycling rate of the target factory, thereby obtaining an evaluation result output by the preset evaluation unit; wherein the preset evaluation unit evaluates the energy efficiency, emission intensity, and resource recycling rate of the factory based on the green manufacturing standard; If the evaluation result does not meet the green manufacturing standard, optimizing the mapping data using the preset optimization unit to obtain optimized mapping data, and re-performing the step of using the mapping data as input data of the preset evaluation unit based on the optimized mapping data, until the evaluation result corresponding to the optimized mapping data meets the green manufacturing standard, thereby determining the final mapping data; The optimization strategy is obtained according to the final mapping data.

3. The factory life cycle management method according to claim 2, characterized in that: The optimizing the mapping data by using the preset optimization unit to obtain the optimized mapping data includes: determining decision variables in the mapping data; Taking maximizing energy efficiency, minimizing emission intensity and maximizing resource recycling rate as optimization objectives, the decision variables are optimized to obtain the optimal decision variables; According to the preferred decision variables, the three-dimensional twin model is dynamically updated, and based on the updated three-dimensional twin model, the target factory is dynamically mapped to obtain optimized mapping data.

4. The factory life cycle management method according to claim 2, characterized in that: The green manufacturing standards include energy efficiency standards, emission intensity standards, and resource recycling rate standards for factories; The mapping data is used as input data of the preset evaluation unit to evaluate the energy efficiency, emission intensity, and resource recycling rate of the target plant using the preset evaluation unit, and an evaluation result output by the preset evaluation unit is obtained, including: using the mapped data as input data for the preset evaluation unit, determining the energy efficiency, emission intensity, and resource recycling rate of the target plant, and comparing the energy efficiency, emission intensity, and resource recycling rate of the target plant with energy efficiency standards, emission intensity standards, and resource recycling rate standards for the plant; If the energy efficiency, emission intensity and resource recycling rate of the target factory all meet the energy efficiency standards, emission intensity standards and resource recycling rate standards of the factory, then the evaluation result is judged to meet the green manufacturing standards; otherwise, the evaluation result is judged not to meet the green manufacturing standards.

5. The factory life cycle management method according to claim 4, characterized in that: The full life cycle includes the planning and design phase of the target factory. The corresponding mapping data includes the energy consumption distribution data and carbon footprint flow data of the target factory. The decision variables in the energy consumption distribution data and the carbon footprint flow data include the installed capacity of renewable energy, the configuration ratio of the energy storage system, the layout parameters of the waste heat recovery pipeline network, the carbon emission coefficient of the process route, the raw material procurement and transportation route, and the waste material recycling and treatment method. The full life cycle includes the operation stage of the target factory, and the corresponding mapping data includes equipment scheduling data and process parameter data. The decision variables in the equipment scheduling data and the process parameter data include equipment start and stop timing, production scheduling priority, process parameter setting value, equipment maintenance cycle, energy allocation ratio and production line cycle time.

6. The factory life cycle management method according to any one of claims 1 to 5, characterized in that: After dynamically mapping the target plant based on the three-dimensional twin model dynamically associated with the entity of the target plant to obtain mapping data, the method further includes: Inputting the mapping data of the target plant's operating phase into a pre-trained fault prediction model to obtain a failure probability value of the corresponding equipment within a preset future time period output by the fault prediction model; wherein the fault prediction model is used to predict the failure probability of the target plant's operating equipment; If the fault probability value is greater than a preset threshold, fault warning information is generated.

7. The factory life cycle management method according to any one of claims 1 to 5, characterized in that: Before dynamically mapping the target plant based on the three-dimensional twin model dynamically associated with the entity of the target plant to obtain mapping data, the method further includes: Acquiring geometric modeling data, physical property data, and real-time sensor data of the target factory; Based on the geometric modeling data, the physical property data and the real-time sensing data, the factory buildings, production line equipment, logistics system and energy consumption units of the target factory are digitally modeled to obtain the three-dimensional twin model.

8. A factory life cycle management and control device, characterized in that: Applied to a digital twin platform, the device includes: a mapping unit, configured to dynamically map the target plant based on a three-dimensional twin model dynamically associated with the target plant's entity to obtain mapping data; wherein the three-dimensional twin model is a virtual model obtained by digitally mirroring the target plant; an optimization unit configured to optimize the entire life cycle of the target factory based on the mapping data and a preset optimization model to obtain an optimization strategy; wherein the preset optimization model is configured to optimize the mapping data of the factory so that the energy efficiency, emission intensity, and resource recycling rate of the factory throughout its entire life cycle meet green manufacturing standards; The execution unit is used to manage and control the entire life cycle of the target factory according to the optimization strategy.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the factory life cycle management method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the factory life cycle management method according to any one of claims 1 to 7 is implemented.

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