A digital twin system for energy efficiency optimization of industrial parks based on large model algorithm

Abstract

The present invention provides an industrial park energy efficiency optimization digital twin system based on a large model algorithm, which relates to the field of electrical digital data processing, and includes a data acquisition module, a digital twin module, an energy efficiency calculation module and a simulation optimization module. The data acquisition module is used to collect operation information in the park, the digital twin module constructs a digital twin model according to the operation information, the energy efficiency calculation module is used to calculate the energy efficiency information of the operation, and the simulation optimization module is used to simulate the operation of the digital twin module and give process optimization suggestions; this system digitizes all operations in the park, calculates and processes energy efficiency, simulates and analyzes event strategies based on historical data, and selects the strategy with the highest energy efficiency to optimize the park's processes.

Description

Technical Field

[0001] The present invention relates to the field of electrical digital data processing, and in particular to a digital twin system for optimizing energy efficiency of an industrial park based on a large model algorithm. Background Art

[0002] Industrial parks bring together highly related operations such as production, R&D, and services, which can effectively improve industrial efficiency. However, these operations are usually managed manually, making it difficult to better tap the potential energy efficiency in the park. Therefore, a system is needed to coordinate the operations within the park and further improve the park's energy efficiency.

[0003] The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion does not acknowledge or admit that any of the material referred to is part of the common general knowledge.

[0004] Many industrial park management systems have been developed. After a lot of searching and reference, it is found that the existing park management system is such as the system disclosed in the publication number CN116739175A. These system methods generally include: data collection, statistical analysis, energy consumption prediction, energy consumption warning and other processes. Various sensors and detection instruments are used to collect energy consumption data of industrial parks, upload them to the edge computing gateway for statistical analysis; use the improved particle swarm intelligent optimization algorithm to optimize parameters, establish a support vector regression energy consumption prediction model based on particle swarm optimization, realize real-time prediction of the total energy consumption of the park, and issue over-value warnings based on the energy consumption prediction results, in order to reduce the energy consumption of the park. However, this system only manages energy consumption, does not optimize the coordination between different operations, and cannot further improve efficiency. Summary of the invention

[0005] The purpose of the present invention is to propose a digital twin system for energy efficiency optimization of industrial parks based on a large model algorithm to address the existing deficiencies.

[0006] The present invention adopts the following technical solution:

[0007] A digital twin system for energy efficiency optimization of industrial parks based on a large model algorithm, including a data acquisition module, a digital twin module, an energy efficiency calculation module and a simulation optimization module;

[0008] The data acquisition module is used to collect operation information in the park, the digital twin module builds a digital twin model according to the operation information, the energy efficiency calculation module is used to calculate the energy efficiency information of the operation, and the simulation optimization module is used to simulate the operation of the digital twin module and give process optimization suggestions;

[0009] The data acquisition module includes a job monitoring unit, an information collection unit and a data sorting unit. The job monitoring unit is used to monitor the running status of each job, the information collection unit is used to collect data information when the job is running, and the data sorting unit is used to classify and sort the collected data information;

[0010] The digital twin module includes an operation basic unit, an operation linkage unit, a data storage unit and a real-time display unit. The operation basic unit is used to record the basic data of each operation, the operation linkage unit is used to record the linkage relationship and linkage data between operations, the data storage unit is used to save the collected data information, and the real-time display unit is used to display real-time operation information;

[0011] The energy efficiency calculation module includes an energy consumption calculation unit, an efficiency calculation unit and a comprehensive calculation unit. The energy efficiency calculation unit is used to calculate the energy consumption information of the operation, the efficiency calculation unit is used to calculate the efficiency information of the operation, and the comprehensive calculation unit is used to calculate the energy efficiency information of the entire park;

[0012] The simulation optimization module includes a model building unit, a simulation operation unit and an optimization output unit. The model building unit builds a data conversion model based on historical data. The simulation operation unit simulates the operation of jobs under different strategies based on real-time data and the data conversion model. The optimization output unit is used to select the strategy with the best energy efficiency as the optimization solution.

[0013] Furthermore, the energy consumption calculation unit includes a relative energy consumption conversion processor and a component integration processor, wherein the relative energy consumption conversion processor is used to convert a single type of energy consumption into a relative energy consumption value, and the component integration processor is used to obtain all types of energy consumption data of the digital operation component when completing an event and calculate the event energy consumption value;

[0014] Furthermore, the efficiency calculation unit includes a value conversion processor and a value-added efficiency processor, wherein the value conversion processor is used to calculate the value of each object, and the value-added efficiency processor is used to calculate the value-added efficiency of the digital operation element within a specific time period;

[0015] The value-added efficiency processor calculates the value-added efficiency Vae of the digital operation element according to the following formula:

[0016] ;

[0017] in, represents the value of the i-th type of object invested, represents the value of the i-th type of object produced, n1 is the number of object types input, n2 is the number of object types produced, and T is the length of a specific time period;

[0018] Furthermore, the comprehensive calculation unit includes an event matching processor and an energy efficiency calculation processor, wherein the event matching processor is used to set a specific time period and match and obtain event energy consumption values ​​within the specific time period, and the energy efficiency calculation processor calculates the energy efficiency index of the entire park within the specific time period based on the matching information;

[0019] The energy efficiency calculation processor calculates the energy efficiency index P of the entire park according to the following formula:

[0020] ;

[0021] Where Vae(i) represents the value-added efficiency of the i-th digital operating element, Ee(i, j) represents the energy consumption value of the j-th event of the i-th digital operating element, N is the number of digital operating elements, M i is the number of events of the ith digital operation element in a specific time period, a is the efficiency weight, and b is the energy consumption weight;

[0022] Further, the simulation operation unit includes a strategy generation processor and a model execution processor, the strategy generation processor is used to obtain real-time data and formulate future events, and the model execution processor is used to use the real-time data and future events as inputs of the data conversion model and start the data conversion model;

[0023] The strategy generation processor calculates the limit time t of future events in the strategy according to the following formula:

[0024] ;

[0025] Among them, N s is the number of sample data, P max is the maximum energy efficiency index in the sample data, t max is the time of the corresponding event in the maximum energy efficiency index sample data, P i is the energy efficiency index of the i-th sample data, t i is the interval between corresponding events in the i-th sample data;

[0026] The strategy generation processor is in [t, t max ] or [t max , t] uniformly takes the value as the plan time corresponding to the future event. The strategy generation processor combines different future event plan times to form a strategy, and different combinations constitute different strategies.

[0027] The beneficial effects achieved by the present invention are:

[0028] This system digitally twins the operation information of the industrial park, and can control the operation status within the park in real time. By calculating the energy consumption, value-added efficiency and comprehensive energy efficiency of the operation, it can more intuitively compare the advantages and disadvantages of different operating conditions. By analyzing historical data and simulating the energy efficiency after the implementation of different strategies, it can select the best strategy to coordinate operations and improve the overall operation level of the park.

[0029] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the overall structural framework of the present invention;

[0031] Figure 2 This is a schematic diagram of the data acquisition module of the present invention;

[0032] Figure 3 This is a schematic diagram of the energy efficiency calculation module of the present invention;

[0033] Figure 4 This is a schematic diagram of the simulation optimization module of the present invention;

[0034] Figure 5 This is a schematic diagram of optimizing the output unit structure of the present invention. DETAILED DESCRIPTION

[0035] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention. Example

[0036] This embodiment provides an industrial park energy efficiency optimization digital twin system based on a large model algorithm, combined with Figure 1 , including data acquisition module, digital twin module, energy efficiency calculation module and simulation optimization module;

[0037] The data acquisition module is used to collect operation information in the park, the digital twin module builds a digital twin model according to the operation information, the energy efficiency calculation module is used to calculate the energy efficiency information of the operation, and the simulation optimization module is used to simulate the operation of the digital twin module and give process optimization suggestions;

[0038] The data acquisition module includes a job monitoring unit, an information collection unit and a data sorting unit. The job monitoring unit is used to monitor the running status of each job, the information collection unit is used to collect data information when the job is running, and the data sorting unit is used to classify and sort the collected data information;

[0039] The digital twin module includes an operation basic unit, an operation linkage unit, a data storage unit and a real-time display unit. The operation basic unit is used to record the basic data of each operation, the operation linkage unit is used to record the linkage relationship and linkage data between operations, the data storage unit is used to save the collected data information, and the real-time display unit is used to display real-time operation information;

[0040] The energy efficiency calculation module includes an energy consumption calculation unit, an efficiency calculation unit and a comprehensive calculation unit. The energy efficiency calculation unit is used to calculate the energy consumption information of the operation, the efficiency calculation unit is used to calculate the efficiency information of the operation, and the comprehensive calculation unit is used to calculate the energy efficiency information of the entire park;

[0041] The simulation optimization module includes a model building unit, a simulation operation unit and an optimization output unit. The model building unit builds a data conversion model based on historical data. The simulation operation unit simulates the operation of jobs under different strategies based on real-time data and the data conversion model. The optimization output unit is used to select the strategy with the best energy efficiency as the optimization solution.

[0042] The energy consumption calculation unit includes a relative energy consumption conversion processor and a component integration processor, wherein the relative energy consumption conversion processor is used to convert a single type of energy consumption into a relative energy consumption value, and the component integration processor is used to obtain all types of energy consumption data of the digital operation component when completing an event and calculate the event energy consumption value;

[0043] The efficiency calculation unit includes a value conversion processor and a value-added efficiency processor, wherein the value conversion processor is used to calculate the value of each object, and the value-added efficiency processor is used to calculate the value-added efficiency of the digital operation element within a specific time period;

[0044] The value-added efficiency processor calculates the value-added efficiency Vae of the digital operation element according to the following formula:

[0045] ;

[0046] in, represents the value of the i-th type of object invested, represents the value of the i-th type of object produced, n1 is the number of object types input, n2 is the number of object types produced, and T is the length of a specific time period;

[0047] The comprehensive calculation unit includes an event matching processor and an energy efficiency calculation processor. The event matching processor is used to set a specific time period and match and obtain the event energy consumption value within the specific time period. The energy efficiency calculation processor calculates the energy efficiency index of the entire park within the specific time period based on the matching information.

[0048] The energy efficiency calculation processor calculates the energy efficiency index P of the entire park according to the following formula:

[0049] ;

[0050] Where Vae(i) represents the value-added efficiency of the i-th digital operating element, Ee(i, j) represents the energy consumption value of the j-th event of the i-th digital operating element, N is the number of digital operating elements, M i is the number of events of the ith digital operation element in a specific time period, a is the efficiency weight, and b is the energy consumption weight;

[0051] The simulation operation unit includes a strategy generation processor and a model execution processor, wherein the strategy generation processor is used to obtain real-time data and formulate future events, and the model execution processor is used to use the real-time data and future events as inputs of a data conversion model and start the data conversion model;

[0052] The strategy generation processor calculates the limit time t of future events in the strategy according to the following formula:

[0053] ;

[0054] Among them, N s is the number of sample data, P max is the maximum energy efficiency index in the sample data, t max is the time of the corresponding event in the maximum energy efficiency index sample data, P i is the energy efficiency index of the i-th sample data, t i is the interval between corresponding events in the i-th sample data;

[0055] The strategy generation processor is in [t, t max ] or [t max , t] uniformly takes the value as the plan time corresponding to the future event. The strategy generation processor combines different future event plan times to form a strategy, and different combinations constitute different strategies. Example

[0056] This embodiment includes all the contents of the first embodiment, and provides an industrial park energy efficiency optimization digital twin system based on a large model algorithm, including a data acquisition module, a digital twin module, an energy efficiency calculation module, and a simulation optimization module;

[0057] The data acquisition module is used to collect operation information in the park, the digital twin module builds a digital twin model according to the operation information, the energy efficiency calculation module is used to calculate the energy efficiency information of the operation, and the simulation optimization module is used to simulate the operation of the digital twin module and give process optimization suggestions;

[0058] Combination Figure 2 The data acquisition module includes a job monitoring unit, an information collection unit and a data sorting unit. The job monitoring unit is used to monitor the running status of each job, the information collection unit is used to collect data information when the job is running, and the data sorting unit is used to classify and sort the collected data information;

[0059] The digital twin module includes an operation basic unit, an operation linkage unit, a data storage unit and a real-time display unit. The operation basic unit is used to record the basic data of each operation, the operation linkage unit is used to record the linkage relationship and linkage data between operations, the data storage unit is used to save the collected data information, and the real-time display unit is used to display real-time operation information;

[0060] Basic data refers to the data information of the smallest operation in the industrial park. Different industrial parks have different operation contents and corresponding basic data. For example, the data information of the smallest operation such as producing a certain part or processing a certain part is the basic data.

[0061] The linkage relationship refers to the process-adjacent relationship between the smallest operations. For example, the production of a part A and the further processing of part A are the two smallest operations that have a linkage relationship.

[0062] Linkage data refers to the data relationship between the smallest operations with linkage relationship. For example, the linkage relationship between the three smallest operations of producing part A, producing part B, and combining part A and part B into part C, and a part C is composed of two parts A and one part B. 2:1:1 is the linkage data of these three smallest operations.

[0063] Combination Figure 3 The energy efficiency calculation module includes an energy consumption calculation unit, an efficiency calculation unit and a comprehensive calculation unit. The energy efficiency calculation unit is used to calculate the energy consumption information of the operation, the efficiency calculation unit is used to calculate the efficiency information of the operation, and the comprehensive calculation unit is used to calculate the energy efficiency information of the entire park;

[0064] Combination Figure 4 The simulation optimization module includes a model building unit, a simulation operation unit and an optimization output unit. The model building unit builds a data conversion model based on historical data. The simulation operation unit simulates the operation of jobs under different strategies based on real-time data and the data conversion model. The optimization output unit is used to select the strategy with the best energy efficiency as the optimization solution;

[0065] The operation basic unit includes a model data register and a twin mapping processor, wherein the model data register is used to store a prototype frame of each operation, and the twin mapping processor is used to assign component numbers and parameters to the prototype frame to obtain a digital operation component corresponding to the actual operation;

[0066] The operation linkage unit includes a linkage relationship register and an event-driven processor, wherein the linkage relationship register is used to store the linkage relationship between the digital operation elements, and the event-driven processor is used to record the data parameters of each linkage event;

[0067] The data storage unit includes a historical data register, a real-time data register and a packaging and archiving processor, wherein the historical data register is used to store historical operation data, the real-time data register is used to store real-time operation data, and the packaging and archiving processor is used to package the data in the real-time data register into the historical data register and to archive the data in the historical data register;

[0068] The real-time display unit includes an operation layout processor and a data conversion processor. The operation layout processor is used to form a park base disk with each digital operation element in the form of a corresponding image for display. The data conversion processor is used to convert the data in the real-time data register into a picture form and then overlay and display it on the park base disk.

[0069] The energy consumption calculation unit includes a relative energy consumption conversion processor and a component integration processor, wherein the relative energy consumption conversion processor is used to convert a single type of energy consumption into a relative energy consumption value, and the component integration processor is used to obtain all types of energy consumption data of the digital operation component when completing an event and calculate the event energy consumption value;

[0070] The relative conversion processor calculates the relative energy consumption value Er according to the following formula:

[0071] ;

[0072] Among them, k is the conversion coefficient, and Ea is the absolute energy consumption;

[0073] The component integrated processor calculates the event energy consumption value Ee according to the following formula:

[0074] ;

[0075] Where n is the number of energy consumption types, Er(i) represents the relative energy consumption value of the i-th type;

[0076] The efficiency calculation unit includes a value conversion processor and a value-added efficiency processor, wherein the value conversion processor is used to calculate the value of each object, and the value-added efficiency processor is used to calculate the value-added efficiency of the digital operation element within a specific time period;

[0077] The value conversion processor calculates the value V of the object according to the following formula:

[0078] ;

[0079] Where m is the number of objects, is the unit value of the object;

[0080] It is important to note that objects can be both physical items and virtual services;

[0081] The value-added efficiency processor calculates the value-added efficiency Vae of the digital operation element according to the following formula:

[0082] ;

[0083] in, represents the value of the i-th type of object invested, represents the value of the i-th type of object produced, n1 is the number of object types input, n2 is the number of object types produced, and T is the length of a specific time period;

[0084] The comprehensive calculation unit includes an event matching processor and an energy efficiency calculation processor. The event matching processor is used to set a specific time period and match and obtain the event energy consumption value within the specific time period. The energy efficiency calculation processor calculates the energy efficiency index of the entire park within the specific time period based on the matching information.

[0085] The energy efficiency calculation processor calculates the energy efficiency index P of the entire park according to the following formula:

[0086] ;

[0087] Where Vae(i) represents the value-added efficiency of the i-th digital operating element, Ee(i, j) represents the energy consumption value of the j-th event of the i-th digital operating element, N is the number of digital operating elements, M i is the number of events of the ith digital operation element in a specific time period, a is the efficiency weight, and b is the energy consumption weight;

[0088] The model building unit includes a material acquisition processor, a data fitting processor and a model generation processor, wherein the material acquisition processor is used to collect parameter data in the operation basic unit and the operation linkage unit and historical operation data in the historical data register, the data fitting processor is used to perform fitting processing on the historical operation data to obtain a fitting function, and the model generation processor generates a data conversion model based on the parameter data and the fitting function;

[0089] Historical operation data refers to the data information of each minimum operation during the actual operation of the industrial park. For example, the two minimum operations of producing part A and transporting part A. When the number of produced part A reaches a target value, the produced part A will be packaged and transported. This target value may be different during the actual operation process and will be recorded as historical data.

[0090] The target values ​​in the historical data correspond to different efficiencies and energy consumptions. By fitting the discrete points formed by these target values ​​and efficiency and energy consumption into a curve, the functional relationship between the target value and efficiency and energy consumption can be obtained, which is called the fitting function. The data conversion model is composed of fitting functions between multiple minimum operations with linkage relationships.

[0091] There are many ways to fit discrete points into curves in the prior art, which will not be described in detail here;

[0092] The simulation operation unit includes a strategy generation processor and a model execution processor, wherein the strategy generation processor is used to obtain real-time data and formulate future events, and the model execution processor is used to use the real-time data and future events as inputs of a data conversion model and start the data conversion model;

[0093] The data information output by the data conversion model is the absolute energy consumption of the event and the change in the number of objects;

[0094] Combination Figure 5 , the optimization output unit includes a calculation calling processor, a result cache processor and an output control processor, the calculation calling processor is used to call the energy efficiency calculation module to obtain the energy efficiency index, the result cache processor is used to store the energy efficiency indexes of different strategies, and the output control processor is used to select the strategy with the largest energy efficiency index and output the recommended instructions for future events to the real-time display unit;

[0095] Jobs are divided into continuous jobs and instant jobs. The future events formulated by the strategy generation processor include the start of an instant job or the switching of a continuous job.

[0096] The strategy generation processor obtains approximate data from historical data based on real-time data, and then selects sample data with a higher energy efficiency index from the approximate data, and formulates multiple strategies based on the sample data;

[0097] The strategy generation processor calculates the limit time t of future events in the strategy according to the following formula:

[0098] ;

[0099] Among them, N s is the number of sample data, P max is the maximum energy efficiency index in the sample data, t max is the time of the corresponding event in the maximum energy efficiency index sample data, P i is the energy efficiency index of the i-th sample data, t i is the interval between corresponding events in the i-th sample data;

[0100] The strategy generation processor is in [t, t max ] or [t max , t] uniformly takes the value as the plan time corresponding to the future event, and the strategy generation processor combines different future event plan times to form a strategy, and different combinations constitute different strategies;

[0101] The i and j appearing in the above text are both ordinal numbers used to indicate serial numbers.

[0102] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A digital twin system for energy efficiency optimization of industrial parks based on a large model algorithm, characterized in that: It includes data acquisition module, digital twin module, energy efficiency calculation module and simulation optimization module; The data acquisition module is used to collect operation information in the park, the digital twin module builds a digital twin model according to the operation information, the energy efficiency calculation module is used to calculate the energy efficiency information of the operation, and the simulation optimization module is used to simulate the operation of the digital twin module and give process optimization suggestions; The data acquisition module includes a job monitoring unit, an information collection unit and a data sorting unit. The job monitoring unit is used to monitor the running status of each job, the information collection unit is used to collect data information when the job is running, and the data sorting unit is used to classify and sort the collected data information; The digital twin module includes an operation basic unit, an operation linkage unit, a data storage unit and a real-time display unit. The operation basic unit is used to record the basic data of each operation, the operation linkage unit is used to record the linkage relationship and linkage data between operations, the data storage unit is used to save the collected data information, and the real-time display unit is used to display real-time operation information; The energy efficiency calculation module includes an energy consumption calculation unit, an efficiency calculation unit and a comprehensive calculation unit. The energy consumption calculation unit is used to calculate the energy consumption information of the operation, the efficiency calculation unit is used to calculate the efficiency information of the operation, and the comprehensive calculation unit is used to calculate the energy efficiency information of the entire park; The simulation optimization module includes a model building unit, a simulation operation unit and an optimization output unit. The model building unit builds a data conversion model based on historical data. The simulation operation unit simulates the operation of jobs under different strategies based on real-time data and the data conversion model. The optimization output unit is used to select the strategy with the best energy efficiency as the optimization solution. The energy consumption calculation unit includes a relative energy consumption conversion processor and a component integration processor, wherein the relative energy consumption conversion processor is used to convert a single type of energy consumption into a relative energy consumption value, and the component integration processor is used to obtain all types of energy consumption data of the digital operation component when completing an event and calculate the event energy consumption value; The efficiency calculation unit includes a value conversion processor and a value-added efficiency processor, wherein the value conversion processor is used to calculate the value of each object, and the value-added efficiency processor is used to calculate the value-added efficiency of the digital operation element within a specific time period; The value-added efficiency processor calculates the value-added efficiency Vae of the digital operation element according to the following formula: ； in, represents the value of the i-th type of object invested, represents the value of the i-th type of object produced, n1 is the number of object types input, n2 is the number of object types produced, and T is the length of a specific time period; The comprehensive calculation unit includes an event matching processor and an energy efficiency calculation processor. The event matching processor is used to set a specific time period and match and obtain the event energy consumption value within the specific time period. The energy efficiency calculation processor calculates the energy efficiency index of the entire park within the specific time period based on the matching information. The energy efficiency calculation processor calculates the energy efficiency index P of the entire park according to the following formula: ； Where Vae(i) represents the value-added efficiency of the i-th digital operating element, Ee(i, j) represents the energy consumption value of the j-th event of the i-th digital operating element, N is the number of digital operating elements, M i is the number of events of the ith digital operation element in a specific time period, a is the efficiency weight, and b is the energy consumption weight; The simulation operation unit includes a strategy generation processor and a model execution processor, wherein the strategy generation processor is used to obtain real-time data and formulate future events, and the model execution processor is used to use the real-time data and future events as inputs of a data conversion model and start the data conversion model; The strategy generation processor calculates the limit time t of future events in the strategy according to the following formula: ； Among them, N s is the number of sample data, P max is the maximum energy efficiency index in the sample data, t max is the time of the corresponding event in the maximum energy efficiency index sample data, P i is the energy efficiency index of the i-th sample data, t i is the time of the corresponding event in the i-th sample data; The strategy generation processor is in [t, t max ] or [t max , t] uniformly takes the value as the plan time corresponding to the future event. The strategy generation processor combines different future event plan times to form a strategy, and different combinations constitute different strategies.

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