Energy management optimization method and system based on digital workshop

Abstract

The invention relates to the technical field of industrial production, particularly discloses an energy management optimization method and system based on a digital workshop, and aims to solve the problems of limited energy consumption data processing capability, single energy consumption optimization strategy, lack of effective feedback mechanism and the like in the prior art. The method comprises the following steps: collecting current and voltage data of equipment in real time, and processing to obtain an instantaneous energy consumption value of the equipment; drawing an actual curve graph of energy consumption changing along with time based on the energy consumption value, and generating an energy consumption abnormal signal through analysis; determining the operation load of the equipment according to the energy consumption abnormal signal, and generating an operation load abnormal signal; further analyzing the relationship between the energy consumption and the operation load to obtain a target operation load; finally, the target rotating speed of the equipment is calculated according to the target operation load, and the rotating speed of the equipment is regulated and controlled, effective monitoring and optimal management of the energy consumption of the equipment in the workshop can be achieved, and improvement of the overall production efficiency and the energy-saving and emission-reducing effects is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production, and in particular to an optimization method and system for energy management based on a digital workshop. Background Art

[0002] With the development of Industry 4.0 and the popularization of the concept of smart factories, more and more companies are beginning to pay attention to how to improve production efficiency and reduce energy consumption through intelligent means. In modern manufacturing, various equipment in the workshop consumes a lot of energy, and the effective management and utilization of energy is of great significance to improving the competitiveness of enterprises. At present, most companies still use traditional manual monitoring and recording methods in energy management. This method is not only time-consuming and labor-intensive, but also prone to errors. Therefore, there is an urgent need for a method that can automatically monitor and analyze equipment energy consumption to achieve refined energy management.

[0003] The current digital workshop energy management systems mostly use manual recording and analysis, simple automated monitoring or rule-based automatic analysis. Manual recording and analysis methods are inefficient and prone to errors. Although simple automated monitoring improves the efficiency of data collection, it has limited capabilities in data analysis. Although rule-based automatic analysis methods are simple and easy to use, they have poor adaptability and are difficult to cope with complex and changing production environments. These methods all have problems such as low monitoring accuracy and limited data analysis capabilities, and are difficult to meet the growing demand for energy management in modern manufacturing companies.

[0004] The present invention provides an intelligent energy management system and method for digital workshops, which aims to achieve real-time monitoring, precise analysis and predictive optimization of energy consumption in the workshop by integrating advanced sensing technology, big data analysis technology and artificial intelligence algorithms. The system can significantly improve energy utilization efficiency, reduce unnecessary waste, and automatically adjust energy allocation strategies according to production plans, thereby effectively reducing production costs and enhancing the competitiveness of enterprises. Summary of the invention

[0005] The purpose of the present invention is to provide an optimization method and system for energy management based on a digital workshop, which solves the problems of lack of adaptability to different equipment types and application scenarios, poor real-time performance and high implementation difficulty.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] An optimization method for energy management based on a digital workshop comprises the following steps:

[0008] Obtain the current and voltage of the device, process the current and voltage, and obtain the energy consumption value of the device;

[0009] Process the energy consumption value of the equipment to obtain an actual energy consumption curve showing the energy consumption of the equipment changing over time;

[0010] Analyze the actual energy consumption curve to generate abnormal energy consumption signals and normal energy consumption signals;

[0011] Based on the abnormal energy consumption signal, the operating load of the equipment is obtained;

[0012] By processing the operating load of the equipment, an abnormal operating load signal and a normal operating load signal are generated;

[0013] Based on the abnormal operating load signal, analyze the equipment energy consumption under different operating loads and generate a scatter plot of operating load and equipment energy consumption;

[0014] Analyze the scatter plot to obtain the target operating load;

[0015] The target operating load is processed to obtain the target speed of the equipment, and the target speed of the equipment is regulated.

[0016] As a further solution of the present invention: the specific process of obtaining the energy consumption value of the equipment is:

[0017] Divide the monitoring cycle into several time sub-units of equal length, divide the time sub-unit into several time small units of equal length, and obtain the current and voltage of the device at the middle moment of the time small unit;

[0018] The instantaneous power Ps of the equipment is obtained by multiplying the current and voltage at the middle moment of the time unit.

[0019] Process all instantaneous powers in the time subunit to obtain a unit instantaneous power group;

[0020] The energy consumption value En of the equipment in the time subunit is calculated by the formula En = ∫Ps(t)dt;

[0021] Wherein, Ps(t) is the instantaneous power varying with time, and t is the number of time subunits in the time subunit.

[0022] As a further solution of the present invention: the specific construction process of the actual energy consumption curve diagram is:

[0023] All energy consumption values ​​within the monitoring period are processed to obtain equipment energy consumption groups;

[0024] Based on the equipment energy consumption group, a plane coordinate system is constructed, with the equipment running time as the horizontal axis and the equipment energy consumption as the vertical axis, to obtain an actual energy consumption curve showing the change of equipment energy consumption over time.

[0025] As a further solution of the present invention: the process of analyzing the actual energy consumption curve is:

[0026] Based on the actual energy consumption curve, all energy consumption values ​​in the actual energy consumption curve are integrated and all energy consumption values ​​are arranged in ascending order;

[0027] The value corresponding to the boundary of the middle 25% of the data set is defined as the lower quartile Q1;

[0028] The value corresponding to the boundary of the middle 75% of the data set is defined as the upper quartile Q3;

[0029] Calculate the difference between Q3 and Q1 to obtain the normal baseline IQR of the energy consumption index;

[0030] According to the formula S1=Q1-1.5*IQR, the upper boundary value S1 of the normal energy consumption interval is obtained, and an upper boundary line of the normal energy consumption interval parallel to the horizontal axis is drawn with the upper boundary value S1 of the normal energy consumption interval;

[0031] According to the formula S2=Q3-1.5*IQR, the lower boundary value S2 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is drawn with the lower boundary value S2 of the normal energy consumption interval;

[0032] The area where the energy consumption curve is above the lower boundary line of the normal energy consumption range and below the upper boundary line of the normal energy consumption range is recorded as the actual normal energy consumption range;

[0033] The area where the energy consumption curve is below the lower boundary line of the normal energy consumption range or above the upper boundary line of the normal energy consumption range is recorded as the actual abnormal energy consumption range.

[0034] As a further solution of the present invention: the process of energy consumption abnormal signal and energy consumption non-abnormal signal is:

[0035] Obtain the abnormal time period corresponding to the actual abnormal energy consumption interval, integrate all abnormal time periods within the monitoring period, and record them as the total abnormal time;

[0036] If the total abnormal time is greater than or equal to the preset time threshold, it means that the equipment motor is in an abnormal energy consumption state, and an abnormal energy consumption signal is generated;

[0037] If the total abnormal time is less than the preset time threshold, it means that the equipment motor is in a non-abnormal energy consumption state, and a non-abnormal energy consumption signal is generated.

[0038] As a further solution of the present invention, the specific process of generating the abnormal operation load signal and the normal operation load signal is as follows:

[0039] Integrate all operating loads within the monitoring period to obtain the equipment operating load group;

[0040] The equipment's operating load group is processed according to the mean value calculation formula to obtain the average load of the equipment during the monitoring period;

[0041] The equipment's operating load group is processed according to the standard deviation calculation formula to obtain the standard deviation of the equipment's operating load within the monitoring period;

[0042] The difference between the operating load and the average load is calculated by the ratio of the standard deviation of the operating load to obtain

[0043] Standard value of operating load;

[0044] comparing the standard value of the operating load with a threshold value of the operating load;

[0045] If the standard value of the operating load of the equipment during the monitoring period is greater than or equal to the threshold value of the operating load, it means that the operating load exceeds the normal range, and an abnormal operating load signal is generated;

[0046] If the standard value of the operating load of the equipment during the monitoring period is less than the threshold value of the operating load, it means that the operating load is within the normal range, and a normal operating load signal is generated.

[0047] As a further solution of the present invention: the process of analyzing the energy consumption of equipment under different operating loads is:

[0048] Based on the abnormal signal of operating load, the formula Get the coefficient b of equipment energy consumption and operating load 1；

[0049] Where n is the number of time subunits, YF is the operating load of the equipment, En is the energy consumption value of the equipment, i is the number of time subunits, i = 1, 2, ..., n;

[0050] Based on the coefficient b1, through the formula Obtain the coefficient b2 of equipment energy consumption and operating load;

[0051] The energy consumption value Eny of the equipment under different operating loads is obtained through the formula Eny=b1+b2×YF.

[0052] As a further solution of the present invention: the process of generating a scatter plot of operating load and equipment energy consumption is:

[0053] Based on the equipment energy consumption values ​​under different operating loads, a scatter plot of operating load and equipment energy consumption is drawn with the operating load as the horizontal axis and the equipment energy consumption as the vertical axis.

[0054] As a further solution of the present invention: the specific process of obtaining the target speed of the equipment is:

[0055] Based on the scatter plot of operating load and equipment energy consumption, find the operating load with the lowest equipment energy consumption and record it as the target operating load;

[0056] The target operating load is multiplied by the theoretical maximum output power of the equipment to obtain the target actual output power;

[0057] The current rotation speed of the device is obtained, and the target rotation speed of the device is obtained by multiplying the ratio of the target actual output power to the actual output power of the device by the current rotation speed of the device.

[0058] As a further solution of the present invention: an optimization system for energy management based on a digital workshop, characterized by comprising: a data acquisition module, an energy consumption analysis module, an operation load analysis module and a control module;

[0059] The data acquisition module is used to obtain the current and voltage of the equipment, process the current and voltage, and obtain the energy consumption value of the equipment;

[0060] The energy consumption analysis module processes the energy consumption value of the equipment and obtains an actual energy consumption curve chart showing the energy consumption of the equipment changing over time;

[0061] Analyze the actual energy consumption curve to generate abnormal energy consumption signals and normal energy consumption signals;

[0062] Based on the abnormal energy consumption signal, the operating load of the equipment is obtained;

[0063] The operation load analysis module processes the operation load of the equipment to generate an abnormal operation load signal and a normal operation load signal;

[0064] The control module analyzes the relationship between energy consumption and operating load based on the abnormal operating load signal, and generates a scatter plot of operating load and equipment energy consumption;

[0065] Analyze the scatter plot to obtain the target operating load;

[0066] The target operating load is processed to obtain the target speed of the equipment, and the target speed of the equipment is regulated.

[0067] Beneficial effects of the present invention:

[0068] (1) The present invention achieves comprehensive monitoring and optimization of energy consumption within the factory by introducing an advanced energy management system. Through real-time monitoring and data analysis of various energy sources (such as electricity, water power, natural gas, etc.), it can timely discover and solve energy waste problems, significantly reduce energy consumption, and achieve the purpose of energy conservation and emission reduction. For example, in terms of electricity consumption, the system can identify high-energy consumption periods and avoid these periods by adjusting production plans, thereby effectively reducing electricity bills. In addition, the present invention also optimizes energy allocation strategies, realizes intelligent management of energy use, and improves energy utilization efficiency. This intelligent management model not only reduces unnecessary energy waste, but also greatly improves the operational efficiency of the enterprise;

[0069] (2) Another major feature of the present invention is that it has a predictive maintenance function. Through analysis technology, the system can predict possible problems of the equipment based on historical data and current operating status, take preventive maintenance measures in advance, and effectively reduce unplanned downtime caused by equipment failure. This function is crucial to ensure the stable operation of the production line and helps to avoid economic losses caused by sudden failures. In addition, through precise control of energy consumption, excessive use of energy is avoided, thereby reducing production costs. This includes not only direct energy cost savings, but also indirect cost savings due to reduced equipment wear, further enhancing the market competitiveness of enterprises;

[0070] (3) From the perspective of environmental protection, the present invention helps to reduce carbon dioxide emissions by reducing unnecessary energy waste, which is in line with the environmental protection concept of sustainable development. As the world's attention to climate change increases, it is becoming increasingly important for enterprises to adopt greener and more efficient energy management systems. The present invention not only helps enterprises to contribute to energy conservation and emission reduction, but also enhances the image of corporate social responsibility. In addition, the system has good scalability and adaptability, and can be flexibly configured according to the specific needs of different workshops. It is suitable for a variety of industrial scenarios. Whether it is a new factory or the transformation and upgrading of an existing factory, the technical solution of the present invention can be easily applied to meet the production needs of different scales and types. Therefore, the present invention not only improves the intelligent level of energy management, but also brings a double improvement in economic and social benefits to enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The present invention will be further described below in conjunction with the accompanying drawings.

[0072] Figure 1 It is a flow chart of an optimization method of energy management based on a digital workshop of the present invention;

[0073] Figure 2 The invention is a method for optimizing energy management based on a digital workshop to generate energy consumption abnormality signals and energy consumption non-abnormal signal graphs;

[0074] Figure 3 The present invention is a flow chart of an optimization system for energy management based on a digital workshop. DETAILED DESCRIPTION

[0075] The present invention is further described below through specific embodiments in conjunction with the accompanying drawings, but the present invention is not limited thereto. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0076] Example 1

[0077] See also Figure 1 As shown, the present invention is an optimization method for energy management based on a digital workshop, comprising:

[0078] Obtain the current and voltage of the device, process the current and voltage, and obtain the energy consumption value of the device;

[0079] Process the energy consumption value of the equipment to obtain an actual energy consumption curve showing the energy consumption of the equipment changing over time;

[0080] Analyze the actual energy consumption curve to generate abnormal energy consumption signals and normal energy consumption signals;

[0081] Based on the abnormal energy consumption signal, the operating load of the equipment is obtained;

[0082] By processing the operating load of the equipment, an abnormal operating load signal and a normal operating load signal are generated;

[0083] Based on the abnormal operating load signal, the relationship between energy consumption and operating load is analyzed, and a scatter plot of operating load and equipment energy consumption is generated;

[0084] Analyze the scatter plot to obtain the target operating load;

[0085] The target operating load is processed to obtain the target speed of the equipment, and the target speed of the equipment is regulated.

[0086] Example 2

[0087] See also Figure 2 As shown, the monitoring period is divided into several time sub-units of equal length, and the time sub-unit is divided into several time small units of equal length, and the current and voltage of the device at the middle moment of the time small unit are obtained;

[0088] The instantaneous power Ps of the equipment is obtained by multiplying the current and voltage at the middle moment of the time unit.

[0089] Process all instantaneous powers in the time subunit to obtain a unit instantaneous power group;

[0090] Based on the instantaneous power group, the energy consumption value En of the equipment in the time subunit is calculated by the formula En = ∫Ps(t)dt;

[0091] Where Ps(t) is the instantaneous power varying with time, and t is the number of time subunits in the time subunit;

[0092] The energy consumption values ​​in all time sub-units and the corresponding time series are processed to obtain the equipment energy consumption group;

[0093] Based on the equipment energy consumption group, a plane coordinate system is constructed, with the equipment running time as the horizontal axis and the equipment energy consumption as the vertical axis, to obtain an actual energy consumption curve of the equipment energy consumption changing with time;

[0094] Based on the actual energy consumption curve, a normal energy consumption baseline is constructed for the device, specifically:

[0095] Integrate all energy consumption values ​​in the actual energy consumption curve and use the interquartile range method to obtain the energy consumption baseline;

[0096] Arrange all energy consumption values ​​in ascending order, and define the value corresponding to the boundary of the middle 25% of the data set as the lower quartile Q1, that is, at least 25% of the values ​​in the data set are less than or equal to Q1;

[0097] The value corresponding to the 75% boundary in the middle of the data set is defined as the upper quartile Q3, that is, at least 75% of the values ​​in the data set are less than or equal to Q3;

[0098] The difference between Q3 and Q1 is calculated to obtain the normal baseline of the energy consumption index, which is recorded as IQR;

[0099] According to the formula S1=Q1-1.5*IQR, the upper boundary value S1 of the normal energy consumption interval is obtained, and an upper boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the upper boundary value S1 of the normal energy consumption interval. According to the formula S2=Q3-1.5*IQR, the lower boundary value S2 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is made with the lower boundary value S2 of the normal energy consumption interval;

[0100] The normal range of energy consumption during the actual operation of the equipment is obtained by the above method, that is, the area where the energy consumption curve is above the lower boundary line of the normal energy consumption range and below the upper boundary line of the normal energy consumption range is recorded as the actual normal energy consumption range;

[0101] The area where the energy consumption curve is below the lower boundary line of the normal energy consumption range or above the upper boundary line of the normal energy consumption range is recorded as the actual abnormal energy consumption range;

[0102] Obtain the real-time energy consumption of the equipment motor in the time sub-unit, and compare it with the actual normal energy consumption range after processing;

[0103] Specific:

[0104] Obtain the abnormal time period corresponding to the actual abnormal energy consumption interval, integrate all abnormal time periods within the monitoring period, and record them as the total abnormal time;

[0105] If the total abnormal time is greater than or equal to the preset time threshold, it means that the equipment motor is in an abnormal energy consumption state, and an abnormal energy consumption signal is generated;

[0106] If the total abnormal time is less than the preset time threshold, it means that the equipment motor is in a non-abnormal energy consumption state, and a non-abnormal energy consumption signal is generated;

[0107] It should be noted that the abnormal energy consumption signal means that the energy consumption of the equipment is too high during operation. Excessive energy consumption of industrial equipment will lead to a series of problems such as increased production costs, weakened corporate competitiveness, and increased environmental pollution. These problems not only have a negative impact on the company itself, but also impose a burden on the equipment and the environment.

[0108] Example 3

[0109] Based on the abnormal energy consumption signal of the equipment generated above, since the abnormal operation load of the equipment will lead to the abnormal energy efficiency of the equipment, it is analyzed and judged whether the operation load of the equipment is related to the abnormal energy efficiency of the equipment. If there is a relationship of the operation load, it is convenient to adjust the operation load in time during the subsequent operation of the equipment to reduce the impact of the operation load of the equipment on the energy efficiency, effectively improve the energy efficiency of the equipment, and then improve the energy utilization rate of the equipment. Specifically:

[0110] Obtain the theoretical maximum output power of the device and the actual output power of the device when the time subunit is in the middle moment;

[0111] Calculate the ratio of the actual output power of the equipment when the time sub-unit is in the middle moment to the theoretical maximum output power of the equipment to obtain the operating load YF of the equipment in the time sub-unit;

[0112] Integrate all operating loads within the monitoring period to obtain the equipment operating load group;

[0113] The equipment's operating load group is processed according to the mean value calculation formula to obtain the equipment's average load YFJ during the monitoring period;

[0114] The equipment's operating load group is processed according to the standard deviation formula to obtain the standard deviation YFB of the equipment's operating load during the monitoring period;

[0115] Based on the average load YFJ and standard deviation YFB of the operating load, the formula Calculate the standard value of the operating load YFZ;

[0116] Compare the standard value YFZ of the operating load of the equipment during the monitoring period with the threshold value of the operating load;

[0117] If the standard value YFZ of the equipment's operating load during the monitoring period is greater than or equal to the threshold value of the operating load, it means that the operating load exceeds the normal range, and an abnormal operating load signal is generated;

[0118] If the standard value YFZ of the operating load of the equipment during the monitoring period is less than the threshold value of the operating load, it means that the operating load is within the normal range, and a normal operating load signal is generated.

[0119] Example 4

[0120] Based on the above-mentioned abnormal operating load signals, by analyzing the energy consumption of equipment under different operating loads, more sophisticated load management can be achieved, so that enterprises can effectively control the energy consumption of equipment, improve energy utilization efficiency, and reduce operating costs;

[0121] Analyze the energy consumption of equipment under different operating loads, specifically:

[0122] Based on the equipment energy consumption group and the equipment operation load group, the formula Obtain the coefficient b1 of equipment energy consumption and operating load, where n is the number of time subunits, i is the number of time subunits, i = 1, 2, ..., n;

[0123] Based on the coefficient b1, through the formula Obtain the coefficient b2 of equipment energy consumption and operating load;

[0124] The energy consumption value Eny of the equipment under different operating loads is obtained through the formula Eny=b1+b2×YF;

[0125] Based on the equipment energy consumption value Eny under different operating loads, a scatter plot of operating load and equipment energy consumption is drawn with operating load as the horizontal axis and equipment energy consumption as the vertical axis;

[0126] Based on the scatter plot of operating load and equipment energy consumption, find the operating load with the lowest equipment energy consumption, which is recorded as the target operating load YFmin;

[0127] Based on the target operating load YFmin, the target operating load is multiplied by the theoretical maximum output power of the equipment to obtain the target actual output power PMB;

[0128] Get the current speed Nc of the device through the formula Calculate the target speed of the equipment Nm;

[0129] Among them, PSJ is the actual output power of the device;

[0130] Based on the target speed of the equipment, use the frequency converter to adjust the speed of the equipment. Specifically:

[0131] By formula Calculate and obtain the output frequency of the equipment inverter;

[0132] It should be noted that there is a direct relationship between the equipment speed and the operating load. When the equipment speed increases, the output power of the equipment will usually increase, thereby increasing the operating load. Conversely, reducing the motor speed will reduce the output power of the equipment, thereby reducing the operating load. By changing the output frequency of the inverter to adjust the speed of the equipment motor to the target speed, the operating load of the equipment can be adjusted to the optimal operating load, thereby achieving the purpose of optimizing equipment energy consumption.

[0133] Example 5

[0134] See also Figure 3 As shown, the present invention is an optimization system for energy management based on a digital workshop, comprising: a data acquisition module, an energy consumption analysis module, an operation load analysis module and a control module;

[0135] The data acquisition module is used to obtain the current and voltage of the equipment, process the current and voltage, and obtain the energy consumption value of the equipment;

[0136] The energy consumption analysis module processes the energy consumption value of the equipment and obtains an actual energy consumption curve chart showing the energy consumption of the equipment changing over time;

[0137] Analyze the actual energy consumption curve to generate abnormal energy consumption signals and normal energy consumption signals;

[0138] Based on the abnormal energy consumption signal, the operating load of the equipment is obtained;

[0139] The operation load analysis module processes the operation load of the equipment to generate an abnormal operation load signal and a normal operation load signal;

[0140] The control module analyzes the relationship between energy consumption and operating load based on the abnormal operating load signal, and generates a scatter plot of operating load and equipment energy consumption;

[0141] Analyze the scatter plot to obtain the target operating load;

[0142] The target operating load is processed to obtain the target speed of the equipment, and the target speed of the equipment is regulated.

[0143] Working principle of the invention: The invention relates to an integrated energy management system and its working method, which are used for energy monitoring and optimization in the field of industrial production. The system collects real-time data (such as current, voltage, temperature, pressure, etc.) through sensors installed at key positions, and transmits these data to the central processing unit for preprocessing and analysis to extract valuable energy consumption information. Then, the system uses machine learning algorithms and big data analysis technology to conduct in-depth analysis, identify energy waste phenomena and propose improvement measures. At the same time, it also uses artificial intelligence algorithms to optimize energy allocation strategies to maximize energy utilization. In addition, the present invention also has a predictive maintenance function, which predicts potential failure risks by analyzing historical equipment data, and takes maintenance measures in advance. At the same time, the production plan is dynamically adjusted according to the prediction results to ensure that the equipment is always in the best working condition, thereby reducing energy consumption, improving production efficiency and reducing environmental pollution.

[0144] The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. An optimization method for energy management based on a digital workshop, characterized in that: The following steps are involved: Obtain the current and voltage of the device, process the current and voltage, and obtain the energy consumption value of the device; Process the energy consumption value of the equipment to obtain an actual energy consumption curve showing the energy consumption of the equipment changing over time; Analyze the actual energy consumption curve to generate abnormal energy consumption signals and normal energy consumption signals; Based on the abnormal energy consumption signal, the operating load of the equipment is obtained; By processing the operating load of the equipment, an abnormal operating load signal and a normal operating load signal are generated; Based on the abnormal operating load signal, analyze the equipment energy consumption under different operating loads and generate a scatter plot of operating load and equipment energy consumption; Analyze the scatter plot to obtain the target operating load; The target operating load is processed to obtain the target speed of the equipment, and the target speed of the equipment is regulated.

2. The optimization method for energy management based on a digital workshop according to claim 1 is characterized in that: The specific process of obtaining the equipment energy consumption value is as follows: Divide the monitoring cycle into several time sub-units of equal length, divide the time sub-unit into several time small units of equal length, and obtain the current and voltage of the device at the middle moment of the time small unit; The instantaneous power Ps of the equipment is obtained by multiplying the current and voltage at the middle moment of the time unit. Process all instantaneous powers in the time subunit to obtain a unit instantaneous power group; The energy consumption value En of the equipment in the time subunit is calculated by the formula En = ∫Ps(t)dt; Wherein, Ps(t) is the instantaneous power varying with time, and t is the number of time subunits in the time subunit.

3. The optimization method for energy management based on digital workshop according to claim 2 is characterized in that: The specific construction process of the actual energy consumption curve is as follows: All energy consumption values ​​within the monitoring period are processed to obtain equipment energy consumption groups; Based on the equipment energy consumption group, a plane coordinate system is constructed, with the equipment running time as the horizontal axis and the equipment energy consumption as the vertical axis, to obtain an actual energy consumption curve showing the change of equipment energy consumption over time.

4. The optimization method for energy management based on digital workshop according to claim 3 is characterized in that: The process of analyzing the actual energy consumption curve is as follows: Based on the actual energy consumption curve, all energy consumption values ​​in the actual energy consumption curve are integrated and all energy consumption values ​​are arranged in ascending order; The value corresponding to the boundary of the middle 25% of the data set is defined as the lower quartile Q1; The value corresponding to the boundary of the middle 75% of the data set is defined as the upper quartile Q3; Calculate the difference between Q3 and Q1 to obtain the normal baseline IQR of the energy consumption index; According to the formula S1=Q1-1.5*IQR, the upper boundary value S1 of the normal energy consumption interval is obtained, and an upper boundary line of the normal energy consumption interval parallel to the horizontal axis is drawn with the upper boundary value S1 of the normal energy consumption interval; According to the formula S2=Q3-1.5*IQR, the lower boundary value S2 of the normal energy consumption interval is obtained, and a lower boundary line of the normal energy consumption interval parallel to the horizontal axis is drawn with the lower boundary value S2 of the normal energy consumption interval; The area where the energy consumption curve is above the lower boundary line of the normal energy consumption range and below the upper boundary line of the normal energy consumption range is recorded as the actual normal energy consumption range; The area where the energy consumption curve is below the lower boundary line of the normal energy consumption range or above the upper boundary line of the normal energy consumption range is recorded as the actual abnormal energy consumption range.

5. The optimization method for energy management based on digital workshop according to claim 4 is characterized in that: The process of energy consumption abnormal signal and energy consumption non-abnormal signal is as follows: Obtain the abnormal time period corresponding to the actual abnormal energy consumption interval, integrate all abnormal time periods within the monitoring period, and record them as the total abnormal time; If the total abnormal time is greater than or equal to the preset time threshold, it means that the equipment motor is in an abnormal energy consumption state, and an abnormal energy consumption signal is generated; If the total abnormal time is less than the preset time threshold, it means that the equipment motor is in a non-abnormal energy consumption state, and a non-abnormal energy consumption signal is generated.

6. The optimization method for energy management based on digital workshop according to claim 1 is characterized in that: The specific process of generating the abnormal operation load signal and the normal operation load signal is as follows: Integrate all operating loads within the monitoring period to obtain the equipment operating load group; The equipment's operating load group is processed according to the mean value calculation formula to obtain the average load of the equipment during the monitoring period; The equipment's operating load group is processed according to the standard deviation calculation formula to obtain the standard deviation of the equipment's operating load within the monitoring period; The difference between the operating load and the average load is calculated by the ratio of the standard deviation of the operating load to obtain Standard value of operating load; comparing the standard value of the operating load with a threshold value of the operating load; If the standard value of the operating load of the equipment during the monitoring period is greater than or equal to the threshold value of the operating load, it means that the operating load exceeds the normal range, and an abnormal operating load signal is generated; If the standard value of the operating load of the equipment during the monitoring period is less than the threshold value of the operating load, it means that the operating load is within the normal range, and a normal operating load signal is generated.

7. The optimization method for energy management based on digital workshop according to claim 6 is characterized in that: The process of analyzing the energy consumption of equipment under different operating loads is; Based on the abnormal signal of operating load, the formula Get the coefficient b of equipment energy consumption and operating load 1； Where n is the number of time subunits, YF is the operating load of the equipment, En is the energy consumption value of the equipment, i is the number of time subunits, i = 1, 2, ..., n; Based on the coefficient b1, through the formula Obtain the coefficient b2 of equipment energy consumption and operating load; The energy consumption value Eny of the equipment under different operating loads is obtained through the formula Eny=b1+b2×YF.

8. The optimization method for energy management based on digital workshop according to claim 7 is characterized in that: The process of generating a scatter plot of operating load and equipment energy consumption is: Based on the equipment energy consumption values ​​under different operating loads, a scatter plot of operating load and equipment energy consumption is drawn with the operating load as the horizontal axis and the equipment energy consumption as the vertical axis.

9. The optimization method for energy management based on digital workshop according to claim 8 is characterized in that: The specific process of obtaining the target speed of the equipment is: Based on the scatter plot of operating load and equipment energy consumption, find the operating load with the lowest equipment energy consumption and record it as the target operating load; The target operating load is multiplied by the theoretical maximum output power of the equipment to obtain the target actual output power; The current rotation speed of the device is obtained, and the target rotation speed of the device is obtained by multiplying the ratio of the target actual output power to the actual output power of the device by the current rotation speed of the device.

10. An energy management optimization system based on a digital workshop, characterized in that: include: Data acquisition module, energy consumption analysis module, operation load analysis module and control module; The data acquisition module is used to obtain the current and voltage of the equipment, process the current and voltage, and obtain the energy consumption value of the equipment; The energy consumption analysis module processes the energy consumption value of the equipment and obtains an actual energy consumption curve chart showing the energy consumption of the equipment changing over time; Analyze the actual energy consumption curve to generate abnormal energy consumption signals and normal energy consumption signals; Based on the abnormal energy consumption signal, the operating load of the equipment is obtained; The operation load analysis module processes the operation load of the equipment to generate an abnormal operation load signal and a normal operation load signal; The control module analyzes the relationship between energy consumption and operating load based on the abnormal operating load signal, and generates a scatter plot of operating load and equipment energy consumption; Analyze the scatter plot to obtain the target operating load; The target operating load is processed to obtain the target speed of the equipment, and the target speed of the equipment is regulated.

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