An energy-saving and carbon-reducing management and control system based on intelligent technology

Abstract

The application discloses an energy-saving and carbon-reducing management and control system based on intelligent technology, and a method thereof comprises the following steps: acquiring energy-saving data of each device in real time, calculating carbon emission of each device according to each energy-saving data; acquiring carbon emission plan and characteristic curve of each device, determining optimal carbon emission of target factory; determining target power distribution plan of each device of target factory in future preset time period according to optimal carbon emission; receiving plan execution data of each device, calculating actual carbon emission and actual carbon-saving amount of each device according to plan execution data and carbon emission of each device; according to each device ID and corresponding actual carbon emission and actual carbon-saving amount, adding attribute of data object of each device in data twin model of target factory, so as to display actual carbon-saving amount and actual carbon emission of each device of target factory in real time through data object in data twin model in data twin module.

Description

Technical Field

[0001] This invention relates to the field of energy-saving and carbon-reducing management systems, and in particular to an energy-saving and carbon-reducing management system based on intelligent technology. Background Technology

[0002] The energy-saving and carbon-reduction management system is a closed-loop management system that integrates intelligent technologies to monitor, analyze, optimize, control, and trace the entire process of energy consumption and carbon emissions. Its core objective is to achieve the dual goals of "reducing energy consumption and reducing carbon emissions" through precise management and control, while ensuring production / operation efficiency. It is widely used in industrial enterprises, smart parks, commercial buildings, transportation hubs, and other scenarios, and is a core technological tool for achieving the "dual carbon" goal. Currently, factories typically ensure the normal operation of motors and frequency converters by regularly maintaining and inspecting them. They also control energy consumption and carbon emissions by reducing the operating power of motors and frequency converters during specific periods or by decreasing their operating time when the load is low. However, these methods are performed periodically and cannot monitor the operating status of motors and frequency converters in real time, easily leading to fluctuations in energy consumption and carbon emissions. To address these issues, some factories have implemented real-time monitoring of motors and frequency converters by introducing automation systems, such as PLCs (Programmable Logic Controllers), to control the start-up, shutdown, and operating parameters of motors and frequency converters. However, the feedback mechanism of PLCs is usually quite simple, relying mainly on limited data collected by sensors. Furthermore, PLCs can only execute simple preset tasks, such as starting and stopping the operation of motors and frequency converters during specific periods or reducing their operating time when the load is low. While these preset tasks can reduce energy consumption to some extent, they often cannot be dynamically adjusted according to the actual operating status of motors and frequency converters due to the lack of real-time data support. Therefore, their effect on energy saving and carbon reduction is limited. To address this, we propose an energy-saving and carbon-reducing management system based on intelligent technology. Summary of the Invention

[0003] The main objective of this invention is to provide an energy-saving and carbon-reducing management system based on intelligent technology, which can effectively solve the problems in the background technology. To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, an energy-saving and carbon-reduction management system based on intelligent technology is provided, which is applied to a server. The server is equipped with a comprehensive management platform, which includes a data twin module. The comprehensive management platform and the energy-saving management cloud control platform transmit data through a preset interface. The method includes: acquiring energy-saving data of each device in the target factory in real time through the energy-saving management cloud control platform, and calculating the carbon emissions of each device based on the energy-saving data of each device. The carbon emission plan of the target factory and the characteristic curve of each of the devices are obtained, and the optimal carbon emission of the target factory is determined based on the carbon emission plan and the characteristic curve of each of the devices using the first AI processing logic. Using a second AI processing logic, a target power allocation plan for each piece of equipment in the target factory is determined based on the optimal carbon emission amount for a future preset time period. The target power allocation plan is then sent to the energy-saving management cloud control platform, which generates equipment control instructions for each piece of equipment based on the target power allocation plan and issues the equipment control instructions to the corresponding equipment. In response to receiving the planned execution data of each device returned by the energy-saving management cloud control platform, the actual carbon emissions and actual carbon savings of each device in the target factory are calculated based on the planned execution data and the carbon emissions of each device. Obtain the device ID of each device, and based on the device ID of each device, the corresponding actual carbon emissions, and the corresponding actual carbon savings, add attributes of the data object of each device in the data twin model of the target factory, so as to display the actual carbon savings and actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module. In one possible implementation of the first aspect, the first AI processing logic includes: determining the operating range and upper limit carbon emissions of each device based on the carbon emission plan and the characteristic curve of each device; Construct a multi-objective function, wherein the multi-objective function includes a function to minimize total carbon emissions, a function to maximize equipment efficiency, and a function to minimize energy consumption cost, wherein the function to minimize total carbon emissions is the primary objective function, and the function to maximize equipment efficiency and the function to minimize energy consumption cost are secondary objective functions; The operating range of each device and the upper limit carbon emission of the target factory are both used as the first constraints of the main objective function, and the main objective function is solved using a preset first solution algorithm to obtain the first total carbon emission. The secondary objective function is used as the second constraint condition of the main objective function, and the main objective function is solved using a preset second solution algorithm to obtain the second total carbon emissions. The minimum of the first total carbon emissions and the second total carbon emissions is taken as the optimal carbon emissions for the target plant. In another possible implementation of the first aspect, the step of using the secondary objective function as a second constraint condition of the primary objective function and solving the primary objective function using a preset second solution algorithm to obtain the second total carbon emissions includes: converting the secondary objective function into a second constraint condition, wherein the second constraint condition includes that the solution of the objective function for maximizing equipment efficiency is greater than or equal to a preset minimum efficiency, and the solution of the objective function for minimizing energy consumption cost is less than or equal to a preset maximum cost. The main objective function with the second constraint condition is solved by a preset second solution algorithm, and the minimum energy consumption cost objective function is solved by a preset optimization algorithm, so as to optimize the expected energy consumption of each device and minimize the value of the main objective function. The minimum value of the main objective function is taken as the second total carbon emissions. In another possible implementation of the first aspect, the second AI processing logic includes: obtaining the predicted electricity demand and grid load for a future preset time period; Based on the predicted electricity demand and the power grid load, the future preset time period is divided into peak period and / or off-peak period; The target power allocation plan for each of the devices in the target plant during the peak and / or off-peak periods is determined based on the optimized expected energy consumption of each device and the optimal carbon emissions. In another possible implementation of the first aspect, determining the target power allocation plan for each of the devices in the target factory during the peak and / or off-peak periods based on the optimized expected energy consumption of each device and the optimal carbon emission includes: obtaining electricity price information for the peak and off-peak periods if the future preset time period includes the peak and off-peak periods; The power demand of each device during the peak and off-peak periods is predicted based on a pre-built regression model. Based on the electricity price information, the electricity demand of each device during peak and off-peak periods, the optimized expected energy consumption of each device, and the optimal carbon emission level, a total cost minimization objective function is constructed. The total cost minimization includes minimizing electricity cost and minimizing carbon emission cost. The third constraint of the total cost minimization objective function includes that the electricity allocated to each device is greater than or equal to the corresponding electricity demand, the expected energy consumption of each device is less than or equal to the allocated electricity, and the total carbon emission generated by the electricity allocated to all devices is less than or equal to the optimal carbon emission level. The objective function of minimizing total cost is solved using a preset genetic algorithm. The power allocation plan that satisfies all the third constraints and achieves the optimal solution of the objective function of minimizing total cost is taken as the target power allocation plan. In another possible implementation of the first aspect, the plan execution data includes the power consumption value of each of the devices, and the calculation of the actual carbon emissions and actual carbon savings of each of the devices in the target factory based on the plan execution data and the carbon emissions of each of the devices includes: Obtain the carbon emission factor for each of the aforementioned devices; For each of the devices, the product of the corresponding carbon emission factor and the corresponding power consumption value is taken as the actual carbon emission of each of the devices in the target factory. The difference between the carbon emissions of each device and the corresponding actual carbon emissions is taken as the actual carbon saving amount of each device in the target factory. In another possible implementation of the first aspect, the step of adding attributes of a data object for each device in the data twin model of the target factory based on the device ID of each device and the corresponding actual carbon emissions and the corresponding actual carbon savings, so as to display the actual carbon savings and actual carbon emissions of each device in the target factory in real time through the data object in the data twin model in the data twin module, includes: searching for the corresponding data object in the data twin model based on the device ID; The actual carbon emissions and actual carbon savings corresponding to the device ID are added as new attributes to the attributes of the data object to update the data object of the data twin model; The updated data objects are synchronized to the database of the data twin model; In response to receiving a query command corresponding to the device ID, the updated data object corresponding to the device ID is retrieved from the database, and the updated data object is displayed in the visualization interface of the data twin model to display the actual carbon emissions and the actual carbon savings corresponding to the device ID in real time. In another possible implementation of the first aspect, the method further includes: in response to receiving an interaction request from the associated platform to the integrated management platform, obtaining the version number of the associated platform; Using a preset version control logic, the version number is routed to the corresponding API interface of the integrated management platform. Secondly, this application provides a server, including: a memory configured to store instructions; and a processor configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned AI-assisted energy-saving and carbon-reduction control method. Thirdly, this application provides an integrated management platform, which includes a data twin module. The integrated management platform and the energy-saving management cloud control platform transmit data through a preset interface. It also includes a first data acquisition module, which is used to acquire energy-saving data of each device in the target factory in real time through the energy-saving management cloud control platform, and calculate the carbon emissions of each device based on the energy-saving data of each device. The first AI calculation module is used to obtain the carbon emission plan of the target factory and the characteristic curve of each of the devices, and to use the first AI processing logic to determine the optimal carbon emission of the target factory based on the carbon emission plan and the characteristic curve of each of the devices. The second AI calculation module is used to use the second AI processing logic to determine the target power allocation plan for each of the devices in the target factory in the future preset time period based on the optimal carbon emission, and send the target power allocation plan to the energy-saving management cloud control platform, so that the energy-saving management cloud control platform generates equipment control instructions for each device according to the target power allocation plan, and issues the equipment control instructions to the corresponding devices. The second data acquisition module is used to respond to receiving the planned execution data of each of the devices returned by the energy-saving management cloud control platform, and to calculate the actual carbon emissions and actual carbon savings of each of the devices in the target factory based on the planned execution data and the carbon emissions of each of the devices. The display module is used to obtain the device ID of each device, and based on the device ID of each device and the corresponding actual carbon emissions and the corresponding actual carbon savings, add attributes of the data object of each device in the data twin model of the target factory, so as to display the actual carbon savings and the actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module. In summary, due to the adoption of the above technical solution, the beneficial effects of this application are: Through the aforementioned technical solution, the energy-saving data of each piece of equipment in the target factory is acquired in real time via the energy-saving management cloud control platform, and the carbon emissions of each piece of equipment are calculated. This effectively ensures real-time monitoring of equipment operating status, enabling timely updates of energy consumption and carbon emission data and avoiding the lag of traditional periodic maintenance and inspection methods. With the support of real-time data, the energy consumption and carbon emissions of equipment can be assessed more accurately. Using the first AI processing logic, the optimal carbon emission level for the target factory is determined based on the target factory's carbon emission plan and the characteristic curves of each piece of equipment. This optimal carbon emission level comprehensively considers the operating characteristics of the equipment and the factory's carbon emission targets, ensuring both the achievement of the target factory's carbon emission goals and the maintenance of equipment operating efficiency, preventing excessive energy saving from impacting production efficiency. Furthermore, using the second AI processing logic, the target power allocation plan for each piece of equipment in the target factory is determined based on the optimal carbon emission level for a preset time period in the future. This plan is then sent to the energy-saving management cloud control platform, enabling dynamic adjustment of equipment energy consumption. This allows equipment operating parameters to be optimized according to actual needs. Through intelligent allocation by AI, energy consumption and carbon emissions can be minimized while ensuring production needs are met, achieving the dual goals of energy conservation and carbon reduction. Based on the planned execution data of each device returned by the energy-saving management cloud control platform, real-time evaluation of energy-saving and carbon-reduction effects can be achieved. Finally, the device ID of each device is obtained, and based on the device ID and the corresponding actual carbon emissions and actual carbon savings, attributes of each device's data object are added to the data twin model of the target factory. This allows for real-time display of the actual carbon savings and emissions of each device in the target factory through the data twin model within the data twin module, achieving a visual representation of energy-saving and carbon-reduction effects. This enables factory managers to intuitively understand the equipment's operating status and energy-saving and carbon-reduction effects. The application of data twin technology enables factories to achieve more efficient energy-saving and carbon-reduction management. In summary, the above technical solution enables real-time monitoring and dynamic adjustment of equipment energy consumption and carbon emissions, ultimately effectively controlling equipment energy consumption and carbon emissions. It not only solves the problem of lag in traditional periodic maintenance and inspection methods but also overcomes the limitations of PLC feedback mechanisms, which are simple and lack real-time data support. Through the application of AI intelligent computing and data twin technology, more accurate decision support can be provided, helping factories achieve more efficient energy-saving and carbon-reduction management. Ultimately, through AI-assisted energy-saving and carbon-reduction control methods, real-time monitoring and dynamic adjustment of equipment energy consumption and carbon emissions are achieved, which can effectively control the energy consumption and carbon emissions of the equipment. Attached Figure Description Figure 1 A first process diagram of an energy-saving and carbon-reducing management system based on intelligent technology is provided for an embodiment of this application; Figure 2A schematic diagram of the overall process of an energy-saving and carbon-reducing management system based on intelligent technology is provided for embodiments of this application; Figure 3 This is a flowchart illustrating the data interface transmission process of an integrated management platform provided in an embodiment of this application. Detailed Implementation To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Figure 1 The illustration shows a flowchart of an energy-saving and carbon-reducing management system based on intelligent technology according to an embodiment of this application. Figure 1 As shown in the figure, this application embodiment provides an energy-saving and carbon-reduction management system based on intelligent technology, which is applied to a server. The server is equipped with a comprehensive management platform, which includes a data twin module. The comprehensive management platform and the energy-saving management cloud control platform transmit data through a preset interface. The method may include the following steps. S110. Obtain energy-saving data of each piece of equipment in the target factory in real time through the energy-saving management cloud control platform, and calculate the carbon emissions of each piece of equipment based on the energy-saving data of each piece of equipment; S120: Obtain the carbon emission plan of the target factory and the characteristic curve of each piece of equipment, and use the first AI processing logic to determine the optimal carbon emission of the target factory based on the carbon emission plan and the characteristic curve of each piece of equipment. S130. Using the second AI processing logic, determine the target power allocation plan for each piece of equipment in the target factory in the future preset time period based on the optimal carbon emission, and send the target power allocation plan to the energy management cloud control platform, so that the energy management cloud control platform can generate equipment control instructions for each piece of equipment according to the target power allocation plan, and issue the equipment control instructions to the corresponding equipment. S140. In response to receiving the planned execution data of each device returned by the energy-saving management cloud control platform, calculate the actual carbon emissions and actual carbon savings of each device in the target factory based on the planned execution data and the carbon emissions of each device. S150. Obtain the device ID of each device, and based on the device ID of each device and the corresponding actual carbon emissions and actual carbon savings, add the attributes of the data object of each device in the data twin model of the target factory, so as to display the actual carbon savings and actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module. In this embodiment, the energy-saving management cloud control platform acquires energy-saving data in real time from each piece of equipment in the target factory through a preset interface. This energy-saving data includes the equipment's operating status, power consumption, and operating time; the equipment includes motors and frequency converters. The carbon emissions of each piece of equipment can be calculated using this data. Specifically, the carbon emission calculation formula is typically based on the equipment's power consumption and operating time, combined with a carbon emission coefficient. For example, if a piece of equipment consumes 1 kilowatt-hour (kWh) of electricity in a certain unit of time, equivalent to consuming 0.404 kg of standard coal, and the carbon emission per kWh is 0.272 kg, then the carbon emissions generated by that equipment in that unit of time are 0.272 kg. In this way, the carbon emissions of each piece of equipment can be monitored in real time, providing data support for subsequent energy-saving and carbon-reduction decisions. The integrated management platform acquires the target factory's carbon emission plan and the characteristic curves (including no-load and load characteristic curves) of each piece of equipment. These characteristic curves describe the energy consumption characteristics of the equipment under different load conditions. The first AI processing logic can calculate the optimal carbon emission level for the target factory based on the equipment's energy consumption characteristics under different load conditions and the factory's carbon emission plan. Specifically, the first AI processing logic can analyze the equipment's characteristic curves, identify changes in energy consumption under different load conditions, and, combined with the factory's carbon emission targets, calculate an optimal carbon emission level that meets production needs while minimizing carbon emissions. For example, if the factory's goal is to reduce carbon emissions by 10% within the next month, the first AI processing logic can calculate a new carbon emission target based on the equipment's characteristic curves and current operating data, ensuring that emission reduction targets are achieved while meeting production needs. The second AI processing logic determines the target power allocation plan for each piece of equipment in the target factory over a preset time period based on optimal carbon emission levels. This target power allocation plan details the power distribution for each piece of equipment across different time periods to ensure that equipment operation meets optimal carbon emission requirements. For example, if a piece of equipment consumes more energy during peak hours, the second AI processing logic will suggest reducing its operating time or power during peak hours to reduce carbon emissions. Once generated, the target power allocation plan can be sent to the energy management cloud control platform via a preset interface. The energy management cloud control platform can then generate corresponding equipment control instructions based on the power allocation plan and distribute them to the corresponding equipment. For example, the energy management cloud control platform might generate an instruction requiring a piece of equipment to reduce its operating power during a specific time period to reduce carbon emissions. Once the energy-saving management cloud control platform receives the planned execution data from each device, the integrated management platform calculates the actual carbon emissions and actual carbon savings for each device in the target factory based on this data and the carbon emissions of each device. Specifically, the platform compares the planned execution data with the actual operating data to calculate the actual carbon emissions of each device after executing the target electricity allocation plan. Then, by comparing the actual carbon emissions with the planned carbon emissions, it calculates the actual carbon savings for each device. For example, if a device's actual carbon emissions are 20 kg less than the planned carbon emissions after executing the plan, then the actual carbon savings for that device are 20 kg. In this way, the integrated management platform can monitor the energy-saving and carbon-reduction effects of each device in real time. After obtaining the actual carbon emissions and carbon savings of each piece of equipment, the integrated management platform acquires the equipment ID for each device. Based on the equipment ID and the corresponding actual carbon emissions and carbon savings, it adds attributes to the data object of each device in the data twin model of the target factory. These attributes include the actual carbon emissions and carbon savings of the equipment, as well as other relevant operational data. Through the data twin model in the data twin module, the data object can display the actual carbon savings and emissions of each piece of equipment in the target factory in real time. For example, the data twin model can show that a certain piece of equipment has an actual carbon emission of 50 kg and an actual carbon savings of 10 kg over a certain period, allowing factory managers to intuitively understand the operating status and energy-saving and carbon-reduction effects of each piece of equipment. In practical implementation, the integrated management platform can also perform fault detection on the motor. Specifically, temperature sensors are pre-installed on the motor windings and front and rear bearings, and vibration sensors are installed on the motor housing. Based on these sensors, the motor temperature, the front and rear bearing temperatures, and the motor vibration status can be obtained. In addition, the motor's operating status can be acquired, and motor parameters (such as current and voltage) can be collected through the frequency converter. The above data (motor temperature, front and rear bearing temperatures, vibration status, operating status, and motor parameters) are sent to the integrated management platform. The AI ​​algorithm pre-installed in the integrated management platform can determine whether the motor is faulty based on the above data and issue an early warning when a motor fault occurs. "Motor 1 temperature abnormal 2024 / 09 / 08" indicates that the temperature of Motor 1 is abnormal and a fault may occur, reminding operators to check and maintain it. To further improve management efficiency and operational intuitiveness, the integrated management platform incorporates a BIM model, enabling real-time visual monitoring of motor position and status. The specific implementation steps are as follows: 1. BIM Model Import: Import the BIM model of the factory or equipment into the integrated management platform to ensure that each motor in the model has a unique identifier. 2. Data Mapping: Map the real-time collected motor data (such as temperature, vibration, operating status, etc.) to the motor identifiers in the BIM model to ensure that the status information of each motor can be updated in the BIM model in real time. 3. Visual Interface: On the visual interface of the integrated management platform, operators can intuitively see the location, current status (such as normal, abnormal temperature, abnormal vibration, etc.) and related operating data (such as temperature curve, vibration frequency, etc.) of each motor through the BIM model. The integrated management platform enables real-time data retrieval. Specifically, operators can directly access real-time data for a specific motor, such as temperature and vibration status, through the BIM model without switching to other interfaces. When a motor malfunctions, the corresponding motor icon in the BIM model will be highlighted, and a warning message will pop up to remind operators to take timely action. By integrating motor condition monitoring with BIM models, the integrated management platform can not only provide early warnings of motor faults, but also offer an intuitive and real-time visual monitoring interface, greatly improving the operation and maintenance efficiency and safety of industrial equipment. The following example uses the target factory, whose data twin model (BIM model) has been imported into the integrated management platform. The BIM model contains the factory layout diagram, and each motor has a unique identifier in the model (such as "Motor 1", "Motor 2", etc.). Each motor's sensor data (such as temperature, vibration, and operating status) is mapped to a motor identifier in the BIM model. For example, the temperature sensor data for "Motor 1" is associated with the "Motor 1" icon in the BIM model. Operators log into the integrated management platform and access the BIM visualization interface. Clicking the "Motor 1" icon in the BIM model brings up a detailed information window. The window displays the current status of "Motor 1": Temperature: 95℃ (abnormal), Vibration: 0.5mm / s (abnormal), Operating Status: Running. When the temperature and vibration data of "Motor 1" are abnormal, the "Motor 1" icon in the BIM model will be highlighted, and a warning message will pop up: "Motor 1 Temperature Abnormal 2024 / 09 / 08 14:30". After receiving the warning message, operators can click on it to view detailed abnormal data for further analysis and processing. Operators can directly access historical data for "Motor 1" through the BIM model, viewing historical temperature and vibration data to analyze the causes of anomalies. All real-time data and warning information for all motors are recorded in the integrated management platform's database, facilitating subsequent analysis and report generation. Figure 2 This application provides an embodiment of an energy-saving and carbon-reduction management system based on intelligent technology, illustrated in the following diagram. Figure 2As shown, the integrated management platform can interact with the energy-saving management cloud control platform, and at the same time, it can use AI to determine the optimal carbon emission and power allocation plan, and return the power allocation plan to the energy-saving management cloud control platform for execution. This enables real-time monitoring and dynamic adjustment of equipment energy consumption and carbon emissions, helping factories achieve more efficient energy-saving and carbon reduction management. This embodiment acquires real-time energy-saving data for each piece of equipment in the target factory through an energy-saving management cloud control platform and calculates the carbon emissions of each piece of equipment. This effectively ensures real-time monitoring of equipment operating status, enabling timely updates of energy consumption and carbon emission data and avoiding the lag of traditional periodic maintenance and inspection methods. With the support of real-time data, the energy consumption and carbon emissions of equipment can be assessed more accurately. Using a first AI processing logic, the optimal carbon emission level for the target factory is determined based on the target factory's carbon emission plan and the characteristic curves of each piece of equipment. This comprehensively considers the operating characteristics of the equipment and the factory's carbon emission targets, resulting in the optimal carbon emission level. While achieving the target factory's carbon emission targets, it also ensures the operating efficiency of the equipment, avoiding the impact on production efficiency due to excessive energy saving. Furthermore, using a second AI processing logic, the target power allocation plan for each piece of equipment in the target factory is determined based on the optimal carbon emission level for a preset time period in the future. This plan is then sent to the energy-saving management cloud control platform, enabling dynamic adjustment of equipment energy consumption. This allows the operating parameters of the equipment to be optimized according to actual needs. Through intelligent allocation by AI, energy consumption and carbon emissions can be minimized while ensuring production needs are met, achieving the dual goals of energy saving and carbon reduction. Based on the planned execution data of each device returned by the energy-saving management cloud control platform, real-time assessment of energy-saving and carbon-reduction effects can be achieved. Finally, the device ID of each device is obtained, and based on the device ID and the corresponding actual carbon emissions and actual carbon savings, attributes of each device's data object are added to the data twin model of the target factory. This allows for real-time display of the actual carbon savings and emissions of each device in the target factory through the data twin model within the data twin module, achieving a visual representation of energy-saving and carbon-reduction effects. This enables factory managers to intuitively understand the equipment's operating status and energy-saving and carbon-reduction effects. The application of data twin technology enables factories to achieve more efficient energy-saving and carbon-reduction management. In summary, the above technical solution enables real-time monitoring and dynamic adjustment of equipment energy consumption and carbon emissions, ultimately effectively controlling equipment energy consumption and carbon emissions. It not only solves the problem of lag in traditional periodic maintenance and inspection methods but also overcomes the limitations of PLC feedback mechanisms, which are simple and lack real-time data support. Through the application of AI intelligent computing and data twin technology, more accurate decision support can be provided, helping factories achieve more efficient energy-saving and carbon-reduction management. Ultimately, through AI-assisted energy-saving and carbon-reduction control methods, real-time monitoring and dynamic adjustment of equipment energy consumption and carbon emissions are achieved, which can effectively control the energy consumption and carbon emissions of the equipment. In one embodiment of this example, the first AI processing logic includes the following steps: S210. Based on the carbon emission plan and the characteristic curve of each piece of equipment, determine the operating range and upper limit of carbon emissions for each piece of equipment; S220. Construct a multi-objective function, which includes a function to minimize total carbon emissions, a function to maximize equipment efficiency, and a function to minimize energy consumption cost. The function to minimize total carbon emissions is the primary objective function, while the functions to maximize equipment efficiency and the function to minimize energy consumption cost are secondary objective functions. S230. The operating range of each device and the upper limit of carbon emissions of the target factory are taken as the first constraints of the main objective function, and the main objective function is solved by the preset first solution algorithm to obtain the first total carbon emissions. S240. Using the secondary objective function as the second constraint condition of the main objective function, the main objective function is solved using a preset second solution algorithm to obtain the second total carbon emissions. S250. The minimum of the first total carbon emissions and the second total carbon emissions shall be taken as the optimal carbon emissions for the target plant. In this embodiment, the operating range and upper limit carbon emissions for each piece of equipment are first determined based on the carbon emission plan and the characteristic curve of each device. First, the carbon emission plan provides the factory's carbon emission targets for a specific period, such as monthly or annual carbon emission limits. The characteristic curve describes the energy consumption characteristics of the equipment under different load conditions, including power consumption under no-load and load conditions. Using the carbon emission plan and the characteristic curve of each device, the operating range of each device under different load conditions can be determined. For example, a device's characteristic curve might show a rated power of 200kW and a rated current of 400A, but the actual operating current is between 200A and 400A, and the power consumption is between 100kW and 200kW. Based on this data, the operating range of the device can be determined to be 50% to 100% load. Next, combined with the carbon emission plan, the upper limit carbon emissions for each device are calculated. For example, if the factory's target is to limit monthly carbon emissions to no more than 1000kg, and the device operates for 100 hours in a month, then the upper limit carbon emissions for that device is 10kg / h. In this way, the operating range and upper limit carbon emissions of each device can be determined. To determine the optimal carbon emissions for the target plant, a multi-objective function is first constructed, including a function to minimize total carbon emissions, a function to maximize equipment efficiency, and a function to minimize energy costs. The function to minimize total carbon emissions is the primary objective function, aiming to reduce the plant's total carbon emissions to the greatest extent possible. The functions to maximize equipment efficiency and minimize energy costs are secondary objective functions, aiming to improve equipment operating efficiency and reduce energy costs, respectively. Specifically, the function to minimize total carbon emissions can be expressed as: Where Ci represents the carbon emissions of the i-th device. The objective function for maximizing device efficiency can be expressed as: Where Ei represents the efficiency of the i-th device. The objective function for minimum energy consumption cost can be expressed as: Where Pi represents the energy consumption cost of the i-th device. By constructing the above objective function, it is possible to optimize equipment operating efficiency and energy consumption costs while reducing carbon emissions. The operating range of each piece of equipment and the upper limit of carbon emissions for the target factory are both used as the first constraints of the main objective function. A pre-defined first solution algorithm is then used to solve the main objective function to obtain the first total carbon emissions. Specifically, the operating range and upper limit of carbon emissions for each piece of equipment are first added as constraints to the main objective function. For example, if a piece of equipment operates at 50% to 100% load and has an upper limit of carbon emissions of 10 kg / h, then the constraints can be expressed as follows: 50%≤Li≤100%, Ci≤10kg / h; Here, Li represents the load of the i-th device. Next, a pre-defined first solution algorithm (such as linear programming or genetic algorithm) is used to solve the main objective function to obtain the first total carbon emissions. For example, using a linear programming algorithm, under the premise of satisfying all constraints, a device operation scheme that minimizes total carbon emissions can be found. The secondary objective function is used as a second constraint on the primary objective function. A pre-defined second solution algorithm is then used to solve the primary objective function to obtain the second total carbon emissions. Specifically, the secondary objective functions (maximizing equipment efficiency and minimizing energy cost) are first added as constraints to the primary objective function. For example, if the constraints for maximizing equipment efficiency and minimizing energy cost are as follows: Ei≥Emin, Pi≤Pmax; Here, Emin and Pmax represent the minimum equipment efficiency and the maximum energy cost, respectively. Next, a pre-defined second solution algorithm (such as a multi-objective optimization algorithm) is used to solve the main objective function to obtain the second total carbon emissions. For example, through a multi-objective optimization algorithm, a device operation scheme that minimizes total carbon emissions can be found while satisfying all constraints, simultaneously optimizing equipment efficiency and energy cost. The minimum of the first and second total carbon emissions is taken as the optimal carbon emission level for the target plant. Specifically, the first and second total carbon emissions are compared, and the smaller value is selected as the optimal carbon emission level for the target plant. For example, if the first total carbon emission is 1000 kg and the second total carbon emission is 950 kg, then the optimal carbon emission level is 950 kg. In this way, the equipment operation scheme that minimizes total carbon emissions can be found while satisfying all constraints, simultaneously optimizing equipment efficiency and energy costs. Ultimately, a scientifically sound optimal carbon emission level is obtained, providing decision support for the plant's energy conservation and carbon reduction management. This implementation method achieves multi-objective optimization of equipment energy consumption and carbon emissions, ultimately effectively controlling these two aspects. It not only solves the problem of lag in traditional periodic maintenance and inspection methods but also overcomes the limitations of PLC feedback mechanisms, such as their simplicity and lack of real-time data support. Through the application of AI's intelligent computing and multi-objective optimization technology, more precise decision support can be provided, helping factories achieve more efficient energy-saving and carbon-reduction management. Ultimately, this can significantly reduce the factory's energy consumption and carbon emissions. In one embodiment of this invention, the secondary objective function is used as the second constraint condition of the primary objective function, and a preset second solution algorithm is used to solve the primary objective function to obtain the second total carbon emissions, including the following steps: S310. Transform the secondary objective function into a second constraint condition. The second constraint condition includes that the solution of the objective function for maximizing equipment efficiency is greater than or equal to the preset minimum efficiency, and the solution of the objective function for minimizing energy consumption cost is less than or equal to the preset maximum cost. S320. The preset second solution algorithm is used to solve the main objective function with the second constraint condition, and the preset optimization algorithm is used to solve the minimum energy consumption cost objective function, so as to optimize the expected energy consumption of each device and minimize the value of the main objective function. S330. The value of the minimum main objective function is taken as the second total carbon emissions. In this embodiment, the secondary objective functions are first transformed into secondary constraints. Specifically, the secondary objective functions include a function to maximize equipment efficiency and a function to minimize energy consumption cost. To transform these secondary objective functions into constraints, preset minimum efficiency and maximum cost need to be set. For example, assume the preset minimum efficiency is 90% and the maximum cost is 0.6 yuan per kilowatt-hour. Then, the solution of the objective function to maximize equipment efficiency is greater than or equal to the preset minimum efficiency, and the solution of the objective function to minimize energy consumption cost is less than or equal to the preset maximum cost, are used as the second constraints. Specifically, the second constraints can be expressed as follows: Ei≥0.9, Pi≤0.6; Where Ei represents the efficiency of the i-th device, and Pi represents the energy cost of the i-th device. Next, a pre-defined second solution algorithm is used to solve the main objective function with the second constraint condition, and a pre-defined optimization algorithm is used to solve the minimum energy consumption cost objective function to optimize the expected energy consumption of each device and minimize the value of the main objective function. Specifically, the second constraint condition is first added to the main objective function to form a new optimization problem. Then, the pre-defined second solution algorithm (such as a multi-objective optimization algorithm) is used to solve the new optimization problem to obtain the minimum value of the main objective function. At the same time, a pre-defined optimization algorithm (such as linear programming or a genetic algorithm) is used to solve the minimum energy consumption cost objective function to optimize the expected energy consumption of each device. For example, using a linear programming algorithm, the device operation scheme that minimizes energy consumption cost can be found while satisfying all constraints. In this way, energy consumption costs of equipment can be optimized while reducing carbon emissions. The minimum value of the primary objective function is taken as the second total carbon emission. Specifically, the minimum value of the primary objective function under different constraints is compared, and the minimum value is selected as the second total carbon emission. For example, if the minimum value of the primary objective function under the first constraint is 1000 kg of carbon emissions, and the minimum value under the second constraint is 950 kg of carbon emissions, then the second total carbon emission is 950 kg. In summary, under the premise of satisfying all constraints, the equipment operation scheme that minimizes the total carbon emissions can be found, while optimizing the equipment efficiency and energy consumption costs. Ultimately, a reasonable second total carbon emission is obtained. This implementation method, through the application of AI's intelligent computing and multi-objective optimization technology, can provide more accurate decision support, help factories achieve more efficient energy-saving and carbon-reduction management, significantly reduce the factory's energy consumption and carbon emissions, achieve multi-objective optimization of equipment energy consumption and carbon emissions, and ultimately effectively control the energy consumption and carbon emissions of equipment. In one embodiment of this example, the second AI processing logic includes the following steps: S410. Obtain the predicted electricity demand and grid load for a future preset time period; S420. Based on the predicted electricity demand and grid load, the future preset time period is divided into peak period and / or off-peak period; S430. Determine the target power allocation plan for each device in the target plant during peak and / or off-peak periods based on the optimized expected energy consumption and optimal carbon emissions for each device. In the second AI processing logic, the projected electricity demand and grid load for a preset future time period are first obtained. Specifically, based on historical data and current electricity consumption trends, electricity demand for the near future is predicted. For example, if it is currently summer, it is predicted that daily electricity demand will gradually increase over the next week due to increased air conditioning usage. Simultaneously, the grid load is obtained, including the current load, peak load, and load fluctuations. For instance, the grid may reach peak load between 2 PM and 4 PM daily, while the load is lower between 1 AM and 5 AM. This data provides a comprehensive understanding of the electricity demand and grid load for the preset future time period, laying the foundation for subsequent power allocation planning. Based on predicted electricity demand and grid load, the future time period is divided into peak and / or off-peak periods. Specifically, by first analyzing the predicted electricity demand and grid load data, peak and off-peak periods can be identified. For example, if the forecast shows that both electricity demand and grid load peak between 2 PM and 4 PM each day, this period can be classified as a peak period. Conversely, if the forecast shows that both electricity demand and grid load are low between 1 AM and 5 AM each day, this period can be classified as an off-peak period. In this way, the future time period can be divided into different time segments to facilitate the subsequent determination of the target power allocation plan. Specifically, based on the optimized expected energy consumption and optimal carbon emissions of each piece of equipment, a target power allocation plan for each piece of equipment in the target factory during peak and / or off-peak periods is determined. More specifically, a power allocation plan for each piece of equipment at different time periods can be developed by combining its expected energy consumption and optimal carbon emissions. For example, if a piece of equipment has higher expected energy consumption during peak periods, its operating time or power can be reduced during peak periods to decrease carbon emissions. During off-peak periods, the operating time or power of the equipment can be increased to fully utilize electricity during low-load periods, meeting electricity demand while minimizing carbon emissions and achieving the goal of energy conservation and carbon reduction. This implementation method, through the second AI processing logic, can achieve dynamic adjustment of the target power allocation plan, which helps the target factory achieve more efficient energy-saving and carbon-reduction management, effectively control the energy consumption and carbon emissions of equipment, and thus significantly reduce the factory's energy consumption and carbon emissions. In one embodiment of this example, determining the target power allocation plan for each device in the target factory during peak and / or off-peak periods based on the optimized expected energy consumption and optimal carbon emissions for each device includes the following steps: S510: If the future preset time period includes peak and off-peak periods, obtain the electricity price information for peak and off-peak periods; S520. Based on the pre-built regression model, the power demand of each device during peak and off-peak periods is predicted. S530. Based on electricity price information, the electricity demand of each device during peak and off-peak periods, the optimized expected energy consumption of each device, and the optimal carbon emissions, construct a total cost minimization objective function. Minimizing total cost includes minimizing electricity cost and minimizing carbon emission cost. The third constraint of minimizing total cost objective function includes that the electricity allocated to each device is greater than or equal to the corresponding electricity demand, the expected energy consumption of each device is less than or equal to the allocated electricity, and the total carbon emissions generated by the electricity allocated to all devices are less than or equal to the optimal carbon emissions. S540. Use a preset genetic algorithm to solve the objective function of minimizing the total cost, and take the power allocation plan that satisfies all the third constraints and minimizes the objective function of the total cost to reach the optimal solution as the target power allocation plan. Given a future preset time period that includes both peak and off-peak periods, the system obtains electricity price information for both. Specifically, this information can be obtained from the electricity market or electricity suppliers for different time periods within the preset time period. For example, the peak electricity price might be 0.8 yuan per kilowatt-hour, while the off-peak price might be 0.6 yuan per kilowatt-hour. Then, the pre-built regression model predicts the power demand of each device during peak and off-peak periods. Specifically, historical data and current electricity consumption trends can be used to predict the power demand of each device within a preset time period using the pre-built regression model. For example, if the daily power demand of a certain device has gradually increased over the past week, it is predicted that the power demand of that device will also gradually increase over the next week. In this way, the system can accurately predict the power demand of each device during peak and off-peak periods, providing data support for subsequent power allocation planning. The regression model is built based on historical data, which can include device power consumption records, production plans, weather conditions, etc. The historical data can be used to train machine learning algorithm models to obtain regression models that can predict future power demand based on input features (time, production plans, weather, etc.). For example, for a motor, the model predicts that it will need 80 kWh of power per hour during peak periods (such as weekdays 9:00-17:00) and only 30 kWh per hour during off-peak periods (such as nighttime 22:00-6:00). The above predictions will be made for each piece of equipment in the factory, taking into account the characteristics and usage patterns of the equipment. Based on electricity price information, the peak and off-peak electricity demand of each device, the optimized expected energy consumption of each device, and the optimal carbon emissions, a total cost minimization objective function is constructed. Specifically, minimizing the total cost includes minimizing the electricity cost and minimizing the carbon emission cost. The electricity cost can be calculated using electricity prices and electricity demand, while the carbon emission cost can be calculated using carbon emission amounts and carbon emission coefficients. The third constraint of the total cost minimization objective function includes that the electricity allocated to each device is greater than or equal to the corresponding electricity demand, the expected energy consumption of each device is less than or equal to the allocated electricity, and the total carbon emissions generated by the electricity allocated to all devices are less than or equal to the optimal carbon emissions. Specifically, to ensure the normal operation of the equipment, it should be ensured that each piece of equipment can meet its basic power needs during operation. The first constraint is that the power allocated to each piece of equipment is greater than or equal to its corresponding power demand. To ensure that the actual energy consumption of the equipment does not exceed the allocated power, thus avoiding equipment overload or excessive power consumption and reducing power costs and carbon emissions, a second constraint is established to limit the expected energy consumption of the equipment, ensuring that the expected energy consumption of each piece of equipment is less than or equal to the allocated power. To ensure that the total carbon emissions of the entire factory do not exceed the set optimal carbon emissions, achieving energy conservation and carbon reduction, a third constraint is established: the total carbon emissions generated by the power allocated to all equipment are less than or equal to the optimal carbon emissions. In practice, the first step is to define variables, such as Xij representing the electrical energy allocated to device i during time period j. Then, the objective function is constructed: min(∑(xij×Pj)+α×∑(xij×Ei)); Where Pj is the electricity price in time period j, Ei is the carbon emission factor of equipment i, and α is the weighting coefficient of carbon emission cost. At the same time, three key constraints need to be considered: (1) xij ≥ Dij, where Dij is the predicted electricity demand of equipment i in time period j; (2) Ci ≤ xij, where Ci is the expected energy consumption of equipment i. (3) ∑(xij×Ei)≤Cmax; Cmax represents the optimal carbon emissions. The objective function constructed based on these three constraints considers both economic costs and environmental factors in the decision-making process, thus achieving a balance between economic and environmental benefits. By appropriately setting the weighting coefficient α, the relative importance of economic and environmental objectives can be adjusted, ensuring that the final optimization result better meets the specific needs of enterprises and policy requirements. In summary, it is possible to find the power allocation plan that minimizes the total cost while satisfying all constraints. A pre-defined genetic algorithm is used to solve the objective function of minimizing the total cost. The power allocation plan that satisfies all the third constraints and achieves the optimal solution for minimizing the total cost objective function is taken as the target power allocation plan. Specifically, the genetic algorithm is an optimization algorithm that gradually optimizes the objective function by simulating natural selection and genetic mechanisms. First, the objective function of minimizing the total cost and the third constraints are input into the genetic algorithm. Through multiple iterations, the power allocation plan that satisfies all constraints and minimizes the total cost is found. For example, the genetic algorithm can find the equipment operation scheme that minimizes the total cost while satisfying all constraints. For future preset time periods that only include peak periods or only include off-peak periods, the steps S510-S540 above can also be used. However, during implementation, the chromosome encoding in the genetic algorithm will be simplified. For example, for a problem with 10 devices, if the future preset time period only includes peak periods or only includes off-peak periods, it may only require a 10-dimensional real-number encoded chromosome instead of the previous 240 dimensions, in order to reduce computational complexity and speed up the solution. This implementation method, through AI-powered intelligent calculations and dynamic adjustments to equipment energy consumption and carbon emissions, can effectively control equipment energy consumption and carbon emissions, and provide more precise decision support, helping factories achieve more efficient energy-saving and carbon-reduction management. Ultimately, it can significantly reduce the factory's energy consumption and carbon emissions. In one embodiment of this example, the planned execution data includes the power consumption value, actual operating parameters (power, current, voltage, etc.), and operating time of each device. The actual carbon emissions and actual carbon savings of each device in the target factory are calculated based on the planned execution data and the carbon emissions of each device, including the following steps: S610, Obtain the carbon emission factor for each device; S620. For each piece of equipment, the product of the corresponding carbon emission factor and the corresponding power consumption value shall be used as the actual carbon emission of each piece of equipment in the target factory. S630: The difference between the carbon emissions of each piece of equipment and the corresponding actual carbon emissions shall be used as the actual carbon savings of each piece of equipment in the target factory. First, obtain the carbon emission factor for each device. Specifically, the carbon emission factor is a constant representing the amount of carbon emissions generated per unit of electricity consumed. Carbon emission factors are typically provided by electricity suppliers or relevant agencies and reflect the carbon emissions during the electricity production process. For example, if a region calculates its carbon emissions per kilowatt-hour based on the carbon emission factor as 0.272 kilograms, this means that consuming 1 kilowatt-hour of electricity will generate 0.272 kilograms of carbon emissions. For each piece of equipment, the product of its corresponding carbon emission factor and its corresponding electricity consumption value is taken as the actual carbon emission of each piece of equipment in the target factory. Specifically, the actual carbon emission of each piece of equipment can be calculated based on its electricity consumption value and carbon emission factor. For example, if a piece of equipment consumes 1 kilowatt-hour (kWh) of electricity in a certain unit of time, equivalent to consuming 0.404 kg of standard coal, and the carbon emission per kWh is 0.272 kg, then the actual carbon emission generated by that equipment in that unit of time is 0.272 kg. Using this method, the actual carbon emission of each piece of equipment can be accurately calculated. The difference between the carbon emissions of each piece of equipment and its corresponding actual carbon emissions is taken as the actual carbon saving for each piece of equipment in the target factory. Specifically, the actual carbon saving for each piece of equipment can be calculated by comparing its carbon emissions with the actual carbon emissions. For example, if a piece of equipment has a carbon emission of 60 kg and an actual carbon emission of 50 kg, then the actual carbon saving for that piece of equipment is 10 kg. This implementation method enables real-time monitoring and dynamic adjustment of equipment energy consumption and carbon emissions, ultimately effectively controlling equipment energy consumption and carbon emissions. In one embodiment of this example, based on the device ID of each device and its corresponding actual carbon emissions and actual carbon savings, attributes of the data objects for each device are added to the data twin model of the target factory. This allows the actual carbon savings and actual carbon emissions of each device in the target factory to be displayed in real time through the data objects in the data twin model of the data twin module. The steps include the following: S710. Locate the corresponding data object in the data twin model based on the device ID; S720. Add the actual carbon emissions and actual carbon savings corresponding to the device ID as new attributes to the attributes of the data object to update the data object of the data twin model. S730. Synchronize the updated data objects to the database of the data twin model; S740, in response to receiving a query command corresponding to the device ID, retrieves the updated data object corresponding to the device ID from the database and displays the updated data object in the visualization interface of the data twin model to display the actual carbon emissions and actual carbon savings corresponding to the device ID in real time. In this embodiment, the corresponding data object is located in the data twin model based on the device ID. The data twin model is a virtual factory model containing detailed information about all equipment and systems within the factory. Each device has a corresponding data object in the data twin model, which contains the device's basic information and operational data. The corresponding data object can be located in the data twin model based on the device ID. For example, if a device's device ID is "M12345", the data object with the ID "M12345" can be found in the data twin model. Next, the actual carbon emissions and actual carbon savings corresponding to the device ID are added as new attributes to the data object to update the data twin model's data object. Specifically, the actual carbon emissions and actual carbon savings for each device can be added as new attributes to the corresponding data object. For example, if a device's actual carbon emissions are 50 kg and its actual carbon savings are 10 kg, these two values ​​can be added as new attributes to that device's data object, thus updating the data object for each device in real time and ensuring that the data in the data twin model is always up-to-date. The updated data objects are synchronized to the database of the data twin model. Specifically, the updated data objects can be saved to the database of the data twin model for subsequent querying and analysis. For example, the data object with the ID "M12345", along with its actual carbon emissions and actual carbon savings, can be saved to the database to ensure data consistency and integrity, facilitating subsequent visualization. In response to a query command corresponding to a device ID, the system retrieves the updated data object corresponding to the device ID from the database and displays it in the visualization interface of the data twin model. This allows for real-time display of the actual carbon emissions and carbon savings associated with each device ID. Specifically, when a user or system issues a query command, the updated data object corresponding to the device ID can be retrieved from the database and displayed in the visualization interface of the data twin model. For example, if a user queries the actual carbon emissions and carbon savings of device ID "M12345", the data object can be retrieved from the database, and the visualization interface will show that its actual carbon emissions are 50 kg and its actual carbon savings are 10 kg. In this way, the actual carbon emissions and carbon savings of each device can be displayed in real time, providing intuitive data support for the factory's energy conservation and carbon reduction management. This implementation method can visualize the actual carbon emissions and actual carbon savings of the equipment in a data twin model, enabling factory managers to intuitively understand the operating status of the equipment and the energy-saving and carbon-reduction effects. In one embodiment of this invention, the following steps are also included: S810. In response to receiving an interaction request from the associated platform to the integrated management platform, obtain the version number of the associated platform; S820 uses a preset version control logic to route the version number to the corresponding API interface of the integrated management platform. Figure 3 This application illustrates a data interface transmission flowchart of an integrated management platform according to an embodiment of the present application. Figure 3 As shown, in response to receiving an interaction request from an associated platform to the integrated management platform, the version number of the associated platform is obtained. Specifically, when an associated platform (such as other systems or applications) sends an interaction request to the integrated management platform, its version number can be automatically identified and obtained. A version number is an identifier used to distinguish different versions of software or systems. For example, if the associated platform is an energy management system, its version number might be "V1.2.3". The version number can be extracted from the interaction request, providing a basis for subsequent version control. A pre-defined version control logic is used to route version numbers to the corresponding API interfaces of the integrated management platform. Specifically, the obtained version number can be routed to the corresponding API interface in the integrated management platform according to the pre-defined version control logic. This version control logic typically includes a mapping table that records the API interfaces corresponding to different version numbers. For example, if a related platform with version number "V1.2.3" needs to access a specific function of the integrated management platform, the request can be routed to the corresponding API interface according to the mapping table. This ensures that related platforms of different versions can correctly access the corresponding functions of the integrated management platform, avoiding functional abnormalities or data errors caused by version incompatibility. This implementation achieves version control for the interaction between the associated platform and the integrated management platform, ensuring that different versions of the associated platform can correctly access the corresponding functions of the integrated management platform. It not only solves the system anomalies caused by traditional version incompatibility but also improves system stability and reliability. Through preset version control logic, more precise API interface routing can be provided, helping associated platforms achieve more efficient system integration. This application also provides a server, including: a memory configured to store instructions; and a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the above-described AI-assisted energy-saving and carbon-reduction control method. This application embodiment also provides an integrated management platform, which includes a data twin module. The integrated management platform and the energy-saving management cloud control platform transmit data through a preset interface. It also includes a first data acquisition module 10, which is used to acquire the energy-saving data of each device in the target factory in real time through the energy-saving management cloud control platform, and calculate the carbon emissions of each device based on the energy-saving data of each device. The first AI calculation module 20 is used to obtain the carbon emission plan of the target factory and the characteristic curve of each device, and to use the first AI processing logic to determine the optimal carbon emission of the target factory based on the carbon emission plan and the characteristic curve of each device. The second AI computing module 30 is used to use the second AI processing logic to determine the target power allocation plan for each device in the target factory in the future preset time period based on the optimal carbon emission, and send the target power allocation plan to the energy management cloud control platform so that the energy management cloud control platform can generate equipment control instructions for each device according to the target power allocation plan and issue the equipment control instructions to the corresponding devices. The second data acquisition module 40 is used to respond to the planned execution data of each device returned by the energy-saving management cloud control platform, and calculate the actual carbon emissions and actual carbon savings of each device in the target factory based on the planned execution data and the carbon emissions of each device. Display module 50 is used to obtain the device ID of each device, and based on the device ID of each device and the corresponding actual carbon emissions and corresponding actual carbon savings, add the attributes of the data object of each device in the data twin model of the target factory, so as to display the actual carbon savings and actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes. In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media. Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves. The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An energy-saving and carbon-reducing management system based on intelligent technology, characterized in that: Applied to a server, the server is equipped with a comprehensive management platform, the comprehensive management platform including a data twin module, the comprehensive management platform and an energy-saving management cloud control platform transmit data through a preset interface, the method includes: The energy-saving management cloud control platform acquires energy-saving data for each piece of equipment in the target factory in real time, and calculates the carbon emissions of each piece of equipment based on the energy-saving data of each piece of equipment, wherein the equipment includes motors and frequency converters; The carbon emission plan of the target factory and the characteristic curve of each of the devices are obtained, and the optimal carbon emission of the target factory is determined based on the carbon emission plan and the characteristic curve of each of the devices using the first AI processing logic. Using a second AI processing logic, a target power allocation plan for each piece of equipment in the target factory is determined based on the optimal carbon emission amount for a future preset time period. The target power allocation plan is then sent to the energy-saving management cloud control platform, which generates equipment control instructions for each piece of equipment based on the target power allocation plan and issues the equipment control instructions to the corresponding equipment. In response to receiving the planned execution data of each device returned by the energy-saving management cloud control platform, the actual carbon emissions and actual carbon savings of each device in the target factory are calculated based on the planned execution data and the carbon emissions of each device. Obtain the device ID of each device, and based on the device ID of each device, the corresponding actual carbon emissions, and the corresponding actual carbon savings, add attributes of the data object of each device in the data twin model of the target factory, so as to display the actual carbon savings and actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module.

2. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 1, characterized in that: Based on the carbon emission plan and the characteristic curve of each device, determine the operating range and upper limit carbon emission of each device; Construct a multi-objective function, wherein the multi-objective function includes a function to minimize total carbon emissions, a function to maximize equipment efficiency, and a function to minimize energy consumption cost, wherein the function to minimize total carbon emissions is the primary objective function, and the function to maximize equipment efficiency and the function to minimize energy consumption cost are secondary objective functions; The operating range of each device and the upper limit carbon emission of the target factory are both used as the first constraints of the main objective function, and the main objective function is solved using a preset first solution algorithm to obtain the first total carbon emission. The secondary objective function is used as the second constraint condition of the main objective function, and the main objective function is solved using a preset second solution algorithm to obtain the second total carbon emissions. The minimum of the first total carbon emissions and the second total carbon emissions is taken as the optimal carbon emissions for the target plant.

3. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 2, characterized in that: The step of using the secondary objective function as the second constraint condition of the primary objective function and solving the primary objective function using a preset second solution algorithm to obtain the second total carbon emissions includes: converting the secondary objective function into a second constraint condition, wherein the second constraint condition includes that the solution of the objective function for maximizing equipment efficiency is greater than or equal to a preset minimum efficiency, and the solution of the objective function for minimizing energy consumption cost is less than or equal to a preset maximum cost; The main objective function with the second constraint condition is solved by a preset second solution algorithm, and the minimum energy consumption cost objective function is solved by a preset optimization algorithm, so as to optimize the expected energy consumption of each device and minimize the value of the main objective function. The minimum value of the main objective function is taken as the second total carbon emissions.

4. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 3, characterized in that: The second AI processing logic includes: obtaining the predicted electricity demand and grid load for a future preset time period; Based on the predicted electricity demand and the power grid load, the future preset time period is divided into peak period and / or off-peak period; The target power allocation plan for each of the devices in the target plant during the peak and / or off-peak periods is determined based on the optimized expected energy consumption of each device and the optimal carbon emissions.

5. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 4, characterized in that: The step of determining the target power allocation plan for each of the devices in the target factory during the peak and / or off-peak periods based on the optimized expected energy consumption of each device and the optimal carbon emission includes: obtaining electricity price information for the peak and off-peak periods when the future preset time period includes the peak and off-peak periods; The power demand of each device during the peak and off-peak periods is predicted based on a pre-built regression model. Based on the electricity price information, the electricity demand of each device during peak and off-peak periods, the optimized expected energy consumption of each device, and the optimal carbon emission level, a total cost minimization objective function is constructed. The total cost minimization includes minimizing electricity cost and minimizing carbon emission cost. The third constraint of the total cost minimization objective function includes that the electricity allocated to each device is greater than or equal to the corresponding electricity demand, the expected energy consumption of each device is less than or equal to the allocated electricity, and the total carbon emission generated by the electricity allocated to all devices is less than or equal to the optimal carbon emission level. The objective function of minimizing total cost is solved using a preset genetic algorithm. The power allocation plan that satisfies all the third constraints and achieves the optimal solution of the objective function of minimizing total cost is taken as the target power allocation plan.

6. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 5, characterized in that: The plan execution data includes the power consumption value of each of the devices. The step of calculating the actual carbon emissions and actual carbon savings of each of the devices in the target factory based on the plan execution data and the carbon emissions of each of the devices includes: obtaining the carbon emission factor of each of the devices. For each of the devices, the product of the corresponding carbon emission factor and the corresponding power consumption value is taken as the actual carbon emission of each of the devices in the target factory. The difference between the carbon emissions of each device and the corresponding actual carbon emissions is taken as the actual carbon saving amount of each device in the target factory.

7. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 6, characterized in that: The step of adding attributes of a data object for each device in the data twin model of the target factory based on the device ID of each device, the corresponding actual carbon emissions, and the corresponding actual carbon savings, so as to display the actual carbon savings and actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module, includes: finding the corresponding data object in the data twin model based on the device ID; The actual carbon emissions and actual carbon savings corresponding to the device ID are added as new attributes to the attributes of the data object to update the data object of the data twin model; The updated data objects are synchronized to the database of the data twin model; In response to receiving a query command corresponding to the device ID, the updated data object corresponding to the device ID is retrieved from the database, and the updated data object is displayed in the visualization interface of the data twin model to display the actual carbon emissions and the actual carbon savings corresponding to the device ID in real time.

8. The energy-saving and carbon-reducing control system based on intelligent technology according to claim 7, characterized in that: The method further includes: in response to receiving an interaction request from the associated platform to the integrated management platform, obtaining the version number of the associated platform; Using a preset version control logic, the version number is routed to the corresponding API interface of the integrated management platform.

9. A server, characterized in that, include: The memory is configured to store instructions; And a processor configured to retrieve the instructions from the memory and, when executing the instructions, to implement the AI-assisted energy-saving and carbon-reduction control method according to any one of claims 1 to 8.

10. A comprehensive management platform, characterized in that, The energy-saving and carbon-reduction control method based on AI intelligent assistance as described in any one of claims 1-8, wherein the integrated management platform includes a data twin module, the integrated management platform and the energy-saving management cloud control platform transmit data through a preset interface, and further includes: The first data acquisition module is used to acquire energy-saving data of each piece of equipment in the target factory in real time through the energy-saving management cloud control platform, and calculate the carbon emissions of each piece of equipment based on the energy-saving data of each piece of equipment. The first AI calculation module is used to obtain the carbon emission plan of the target factory and the characteristic curve of each of the devices, and to use the first AI processing logic to determine the optimal carbon emission of the target factory based on the carbon emission plan and the characteristic curve of each of the devices. The second AI calculation module is used to use the second AI processing logic to determine the target power allocation plan for each of the devices in the target factory in the future preset time period based on the optimal carbon emission, and send the target power allocation plan to the energy-saving management cloud control platform, so that the energy-saving management cloud control platform generates equipment control instructions for each device according to the target power allocation plan, and issues the equipment control instructions to the corresponding devices. The second data acquisition module is used to respond to receiving the planned execution data of each of the devices returned by the energy-saving management cloud control platform, and to calculate the actual carbon emissions and actual carbon savings of each of the devices in the target factory based on the planned execution data and the carbon emissions of each of the devices. The display module is used to obtain the device ID of each device, and based on the device ID of each device and the corresponding actual carbon emissions and the corresponding actual carbon savings, add attributes of the data object of each device in the data twin model of the target factory, so as to display the actual carbon savings and the actual carbon emissions of each device in the target factory in real time through the data object in the data twin model of the data twin module.

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