Integrated Control Method and System for High-Efficiency and Energy-Saving Industrial Automation Products

Abstract

The present invention relates to the field of automation control, and discloses an integrated control method and system for high-efficiency and energy-saving industrial automation products, including: for the automation equipment of the target industrial products, first obtain its historical energy consumption data, identify abnormal points to determine the basic control mode, construct an energy consumption optimization process based on this, determine the optimal operation mode according to the energy consumption efficiency of different process links, generate an energy-saving operation strategy, then evaluate the energy consumption performance under different working conditions, find out the energy consumption bottleneck and its occurrence conditions, construct a dynamic adjustment mechanism, generate an adjustment task list and extract detailed adjustment tasks, and finally analyze the energy-saving target, identify the obstructive factors, and generate an energy consumption control plan based on the production feedback data. The present invention can improve the control efficiency of the industrial automation system.

Description

Technical Field

[0001] The present invention relates to an integrated control method and system for high - efficiency and energy - saving industrial automation products, belonging to the field of automation control. Background Art

[0002] In modern industrial production, industrial automation products are widely used. They play a crucial role in improving production efficiency and optimizing production processes, and are widely applied in many industrial scenarios such as manufacturing, energy industry, chemical industry, etc. High - efficiency and energy - saving industrial automation products and their integrated control methods have become extremely important.

[0003] Currently, the integrated control methods adopted by most industrial automation products have obvious shortcomings in terms of energy conservation and efficiency. On the one hand, traditional integrated control methods often lack accurate real - time monitoring and intelligent regulation of equipment operating states, resulting in energy waste during equipment operation. For example, motors still maintain high power operation under light load, greatly increasing energy consumption. On the other hand, the existing integrated control methods are relatively inefficient when coordinating multiple automation products to work together. The communication delay and poor data interaction between devices make it difficult to achieve efficient connection of production processes, seriously restricting the improvement of overall production efficiency. Therefore, a high - efficiency and energy - saving integrated control method for industrial automation products is needed to improve the control efficiency of industrial automation systems. Summary of the Invention

[0004] The present invention provides an integrated control method and system for high - efficiency and energy - saving industrial automation products, and its main purpose is to improve the control efficiency of industrial automation systems.

[0005] To achieve the above - mentioned purpose, an integrated control method for high - efficiency and energy - saving industrial automation products provided by the present invention includes:

[0006] Obtain the automation equipment corresponding to the target industrial product, query the historical energy consumption data corresponding to the automation equipment, and identify the abnormal energy consumption points in the historical energy consumption data that exceed the preset energy consumption threshold. Based on the abnormal energy consumption points, determine the basic control mode corresponding to the target industrial product;

[0007] Based on the basic control mode, construct the energy - consumption optimization process corresponding to the target industrial product, identify the process links in the energy - consumption optimization process, calculate the energy - consumption efficiency of the target industrial product under different process links, and according to the energy - consumption efficiency, determine the optimal operation mode corresponding to the target industrial product. Based on the optimal operation mode, generate the energy - saving operation strategy corresponding to the target industrial product;

[0008] Based on the energy-saving operation strategy, evaluate the energy consumption performance of the target industrial product under different working conditions. Based on the energy consumption performance, query the energy consumption bottlenecks existing in the target industrial product, and analyze the occurrence conditions corresponding to the energy consumption bottlenecks;

[0009] Based on the energy consumption bottlenecks and the occurrence conditions, combined with the current product state corresponding to the target industrial product, construct a dynamic adjustment mechanism corresponding to the target industrial product. Based on the dynamic adjustment mechanism, generate an adjustment task list corresponding to the target industrial product, and extract the detailed adjustment tasks in the adjustment task list;

[0010] Analyze the energy-saving goals corresponding to the detailed adjustment tasks, identify the energy-saving obstacle factors in the energy-saving goals, based on the energy-saving obstacle factors, detect the production feedback data corresponding to the target industrial product, and based on the production feedback data, generate an energy consumption control plan corresponding to the target industrial product.

[0011] Optionally, the determining the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points includes:

[0012] Analyze the time series characteristics of the occurrence of the abnormal energy consumption points;

[0013] Sort out the energy consumption fluctuation law corresponding to the time series characteristics;

[0014] Identify the abnormal working condition energy consumption corresponding to the energy consumption fluctuation law;

[0015] Formulate an energy consumption adjustment strategy corresponding to the abnormal working condition energy consumption;

[0016] Based on the energy consumption adjustment strategy, determine the basic control mode corresponding to the target industrial product.

[0017] Optionally, the calculating the energy consumption efficiency of the target industrial product under different process links includes:

[0018] Use the following formula to calculate the energy consumption efficiency of the target industrial product under different process links:

[0019] ;

[0020] Wherein, represents the energy consumption efficiency of the target industrial product under different process links, represents the number of types corresponding to the target industrial product, the type index corresponding to the target industrial product, represents the unit value corresponding to the th type of the target industrial product, represents the The process output corresponding to the target industrial product Indicates the number of process steps corresponding to the process link Indicates the quantity index corresponding to the process link Indicates the start time of the process link Indicates the end time of the process link Indicates the th process link's power consumption function at time moment

[0021] Optionally, generating the energy-saving operation strategy corresponding to the target industrial product based on the optimal operation mode includes:

[0022] Analyzing the real-time energy consumption data of each device under the optimal operation mode;

[0023] Querying the optimizable adjustment parameters in the real-time energy consumption data;

[0024] Performing multi-combination simulation tests on the optimizable adjustment parameters to obtain a simulation test group;

[0025] Calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group;

[0026] Generating the energy-saving operation strategy corresponding to the target industrial product based on the energy consumption reduction ratio.

[0027] Optionally, calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group includes:

[0028] Using the following formula to calculate the energy consumption reduction ratio corresponding to the parameters in the simulation test group:

[0029] ;

[0030] Where Indicates the energy consumption reduction ratio corresponding to the parameters in the simulation test group Indicates the number of test cases in the simulation test group Indicates the quantity index of the test cases Indicates the th test case's energy consumption baseline value of the device within the time interval Indicates the start time of the simulation test Indicates the end time of the simulation test Indicates in the th test case, the device's power consumption function at time moment

[0031] ​Optionally, based on the energy-saving operation strategy, evaluate the energy consumption performance of the target industrial product under different working conditions, including:

[0032] Sort out the key strategy points in the energy-saving operation strategy;

[0033] Based on the key strategy points, determine the parameter change gradient of the target industrial product under different working conditions;

[0034] Based on the parameter change gradient, query the parameter adjustment range under different working conditions;

[0035] Analyze the actual energy consumption indicators corresponding to the parameter adjustment range;

[0036] Based on the actual energy consumption indicators, evaluate the energy consumption performance of the target industrial product under different working conditions.

[0037] Optionally, based on the energy consumption bottleneck and the occurrence conditions, combined with the current product state of the target industrial product, construct a dynamic adjustment mechanism for the target industrial product, including:

[0038] Analyze the energy consumption threshold interval corresponding to the energy consumption bottleneck and the occurrence conditions;

[0039] Based on the energy consumption threshold interval and the current product state, generate a state logic chain for the target industrial product;

[0040] Extract the logical working condition factors in the state logic chain;

[0041] Based on the logical working condition factors, match the optimal adjustment strategy in the preset adjustment strategy library;

[0042] Based on the optimal adjustment strategy, construct a dynamic adjustment mechanism for the target industrial product.

[0043] Optionally, based on the dynamic adjustment mechanism, generate an adjustment task list for the target industrial product, including:

[0044] Analyze the core adjustment path corresponding to the dynamic adjustment mechanism;

[0045] Collect multi-source information nodes in the core adjustment path;

[0046] Based on the multi-source information nodes, clarify the task adjustment direction of the target industrial product;

[0047] Based on the task adjustment direction, formulate a task execution process for the target industrial product;

[0048] Based on the task execution process, generate an adjustment task list for the target industrial product.

[0049] Optionally, analyzing the energy-saving target corresponding to the detailed adjustment task includes:

[0050] Querying the energy-saving reference data corresponding to the energy-saving target;

[0051] Based on the energy-saving reference data, analyzing the energy consumption ratio index corresponding to the energy-saving target;

[0052] Based on the energy consumption ratio index, generating an energy consumption change curve corresponding to the energy-saving target;

[0053] Extracting the energy consumption related nodes in the energy consumption change curve;

[0054] Based on the energy consumption related nodes, analyzing the energy-saving target corresponding to the detailed adjustment task.

[0055] To solve the above problems, the present invention also provides an integrated control system for an energy-efficient industrial automation product, and the system includes:

[0056] A mode determination module, configured to obtain the automation equipment corresponding to the target industrial product, query the historical energy consumption data corresponding to the automation equipment, identify the abnormal energy consumption points exceeding the preset energy consumption threshold in the historical energy consumption data, and based on the abnormal energy consumption points, determine the basic control mode corresponding to the target industrial product;

[0057] A strategy generation module, configured to construct an energy consumption optimization process corresponding to the target industrial product based on the basic control mode, identify the process links in the energy consumption optimization process, calculate the energy consumption efficiency of the target industrial product under different process links, and based on the energy consumption efficiency, determine the optimal operation mode corresponding to the target industrial product, and based on the optimal operation mode, generate an energy-saving operation strategy corresponding to the target industrial product;

[0058] A condition analysis module, configured to evaluate the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy, query the energy consumption bottlenecks existing in the target industrial product based on the energy consumption performance, and analyze the occurrence conditions corresponding to the energy consumption bottlenecks;

[0059] A task extraction module, configured to construct a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence conditions in combination with the current product state corresponding to the target industrial product, generate an adjustment task list corresponding to the target industrial product based on the dynamic adjustment mechanism, and extract the detailed adjustment tasks in the adjustment task list;

[0060] A solution generation module is used to analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacle factors in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacle factors, and generate an energy consumption control solution corresponding to the target industrial product based on the production feedback data.

[0061] Compared with the problems described in the background art, the present invention can accurately locate abnormal energy consumption points by obtaining the automation equipment corresponding to the target industrial product, and then determine the basic control mode. At the same time, this helps to coordinate the collaborative work of equipment, reduce communication delays and poor data interaction, improve the overall production efficiency, and achieve the efficient energy-saving operation of the industrial automation system. Based on the basic control mode, the present invention constructs an energy consumption optimization process corresponding to the target industrial product and identifies the process links in the energy consumption optimization process, which helps to accurately locate the key nodes of energy consumption optimization. For example, exclusive energy-saving measures can be formulated in links such as equipment start-stop and operating parameter adjustment, which can greatly improve the energy utilization efficiency, reduce production costs, and thus improve the balance between energy conservation and production benefits. Further, based on the energy-saving operation strategy, the present invention evaluates the energy consumption performance of the target industrial product under different working conditions, which can intuitively present the product energy consumption status and accurately locate high-energy consumption working conditions. This can optimize the strategy accordingly, reduce energy waste, improve the energy utilization efficiency, and at the same time provide a basis for product R & D improvement, helping to create more energy-saving products and promoting the development of industrial production towards the direction of green and high efficiency. Further, based on the energy consumption bottleneck and the occurrence conditions, combined with the current product state corresponding to the target industrial product, the present invention constructs a dynamic adjustment mechanism corresponding to the target industrial product, which can automatically adjust the equipment operating parameters according to the real-time situation, accurately avoid the energy consumption bottleneck, reduce energy waste, and at the same time, can adapt to the change of the product state, continuously optimize the energy consumption, and ensure the efficient and energy-saving operation of production. Finally, by analyzing the energy-saving target corresponding to the detailed adjustment task, the present invention clarifies the expected energy-saving effect of each task, focuses on the key links, and at the same time helps to accurately evaluate the effectiveness of the adjustment strategy, timely adjust the task execution direction, and ensure the steady reduction of the energy consumption of industrial products, realizing efficient energy-saving production. Therefore, the integrated control method and system of the high-efficiency energy-saving industrial automation product provided by the embodiments of the present invention can improve the control efficiency of the industrial automation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic flowchart of an integrated control method of a high-efficiency energy-saving industrial automation product provided by an embodiment of the present invention;

[0063] Figure 2 It is a schematic diagram of modules of an integrated control system for realizing the high-efficiency energy-saving industrial automation product provided by an embodiment of the present invention.

[0064] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0065] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0066] The embodiment of the present application provides an integrated control method for an energy-efficient industrial automation product. The execution subject of the integrated control method for an energy-efficient industrial automation product includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the integrated control method for an energy-efficient industrial automation product can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0067] Embodiment 1:

[0068] Reference Figure 1 FIG. 1 is a flow chart of an integrated control method for an efficient and energy-saving industrial automation product provided by an embodiment of the present invention. In this embodiment, the integrated control method for an efficient and energy-saving industrial automation product includes:

[0069] S1. Obtain the automation equipment corresponding to the target industrial product, query the historical energy consumption data corresponding to the automation equipment, and identify abnormal energy consumption points in the historical energy consumption data that exceed a preset energy consumption threshold, and determine the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points.

[0070] By acquiring the automation equipment corresponding to the target industrial product, the present invention can accurately locate abnormal energy consumption points and then determine the basic control mode. At the same time, this helps to coordinate the collaborative work of equipment, reduce communication delays and poor data interaction, improve overall production efficiency, and achieve efficient and energy-saving operation of the industrial automation system.

[0071] Among them, the target industrial product refers to a product with an extremely wide coverage. In the manufacturing industry, for example, in the field of automobile manufacturing, the whole vehicle product requires efficient and energy-saving control in the entire production process from parts processing to vehicle assembly to reduce costs and increase production capacity; in the manufacturing of electronic products, products such as mobile phones and computers have strict requirements for the energy consumption and operation efficiency of equipment during their production process to meet the needs of large-scale production and environmental protection. The automation equipment refers to the key carrier for realizing the efficient and energy-saving production of target industrial products. In the manufacturing industry, automated production line equipment includes automated stamping machines, welding robots, painting equipment, etc. Through precise program control, it can, while ensuring product quality, adjust operation parameters in real time according to production tasks and reduce energy waste. Industrial robots are more widely used in links such as material handling and part assembly, and can efficiently complete complex tasks according to preset instructions, improving production efficiency. In the energy industry, automated monitoring and control devices for power generation can monitor the operating status of power generation equipment in real time, such as the boiler control system in thermal power generation and the pitch and speed control system of wind turbines in wind power generation, and automatically adjust according to energy demand and equipment conditions to achieve efficient power generation and energy conservation. Optionally, the acquisition of the automation equipment corresponding to the target industrial product can be achieved through equipment identification technology. For example, cameras are arranged in the workshop to collect images, and algorithms identify features such as the shape and identification of the equipment to determine whether it is the equipment required for the target industrial product. Combining the equipment networking information, the corresponding automation equipment can be determined.

[0072] Furthermore, by querying the historical energy consumption data corresponding to the automation equipment and identifying the abnormal energy consumption points that exceed the preset energy consumption threshold in the historical energy consumption data, the root cause of excessive equipment energy consumption can be accurately located, which helps to timely discover potential equipment failure hazards, avoid resource waste caused by high-energy consumption operation, and thus significantly improve the energy-saving benefits of industrial production and the operation stability of equipment.

[0073] Among them, the historical energy consumption data refers to the detailed record set of energy consumption of automated equipment during past operating cycles. These data cover different time nodes, such as the energy consumption amounts of electricity, fuel, steam, etc. counted by hour, day, and month, and are also associated with equipment operation status information, such as equipment rotation speed, load rate, operation duration, etc. Taking the motor equipment in a factory as an example, its historical energy consumption data will include the power consumption of different shifts on each working day in a year, as well as the load conditions of the motor during the corresponding periods. It is the basic data source for analyzing the energy consumption trends and patterns of equipment; the preset energy consumption threshold refers to the energy consumption limit value set based on the design specifications of the equipment, the industry average energy consumption standard, the enterprise's energy-saving goals, and long-term historical operation data analysis. It can be a fixed value. For example, for a certain type of energy-saving lamp, according to its rated power and ideal working efficiency, the daily power consumption threshold is set to 0.5 degrees; it can also be a dynamic range. For example, for a refrigeration equipment with obvious seasonal changes, according to the ambient temperature and usage frequency in different seasons, different energy consumption ranges are set for summer and winter; the abnormal energy consumption point refers to a specific data point in the historical energy consumption data where the energy consumption value significantly deviates from the preset energy consumption threshold. For example, during the normal processing process of a numerically controlled machine tool, the energy consumption per unit time is stable at 2-3 degrees of electricity, but at a certain moment, the energy consumption suddenly soars to 5 degrees of electricity. This time point with abnormal increase in energy consumption value is the abnormal energy consumption point. Optionally, querying the historical energy consumption data corresponding to the automated equipment can be achieved through the database query method. For example: by writing an SQL query statement and using the unique identifier of the equipment (such as equipment ID) as the query condition and associating with the energy consumption data table, the historical energy consumption data of the specified automated equipment can be obtained; identifying the abnormal energy consumption points in the historical energy consumption data that exceed the preset energy consumption threshold can be achieved through the Isolation Forest algorithm. For example: after constructing an Isolation Forest model, new data points are predicted, and the points with scores exceeding a certain threshold are determined as abnormal energy consumption points, that is, data points deviating from the normal energy consumption mode.

[0074] Furthermore, based on the abnormal energy consumption points, the present invention determines the basic control mode corresponding to the target industrial product, accurately indicates the deficiencies of the current control mode of the equipment, and adjusts the basic control mode accordingly, which can make the equipment operation more in line with the actual requirements, avoid energy waste, improve the overall energy utilization efficiency, and thus achieve the goal of energy conservation and efficiency improvement.

[0075] Among them, the basic control mode refers to the comprehensive energy consumption regulation strategy, which is a set of basic equipment operation control methods constructed for the target industrial product. It integrates and optimizes various energy consumption regulation strategies for different abnormal working conditions to form a systematic and executable control framework. Under this framework, the equipment can automatically or manually switch to the corresponding energy consumption regulation strategy according to different working conditions to achieve energy-saving operation.

[0076] As an embodiment of the present invention, determining the basic control mode corresponding to the target industrial product based on the abnormal energy consumption point includes: analyzing the time series characteristics of the occurrence of the abnormal energy consumption point; sorting out the energy consumption fluctuation law corresponding to the time series characteristics; identifying the abnormal working condition energy consumption corresponding to the energy consumption fluctuation law; formulating an energy consumption adjustment strategy corresponding to the abnormal working condition energy consumption; and determining the basic control mode corresponding to the target industrial product based on the energy consumption adjustment strategy.

[0077] Among them, the time series characteristics refer to the set of characteristics presented by the abnormal energy consumption point in the time dimension. For example, whether it appears periodically or randomly and suddenly; the time point distribution, such as concentrated in a specific period of weekdays or frequently appearing in the peak production season; and the energy consumption change trend over time, like whether the abnormal energy consumption value gradually increases or decreases over time, etc.; the energy consumption fluctuation law refers to the regular pattern of the energy consumption of the automation equipment changing over time summarized based on the time series characteristics. For example, it may show a pattern that the energy consumption rapidly rises at the initial stage of equipment startup and then tends to be stable, and there is a large fluctuation in energy consumption when switching specific production tasks; or at a fixed time of each day, due to production process adjustment, the energy consumption shows periodic high and low fluctuations; the abnormal working condition energy consumption refers to the energy consumption under a specific working condition corresponding to the abnormal energy consumption situation in the energy consumption fluctuation law. For example, when the equipment load is too high or too low, exceeding the reasonable design range, the high energy consumption generated at this time is the abnormal working condition energy consumption; or when the equipment is aging, parts are worn, etc., although the operation task remains unchanged, the energy consumption increases significantly, and this energy consumption state also belongs to the abnormal working condition energy consumption; the energy consumption adjustment strategy refers to a series of methods and measures formulated according to the identified abnormal working condition energy consumption, aiming to reduce energy consumption and optimize equipment operation. It can include adjusting equipment operation parameters, such as reducing the motor speed when the equipment is lightly loaded to reduce unnecessary energy consumption; changing the production process sequence, reasonably arranging the start and stop time of the equipment to avoid equipment idling and consuming energy; or maintaining and servicing the equipment, replacing aging parts, improving equipment operation efficiency, and thus reducing energy consumption.

[0078] Further, the analysis of the time series characteristics of the abnormal energy consumption points can be achieved through the autocorrelation function. For example, by plotting the ACF and PACF graphs, the correlation characteristics of the time series can be visually observed, thereby obtaining the time series characteristics of the abnormal energy consumption points. The sorting out of the energy consumption fluctuation law corresponding to the time series characteristics can be achieved through the time series decomposition algorithm. For example, by observing the changes in trends, seasonality, and residuals, the energy consumption fluctuation law can be sorted out, such as whether there is a long-term upward or downward trend and the seasonal fluctuation pattern. The identification of the abnormal working condition energy consumption corresponding to the energy consumption fluctuation law can be achieved through the clustering algorithm. For example, the energy consumption data is organized into a suitable format and clustered using DBSCAN(eps=3, min_samples=2).fit. The energy consumption corresponding to the points with the label of -1 is the abnormal working condition energy consumption. The formulation of the energy consumption adjustment strategy corresponding to the abnormal working condition energy consumption can be achieved through the reinforcement learning algorithm. For example, the abnormal working condition energy consumption is used as the environmental state, and the energy consumption adjustment strategy is used as the action. With the goal of reducing energy consumption as the reward, the agent is trained to learn the optimal adjustment strategy. The determination of the basic control mode corresponding to the target industrial product can be achieved through the analytic hierarchy process. For example, multiple evaluation indicators of the basic control mode (such as energy consumption, production efficiency, equipment life, etc.) are hierarchically divided, and the weights of each indicator are determined through pairwise comparison. Then, the optimal basic control mode is determined by synthesizing the scores of each energy consumption adjustment strategy.

[0079] S2. Based on the basic control mode, construct the energy consumption optimization process corresponding to the target industrial product, identify the process links in the energy consumption optimization process, calculate the energy consumption efficiency of the target industrial product under different process links, and based on the energy consumption efficiency, determine the optimal operation mode corresponding to the target industrial product. Based on the optimal operation mode, generate the energy-saving operation strategy corresponding to the target industrial product.

[0080] Based on the basic control mode, the present invention constructs the energy consumption optimization process corresponding to the target industrial product and identifies the process links in the energy consumption optimization process, which helps to accurately locate the key nodes of energy consumption optimization. For example, formulating exclusive energy-saving measures in links such as equipment start-stop and operating parameter adjustment can greatly improve the energy utilization efficiency, reduce the production cost, and thus improve the balance between energy conservation and production benefits.

[0081] Among them, the energy consumption optimization process refers to a systematic solution carefully designed to reduce energy consumption in the production process of target industrial products. For example, in chemical production, the energy consumption optimization process can cover a series of coherent and interrelated steps from precise energy input in the raw material pretreatment stage, to real-time regulation of equipment energy consumption according to the reaction progress during the reaction process, to waste heat recovery and utilization in the product separation and purification stage; the process links refer to the specific components of the energy consumption optimization process. Each link has specific functions and goals, and plays a key role in the overall energy consumption optimization. These links are divided according to different stages of industrial production and energy consumption characteristics. For example, the equipment operation link involves operations such as equipment startup, stable operation, and load adjustment. Energy consumption can be directly affected by reasonably arranging the equipment operation status; the energy distribution link is responsible for accurately delivering energy to areas or equipment with different energy consumption requirements in the production process to ensure the rationality of energy distribution; the maintenance link regularly inspects, repairs and maintains the equipment to ensure that the equipment is in an efficient operating state and reduces additional energy consumption caused by equipment aging and failure. Optionally, the energy consumption optimization process corresponding to the target industrial product can be constructed through a linear programming algorithm, such as: abstracting the production process of the target industrial product into a mathematical model, and solving the model through a linear programming algorithm to obtain the optimal resource allocation and energy use plan for each production link under the premise of meeting production needs, thereby constructing an energy consumption optimization process; the identification of the process links in the energy consumption optimization process can be achieved through a cluster analysis algorithm, such as: through cluster analysis, it is found that within a certain period of equipment operation, energy consumption and production efficiency present a specific pattern, and the production operation corresponding to this time period is determined as a process link.

[0082] Furthermore, by calculating the energy efficiency of the target industrial product in different process links, the present invention can accurately locate the links with high energy consumption and low efficiency, provide a clear direction for targeted optimization, help enterprises to reasonably allocate energy-saving resources, give priority to improving key links, and thus significantly improve the overall energy utilization efficiency.

[0083] Among them, the energy consumption efficiency refers to the ratio of the input energy converted into effective output in different process links of the target industrial product. In the early stage, it can measure the total value of the product obtained by consuming a certain amount of energy in a specific process link. The higher the value, the more sufficient the energy utilization is, and the better the energy utilization efficiency of the process link.

[0084] As an embodiment of the present invention, the calculating the energy consumption efficiency of the target industrial product in different process links includes:

[0085] The energy efficiency of the target industrial product in different process links is calculated using the following formula:

[0086] ;

[0087] Among them, represents the energy consumption efficiency of the target industrial product under different process links, represents the number of types corresponding to the target industrial product, the type index corresponding to the target industrial product, represents the unit value corresponding to the th type of target industrial product, represents the process output corresponding to the th type of target industrial product, represents the number of links corresponding to the process link, represents the start time of the process link, represents the end time of the process link, represents the th process link's power consumption function at time moment.

[0088] Specifically, the unit value refers to, for the th type of target industrial product, the unit value is the economic value of a single product, which can be comprehensively determined according to factors such as the market price of the product, the technical content of the product itself, and the scarcity degree of market demand; the process output refers to the production quantity of the th type of target industrial product in this process link , which reflects the output scale of this product in this process link. For example, in an assembly link on an automobile production line, the number of automobiles assembled in a day is the process output of the automobile product in this link; the power consumption function refers to the power consumption function of the th process link at a time moment , which describes the change of energy consumption power at different moments during the entire operation time of this process link.

[0089] Furthermore, based on the energy consumption efficiency, the present invention determines the optimal operation mode corresponding to the target industrial product, can accurately find the efficient interval of energy utilization, reduce unnecessary energy waste, and greatly reduce production costs. This not only helps enterprises improve their competitiveness in the market but also promotes the development of industrial production towards a green and sustainable direction.

[0090] Among them, the optimal operation mode refers to a mode of operation formed by comprehensively considering various factors such as energy consumption efficiency, equipment performance, production time, and product quality in the process of producing the target industrial product. After optimizing the configuration of production parameters, equipment operation status, operation sequence, etc., in this mode, products meeting quality standards can be produced with the least energy consumption, the shortest production cycle, and lower costs. Optionally, determining the optimal operation mode corresponding to the target industrial product can be achieved through the experimental design method. For example, in the production of chemical products, multiple groups of experiments are carried out by changing the reaction temperature, pressure, and raw material ratio, and these experimental data are analyzed to find out the combination of conditions that can make the energy consumption efficiency of the target industrial product high and meet the quality requirements, so as to determine the optimal operation mode.

[0091] Furthermore, based on the optimal operation mode, the present invention generates an energy-saving operation strategy corresponding to the target industrial product, which can make the equipment operate more reasonably, reduce excessive wear of the equipment, extend the service life of the equipment, reduce the maintenance cost, and at the same time, help to achieve the goal of energy conservation and emission reduction, enhance the image of the enterprise in the field of environmental protection, and strengthen the market competitiveness.

[0092] Among them, the energy-saving operation strategy refers to a set of specific plans for guiding the operation of equipment in the production process of the target industrial product, which is formulated by comprehensively considering factors such as the actual production demand and equipment performance limitations based on the energy consumption reduction ratio.

[0093] As an embodiment of the present invention, generating the energy-saving operation strategy corresponding to the target industrial product based on the optimal operation mode includes: analyzing the real-time energy consumption data of each device in the optimal operation mode; querying the optimizable adjustment parameters in the real-time energy consumption data; performing multi-combination simulation tests on the optimizable adjustment parameters to obtain simulation test groups; calculating the energy consumption reduction ratios corresponding to the parameters in the simulation test groups; and generating the energy-saving operation strategy corresponding to the target industrial product based on the energy consumption reduction ratios.

[0094] Among them, the real-time energy consumption data refers to the specific numerical records of the energy consumed by each production device at the current moment and within a continuous time period when the target industrial product is in the optimal operation mode. These data are obtained by real-time collection through various energy monitoring devices, such as smart meters, gas flow sensors, etc.; the optimizable adjustment parameters refer to the operation parameters that can be mined from the real-time energy consumption data and whose adjustment can affect the energy consumption of the device. For example, for motor devices, changes in parameters such as speed and voltage will directly affect their energy consumption, and these are the optimizable adjustment parameters; the simulation test group refers to a series of parameter sets formed after different combinations of the optimizable adjustment parameters are set for simulation tests. For example, for the two optimizable adjustment parameters of the speed and voltage of a motor device, if the speed is set to three gears: low, medium, and high, and the voltage is set to three levels: normal, slightly higher, and slightly lower, then 9 different simulation test cases will be formed; the energy consumption reduction ratio refers to the degree of reduction in the energy consumption of the device corresponding to each parameter combination in the simulation test group compared to the energy consumption in the original optimal operation mode, presented in percentage form.

[0095] Furthermore, the analysis of the real-time energy consumption data of each device in the optimal operation mode can be achieved through the sliding window algorithm. For example: by setting a 30-minute sliding window to calculate the energy consumption mean, automatically marking the devices whose mean deviates from the historical baseline by more than 20%, outputting the device energy consumption fluctuation ranking, and finally obtaining the real-time energy consumption data; the query of the optimizable adjustment parameters in the real-time energy consumption data can be achieved through the decision tree algorithm. For example: using the C4.5 algorithm to construct a tree of energy consumption influencing factors, finding that the set value of the dryer temperature (50 - 80 °C) has an influence weight of 37% on energy consumption and determining it as an optimizable parameter; the multi-combination simulation test of the optimizable adjustment parameters can be achieved through the Monte Carlo simulation method. For example: setting random perturbations for the parameters (such as temperature ±2 °C), generating 1000 groups of random parameter combinations, and calculating the energy consumption simulation values of each group through GPU acceleration to finally obtain the simulation test group; the calculation of the energy consumption reduction ratio corresponding to the parameters in the simulation test group can be achieved through the following calculation formula; the generation of the energy-saving operation strategy corresponding to the target industrial product can be achieved through the logical closed-loop verification method. For example: by deploying multiple energy consumption sensors to collect data in real time, using the decision tree to identify the key parameter of the slider speed of the press, and obtaining the optimal speed combination through orthogonal experiment simulation to finally generate the energy-saving operation strategy.

[0096] As an embodiment of the present invention, the calculation of the energy consumption reduction ratio corresponding to the parameters in the simulation test group includes:

[0097] Using the following formula to calculate the energy consumption reduction ratio corresponding to the parameters in the simulation test group:

[0098] ;

[0099] Among them, represents the energy consumption reduction ratio corresponding to the parameters in the simulation test group, represents the number of test cases in the simulation test group, represents the index of the number of test cases, represents the energy consumption benchmark value of the device corresponding to the th test case within the time interval represents the start time of the simulation test, represents the end time of the simulation test, represents the energy consumption power function of the device at time in the th test case.

[0100] Specifically, the energy consumption reduction ratio refers to the amplitude of the energy consumption decrease obtained by comparing the actual energy consumption of the device under different test cases with the original energy consumption benchmark value in the simulation test group, presented in the form of a percentage. This value intuitively reflects the degree of energy consumption reduction of the device after different combinations of adjustable parameters are set. The higher the value, the better the energy-saving effect under the corresponding parameter combination; the test case refers to each set of parameter settings formed by arranging and combining various adjustable parameters according to different values during the multi-combination simulation test of the adjustable parameters. Each test case represents a combination mode of the device operation parameters. Through the simulation tests of these different combinations, the energy consumption of the device under different parameter settings is evaluated; the energy consumption benchmark value refers to the energy consumption within the time interval before the start of the simulation test when the device is in the original optimal operation mode, used to compare with the actual energy consumption during the simulation test of this test case to obtain the energy consumption reduction situation; the energy consumption power function refers to the function that describes the change of the energy consumption power of the device at time , and through this function, the actual energy consumption of the device within the entire simulation test time interval can be calculated.

[0101] S3. Based on the energy-saving operation strategy, evaluate the energy consumption performance of the target industrial product under different working conditions. Based on the energy consumption performance, query the energy consumption bottlenecks existing in the target industrial product and analyze the occurrence conditions corresponding to the energy consumption bottlenecks.

[0102] Based on the energy-saving operation strategy, the present invention evaluates the energy consumption performance of the target industrial product under different working conditions, which can visually present the product's energy consumption status and accurately locate high-energy-consuming working conditions. Based on this, the strategy can be optimized to reduce energy waste and improve energy utilization efficiency. At the same time, it provides a basis for product R & D and improvement, helps to create more energy-saving products, and promotes the development of industrial production towards the direction of green and high efficiency.

[0103] Among them, the energy consumption performance refers to the overall evaluation of the energy utilization efficiency, energy-saving effect, etc. of the target industrial product under different working conditions by integrating information such as actual energy consumption indicators. For example, whether the energy consumption is within a reasonable range, the gap from the expected energy-saving target, etc., which is used to measure the quality of the product's energy use under different working conditions.

[0104] As an embodiment of the present invention, the evaluation of the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy includes: sorting out the key strategy points in the energy-saving operation strategy; determining the parameter change gradient of the target industrial product under different working conditions based on the key strategy points; querying the parameter adjustment range under different working conditions based on the parameter change gradient; analyzing the actual energy consumption indicators corresponding to the parameter adjustment range; and evaluating the energy consumption performance of the target industrial product under different working conditions based on the actual energy consumption indicators.

[0105] Among them, the key strategy points refer to the key points in the energy-saving operation strategy that play a core role in reducing the energy consumption of the target industrial product. For example, the optimized arrangement of equipment start-stop time, the setting range of specific operating parameters, etc. These key points are the core components of the strategy; the parameter change gradient refers to the change amplitude and rhythm of the operating parameters of the target industrial product determined based on the key strategy points under different working conditions. For example, under different working conditions of equipment load change, the motor speed parameter is adjusted according to a certain ratio and frequency, and this ratio and frequency are the parameter change gradient, which reflects the law of parameter change; the parameter adjustment range refers to the interval within which the operating parameters can be adjusted according to the parameter change gradient for different working conditions. For example, under high-temperature working conditions, the temperature control parameter of the industrial furnace can be adjusted within a certain specific temperature interval, and this interval is the parameter adjustment range, which limits the boundary of parameter adjustment; the actual energy consumption indicators refer to the energy consumption-related data generated during the actual operation of the target industrial product within the parameter adjustment range, such as the energy consumption per unit time, the energy consumption for producing a unit product, etc. These indicators reflect the energy consumption status of the product during actual operation.

[0106] Furthermore, the key strategic points in the energy-saving operation strategy can be identified through association rule mining algorithms. For example, the Apriori algorithm can be used to mine association rules in data, find out frequently occurring strategy combinations that have an important impact on energy conservation, and the key points of the strategies in these combinations are the key strategic points. The determination of the parameter change gradient of the target industrial product under different working conditions can be achieved through an adaptive control algorithm. For example, in a smart home system, the air conditioner adjusts parameters such as the compressor speed according to working condition data such as indoor temperature and humidity through an adaptive control algorithm, achieving energy conservation while ensuring comfort. The parameter adjustment rule formed during this process is the parameter change gradient. The query of the parameter regulation range under different working conditions can be achieved through a clustering algorithm. For example, the K-Means algorithm is used to cluster the operating conditions of equipment in different production batches, and analyze the parameter regulation ranges such as the temperature and operating frequency of the equipment under each type of working condition. The analysis of the actual energy consumption indicators corresponding to the parameter regulation range can be achieved through a data analysis platform. For example, using the Tableau platform to draw an energy consumption line chart under different temperature settings, visually observing the energy consumption changes when the temperature parameter changes within the regulation range, so as to determine the actual energy consumption indicators. The evaluation of the energy consumption performance of the target industrial product under different working conditions can be achieved through the analytic hierarchy process. For example, when evaluating the energy consumption performance of a large industrial plant, combining AHP and grey relational analysis, considering the energy consumption indicators in multiple aspects such as lighting, air conditioning, and equipment operation in the plant, and evaluating its energy consumption performance in different seasons (working conditions).

[0107] Based on the energy consumption performance, the present invention queries the energy consumption bottlenecks of the target industrial product and analyzes the occurrence conditions corresponding to the energy consumption bottlenecks, which helps to formulate targeted energy-saving improvement plans, avoid blind measures, efficiently utilize resources. At the same time, it can prevent the occurrence of high energy consumption situations in advance, continuously optimize the product energy consumption, and improve the energy utilization efficiency.

[0108] Among them, the energy consumption bottleneck refers to the key links, equipment or factors that cause excessive energy consumption and are difficult to reduce in the production process or operation process of the target industrial product. For example, in a certain chemical production, due to the outdated technology of a specific reaction equipment and low energy conversion rate, this equipment becomes the energy consumption bottleneck in the whole production process; the occurrence conditions refer to various internal and external environmental factors, operating parameter settings or specific scenarios that trigger the appearance of the energy consumption bottleneck. It includes the operating state of the equipment (such as long-term high-load operation), production process conditions (such as a specific temperature and pressure range), raw material characteristics (specific purity or composition), and external environmental factors (such as high temperature and high humidity environment), etc. For example, in the manufacturing process of a certain electronic device, when the production line is in continuous high-intensity operation and the environmental temperature is too high, the burden on the equipment cooling system increases, becoming an energy consumption bottleneck, resulting in a significant increase in energy consumption. Here, the continuous high-intensity operation and high temperature environment are the occurrence conditions of the energy consumption bottleneck. Optionally, querying the energy consumption bottleneck existing in the target industrial product can be achieved through an energy management system. For example, in a certain steel plant, the EMS system shows that the energy consumption in the blast furnace ironmaking process fluctuates greatly and the overall energy consumption exceeds the expectation. After in-depth investigation, it is found that the serious heat loss is caused by the damage of the blast furnace lining, and then it is determined that the problem of the blast furnace lining is the energy consumption bottleneck; analyzing the occurrence conditions corresponding to the energy consumption bottleneck can be achieved through an association rule mining algorithm. For example, in a pharmaceutical factory, through the Apriori algorithm analysis, it is found that when the temperature of the drug drying equipment is 10°C higher than the set value, the wind speed is 20% lower than the normal range, and the moisture content of the raw material is 5% higher than the standard, the energy consumption in the drying process increases significantly, becoming an energy consumption bottleneck. These parameter conditions are the occurrence conditions of the energy consumption bottleneck.

[0109] S4. Based on the energy consumption bottleneck and the occurrence conditions, combined with the current product state corresponding to the target industrial product, construct a dynamic adjustment mechanism corresponding to the target industrial product. Based on the dynamic adjustment mechanism, generate an adjustment task list corresponding to the target industrial product, and extract the detailed adjustment tasks in the adjustment task list.

[0110] Based on the energy consumption bottleneck and the occurrence conditions, combined with the current product state corresponding to the target industrial product, the present invention constructs a dynamic adjustment mechanism corresponding to the target industrial product, which can automatically adjust the equipment operating parameters according to the real-time situation, accurately avoid the energy consumption bottleneck, reduce energy waste. At the same time, it can adapt to the change of the product state, continuously optimize the energy consumption, and ensure the efficient and energy-saving operation of the production.

[0111] Among them, the current product state refers to multiple key aspects covering the target industrial product during the production or operation process. For example, from the perspective of production progress, it includes which stage of production the product is in, whether it is the initial processing, intermediate assembly, or final inspection link; in terms of product quality, it involves whether the product meets the established standards and the risk of defective products; in terms of production environmental factors, it covers the impact of conditions such as the temperature, humidity, and cleanliness of the workshop on the product. The dynamic adjustment mechanism refers to a comprehensive system that integrates a series of links such as the analysis of energy consumption bottlenecks and occurrence conditions, the monitoring of the current product state, the construction of a state logic chain, the extraction of logical operating condition factors, the matching of optimal adjustment strategies, and the implementation and feedback of adjustment strategies.

[0112] As an embodiment of the present invention, constructing the dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence conditions in combination with the current product state corresponding to the target industrial product includes: analyzing the energy consumption threshold interval corresponding to the energy consumption bottleneck and the occurrence conditions; generating a state logic chain corresponding to the target industrial product based on the energy consumption threshold interval and the current product state; extracting logical operating condition factors in the state logic chain; matching the optimal adjustment strategy in the preset adjustment strategy library based on the logical operating condition factors; and constructing the dynamic adjustment mechanism corresponding to the target industrial product based on the optimal adjustment strategy.

[0113] Among them, the energy consumption threshold range refers to a range of energy consumption values determined for the energy consumption bottleneck and its occurrence conditions during the production process of the target industrial product. For example, in a certain electronic device manufacturing process, when a specific device is in a certain operating condition (such as continuous high-load operation), the normal energy consumption range is 10-15 degrees per hour. Once it exceeds 15 degrees, it may trigger an energy consumption bottleneck. Then 15 degrees is the upper limit value of the energy consumption threshold range under this specific condition; the state logic chain refers to a logical association structure constructed based on the energy consumption threshold range and the current product state. For example, when the product is in the initial processing state and the equipment is running smoothly, it corresponds to an energy consumption threshold range; as the production progress advances to the assembly stage, if the equipment load increases and the energy consumption threshold range changes, the state logic chain clearly shows the dynamic changes in the energy consumption threshold range and the relevant logical relationships when changing from one product state to another; the logical operating condition factors refer to various factors extracted from the state logic chain that can affect the product energy consumption state. These factors include but are not limited to equipment operating parameters (such as rotational speed, power, etc.), production process conditions (such as temperature, pressure, etc.), raw material characteristics (such as purity, composition, etc.), and external environmental factors (such as workshop temperature, humidity, etc.); the preset adjustment strategy library refers to a set of adjustment strategies formulated and stored in advance for different energy consumption bottlenecks and logical operating condition factors. For example, for the situation where a certain equipment has excessive energy consumption due to long-term high-load operation, the preset adjustment strategy library includes various strategies such as reducing the equipment operating load, optimizing the equipment operating parameters, or performing equipment maintenance; the optimal adjustment strategy refers to the adjustment strategy determined in the preset adjustment strategy library according to the logical operating condition factors in the current product state through a certain screening and matching algorithm, which is most suitable for solving the current energy consumption bottleneck problem and achieving energy consumption optimization.

[0114] Furthermore, the analysis of the energy consumption bottleneck and the corresponding energy consumption threshold range of the occurrence conditions can be achieved through machine learning algorithms. For example, the support vector machine algorithm can be used to classify energy consumption data, identify abnormal energy consumption points, and determine the threshold range. The generation of the state logic chain corresponding to the target industrial product can be achieved through process modeling tools. For example, in the production process modeling of a large steel plant, the BPMN 2.0 modeling tool is used to clearly show the state changes of each production link and the state transition logic related to energy consumption during the process from iron ore feeding to the output of finished steel. The extraction of the logical working condition factors in the state logic chain can be achieved through rule matching methods. For example, based on keyword matching, defining "temperature", "humidity", "rotation speed", "pressure", etc. as keywords for logical working condition factors, when these keywords appear in the state description, the relevant numerical or state information is extracted as logical working condition factors. The matching of the optimal adjustment strategy in the preset adjustment strategy library can be achieved through expert system tools. For example, tools such as CLIPS and Drools can be used. The construction of the dynamic adjustment mechanism corresponding to the target industrial product can be achieved through adaptive control algorithms. For example, in the energy management system of a new energy vehicle, an energy consumption model is established as a reference model, and the actual energy consumption data and operating state information of the vehicle are collected in real time through sensors. The MRAC controller dynamically adjusts the control parameters of the motor, the charge and discharge strategy of the battery, etc. according to the error between the actual energy consumption and the energy consumption of the reference model, realizes the optimal control of the vehicle's energy consumption, and constructs a dynamic adjustment mechanism.

[0115] Based on the dynamic adjustment mechanism, the present invention generates an adjustment task list corresponding to the target industrial product, clarifies the specific adjustment tasks to be executed in each stage, enables a quick understanding of the work focus, and avoids blind operations. At the same time, the list can be used as an execution basis and a progress tracking tool to ensure the orderly progress of adjustment measures, effectively improve energy utilization efficiency, and contribute to energy-saving and efficient product production.

[0116] Among them, the adjustment task list refers to further refining and rationalizing the task execution process and presenting it in the form of a list. The list contains information such as detailed descriptions of each specific task, executors, estimated execution times, and required resources.

[0117] As an embodiment of the present invention, the generation of the adjustment task list corresponding to the target industrial product based on the dynamic adjustment mechanism includes: analyzing the core adjustment path corresponding to the dynamic adjustment mechanism; collecting multi-source information nodes in the core adjustment path; clarifying the task adjustment direction corresponding to the target industrial product based on the multi-source information nodes; formulating the task execution process corresponding to the target industrial product based on the task adjustment direction; and generating the adjustment task list corresponding to the target industrial product based on the task execution process.

[0118] Among them, the core adjustment path refers to the key thread of energy consumption optimization in the dynamic adjustment mechanism. For example, in chemical production, from the adjustment of raw material ratio, to the control of reaction temperature and pressure, and then to the energy consumption adjustment in the product separation link, these closely related steps constitute the core adjustment path; the multi-source information nodes refer to the key positions distributed on the core adjustment path for collecting different types of information. For example, in an automobile manufacturing factory, the real-time energy consumption data of equipment on the production line, the environmental temperature and humidity data in the painting workshop, and the feedback on product quality defects in the assembly link are all information collected by multi-source information nodes; the task adjustment direction refers to the specific direction of energy consumption adjustment of the target industrial product obtained through comprehensive analysis based on the data collected by the multi-source information nodes. For example, if the multi-source information shows that the high energy consumption of a certain equipment is due to long-term high-load operation, the task adjustment direction can be to reduce the equipment load or optimize the equipment operation mode; the task execution process refers to a series of specific operation steps and their sequence carefully planned around the task adjustment direction. For example, to reduce the equipment load, the task execution process can first be to evaluate the load range that the equipment can bear, then formulate specific operation steps for reducing the equipment load (such as adjusting the production rhythm, reducing the processing volume, etc.), monitor the equipment operation status in real time during the execution process, and check whether the energy consumption has been reduced to the expected range after completion.

[0119] Furthermore, the analysis of the core adjustment path corresponding to the dynamic adjustment mechanism can be achieved through the critical path algorithm. For example: using the PERT (Program Evaluation and Review Technique) algorithm, by calculating the earliest start time, earliest completion time, latest start time, and latest completion time of tasks, determine the path with the longest total duration, that is, the core adjustment path; the collection of multi-source information nodes in the core adjustment path can be achieved through the data collection scheduling algorithm. For example: the oil temperature sensor of the main transformer in the substation and the current sensor data of the high-voltage transmission line, and through reasonable scheduling, ensure that under limited network resources, the data of multi-source information nodes is collected comprehensively and in a timely manner; the clarification of the task adjustment direction corresponding to the target industrial product can be achieved through the decision tree algorithm. For example: according to the decision tree model, predict the energy consumption reduction effects under different adjustment measures such as adjusting the reaction temperature and changing the raw material ratio, and select the direction corresponding to the adjustment measure with the best energy consumption reduction effect as the task adjustment direction; the formulation of the task execution process corresponding to the target industrial product can be achieved through the genetic algorithm. For example: using the genetic algorithm, encode information such as the sequence of equipment adjustment operations and parameter settings into chromosomes, calculate the fitness corresponding to each chromosome (such as a comprehensive index of energy consumption reduction and execution time), select chromosomes with high fitness for crossover and mutation operations, and after multiple generations of evolution, obtain the optimal task execution process; the generation of the adjustment task list corresponding to the target industrial product can be achieved through task generation tools. For example: tools such as Todoist and Wunderlist.

[0120] By extracting the detailed adjustment tasks in the adjustment task list, the present invention can reasonably allocate resources and plan time according to the detailed tasks, greatly improving the efficiency and effectiveness of the energy consumption adjustment work and helping to steadily achieve the energy-saving goal of industrial products.

[0121] Among them, the detailed adjustment task refers to the refined breakdown of the energy consumption optimization work, which covers all links from pre-preparation to specific operations and then to subsequent inspections. For example, when adjusting the energy consumption of equipment, the detailed adjustment task includes preparing the professional tools required for adjustment, adjusting the operating state of the equipment according to specific processes and parameters, and checking the operating stability of the equipment and the energy consumption change situation after the adjustment. Optionally, the extraction of the detailed adjustment tasks in the adjustment task list can be achieved through task extraction tools, such as tools like Jira and Trello.

[0122] S5. Analyze the energy-saving goals corresponding to the detailed adjustment tasks, identify the energy-saving obstacle factors in the energy-saving goals, based on the energy-saving obstacle factors, detect the production feedback data corresponding to the target industrial product, and based on the production feedback data, generate the energy consumption control plan corresponding to the target industrial product.

[0123] By analyzing the energy-saving goals corresponding to the detailed adjustment tasks, the present invention clarifies the expected energy-saving effect of each task, focuses on the key links, and at the same time helps to accurately evaluate the effectiveness of the adjustment strategy, timely adjust the task execution direction, ensure the steady reduction of the energy consumption of industrial products, and achieve efficient energy-saving production.

[0124] Among them, the energy-saving goal refers to the specific index of the expected energy consumption reduction or energy utilization efficiency improvement after the implementation of the detailed adjustment task. For example, an enterprise plans to reduce the unit energy consumption of a certain product by 15% within the next six months through a series of detailed adjustment tasks, and this 15% energy consumption reduction ratio is the energy-saving goal corresponding to this product.

[0125] As an embodiment of the present invention, the analysis of the energy-saving goals corresponding to the detailed adjustment tasks includes: querying the energy-saving benchmark data corresponding to the energy-saving goals; based on the energy-saving benchmark data, analyzing the energy consumption ratio index corresponding to the energy-saving goals; based on the energy consumption ratio index, generating the energy consumption change curve corresponding to the energy-saving goals; extracting the energy consumption correlation nodes in the energy consumption change curve; and based on the energy consumption correlation nodes, analyzing the energy-saving goals corresponding to the detailed adjustment tasks.

[0126] Among them, the energy-saving benchmark data refers to the basic reference data for measuring the energy-saving effect, which covers various energy consumption-related indicators under the normal production operation state of the target industrial product before any detailed adjustment tasks are implemented. For example, in a certain factory, a certain production line consumed 5,000 degrees of electricity for every 100 products produced on average in the past month. The 5,000 degrees of electricity / 100 products is the energy consumption benchmark data of this production line during this period; the energy consumption ratio index refers to a relative index obtained by comparing the energy consumption data after implementing energy-saving measures with the energy-saving benchmark data. For example, after a series of detailed adjustment tasks, the electricity consumption for producing 100 products on this production line has dropped to 4,000 degrees. Then the energy consumption ratio index of the electricity consumption is (4,000÷5,000)×100% = 80%, indicating that the energy consumption has been reduced to 80% of the original compared to the benchmark state; the energy consumption change curve refers to a curve plotted with time or production process as the horizontal axis and the energy consumption ratio index or actual energy consumption value as the vertical axis. For example, within a week of parameter optimization adjustment of a certain industrial equipment, the energy consumption data of the equipment is recorded every day and the energy consumption ratio index is calculated. The curve formed by connecting these data points can clearly show how the energy consumption gradually decreases or fluctuates in some stages; the energy consumption correlation node refers to a point on the energy consumption change curve that has special significance or important value for energy-saving analysis. For example, in the energy consumption change curve, when the equipment replaces energy-saving components, the energy consumption drops significantly. The point on the curve corresponding to this time point is an energy consumption correlation node.

[0127] Further, the query of the energy-saving benchmark data corresponding to the energy-saving target can be achieved through time series decomposition algorithms. For example, in a certain chemical enterprise, the monthly energy consumption data for the past three years is decomposed using the STL algorithm to obtain the trend and seasonal characteristics of the energy consumption, and the energy-saving benchmark energy consumption data for each month in the next year is predicted. The analysis of the energy consumption ratio index corresponding to the energy-saving target can be achieved through moving average algorithms. For example, setting the moving average period to 5 time points, for the energy consumption ratio index at the nth time point, calculate the average value of the energy consumption ratio indexes from the (n - 4)th to the nth time point as the smoothed energy consumption ratio index. The generation of the energy consumption change curve corresponding to the energy-saving target can be achieved through data visualization methods. For example, taking time as the horizontal axis and the energy consumption ratio index or real-time energy consumption data as the vertical axis, mark the energy consumption data corresponding to each time point in the coordinate system, and then connect these points with line segments in sequence to form an energy consumption change curve. The extraction of the energy consumption correlation nodes in the energy consumption change curve can be achieved through data mining tools. For example, organize the energy consumption change curve data into a format recognizable by the Weka tool, run the DBSCAN algorithm, and set appropriate parameters, and the algorithm will automatically mark the energy consumption correlation nodes. The analysis of the energy-saving target corresponding to the detailed adjustment task can be achieved through multi-objective optimization algorithms, such as the NSGA-II algorithm, etc.

[0128] By identifying the energy-saving hindrance factors in the energy-saving target, the present invention can provide a clear improvement direction for energy-saving work, formulate targeted countermeasures, avoid blind resource investment, improve the effectiveness of energy-saving measures, and at the same time, help anticipate potential risks in advance, ensure the smooth progress of energy-saving projects, and ensure the efficient achievement of energy-saving targets.

[0129] Among them, the energy-saving hindrance factors refer to various elements that impede energy consumption reduction and energy utilization efficiency improvement in the process of achieving the energy-saving target. At the equipment level, it can be that old and inefficient equipment has high energy consumption and backward technology, making it difficult to achieve energy savings through conventional adjustments. In terms of processes, complex, cumbersome or unreasonable process flows will increase energy consumption. For example, in chemical production, there are too many reaction steps and large energy losses. Optionally, the identification of the energy-saving hindrance factors in the energy-saving target can be achieved through energy audit tools. For example, in a certain machinery manufacturing enterprise, through the analysis using the EnergiSEER tool, it is found that some high-energy-consuming equipment shares the same energy line with low-energy-consuming equipment, resulting in large energy transmission losses, and this energy distribution problem is identified as an energy-saving hindrance factor.

[0130] Further, based on the energy-saving hindrance factors, the present invention detects the production feedback data corresponding to the target industrial product, can quickly judge how the hindrance factors affect the production link, and then evaluate the effect of energy-saving measures, which helps to adjust strategies in a timely manner, efficiently break through the energy-saving bottleneck, promote the continuous optimization of the production of industrial products towards the energy-saving target, and improve the overall energy utilization efficiency.

[0131] Among them, the production feedback data refers to various types of information collected from each link in the industrial production process that reflects the actual production situation. For example, the power and heat energy consumption values at each production stage directly reflect the energy utilization situation; product quality data, including the qualified rate, defective rate of products, and specific quality inspection indicators, can reflect the impact of the production process on product quality; and there is also production process data, such as the duration and connection sequence of each process. Optionally, the detection of the production feedback data corresponding to the target industrial product can be achieved through a data fusion algorithm. For example, in chemical production, multiple temperature sensors measure the temperature inside the reaction kettle, and the Kalman filtering algorithm is used to fuse these measurement data to obtain more accurate reaction kettle temperature data as part of the production feedback data.

[0132] Furthermore, based on the production feedback data, the present invention generates an energy consumption control scheme corresponding to the target industrial product, which can identify key energy consumption control points, formulate targeted strategies, and avoid blind implementation. This can not only effectively reduce energy consumption, but also improve production efficiency, reduce energy waste, and help achieve a win-win situation of energy conservation and benefits in the production of industrial products.

[0133] Among them, the energy consumption control scheme refers to a set of systematic management strategies for energy consumption in the industrial production process, which covers specific measures in multiple aspects such as the equipment level, process links, and management processes. At the equipment level, it includes the upgrade and transformation plan for old equipment and the energy-saving selection criteria for new equipment; in terms of technology, it involves the optimization and adjustment of the existing production process, such as simplifying operation steps and improving reaction efficiency; the management process includes formulating energy usage specifications and establishing an energy consumption monitoring and assessment mechanism. Optionally, the generation of the energy consumption control scheme corresponding to the target industrial product can be achieved through industrial simulation tools, such as Aspen Plus, Arena, etc.

[0134] Compared with the problems described in the background technology, the present invention can accurately locate abnormal energy consumption points by acquiring the automation equipment corresponding to the target industrial product, and then determine the basic control mode. At the same time, this helps to coordinate the collaborative work of equipment, reduce communication delays and poor data interaction, improve overall production efficiency, and achieve efficient and energy-saving operation of the industrial automation system. Based on the basic control mode, the present invention constructs an energy consumption optimization process corresponding to the target industrial product, and identifies the process links in the energy consumption optimization process, which helps to accurately locate the key nodes of energy consumption optimization. For example, exclusive energy-saving measures are formulated in the links such as equipment start-up and shutdown, and operation parameter adjustment, which can greatly improve energy utilization efficiency and reduce production costs, thereby improving the balance between energy saving and production benefits. Furthermore, based on the energy-saving operation strategy, the present invention evaluates the energy consumption performance of the target industrial product under different working conditions, can intuitively present the product energy consumption status, and accurately locate high-energy consumption conditions. This can optimize the strategy, reduce energy waste, and improve energy utilization efficiency. At the same time, it provides a basis for product research and development and improvement, helps to create more energy-saving products, and promotes industrial production to develop in a green and efficient direction. Furthermore, the present invention is based on the energy consumption bottleneck and the occurrence conditions combined with the current product status corresponding to the target industrial product, and constructs a dynamic adjustment mechanism corresponding to the target industrial product. It can automatically adjust the equipment operation parameters according to the real-time situation, accurately avoid energy consumption bottlenecks, and reduce energy waste. At the same time, it can adapt to changes in product status, continuously optimize energy consumption, and ensure efficient and energy-saving production. Finally, the present invention analyzes the energy-saving goals corresponding to the detailed adjustment tasks, clarifies the energy-saving results expected to be achieved by each task, and focuses on key links. At the same time, it helps to accurately evaluate the effectiveness of the adjustment strategy, timely adjust the direction of task execution, ensure that the energy consumption of industrial products is steadily reduced, and achieve efficient and energy-saving production. Therefore, the integrated control method and system of efficient and energy-saving industrial automation products provided by the embodiments of the present invention can improve the control efficiency of industrial automation systems.

[0135] Embodiment 2:

[0136] like Figure 2 The figure shows a functional module diagram of an integrated control system of a high-efficiency and energy-saving industrial automation product of the present invention.

[0137] The integrated control system 200 of an efficient and energy-saving industrial automation product described in the present invention can be installed in an electronic device. According to the functions to be implemented, the integrated control system of the efficient and energy-saving industrial automation product can include a mode determination module 201, a strategy generation module 202, a condition analysis module 203, a task extraction module 204 and a solution generation module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0138] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0139] The mode determination module 201 is configured to obtain the automation equipment corresponding to the target industrial product, query the historical energy consumption data corresponding to the automation equipment, identify the abnormal energy consumption points exceeding the preset energy consumption threshold in the historical energy consumption data, and determine the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points;

[0140] The strategy generation module 202 is configured to construct an energy consumption optimization process corresponding to the target industrial product based on the basic control mode, identify the process links in the energy consumption optimization process, calculate the energy consumption efficiency of the target industrial product under different process links, determine the optimal operation mode corresponding to the target industrial product according to the energy consumption efficiency, and generate an energy-saving operation strategy corresponding to the target industrial product based on the optimal operation mode;

[0141] The condition analysis module 203 is configured to evaluate the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy, query the energy consumption bottlenecks existing in the target industrial product based on the energy consumption performance, and analyze the occurrence conditions corresponding to the energy consumption bottlenecks;

[0142] The task extraction module 204 is configured to construct a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck, the occurrence conditions, and the current product state corresponding to the target industrial product, generate an adjustment task list corresponding to the target industrial product based on the dynamic adjustment mechanism, and extract the detailed adjustment tasks in the adjustment task list;

[0143] The solution generation module 205 is configured to analyze the energy-saving goals corresponding to the detailed adjustment tasks, identify the energy-saving obstacle factors in the energy-saving goals, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacle factors, and generate an energy consumption control solution corresponding to the target industrial product based on the production feedback data.

[0144] Specifically, each module in the integrated control system 200 of the high-efficiency energy-saving industrial automation product in the embodiments of the present invention uses the same technical means as the Figure 1 integrated control method of the high-efficiency energy-saving industrial automation product described above, and can produce the same technical effects, which will not be elaborated here.

[0145] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An integrated control method for an energy-efficient industrial automation product, characterized in that, The method includes: Obtain the automation equipment corresponding to the target industrial product, query the historical energy consumption data corresponding to the automation equipment, and identify the abnormal energy consumption points in the historical energy consumption data that exceed the preset energy consumption threshold. Based on the abnormal energy consumption points, determine the basic control mode corresponding to the target industrial product. Among them, the step of determining the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points includes: Analyze the time series characteristics of the occurrence of the abnormal energy consumption points; Sort out the energy consumption fluctuation law corresponding to the time series characteristics; Identify the abnormal working condition energy consumption corresponding to the energy consumption fluctuation law; Formulate an energy consumption adjustment strategy corresponding to the abnormal working condition energy consumption; Based on the energy consumption adjustment strategy, determine the basic control mode corresponding to the target industrial product; Based on the basic control mode, construct an energy consumption optimization process corresponding to the target industrial product, identify the process links in the energy consumption optimization process, and calculate the energy consumption efficiency of the target industrial product under different process links. Among them, the step of calculating the energy consumption efficiency of the target industrial product under different process links includes: Use the following formula to calculate the energy consumption efficiency of the target industrial product under different process links: ； Among them, represents the energy consumption efficiency of the target industrial product under different process links, represents the number of types corresponding to the target industrial product, represents the type index corresponding to the target industrial product, represents the unit value corresponding to the th target industrial product, represents the process output corresponding to the th target industrial product, represents the number of links corresponding to the process link, represents the start time of the process link, represents the end time of the process link, represents the th process link's power consumption function at time Determine the optimal operation mode corresponding to the target industrial product according to the energy consumption efficiency, and generate an energy-saving operation strategy corresponding to the target industrial product based on the optimal operation mode; Based on the energy-saving operation strategy, evaluate the energy consumption performance of the target industrial product under different working conditions. Among them, the step of evaluating the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy includes: Sort out the key strategy points in the energy-saving operation strategy; Based on the key strategy points, determine the parameter change gradient of the target industrial product under different working conditions; Based on the parameter change gradient, query the parameter regulation range under different working conditions; Analyze the actual energy consumption index corresponding to the parameter regulation range; Based on the actual energy consumption index, evaluate the energy consumption performance of the target industrial product under different working conditions. Based on the energy consumption performance, query the energy consumption bottleneck existing in the target industrial product and analyze the occurrence conditions corresponding to the energy consumption bottleneck; Based on the energy consumption bottleneck and the occurrence conditions, combined with the current product state corresponding to the target industrial product, construct a dynamic adjustment mechanism corresponding to the target industrial product. Among them, the step of constructing a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence conditions, combined with the current product state corresponding to the target industrial product, includes: Analyze the energy consumption threshold interval corresponding to the energy consumption bottleneck and the occurrence conditions; Based on the energy consumption threshold interval and the current product state, generate a state logic chain corresponding to the target industrial product; Extract the logical working condition factors in the state logic chain; Based on the logical working condition factors, match the optimal adjustment strategy in the preset adjustment strategy library; Based on the optimal adjustment strategy, construct a dynamic adjustment mechanism corresponding to the target industrial product. Based on the dynamic adjustment mechanism, generate an adjustment task list corresponding to the target industrial product, and extract the detailed adjustment tasks in the adjustment task list; Analyze the energy-saving objectives corresponding to the detailed adjustment tasks, identify the energy-saving obstacles in the energy-saving objectives, based on the energy-saving obstacles, detect the production feedback data corresponding to the target industrial product, and based on the production feedback data, generate an energy consumption control plan for the target industrial product.

2. The integrated control method of the high-efficiency and energy-saving industrial automation product according to claim 1, characterized in that, The generating of the energy-saving operation strategy for the target industrial product based on the optimal operation mode includes: Analyze the real-time energy consumption data of each device under the optimal operation mode; Query the optimizable adjustment parameters in the real-time energy consumption data; Conduct multi-combination simulation tests on the optimizable adjustment parameters to obtain a simulation test group; Calculate the energy consumption reduction ratio corresponding to the parameters in the simulation test group; Based on the energy consumption reduction ratio, generate an energy-saving operation strategy for the target industrial product.

3. The integrated control method of the high-efficiency and energy-saving industrial automation product according to claim 2, characterized in that, The calculating of the energy consumption reduction ratio corresponding to the parameters in the simulation test group includes: Use the following formula to calculate the energy consumption reduction ratio corresponding to the parameters in the simulation test group: ； Among them, represents the energy consumption reduction ratio corresponding to the parameters in the simulation test group, represents the number of test cases in the simulation test group, represents the index of the number of test cases, represents the th energy consumption baseline value of the device corresponding to the test case within the time interval , represents the start time of the simulation test, represents the end time of the simulation test, represents in the th test case, the energy consumption power function of the device at time .

4. The integrated control method of the high-efficiency energy-saving industrial automation product according to claim 1, characterized in that, The generating of the adjustment task list for the target industrial product based on the dynamic adjustment mechanism includes: Analyze the core adjustment path corresponding to the dynamic adjustment mechanism; Collect multi-source information nodes in the core adjustment path; Based on the multi-source information nodes, clarify the task adjustment direction for the target industrial product; Based on the task adjustment direction, formulate a task execution process for the target industrial product; Based on the task execution process, generate an adjustment task list for the target industrial product.

5. The integrated control method of the high-efficiency energy-saving industrial automation product according to claim 1, characterized in that The analyzing of the energy-saving objectives corresponding to the detailed adjustment tasks includes: Query the energy-saving benchmark data corresponding to the energy-saving objectives; Based on the energy-saving benchmark data, analyze the energy consumption ratio index corresponding to the energy-saving objectives; Based on the energy consumption ratio index, generate an energy consumption change curve for the energy-saving objectives; Extract the energy consumption correlation nodes in the energy consumption change curve; Based on the energy consumption correlation nodes, analyze the energy-saving objectives corresponding to the detailed adjustment tasks.

6. An integrated control system for an energy-efficient industrial automation product, characterized in that, The system includes: A mode determination module, configured to obtain the automation equipment corresponding to the target industrial product, query the historical energy consumption data corresponding to the automation equipment, and identify the abnormal energy consumption points in the historical energy consumption data that exceed the preset energy consumption threshold. Based on the abnormal energy consumption points, determine the basic control mode for the target industrial product. Among them, the determining of the basic control mode for the target industrial product based on the abnormal energy consumption points includes: Analyze the time series characteristics of the occurrence of the abnormal energy consumption points; Sort out the energy consumption fluctuation law corresponding to the time series characteristics; Identify the abnormal working condition energy consumption corresponding to the energy consumption fluctuation law; Formulate an energy consumption adjustment strategy for the abnormal working condition energy consumption; Based on the energy consumption adjustment strategy, determine the basic control mode for the target industrial product; A strategy generation module, configured to construct an energy consumption optimization process for the target industrial product based on the basic control mode, identify the process links in the energy consumption optimization process, and calculate the energy consumption efficiency of the target industrial product under different process links. Among them, the calculating of the energy consumption efficiency of the target industrial product under different process links includes: Calculate the energy consumption efficiency of the target industrial product under different process links using the following formula: ； Among them, represents the energy consumption efficiency of the target industrial product under different process links, represents the number of types corresponding to the target industrial product, represents the type index corresponding to the target industrial product, represents the unit value corresponding to the th type of the target industrial product, represents the process output corresponding to the th type of the target industrial product, represents the number of links corresponding to the process link, represents the start time of the process link, represents the end time of the process link, represents the th process link's power consumption function at time moment. According to the energy consumption efficiency, determine the optimal operation mode corresponding to the target industrial product, and based on the optimal operation mode, generate an energy-saving operation strategy corresponding to the target industrial product; A condition analysis module, configured to evaluate the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy. Among them, evaluating the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy includes: Sort out the key strategy points in the energy-saving operation strategy; Based on the key strategy points, determine the parameter change gradient of the target industrial product under different working conditions; Based on the parameter change gradient, query the parameter adjustment range under different working conditions; Analyze the actual energy consumption indicators corresponding to the parameter adjustment range; Based on the actual energy consumption indicators, evaluate the energy consumption performance of the target industrial product under different working conditions. Based on the energy consumption performance, query the energy consumption bottlenecks existing in the target industrial product and analyze the occurrence conditions corresponding to the energy consumption bottlenecks; A task extraction module, configured to construct a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck, the occurrence conditions, and the current product state corresponding to the target industrial product. Among them, constructing a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck, the occurrence conditions, and the current product state corresponding to the target industrial product includes: Analyze the energy consumption threshold interval corresponding to the energy consumption bottleneck and the occurrence conditions; Based on the energy consumption threshold interval and the current product state, generate a state logic chain corresponding to the target industrial product; Extract the logical working condition factors in the state logic chain; Based on the logical working condition factors, match the optimal adjustment strategy in the preset adjustment strategy library; Based on the optimal adjustment strategy, construct a dynamic adjustment mechanism corresponding to the target industrial product. Based on the dynamic adjustment mechanism, generate an adjustment task list corresponding to the target industrial product, and extract the detailed adjustment tasks in the adjustment task list; A solution generation module, configured to analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacle factors in the energy-saving target, based on the energy-saving obstacle factors, detect the production feedback data corresponding to the target industrial product, and based on the production feedback data, generate an energy consumption control solution corresponding to the target industrial product.

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