A production energy consumption analysis system based on big data

Abstract

The present application relates to the technical field of big data, and discloses a production energy consumption analysis system based on big data, which comprises an energy consumption benchmark constraint condition acquisition module, a production energy consumption data acquisition module, an energy consumption processing dynamic switching module, an energy consumption microwave fluctuation sensitive analysis module, an energy consumption sudden increase fault discrimination module and a comprehensive regulation and control early warning module. The production energy consumption data of the running equipment in the workshop production process are acquired, the controller data of the running equipment are read through an industrial communication protocol, the read controller data of the running equipment are projected to the preset energy consumption fluctuation interval of the running equipment, the energy consumption processing dynamic switching instruction is issued according to the projection result, the energy consumption microwave fluctuation sensitive analysis and the energy consumption sudden increase fault discrimination analysis are carried out on the running equipment according to the instruction, the regulation and control instruction is issued and the fault type is calibrated according to the analysis result, and the accuracy and efficiency of energy consumption management are further improved.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and more specifically to a production energy consumption analysis system based on big data. Background Technology

[0002] With the continuous rise in global energy consumption and the increasing severity of environmental problems, effectively reducing energy consumption has become a key concern for all industries. Particularly in the manufacturing sector, workshop energy consumption accounts for a significant proportion of a company's overall energy consumption. Machine tools, as crucial equipment in industrial production, are particularly noteworthy for their energy consumption. In light of this, the government has increasingly emphasized energy conservation and emission reduction, successively introducing a series of policies and measures aimed at promoting these efforts. The implementation of these policies has brought pressure and challenges to enterprises in terms of energy conservation and consumption reduction. Simultaneously, with intensifying market competition, companies are actively seeking various methods to reduce production costs to maintain their competitiveness. Against this backdrop, reducing machine tool energy consumption not only helps companies reduce energy expenditures but is also a key way to improve economic efficiency and enhance market competitiveness.

[0003] The maturity of technologies such as big data and the Internet of Things (IoT) has provided technical support for energy consumption analysis in machine tool production. Through IoT devices, the operating status and energy consumption data of machine tools can be collected in real time, which makes it possible to optimize energy consumption. However, the analysis of production energy consumption is the premise and foundation for achieving energy consumption optimization. But in the process of analyzing energy consumption fluctuation range, there is a lack of sufficiently comprehensive and rich data support, making it difficult to accurately grasp the energy consumption fluctuation pattern under different working conditions. It is impossible to accurately analyze the fluctuation range of machine tool energy consumption under different conditions, resulting in blind energy consumption management and difficulty in formulating scientific and reasonable energy consumption management strategies. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a production energy consumption analysis system based on big data to solve the problems existing in the background art.

[0005] This invention provides the following technical solution: a production energy consumption analysis system based on big data, comprising: an energy consumption benchmark constraint acquisition module, a production energy consumption data acquisition module, an energy consumption processing dynamic switching module, an energy consumption micro-fluctuation sensitivity analysis module, an energy consumption sudden increase fault discrimination module, and a comprehensive control and early warning module;

[0006] The energy consumption benchmark constraint acquisition module is used to acquire the energy consumption benchmark constraint of the operating equipment during the workshop production process. The energy consumption benchmark constraint includes the rated value fluctuation range and the preset energy consumption fluctuation range of the operating equipment.

[0007] The production energy consumption data acquisition module is used to collect production energy consumption data of the operating equipment in the workshop production process, and read the controller data of the operating equipment through the industrial communication protocol. The controller data represents the energy consumption value of the operating equipment at different times.

[0008] The energy consumption processing dynamic switching module is used to project the controller data of the operating equipment into the preset energy consumption fluctuation range of the operating equipment, and issue an energy consumption processing dynamic switching command based on the projection result. The energy consumption processing dynamic switching command includes a micro fluctuation processing command and a sudden increase processing command.

[0009] The energy consumption micro-fluctuation sensitivity analysis module performs energy consumption micro-fluctuation sensitivity analysis on the operating equipment based on the received micro-fluctuation processing instructions, issues control instructions based on the sensitivity analysis results, and outputs the judgment results to the comprehensive control and early warning module.

[0010] The energy consumption surge fault detection module performs energy consumption surge fault detection analysis on the operating equipment based on the received surge processing command, identifies the fault type, and outputs the identification result to the integrated control and early warning module.

[0011] The integrated control and early warning module completes the integrated control and early warning processing of operating equipment during the workshop production process.

[0012] Preferably, the energy consumption baseline constraint acquisition module is used to acquire the energy consumption baseline constraint conditions of the operating equipment during the workshop production process, wherein the operating equipment refers to the machine tools operating in the workshop.

[0013] Multi-parameter rated value fluctuation range constraints are imposed on the operating equipment in the workshop production process. When the fluctuation range of the multi-parameter values ​​exceeds the rated value fluctuation range, the protection mechanism is triggered.

[0014] Based on historical energy consumption data, a preset energy consumption fluctuation range is set to constrain the energy consumption fluctuation range of operating equipment in the workshop production process.

[0015] Preferably, the production energy consumption data acquisition module is used to collect production energy consumption data of the operating equipment in the workshop production process. The production energy consumption data includes: voltage value, current value and frequency value of the operating equipment at different times.

[0016] The controller data of the operating equipment is read through the industrial communication protocol. The controller data represents the energy consumption value of the operating equipment at different times. The principle of reading the energy consumption value is as follows: when the machine tool is running, the current flows through the power supply line. The controller senses an electrical signal proportional to the current and voltage based on the magnitude of the current and voltage. The magnitude of the electrical signal is linearly related to the production energy consumption of the machine tool. The value of the electrical signal is then expressed as the energy consumption value of the operating equipment.

[0017] Preferably, the specific content of the energy consumption processing dynamic switching module is as follows:

[0018] Read the controller data of the operating equipment and obtain the maximum and minimum values ​​of the data read at different times;

[0019] The maximum and minimum values ​​are projected onto the preset energy consumption fluctuation range of the operating equipment. If the maximum and minimum values ​​are within the preset energy consumption fluctuation range, the production energy consumption of the operating equipment is normal; otherwise, the production energy consumption of the operating equipment is abnormal.

[0020] When the energy consumption of the operating equipment is abnormal, it means that both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range or both are less than the minimum value of the preset energy consumption fluctuation range. When both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range, and the difference between the maximum value and the maximum value of the preset energy consumption fluctuation range exceeds the preset fluctuation judgment threshold, a sudden increase processing command is issued; otherwise, a micro fluctuation processing command is issued.

[0021] When the energy consumption of the operating equipment is abnormal, it means that both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range or both are less than the minimum value of the preset energy consumption fluctuation range. When both the maximum and minimum values ​​are less than the minimum value of the preset energy consumption fluctuation range, and the difference between the minimum value and the minimum value of the preset energy consumption fluctuation range exceeds the preset fluctuation judgment threshold, a sudden increase processing command is issued; otherwise, a micro fluctuation processing command is issued.

[0022] Preferably, the specific contents of the energy consumption micro-fluctuation sensitivity analysis module are as follows:

[0023] Based on the received micro-fluctuation processing instructions, wavelet time-frequency processing is performed on the controller data of the operating equipment read at different times to obtain the energy values ​​at different times and frequencies.

[0024] A parameter sequence is constructed from the voltage, current, and frequency values ​​of the operating device read at different times, and is represented as follows: , and t = 1, 2, 3, ..., T, where t represents the number of different times and T represents the total number of data reads;

[0025] The energy values ​​at different times and frequencies are summed, and the average energy value at different times is calculated by averaging. ;

[0026] The ratio of the average energy value at different times to the energy reference value is used as the numerator, and the ratios of the voltage, current and frequency values ​​of the operating equipment read at different times to the voltage reference value, current reference value and frequency reference value are used as the denominators. The energy consumption micro-fluctuation sensitivity analysis of the operating equipment is performed, and the sensitivity coefficients of the voltage, current and frequency of the operating equipment are calculated.

[0027] Based on the calculated sensitivity coefficients of the voltage, current, and frequency of the operating equipment, the sensitivity coefficients of the voltage, current, and frequency of the operating equipment are compared to obtain the maximum sensitivity coefficient. The operating equipment parameter corresponding to the maximum sensitivity coefficient is then labeled as the fault type and a control command is issued.

[0028] Preferably, the specific content of the energy consumption surge fault detection module is as follows:

[0029] Based on the received surge processing instructions, the voltage values ​​of the operating equipment at different times are compared with the voltage reference value to obtain the voltage deviation at different times; the current values ​​of the operating equipment at different times are compared with the current reference value to obtain the current deviation at different times; and the frequency values ​​of the operating equipment at different times are compared with the frequency reference value to obtain the frequency deviation at different times.

[0030] The maximum values ​​of voltage deviation, current deviation, and frequency deviation of the operating equipment at different times are obtained: the maximum voltage deviation and the time when the maximum voltage deviation occurs are obtained, the maximum current deviation and the time when the maximum current deviation occurs are obtained, and the maximum frequency deviation and the time when the maximum frequency deviation occurs are obtained.

[0031] A time synchronization analysis was performed on the times when the voltage deviation, current deviation, and frequency deviation reached their maximum values.

[0032] For equipment operating with sudden increases in energy consumption, if the result of the time synchronization analysis is greater than or equal to the preset threshold, the result is a comprehensive fault and the fault type is marked. Conversely, if the result of the time synchronization analysis is less than the preset threshold, the highest priority is marked as the fault type according to the preset priority.

[0033] Preferably, the specific contents of the integrated control and early warning module are as follows:

[0034] Receive the calibration results of the energy consumption micro-fluctuation sensitivity analysis module: Based on the fault type calibrated by the energy consumption micro-fluctuation sensitivity analysis module and the control instructions issued, complete the control of the operating equipment parameters corresponding to the maximum sensitivity coefficient;

[0035] Receive the calibration results of the power consumption surge fault discrimination module: When the fault type is calibrated as a comprehensive fault, a warning message is sent to the user terminal. The user terminal receives the warning message, performs a power-off operation on the operating equipment, and performs a detection on the operating equipment. When the highest priority is calibrated as the fault type, the operating equipment is regulated according to the highest priority.

[0036] The technical effects and advantages of this invention are as follows:

[0037] This invention reads the controller data of the operating equipment, obtains the maximum and minimum values ​​of the read data at different times, and projects them onto the preset energy consumption fluctuation range of the operating equipment to make anomaly judgments on production energy consumption and promptly detect energy consumption anomalies.

[0038] The energy consumption micro-fluctuation sensitivity analysis module can accurately capture the micro-fluctuations in the energy consumption of operating equipment. By calculating the sensitivity coefficient, it issues control commands based on the maximum sensitivity coefficient, providing strong support for early fault warning and timely handling. The energy consumption surge fault identification module focuses on dealing with sudden increases in energy consumption. Through deviation calculation and real-time synchronization analysis, it can accurately identify comprehensive faults or single fault types.

[0039] As the core control unit of the entire system, the integrated control and early warning module executes corresponding control commands or early warning operations based on the calibration results of the energy consumption micro-fluctuation sensitivity analysis module and the energy consumption surge fault judgment module, ensuring that energy consumption in the workshop production process is always under control and effectively avoiding the risks of energy waste and equipment damage. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of a production energy consumption analysis system based on big data. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The production energy consumption analysis system based on big data involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, the present invention provides a production energy consumption analysis system based on big data, including: an energy consumption benchmark constraint acquisition module, a production energy consumption data acquisition module, an energy consumption processing dynamic switching module, an energy consumption micro-fluctuation sensitivity analysis module, an energy consumption sudden increase fault identification module, and a comprehensive control and early warning module;

[0043] The energy consumption benchmark constraint acquisition module is used to acquire the energy consumption benchmark constraint of the operating equipment during the workshop production process. The constraint includes the rated value fluctuation range and the preset energy consumption fluctuation range of the operating equipment.

[0044] The production energy consumption data acquisition module collects production energy consumption data of the operating equipment in the workshop production process, and reads the controller data of the operating equipment through the industrial communication protocol. The controller data represents the energy consumption value of the operating equipment at different times.

[0045] The energy consumption processing dynamic switching module projects the controller data of the operating equipment into the preset energy consumption fluctuation range of the operating equipment, and issues an energy consumption processing dynamic switching command based on the projection result. The energy consumption processing dynamic switching command includes a micro fluctuation processing command and a sudden increase processing command.

[0046] The energy consumption micro-fluctuation sensitivity analysis module performs energy consumption micro-fluctuation sensitivity analysis on the operating equipment based on the received micro-fluctuation processing instructions, issues control instructions based on the sensitivity analysis results, and outputs the judgment results to the comprehensive control and early warning module.

[0047] The energy consumption surge fault detection module performs energy consumption surge fault detection analysis on the operating equipment based on the received surge processing command, identifies the fault type, and outputs the identification result to the integrated control and early warning module.

[0048] The integrated control and early warning module completes the integrated control and early warning processing of operating equipment during the workshop production process.

[0049] In this embodiment, it should be specifically noted that the energy consumption baseline constraint acquisition module is used to acquire the energy consumption baseline constraint conditions of the operating equipment during the workshop production process, where the operating equipment refers to the machine tools operating in the workshop.

[0050] The system constrains the fluctuation range of multiple parameters of the operating equipment in the workshop production process. When the fluctuation range of multiple parameters exceeds the fluctuation range of multiple parameters, a protection mechanism is triggered. The protection mechanism is as follows: when the parameter exceeds the rated range, the system notifies the operator through audible and visual alarms, SMS or email.

[0051] Based on historical energy consumption data, a preset energy consumption fluctuation range is set to constrain the energy consumption fluctuation range of operating equipment in the workshop production process.

[0052] In this embodiment, it should be specifically noted that the production energy consumption data acquisition module collects production energy consumption data of the operating equipment in the workshop production process. The production energy consumption data includes: voltage value, current value and frequency value of the operating equipment at different times.

[0053] The controller data of the operating equipment is read through the industrial communication protocol. The controller data represents the energy consumption value of the operating equipment at different times. The principle of reading the energy consumption value is as follows: when the machine tool is running, the current flows through the power supply line. The controller senses an electrical signal proportional to the current and voltage based on the magnitude of the current and voltage. The magnitude of the electrical signal is linearly related to the production energy consumption of the machine tool. The value of the electrical signal is then expressed as the energy consumption value of the operating equipment.

[0054] In this embodiment, it should be specifically explained that the specific content of the energy consumption processing dynamic switching module is as follows:

[0055] Read the controller data of the operating equipment and obtain the maximum and minimum values ​​of the data read at different times;

[0056] The maximum and minimum values ​​are projected onto the preset energy consumption fluctuation range of the operating equipment. If the maximum and minimum values ​​are within the preset energy consumption fluctuation range, the production energy consumption of the operating equipment is normal; otherwise, the production energy consumption of the operating equipment is abnormal.

[0057] When the energy consumption of the operating equipment is abnormal, it means that both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range or both are less than the minimum value of the preset energy consumption fluctuation range. When both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range, and the difference between the maximum value and the maximum value of the preset energy consumption fluctuation range exceeds the preset fluctuation judgment threshold, a sudden increase processing command is issued; otherwise, a micro fluctuation processing command is issued.

[0058] When the energy consumption of the operating equipment is abnormal, it means that both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range or both are less than the minimum value of the preset energy consumption fluctuation range. When both the maximum and minimum values ​​are less than the minimum value of the preset energy consumption fluctuation range, and the difference between the minimum value and the minimum value of the preset energy consumption fluctuation range exceeds the preset fluctuation judgment threshold, a sudden increase processing command is issued; otherwise, a micro fluctuation processing command is issued.

[0059] In this embodiment, it should be specifically explained that the specific content of the energy consumption micro-fluctuation sensitivity analysis module is as follows:

[0060] Based on the received micro-wave processing instructions, wavelet time-frequency processing is performed on the controller data of the operating equipment read at different times to obtain the energy values ​​at different times and frequencies. The energy value represents the energy concentration of the microwave signal at a specific time and frequency range. Wavelet time-frequency processing means that the controller data of the operating equipment read at different times is decomposed into different frequency units through continuous wavelet transform. Energy calculation can be directly implemented using MATLAB Wavelet Toolbox.

[0061] A parameter sequence is constructed from the voltage, current, and frequency values ​​of the operating device read at different times, and is represented as follows: , and t = 1, 2, 3, ..., T, where t represents the number of different times and T represents the total number of data reads;

[0062] The energy values ​​at different times and frequencies are summed, and the average energy value at different times is calculated by averaging. ;

[0063] Using the ratio of the average energy value to the energy reference value at different times as the numerator, and the ratios of the voltage, current, and frequency values ​​of the operating equipment read at different times to the voltage reference value, current reference value, and frequency reference value as the denominator, a sensitivity analysis of energy consumption micro-fluctuations is performed on the operating equipment. The sensitivity coefficients of the operating equipment's voltage, current, and frequency are calculated. The formula for calculating the voltage sensitivity coefficient of the operating equipment is as follows: ,in This represents the voltage sensitivity coefficient of the operating equipment. Indicates the energy reference value. The voltage reference value is represented; the formula for calculating the current sensitivity coefficient of the operating equipment is: ,in This represents the current sensitivity coefficient of the operating equipment. Indicates the energy reference value. The reference current value is represented by the formula for calculating the frequency sensitivity coefficient of the operating equipment. ,in This represents the frequency sensitivity coefficient of the operating equipment. Indicates the energy reference value. Indicates the frequency reference value;

[0064] Based on the calculated sensitivity coefficients of the voltage, current and frequency of the operating equipment, the sensitivity coefficients of the voltage, current and frequency of the operating equipment are compared to obtain the maximum sensitivity coefficient, and the operating equipment parameter corresponding to the maximum sensitivity coefficient is labeled as the fault type and a control command is issued.

[0065] Voltage, current, and frequency are the basic electrical parameters for equipment operation. Energy is the comprehensive performance of equipment operation. The design logic of the sensitivity coefficient is to build a correlation between the relative change of energy and the relative change of each electrical parameter. The sensitivity relationship between energy and parameters is quantified by summing the logarithmic function to avoid the distortion of the sensitivity coefficient due to excessively large values. Adding 1 is to ensure that the input of the logarithmic function is positive.

[0066] The formulas for voltage, current, and frequency are completely identical, all being the ratio of the relative change in energy to the relative change in parameters. After logarithmic transformation, they are summed. Through a unified mathematical form, the sensitivity coefficients corresponding to parameters with different physical meanings become correlation strength indicators under the same mathematical framework, which can naturally be compared. The purpose of calculating the sensitivity coefficients of voltage, current, and frequency of the operating equipment is to determine the most sensitive parameter among the operating equipment parameters, thereby determining the fault type.

[0067] In this embodiment, it should be specifically explained that the specific content of the energy consumption surge fault detection module is as follows:

[0068] Based on the received surge processing instructions, the voltage values ​​of the operating equipment at different times are compared with the voltage reference value to obtain the voltage deviation at different times; the current values ​​of the operating equipment at different times are compared with the current reference value to obtain the current deviation at different times; and the frequency values ​​of the operating equipment at different times are compared with the frequency reference value to obtain the frequency deviation at different times.

[0069] The maximum values ​​of voltage deviation, current deviation, and frequency deviation of the operating equipment at different times are obtained: the maximum voltage deviation and the time when the maximum voltage deviation occurs are obtained, the maximum current deviation and the time when the maximum current deviation occurs are obtained, and the maximum frequency deviation and the time when the maximum frequency deviation occurs are obtained.

[0070] A time synchronization analysis was performed on the times when the voltage deviation, current deviation, and frequency deviation reached their maximum values. The calculation formula is as follows: ,in This indicates the results of the time synchronization analysis. This indicates the time when the maximum voltage deviation occurs. This indicates the moment when the maximum current deviation occurs. This indicates the moment when the maximum frequency deviation occurs, where e represents the natural constant and k represents the time constant;

[0071] For the equipment, a sudden increase in energy consumption is detected: if the result of the time synchronization analysis is greater than or equal to the preset threshold, the result is a comprehensive fault and the fault type is marked. Conversely, if the result of the time synchronization analysis is less than the preset threshold, the highest priority is marked as the fault type according to the preset priority. In this embodiment, the preset priority order is represented as voltage regulation > current regulation > frequency regulation.

[0072] The timing synchronization formula is designed to quantify the synchronicity at the moment of maximum voltage, current, and frequency deviation. It transforms the proximity of the three deviation moments into a comprehensive index through an exponential decay function of the time difference. e is a natural constant to ensure smooth function decay, and k is a time constant to control the decay rate. If the voltage, current, and frequency deviations occur synchronously (… A large deviation indicates a complex fault; if the deviation is dispersed over time ( If the fault is small, then it is determined to be a single fault;

[0073] The method for setting the preset threshold is as follows: The device is simultaneously triggered to measure voltage, current, and frequency deviations through experimentation, such as by artificially injecting disturbances, so that the maximum values ​​of these three deviations are synchronized at all times. Multiple sets of data are collected. The minimum value of the data represents the minimum synchronization index of the comprehensive fault. The voltage, current and frequency deviations are triggered individually. For example, the voltage is adjusted to maximize the deviation, and the current and frequency are controlled to be stable. Multiple sets of λ data are collected, and the maximum value represents the maximum synchronization index of a single fault. The threshold is set to any value between the minimum synchronization index and the maximum synchronization index based on engineering experience.

[0074] In this embodiment, it should be specifically explained that the specific content of the integrated control and early warning module is as follows:

[0075] Receive the calibration results of the energy consumption micro-fluctuation sensitivity analysis module: Based on the fault type calibrated by the energy consumption micro-fluctuation sensitivity analysis module and the control instructions issued, complete the control of the operating equipment parameters corresponding to the maximum sensitivity coefficient;

[0076] Receive the calibration results of the power consumption surge fault discrimination module: When the fault type is calibrated as a comprehensive fault, a warning message is sent to the user terminal. The user terminal receives the warning message, performs a power-off operation on the operating equipment, and performs a detection on the operating equipment. When the highest priority is calibrated as the fault type, the operating equipment is regulated according to the highest priority.

[0077] In this embodiment, it should be specifically noted that the main difference between this embodiment and the prior art is that this embodiment reads the controller data of the operating equipment, obtains the maximum and minimum values ​​of the read data at different times, and projects them onto the preset energy consumption fluctuation range of the operating equipment to make anomaly judgment on production energy consumption and promptly detect energy consumption anomalies.

[0078] The energy consumption micro-fluctuation sensitivity analysis module can accurately capture the micro-fluctuations in the energy consumption of operating equipment. By calculating the sensitivity coefficient, it issues control commands based on the maximum sensitivity coefficient, providing strong support for early fault warning and timely handling. The energy consumption surge fault identification module focuses on dealing with sudden increases in energy consumption. Through deviation calculation and real-time synchronization analysis, it can accurately identify comprehensive faults or single fault types.

[0079] As the core control unit of the entire system, the integrated control and early warning module executes corresponding control commands or early warning operations based on the calibration results of the energy consumption micro-fluctuation sensitivity analysis module and the energy consumption surge fault judgment module, ensuring that energy consumption in the workshop production process is always under control and effectively avoiding the risks of energy waste and equipment damage.

[0080] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A big data-based production energy consumption analysis system, characterized by, include: The module includes: energy consumption baseline constraint acquisition module, production energy consumption data acquisition module, energy consumption processing dynamic switching module, energy consumption micro-fluctuation sensitivity analysis module, energy consumption sudden increase fault detection module, and comprehensive control and early warning module. The energy consumption benchmark constraint acquisition module is used to acquire the energy consumption benchmark constraint of the operating equipment during the workshop production process. The energy consumption benchmark constraint includes the rated value fluctuation range and the preset energy consumption fluctuation range of the operating equipment. The production energy consumption data acquisition module is used to collect production energy consumption data of the operating equipment in the workshop production process, and read the controller data of the operating equipment through the industrial communication protocol. The controller data represents the energy consumption value of the operating equipment at different times. The energy consumption processing dynamic switching module is used to project the controller data of the operating equipment into the preset energy consumption fluctuation range of the operating equipment, and issue an energy consumption processing dynamic switching command based on the projection result. The energy consumption processing dynamic switching command includes a micro fluctuation processing command and a sudden increase processing command. The specific details of the dynamic switching module for energy consumption processing are as follows: Read the controller data of the operating equipment and obtain the maximum and minimum values ​​of the data read at different times; The maximum and minimum values ​​are projected onto the preset energy consumption fluctuation range of the operating equipment. If the maximum and minimum values ​​are within the preset energy consumption fluctuation range, the production energy consumption of the operating equipment is normal; otherwise, the production energy consumption of the operating equipment is abnormal. When the energy consumption of the operating equipment is abnormal, it means that both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range or both are less than the minimum value of the preset energy consumption fluctuation range. When both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range, and the difference between the maximum value and the maximum value of the preset energy consumption fluctuation range exceeds the preset fluctuation judgment threshold, a sudden increase processing command is issued; otherwise, a micro fluctuation processing command is issued. When the energy consumption of the operating equipment is abnormal, it means that both the maximum and minimum values ​​are greater than the maximum value of the preset energy consumption fluctuation range or both are less than the minimum value of the preset energy consumption fluctuation range. When both the maximum and minimum values ​​are less than the minimum value of the preset energy consumption fluctuation range, and the difference between the minimum value and the minimum value of the preset energy consumption fluctuation range exceeds the preset fluctuation judgment threshold, a sudden increase processing command is issued; otherwise, a micro fluctuation processing command is issued. The energy consumption micro-fluctuation sensitivity analysis module performs energy consumption micro-fluctuation sensitivity analysis on the operating equipment based on the received micro-fluctuation processing instructions, issues control instructions based on the sensitivity analysis results, and outputs the judgment results to the comprehensive control and early warning module. The specific contents of the energy consumption micro-fluctuation sensitivity analysis module are as follows: Based on the received micro-fluctuation processing instructions, wavelet time-frequency processing is performed on the controller data of the operating equipment read at different times to obtain the energy values ​​at different times and frequencies. The parameter sequence is constructed for the voltage value, the current value and the frequency value of the running equipment read at different time, respectively represented as , and , t = 1, 2, 3,..., T, wherein t represents the number of different time, and T represents the total number of data reading; The energy values ​​at different times and frequencies are summed, and the average energy value at different times is calculated by averaging. ; The ratio of the average energy value at different times to the energy reference value is used as the numerator, and the ratios of the voltage, current and frequency values ​​of the operating equipment read at different times to the voltage reference value, current reference value and frequency reference value are used as the denominators. The energy consumption micro-fluctuation sensitivity analysis of the operating equipment is performed, and the sensitivity coefficients of the voltage, current and frequency of the operating equipment are calculated. Based on the calculated sensitivity coefficients of the voltage, current and frequency of the operating equipment, the sensitivity coefficients of the voltage, current and frequency of the operating equipment are compared to obtain the maximum sensitivity coefficient, and the operating equipment parameter corresponding to the maximum sensitivity coefficient is labeled as the fault type and a control command is issued. The energy consumption surge fault detection module performs energy consumption surge fault detection analysis on the operating equipment based on the received surge processing command, identifies the fault type, and outputs the identification result to the integrated control and early warning module. The specific contents of the energy consumption surge fault detection module are as follows: Based on the received surge processing instructions, the voltage values ​​of the operating equipment at different times are compared with the voltage reference value to obtain the voltage deviation at different times; the current values ​​of the operating equipment at different times are compared with the current reference value to obtain the current deviation at different times; and the frequency values ​​of the operating equipment at different times are compared with the frequency reference value to obtain the frequency deviation at different times. The maximum values ​​of voltage deviation, current deviation, and frequency deviation of the operating equipment at different times are obtained: the maximum voltage deviation and the time when the maximum voltage deviation occurs are obtained, the maximum current deviation and the time when the maximum current deviation occurs are obtained, and the maximum frequency deviation and the time when the maximum frequency deviation occurs are obtained. A time synchronization analysis was performed on the times when the voltage deviation, current deviation, and frequency deviation reached their maximum values. For equipment operating, a sudden increase in energy consumption is detected. If the result of the time synchronization analysis is greater than or equal to the preset threshold, the detection result is a comprehensive fault and the fault type is marked. Conversely, if the result of the time synchronization analysis is less than the preset threshold, the highest priority is marked as the fault type according to the preset priority. The integrated control and early warning module completes the integrated control and early warning processing of operating equipment during the workshop production process.

2. The production energy consumption analysis system based on big data according to claim 1, characterized in that: The energy consumption baseline constraint acquisition module is used to acquire the energy consumption baseline constraints of the operating equipment during the workshop production process, wherein the operating equipment refers to the machine tools operating in the workshop. Multi-parameter rated value fluctuation range constraints are imposed on the operating equipment in the workshop production process. When the fluctuation range of the multi-parameter values ​​exceeds the rated value fluctuation range, the protection mechanism is triggered. Based on historical energy consumption data, a preset energy consumption fluctuation range is set to constrain the energy consumption fluctuation range of operating equipment in the workshop production process.

3. The production energy consumption analysis system based on big data according to claim 1, characterized in that: The production energy consumption data acquisition module is used to collect production energy consumption data of the operating equipment in the workshop production process. The production energy consumption data includes: voltage value, current value and frequency value of the operating equipment at different times. The controller data of the operating equipment is read through the industrial communication protocol. The controller data represents the energy consumption value of the operating equipment at different times. The principle of reading the energy consumption value is as follows: when the machine tool is running, the current flows through the power supply line. The controller senses an electrical signal proportional to the current and voltage based on the magnitude of the current and voltage. The magnitude of the electrical signal is linearly related to the production energy consumption of the machine tool. The value of the electrical signal is then expressed as the energy consumption value of the operating equipment.

4. The production energy consumption analysis system based on big data according to claim 1, characterized in that: The specific contents of the comprehensive regulation and early warning module are as follows: Receive the calibration results of the energy consumption micro-fluctuation sensitivity analysis module: Based on the fault type calibrated by the energy consumption micro-fluctuation sensitivity analysis module and the control instructions issued, complete the control of the operating equipment parameters corresponding to the maximum sensitivity coefficient; Receive the calibration results of the power consumption surge fault discrimination module: When the fault type is calibrated as a comprehensive fault, a warning message is sent to the user terminal. The user terminal receives the warning message, performs a power-off operation on the operating equipment, and performs a detection on the operating equipment. When the highest priority is calibrated as the fault type, the operating equipment is regulated according to the highest priority.

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