A process manufacturing energy consumption and equipment state collaborative optimization monitoring method and system

Abstract

This invention relates to the field of industrial manufacturing equipment maintenance technology, and particularly to a method and system for collaboratively optimizing and monitoring energy consumption and equipment status in process manufacturing. The method includes monitoring and calculating the probability of failure using sensors; when the failure probability reaches a threshold, collecting failure status data and energy consumption data before and after the corresponding timestamp, defining before and after time windows; determining the energy consumption status of equipment within the before time window based on historical energy consumption baselines; calculating the total energy consumption increment for standby equipment switching and steady-state operation within the after time window based on historical production line data; generating failure risk constraints based on the failure probability change rate; setting energy efficiency thresholds based on production line energy consumption control targets and generating energy efficiency constraints; and outputting a decision result of delayed shutdown or immediate shutdown by comprehensively considering the energy consumption status and the two types of constraints. This invention achieves a collaborative balance between equipment failure risk prevention and energy consumption optimization, avoiding energy waste caused by blind shutdowns under low-energy steady-state conditions.

Description

Technical Field

[0001] This invention relates to the field of industrial manufacturing equipment maintenance technology, and in particular to a method and system for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status. Background Technology

[0002] In the process manufacturing field, stable equipment operation and energy consumption optimization are the core requirements for ensuring production efficiency and reducing operating costs. Among them, the scientific nature of equipment condition monitoring and maintenance decisions directly affects the overall benefits of the production line. In existing technologies, when monitoring the status of equipment, maintenance is determined by whether a threshold for the probability of failure is triggered. However, if the failure probability of the equipment reaches the threshold at a certain point in time, the system immediately triggers a shutdown for maintenance. But at this time, the equipment is operating in a low-energy consumption range. Direct shutdown for maintenance will cause the production line to switch to a backup fan. The backup fan consumes much more energy than the original equipment when it was operating in a low-energy consumption range. At the same time, the production line load adjustment during the switching process will also generate additional transitional energy consumption. Ultimately, the total energy consumption of switching to the backup equipment is much higher than the energy consumption of the original equipment continuing to operate until a reasonable maintenance window, resulting in serious energy waste. In some scenarios, although the probability of equipment failure reaches the threshold, the overall failure risk is stable or decreasing. Continuing to operate will not pose a risk of failure expansion in the short term. If a shutdown for maintenance is performed directly, not only will the low-energy operation advantage of the original equipment be sacrificed, but the subsequent energy consumption rebound and increased equipment wear may also be caused by operations such as switching to the backup equipment and frequent start-ups and shutdowns of the original equipment.

[0003] Therefore, it is necessary to propose a collaborative optimization monitoring method and system for process manufacturing energy consumption and equipment status to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for co-optimizing and monitoring process manufacturing energy consumption and equipment status, in order to solve the problems that direct shutdown maintenance will lead to energy consumption that is much higher than the energy consumption when the equipment is operating in the low energy consumption range, and that direct shutdown maintenance will sacrifice the low energy consumption operation advantage of the original equipment, and lead to subsequent energy consumption rebound and increased equipment wear.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for collaborative optimization and monitoring of energy consumption and equipment status in process manufacturing includes the following steps: S1: Utilize the condition monitoring sensors deployed within the original equipment to monitor and calculate the failure probability of the original equipment. Set the failure probability threshold for the original equipment. And based on failure probability Collect fault status data and energy consumption data of the original equipment, and collect fault probability. Reaching the failure probability threshold The corresponding timestamp And based on timestamps Delineate the previous time window and subsequent time window ; S2: Set the historical energy consumption baseline of the original equipment and generate the previous time window based on the energy consumption data. Energy consumption status of internal equipment; S3: Based on historical production line data, calculate the energy consumption increment when switching from the original equipment to the standby equipment. And the energy consumption increment of backup equipment when it reaches steady-state operation According to the increase in energy consumption and energy consumption increment Obtain the subsequent time window Total energy consumption increment ; S4: Calculate the failure probability based on the failure status data. rate of change According to the rate of change Generate fault risk constraints; S5: Set energy efficiency threshold Combined with the increase in total energy consumption Generate energy consumption efficiency constraints; S6: Based on the previous time window The system monitors the equipment based on its energy consumption status, fault risk constraints, and energy efficiency constraints, in order to output decision results.

[0006] Preferably, the step involves using condition monitoring sensors deployed within the original equipment to monitor and calculate the failure probability of the original equipment. Set the failure probability threshold for the original equipment. And based on failure probability The steps for collecting fault status data and energy consumption data from the original equipment include: When the status monitoring sensor detects the timestamp Failure probability value When a data acquisition command is triggered, fault status data is collected, including the original device timestamp. Failure probability sequence before and after ,in, , For timestamps The previous collection time, For timestamps Subsequent data collection duration; After triggering the data acquisition command, energy consumption data is collected, including data related to the fault probability sequence. Energy consumption per unit of product during the same time period Production line capacity data .

[0007] Preferably, the timestamp Delineate the previous time window and subsequent time window The steps include: By timestamp Set to get the previous time window and subsequent time window The preceding time window The time window is The post-time window The time window is .

[0008] Preferably, the historical energy consumption baseline of the original equipment is set, and a previous time window is generated by combining the energy consumption data. The steps for determining the energy consumption status of internal equipment include: The historical energy consumption baseline includes the energy consumption range of the low-energy steady-state interval. and energy consumption fluctuation threshold ,in, For the lowest unit energy consumption, This represents the steady-state average energy consumption. Energy consumption sequence per unit product from energy consumption data mean Maximum value and minimum value The previous time window was calculated. Fluctuation rate of energy consumption of internal raw equipment ; The energy consumption state includes a low energy consumption steady-state range and a high energy consumption fluctuation range; If the mean and This is determined to be a low-energy-consumption steady-state range. If the mean or It was determined to be in the high energy consumption fluctuation range.

[0009] Preferably, the step of calculating the energy consumption increment when switching from the original equipment to the standby equipment is based on historical production line data. And the energy consumption increment of backup equipment when it reaches steady-state operation According to the increase in energy consumption and energy consumption increment Obtain the subsequent time window Total energy consumption increment The steps include: The energy consumption increment is calculated based on the peak energy consumption of standby equipment startup and the energy consumption of production line load adjustment. ; Based on the original equipment in the previous time window Average energy consumption per unit product Combined with the later time window in energy consumption data Production line capacity data The energy consumption increment was calculated. ; Based on energy consumption increment and energy consumption increment The total energy consumption increment was calculated. .

[0010] Preferably, the fault probability is calculated based on the fault status data. rate of change According to the rate of change The steps for generating failure risk constraints include: Based on the fault status data, the original device timestamp Failure probability sequence before and after and timestamp Corresponding failure probability value The previous time window was calculated. Internal Failure Probability rate of change ; Based on rate of change Generate fault risk constraints, which include: like A value greater than 0 indicates an upward trend in the risk of failure. like =0, indicating a stable trend in fault risk; like A value less than 0 indicates a decreasing trend in fault risk.

[0011] Preferably, the setting of the energy efficiency threshold Combined with the increase in total energy consumption The steps for generating energy efficiency constraints include: Based on the energy consumption control targets of the production line, set an energy efficiency threshold. ; Combined with the increase in total energy consumption Generate energy efficiency constraints, which include: If the total energy consumption increment Energy efficiency threshold The system was deemed to have excessively low energy efficiency when switching to backup equipment. If the total energy consumption increment ≤ Energy efficiency threshold The energy consumption efficiency of switching to backup equipment was deemed acceptable.

[0012] Preferably, the step of basing the time window on the previous time window The steps for monitoring equipment based on its energy consumption status, failure risk constraints, and energy efficiency constraints to output decision results include: Current time window When the energy consumption state is in the low-energy-consumption steady-state range, if the fault risk stability trend or the fault risk decreasing trend in the fault risk constraint is met, and the total energy consumption increment in the energy consumption benefit constraint is also met, then... Energy efficiency threshold The output decision is to delay the shutdown of the original equipment and continue its operation. Current time window When the energy consumption state is in the low energy consumption steady state range, if the failure risk increases in the failure risk constraint condition, the output decision result is that the original equipment should be shut down immediately and the standby equipment should be switched. Current time window When the energy consumption state is in the high energy consumption fluctuation range, if the total energy consumption increment is satisfied... ≤ Energy efficiency threshold The output decision is to immediately shut down the original equipment and switch to the backup equipment.

[0013] A process manufacturing energy consumption and equipment status collaborative optimization monitoring system, applying the aforementioned process manufacturing energy consumption and equipment status collaborative optimization monitoring method, the system includes: The status monitoring module is used to monitor and calculate the failure probability of the original equipment in real time through the status monitoring sensors deployed in the original equipment. The data acquisition module is used to collect fault probability sequences, unit product energy consumption, and production line capacity data when the equipment failure probability reaches the failure probability threshold. The delineation module is used to delineate the pre-time window and the post-time window based on the timestamp when the failure probability reaches the threshold. The analysis module is used to calculate the average and volatility of unit product energy consumption within the previous time window based on the original equipment's historical energy consumption baseline. The comparison module is used to compare the average energy consumption per unit of product in the previous time window with the fluctuation rate of the original equipment energy consumption to obtain the energy consumption status. The prediction module is used to calculate the energy consumption increment when switching from the original equipment to the standby equipment and the energy consumption increment when the standby equipment reaches steady-state operation based on historical data of the production line, so as to obtain the total energy consumption increment within the subsequent time window. The constraint setting module is used to set the failure risk constraint corresponding to the failure probability change rate and the energy efficiency threshold constraint based on the production line energy consumption control target. The decision module is used to comprehensively consider the energy consumption status of the previous time window, the fault risk constraints, and the energy consumption benefit constraints, and to determine whether to delay the shutdown of the original equipment and continue to operate or to immediately shut down the equipment and switch to the backup equipment.

[0014] The technical effects and advantages of the present invention in the above technical solution are as follows: This invention captures the steady-state energy consumption characteristics and the evolution trend of fault risk before equipment fault warning by associating time windows before and after timestamps. Combined with historical production line data, it quantifies the total energy consumption increment of switching to standby equipment. Through multi-constraint linkage judgment, it avoids energy waste caused by blindly shutting down and switching to standby equipment under low energy consumption steady state. Moreover, it can trigger maintenance in a timely manner when fault risk increases or equipment energy consumption fluctuates, effectively balancing fault risk prevention and control with energy consumption efficiency optimization. The system has clear division of labor among its modules, closed-loop data flow, and scientific and traceable decision-making logic. It is adapted to the operating characteristics of different types of equipment in the process manufacturing field, improving the intelligence level of production line maintenance decision-making and overall operational efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for collaborative optimization and monitoring of energy consumption and equipment status in process manufacturing, according to the present invention.

[0016] Figure 2 This is a flowchart of a process manufacturing energy consumption and equipment status collaborative optimization monitoring system according to the present invention.

[0017] Figure 3 This is a structural diagram of a process manufacturing energy consumption and equipment status collaborative optimization monitoring system according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, as Figure 1 , Figure 2 As shown, this embodiment provides a method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status, including the following steps: S1: Utilize the condition monitoring sensors deployed within the original equipment to monitor and calculate the failure probability of the original equipment. Set the failure probability threshold for the original equipment. And based on failure probability Collect fault status data and energy consumption data of the original equipment, and collect fault probability. Reaching the failure probability threshold The corresponding timestamp And based on timestamps Delineate the previous time window and subsequent time window ; S2: Set the historical energy consumption baseline of the original equipment and generate the previous time window based on the energy consumption data. Energy consumption status of internal equipment; S3: Based on historical production line data, calculate the energy consumption increment when switching from the original equipment to the standby equipment. And the energy consumption increment of backup equipment when it reaches steady-state operation According to the increase in energy consumption and energy consumption increment Obtain the subsequent time window Total energy consumption increment ; S4: Calculate the failure probability based on the failure status data. rate of change According to the rate of change Generate fault risk constraints; S5: Set energy efficiency threshold Combined with the increase in total energy consumption Generate energy consumption efficiency constraints; S6: Based on the previous time window The system monitors the equipment based on its energy consumption status, fault risk constraints, and energy efficiency constraints, in order to output decision results.

[0020] In one embodiment of the present invention, the condition monitoring sensor deployed within the original equipment is used to monitor and calculate the failure probability of the original equipment. Set the failure probability threshold for the original equipment. And based on failure probability The steps for collecting fault status data and energy consumption data from the original equipment include: S11: When the status monitoring sensor detects a timestamp Failure probability value When a data acquisition command is triggered, fault status data is collected, including the original device timestamp. Failure probability sequence before and after ,in, , For timestamps The previous collection time, For timestamps Subsequent data collection duration; S12: After triggering the data acquisition command, collect energy consumption data, which includes a fault probability sequence. Energy consumption per unit of product during the same time period Production line capacity data .

[0021] In this embodiment of the invention, as shown in steps S11-S12 above, a status monitoring sensor deployed within the original device is used to monitor and calculate the failure probability of the original device. This condition monitoring sensor can utilize multiple types of sensors, such as vibration sensors, temperature sensors, and wear sensors, to monitor conditions. By combining vibration, temperature, and wear data, a failure probability value is calculated. The value ranges from 0 to 1, representing the fault probability threshold. Set to 0.7, that is, when When the value is ≥0.7, a data acquisition command is triggered to collect fault status data and obtain the original device timestamp. Fault probability sequence with pre- and post-set time intervals , ,for The settings need to be determined based on the steady-state cycle of the industry to which the equipment belongs and the stable duration of energy consumption data during the equipment's historical normal operation. The data acquisition frequency of the condition monitoring sensor needs to be matched. The settings need to be configured in conjunction with the actual switching time of the production line's standby equipment, the production line's capacity plan within the window, and the main periods of energy consumption increments in historical fault handling. For energy consumption data collection, it is necessary to collect data along with the fault probability sequence. Energy consumption per unit of product within the same time period and production line capacity data .

[0022] In one embodiment of the present invention, the step is based on a timestamp. Delineate the previous time window and subsequent time window The steps include: S13: By timestamp Set to get the previous time window and subsequent time window The preceding time window The time window is The post-time window The time window is .

[0023] In one embodiment of the present invention, the historical energy consumption baseline of the original device is set, and a previous time window is generated by combining the energy consumption data. The steps for determining the energy consumption status of internal equipment include: S21: The historical energy consumption baseline includes the energy consumption range of the low-energy-consumption steady-state interval. and energy consumption fluctuation threshold ,in, For the lowest unit energy consumption, This represents the steady-state average energy consumption. S22: Energy consumption sequence per unit product from energy consumption data mean Maximum value and minimum value The previous time window was calculated. Fluctuation rate of energy consumption of internal raw equipment ; S23: The energy consumption state includes a low energy consumption steady-state range and a high energy consumption fluctuation range; S24: If the mean and This is determined to be a low-energy-consumption steady-state range. S25: If the mean or It was determined to be in the high energy consumption fluctuation range.

[0024] In this embodiment of the invention, as shown in steps S21-S25 above, a historical energy consumption baseline is constructed using the historical operating data of the original equipment, wherein the energy consumption range of the low-energy-consumption steady-state interval is set to... , To determine the lowest unit energy consumption of equipment during its historical operation within the optimal load range, this is achieved by statistically analyzing historical unit product energy consumption sequences. The minimum value is used to determine this. The average energy consumption per unit of product during historical steady-state operation. energy consumption fluctuation threshold Setting parameters through production line energy consumption control, targeting the preceding time window. Unit product energy consumption sequence collected internally Calculate its mean Maximum value and minimum value And through calculation formula The previous time window was calculated. Fluctuation rate of energy consumption of internal raw equipment Regarding energy consumption status, it is specifically divided into a low-energy-consumption steady-state range and a high-energy-consumption fluctuation range, where the mean... and This is determined to be a low-energy-consumption steady-state range, indicating that the equipment's current energy consumption level is at its optimal and operating stably; if the mean... or If the original equipment is identified as being in a high energy consumption fluctuation range, it indicates that the equipment has redundant energy consumption or an unstable operating state.

[0025] In one embodiment of the present invention, the step of calculating the energy consumption increment when switching from the original equipment to the standby equipment based on historical production line data is described. And the energy consumption increment of backup equipment when it reaches steady-state operation According to the increase in energy consumption and energy consumption increment Obtain the subsequent time window Total energy consumption increment The steps include: S31: Calculate the energy consumption increment based on the peak energy consumption of standby equipment startup and the energy consumption adjustment of the production line load. ; S32: Based on the original device's previous time window Average energy consumption per unit product Combined with the later time window in energy consumption data Production line capacity data The energy consumption increment was calculated. ; S33: Based on energy consumption increment and energy consumption increment The total energy consumption increment was calculated. .

[0026] In this embodiment of the invention, as shown in steps S31-S33 above, based on historical operation and maintenance data of standby equipment switching on the production line, the peak energy consumption during the process of starting the standby equipment from standby state until reaching rated speed or load is statistically analyzed using the historical operation and maintenance data. This peak energy consumption is then added to the energy consumption for load fluctuation adjustments caused by equipment switching on the production line. The summation yields the energy consumption increment. Regarding the increase in energy consumption Through the previous time window Average energy consumption per unit product of internal original equipment Combined with the post-time window The production capacity data of the internal production line has been determined. The unit product energy consumption of the standby equipment under this production capacity is predicted. Using calculation formula Perform calculations, where For the later time window The production capacity data of the internal production line has been determined. , The duration of the subsequent time window is used to calculate the energy consumption increment. Then, it is compared with the increase in energy consumption. The total energy consumption increment is obtained by superimposing the results. .

[0027] In one embodiment of the present invention, the fault probability is calculated based on the fault status data. rate of change According to the rate of change The steps for generating failure risk constraints include: S41: Based on the original device timestamp in the fault status data Failure probability sequence before and after and timestamp Corresponding failure probability value The previous time window was calculated. Internal Failure Probability rate of change ; S42: Based on rate of change Generate fault risk constraints, which include: like A value greater than 0 indicates an upward trend in the risk of failure. like =0, indicating a stable trend in fault risk; like A value less than 0 indicates a decreasing trend in fault risk.

[0028] In this embodiment of the invention, as shown in steps S41-S42 above, according to the previous time window time window Internal Fault Probability Sequence Extract the start time of the window. Failure probability value And extract the timestamp Corresponding failure probability value The linear slope calculation method is used, through the formula. , For the previous time window The duration was used to calculate the rate of change. Based on the rate of change Generate fault risk constraints, if A value >0 indicates that the probability of failure continues to increase over time, and is judged as an upward trend in failure risk; if When = 0, it indicates that the probability of failure is within the entire previous time window. The internal stability remained unchanged, without significant fluctuations, indicating a stable trend in fault risk; if When <0, it indicates that the failure probability is calculated from the start time of the window. to As the risk of equipment failure gradually decreases, it is determined to be a downward trend in failure risk, and failure risk constraints are generated comprehensively.

[0029] In one embodiment of the present invention, the setting of the energy efficiency threshold is... Combined with the increase in total energy consumption The steps for generating energy efficiency constraints include: S51: Set energy efficiency thresholds based on production line energy consumption control targets. ; S52: Combining total energy consumption increment Generate energy efficiency constraints, which include: If the total energy consumption increment Energy efficiency threshold The system was deemed to have excessively low energy efficiency when switching to backup equipment. If the total energy consumption increment ≤ Energy efficiency threshold The energy consumption efficiency of switching to backup equipment was deemed acceptable.

[0030] In this embodiment of the invention, as shown in steps S51-S52 above, regarding the energy consumption efficiency constraint, when the total energy consumption increment... Energy efficiency threshold If the switch to backup equipment is deemed to have excessively low energy efficiency, it means that the additional energy consumption generated by switching to backup equipment exceeds the production line's tolerance, leading to increased energy costs per unit product and failure to achieve energy-saving targets; when the total energy consumption increase... ≤ Energy efficiency threshold If the energy consumption of switching to backup equipment is deemed acceptable, it indicates that the energy consumption increase generated by the switch is within the control target and that subsequent higher energy consumption losses or downtime losses can be avoided through maintenance of the original equipment.

[0031] In one embodiment of the present invention, the step of basing the time window on the previous time window The steps for monitoring equipment based on its energy consumption status, failure risk constraints, and energy efficiency constraints to output decision results include: S61: Current Time Window When the energy consumption state is in the low-energy-consumption steady-state range, if the fault risk stability trend or the fault risk decreasing trend in the fault risk constraint is met, and the total energy consumption increment in the energy consumption benefit constraint is also met, then... Energy efficiency threshold The output decision is to delay the shutdown of the original equipment and continue its operation. S62: Current Time Window When the energy consumption state is in the low energy consumption steady state range, if the failure risk increases in the failure risk constraint condition, the output decision result is that the original equipment should be shut down immediately and the standby equipment should be switched. S63: Current Time Window When the energy consumption state is in the high energy consumption fluctuation range, if the total energy consumption increment is satisfied... ≤ Energy efficiency threshold The output decision is to immediately shut down the original equipment and switch to the backup equipment.

[0032] In this embodiment of the invention, as shown in steps S61-S63 above, the time window before determination is performed. When the energy consumption state is in the low-energy-consumption steady-state range, according to the fault risk constraint, if the rate of change of fault probability... =0, corresponding to a stable risk trend, or if A value less than 0 indicates a downward trend in risk, which determines the increase in total energy consumption. Energy efficiency threshold This indicates that the current equipment is operating at its optimal energy consumption and there is no urgent risk of failure. Switching to the backup equipment would cause unnecessary energy waste. Therefore, the decision is made to delay the shutdown of the original equipment and continue operation, with a maximum delay duration set, for example, the maximum delay duration can be set to the duration of the subsequent time window. If the fault risk constraint condition in the low-energy steady-state range is: A value >0 indicates an upward trend in risk, meaning that although the equipment currently has optimal energy consumption, the risk of failure continues to increase. Continuing operation may lead to equipment failure, downtime, and greater losses. In this case, energy efficiency constraints are no longer necessary, and the decision to immediately shut down the original equipment and switch to the backup equipment is directly output. (When determining the time window before...) When the energy consumption state is in the high energy consumption fluctuation range, if the total energy consumption increment ≤ Energy efficiency threshold This indicates that the energy consumption increase from switching to the backup equipment is within the controllable range, and the high energy consumption problem can be solved by maintaining the original equipment. Therefore, the decision is to immediately shut down the original equipment and switch to the backup equipment. At the same time, the decision result is pushed to the production line system and operation and maintenance management platform to trigger the generation of corresponding equipment control commands and maintenance work orders, so as to ensure the timeliness and closed-loop of decision execution.

[0033] Example 2, as Figure 3 As shown, this embodiment provides a process manufacturing energy consumption and equipment status collaborative optimization monitoring system, the system including: The status monitoring module is used to monitor and calculate the failure probability of the original equipment in real time through the status monitoring sensors deployed in the original equipment. The data acquisition module is used to collect fault probability sequences, unit product energy consumption, and production line capacity data when the equipment failure probability reaches the failure probability threshold. The delineation module is used to delineate the pre-time window and the post-time window based on the timestamp when the failure probability reaches the threshold. The analysis module is used to calculate the average and volatility of unit product energy consumption within the previous time window based on the original equipment's historical energy consumption baseline. The comparison module is used to compare the average energy consumption per unit of product in the previous time window with the fluctuation rate of the original equipment energy consumption to obtain the energy consumption status. The prediction module is used to calculate the energy consumption increment when switching from the original equipment to the standby equipment and the energy consumption increment when the standby equipment reaches steady-state operation based on historical data of the production line, so as to obtain the total energy consumption increment within the subsequent time window. The constraint setting module is used to set the failure risk constraint corresponding to the failure probability change rate and the energy efficiency threshold constraint based on the production line energy consumption control target. The decision module is used to comprehensively consider the energy consumption status of the previous time window, the fault risk constraints, and the energy consumption benefit constraints, and to determine whether to delay the shutdown of the original equipment and continue to operate or to immediately shut down the equipment and switch to the backup equipment.

[0034] In this embodiment of the invention, the status monitoring module monitors and calculates the failure probability in real time through sensors within the original equipment. When the failure probability reaches a set threshold, the data acquisition module simultaneously collects the failure probability sequence, unit product energy consumption, and production line capacity data before and after the corresponding timestamp. The delineation module delineates the before and after time windows based on this timestamp. The analysis module calculates the average energy consumption and volatility within the before time window by combining the historical energy consumption baseline of the original equipment. The comparison module determines the energy consumption status as a low-energy-consumption steady state or a high-energy-consumption fluctuation range based on the average energy consumption and volatility. The prediction module calculates the total energy consumption increment for switching to standby equipment and steady-state operation within the after time window based on historical production line data. The constraint setting module sets failure risk constraints and energy consumption benefit constraints simultaneously based on the failure probability change rate. The judgment module integrates the energy consumption status and the two types of constraints to output the decision result of whether to delay the shutdown of the original equipment and continue operation or immediately shut down and switch to standby equipment, thus realizing the collaborative optimization monitoring of equipment status and energy consumption.

[0035] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for collaborative optimization and monitoring of energy consumption and equipment status in process manufacturing, characterized in that, Includes the following steps: S1: Utilize the condition monitoring sensors deployed within the original equipment to monitor and calculate the failure probability of the original equipment. Set the failure probability threshold for the original equipment. And based on failure probability Collect fault status data and energy consumption data of the original equipment, and collect fault probability. Reaching the failure probability threshold The corresponding timestamp And based on timestamps Delineate the previous time window and subsequent time window ; S2: Set the historical energy consumption baseline of the original equipment and generate the previous time window based on the energy consumption data. Energy consumption status of internal equipment; S3: Based on historical production line data, calculate the energy consumption increment when switching from the original equipment to the standby equipment. And the energy consumption increment of backup equipment when it reaches steady-state operation According to the increase in energy consumption and energy consumption increment Obtain the subsequent time window Total energy consumption increment ; S4: Calculate the failure probability based on the failure status data. rate of change According to the rate of change Generate fault risk constraints; S5: Set energy efficiency threshold Combined with the increase in total energy consumption Generate energy consumption efficiency constraints; S6: Based on the previous time window The system monitors the equipment based on its energy consumption status, fault risk constraints, and energy efficiency constraints, in order to output decision results.

2. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The method utilizes status monitoring sensors deployed within the original equipment to monitor and calculate the failure probability of the original equipment. Set the failure probability threshold for the original equipment. And based on failure probability The steps for collecting fault status data and energy consumption data from the original equipment include: When the status monitoring sensor detects the timestamp Failure probability value When a data acquisition command is triggered, fault status data is collected, including the original device timestamp. Failure probability sequence before and after ,in, , For timestamps The previous collection time, For timestamps Subsequent data collection duration; After triggering the data acquisition command, energy consumption data is collected, including data related to the fault probability sequence. Energy consumption per unit of product during the same time period Production line capacity data .

3. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The above and based on timestamps Delineate the previous time window and subsequent time window The steps include: By timestamp Set to get the previous time window and subsequent time window The preceding time window The time window is The post-time window The time window is .

4. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The system sets the historical energy consumption baseline of the original equipment and combines it with energy consumption data to generate a previous time window. The steps for determining the energy consumption status of internal equipment include: The historical energy consumption baseline includes the energy consumption range of the low-energy steady-state interval. and energy consumption fluctuation threshold ,in, For the lowest unit energy consumption, This represents the steady-state average energy consumption. Energy consumption sequence per unit product from energy consumption data mean Maximum value and minimum value The previous time window was calculated. Fluctuation rate of energy consumption of internal raw equipment ; The energy consumption state includes a low energy consumption steady-state range and a high energy consumption fluctuation range; If the mean and This is determined to be a low-energy-consumption steady-state range. If the mean or It was determined to be in the high energy consumption fluctuation range.

5. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The calculation of energy consumption increment when switching from the original equipment to the standby equipment is based on historical production line data. And the energy consumption increment of backup equipment when it reaches steady-state operation According to the increase in energy consumption and energy consumption increment Obtain the subsequent time window Total energy consumption increment The steps include: The energy consumption increment is calculated based on the peak energy consumption of standby equipment startup and the energy consumption of production line load adjustment. ; Based on the original equipment in the previous time window Average energy consumption per unit of product Combined with the later time window in energy consumption data Production line capacity data The energy consumption increment was calculated. ; Based on energy consumption increment and energy consumption increment The total energy consumption increment was calculated. .

6. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The fault probability is calculated based on the fault status data. rate of change According to the rate of change The steps for generating failure risk constraints include: Based on the fault status data, the original device timestamp Failure probability sequence before and after and timestamp Corresponding failure probability value The previous time window was calculated. Internal Failure Probability rate of change ; Based on rate of change Generate fault risk constraints, which include: like A value greater than 0 indicates an upward trend in the risk of failure. like =0, indicating a stable trend in fault risk; like A value less than 0 indicates a decreasing trend in fault risk.

7. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The setting of energy efficiency threshold Combined with the increase in total energy consumption The steps for generating energy efficiency constraints include: Based on the energy consumption control targets of the production line, set an energy efficiency threshold. ; Combined with the increase in total energy consumption Generate energy efficiency constraints, which include: If the total energy consumption increment Energy efficiency threshold The system was deemed to have excessively low energy efficiency when switching to backup equipment. If the total energy consumption increment ≤ Energy efficiency threshold The energy consumption efficiency of switching to backup equipment was deemed acceptable.

8. The method for collaborative optimization and monitoring of process manufacturing energy consumption and equipment status according to claim 1, characterized in that, The method based on the previous time window The steps for monitoring equipment based on its energy consumption status, failure risk constraints, and energy efficiency constraints to output decision results include: Current time window When the energy consumption state is in the low-energy-consumption steady-state range, if the fault risk stability trend or the fault risk decrease trend in the fault risk constraint conditions are met, and the total energy consumption increment in the energy consumption benefit constraint conditions are also met, then... Energy efficiency threshold The output decision is to delay the shutdown of the original equipment and continue its operation. Current time window When the energy consumption state is in the low energy consumption steady state range, if the failure risk increases in the failure risk constraint condition, the output decision result is that the original equipment should be shut down immediately and the standby equipment should be switched. Current time window When the energy consumption state is in the high energy consumption fluctuation range, if the total energy consumption increment is satisfied... ≤ Energy efficiency threshold The output decision is to immediately shut down the original equipment and switch to the backup equipment.

9. A process manufacturing energy consumption and equipment status collaborative optimization monitoring system, employing the process manufacturing energy consumption and equipment status collaborative optimization monitoring method according to any one of claims 1-8, characterized in that, The system includes: The status monitoring module is used to monitor and calculate the failure probability of the original equipment in real time through the status monitoring sensors deployed in the original equipment. The data acquisition module is used to collect fault probability sequences, unit product energy consumption, and production line capacity data when the equipment failure probability reaches the failure probability threshold. The delineation module is used to delineate the pre-time window and the post-time window based on the timestamp when the failure probability reaches the threshold. The analysis module is used to calculate the average and volatility of unit product energy consumption within the previous time window based on the original equipment's historical energy consumption baseline. The comparison module is used to compare the average energy consumption per unit of product in the previous time window with the fluctuation rate of the original equipment energy consumption to obtain the energy consumption status. The prediction module is used to calculate the energy consumption increment when switching from the original equipment to the standby equipment and the energy consumption increment when the standby equipment reaches steady-state operation based on historical data of the production line, so as to obtain the total energy consumption increment within the subsequent time window. The constraint setting module is used to set the failure risk constraint corresponding to the failure probability change rate and the energy efficiency threshold constraint based on the production line energy consumption control target. The decision module is used to comprehensively consider the energy consumption status of the previous time window, the fault risk constraints, and the energy consumption benefit constraints, and to determine whether to delay the shutdown of the original equipment and continue to operate or to immediately shut down the equipment and switch to the backup equipment.

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