Grease processing energy consumption monitoring system

Abstract

The invention relates to the technical field of energy consumption monitoring, in particular to a grease processing energy consumption monitoring system which comprises a temperature difference input sensing module, an energy consumption fluctuation extraction module, an abnormal attribution recognition module, a single-point mutation screening module and a partition early warning module. According to the invention, through deep analysis of the energy consumption data in the continuous time period, a specific section with abnormal energy consumption can be identified, the risks of energy waste and equipment overload are effectively prevented, and through analysis of the association between the energy consumption of equipment operation and the working mode, data support is provided for equipment maintenance and energy saving. Through detailed recording and analysis of specific data of an abnormal section, energy management personnel can rapidly locate problem reasons, shorten response time, formulate more targeted improvement measures, monitor single-point sudden change and refine monitoring granularity, so that energy consumption of each key device is strictly monitored, and the monitoring efficiency is improved. And the chain reaction risk caused by abnormal energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption monitoring, and particularly to an energy consumption monitoring system for oil processing. Background Art

[0002] The technical field of energy consumption monitoring involves the dynamic acquisition, processing, analysis, and management of various energy usage situations, mainly including the monitoring and evaluation of energy consumption forms such as electricity, gas, steam, water, and heat energy. This technical field encompasses the deployment of hardware sensing devices, data acquisition terminals, data transmission networks, edge computing nodes, and software analysis systems based on platforms, and is widely applied in multiple scenarios such as industrial manufacturing, building management, transportation, agriculture, and public facilities. Technical means include multi-point data acquisition, protocol parsing, edge fusion processing, energy consumption index modeling, power consumption trend prediction, energy efficiency comparison analysis, and energy consumption anomaly warning, etc., to achieve energy usage visualization, anomaly traceability, system optimization, and energy efficiency improvement.

[0003] Among them, the energy consumption monitoring system for oil processing is a system used to monitor, record, and analyze the energy consumption data of each production unit during the oil processing production process. Its uses include real-time collection of energy data such as electric energy, heat energy, and water consumption at each stage of oil processing, identification of energy consumption peaks, low energy efficiency, or abnormal equipment operating conditions, and then assisting management personnel to achieve precise energy usage scheduling, energy efficiency evaluation, and implementation of energy-saving improvement measures, reducing the energy consumption per unit product, and improving energy utilization efficiency.

[0004] Traditional monitoring systems mostly focus on the statistics and monitoring of overall energy consumption, without delving into the specific energy consumption dynamics of each individual section or equipment, resulting in insufficiently timely responses to energy consumption anomalies, inability to effectively predict and control energy consumption peaks, and affecting the energy efficiency and cost control of the entire production. For example, in the case of failure to timely identify abnormal equipment operating conditions, it will cause the equipment to operate under overload, not only increasing energy consumption, but also shortening the service life of the equipment and increasing maintenance costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an energy consumption monitoring system for oil processing.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: An energy consumption monitoring system for oil processing, the system includes: The temperature difference input perception module is based on the oil pretreatment section, calls the timestamp sequence to perform hourly difference operations on the temperature difference value between the heat exchange fluid inlet and the external temperature difference, determines whether there is a positive driving relationship between the change in unit heat load and the temperature difference difference, and generates a temperature difference coupling correlation status identifier; Based on the temperature difference coupling correlation status identifier, the energy consumption fluctuation extraction module constructs an energy consumption difference sequence according to the equipment category, calculates the fluctuation amplitude between the maximum value and the minimum value in the sequence, identifies the over-limit equipment section, and generates the periodic energy consumption abnormal section information; According to the periodic energy consumption abnormal section information, the abnormal cause identification module calculates the ratio of the running duration to the start-stop frequency, multiplies it with the energy consumption per unit time for judgment. If the ratio is lower than the preset standard and the product value is higher than the standard rated load of the same type of equipment, it is determined as an unstable induced abnormality, and a work section-level energy consumption cause attribution structure map is generated; Based on the work section-level energy consumption cause attribution structure map, the single-point mutation screening module calculates the power consumption deviation rate per unit material for each equipment. If the deviation rate is greater than the power consumption abnormal deviation threshold, it is marked as a jump node and the abnormal equipment type is attached, generating an equipment-level single-point energy consumption jump identification set.

[0007] The improvements of the present invention are as follows. The temperature difference coupling correlation status identifier includes the temperature difference interval feature, the unit heat load coupling coefficient, and the heat exchange equipment response category. The periodic energy consumption abnormal section information includes the abnormal equipment number, the energy consumption fluctuation amplitude index, and the work section time identifier. The work section-level energy consumption cause attribution structure map specifically refers to the equipment energy consumption cause type, the work section to which the attributed equipment belongs, and the attribution determination intensity level. The equipment-level single-point energy consumption jump identification set specifically refers to the jump node number, the jump deviation degree classification, and the corresponding processing process stage of the jump.

[0008] The improvements of the present invention are as follows. The temperature difference input perception module includes: Based on the oil pretreatment work section, the heat environment temperature difference analysis sub-module extracts the inlet temperature, outlet temperature, heating medium temperature, and ambient air temperature of the heat exchange device, collects the real-time temperature data of the heat exchange fluid inlet and outlet of the heat exchange device and records the corresponding time stamp sequence, calls the ambient air temperature value at the corresponding moment and performs hourly difference calculation with the heat exchange fluid inlet temperature, generating a heat environment temperature difference sequence; The temperature difference matching trend sub-module calls the heat environment temperature difference sequence, synchronously retrieves the heat exchange amount per unit time of the heat exchange device and the recorded value of the equipment heat load, performs one-to-one mapping of the heat load value and the temperature difference sequence under the corresponding time sequence and calculates the matching coefficient, establishing a dual-sequence response trend between the heat exchange driving parameter and the change of the unit heat load, obtaining a heat load temperature difference matching trend sequence; Based on the heat load temperature difference matching trend sequence, the temperature difference coupling correlation analysis sub-module judges the symbol consistency of the change direction of the temperature difference and the change direction of the unit heat load in each time period. If the two directions are consistent in consecutive cycles and the matching trend slope is greater than the trend reference slope value, it is marked as a coupling section and the equipment number and the coupling section time period information are collected, generating a temperature difference coupling correlation status identifier.

[0009] The present invention is improved in that the energy consumption fluctuation extraction module comprises: The data extraction submodule extracts the unit output steam consumption, unit output electricity consumption and unit output water consumption data of the pretreatment tank, heat exchange device and separation centrifuge in the oil refining section based on the upper temperature difference coupling association state identification, and aggregates the unit energy consumption values collected by each device in three consecutive process cycles in a time series structure, generates three types of energy consumption sequences of steam, electricity and water according to the type of equipment, and obtains the unit energy consumption sequence set of equipment category; The difference calculation submodule calls the unit energy consumption sequence set of the equipment category, identifies the maximum and minimum values in each category of sequence, performs difference calculation on the fluctuation amplitude of each equipment unit energy consumption sequence, extracts the maximum section of continuous cycle fluctuation in the sequence, constructs the total difference amount according to the equipment, constructs the energy consumption fluctuation intensity array according to the equipment category archive, and calculates and obtains the energy consumption fluctuation index value of the equipment; The over-limit identification submodule reads the allowable fluctuation limit of the heat load corresponding to the equipment type according to the energy consumption fluctuation index value of the equipment, compares whether the fluctuation index value exceeds the corresponding limit, marks the equipment that exceeds the limit, and associates the corresponding time period, the equipment number and the process section information to establish an abnormal section index list and generate periodic energy consumption abnormal section information.

[0010] The present invention is improved in that the abnormal attribution identification module includes: The equipment data aggregation submodule obtains the working duration, start-stop frequency, and total energy consumption per unit time of the heat exchange device and the material conveying device according to the abnormal period energy consumption section information, and classifies them according to the equipment numbers, integrates the classified data structure by time axis, and organizes each parameter value of the equipment in the same period into an equipment time period data group to generate an equipment period operation data group set; The load structure calculation submodule calculates the ratio of the operation duration to the start-stop frequency for each set of data based on the equipment periodic operation data set, multiplies the ratio by the total energy consumption per unit time, constructs a load value sequence for equipment operation stability, performs a difference judgment between the result and the standard rated load of similar equipment, screens data segments that do not meet the operation stability standard and have a high load value, and obtains an unstable load characteristic value sequence; The section abnormality classification submodule obtains the corresponding grease processing section name according to the unstable load characteristic value sequence and the equipment number, binds the data segment marked as unstable load to the corresponding section identifier, maps and marks the classification items according to the abnormal intensity level, establishes a comparison matrix between section numbers and abnormal categories, and generates a section-level energy consumption attribution structure map.

[0011] The present invention is improved in that the single point mutation screening module comprises: The operation data extraction sub-module retrieves the operation cycle lengths, material processing amounts, and unit cycle power consumption values of the stirring device, degumming pump, and vacuum drying equipment in the refining section based on the energy consumption attribution structure map at the section level, classifies them according to the equipment numbers, horizontally integrates the classified data according to the time cycle structure, obtains the operation behavior data combination within the corresponding cycle of the equipment numbers, and acquires the equipment cycle operation information set; The power consumption offset calculation sub-module calculates the unit power consumption ratio corresponding to the unit material processing amount for each device according to the equipment cycle operation information set, extracts the unit material power consumption value for each cycle of the device, calculates the unit material power consumption offset value for each device in each cycle through operation, screens the equipment cycle data with offset values greater than the power consumption abnormal offset threshold, and generates a power consumption offset mutation structure array; The abnormal node identification sub-module calls the power consumption offset mutation structure array, screens the equipment numbers and corresponding process section identifiers, identifies the data segments where the continuous offset nodes in the offset value sequence are greater than the threshold, attaches the equipment type and abnormal types, completes the binding of the three elements of equipment number, cycle position, and abnormal label, and generates an equipment-level single-point energy consumption jump identification set.

[0012] The improvement of the present invention is that the system further includes: The partition warning module calls the equipment-level single-point energy consumption jump identification set, calibrates the section partition of the oil processing stage where the equipment is located, calculates the jump concentration by performing ratio conversion based on the total number of equipment and the number of abnormal jump equipment in each section, matches the concentration with the risk grading value, obtains the risk level corresponding to the section, and generates an energy consumption monitoring partition warning level label; The energy consumption monitoring partition warning level label includes the section risk level number, the equipment abnormal concentration ratio level, and the process link abnormal label.

[0013] The improvement of the present invention is that the partition warning module includes: The partition calibration sub-module calls the equipment-level single-point energy consumption jump identification set, identifies the process stage corresponding to each abnormal equipment, extracts the processing flow identifier and section structure number where it is located, establishes a section partition identification index set according to the mapping result of the equipment number and the processing stage, and generates an equipment segmentation attribution structure information set; The jump ratio calculation sub-module calculates the total number of equipment and the number of abnormal equipment in each section according to the equipment segmentation attribution structure information set, calculates the corrected jump concentration value for each section through operation, and generates a section abnormal concentration parameter set; The risk level matching sub-module calls the section abnormal concentration parameter set, compares the jump concentration value of each section with the energy consumption abnormal risk grading standard table, assigns the corresponding grading level identifier to the corresponding section number after comparison, generates a risk level label list, and obtains the energy consumption monitoring partition warning level label.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the fine monitoring and analysis of temperature difference, energy consumption fluctuation, abnormal state, single-point mutation and regional early warning in the oil processing process, the accuracy and efficiency of energy utilization are improved. In the utilization of temperature difference data, by real-time monitoring the difference between the temperature change of the heat exchange fluid and the external temperature, the efficiency of heat energy use can be analyzed more accurately, and an instant response to the trend of heat energy use can be achieved. Through the in-depth analysis of energy consumption data within a continuous time period, the specific section with abnormal energy consumption can be identified, effectively preventing the risks of energy waste and equipment overloading. By analyzing the correlation between the energy consumption of equipment operation and the working mode, data support is provided for equipment maintenance and energy conservation. By detailed recording and analyzing the specific data of the abnormal section, energy management personnel can quickly locate the cause of the problem, shorten the response time, and formulate more targeted improvement measures. The monitoring of single-point mutation refines the monitoring granularity, enabling the energy consumption of each key equipment to be strictly monitored, reducing the risk of chain reaction caused by abnormal energy consumption, ensuring the stability of the production process, and reducing the energy cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the temperature difference input perception module of the present invention; Figure 3 is the flow chart of the energy consumption fluctuation extraction module of the present invention; Figure 4 is the flow chart of the abnormal attribution recognition module of the present invention; Figure 5 is the flow chart of the single-point mutation screening module of the present invention; Figure 6 is the flow chart of the regional early warning module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the present invention provides a technical solution: an energy consumption monitoring system for oil processing, the system includes: The temperature difference input perception module is based on the oil pretreatment section, extracts the inlet temperature, outlet temperature, heating medium temperature and ambient air temperature of the heat exchange device, calls the timestamp sequence to perform hourly difference calculation on the heat exchange fluid inlet temperature difference value and the external temperature difference value, and performs dual-sequence matching analysis on the difference value and the unit heat load in the corresponding period to determine whether there is a positive driving relationship between the change of the unit heat load and the temperature difference difference, and generates a temperature difference coupling correlation status identifier; The energy consumption fluctuation extraction module extracts the steam consumption per unit output, power consumption per unit output and water consumption per unit output in three consecutive time periods of the process equipment in the oil refining section based on the temperature difference coupling correlation status identifier, constructs an energy consumption difference sequence according to the equipment category, and calculates the difference in the fluctuation range between the maximum value and the minimum value in each sequence, and compares the difference result with the set allowable fluctuation limit of the heat load to identify the over-limit equipment section and generate periodic energy consumption abnormal section information; The abnormal cause identification module obtains the working duration, start-stop frequency and total energy consumption per unit time of the heat exchange device and the material conveying device in the abnormal section according to the periodic energy consumption abnormal section information, aggregates each item of data according to the equipment number, calculates the ratio of the running duration to the start-stop frequency, and multiplies it with the energy consumption per unit time for judgment. If the ratio is lower than the preset standard and the product value is higher than the standard rated load of the same type of equipment, it is determined as an unstable induced abnormality, and it is classified into the corresponding oil treatment section according to the equipment number, and a section-level energy consumption cause attribution structure map is generated; The ratio of the running duration to the start-stop frequency is an important factor for measuring the running stability of the equipment. The product and the energy consumption are used to judge the load consistency. The reference value limit can be set based on the upper and lower limits of the rated load specified in the equipment manual; The single-point mutation screening module, based on the energy consumption attribution structure map at the work section level, retrieves the operation cycle lengths, material processing volumes, and corresponding unit power consumption values per cycle of the stirring device, degumming pump, and vacuum drying equipment in the refining section. It calculates the power consumption deviation rate per unit of material for each device and performs difference normalization with the average value of the same type of device. If the deviation rate is greater than the power consumption abnormal deviation threshold, it is marked as a jump node and the abnormal device type is appended to generate a device-level single-point energy consumption jump identification set; The power consumption deviation rate per unit of material refers to the proportion of the difference between the actual power consumption for unit material processing and the historical equipment average value; The zoning warning module calls the device-level single-point energy consumption jump identification set, calibrates the work section zoning for the oil processing stage where the device is located, calculates the jump concentration by performing ratio conversion based on the total number of devices and the number of abnormal jump devices in each work section, matches the concentration with the risk classification value to obtain the corresponding risk level for the work section, and generates an energy consumption monitoring zoning warning level label; The temperature difference coupling correlation status identification includes the temperature difference interval characteristics, unit heat load coupling coefficient, and heat exchange equipment response category. The information on the abnormal section of periodic energy consumption includes the abnormal device number, energy consumption fluctuation amplitude index, and work section time identification. The energy consumption attribution structure map at the work section level specifically refers to the device energy consumption cause type, the work section to which the attributed device belongs, and the attribution determination intensity level. The device-level single-point energy consumption jump identification set specifically refers to the jump node number, jump deviation degree classification, and the corresponding processing technology stage of the jump. The energy consumption monitoring zoning warning level label includes the work section risk level number, device abnormal concentration ratio level, and process link abnormal label.

[0019] Please refer to Figure 2 , the temperature difference input perception module includes: The hot environment temperature difference analysis sub-module, based on the oil pretreatment work section, extracts the inlet temperature, outlet temperature, heating medium temperature, and ambient air temperature of the heat exchange device, collects the real-time temperature data of the inlet and outlet of the heat exchange fluid of the heat exchange device and records the corresponding timestamp sequence, calls the ambient air temperature value at the corresponding moment and performs hourly difference calculation with the inlet temperature of the heat exchange fluid to generate a hot environment temperature difference sequence; Obtain the inlet temperature, outlet temperature, heating medium temperature, and ambient air temperature of the heat exchange device in the oil pretreatment section. During the process of collecting real-time temperature data of the inlet and outlet of the heat exchange fluid, high-precision thermocouple temperature sensors can be installed on each heat exchange device, and a data collector with timestamp recording function can be configured to record temperature data at a frequency of once per minute. The ambient air temperature at the corresponding moment is measured by the ambient temperature sensor in the adjacent area. For example, in a typical sample, assume that at a certain moment, the inlet temperature of the heat exchange fluid is 92.6 °C, the outlet temperature is 68.1 °C, the ambient temperature is 36.4 °C, and the heating medium temperature is 115.2 °C. Then record this moment as "2025-04-25 09:15:00". Subtract the ambient temperature of 36.4 °C from the inlet temperature of 92.6 °C to get a difference of 56.2 °C. Bind this difference with the timestamp and arrange it in chronological order to form a sequence of heat environment temperature difference values. For the "collection" and "calculation" operations in the above process, the execution logic is as follows: The collected temperature values are converted through analog voltage signals and are converted into numerical records in real time by the A / D conversion unit. For the "difference calculation" process, it is to perform a point-by-point subtraction operation between the inlet temperature and the ambient temperature to form a difference sequence. Set the data sequence as follows: °C, °C, then the calculated difference sequence is °C, and use the timestamps "09:15", "09:16", "09:17" to identify and form a sequence of heat environment temperature difference values respectively. This sequence constitutes the basic input for subsequent temperature difference trend judgment and generates a sequence of heat environment temperature difference values.

[0020] The temperature difference matching trend sub-module calls the sequence of heat environment temperature difference values, synchronously retrieves the heat exchange amount and the recorded value of the equipment heat load of the heat exchange device per unit time, and performs a one-to-one mapping and calculates the matching coefficient between the heat load value and the temperature difference value sequence under the corresponding time series, establishing a dual-sequence response trend between the heat transfer driving parameter and the change of the unit heat load to obtain a heat load temperature difference matching trend sequence; Call the sequence of heat environment temperature difference values, synchronously retrieve the heat exchange amount and the recorded value of the equipment heat load of the heat exchange device per unit time, and it is necessary to ensure that the above three types of data have timestamp consistency. The heat exchange amount per unit time can be calculated by measuring the temperature difference between the inlet and outlet of the heat transfer medium and combining the flow data. The heat load value is provided by the control system of the heat exchange device. Set the sampling time interval to 1 minute, and the heat exchange amount The calculation formula in this minute is: ; Where is the specific heat capacity, taking water as 4.18 kJ / kg·K, is the density, taking 1 kg / L, is the flow rate, set to 3.2 L / min. If °C, °C, then kJ, the equipment heat load is recorded as 340.5 kJ at this moment. According to the temperature difference of 56.2 °C at this time point, perform one-to-one mapping and matching, parallelize these three quantity values into data nodes, and record the next cycle node at the same time. Arrange all nodes in chronological order, calculate the matching coefficient between the heat load and the temperature difference sequence through the linear correlation function, and use the Pearson correlation coefficient formula: ; where is the temperature difference sequence, is the heat load sequence, and substitute the values of the three time points: , , and after calculation, is obtained, indicating a strong linear correlation between the two. Then, according to the chronological order, establish data pairs for the temperature difference differences and the corresponding unit heat load values at each time point according to the corresponding relationship, and generate a heat load-temperature difference matching trend sequence.

[0021] Based on the heat load-temperature difference matching trend sequence, the sub-module for temperature difference coupling correlation analysis judges the sign consistency of the change direction of the temperature difference difference and the change direction of the unit heat load within each time period. If the directions of the two are consistent in consecutive cycles and the matching trend slope is greater than the trend reference slope value, it is marked as a coupling section and the equipment number and the time period information of the coupling section are collected to generate a temperature difference coupling correlation status identifier; Based on the heat load-temperature difference matching trend sequence, judge the sign consistency of the change direction of the temperature difference difference and the change direction of the unit heat load within each time period. It is necessary to judge the sign of the difference change according to the data at two adjacent moments in the sequence. For example, the temperature difference sequence is , and the heat load is , and the change directions are negative, negative, and negative respectively. Therefore, the two have direction consistency. At the same time, it is necessary to judge whether the trend slope exceeds the trend reference slope value. The trend slope is calculated by the value of linear fitting. This reference slope value is the system preset judgment standard for distinguishing the difference in trend intensity. Its setting basis is the average value of the minimum linear fitting slope values between the normal working condition heat load and the temperature difference change in multiple batches of debugging results. The actual setting is . In actual operation, 10 groups of working condition data are collected, and the slopes are extracted by linear regression respectively. The obtained slope range is 1.65 to 2.03. Therefore, take the median and trim it downward to be set as 1.85 as the stability judgment benchmark for trend response. If the fitting trend is , substitute the above three points for calculation, and the obtained fitting slope is , it is considered that this trend meets the coupling standard, and these three time periods are marked as coupling sections. The corresponding device number and the corresponding timestamp interval "2025-04-25 09:15:00" to "2025-04-25 09:17:00" are called, the coupling label is recorded, and a double matching dictionary structure of the device number and the coupling time period is constructed, and finally the temperature difference coupling association state identification is generated.

[0022] See also Figure 3 , the energy consumption fluctuation extraction module includes: The data extraction submodule extracts the unit output steam consumption, unit output electricity consumption and unit output water consumption data of the pretreatment tank, heat exchange device and separation centrifuge in the oil refining section based on the upper temperature difference coupling association state identification, and aggregates the unit energy consumption values collected by each device in three consecutive process cycles in a time series structure, generates three types of energy consumption sequences of steam, electricity and water according to the type of equipment, and obtains the unit energy consumption sequence set of equipment category; Based on the temperature difference coupling association state identification, the unit output steam consumption, unit output power consumption and unit output water consumption data of the pretreatment tank, heat exchange device and separation centrifuge in the oil refining section are extracted. First, the output benchmark of each type of equipment in three consecutive production cycles must be determined, and normalized with "tons of oil" as the unit. When obtaining the real-time energy consumption record of the equipment, the steam consumption can be directly recorded by the flow meter The steam flow rate is integrated over time to obtain the cumulative value, the power consumption is determined by the difference in the meter readings, and the water consumption is provided by the cumulative value of the water flow meter. The normalization method is to divide the energy consumption of each cycle by the corresponding cycle output tons to form a unit energy consumption value. In the example, it is assumed that the heat exchange device consumes 210 kg of steam in a certain cycle and the output is 2 tons of oil, then the unit steam consumption is 105 kg / t, the power consumption is set to 132 kWh, the unit power consumption is 66 kWh / t, the water consumption is 920L, and the unit water consumption is 460 L / t, extract the above indicators in three consecutive cycles, combine the unit energy consumption values of the three types of equipment in the three cycles to form the original energy consumption array, and divide it into three sequence structures of steam, electricity, and water according to the equipment category. After alignment according to the timestamp sequence, the structure is as follows: Table 1 Example of unit energy consumption time series

[0023] As shown in Table 1, the unit energy consumption data are organized into a time series, which is then packaged into a structured array according to the equipment category to generate a unit energy consumption series set by equipment category.

[0024] The difference calculation sub-module calls the equipment category unit energy consumption sequence set, identifies the maximum and minimum values within each type of sequence, calculates the difference in the fluctuation range of each equipment unit energy consumption sequence, extracts the section with the largest continuous periodic fluctuation in the sequence, constructs the total difference according to the equipment, archives according to the equipment category to construct the energy consumption fluctuation intensity array, and uses the formula: ; Calculate the energy consumption fluctuation index value of the equipment through operation; Among them, represents the energy consumption fluctuation index value of the th type of equipment, , respectively represent the maximum and minimum normalized values of the unit output energy consumption of the th type of equipment, represents the normalized value of the unit output energy consumption of the th type of equipment in the rd cycle, represents the normalized average value of the unit output energy consumption of the th type of equipment in all cycles, represents the number of monitored time cycles; Call the equipment category unit energy consumption sequence set, first extract the maximum and minimum values in each type of sequence. For example, the unit steam consumption sequence of the heat exchange device is [105, 113, 97] kg / t, where the maximum value , the minimum value , the fluctuation range is the absolute difference kg / t. Then, calculate the standard deviation of each equipment unit energy consumption sequence to judge the degree of fluctuation, and process the standard deviation part by introducing the periodic value of the normalized unit output energy consumption. The formula is as follows: ; The description of the formula parameters is as follows: : The energy consumption fluctuation index value of the th type of equipment, indicating the degree of fluctuation of the unit output energy consumption in multiple cycles; , : Respectively, the maximum and minimum values of the unit output energy consumption of the th type of equipment within the monitored cycle, both of which are normalized unit values here; : The normalized value of the unit output energy consumption of the th type of equipment in the st cycle, : The average normalized value of the unit energy consumption of the th type of equipment; : The number of statistical time periods is set to 3.

[0025] Taking the steam sequence as an example: [105, 113, 97], its average value , and the sum of squared differences is , and the standard deviation part is , and the final fluctuation index is: , indicating that the energy consumption fluctuation level of this equipment type under this sequence is 104.48.

[0026] The over-limit identification sub-module reads the allowable fluctuation limit of the heat load corresponding to the equipment type according to the energy consumption fluctuation index value of the equipment, compares whether the fluctuation index value exceeds the corresponding limit, marks the exceeded equipment, and associates the corresponding time period, the equipment number and the process section information, establishes an abnormal section index list, and generates the information of the abnormal section of the periodic energy consumption; According to the energy consumption fluctuation index value of the equipment, read the preset allowable fluctuation limit of the heat load for each type of equipment. The basis for setting this limit is the allowable range of load fluctuation of typical industrial equipment in the national standard GB / T 2589-2020. Combine the fluctuation tolerance of the equipment category to design and set the limit. For example, for the heat exchange device, the allowable limit of heat load fluctuation is set to 92.00 kJ / t. If the calculated fluctuation index value is 104.48, it exceeds this limit and is identified as abnormal. The equipment number is set to HXZ-02, the corresponding period is "09:30–10:00", and it belongs to the deacidification section. Then generate the identification key-value pair in the abnormal record structure, including the equipment number, the time section and the name of the affiliated section, and finally construct the abnormal section index list to generate the information of the abnormal section of the periodic energy consumption.

[0027] The setting process of this value is described as follows: Under the preset conditions, conduct multi-batch measurements on the operation samples of the same type of equipment, and statistically calculate the heat load fluctuation range under full load, half load and variable load operations. Obtain 10 groups of samples, which are: 88.6, 91.2, 92.3, 90.5, 93.4, 89.7, 91.6, 90.1, 92.8, 89.9. Take their arithmetic mean as the setting reference value, and calculate it as , and combine the equipment safety margin to be weighted and adjusted to 92.00 kJ / t. This result shows that 104.48 > 92.00, indicating that the energy consumption fluctuation range of the current heat exchange device exceeds the stable operation range and needs to enter the abnormal identification process.

[0028] Please refer to Figure 4 , the abnormal cause identification module includes: According to the information of the abnormal section of the periodic energy consumption, the device data aggregation sub-module obtains the working duration, start-stop frequency, and total energy consumption per unit time of the heat exchange device and the material conveying device, classifies them according to the device number respectively, integrates the classified data structure by time axis segmentation, forms a device time period data group with each parameter value of the device in the same period, and generates a set of device cycle operation data groups; When obtaining the working duration, start-stop frequency, and total energy consumption per unit time of the heat exchange device and the material conveying device in the information of the abnormal section of the periodic energy consumption, it is necessary to separately retrieve the energy consumption monitoring records and operation logs of the corresponding devices. The working duration can be calculated by the difference in the time points of the device state changes. For example, if a heat exchange device starts running at 08:00 and stops at 08:45, the working duration is 45 minutes. The start-stop frequency is obtained by counting the total number of start signals and stop signals within the period. The total energy consumption per unit time is achieved by integrating the energy consumption cumulative curve within the period. In the system, all records are classified according to the device number. For example, device HXZ-01 corresponds to three groups of data: [45 min, 3 times, 12.8 kWh], [43min, 2 times, 11.6 kWh], [46 min, 3 times, 13.2 kWh]. The classified data is integrated by time axis segmentation, and the working duration, start-stop times, and total energy consumption within each time period are aggregated into a single record, and an operation status record matrix of the device is formed in sequence, thus constituting a set of device cycle operation data groups.

[0029] Based on the set of device cycle operation data groups, the load structure calculation sub-module calculates the ratio of the operation duration to the start-stop frequency for each group of data, multiplies the ratio by the total energy consumption per unit time, constructs a sequence of device operation stability load values, judges the difference between the result and the standard rated load of similar devices, and screens out the data segments that do not meet the operation stability standard and have a high load value to obtain a sequence of non-stable load characteristic values; Based on the set of device cycle operation data groups, calculate the ratio of the operation duration to the start-stop frequency for each group of data. If a device runs for 45 minutes and the start-stop times are 3 times within a period, the ratio is 15. If the total energy consumption per unit time in this period is 12.8 kWh, the operation stability load value is , this process is performed once for each data segment to obtain a sequence , construct the sequence of the device operation stability load values, and judge the difference between this sequence and the standard rated load of similar devices. The standard rated load value is set to 200, which is obtained by conversion after normalization under the rated operation energy consumption in the device manual. The rated continuous operation power consumption is 13.3 kWh, the average start-stop frequency is 2.5 times, and the standard time ratio is 14 minutes, then the set load is , after adjusting the normalization coefficient to 2.68 times and rounding it up, we get 200. If the load value is greater than this value and the ratio is less than 12, it is determined as an unstable operation section. Based on this, the paragraph data in the sequence that is greater than 200 and the corresponding ratio is less than 12 is marked, and its time position and equipment number are extracted to obtain the non-stable load eigenvalue sequence.

[0030] The abnormal classification sub-module for the work section traces back according to the equipment number in the non-stable load eigenvalue sequence to obtain the corresponding grease treatment work section name, binds the data segments marked as non-stable load to the corresponding work section identifier, and maps and marks the classification items according to the abnormal intensity level, establishes a control matrix of work section number and abnormal category, and generates a work section-level energy consumption attribution structure map; According to the non-stable load eigenvalue sequence, trace back upward according to the equipment number, and retrieve the work section identifier registered for the equipment number in the system deployment table. For example, if the number HXZ-01 corresponds to the pretreatment work section in the table, then bind the mark of this equipment during the abnormal time period "09:00–09:30" to the "pretreatment work section". At the same time, sort all the abnormal mark entries by work section, perform the mapping of the abnormal intensity level. The level division rule is: if the proportion of the number of abnormalities in the total number of work section equipment is within 0–20%, it is marked as L1, within 21%–50% as L2, and more than 50% as L3. Suppose a work section has a total of 6 devices, and 3 of them are marked as abnormal, then the level is L2, and the mapping item {"pretreatment work section": L2} is established. In this way, all abnormal work sections are uniformly generated into a two-field structure map record entry "work section number + abnormal level", and the aggregation result obtains the work section-level energy consumption attribution structure map.

[0031] Please refer to Figure 5 , the single-point mutation screening module includes: The operation data extraction sub-module, based on the work section-level energy consumption attribution structure map, retrieves the operation cycle length, material processing volume, and unit cycle power consumption value of the stirring device, degumming pump, and vacuum drying equipment in the refining section, and classifies them according to the equipment number. The classified data is horizontally integrated according to the time cycle structure to obtain the operation behavior data combination within the corresponding cycle of the equipment number, and obtains the equipment cycle operation information set; Based on the energy consumption attribution structure map at the workshop level, when retrieving the operation cycle length, material throughput, and unit cycle power consumption values of the stirring device, degumming pump, and vacuum drying equipment in the refining section, the system retrieves the equipment operation logs and material transfer records according to the equipment numbers respectively, and extracts the data within the corresponding time window with the cycle length as the query condition. The equipment operation cycle length is obtained by recording the time difference of the equipment start and stop signals. For example, a certain stirring device starts at 09:00 and stops at 09:45, and the cycle length is 45 minutes. The material throughput is collected by the on-line weighing system or flowmeter, and the unit cycle power consumption value is obtained by the energy consumption monitoring module summarizing the energy consumption readings within the cycle and calculating the normalized value in combination with the output. Taking the stirring device SP-01 as an example, it processed 3.6 tons of materials during the operation cycle from 09:00 to 09:45, and the total energy consumption was 16.2 kWh, then the unit cycle power consumption value was 4.5 kWh / t. The above data is classified in the system according to the equipment numbers and organized in a time series format to form a structured data group. During the horizontal integration process, the operation cycle length, material throughput, and unit energy consumption value of each equipment in each cycle are combined into a ternary data structure group, and after sorting, an equipment cycle operation information set is formed. For example, as shown below: Table 2 Sample Table of Equipment Operation Information Data

[0032] As shown in Table 2, each group of data is organized according to the time cycle, and the three core indicators are updated in real time through the data acquisition system.

[0033] The power consumption deviation calculation sub-module calculates the unit power consumption ratio corresponding to the unit material throughput for each equipment according to the equipment cycle operation information set, extracts the unit material power consumption value of each equipment in each cycle, and uses the formula: ; Calculate to obtain the unit material power consumption deviation value of each equipment in each cycle, screen the equipment cycle data with the deviation value greater than the power consumption abnormal deviation threshold, and generate a power consumption deviation mutation structure array; Among them, represents the unit material power consumption deviation value of the th equipment in the th cycle, represents the normalized value of the energy consumption of the th equipment in the th cycle, represents the normalized value of the material throughput of the th equipment in the th cycle, is the number of time cycles of the same type of equipment, is the equipment type adjustment factor, is the th equipment in the Periodic operation time is the sum of the start-stop frequencies of the th device during all cycles; According to the device cycle operation information set, calculate the unit power consumption ratio corresponding to the unit material processing volume for each device, extract the unit material power consumption in each cycle, calculate the difference ratio relative to the average unit power consumption of the device category, and then calculate the power consumption offset value in combination with the amplitude modulation factor. Taking device SP-01 as an example, its unit power consumption values in three cycles are 4.5, 4.82, and 4.35 kWh / t respectively, and the average value is kWh / t. For the second cycle, the offset part of the unit material power consumption is , assuming the device type adjustment factor , operation time min, the sum of start-stop frequencies , then use the formula: ; Substitute the values: ; If the system sets the power consumption abnormal offset threshold to 0.28 (this value is set by the boundary value of the normal offset range of more than 90% of the same type of equipment), then the offset value of this cycle is greater than this threshold, and it is determined as an abnormal offset segment. Finally, screen and collect the cycle data with offset values greater than the threshold, and summarize them into a power consumption offset mutation structure array.

[0034] The abnormal node identification sub-module calls the power consumption offset mutation structure array, screens the device number and the corresponding process section identifier, and identifies the data segment where the continuous offset nodes in the offset value sequence are greater than the threshold, attaches the device type and the type of abnormality, completes the binding of the three elements of the device number, cycle position, and abnormal label, and generates a device-level single-point energy consumption jump identification set; Call the power consumption offset mutation structure array. First, according to the device number bound to each offset record, trace back to obtain the corresponding process section information in the device configuration list. For example, the number SP-01 corresponds to the degumming section. Extract the number, cycle time, offset value, and device type label of the device cycle segments determined to be abnormal in turn, and further identify whether there are two or more consecutive abnormal records in the offset value sequence. If so, mark it as "continuous offset", otherwise mark it as "isolated offset". For example, if the offset values of a device in two consecutive cycles are 0.35 and 0.31 respectively, both greater than the threshold 0.28, then it is determined as a continuous offset segment. Bind the above results to the three contents of the device number, cycle position, and abnormal label, and represent them in a triple structure as (SP-01, 10:00–11:30, continuous offset). Finally, output in a list structure to generate a device-level single-point energy consumption jump identification set.

[0035] Please refer toFigure 6 , the partition warning module includes: The partition calibration sub-module calls the device-level single-point energy consumption jump identification set, identifies the process stage corresponding to each abnormal device, extracts the processing flow identification and section structure number where it is located, establishes a section partition identification index set according to the mapping result of the device number and the processing stage, and generates a device section attribution structure information set; When calling the device-level single-point energy consumption jump identification set, it is necessary to extract the abnormal device number, the bound period and the device type label from the record, obtain the corresponding processing flow identification (such as "degumming", "drying") and section structure number (such as "DGS-03") by matching the device number with the device information library, and bind the process stage and the structure number into a mapping pair with the device number as the primary key. Taking the number SP-02 as an example, its bound label is "vacuum drying equipment", and the system queries the device list to obtain its belonging processing flow as "drying section" and the structure number as "DRY-02". Structure the above device records as (SP-02, drying section, DRY-02), and classify the process identifications of all abnormal devices in this way. Finally, summarize and generate a set of structure items for subsequent attribution partition and jump intensity calculation, forming a device section attribution structure information set.

[0036] The jump ratio calculation sub-module calculates the total number of devices and the number of abnormal devices in each section according to the device section attribution structure information set, and uses the formula: ; Operate to obtain the corrected jump concentration value of each section, and generate a section abnormal concentration parameter set; Among them, represents the corrected jump concentration value of the th section, is the number of devices marked as abnormal in the th section, is the total number of devices in the th section, is the number of corresponding processing cycles in the th section, is the device layout concentration coefficient in the According to the device section attribution structure information set, count the total number of devices in each section and the number of devices with jump anomalies , and calculate its basic jump ratio , introduce the processing cycle number factor and the device concentration coefficient Perform dynamic correction. The equipment concentration coefficient is reflected by the number of equipment per unit area in the section layout, and the number of processing cycles is measured by the total number of cycles. In the example, there are 10 pieces of equipment in the degumming section (number DG-01), 3 of which are marked as jump anomalies, the number of processing cycles is 24 cycles, and the equipment density concentration coefficient is 3.2 (that is, 10 pieces of equipment occupy a 3.1-square-meter working area). The calculation process is as follows: ; The jump correction concentration of the degumming section is obtained as 3.828. If it needs to be retained to two decimal places according to the rule, the value is 3.83. This result is written into the section anomaly record structure to form the section anomaly concentration parameter set.

[0037] The risk level matching sub-module calls the section anomaly concentration parameter set, compares the jump concentration value of each section with the energy consumption anomaly risk grading standard table, assigns the corresponding grading level identifier after comparison to the corresponding section number, and generates a risk level label list to obtain the energy consumption monitoring area warning level label; Call the section anomaly concentration parameter set to map and match the corrected jump concentration value of each section with the preset energy consumption anomaly risk grading standard table. Assume the risk standard is: is level L1, is L2, is L3. Taking the aforementioned degumming section as an example, its jump concentration value is 3.83, and the corresponding risk level is L3. Bind the section number DG-01 to its level L3 to form a structure record (DG-01, L3). All matching results are uniformly sorted into a list form to form the energy consumption monitoring area warning level label. Each label record is used to prompt the regional energy consumption operation status and support the subsequent configuration and analysis processes of the management system.

[0038] The above is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. Oil processing energy consumption monitoring system, characterized in that, The system includes: Based on the grease pretreatment section, the temperature difference input perception module calls the timestamp sequence to perform hourly difference calculation on the temperature difference value between the inlet and outlet of the heat exchange fluid and the external temperature difference, judges whether there is a positive driving relationship between the unit heat load change and the temperature difference difference, and generates a temperature difference coupling correlation status flag; Based on the temperature difference coupling correlation status flag, the energy consumption fluctuation extraction module constructs an energy consumption difference sequence according to the equipment category, calculates the fluctuation range between the maximum value and the minimum value in the sequence, identifies the over-limit equipment section, and generates the periodic energy consumption abnormal section information; According to the periodic energy consumption abnormal section information, the abnormal cause identification module calculates the ratio of the running duration to the start-stop frequency, multiplies it with the energy consumption per unit time for judgment. If the ratio is lower than the preset standard and the product value is higher than the standard rated load of the same type of equipment, it is determined as an unstable-induced abnormality, and a section-level energy consumption cause attribution structure map is generated; Based on the section-level energy consumption cause attribution structure map, the single-point mutation screening module calculates the unit material power consumption deviation rate for each equipment. If the deviation rate is greater than the power consumption abnormal deviation threshold, it is marked as a jump node and the abnormal equipment type is attached, and a device-level single-point energy consumption jump identification set is generated.

2. The grease processing energy consumption monitoring system according to claim 1, wherein The temperature difference coupling correlation status flag includes the temperature difference difference interval characteristics, the unit heat load coupling coefficient, and the heat exchange equipment response category. The periodic energy consumption abnormal section information includes the abnormal equipment number, the energy consumption fluctuation amplitude index, and the section time identifier. The section-level energy consumption cause attribution structure map is specifically the equipment energy consumption cause type, the section to which the attributed equipment belongs, and the attribution determination intensity level. The device-level single-point energy consumption jump identification set specifically refers to the jump node number, the jump deviation degree classification, and the corresponding processing process stage of the jump.

3. The energy consumption monitoring system for oil processing according to claim 2, wherein The temperature difference input perception module includes: Based on the grease pretreatment section, the thermal environment temperature difference analysis sub-module extracts the inlet temperature, outlet temperature, heating medium temperature, and ambient air temperature of the heat exchange device, collects the real-time temperature data of the inlet and outlet of the heat exchange fluid of the heat exchange device and records the corresponding timestamp sequence, calls the ambient air temperature value at the corresponding moment and performs hourly difference calculation with the inlet temperature of the heat exchange fluid, and generates a thermal environment temperature difference sequence; The temperature difference matching trend sub-module calls the thermal environment temperature difference sequence, synchronously retrieves the heat exchange amount and the equipment heat load record value of the heat exchange device per unit time, performs one-to-one mapping on the heat load value and the temperature difference sequence under the corresponding time sequence and calculates the matching coefficient, establishes a double-sequence response trend between the heat transfer driving parameter and the unit heat load change, and obtains a heat load temperature difference matching trend sequence; Based on the heat load temperature difference matching trend sequence, the temperature difference coupling correlation analysis sub-module judges the symbol consistency of the change direction of the temperature difference difference and the unit heat load change direction in each time period. If the two directions are consistent in consecutive periods and the matching trend slope is greater than the trend reference slope value, it is marked as a coupling section and the equipment number and the coupling section time period information are collected, and a temperature difference coupling correlation status flag is generated.

4. The energy consumption monitoring system for oil processing according to claim 3, wherein, The energy consumption fluctuation extraction module includes: The data extraction submodule extracts the unit output steam consumption, unit output electricity consumption and unit output water consumption data of the pretreatment tank, heat exchange device and separation centrifuge in the oil refining section based on the upper temperature difference coupling association state identification, and aggregates the unit energy consumption values collected by each device in three consecutive process cycles in a time series structure, generates three types of energy consumption sequences of steam, electricity and water according to the type of equipment, and obtains the unit energy consumption sequence set of equipment category; The difference calculation submodule calls the unit energy consumption sequence set of the equipment category, identifies the maximum and minimum values in each category of sequence, performs difference calculation on the fluctuation amplitude of each equipment unit energy consumption sequence, extracts the maximum section of continuous cycle fluctuation in the sequence, constructs the total difference amount according to the equipment, constructs the energy consumption fluctuation intensity array according to the equipment category archive, and calculates and obtains the energy consumption fluctuation index value of the equipment; The over-limit identification submodule reads the allowable fluctuation limit of the heat load corresponding to the equipment type according to the energy consumption fluctuation index value of the equipment, compares whether the fluctuation index value exceeds the corresponding limit, marks the equipment that exceeds the limit, and associates the corresponding time period, the equipment number and the process section information to establish an abnormal section index list and generate periodic energy consumption abnormal section information.

5. The oil processing energy consumption monitoring system according to claim 4, characterized in that, The abnormal attribution identification module includes: The equipment data aggregation submodule obtains the working duration, start-stop frequency, and total energy consumption per unit time of the heat exchange device and the material conveying device according to the abnormal period energy consumption section information, and classifies them according to the equipment numbers, integrates the classified data structure by time axis, and organizes each parameter value of the equipment in the same period into an equipment time period data group to generate an equipment period operation data group set; The load structure calculation submodule calculates the ratio of the operation duration to the start-stop frequency for each set of data based on the equipment periodic operation data set, multiplies the ratio by the total energy consumption per unit time, constructs a load value sequence for equipment operation stability, performs a difference judgment between the result and the standard rated load of similar equipment, screens data segments that do not meet the operation stability standard and have a high load value, and obtains an unstable load characteristic value sequence; The section abnormality classification submodule obtains the corresponding grease processing section name according to the unstable load characteristic value sequence and the equipment number, binds the data segment marked as unstable load to the corresponding section identifier, maps and marks the classification items according to the abnormal intensity level, establishes a comparison matrix between section numbers and abnormal categories, and generates a section-level energy consumption attribution structure map.

6. The energy consumption monitoring system for oil processing according to claim 5, wherein The single point mutation screening module comprises: The operation data extraction submodule retrieves the operation cycle length, material handling volume and unit cycle power consumption value of the stirring device, degumming pump and vacuum drying equipment in the refining section based on the section-level energy consumption attribution structure map, and classifies them according to the equipment number. The classified data is horizontally integrated according to the time cycle structure to obtain the operation behavior data combination within the cycle corresponding to the equipment number, and obtain the equipment cycle operation information set; The power consumption offset calculation sub-module calculates the unit power consumption ratio corresponding to the unit material processing volume for each device according to the device cycle operation information set, extracts the unit material power consumption value for each device in each cycle, calculates the unit material power consumption offset value for each device in each cycle through operation, filters the device cycle data with the offset value greater than the power consumption abnormal offset threshold, and generates a power consumption offset mutation structure array. The abnormal node identification sub-module calls the power consumption offset mutation structure array, filters the device numbers and corresponding process section identifiers, identifies the data segments where the consecutive offset nodes in the offset value sequence are greater than the threshold, attaches the device type and abnormal types, completes the binding of the three elements of device number, cycle position and abnormal label, and generates a device-level single-point energy consumption jump identification set.

7. The energy consumption monitoring system for oil processing according to claim 6, wherein The system further includes: The partition warning module calls the device-level single-point energy consumption jump identification set, calibrates the process section partition for the grease treatment stage where the device is located, calculates the jump concentration by performing ratio conversion based on the total number of devices and the number of abnormal jump devices in each process section, matches the concentration with the risk grading value, obtains the risk level corresponding to the process section, and generates an energy consumption monitoring partition warning level label. The energy consumption monitoring partition warning level label includes the process section risk level number, the device abnormality concentration ratio level and the process link abnormality label.

8. The energy consumption monitoring system for oil processing according to claim 7, wherein, The partition warning module includes: The partition calibration sub-module calls the device-level single-point energy consumption jump identification set, identifies the process stage corresponding to each abnormal device, extracts the processing flow identifier and the process section structure number where it is located, establishes a process section partition identification index set according to the mapping result of the device number and the processing stage, and generates a device segmented attribution structure information set. The jump ratio calculation sub-module calculates the total number of devices and the number of abnormal devices in each process section according to the device segmented attribution structure information set, calculates the corrected jump concentration value for each process section through operation, and generates a process section abnormality concentration parameter set. The risk level matching sub-module calls the process section abnormality concentration parameter set, compares the jump concentration value of each process section with the energy consumption abnormal risk grading standard table, assigns the corresponding grading level identifier after comparison to the corresponding process section number, generates a risk level label list, and obtains the energy consumption monitoring partition warning level label.

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