Integrated control method and system for efficient and energy-saving industrial automation product

Abstract

The invention relates to the field of automation control, and discloses an integrated control method and system for an efficient and energy-saving industrial automation product, and the method comprises the steps: firstly obtaining the historical energy consumption data of automation equipment of a target industrial product, recognizing an abnormal point to determine a basic control mode, and constructing an energy consumption optimization process according to the basic control mode; the method comprises the steps of determining an optimal operation mode and generating an energy-saving operation strategy according to energy consumption efficiency of different process links, then evaluating energy consumption performance under different working conditions, finding out energy consumption bottlenecks and occurrence conditions, constructing a dynamic adjustment mechanism, generating an adjustment task list and extracting detailed adjustment tasks, and finally analyzing an energy-saving target and identifying hindering factors. And generating an energy consumption control scheme based on the production feedback data. The control efficiency of the industrial automation system can be improved.

Description

Technical Field

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

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

[0003] At present, the integrated control methods adopted by most industrial automation products have obvious shortcomings in energy saving and efficiency. On the one hand, traditional integrated control methods often lack accurate real-time monitoring and intelligent regulation of the equipment's operating status, resulting in frequent energy waste during the operation of the equipment. For example, the motor still maintains high power operation when it is lightly loaded, which greatly increases energy consumption. On the other hand, the existing integrated control methods are relatively inefficient when coordinating multiple automation products to work together. Communication delays and poor data interaction between devices make it difficult to achieve efficient connection in the production process, which seriously restricts the improvement of overall production efficiency. Therefore, an efficient 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 a high-efficiency and energy-saving industrial automation product, the main purpose of which is to improve the control efficiency of the industrial automation system.

[0005] To achieve the above-mentioned purpose, the present invention provides an integrated control method for a high-efficiency and energy-saving industrial automation product, comprising: Acquire 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, and determine the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points; 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, calculate the energy consumption efficiency of the target industrial product in 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; Based on the energy-saving operation strategy, the energy consumption performance of the target industrial product under different working conditions is evaluated, based on the energy consumption performance, the energy consumption bottleneck of the target industrial product is queried, and the occurrence conditions corresponding to the energy consumption bottleneck are analyzed; Based on the energy consumption bottleneck and the occurrence conditions combined with the current product status corresponding to the target industrial product, a dynamic adjustment mechanism corresponding to the target industrial product is constructed, based on the dynamic adjustment mechanism, an adjustment task list corresponding to the target industrial product is generated, and detailed adjustment tasks in the adjustment task list are extracted; Analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacles in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacles, and generate the energy consumption control plan corresponding to the target industrial product based on the production feedback data.

[0006] Optionally, determining the basic control mode corresponding to the target industrial product based on the abnormal energy consumption point includes: Analyze the time series characteristics of the abnormal energy consumption points; Sorting out the energy consumption fluctuation patterns corresponding to the time series characteristics; Identify the abnormal operating condition energy consumption corresponding to the energy consumption fluctuation law; Formulate an energy consumption adjustment strategy corresponding to the energy consumption under the abnormal operating condition; Based on the energy consumption regulation strategy, a basic control mode corresponding to the target industrial product is determined.

[0007] Optionally, the calculating the energy consumption efficiency of the target industrial product in different process links includes: The energy efficiency of the target industrial product in different process links is calculated using the following formula: ; in, Indicates the energy efficiency of the target industrial product in different process links, Indicates the number of types corresponding to the target industrial product, The category index corresponding to the target industrial product, Indicates The unit value corresponding to the target industrial product, Indicates The process output corresponding to the target industrial product, Indicates the number of links 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 process links in time Power consumption function at time.

[0008] Optionally, generating an energy-saving operation strategy corresponding to 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; Querying the optimizable adjustable parameters in the real-time energy consumption data; Performing multiple combination simulation tests on the optimizable adjustable parameters to obtain a simulation test group; Calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group; Based on the energy consumption reduction ratio, an energy-saving operation strategy corresponding to the target industrial product is generated.

[0009] Optionally, the calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group includes: The energy consumption reduction ratio corresponding to the parameters in the simulation test group is calculated using the following formula: ; in, Indicates 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, Indicates the number index of test cases, Indicates The device corresponding to the test case is in the time interval Energy consumption benchmark value within Indicates the start time of the simulation test. Indicates the end time of the simulation test. Indicated in In the test case, the device Energy consumption power function at time.

[0010] Optionally, the evaluating 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 strategic points in the energy-saving operation strategy; Based on the key strategic points, determining the parameter change gradient of the target industrial product under different working conditions; Based on the parameter change gradient, query the parameter control range under different working conditions; Analyze the actual energy consumption index corresponding to the parameter control range; Based on the actual energy consumption index, the energy consumption performance of the target industrial product under different working conditions is evaluated.

[0011] Optionally, the constructing of a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence condition in combination with the current product state corresponding to the target industrial product includes: Analyze the energy consumption bottleneck and the energy consumption threshold interval corresponding to the occurrence condition; Based on the energy consumption threshold interval and the current product state, generating a state logic chain corresponding to the target industrial product; Extracting logical operating conditions factors in the state logic chain; Based on the logical operating conditions, matching the optimal regulation strategy in the preset regulation strategy library; Based on the optimal regulation strategy, a dynamic regulation mechanism corresponding to the target industrial product is constructed.

[0012] Optionally, generating an adjustment task list corresponding to the target industrial product based on the dynamic adjustment mechanism includes: Analyze the core regulation path corresponding to the dynamic regulation mechanism; Collecting multi-source information nodes in the core regulation path; Based on the multi-source information nodes, clarify the task adjustment direction corresponding to the target industrial product; Based on the task adjustment direction, formulate the task execution process corresponding to the target industrial product; Based on the task execution process, an adjustment task list corresponding to the target industrial product is generated.

[0013] Optionally, the analyzing the energy saving target corresponding to the detailed adjustment task includes: Query the energy-saving benchmark data corresponding to the energy-saving target; Based on the energy-saving benchmark data, analyzing the energy consumption proportional index corresponding to the energy-saving target; Based on the energy consumption geometric index, generating an energy consumption change curve corresponding to the energy saving target; Extracting energy consumption related nodes in the energy consumption change curve; Based on the energy consumption associated nodes, the energy saving target corresponding to the detailed adjustment task is analyzed.

[0014] In order to solve the above problems, the present invention also provides an integrated control system for high-efficiency and energy-saving industrial automation products, the system comprising: A mode determination module is used to 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; A strategy generation module is used 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 in 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; A condition analysis module, for evaluating the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy, querying the energy consumption bottleneck of the target industrial product based on the energy consumption performance, and analyzing the occurrence conditions corresponding to the energy consumption bottleneck; A task extraction module is used to construct a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence condition combined with the current product status 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 detailed adjustment tasks in the adjustment task list; A scheme generation module is used to analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacles in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacles, and generate an energy consumption control scheme corresponding to the target industrial product based on the production feedback data.

[0015] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of an integrated control method for an energy-efficient industrial automation product provided by an embodiment of the present invention; Figure 2 A schematic diagram of a module for implementing an integrated control system for an efficient and energy-saving industrial automation product provided by an embodiment of the present invention.

[0017] 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

[0018] 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.

[0019] 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.

[0020] Embodiment 1: 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: 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.

[0021] 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.

[0022] Among them, the target industrial products refer to a very wide range of products. In the manufacturing industry, for example, complete vehicle products in the field of automobile manufacturing, from parts processing to vehicle assembly, the entire production process requires efficient and energy-saving control to reduce costs and increase production capacity; in the manufacturing of electronic products, mobile phones, computers and other products have strict requirements on the energy consumption and operating efficiency of equipment during their production process to meet large-scale production and environmental protection needs; the automation equipment refers to the key carrier for achieving 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., which can adjust operating parameters in real time according to production tasks to reduce energy waste through precise program control while ensuring product quality; Industrial robots are widely used in material handling, parts assembly and other links. They can efficiently complete complex tasks according to preset instructions and improve 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 boiler control systems in thermal power generation, fan pitch and speed regulation systems in wind power generation, etc., and automatically adjust according to energy demand and equipment operating conditions to achieve efficient power generation and energy saving. Optionally, the acquisition of automated equipment corresponding to the target industrial product can be achieved through equipment recognition technology, such as: arranging cameras in the workshop to collect images, and algorithms to identify equipment shapes, logos and other features to determine whether it is the equipment required for the target industrial product. Combined with the equipment networking information, the corresponding automated equipment can be determined.

[0023] Furthermore, the present invention can accurately locate the root cause of excessive energy consumption of the equipment by querying the historical energy consumption data corresponding to the automation equipment and identifying abnormal energy consumption points in the historical energy consumption data that exceed the preset energy consumption threshold. This helps to timely discover potential equipment failure hazards and avoid waste of resources due to high energy consumption operation, thereby significantly improving the energy-saving benefits of industrial production and the stability of equipment operation.

[0024] Among them, the historical energy consumption data refers to a detailed record set of energy consumption of automation equipment in the past operation cycle. These data cover different time nodes, such as electricity, fuel, steam and other energy usage statistics by hour, day and month, and are related to equipment operation status information, such as equipment speed, load rate, operation time, etc. Taking the factory's motor equipment as an example, its historical energy consumption data will include the power consumption of different shifts on each working day of the year, as well as the load conditions of the motors in the corresponding time period. It is the basic data source for analyzing equipment energy consumption trends and rules; the preset energy consumption threshold refers to the energy consumption limit value set according to the design specifications of the equipment, the industry's average energy consumption standards, the company's energy-saving goals and the analysis of past long-term operation data. It can be a fixed value. For example, for a certain model of energy-saving lamps, the daily power consumption threshold is set to 0.5 degrees based on its rated power and ideal working efficiency; it can also be a dynamic range, such as refrigeration equipment with obvious seasonal changes, according to the ambient temperature and The frequency of use sets different energy consumption intervals 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, under a normal processing flow, the energy consumption per unit time of a CNC machine tool is stable at 2-3 kWh, but at a certain moment the energy consumption suddenly soars to 5 kWh. The time point when the energy consumption value abnormally increases is the abnormal energy consumption point. Optionally, the query of the historical energy consumption data corresponding to the automation equipment can be implemented through a database query method, such as: by writing an SQL query statement, using the device unique identifier (such as device ID) as a query condition, and associating the energy consumption data table, the historical energy consumption data of the specified automation equipment can be obtained; the identification of abnormal energy consumption points in the historical energy consumption data that exceed the preset energy consumption threshold can be implemented through an isolation forest algorithm, such as: after constructing an isolation forest model, new data points are predicted, and points with scores exceeding a certain threshold are determined to be abnormal energy consumption points, that is, data points that deviate from the normal energy consumption pattern.

[0025] Furthermore, the present invention determines the basic control mode corresponding to the target industrial product based on the abnormal energy consumption point, accurately indicates the deficiencies of the current control mode of the equipment, and adjusts the basic control mode based on this, so that the operation of the equipment can be more in line with actual needs, avoid energy waste, and improve the overall energy utilization efficiency, thereby achieving the goal of energy saving and efficiency improvement.

[0026] Among them, the basic control mode refers to a comprehensive energy consumption regulation strategy, which is a set of basic equipment operation control methods constructed for target industrial products. 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.

[0027] As an embodiment of the present invention, determining the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points includes: analyzing the time series characteristics of the abnormal energy consumption points; sorting out the energy consumption fluctuation patterns corresponding to the time series characteristics; identifying the abnormal operating energy consumption corresponding to the energy consumption fluctuation patterns; formulating an energy consumption adjustment strategy corresponding to the abnormal operating energy consumption; and determining the basic control mode corresponding to the target industrial product based on the energy consumption adjustment strategy.

[0028] Among them, the time series characteristics refer to the set of characteristics presented by abnormal energy consumption points in the time dimension, such as whether they appear periodically or randomly; the distribution of time points, such as whether they are concentrated in specific time periods on weekdays or frequently appear in the peak production season; and the trend of energy consumption changes over time, such as whether the abnormal energy consumption values ​​gradually increase or decrease over time; the energy consumption fluctuation law refers to the regular pattern of energy consumption changes of automation equipment over time summarized based on time series characteristics. For example, it may show a pattern that energy consumption rises rapidly at the beginning of equipment startup, then tends to be stable, and then energy consumption fluctuates greatly when switching to specific production tasks; or at fixed time periods every day, due to production process adjustments, energy consumption shows periodic ups and downs; the abnormal operating condition energy consumption refers to the energy consumption corresponding to the equipment operation process. Energy consumption under specific working conditions where abnormal energy consumption occurs in the 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 operating task remains unchanged, the energy consumption increases significantly, this energy consumption state also belongs to abnormal working condition energy consumption; the energy consumption adjustment strategy refers to a series of methods and measures formulated based on the identified abnormal working condition energy consumption to reduce energy consumption and optimize equipment operation, which may include adjusting equipment operating parameters, such as reducing motor speed when the equipment is lightly loaded to reduce unnecessary energy consumption; changing the production process sequence, reasonably arranging equipment start and stop time, and avoiding equipment idling energy consumption; or maintaining the equipment, replacing aging parts, and improving equipment operation efficiency, thereby reducing energy consumption.

[0029] Furthermore, the analysis of the time series characteristics of the abnormal energy consumption points can be achieved through the autocorrelation function, such as: by drawing the ACF and PACF diagrams, the correlation characteristics of the time series can be intuitively observed, thereby obtaining the time series characteristics of the abnormal energy consumption points; the combing of the energy consumption fluctuation law corresponding to the time series characteristics can be achieved through the time series decomposition algorithm, such as: by observing the changes in trends, seasonality and residuals, combing out the energy consumption fluctuation law, such as whether there is a long-term upward or downward trend, and seasonal fluctuation patterns; the identification of the abnormal operating condition energy consumption corresponding to the energy consumption fluctuation law can be achieved through a clustering algorithm, such as: arranging the energy consumption data into a suitable format, using DBSCAN (eps=3, min_samples=2).fit is used for clustering, and the energy consumption corresponding to the point with label -1 is the energy consumption under abnormal working condition; the energy consumption adjustment strategy corresponding to the energy consumption under abnormal working condition can be implemented by reinforcement learning algorithm, such as: taking the energy consumption under abnormal working condition as the environmental state, the energy consumption adjustment strategy as the action, and reducing energy consumption as the reward goal, and training the intelligent agent to learn the optimal adjustment strategy; the basic control mode corresponding to the target industrial product can be determined by hierarchical analysis method, such as: dividing the multiple evaluation indicators of the basic control mode (such as energy consumption, production efficiency, equipment life, etc.) into levels, determining the weight of each indicator by pairwise comparison, and then comprehensively determining the optimal basic control mode by the scores of each energy consumption adjustment strategy.

[0030] S2. 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, calculate the energy consumption efficiency of the target industrial product in 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] As an embodiment of the present invention, the calculating the energy consumption efficiency of the target industrial product in different process links includes: The energy efficiency of the target industrial product in different process links is calculated using the following formula: ; in, Indicates the energy efficiency of the target industrial product in different process links, Indicates the number of types corresponding to the target industrial product, The category index corresponding to the target industrial product, Indicates The unit value corresponding to the target industrial product, Indicates The process output corresponding to the target industrial product, Indicates the number of links 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 process links in time Power consumption function at time.

[0036] Specifically, the unit value refers to the For target industrial products, the unit value It is the economic value of a single product, which can be determined based on factors such as the market price of the product, the technical content of the product itself, and the scarcity of market demand. The production quantity of the target industrial product in this process link , reflects the output scale of the product in this process link. For example, in a certain assembly link on an automobile production line, the number of cars assembled in one day is the process output of the automobile products in this link; the power consumption function refers to the Power consumption function of each process link at a certain time , describes the changes in energy consumption power of this process link at different times during the entire running time.

[0037] Furthermore, the present invention determines the optimal operating mode corresponding to the target industrial product based on the energy consumption efficiency, can accurately find the high-efficiency range of energy utilization, reduce unnecessary energy waste, and significantly reduce production costs, which not only helps enterprises improve their competitiveness in the market, but also promotes industrial production to develop in a green and sustainable direction.

[0038] Among them, the optimal operating mode refers to an operating mode formed by optimizing the configuration of production parameters, equipment operating status, operation sequence, etc. by comprehensively considering multiple factors such as energy efficiency of each process link, equipment performance, production time, product quality, etc. in the process of producing the target industrial product. In this mode, products that meet quality standards can be produced with minimal energy consumption, shortest production cycle and lower cost. Optionally, the determination of the optimal operating mode corresponding to the target industrial product can be achieved through experimental design method, such as: in the production of chemical products, changing the reaction temperature, pressure and raw material ratio to conduct multiple groups of experiments, analyzing these experimental data, and finding a combination of conditions that can make the target industrial product energy efficient and meet quality requirements, thereby determining the optimal operating mode.

[0039] Furthermore, based on the optimal operating mode, the present invention generates an energy-saving operating strategy corresponding to the target industrial product, which can promote more reasonable equipment operation, reduce excessive equipment wear, extend equipment service life, and reduce maintenance costs. At the same time, it helps achieve energy conservation and emission reduction goals, enhance the company's image in the field of environmental protection, and enhance market competitiveness.

[0040] Among them, the energy-saving operation strategy refers to a set of specific plans to guide the operation of equipment during the production process of target industrial products, which is formulated based on the energy consumption reduction ratio and comprehensive consideration of factors such as actual production needs and equipment performance limitations.

[0041] As an embodiment of the present invention, the energy-saving operation strategy corresponding to the target industrial product is generated based on the optimal operation mode, including: analyzing the real-time energy consumption data of each device under the optimal operation mode; querying the optimizable adjustable parameters in the real-time energy consumption data; performing multiple combination simulation tests on the optimizable adjustable parameters to obtain a simulation test group; calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group; and generating the energy-saving operation strategy corresponding to the target industrial product based on the energy consumption reduction ratio.

[0042] Among them, the real-time energy consumption data refers to the specific numerical records of the energy consumed by each production equipment at the current moment and in continuous time when the target industrial product is in the optimal operating mode. These data are collected in real time through various energy monitoring equipment, such as smart meters, gas flow sensors, etc.; the optimizable adjustable parameters refer to those operating parameters that can be adjusted to affect the energy consumption of the equipment, which are mined from the real-time energy consumption data. For example, for motor equipment, changes in parameters such as speed and voltage will directly affect its energy consumption. These are optimizable adjustable parameters; the simulation test group refers to a series of parameter sets for simulation testing formed after different combinations of optimizable adjustable parameters are set. For example, for the two optimizable adjustable parameters of the speed and voltage of the motor equipment, the speed is set to three gears of low, medium and high, and the voltage is set to three levels of normal, slightly higher and slightly lower. Then the combination will form 9 different simulation test cases; the energy consumption reduction ratio refers to the degree of reduction in the energy consumption of the equipment corresponding to each parameter combination in the simulation test group compared with the energy consumption under the original optimal operating mode, presented in percentage form.

[0043] Furthermore, the analysis of the real-time energy consumption data of each device under the optimal operating mode can be achieved through a sliding window algorithm, such as: calculating the energy consumption mean by setting a 30-minute sliding window, automatically marking the devices whose mean deviates from the historical baseline by more than 20%, outputting the equipment energy consumption fluctuation ranking, and finally obtaining the real-time energy consumption data; the query of the optimizable adjustable parameters in the real-time energy consumption data can be achieved through a decision tree algorithm, such as: using the C4.5 algorithm to construct an energy consumption influencing factor tree, finding that the influence weight of the dryer temperature setting value (50-80°C) on energy consumption is as high as 37%, and determining it as an optimizable parameter; the multi-combination simulation test of the optimizable adjustable parameters The test can be implemented through the Monte Carlo simulation method, such as: setting random disturbances for the parameters (such as temperature ±2°C), generating 1000 groups of random parameter combinations, and calculating the energy consumption simulation value of each group through GPU acceleration, and finally obtaining a simulation test group; the calculation of the energy consumption reduction ratio corresponding to the parameters in the simulation test group can be implemented by the following calculation formula; the generation of the energy-saving operation strategy corresponding to the target industrial product can be implemented through a logical closed-loop verification method, such as: deploying multiple energy consumption sensors to collect data in real time, using a decision tree to identify the press slide speed as a key parameter, and obtaining the optimal speed combination through orthogonal test simulation, and finally generating an energy-saving operation strategy.

[0044] As an embodiment of the present invention, the calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group includes: The energy consumption reduction ratio corresponding to the parameters in the simulation test group is calculated using the following formula: ; in, Indicates 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, Indicates the number index of test cases, Indicates The device corresponding to the test case is in the time interval Energy consumption benchmark value within Indicates the start time of the simulation test. Indicates the end time of the simulation test. Indicated in In the test case, the device Energy consumption power function at time.

[0045] In detail, the energy consumption reduction ratio refers to the energy consumption reduction rate obtained by comparing the actual energy consumption of the equipment under different test cases with the original energy consumption baseline value in the simulation test group, which is presented in the form of a percentage. This value intuitively reflects the degree of energy consumption reduction of the equipment after different combinations of optimizable 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 group of parameter settings formed by arranging and combining each optimizable adjustable parameter according to different values ​​when performing multiple combination simulation tests on the optimizable adjustable parameters. Each test case represents a combination of equipment operating parameters. By simulating these different combinations, the energy consumption of the equipment under different parameter settings is evaluated; the energy consumption baseline value refers to the time interval before the simulation test starts when the equipment is in the original optimal operating mode. The energy consumption in the test case is compared with the actual energy consumption during the simulation test to obtain the energy consumption reduction situation; the energy consumption power function describes the energy consumption of the device in time The energy consumption changes at each moment, and this function can be used to calculate the energy consumption during the entire simulation test time interval. The actual energy consumption of the equipment inside.

[0046] S3. Based on the energy-saving operation strategy, the energy consumption performance of the target industrial product under different working conditions is evaluated. Based on the energy consumption performance, the energy consumption bottleneck of the target industrial product is queried, and the occurrence conditions corresponding to the energy consumption bottleneck are analyzed.

[0047] 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 energy consumption status of the product, and accurately locate the high-energy consumption working 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.

[0048] Among them, the energy consumption performance refers to the overall evaluation of the energy utilization efficiency, energy-saving effect, etc. of the target industrial products under different working conditions, which is based on information such as comprehensive actual energy consumption indicators. For example, whether the energy consumption is within a reasonable range and the gap with the expected energy-saving target, etc., is used to measure the energy use advantages and disadvantages of the product under different working conditions.

[0049] As an embodiment of the present invention, the energy consumption performance of the target industrial product under different working conditions is evaluated based on the energy-saving operation strategy, including: sorting out the key strategy points in the energy-saving operation strategy; based on the key strategy points, determining the parameter change gradient of the target industrial product under different working conditions; based on the parameter change gradient, querying the parameter control range under different working conditions; analyzing the actual energy consumption index corresponding to the parameter control range; based on the actual energy consumption index, evaluating the energy consumption performance of the target industrial product under different working conditions.

[0050] Among them, the key strategic 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 products, such as the optimization arrangement of the equipment start and 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 target industrial product operating parameters under different working conditions determined based on the key strategic points. For example, under different working conditions of equipment load changes, the motor speed parameter is adjusted according to a certain proportion and frequency. This proportion and frequency are the parameter change gradient, which reflects the law of parameter change; the parameter control range refers to the adjustable interval of the operating parameters determined according to the parameter change gradient for different working conditions. For example, under high temperature conditions, the temperature control parameters of the industrial furnace can be adjusted within a certain temperature range. This range is the parameter control range, which limits the boundary of the parameter adjustment; the actual energy consumption index refers to the energy consumption-related data generated during the actual operation of the target industrial product within the parameter control range, such as the energy consumption per unit time, the energy consumption per unit product, etc. These indicators reflect the energy consumption status of the product during actual operation.

[0051] Furthermore, the combing of the key strategy points in the energy-saving operation strategy can be achieved through an association rule mining algorithm, such as: using the Apriori algorithm to mine the association rules in the data to find the strategy combinations that appear frequently and have an important impact on energy saving. The strategy points in these combinations are the key strategy 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, such as: in a smart home system, the air conditioner adjusts parameters such as the compressor speed through an adaptive control algorithm based on working condition data such as indoor temperature and humidity, so as to achieve energy saving while ensuring comfort. The parameter adjustment law formed in this process is the parameter change gradient; the query of the parameter control range under different working conditions can be achieved through a clustering algorithm, such as: using K-M The eans algorithm clusters the operating conditions of equipment in different production batches, and analyzes the temperature, operating frequency and other parameter control ranges of the equipment under each type of condition; the analysis of the actual energy consumption index corresponding to the parameter control range can be achieved through a data analysis platform, such as: using the Tableau platform to draw an energy consumption line chart under different temperature settings, and intuitively see the energy consumption changes when the temperature parameters change within the control range, thereby determining the actual energy consumption index; the evaluation of the energy consumption performance of the target industrial product under different working conditions can be achieved through the hierarchical analysis method, such as: when evaluating the energy consumption performance of large industrial plants, combining AHP and grey correlation analysis, considering the energy consumption indicators of lighting, air conditioning, equipment operation and other aspects of the plant, and evaluating its energy consumption performance in different seasons (working conditions).

[0052] Based on the energy consumption performance, the present invention queries the energy consumption bottleneck of the target industrial product and analyzes the occurrence conditions corresponding to the energy consumption bottleneck, which helps to formulate energy-saving improvement plans in a targeted manner, avoid blind policy implementation, and efficiently utilize resources. At the same time, it can prevent high energy consumption situations from occurring in advance, continuously optimize product energy consumption, and improve energy utilization efficiency.

[0053] Among them, the energy consumption bottleneck refers to the key link, equipment or factor that causes excessive energy consumption and is difficult to reduce in the production process or operation process of the target industrial product. For example, in a certain chemical production, a specific reaction equipment has outdated technology and low energy conversion rate, making the equipment an energy consumption bottleneck in the entire production process; the occurrence conditions refer to various internal and external environmental factors, operating parameter settings or specific scenarios that cause the energy consumption bottleneck. Including the operating status of the equipment (such as long-term high-load operation), production process conditions (such as a certain temperature, pressure range), raw material characteristics (specific purity or composition) and external environmental factors (such as high temperature, high humidity environment), etc. For example, in the manufacturing process of a certain electronic equipment, when the production line is in continuous high-intensity operation and the ambient temperature is too high, the equipment cooling system is burdened, becoming an energy consumption bottleneck, causing a significant increase in energy consumption. The continuous high-intensity operation and high-temperature environment here are the conditions for the occurrence of energy consumption bottlenecks. Optionally, the query of the energy consumption bottleneck of the target industrial product can be achieved through an energy management system, such as: In a certain steel plant, the EMS system showed that the energy consumption in the blast furnace ironmaking process fluctuated greatly and the overall energy consumption exceeded expectations. In-depth investigation found that the damage to the blast furnace lining caused serious heat loss, and then determined that the blast furnace lining problem was an energy consumption bottleneck; the analysis of the conditions corresponding to the energy consumption bottleneck can be achieved through an association rule mining algorithm. For example, in a pharmaceutical factory, the Apriori algorithm analysis found that when the temperature of the drug drying equipment was 10°C higher than the set value, the wind speed was 20% lower than the normal range, and the moisture content of the raw materials was 5% higher than the standard, the energy consumption in the drying process increased significantly, becoming an energy consumption bottleneck. These parameter conditions are the conditions for the occurrence of the energy consumption bottleneck.

[0054] S4. Based on the energy consumption bottleneck and the occurrence conditions combined with the current product status of the target industrial product, a dynamic adjustment mechanism corresponding to the target industrial product is constructed. Based on the dynamic adjustment mechanism, an adjustment task list corresponding to the target industrial product is generated, and detailed adjustment tasks in the adjustment task list are extracted.

[0055] The present invention constructs 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 status corresponding to the target industrial product. It can automatically adjust the equipment operation parameters according to the real-time situation, accurately avoid the energy consumption bottleneck, 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 operation.

[0056] Among them, the current product status refers to multiple key aspects of the target industrial product in the production or operation process, such as from the perspective of production progress, including which stage of production the product is in, whether it is initial processing, intermediate assembly, or final inspection; in terms of product quality, it involves whether the product meets the established standards and whether there is a risk of defective products; in terms of production environment factors, it covers the impact of workshop conditions such as temperature, humidity, and cleanliness on the product; the dynamic adjustment mechanism refers to a comprehensive system that integrates a series of links such as analysis of energy consumption bottlenecks and occurrence conditions, monitoring of current product status, construction of state logic chains, extraction of logical operating conditions, matching of optimal adjustment strategies, and implementation and feedback of adjustment strategies.

[0057] As an embodiment of the present invention, the dynamic adjustment mechanism corresponding to the target industrial product is constructed based on the energy consumption bottleneck and the occurrence condition in combination with the current product state corresponding to the target industrial product, including: analyzing the energy consumption threshold interval corresponding to the energy consumption bottleneck and the occurrence condition; 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 conditions factors in the state logic chain; matching the optimal adjustment strategy in a preset adjustment strategy library based on the logical operating conditions factors; and constructing a dynamic adjustment mechanism corresponding to the target industrial product based on the optimal adjustment strategy.

[0058] Among them, the energy consumption threshold interval refers to an energy consumption value range determined for energy consumption bottlenecks and their occurrence conditions during the production process of the target industrial product. For example, in a certain electronic equipment 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 kWh per hour. Once it exceeds 15 kWh, it may trigger an energy consumption bottleneck. Then 15 kWh is the upper limit of the energy consumption threshold interval under this specific condition; the state logic chain refers to a logical association structure constructed based on the energy consumption threshold interval and the current product state. For example, when the product is in the initial processing state and the equipment runs smoothly, it corresponds to an energy consumption threshold interval; as the production progress advances to the assembly stage, if the equipment load increases and the energy consumption threshold interval changes, the state logic chain clearly shows the dynamic changes of the energy consumption threshold interval and related 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 energy consumption status of the product. These factors include but are not limited to equipment operating parameters (such as 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 series of adjustment strategy sets for different energy consumption bottlenecks and logical operating condition factors that are pre-formulated and stored. For example, in the case where a certain equipment has excessive energy consumption due to long-term high-load operation, the preset adjustment strategy library contains multiple strategies such as reducing the equipment operating load, optimizing equipment operating parameters, or performing equipment maintenance; the optimal adjustment strategy refers to the adjustment strategy that is most suitable for solving the current energy consumption bottleneck problem and achieving energy consumption optimization in the preset adjustment strategy library based on the logical operating condition factors under the current product status after a certain screening and matching algorithm.

[0059] Furthermore, the analysis of the energy consumption bottleneck and the energy consumption threshold interval corresponding to the occurrence condition can be implemented by a machine learning algorithm, such as a support vector machine algorithm, to classify energy consumption data, identify abnormal energy consumption points and determine the threshold interval; the generation of the state logic chain corresponding to the target industrial product can be implemented by a process modeling tool, such as using BPMN in the production process modeling of a large steel plant. 2.0 modeling tool, which clearly shows the state changes of each production link and the state transfer logic related to energy consumption in the process from iron ore feeding to finished steel output; the extraction of logical operating factors in the state logic chain can be achieved through rule matching methods, such as: it can be based on keyword matching, such as defining "temperature", "humidity", "speed", "pressure" and so on as logical operating factor keywords, and when these keywords appear in the state description, extracting related numerical values ​​or state information as logical operating factors; the matching of the optimal adjustment strategy in the preset adjustment strategy library can be achieved through expert system tools, such as CLIPS, Drools and other tools; the construction of the dynamic adjustment mechanism corresponding to the target industrial product can be achieved through an adaptive control algorithm, such as: in an 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 status information of the vehicle are collected in real time through sensors. The MRAC controller dynamically adjusts the control parameters of the motor, the charging and discharging strategy of the battery, etc. according to the error between the actual energy consumption and the reference model energy consumption, so as to achieve optimal control of the vehicle energy consumption and build a dynamic adjustment mechanism.

[0060] 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 performed at each stage, can quickly understand the work focus, and avoid blind operations. At the same time, the list can be used as an execution basis and progress tracking tool to ensure that the adjustment measures are carried out in an orderly manner, effectively improve energy utilization efficiency, and help product production energy-saving and efficient.

[0061] Among them, the adjustment task list refers to further refining and organizing the task execution process and presenting it in the form of a list. The list contains a detailed description of each specific task, executors, estimated execution time, required resources and other information.

[0062] 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; based on the multi-source information nodes, clarifying the task adjustment direction corresponding to the target industrial product; based on the task adjustment direction, formulating the task execution process corresponding to the target industrial product; based on the task execution process, generating the adjustment task list corresponding to the target industrial product.

[0063] The core regulation path refers to the key context of energy consumption optimization in the dynamic regulation mechanism. For example, in chemical production, from raw material ratio adjustment to reaction temperature and pressure control, and then to energy consumption regulation in the product separation link, these closely related steps constitute the core regulation path; the multi-source information node refers to the key position for collecting different types of information distributed on the core regulation path. For example, in an automobile manufacturing plant, the real-time energy consumption data of equipment on the production line, the ambient temperature and humidity data of the paint shop, and the product quality defect feedback in the assembly link are all information nodes collected by the multi-source information node; the task regulation direction refers to the information collected based on the multi-source information node. The specific direction of energy consumption adjustment of target industrial products obtained through comprehensive analysis of data. For example, if multi-source information shows that the energy consumption of a certain equipment is too high due to long-term high-load operation, the task adjustment direction may 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 sequence planned in detail around the task adjustment direction. For example, to reduce the equipment load, the task execution process may first evaluate the load range that the equipment can withstand, and 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 is reduced to the expected range after completion.

[0064] Furthermore, the analysis of the core regulation path corresponding to the dynamic regulation mechanism can be achieved through a critical path algorithm, such as: 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 the task, determine the path with the longest total construction period, that is, the core regulation path; the collection of multi-source information nodes in the core regulation path can be achieved through a data acquisition scheduling algorithm, such as: the oil temperature sensor of the main transformer of the substation, the current sensor data of the high-voltage transmission line, and through reasonable scheduling, ensure that the multi-source information node data is collected comprehensively and timely under limited network resources; the clarification of the task adjustment direction corresponding to the target industrial product can be achieved through a decision tree algorithm, such as: according to the decision tree model Predict the energy consumption reduction effects under different adjustment measures such as adjusting the reaction temperature, changing the raw material ratio, etc., 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 a genetic algorithm, such as: using a genetic algorithm, encoding information such as the order of equipment adjustment operations and parameter settings into chromosomes, and calculating the fitness corresponding to each chromosome (such as a comprehensive indicator of energy consumption reduction and execution time), selecting chromosomes with high fitness for crossover and mutation operations, and obtaining the optimal task execution process after multiple generations of evolution; the generation of the adjustment task list corresponding to the target industrial product can be achieved through a task generation tool, such as: Todoist, Wunderlist and other tools.

[0065] By extracting detailed adjustment tasks from the adjustment task list, the present invention can reasonably allocate resources and plan time according to the detailed tasks, greatly improve the efficiency and effectiveness of energy consumption adjustment work, and help steadily achieve energy-saving goals for industrial products.

[0066] Among them, the detailed adjustment task refers to the refined decomposition of the energy consumption optimization work, which covers all aspects from preliminary preparation to specific operations to subsequent inspections. For example, when adjusting the energy consumption of equipment, the detailed adjustment task includes preparing the professional tools required for the adjustment, adjusting the equipment operation status according to specific processes and parameters, and checking the equipment operation stability and energy consumption changes after the adjustment is completed. Optionally, the extraction of detailed adjustment tasks in the adjustment task list can be achieved through task extraction tools, such as: Jira, Trello and other tools.

[0067] S5. Analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacles in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacles, and generate an energy consumption control plan corresponding to the target industrial product based on the production feedback data.

[0068] The present invention analyzes the energy-saving targets corresponding to the detailed adjustment tasks, clarifies the energy-saving effects 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 the steady reduction of energy consumption of industrial products, and achieve efficient and energy-saving production.

[0069] Among them, the energy-saving target refers to the specific indicator of energy consumption reduction or energy utilization efficiency improvement that is expected to be achieved after the implementation of detailed adjustment tasks. For example, an enterprise plans to reduce the unit energy consumption of a certain product by 15% through the implementation of a series of detailed adjustment tasks in the next six months. This 15% energy consumption reduction ratio is the energy-saving target corresponding to the product.

[0070] As an embodiment of the present invention, the analysis of the energy-saving target corresponding to the detailed adjustment task includes: querying the energy-saving benchmark data corresponding to the energy-saving target; based on the energy-saving benchmark data, analyzing the energy consumption geometric index corresponding to the energy-saving target; based on the energy consumption geometric index, generating an energy consumption change curve corresponding to the energy-saving target; extracting energy consumption related nodes in the energy consumption change curve; based on the energy consumption related nodes, analyzing the energy-saving target corresponding to the detailed adjustment task.

[0071] The energy-saving benchmark data refers to the basic reference data used to measure the energy-saving effect, which covers various energy consumption-related indicators of the target industrial products under normal production and operation before any detailed adjustment tasks are implemented. For example, a production line of a factory consumed an average of 5,000 kWh of electricity for every 100 products produced in the past month. This 5,000 kWh of electricity / 100 products is the energy consumption benchmark data of the production line during this period; the energy consumption geometric index refers to a relative index calculated by comparing the energy consumption data after the implementation of energy-saving measures with the energy-saving benchmark data. For example, after a series of detailed adjustment tasks, the energy consumption of the production line for producing 100 products is reduced to 4,000 kWh, then the energy consumption geometric index of electricity consumption is (4,000÷5,000)×1 00%=80%, indicating that compared with the baseline state, the energy consumption has been reduced to 80% of the original state; the energy consumption change curve refers to a curve drawn with time or production process as the horizontal axis and the energy consumption proportional index or the actual energy consumption value as the vertical axis. For example, during a week of parameter optimization and adjustment of an industrial equipment, the energy consumption data of the equipment is recorded every day and the energy consumption proportional index is calculated. The curve formed by connecting these data points can clearly show how the energy consumption is gradually reduced or fluctuates at certain stages; the energy consumption associated node refers to a point on the energy consumption change curve that has special significance or is of great value to energy-saving analysis. For example, in the energy consumption change curve, when the equipment is replaced with energy-saving components, the energy consumption shows a significant decrease. The point on the curve corresponding to this time point is an energy consumption associated node.

[0072] Furthermore, the query of the energy-saving benchmark data corresponding to the energy-saving target can be achieved through a time series decomposition algorithm, such as: in a chemical enterprise, the STL decomposition algorithm is used for the monthly energy consumption data of the past three years to obtain the trend and seasonal characteristics of energy consumption, and the energy-saving benchmark energy consumption data for each month of the next year is predicted; the analysis of the energy consumption geometric index corresponding to the energy-saving target can be achieved through a moving average algorithm, such as: setting the moving average period to 5 time points, for the energy consumption geometric index at the nth time point, calculating the average value of the energy consumption geometric index from the n-4th to the nth time point as the smoothed energy consumption geometric index; the generation of the energy consumption change curve corresponding to the energy-saving target It can be achieved through data visualization methods, such as: taking time as the horizontal axis, energy consumption geometric index or real-time energy consumption data as the vertical axis, marking the energy consumption data corresponding to each time point in the coordinate system, and then connecting these points in sequence with line segments to form an energy consumption change curve; the extraction of energy consumption related nodes in the energy consumption change curve can be achieved through data mining tools, such as: arranging the energy consumption change curve data into a format recognizable by the Weka tool, running the DBSCAN algorithm, setting appropriate parameters, and the algorithm will automatically mark the energy consumption related nodes; the analysis of the energy-saving target corresponding to the detailed adjustment task can be achieved through a multi-objective optimization algorithm, such as: NSGA-II algorithm, etc.

[0073] By identifying the energy-saving obstacles in the energy-saving goals, the present invention can provide a clear improvement direction for energy-saving work, formulate targeted response strategies, avoid blind investment of resources, and improve the effectiveness of energy-saving measures. At the same time, it helps to predict potential risks in advance, ensure the smooth implementation of energy-saving projects, and ensure that energy-saving goals are achieved efficiently.

[0074] Among them, the energy-saving obstacles refer to various factors that hinder the reduction of energy consumption and the improvement of energy utilization efficiency in the process of achieving energy-saving goals. At the equipment level, it may be old equipment with high energy consumption and backward technology, which makes it difficult to achieve energy saving through conventional adjustments; in terms of process, complex or unreasonable process flows will increase energy consumption, such as too many reaction steps and large energy losses in chemical production. Optionally, the identification of energy-saving obstacles in the energy-saving goals can be achieved through energy auditing tools. For example, in a certain machinery manufacturing company, the EnergiSEER tool was used to analyze and found that some high-energy-consuming equipment and low-energy-consuming equipment shared the same energy line, resulting in large energy transmission losses. This energy distribution problem was identified as an energy-saving obstacle.

[0075] Furthermore, based on the energy-saving obstacles, the present invention detects the production feedback data corresponding to the target industrial products, and can quickly determine how the obstacles affect the production links, and then evaluate the effectiveness of energy-saving measures, which helps to adjust strategies in a timely manner, efficiently break through energy-saving bottlenecks, and promote the continuous optimization of industrial product production towards energy-saving goals, thereby improving overall energy utilization efficiency.

[0076] Among them, the production feedback data refers to various types of information collected from various links in the industrial production process that reflects the actual production situation, such as the electricity and heat energy consumption values ​​in each production stage, which directly reflect the energy utilization situation; product quality data, including product qualification rate, defective rate and specific quality inspection indicators, which can reflect the impact of production technology on product quality; and production process data, such as the duration of each process, the connection sequence, etc. 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 in the reactor, and the Kalman filter algorithm is used to fuse these measurement data to obtain more accurate reactor temperature data as part of the production feedback data.

[0077] Furthermore, the present invention generates an energy consumption control plan corresponding to the target industrial product based on the production feedback data, can dig out the key control points of energy consumption, formulate strategies in a targeted manner, and avoid blind implementation of policies. It can not only effectively reduce energy consumption, but also improve production efficiency, reduce energy waste, and help industrial product production achieve a win-win situation of energy saving and benefits.

[0078] Among them, the energy consumption control plan refers to a set of systematic management and control strategies for energy consumption in industrial production processes, which covers specific measures from equipment level, process links to management processes. In terms of equipment, it includes upgrade and transformation plans for old equipment and energy-saving selection standards for new equipment; in terms of process, it involves optimization and adjustment of existing process flows, such as simplifying operating steps, improving reaction efficiency, etc.; management processes include formulating energy use specifications, establishing energy consumption monitoring and assessment mechanisms, etc. Optionally, the generation of the energy consumption control plan corresponding to the target industrial product can be achieved through industrial simulation tools, such as: Aspen Plus, Arena and other tools.

[0079] 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.

[0080] Embodiment 2: 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.

[0081] 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.

[0082] In the embodiment of the present invention, the functions of each module / unit are as follows: The mode determination module 201 is used 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, and determine the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points; The strategy generation module 202 is used 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 in 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; The condition analysis module 203 is used 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 bottleneck of the target industrial product based on the energy consumption performance, and analyze the occurrence conditions corresponding to the energy consumption bottleneck; The task extraction module 204 is used to construct a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence condition combined with the current product status 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 detailed adjustment tasks in the adjustment task list; The solution generation module 205 is used to analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacles in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacles, and generate an energy consumption control solution corresponding to the target industrial product based on the production feedback data.

[0083] In detail, each module in the integrated control system 200 of the high-efficiency energy-saving industrial automation product in the embodiment of the present invention is used in the same manner as described above. Figure 1 The integrated control method of high-efficiency and energy-saving industrial automation products described in the invention has the same technical means and can produce the same technical effects, so I will not go into details here.

[0084] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An integrated control method for high-efficiency and energy-saving industrial automation products, characterized in that: The method comprises: Acquire 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, and determine the basic control mode corresponding to the target industrial product based on the abnormal energy consumption points; 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, calculate the energy consumption efficiency of the target industrial product in 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; Based on the energy-saving operation strategy, the energy consumption performance of the target industrial product under different working conditions is evaluated, based on the energy consumption performance, the energy consumption bottleneck of the target industrial product is queried, and the occurrence conditions corresponding to the energy consumption bottleneck are analyzed; Based on the energy consumption bottleneck and the occurrence conditions combined with the current product status corresponding to the target industrial product, a dynamic adjustment mechanism corresponding to the target industrial product is constructed, based on the dynamic adjustment mechanism, an adjustment task list corresponding to the target industrial product is generated, and detailed adjustment tasks in the adjustment task list are extracted; Analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacles in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacles, and generate the energy consumption control plan corresponding to the target industrial product based on the production feedback data.

2. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 1, characterized in that: The determining, based on the abnormal energy consumption point, a basic control mode corresponding to the target industrial product includes: Analyze the time series characteristics of the abnormal energy consumption points; Sorting out the energy consumption fluctuation patterns corresponding to the time series characteristics; Identify the abnormal operating condition energy consumption corresponding to the energy consumption fluctuation law; Formulate an energy consumption adjustment strategy corresponding to the energy consumption under the abnormal operating condition; Based on the energy consumption regulation strategy, a basic control mode corresponding to the target industrial product is determined.

3. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 1, characterized in that: The calculation of the energy consumption efficiency of the target industrial product in different process links includes: The energy efficiency of the target industrial product in different process links is calculated using the following formula: ； in, Indicates the energy efficiency of the target industrial product in different process links, Indicates the number of types corresponding to the target industrial product, The category index corresponding to the target industrial product, Indicates The unit value corresponding to the target industrial product, Indicates The process output corresponding to the target industrial product, Indicates the number of links 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 process links in time Power consumption function at time.

4. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 1, characterized in that: The generating of the energy-saving operation strategy corresponding to 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; Querying the optimizable adjustable parameters in the real-time energy consumption data; Performing multiple combination simulation tests on the optimizable adjustable parameters to obtain a simulation test group; Calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group; Based on the energy consumption reduction ratio, an energy-saving operation strategy corresponding to the target industrial product is generated.

5. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 4, characterized in that: The calculating the energy consumption reduction ratio corresponding to the parameters in the simulation test group includes: The energy consumption reduction ratio corresponding to the parameters in the simulation test group is calculated using the following formula: ； in, Indicates 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, Indicates the number index of test cases, Indicates The device corresponding to the test case is in the time interval Energy consumption benchmark value within Indicates the start time of the simulation test. Indicates the end time of the simulation test. Indicated in In the test case, the device Energy consumption power function at time.

6. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 1, characterized in that: 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: Sorting out the key strategic points in the energy-saving operation strategy; Based on the key strategic points, determining the parameter change gradient of the target industrial product under different working conditions; Based on the parameter change gradient, query the parameter control range under different working conditions; Analyze the actual energy consumption index corresponding to the parameter control range; Based on the actual energy consumption index, the energy consumption performance of the target industrial product under different working conditions is evaluated.

7. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 1, characterized in that: The constructing of a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence condition combined with the current product status corresponding to the target industrial product includes: Analyze the energy consumption bottleneck and the energy consumption threshold interval corresponding to the occurrence condition; Based on the energy consumption threshold interval and the current product state, generating a state logic chain corresponding to the target industrial product; Extracting logical operating conditions factors in the state logic chain; Based on the logical operating conditions, matching the optimal regulation strategy in the preset regulation strategy library; Based on the optimal regulation strategy, a dynamic regulation mechanism corresponding to the target industrial product is constructed.

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

9. The integrated control method for high-efficiency and energy-saving industrial automation products according to claim 1, characterized in that: The analyzing the energy saving target corresponding to the detailed adjustment task includes: Query the energy-saving benchmark data corresponding to the energy-saving target; Based on the energy-saving benchmark data, analyzing the energy consumption proportional index corresponding to the energy-saving target; Based on the energy consumption geometric index, generating an energy consumption change curve corresponding to the energy saving target; Extracting energy consumption related nodes in the energy consumption change curve; Based on the energy consumption associated nodes, the energy saving target corresponding to the detailed adjustment task is analyzed.

10. An integrated control system for high-efficiency and energy-saving industrial automation products, characterized in that: The system comprises: A mode determination module is used to 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; A strategy generation module is used 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 in 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; A condition analysis module, for evaluating the energy consumption performance of the target industrial product under different working conditions based on the energy-saving operation strategy, querying the energy consumption bottleneck of the target industrial product based on the energy consumption performance, and analyzing the occurrence conditions corresponding to the energy consumption bottleneck; A task extraction module is used to construct a dynamic adjustment mechanism corresponding to the target industrial product based on the energy consumption bottleneck and the occurrence condition combined with the current product status 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 detailed adjustment tasks in the adjustment task list; A scheme generation module is used to analyze the energy-saving target corresponding to the detailed adjustment task, identify the energy-saving obstacles in the energy-saving target, detect the production feedback data corresponding to the target industrial product based on the energy-saving obstacles, and generate an energy consumption control scheme corresponding to the target industrial product based on the production feedback data.

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