Smart factory energy efficiency optimization method and system, electronic equipment and storage medium

Abstract

The invention discloses a smart factory energy efficiency optimization method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring energy consumption parameters and energy consumption associated variables of a plurality of production units in a target factory; for each production unit, dividing the energy consumption parameters into energy consumption data sequences corresponding to a plurality of time scales according to a preset time interval, and adjusting a reference energy consumption threshold value based on the energy consumption association variable to obtain a target energy consumption threshold value corresponding to the production unit; calculating an energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, and determining an energy distribution level of the production unit in combination with the energy consumption trend characteristic value of the production unit and a target energy consumption threshold value; and dividing the available energy quota corresponding to each production unit based on the energy distribution level of each production unit and the total energy quota of the target factory. By implementing the technical scheme provided by the invention, the effect of improving the efficiency of energy efficiency optimization of the smart factory is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a smart factory energy efficiency optimization method, system, electronic device and storage medium. Background Art

[0002] With the development of industrial automation and information technology, smart factories have become a key solution for the manufacturing industry to pursue high efficiency, low cost and sustainability. Smart factories use advanced data analysis and automation technology to optimize production processes and maximize energy efficiency.

[0003] At present, the existing factory energy efficiency optimization methods mainly evaluate the energy consumption of smart factories by setting fixed thresholds and comparing and analyzing the collected smart factory data. However, in actual applications, since the energy consumption of factories during the production process is affected by many factors, it is often difficult to dynamically adjust only fixed energy consumption thresholds to reflect the actual energy consumption of factories under different production conditions, thereby reducing the efficiency of smart factory energy efficiency optimization. Summary of the invention

[0004] The present application provides a smart factory energy efficiency optimization method, system, electronic device and storage medium, which can improve the efficiency of smart factory energy efficiency optimization.

[0005] In a first aspect, the present application provides a method for optimizing energy efficiency of a smart factory, comprising: Obtain energy consumption parameters and energy consumption related variables of multiple production units in the target factory; For each of the production units, the energy consumption parameters are divided into energy consumption data sequences corresponding to a plurality of time scales according to preset time intervals, and a reference energy consumption threshold is adjusted based on the energy consumption associated variable to obtain a target energy consumption threshold corresponding to the production unit; Calculating the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each of the time scales, and determining the energy allocation level of the production unit in combination with the energy consumption trend characteristic value of the production unit and the target energy consumption threshold; Based on the energy allocation level of each production unit and the total energy quota of the target factory, the available energy quota corresponding to each production unit is divided.

[0006] In a second aspect of the present application, a smart factory energy efficiency optimization system is provided, the system comprising: A data acquisition module, used to acquire energy consumption parameters and energy consumption related variables of multiple production units in a target factory; A data processing module, configured to divide the energy consumption parameters into energy consumption data sequences corresponding to a plurality of time scales for each of the production units according to preset time intervals, and adjust a reference energy consumption threshold based on the energy consumption associated variable to obtain a target energy consumption threshold corresponding to the production unit; An energy allocation level determination module is used to calculate the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, and determine the energy allocation level of the production unit in combination with the energy consumption trend characteristic value of the production unit and the target energy consumption threshold; The energy quota division module is used to divide the available energy quota corresponding to each of the production units based on the energy allocation level of each of the production units and the total energy quota of the target factory.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a smart factory energy efficiency optimization method when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a method for optimizing energy efficiency of a smart factory.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, the energy consumption parameters and energy consumption-related variables of multiple production units in the target factory are obtained, and the energy consumption parameters are divided into energy consumption data sequences of multiple time scales according to preset time intervals. The benchmark energy consumption threshold is dynamically adjusted based on the energy consumption-related variables to obtain the target energy consumption threshold of each production unit. Subsequently, the method calculates the energy consumption trend characteristic values ​​of the production unit based on the energy consumption data sequences of each time scale, and combines these characteristic values ​​with the target energy consumption threshold to accurately determine the energy allocation level of the production unit. Finally, based on the energy allocation level and the total energy quota of the target factory, the available energy quota of each production unit is reasonably divided. A multi-level and detailed analysis of energy consumption data is achieved, which dynamically adapts to changes in the production environment and effectively reflects the energy consumption status under actual production conditions, thereby optimizing energy allocation, ensuring that the best energy use state can be maintained under different production conditions, and improving the overall energy efficiency optimization efficiency of the smart factory. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a smart factory energy efficiency optimization method provided in an embodiment of the present application; Figure 2It is a structural diagram of a smart factory energy efficiency optimization system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a method for optimizing energy efficiency of a smart factory. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of a smart factory energy efficiency optimization method provided by an embodiment of the present application. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a smart factory energy efficiency optimization system based on the von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain energy consumption parameters and energy consumption-related variables of multiple production units in a target factory.

[0016] Among them, the production unit in the embodiment of the present application refers to each production line or production equipment in the factory, which is the basic unit of energy consumption of the factory.

[0017] In the embodiment of the present application, the energy consumption parameter refers to a parameter reflecting the actual energy consumption level of the production unit, such as directly measured energy consumption data such as power consumption data and peak power data.

[0018] In the embodiment of the present application, the energy consumption-related variables refer to other related parameters that affect the energy consumption of the production unit, such as the production environment temperature, equipment operating time, and other environmental or process parameters that indirectly affect energy consumption.

[0019] Specifically, by installing various sensing devices and data acquisition terminals in the factory, the power consumption data, peak power data and other energy consumption parameters of each production unit are monitored and recorded in real time. At the same time, it is also necessary to obtain associated variables that affect energy consumption, such as environmental parameters such as temperature and humidity in the production workshop, and the actual operating time of the production equipment. These data can be automatically collected by devices such as environmental monitors and time counters. In addition, for different types of production units, other specific associated parameters may also need to be obtained, such as production load, type of raw materials, etc., to fully reflect the various factors affecting energy consumption. The energy consumption parameters and associated variable data obtained through this process provide a complete data basis for subsequent analysis and optimization of energy efficiency. With comprehensive parameter information, it is possible to dynamically adjust the energy consumption evaluation standard according to different production conditions, avoid the shortcomings of using fixed thresholds, and achieve more accurate energy efficiency optimization.

[0020] Step 102: For each production unit, the energy consumption parameter is divided into energy consumption data sequences corresponding to multiple time scales according to preset time intervals, and the benchmark energy consumption threshold is adjusted based on the energy consumption associated variable to obtain the target energy consumption threshold corresponding to the production unit.

[0021] Among them, the energy consumption data sequence in the embodiment of the present application refers to the data sequences corresponding to multiple time scales obtained by sampling the energy consumption parameters of the production unit (such as power consumption, peak power) at different time intervals (seconds, minutes, hours), which respectively reflect the energy consumption fluctuations in different time dimensions.

[0022] In the embodiment of the present application, the reference energy consumption threshold refers to a pre-set reference value or allowable range of energy consumption of a production unit under standard working conditions, which is a reference.

[0023] In the embodiment of the present application, the target energy consumption threshold refers to an energy consumption assessment standard value that is closer to actual working conditions after dynamic adjustment and correction based on the benchmark energy consumption threshold according to related variables such as the actual production environment temperature and equipment operating time.

[0024] Specifically, after obtaining the energy consumption parameters and associated variables of the production unit, it is necessary to divide the energy consumption parameters into data sequences of multiple time scales according to different preset time intervals, and dynamically adjust the benchmark energy consumption threshold according to the associated variables to obtain the target energy consumption threshold corresponding to each production unit. The reason for this is that the actual energy consumption of the production unit shows different fluctuation characteristics at different time scales. For example, at the second time scale, the energy consumption data reflects the instantaneous power fluctuation of the equipment; at the minute level, it reflects the load change of the equipment; at the hour level, it reflects the energy consumption trend of the entire production process. Only by dividing the energy consumption parameters into data sequences of multiple time scales can we comprehensively analyze and grasp the energy consumption behavior characteristics of the production unit at different time dimensions. Specifically, the power consumption data and peak power data are extracted from the energy consumption parameters, and then the data are segmented and sampled according to different preset time intervals such as seconds, minutes, and hours to obtain data sequences corresponding to multiple time scales such as second energy consumption sequence, minute energy consumption sequence, and hour energy consumption sequence. On the other hand, since the changes in associated variables such as production environment temperature and equipment operation time will affect the actual energy consumption level of the production unit, it is necessary to dynamically adjust the benchmark energy consumption threshold based on these variables. A correction factor mapping table can be established in advance to determine the correction factors corresponding to different temperatures and operating hours, and then the baseline threshold is multiplied by these correction factors to obtain the dynamically adjusted target energy consumption threshold. This not only takes into account the standard energy consumption level, but also reflects the impact of actual production conditions, so that the target energy consumption threshold is closer to the actual situation.

[0025] Based on the above embodiment, as an optional embodiment, in step 102: dividing the energy consumption parameter into energy consumption data sequences corresponding to multiple time scales according to preset time intervals, this step may also include the following steps: Step 201: extracting power consumption data and peak power data from energy consumption parameters; sampling the power consumption data and peak power data at second-level time intervals to obtain a second-level energy consumption sequence.

[0026] Specifically, in the energy efficiency optimization scheme of smart factories, it is necessary to extract power consumption data and peak power data from the energy consumption parameters of the production unit, because power consumption and peak power are key parameters that directly reflect the real-time energy consumption level of the equipment. Next, the extracted power consumption and peak power data are sampled at preset second-level time intervals to obtain a second-level energy consumption sequence. This data sequence at a second-level time scale can accurately capture the energy consumption fluctuations and peak characteristics of the equipment during instantaneous operation. Specifically, the data acquisition device is used to continuously record the power consumption readings and power readings of the production equipment every second. Then, according to the preset sampling time interval (such as every 1 second, 5 seconds or 10 seconds, etc.), these raw data are segmented and extracted to obtain the power consumption sequence and peak power sequence in seconds. These two sequences together constitute the second-level energy consumption sequence. The reason why it is necessary to collect this energy consumption sequence data with second-level time accuracy is that it can accurately reflect the power consumption and load changes of the equipment during instantaneous operation. For smart factories that use a large number of high-power devices, these devices will experience short-term power fluctuations and energy consumption peaks when starting, accelerating or when the load changes suddenly. Only energy consumption series with a resolution of seconds can accurately capture these instantaneous energy consumption characteristics and provide data support for energy-saving measures such as optimizing equipment operation modes and peak shaving and valley filling.

[0027] Step 202: Sampling the power consumption data and peak power data at minute-level time intervals to obtain a minute-level energy consumption sequence.

[0028] Specifically, after obtaining the original second-level power consumption and peak power data, these data are resampled and grouped according to the preset minute-level time interval (such as every 1 minute, 5 minutes or 10 minutes, etc.), so as to generate a power consumption sequence and a peak power sequence in minutes, and these two sequences together constitute the minute-level energy consumption sequence. For example, the average power consumption and average power values ​​in each preset minute interval can be calculated as the representative data point of the minute interval, thereby forming a minute-level power consumption and power sequence. The maximum value in the minute interval can also be taken as the data point. Different statistical methods can be determined according to specific needs. The reason why minute-level energy consumption sequence data needs to be collected is that compared with the second-level sequence, it can better reflect the load fluctuation law of production equipment on a longer time scale. The load changes of many production equipment are often periodic, and there are obvious energy consumption fluctuation characteristics on the minute-level time scale. Only by obtaining the minute-level sequence can we comprehensively analyze the impact of factors such as equipment start-stop and production rhythm on energy consumption, so as to optimize production plans and process flows.

[0029] Step 203: Sampling the power consumption data and the peak power data at hourly time intervals to obtain an hourly energy consumption sequence.

[0030] Specifically, starting from the original second-level power consumption and peak power data, these data are regrouped and counted according to the preset hour-level time interval (such as every 1 hour, 2 hours or 4 hours, etc.), and the power consumption sequence and peak power sequence in hours are obtained. These two sets of sequence data together constitute the hour-level energy consumption sequence. For example, the total power consumption and average power value in each preset hourly interval can be calculated as the data point of the time interval, and then the hour-level power consumption and power sequences are formed respectively. Other statistics such as the maximum value or median value in the hourly interval can also be taken as data points, and the method can be determined according to specific needs. The reason why hour-level energy consumption sequence data needs to be collected is that compared with the second-level and minute-level, it can better reflect the overall situation and changing trend of energy consumption of production equipment on a longer time scale. The overall operation mode and rhythm of many production lines will only be reflected on the hour-level time scale, such as the start and stop of equipment, high and low load cycles, etc., which will show regular characteristics in the hour-level energy consumption data.

[0031] Step 204: taking the second-level energy consumption sequence, the energy consumption sequence and the hour-level energy consumption sequence as energy consumption data sequences corresponding to multiple time scales.

[0032] Specifically, the obtained second-level energy consumption series (including second-level power consumption series and peak power series), minute-level energy consumption series, and hour-level energy consumption series are uniformly classified and stored as energy consumption data series representing different time scales. The reason why these three types of time precision sequence data need to be unified is that they respectively reflect the energy consumption behavior characteristics of production equipment in different time dimensions. Only by analyzing them together can we fully grasp the energy consumption status of the equipment. The second-level energy consumption series can capture instantaneous power fluctuations and energy consumption peaks; the minute-level series reveals the regular characteristics of equipment start-up and shutdown and load fluctuations; and the hour-level series reflects the overall operation mode and energy consumption trend of the equipment. Combining the three types of energy consumption data series can obtain complete energy consumption data samples of the equipment at each time scale.

[0033] Based on the above embodiment, as an optional embodiment, in step 102: adjusting the reference energy consumption threshold based on the energy consumption associated variable to obtain the target energy consumption threshold corresponding to the production unit, this step may also include the following steps: Step 205: Obtain the production environment temperature and the equipment operation time in the energy consumption associated variables; determine the first correction coefficient corresponding to the production environment temperature and the second correction coefficient corresponding to the equipment operation time in a preset correction coefficient mapping table.

[0034] Specifically, after obtaining the energy consumption data sequences corresponding to multiple time scales, the energy efficiency optimization scheme of the smart factory needs to dynamically correct the standard energy consumption threshold in combination with the associated variables in the actual production environment to obtain a target energy consumption evaluation standard that is closer to the actual working conditions. First, the real-time values ​​of the two key factors, the production environment temperature and the equipment operation time, are obtained from the energy consumption associated variables. This is because temperature and operation time will significantly affect the actual energy consumption level of the equipment. Next, in the pre-established correction coefficient mapping table, according to the obtained real-time temperature value and operation time value, the corresponding first correction coefficient and second correction coefficient are respectively found and determined. This correction coefficient mapping table is usually obtained through a large amount of historical data analysis and modeling, which quantitatively reflects the influence of factors such as temperature and operation time on the energy consumption of the equipment. Taking temperature as an example, the increase in temperature will increase the heat dissipation load of the equipment, thereby increasing energy consumption; when the temperature is too low, additional heating energy consumption is required to ensure the normal operation of precision equipment. The mapping table gives the correction coefficients corresponding to different temperature ranges, which are used to quantitatively correct the standard energy consumption threshold. For the operation time, the equipment often consumes more energy when it is continuously operated for a long time, while intermittent operation is relatively energy-saving. The correction coefficient of the operating hours in the mapping table reflects this regular influence. Finally, multiply the temperature correction coefficient and the operating hours correction coefficient to obtain a comprehensive correction factor. By using this factor to adjust and correct the standard energy consumption threshold, you can get a target energy consumption evaluation standard that is close to the actual production conditions. The key role of this dynamic correction mechanism is to enable the evaluation standard to be adaptively adjusted and change at any time according to real-time changes in factors such as temperature and operating hours, so as to be closer to the actual energy consumption performance and improve the accuracy and pertinence of the evaluation.

[0035] Step 206: Multiply the reference energy consumption threshold by the first correction coefficient and the second correction coefficient in sequence to obtain the target energy consumption threshold corresponding to the production unit.

[0036] Specifically, first obtain the standard benchmark energy consumption threshold of this type of production equipment or production line. The benchmark threshold is usually an energy consumption reference value obtained through statistical analysis of a large amount of historical data under standard working conditions. Next, the first correction coefficient (corresponding to the production environment temperature) and the second correction coefficient (corresponding to the equipment running time) are respectively multiplied by the benchmark energy consumption threshold. The purpose of this step is to quantify the impact of actual working conditions such as temperature and running time into the benchmark threshold. Take the benchmark threshold as 1000 kWh, the temperature correction coefficient as 1.05, and the running time correction coefficient as 1.1 as an example. Then by multiplying by these two correction coefficients respectively, the final corrected target energy consumption threshold is 1155 kWh. This correction process reflects that under actual working conditions, the ambient temperature and running time faced by the equipment are different from the standard working conditions, which will cause its actual energy consumption performance to deviate from the standard benchmark value. By multiplying by the correction coefficient, these differences can be quantified and dynamically adjusted to the evaluation criteria. The reason why this dynamic correction process is needed is to make the final target energy consumption threshold close to production reality and truly reflect the reasonable energy consumption level of the production unit under the current specific working conditions. This provides a more accurate and targeted reference basis for subsequent energy consumption evaluation and optimization.

[0037] Step 103: Calculate the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, and determine the energy allocation level of the production unit in combination with the energy consumption trend characteristic value of the production unit and the target energy consumption threshold.

[0038] Among them, the energy consumption trend characteristic value refers to a series of characteristic parameters calculated according to the energy consumption data sequence corresponding to different time scales (seconds, minutes, hours), which is used to describe and reflect key information such as energy consumption fluctuations, peak and valley distribution, and load change laws of the production unit in each time dimension.

[0039] The energy allocation level is a level indicator determined according to the comparison between the energy consumption trend characteristic value of the production unit and its target energy consumption threshold value, according to the preset evaluation rules. This energy allocation level reflects the current energy-saving status and optimization priority of the unit. For example, a high energy consumption level means that there is a large space for energy saving and needs to be prioritized.

[0040] Specifically, first, a series of energy consumption trend characteristic values ​​are calculated based on the energy consumption data sequences corresponding to different time scales such as seconds, minutes and hours. These characteristic values ​​reflect key information such as energy consumption fluctuations, peak-valley distribution, and load change rules of the production unit in various time dimensions. For example, characteristic values ​​such as peak power and power fluctuation frequency can be calculated in the second dimension; characteristic values ​​such as production rhythm energy consumption fluctuation cycle and fluctuation amplitude can be calculated in the minute dimension; and characteristic values ​​such as daily total energy consumption, peak time distribution, and load utilization can be calculated in the hour dimension. Next, these multi-dimensional energy consumption trend characteristic values ​​calculated are comprehensively compared and analyzed with the target energy consumption threshold corresponding to the production unit. According to the degree of deviation between the characteristic value and the threshold, the energy allocation level of the production unit is determined according to the pre-set evaluation rules. This energy allocation level reflects the current energy-saving potential and optimization needs of the unit. For example, if the characteristic value exceeds the threshold in all aspects, it can be rated as a high energy consumption level, indicating that there is a large space for energy saving, and the unit needs to be prioritized for energy scheduling; if the characteristic value is generally lower than the threshold, it can be rated as an energy-saving level, and the corresponding energy allocation is relatively reasonable. This comprehensive evaluation process is necessary because there are often many complex factors that affect energy consumption at the production site, and simply relying on energy consumption performance at a certain time scale cannot fully reflect the real situation. Only by combining multi-dimensional data and comparing it with the revised target threshold can a comprehensive and accurate evaluation be made.

[0041] Based on the above embodiment, as an optional embodiment, in step 103: calculating the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, this step may also include the following steps: Step 301: Use a sliding window method to process the energy consumption data sequence corresponding to each time scale to obtain energy consumption data in multiple time windows.

[0042] Specifically, first, a predefined time window length is set on the energy consumption series of different time scales such as seconds, minutes, and hours. For example, the window length can be set to 30 seconds on the second-level sequence and 2 hours on the hour-level. Next, this time window is used to slide and traverse the sequences of each time scale. Taking the sliding of a 30-second window on a second-level sequence as an example, the first window covers data from 1 to 30 seconds, the second window covers data from 2 to 31 seconds, and so on, moving one sampling time interval each time. In each time window, the sequence data within the window coverage range is extracted as an independent subset of energy consumption data. In this way, by continuously sliding the window, multiple continuous subsets of energy consumption data covering the entire time interval can be obtained. The purpose of this sliding window segmentation is to be able to perform partition analysis on different time periods of the production process and discover abnormal or regular changes in energy consumption within a specific time period. For example, in a 30-second window of a second-level sequence, it may be found that a cooling device has a short power peak anomaly when it starts; while in an hour-level sequence of a 2-hour window, it may be found that the energy consumption of the device is high between 10 am and 12 noon. By segmenting the data through the sliding window method, the energy consumption performance of each time period can be finely analyzed, thereby accurately evaluating the factors affecting energy consumption and providing targeted data support for energy-saving optimization.

[0043] Step 302: Calculate the mean and standard deviation of the energy consumption data in each time window; calculate the energy consumption growth rate based on the mean of any two adjacent time windows; calculate the energy consumption stability index based on the standard deviation of each time window.

[0044] Specifically, first, the mean and standard deviation of the energy consumption data in each time window are calculated. The mean reflects the average energy consumption level in the time period, while the standard deviation characterizes the discrete fluctuation degree of the energy consumption data. Taking a 30-second second-level time window as an example, assuming that there are 30 second-level energy consumption sampling values ​​in the window, then the arithmetic mean of these 30 values ​​is calculated as the mean, and the standard deviation of these 30 values ​​relative to the mean is calculated as the standard deviation index of the window. Next, based on the mean of any two adjacent time windows, the energy consumption growth rate is calculated. The growth rate reflects the changing trend of the energy consumption level in the production process between different time periods. Taking the two 30-second windows before and after as an example, if the mean of the latter window is 1200 watts and the mean of the previous window is 1000 watts, then the growth rate of the latter window relative to the previous window is (1200-1000) / 1000=20%. By calculating the growth rate of each two adjacent windows, the growth rate curve of the entire time interval can be obtained. The standard deviation value in each time window is used to calculate the energy consumption stability index of the window. The stability index reflects the degree of fluctuation of energy consumption during the period of time. The smaller the standard deviation, the higher the stability. These characteristics such as mean, standard deviation, growth rate and stability index can quantitatively describe the energy consumption performance in the production process from different dimensions. By analyzing these indicators, key information such as abnormal energy consumption, the root cause of energy consumption fluctuations, and the difference in energy consumption between different production stages can be found.

[0045] Step 303: normalize the energy consumption growth rate and the energy consumption stability index and then superimpose them to obtain an energy consumption trend characteristic value.

[0046] Specifically, the energy consumption growth rate sequence and the stability index sequence are first normalized and mapped uniformly to the range of 0-1. This step is to eliminate the numerical differences between the two indicators of different dimensions so that they can be weighted superimposed at the same order of magnitude. Taking Min-Max normalization as an example, assume that the maximum value of the growth rate sequence is 30%, the minimum value is -10%, and the maximum and minimum values ​​of the stability index sequence are 0.25 and 0.05 respectively. Then the growth rate sequence can be linearly scaled to the interval [0, 1], that is, (original value-minimum value) / (maximum value-minimum value); similarly, the stability index sequence is scaled to the interval [0, 1]. After normalization, the two sequences can be weighted superimposed. The purpose of weighted superposition is to integrate the feature information of the two different dimensions of growth rate and stability to form a comprehensive energy consumption trend evaluation index. When superimposing, it is necessary to predetermine the weight distribution ratio of the two features, for example, the weight can be set to 0.6 for the growth rate sequence and 0.4 for the stability index sequence. Then the two normalized sequences are linearly added according to the weights to obtain the final energy consumption trend feature value sequence. Through this fusion step, the energy consumption trend characteristic value can simultaneously reflect the two key aspects of the growth trend and volatility of energy consumption in the production process, thereby more comprehensively characterizing the dynamic trend characteristics of energy consumption.

[0047] Based on the above embodiment, as an optional embodiment, in step 103: combining the energy consumption trend characteristic value of the production unit and the target energy consumption threshold, determining the energy allocation level of the production unit, this step may also include the following steps: Step 304: Acquire the production activity type of the production unit, and determine the energy consumption fluctuation range corresponding to the production activity type in the database.

[0048] Specifically, first, the specific types of production activities being performed by the current production unit are obtained through the factory manufacturing execution system or other on-site data collection terminals, such as casting, stamping, welding, assembly and other different manufacturing links. Different types of production activities correspond to different energy consumption characteristics. For example, a large number of high-energy-consuming equipment will be put into use during the casting process, and there will be a high basic energy consumption; while stamping is more of a staged peak energy consumption; the energy consumption of the assembly link may be relatively stable, etc. Therefore, before evaluating whether the same energy consumption trend characteristic value is abnormal, it is necessary to determine a reasonable energy consumption floating range as a reference based on the current type of production activity. This step will access the system's production knowledge base database to find the standard energy consumption floating range data corresponding to the current production activity type. This database is pre-established during the system design and debugging phase through a large number of on-site energy consumption sampling and analysis. Taking casting as an example, assuming that the energy consumption floating range corresponding to the casting activity in the database is within ±20% of the average energy consumption, then this 20% floating range will be temporarily locked as the reference standard for this evaluation. Only by comparing the actually calculated energy consumption trend characteristic value with this specific floating range can we determine whether there is abnormal fluctuation in the characteristic value, and then determine the optimization level and subsequent optimization strategy of the production unit accordingly.

[0049] Step 305: Determine the expected energy consumption fluctuation range of the production unit based on the energy consumption fluctuation range and energy consumption trend characteristic value of the production unit.

[0050] Specifically, first, the standard energy consumption floating range is used as the reference value, and the upper and lower boundaries of the range are used as the initial upper and lower limits. Next, the energy consumption trend characteristic value sequence calculated previously is analyzed to identify the peak interval. This step can be achieved using data analysis algorithms such as peak detection and pattern matching. Taking a continuous 24-hour energy consumption trend characteristic value sequence as an example, the system may find that there are large peak intervals in the two time periods of 6-8 hours and 18-20 hours. For these detected peak intervals, the system will use the maximum value of the interval as the adjustment basis and appropriately relax the original upper and lower limits. If the maximum value of the peak interval is 120% of the original upper limit, then the new upper limit will be adjusted to 120% of the original upper limit; similarly, if the maximum value is 80% of the original lower limit, then the new lower limit will be adjusted to 80% of the original lower limit. Through this dynamic adjustment process, the standard floating range can be effectively widened to cover possible temporary peak fluctuations and avoid misjudgment due to the peak being judged as an abnormal value.

[0051] Step 306: Calculate the offset between the estimated energy consumption fluctuation range and the target energy consumption threshold, and determine the energy allocation level corresponding to the offset in a preset energy allocation level mapping table.

[0052] Specifically, first take the upper and lower limits of the expected energy consumption fluctuation range, compare them with the target energy consumption threshold, and obtain the absolute values ​​of the two offsets. Take the positive offset between the upper limit of the range and the threshold as a reference, and select the larger absolute value between the positive offset and the negative offset between the lower limit of the range and the threshold as the final judgment offset. For example, suppose the upper and lower limits of the expected range are 1200 and 800 respectively, and the target threshold is 1000. Then the positive offset is 1200-1000=200, and the negative offset is 1000-800=200. Since the absolute values ​​of the two are equal, the system will select 200 as the final judgment offset. Next, the system will access the pre-established energy allocation level mapping table to find the energy allocation level corresponding to the current offset value. This mapping table was developed during the system design and debugging phase based on a large amount of field data analysis and expert experience summary. It establishes a mapping relationship between the possible offset value range and different optimization levels. Assume that the mapping table stipulates that the optimization level is level 1 when the offset is 0-100, level 2 when 100-300, and level 3 when 300-500, increasing in sequence. In this case, since the offset is 200, it will be mapped to level 2 optimization level. The purpose of determining the optimization level is to guide the refined energy efficiency optimization and resource allocation of subsequent production units. Different optimization levels correspond to different degrees of energy-saving potential and different optimization strategy solutions. The higher the level, the more the actual energy consumption of the unit deviates from the target threshold, the greater the energy-saving potential, and more computing power and resources will be invested in subsequent optimization; on the contrary, the lower the level, the more relaxed the corresponding lightweight optimization strategy can be executed. By discretizing the offset into levels, the complexity of the evaluation calculation can be reduced, so that the system has a basis for optimization decision-making, thereby avoiding blind optimization and waste of resources.

[0053] Step 104: Based on the energy allocation level of each production unit and the total energy quota of the target factory, the available energy quota corresponding to each production unit is divided.

[0054] The total energy quota refers to the maximum total amount of energy that the factory is allowed to consume within a specific time period (such as one day). This total amount can be the upper limit of energy use agreed upon by the factory and the superior department, or it can be the upper limit of energy demand estimated based on the production plan and budget cost of the day. It reflects the total energy budget that the factory can reasonably use within that time period.

[0055] The available energy quota refers to the energy quota that can be allocated to each production unit based on its energy allocation level, divided from the total energy quota. Units with different priorities will receive different available quotas.

[0056] Specifically, the system first needs to know the total energy quota of the current target factory, which can be the daily electricity consumption limit agreed upon by the factory and the superior unit, or the energy consumption limit estimated according to the daily operating cost budget. Next, the system will analyze the optimization level of each production unit one by one. For units with higher priority, that is, units with greater energy-saving potential, the system will allocate a larger available energy quota to them, so that there will be enough resources to achieve fine control in subsequent optimization. For units with lower priority and low energy-saving potential, the system will appropriately compress their available quota to avoid wasting optimization resources. The specific quota allocation principle can be pre-set in the system configuration as a mapping relationship table between level and quota. For example, the 1st level unit can allocate 20% of the total quota, the 2nd level unit 15%, the 3rd level unit 10%, and so on. After clarifying the allocation ratio of each unit, the system will complete the quota split according to these ratios. If there is a surplus after the split, it can be evenly distributed to all level units; it can also be preferentially distributed to high-level units to further amplify the focusing effect of optimized resources. In this way, not only the hard limit of the total energy quota of the factory is met, but more importantly, the effective stratification and efficient allocation of optimized resources are achieved, so that the optimization efficiency can be maximized.

[0057] Based on the above embodiment, as an optional embodiment, in step 104: based on the energy allocation level of each production unit and the total energy quota of the target factory, the available energy quota corresponding to each production unit is divided. This step may also include the following steps: Step 401: Based on the energy allocation level of each production unit, determine the energy quota ratio corresponding to each production unit.

[0058] Specifically, the system will access the pre-established energy quota mapping table, and find the corresponding quota ratio value in the mapping table according to the allocation level of each unit. This quota mapping table is actually an empirical value table developed during the system design and debugging phase through a large amount of field data analysis and expert experience summary. It establishes a mapping relationship between different energy allocation levels and reasonable energy quota ratios. For example, the mapping table may stipulate that the quota ratio corresponding to the level 1 priority unit is 20%, the level 2 is 15%, the level 3 is 12%, and the level 4 is 10%, decreasing in sequence. The quota ratio reflects the relative weight of the system's investment in optimization resources for different units. After determining the quota ratio corresponding to each unit, in the subsequent actual energy allocation, the system can divide a reasonable energy usage indicator for each unit according to these quota values. Take the total energy budget of the factory for a certain period of time as 1,000 units as an example. Assume that there are two production units A and B, A is level 2 priority, and B is level 4. Then according to the mapping table, the quota ratio of A is 15% and that of B is 10%. The actual allocation result will be that the energy index of unit A is 1000×15%=150 units, and that of unit B is 1000×10%=100 units. In this way, the system has a good balance between the energy-saving potential differences of each unit and the optimization of resource allocation. For high-priority and high-potential unit A, more energy index margins are given, creating conditions for it to formulate more active optimization strategies. Although the low-priority unit B has relatively fewer indicators, it also retains the necessary optimization space. If the energy indicators are simply allocated equally without considering the differences in unit energy-saving potential, it is likely to lead to inefficient use of optimization resources. The high-potential unit is limited by the indicators and cannot be fully optimized, wasting benefits; the low-potential unit is allocated too many indicators that cannot be digested in vain.

[0059] Step 402: Multiply the energy quota ratio corresponding to each production unit by the total energy quota of the target factory to obtain the available energy quota corresponding to each production unit.

[0060] Specifically, the system first needs to know the total energy quota value of the target factory within a specific time period, which can be the energy consumption limit agreed upon by the factory and the superior department, or the energy demand estimated based on the daily operating cost budget. Taking the total energy quota of a daily production day as 1,000 units as an example, the system has evaluated the energy quota ratios of two production units A and B as 20% and 15% respectively. Then, for unit A, the system will directly multiply the 20% quota ratio by the total quota of 1,000 units, and obtain the available quota of unit A as 1,000×20%=200 units. Similarly, the available quota of unit B is 1,000×15%=150 units. In this way, the system not only reasonably allocates the relative quota weights between units, but more importantly, converts these weights into available energy index values, providing a clear resource constraint basis for the subsequent refined optimization of each unit.

[0061] Based on the above embodiment, as an optional embodiment, in step 104: based on the energy allocation level of each production unit and the total energy quota of the target factory, the available energy quota corresponding to each production unit is divided. This step may also include the following steps: Step 501: Obtain the cooling tower return water temperature and current energy consumption data of the target factory; calculate the energy consumption deviation between the current energy consumption data and a preset energy consumption reference value, and calculate the temperature deviation between the return water temperature and the reference temperature.

[0062] Specifically, the system first obtains the current real-time value of the return water temperature of the cooling tower from the on-site data acquisition terminal, and also obtains the real-time data of the current total energy consumption of the factory. Then, the system will access two pre-configured benchmark values ​​respectively: one is the benchmark return water temperature value of the cooling tower, and the other is the benchmark energy consumption value of the factory under this operating state. These two benchmark values ​​are usually reasonable expected values ​​formulated during the system design and debugging phase based on a large amount of historical data, on-site expert experience, and relevant industry standards. After determining the four data, the system compares the current return water temperature with the benchmark return water temperature value and calculates the temperature deviation between the two. At the same time, the current total energy consumption data will also be compared with the energy consumption benchmark value to obtain an energy consumption deviation value. Taking a certain point in time as an example, assuming that the current return water temperature is 32°C and the benchmark temperature is 30°C, then the temperature deviation is 32-30=2°C. Similarly, if the current energy consumption data is 2.8 million kWh and the energy consumption benchmark is 2.5 million kWh, then the energy consumption deviation is 280-250=300,000 kWh. The acquisition of these two deviation values ​​lays a data foundation for the subsequent formulation of refined energy-saving optimization strategies.

[0063] Step 502: When the temperature deviation is less than the temperature deviation threshold, and the energy consumption deviation is greater than the energy consumption deviation threshold, the fan speed of the target plant is reduced according to a preset speed reduction step.

[0064] Specifically, when the temperature deviation is small (lower than the preset threshold) but the energy consumption deviation is large (higher than the preset threshold), the system will moderately reduce the fan speed according to the preset speed reduction step. The purpose of reducing the fan speed is to reduce the energy consumption of the refrigeration system. The fan is the main power equipment of the refrigeration device, and its speed directly determines the operating load and energy consumption level of the refrigeration system. When the energy consumption deviation is too high, moderately reducing the fan speed can quickly reduce the system energy consumption. The setting of the speed reduction step requires repeated experiments based on a large amount of field data during system debugging to find an optimal value that can effectively reduce consumption without affecting the normal refrigeration effect. Generally speaking, the smaller the step value, the smaller the impact on the refrigeration effect, but the slower the energy consumption decreases. Taking a certain time point as an example, assuming that the current temperature deviation is 1°C (lower than the threshold of 2°C), the energy consumption deviation is 400,000 kWh (higher than the threshold of 300,000 kWh), and the preset speed reduction step is 5%. Then the optimization system will command to reduce the current fan speed by 5%. If the current speed of the fan is 2800 rpm, it will be adjusted to 2800*95%=2660 rpm after the speed reduction. In this way, moderate energy saving can be achieved, and because the temperature deviation is within the controllable range, it will not have a significant impact on the cooling effect. If the energy consumption still cannot be effectively reduced, the system will continue to reduce the fan speed according to the step size in the next adjustment cycle until the energy consumption deviation drops below the threshold. Compared with not making any adjustments or directly making a cliff-like large speed adjustment on the fan, this progressive small-step adjustment is more robust and precise, avoiding drastic fluctuations in energy consumption and temperature, and making the entire refrigeration system run more smoothly and efficiently. If the temperature deviation has exceeded the threshold, then in order to ensure the cooling effect, the system will no longer continue to reduce the fan speed, but will instead adjust other parameters for optimization to fix the problem of excessive temperature deviation. In this way, the present application scheme realizes a closed-loop intelligent optimization control, making targeted adjustments according to the on-site deviation value at all times, dynamically tracking the system status, and achieving a balance between energy efficiency and cooling effect.

[0065] Reference Figure 2 , is a smart factory energy efficiency optimization system provided by an embodiment of the present application, the system includes: a data acquisition module, a data processing module, an energy allocation level determination module, and an energy quota division module, wherein: A data acquisition module, used to acquire energy consumption parameters and energy consumption related variables of multiple production units in a target factory; A data processing module is used to divide the energy consumption parameters of each production unit into energy consumption data sequences corresponding to multiple time scales according to preset time intervals, and adjust the reference energy consumption threshold based on the energy consumption associated variable to obtain the target energy consumption threshold corresponding to the production unit; The energy allocation level determination module is used to calculate the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, and determine the energy allocation level of the production unit by combining the energy consumption trend characteristic value of the production unit and the target energy consumption threshold; The energy quota division module is used to divide the available energy quota corresponding to each production unit based on the energy allocation level of each production unit and the total energy quota of the target factory.

[0066] Based on the above embodiments, the data processing module is also used to extract power consumption data and peak power data from energy consumption parameters; sample the power consumption data and peak power data at time intervals of seconds to obtain a second-level energy consumption sequence; sample the power consumption data and peak power data at time intervals of minutes to obtain a minute-level energy consumption sequence; sample the power consumption data and peak power data at time intervals of hours to obtain an hour-level energy consumption sequence; and use the second-level energy consumption sequence, energy consumption sequence and hour-level energy consumption sequence as energy consumption data sequences corresponding to multiple time scales.

[0067] Based on the above embodiment, the data processing module is also used to obtain the production environment temperature and equipment operating time in the energy consumption related variables; in the preset correction coefficient mapping table, determine the first correction coefficient corresponding to the production environment temperature and the second correction coefficient corresponding to the equipment operating time; multiply the baseline energy consumption threshold with the first correction coefficient and the second correction coefficient in turn to obtain the target energy consumption threshold corresponding to the production unit.

[0068] On the basis of the above-mentioned embodiment, the energy allocation level determination module is also used to process the energy consumption data sequence corresponding to each time scale using the sliding window method to obtain energy consumption data in multiple time windows; calculate the mean and standard deviation of the energy consumption data in each time window; calculate the energy consumption growth rate based on the mean of any two adjacent time windows; calculate the energy consumption stability index based on the standard deviation of each time window; normalize the energy consumption growth rate and the energy consumption stability index and superimpose them to obtain the energy consumption trend characteristic value.

[0069] Based on the above embodiment, the energy allocation level determination module obtains the production activity type of the production unit, and determines the energy consumption floating range corresponding to the production activity type in the database; based on the energy consumption floating range and energy consumption trend characteristic value of the production unit, determines the expected energy consumption fluctuation range of the production unit; calculates the offset between the expected energy consumption fluctuation range and the target energy consumption threshold, and determines the energy allocation level corresponding to the offset in the preset energy allocation level mapping table.

[0070] Based on the above embodiment, the energy quota division module is also used to determine the energy quota ratio corresponding to each production unit based on the energy allocation level of each production unit; the energy quota ratio corresponding to each production unit is multiplied by the total energy quota of the target factory to obtain the available energy quota corresponding to each production unit.

[0071] Based on the above embodiment, the energy quota division module is also used to obtain the cooling tower return water temperature and current energy consumption data of the target factory; calculate the energy consumption deviation between the current energy consumption data and the preset energy consumption reference value, and calculate the temperature deviation between the return water temperature and the reference temperature; when the temperature deviation is less than the temperature deviation threshold, and the energy consumption deviation is greater than the energy consumption deviation threshold, reduce the fan speed of the target factory according to the preset speed reduction step.

[0072] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0073] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0074] The communication bus 302 is used to realize the connection and communication between these components.

[0075] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0076] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0077] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0078] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a smart factory energy efficiency optimization method.

[0079] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a smart factory energy efficiency optimization method in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0085] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0086] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A smart factory energy efficiency optimization method, characterized in that: include: Obtain energy consumption parameters and energy consumption related variables of multiple production units in the target factory; For each of the production units, the energy consumption parameters are divided into energy consumption data sequences corresponding to a plurality of time scales according to preset time intervals, and a reference energy consumption threshold is adjusted based on the energy consumption associated variable to obtain a target energy consumption threshold corresponding to the production unit; Calculating the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each of the time scales, and determining the energy allocation level of the production unit in combination with the energy consumption trend characteristic value of the production unit and the target energy consumption threshold; Based on the energy allocation level of each production unit and the total energy quota of the target factory, the available energy quota corresponding to each production unit is divided.

2. The smart factory energy efficiency optimization method according to claim 1, characterized in that: The preset time interval includes a second-level time interval, a minute-level time interval, and an hour-level time interval. The energy consumption parameter is divided into energy consumption data sequences corresponding to multiple time scales according to the preset time interval, including: Extracting power consumption data and peak power data from the energy consumption parameters; Sampling the power consumption data and the peak power data at the second-level time interval to obtain a second-level energy consumption sequence; Sampling the power consumption data and peak power data at the minute-level time interval to obtain a minute-level energy consumption sequence; Sampling the power consumption data and the peak power data at the hourly time interval to obtain an hourly energy consumption sequence; The second-level energy consumption sequence, the energy consumption sequence and the hour-level energy consumption sequence are used as energy consumption data sequences corresponding to multiple time scales.

3. The smart factory energy efficiency optimization method according to claim 1, characterized in that: The step of adjusting the reference energy consumption threshold based on the energy consumption associated variable to obtain the target energy consumption threshold corresponding to the production unit includes: Obtaining the production environment temperature and equipment operation time in the energy consumption associated variables; In a preset correction coefficient mapping table, determine a first correction coefficient corresponding to the production environment temperature and a second correction coefficient corresponding to the equipment operation time; The reference energy consumption threshold is multiplied by the first correction coefficient and the second correction coefficient in sequence to obtain a target energy consumption threshold corresponding to the production unit.

4. The smart factory energy efficiency optimization method according to claim 1, characterized in that: The step of calculating the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each of the time scales includes: The energy consumption data sequences corresponding to the time scales are processed by a sliding window method to obtain energy consumption data within multiple time windows; Calculating the mean and standard deviation of the energy consumption data within each of the time windows; Calculate the energy consumption growth rate based on the average of any two adjacent time windows; Calculating an energy consumption stability index based on a standard deviation of each of the time windows; The energy consumption growth rate and the energy consumption stability index are normalized and then superimposed to obtain the energy consumption trend characteristic value.

5. The smart factory energy efficiency optimization method according to claim 1, characterized in that: The step of determining the energy allocation level of the production unit by combining the energy consumption trend characteristic value of the production unit and the target energy consumption threshold value includes: Acquire the production activity type of the production unit, and determine the energy consumption fluctuation range corresponding to the production activity type in a database; Determining an estimated energy consumption fluctuation range of the production unit based on the energy consumption fluctuation range and energy consumption trend characteristic value of the production unit; An offset between the estimated energy consumption fluctuation range and the target energy consumption threshold is calculated, and an energy allocation level corresponding to the offset is determined in a preset energy allocation level mapping table.

6. The smart factory energy efficiency optimization method according to claim 1, characterized in that: The dividing of the available energy quota corresponding to each of the production units based on the energy allocation level of each of the production units and the total energy quota of the target factory includes: Based on the energy allocation level of each of the production units, determining the energy quota ratio corresponding to each of the production units; The energy quota ratio corresponding to each of the production units is multiplied by the total energy quota of the target factory to obtain the available energy quota corresponding to each of the production units.

7. The smart factory energy efficiency optimization method according to claim 1, characterized in that: After dividing the available energy quota corresponding to each production unit based on the energy allocation level of each production unit and the total energy quota of the target factory, the method further includes: Obtaining cooling tower return water temperature and current energy consumption data of the target factory; Calculating the energy consumption deviation between the current energy consumption data and a preset energy consumption reference value, and calculating the temperature deviation between the return water temperature and a reference temperature; When the temperature deviation is less than the temperature deviation threshold, and the energy consumption deviation is greater than the energy consumption deviation threshold, the fan speed of the target plant is reduced according to a preset speed reduction step.

8. A smart factory energy efficiency optimization system, characterized in that: The system comprises: A data acquisition module, used to acquire energy consumption parameters and energy consumption related variables of multiple production units in a target factory; A data processing module, configured to divide the energy consumption parameters into energy consumption data sequences corresponding to a plurality of time scales for each of the production units according to preset time intervals, and adjust a reference energy consumption threshold based on the energy consumption associated variable to obtain a target energy consumption threshold corresponding to the production unit; An energy allocation level determination module is used to calculate the energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, and determine the energy allocation level of the production unit in combination with the energy consumption trend characteristic value of the production unit and the target energy consumption threshold; The energy quota division module is used to divide the available energy quota corresponding to each of the production units based on the energy allocation level of each of the production units and the total energy quota of the target factory.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the smart factory energy efficiency optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the smart factory energy efficiency optimization method as described in any one of claims 1-7 is executed.

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