An Industrial Park Resource Scheduling Method Based on Edge Computing

Abstract

The present invention discloses a resource scheduling method for industrial parks based on edge computing, which relates to the field of resource scheduling. The resource scheduling method for industrial parks based on edge computing obtains the environmental data, historical environmental production equipment performance data, total regional output value, and resource consumption data of each area pre-divided in the industrial park; preprocesses the historical environmental production equipment performance data based on the preset edge computing nodes in each area to obtain the best environmental production equipment performance indicators for each area; and uses the best environmental production equipment performance indicators. The present invention can significantly improve the working efficiency and production capacity utilization rate of production equipment by real-time monitoring and adjusting the production environment to meet the best production equipment performance indicators. This refined management reduces energy waste, optimizes the use of raw materials and human resources at the same time, reduces production costs, and improves the overall resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the field of resource scheduling, and specifically to a method for resource scheduling in industrial parks based on edge computing. Background Art

[0002] With the development of industrialization and technology, the scale and complexity of modern industrial parks are constantly increasing, leading to increasingly strict requirements for resource scheduling and management. Industrial parks usually include various production facilities, involving complex resource consumption and production data management, such as electricity, human resources, raw materials, etc. Traditional resource management methods rely on centralized computing systems, which usually have problems such as processing delays, data loss, and low efficiency.

[0003] In the prior art, traditional industrial park resource scheduling systems often rely on centralized data processing, resulting in delays in data transmission during the process, and it is difficult to make full use of historical and real-time environmental data to optimize resource allocation, such as the allocation of electricity, raw materials, and human resources, leading to resource waste and increased production costs. At the same time, traditional methods often have difficulty fully considering the impact of environmental factors on the performance of production equipment, thus failing to maximize the working efficiency and output of the equipment. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for resource scheduling in industrial parks based on edge computing, which solves the problems that traditional industrial park resource scheduling systems often rely on centralized data processing, resulting in delays in data transmission during the process, and it is difficult to make full use of historical and real-time environmental data to optimize resource allocation, such as the allocation of electricity, raw materials, and human resources, leading to resource waste and increased production costs. At the same time, traditional methods often have difficulty fully considering the impact of environmental factors on the performance of production equipment, thus failing to maximize the working efficiency and output of the equipment.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for resource scheduling in an industrial park based on edge computing, comprising the following steps: obtaining the environmental data, historical environmental production equipment performance data, total regional output value, and resource consumption data of each region pre-divided in the industrial park; preprocessing the historical environmental production equipment performance data based on the preset edge computing nodes in each region to obtain the optimal environmental production equipment performance indicators for each region; sending the optimal environmental production equipment performance indicators, the total regional output value, and the resource consumption data of each region pre-divided in the industrial park to the central server, and determining whether the total park output value meets the expected total park output value; if the total park output value meets the expected total park output value, then respectively determine whether the resource consumption data of each region meets the set regional resource consumption data, and take the first resource scheduling measure for the region corresponding to the resource consumption data that does not meet the set regional resource consumption data; if the total park output value does not meet the expected total park output value, then analyze the output difference based on the total park output value and the expected total park output value, and take the second resource scheduling measure for each region based on the environmental data, the output difference, and the optimal environmental production equipment performance indicators.

[0006] Further, the environmental data is specifically the actual temperature value and actual humidity value at the current moment in the pre-divided regions of the industrial park. The historical environmental production equipment performance data is specifically the historical temperature working efficiency value, historical temperature production capacity utilization rate, historical temperature power consumption value at each historical temperature value of each production equipment in the pre-divided regions of the industrial park, and the historical humidity working efficiency value, historical humidity production capacity utilization rate, historical humidity power consumption value at each humidity value. The resource consumption data is specifically the human resource consumption value, power consumption value, and raw material consumption value. The optimal environmental production equipment performance indicators include the optimal temperature production equipment performance indicators and the optimal humidity production equipment performance indicators.

[0007] Further, the specific steps for pre-dividing the regions in the industrial park are as follows: obtaining the layout planning map of the production area of the industrial park, including roads, buildings, the number of equipment, and the equipment layout; based on the set number of regional equipment, performing regional division on the layout planning map with the minimum resource scheduling cost, where the minimum resource scheduling cost is specifically the shortest travel distance when scheduling human resources and raw materials and the shortest transmission path when scheduling power; and respectively labeling the divided several regions.

[0008] Further, preprocess the historical environmental production equipment performance data based on the preset edge computing nodes in each region to obtain the optimal environmental production equipment performance indicators for each region, specifically: establish a temperature constraint model and a humidity constraint model for each region based on the preset edge computing nodes in each region respectively, and perform constraint analysis to obtain the optimal temperature production equipment performance indicator and the optimal humidity production equipment performance indicator for each region; for the temperature constraint model, take maximizing the temperature working efficiency, temperature production capacity utilization rate, and minimizing the temperature power consumption value as the temperature constraint conditions; for the humidity constraint model, take maximizing the humidity working efficiency, humidity production capacity utilization rate, and minimizing the humidity power consumption value as the humidity constraint conditions.

[0009] Further, the specific process of obtaining the optimal temperature production equipment performance indicator and the optimal humidity production equipment performance indicator for each region is as follows: read the historical temperature working efficiency value, historical temperature production capacity utilization rate, historical temperature power consumption value at each historical temperature value of each production equipment in each region, and the historical humidity working efficiency value, historical humidity production capacity utilization rate, historical humidity power consumption value at each humidity value, and perform scalar processing respectively; perform weighted analysis based on the results after scalar processing to obtain the temperature production equipment performance indicator at each temperature value and the humidity production equipment performance indicator at each humidity value of each production equipment in each region; perform constraint analysis on the temperature production equipment performance indicator at each temperature value of each production equipment in each region based on the temperature constraint model to obtain the temperature production equipment performance indicator of all equipment in each region at the optimal temperature value, and mark it as the optimal temperature production equipment performance indicator, and record the optimal temperature value at the same time; perform constraint analysis on the humidity production equipment performance indicator at each humidity value of each production equipment in each region based on the humidity constraint model to obtain the humidity production equipment performance indicator of all equipment in each region at the optimal humidity value, and mark it as the optimal humidity production equipment performance indicator, and record the optimal humidity value at the same time.

[0010] Further, the calculation formulas for the temperature production equipment performance indicator and the humidity production equipment performance indicator are as follows: Among them, Wds i is the temperature production equipment performance indicator at the historical i-th temperature value, LwG i is the historical temperature working efficiency value at the historical i-th temperature value after scalar processing, LwC i is the historical temperature production capacity utilization rate at the historical i-th temperature value after scalar processing, LwD i is the historical temperature power consumption value at the historical i-th temperature value after scalar processing, α i1 is the set proportionality coefficient of the historical temperature working efficiency value at the historical i-th temperature value after scalar processing, α i2The proportionality coefficient of the historical temperature production capacity utilization rate at the historical i-th temperature value after being processed by a set scalar, α i3 The proportionality coefficient of the historical temperature power consumption value at the historical i-th temperature value after being processed by a set scalar, α i1 +α i2 +α i3 =1, i = 1, 2, 3, …, N, where N is the total number of selected temperature values, Sds j The performance index of the humidity production equipment at the historical j-th humidity value, LsG j The historical humidity working efficiency value at the historical j-th humidity value after being processed by a scalar, LsC j The historical humidity production capacity utilization rate at the historical j-th humidity value after being processed by a scalar, LsD j The historical humidity power consumption value at the historical j-th humidity value after being processed by a scalar, β j1 The proportionality coefficient of the historical humidity working efficiency value at the historical j-th humidity value after being processed by a set scalar, β j2 The proportionality coefficient of the historical humidity production capacity utilization rate at the historical j-th humidity value after being processed by a set scalar, β j3 The proportionality coefficient of the historical humidity power consumption value at the historical j-th humidity value after being processed by a set scalar, β j1 +β j2 +β j3 =1, j = 1, 2, 3, …, M, where M is the total number of selected humidity values.

[0011] Furthermore, the specific calculation formula for the total output value of the park is: YqZ = QyZ1 + QyZ2 + … + QyZ k ; where YqZ is the total output value of the park, k is the area number pre-divided in the industrial park, k is a positive integer, and QyZ1, QyZ2,......, QyZ k are the total output values of each area corresponding to the areas pre-divided in the industrial park respectively.

[0012] Furthermore, it is determined whether the resource consumption data of each region respectively conforms to the set regional resource consumption data, and for the regions corresponding to the resource consumption data that do not conform to the set regional resource consumption data, the first resource scheduling measure is specifically as follows: read the human resource consumption value, power consumption value, and raw material consumption value of each region and compare and judge them with the set human resource consumption value, power consumption value, and raw material consumption value respectively; for the regions where the human resource consumption value is higher than the set human resource consumption value, first mark them as regions with high abnormal human resource consumption, and conduct a high-consumption difference analysis based on the human resource consumption value and the set human resource consumption value. At the same time, send the high-consumption difference analysis result and the regions with high abnormal human resource consumption to relevant staff for human resource scheduling. For the regions where the human resource consumption value is lower than the set human resource consumption value, first mark them as regions with low abnormal human resource consumption, and conduct a low-consumption difference analysis based on the human resource consumption value and the set human resource consumption value. At the same time, send the low-consumption difference analysis result and the regions with low abnormal human resource consumption to relevant staff for human resource scheduling; for the regions where the power consumption value is higher than the set power consumption value, first mark them as regions with high abnormal power consumption, and conduct a high-consumption difference analysis based on the power consumption value and the set power consumption value. At the same time, send the high-consumption difference analysis result to relevant staff to supplement the power consumption in the regions with high abnormal power consumption. For the regions where the power consumption value is lower than the set power consumption value, first mark them as regions with low abnormal power consumption, and conduct a low-consumption difference analysis based on the power consumption value and the set power consumption value. At the same time, send the low-consumption difference analysis result to relevant staff to store the excess power in the regions with low abnormal power consumption. If there are regions with high abnormal power consumption, retrieve the stored power to supplement the power consumption in the regions with high abnormal power consumption; for the regions where the raw material consumption value is higher than the set raw material consumption value, first mark them as regions with high abnormal raw material consumption, and conduct a high-consumption difference analysis based on the raw material consumption value and the set raw material consumption value. At the same time, send the high-consumption difference analysis result to relevant staff for raw material replenishment scheduling in the regions with high abnormal raw material consumption, and check the abnormal situation. For the regions where the raw material consumption value is lower than the set raw material consumption value, first mark them as regions with low abnormal raw material consumption, and conduct a low-consumption difference analysis based on the raw material consumption value and the set raw material consumption value. At the same time, send the low-consumption difference analysis result to relevant staff to store the excess raw materials in the regions with low abnormal raw material consumption. If there are regions with high abnormal raw material consumption, retrieve the stored raw materials to conduct raw material replenishment scheduling in the regions with high abnormal raw material consumption.

[0013] Further, analyze the production difference based on the total output value of the park and the expected total output value of the park, and take the second resource scheduling measure for each region based on the environmental data, production difference, and performance indicators of the best environmental production equipment. Specifically: Read the actual temperature value and actual humidity value at the current moment of each region; Based on the temperature value and humidity value corresponding to the best temperature production equipment performance indicator and the best humidity production equipment performance indicator of each region respectively; Judge whether the actual temperature value and actual humidity value at the current moment of each region respectively meet the temperature value and humidity value corresponding to the best temperature production equipment performance indicator and the best humidity production equipment performance indicator of the corresponding region, and take the second resource scheduling measure for the region based on the judgment result.

[0014] Further, taking the second resource scheduling measure for the region based on the judgment result is specifically as follows: If the actual temperature value at the current moment in the region does not meet the temperature value corresponding to the best temperature production equipment performance indicator, then judge whether the actual temperature value is lower than the temperature value corresponding to the best temperature production equipment performance indicator. If the actual temperature value is lower than the temperature value corresponding to the best temperature production equipment performance indicator, then perform a heating treatment on the region and send the required power value for the heating treatment to the relevant staff for power replenishment. If the actual temperature value is higher than the temperature value corresponding to the best temperature production equipment performance indicator, then perform a cooling treatment on the region and send the required power value for the cooling treatment to the relevant staff for power replenishment; If the actual humidity value at the current moment in the region does not meet the humidity value corresponding to the best humidity production equipment performance indicator, then judge whether the actual humidity value is lower than the humidity value corresponding to the best humidity production equipment performance indicator. If the actual humidity value is lower than the humidity value corresponding to the best humidity production equipment performance indicator, then perform a humidifying treatment on the region and send the required power value for the humidifying treatment to the relevant staff for power replenishment. If the actual humidity value is higher than the humidity value corresponding to the best humidity production equipment performance indicator, then perform a drying treatment on the region and send the required power value for the drying treatment to the relevant staff for power replenishment.

[0015] The present invention has the following beneficial effects:

[0016] (1) The resource scheduling method for industrial parks based on edge computing can significantly improve the working efficiency and production capacity utilization rate of production equipment by monitoring and adjusting the production environment in real time to meet the best production equipment performance indicators. This refined management reduces energy waste, optimizes the use of raw materials and human resources at the same time, reduces production costs, and improves the overall resource utilization rate.

[0017] (2) The industrial park resource scheduling method based on edge computing enables faster data processing through the application of edge computing, reducing the amount of data transmitted to the central server and the processing time. This near-real-time data processing capability enables the park management to quickly respond to production changes, achieve immediate resource adjustment, and enhance the system's adaptability to market demand changes.

[0018] (3) The industrial park resource scheduling method based on edge computing effectively reduces energy consumption and environmental impact through precise control of production environment conditions and dynamic resource scheduling based on real-time data. Especially in abnormal areas with high or low energy consumption, through precise difference analysis and resource scheduling, it can effectively balance energy supply, reduce energy waste, and support sustainable development goals.

[0019] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flowchart of an industrial park resource scheduling method based on edge computing according to the present invention.

[0021] Figure 2 It is a three-dimensional scatter plot of temperature obtained from the analysis of data examples calculated from the performance indicators of temperature production equipment in an industrial park resource scheduling method based on edge computing according to the present invention.

[0022] Figure 3 It is a three-dimensional scatter plot of humidity obtained from the analysis of data examples calculated from the performance indicators of humidity production equipment in an industrial park resource scheduling method based on edge computing according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The embodiments of the present application solve the problems of the traditional industrial park resource scheduling system that often relies on centralized data processing, resulting in delays in data transmission during the process, and it is difficult to fully utilize historical and real-time environmental data to optimize resource allocation, such as the allocation of electricity, raw materials, and human resources, leading to resource waste and increased production costs. At the same time, the traditional method often fails to fully consider the impact of environmental factors on the performance of production equipment, thus failing to maximize the working efficiency and output of the equipment, through an industrial park resource scheduling method based on edge computing.

[0024] The general idea for the problems in the embodiments of the present application is as follows:

[0025] First, collect the real-time environmental data, historical environmental production equipment performance data, total regional output value, and resource consumption data of each area in the industrial park. These data are preprocessed on the edge computing nodes within the area to improve the speed and efficiency of data processing. Use the edge computing nodes to analyze the historical data, establish and apply the constraint models of temperature and humidity, so as to obtain the optimal environmental production equipment performance indicators for each area. These indicators are then sent to the central server for further overall evaluation and decision-making. According to the information obtained from the central server, evaluate the output and resource consumption of the entire park. If the resource consumption of a certain area does not meet the predetermined data, or the environmental conditions do not match the optimal production equipment performance indicators, corresponding adjustment measures are taken, such as resource reallocation or environmental condition adjustment, to ensure the optimal utilization of production efficiency and resources.

[0026] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: a resource scheduling method for an industrial park based on edge computing, including the following steps: obtaining the environmental data, historical environmental production equipment performance data, total regional output value, and resource consumption data of each area pre-divided in the industrial park; preprocessing the historical environmental production equipment performance data based on the preset edge computing nodes within each area to obtain the optimal environmental production equipment performance indicators for each area. The edge computing node, that is, the edge device, refers to a computing device located at the edge or edge position of the network, used for processing data and executing computing tasks, and having certain computing capabilities and storage resources, capable of performing partial data processing and analysis tasks at the place where the data is generated; sending the optimal environmental production equipment performance indicators, the total regional output value and resource consumption data of each area pre-divided in the industrial park to the central server, and judging whether the total park output value meets the expected total park output value; if the total park output value meets the expected total park output value, then respectively judge whether the resource consumption data of each area meets the set regional resource consumption data, and take the first resource scheduling measure for the area corresponding to the resource consumption data that does not meet the set regional resource consumption data; if the total park output value does not meet the expected total park output value, then analyze the output difference based on the total park output value and the expected total park output value, specifically, the expected total park output value minus the total park output value, and take the second resource scheduling measure for each area based on the environmental data, output difference, and optimal environmental production equipment performance indicators.

[0027] The specific calculation formula for the total park output value is: YqZ = QyZ1 + QyZ2 + … + QyZ k ; where YqZ is the total park output value, k is the area number pre-divided in the industrial park, k is a positive integer, and QyZ1, QyZ2,......, QyZ k are the total regional output values corresponding to each area pre-divided in the industrial park respectively.

[0028] The environmental data specifically refers to the actual temperature value and actual humidity value at the current moment within a pre-divided area of the industrial park. The historical environmental production equipment performance data specifically refers to the historical temperature working efficiency value, historical temperature production capacity utilization rate, historical temperature power consumption value of each production equipment within a pre-divided area of the industrial park at each historical temperature value, as well as the historical humidity working efficiency value, historical humidity production capacity utilization rate, and historical humidity power consumption value at each humidity value. The resource consumption data specifically refers to the human resource consumption value, power consumption value, and raw material consumption value. The optimal environmental production equipment performance indicators include the optimal temperature production equipment performance indicator and the optimal humidity production equipment performance indicator.

[0029] Specifically, the specific steps for pre-dividing the area of the industrial park are as follows: Obtain the layout planning map of the production area of the industrial park, including roads, buildings, the number of equipment, and the equipment layout. Based on the set number of regional equipment, perform regional division on the layout planning map with the minimum resource scheduling cost. The minimum resource scheduling cost specifically refers to the shortest travel distance when scheduling human resources and raw materials and the shortest transmission path when scheduling power, and label each of the divided areas respectively. Specifically, different areas can be divided using dashed lines, colors, or other methods to ensure clear boundaries between areas.

[0030] In this implementation plan, by optimizing the resource scheduling path based on the number of production equipment and layout, for example, selecting the shortest travel distance and transmission path, the scheduling costs of human resources, raw materials, and power can be significantly reduced. The short travel distance means faster material transportation and lower energy consumption, thus directly reducing the operating cost. The reasonable regional division and optimization of the resource scheduling path ensure the efficient operation of the production process. Shortening the transportation path not only reduces production delays but also helps to quickly respond to emergencies on the production line, improving the overall production efficiency. Using dashed lines, colors, or other marking methods to clearly divide different areas can clearly identify the functions and resource allocations of each area. This clear physical and visual division helps managers better monitor and manage each area, ensuring the correct allocation and use of resources, and is also convenient for maintenance and safety management. Through precise regional division, managers can more flexibly adjust resource allocation and scheduling strategies to adapt to changes in production demand. For example, in the case of increased demand, the human and material supplies in a specific area can be quickly increased, or the resource input can be reduced during low demand, thereby optimizing the resource utilization efficiency. Minimizing the resource scheduling cost and optimizing the path not only reduce energy consumption but also reduce the environmental impact. This method supports the sustainable development strategy of the industrial park, promoting environmental protection by reducing waste of energy and raw materials.

[0031] Specifically, preprocess the historical performance data of environmental production equipment based on the preset edge computing nodes in each region to obtain the optimal performance indicators of environmental production equipment for each region, specifically: establish temperature constraint models and humidity constraint models for each region respectively based on the preset edge computing nodes in each region for constraint analysis, and obtain the optimal temperature production equipment performance indicators and the optimal humidity production equipment performance indicators for each region; for the temperature constraint model, maximize the temperature working efficiency, temperature production capacity utilization rate, and minimize the temperature power consumption value as the temperature constraint conditions; for the humidity constraint model, maximize the humidity working efficiency, humidity production capacity utilization rate, and minimize the humidity power consumption value as the humidity constraint conditions.

[0032] In this implementation plan, by setting temperature and humidity constraint models with the goals of maximizing working efficiency and production capacity utilization rate and minimizing power consumption, it can ensure that each production equipment operates under its most suitable environmental conditions. This method not only optimizes energy use, reduces unnecessary power waste, but also improves the overall working efficiency of the equipment, thereby enhancing the output efficiency of the production line. By optimizing the performance indicators of equipment in each region, enterprises can allocate resources more effectively, such as power and maintenance expenditures. The optimized environmental control helps reduce the excessive consumption of equipment caused by environmental mismatch, thus reducing operating costs in the long run. The equipment operating under the optimal temperature and humidity conditions can reduce mechanical wear and failures caused by environmental factors. This precise control not only extends the service life of the equipment, but also can significantly reduce the costs generated by frequent maintenance or equipment replacement. Maintaining the stability of the production environment is crucial for ensuring product quality. By precisely controlling the temperature and humidity, it can be ensured that each batch of products is produced under the same optimal conditions, thereby maintaining the high quality and consistency of the products and reducing production defects caused by environmental fluctuations. Edge computing allows data to be processed immediately at the location where the data is generated. This rapid feedback loop enables production decisions to be made more based on the current data situation, enhancing the response speed and flexibility to emergencies.

[0033] Specifically, the specific process of obtaining the optimal temperature production equipment performance indicators and the optimal humidity production equipment performance indicators for each area is as follows: Read the historical temperature working efficiency values, historical temperature production capacity utilization rates, historical temperature power consumption values at each temperature value of each production equipment in each area, and the historical humidity working efficiency values, historical humidity production capacity utilization rates, historical humidity power consumption values at each humidity value, and perform scalar processing on them respectively; Based on the results after scalar processing, conduct weighted analysis to obtain the temperature production equipment performance indicators at each temperature value and the humidity production equipment performance indicators at each humidity value for each production equipment in each area; Based on the temperature constraint model, conduct constraint analysis on the temperature production equipment performance indicators at each temperature value of each production equipment in each area to obtain the temperature production equipment performance indicators of all equipment in each area at the optimal temperature value, that is, the working efficiency values, production capacity utilization rates, and power consumption values of all equipment are in the optimal state at the same temperature value, and mark them as the optimal temperature production equipment performance indicators, and record the optimal temperature value at the same time; Based on the humidity constraint model, conduct constraint analysis on the humidity production equipment performance indicators at each humidity value of each production equipment in each area to obtain the humidity production equipment performance indicators of all equipment in each area at the optimal humidity value, that is, the working efficiency values, production capacity utilization rates, and power consumption values of all equipment are in the optimal state at the same humidity value, and mark them as the optimal humidity production equipment performance indicators, and record the optimal humidity value at the same time.

[0034] The calculation formulas for the temperature production equipment performance indicators and the humidity production equipment performance indicators are as follows: Among them, Wds i is the temperature production equipment performance indicator at the historical i-th temperature value, LwG i is the historical temperature working efficiency value at the historical i-th temperature value after scalar processing, LwC i is the historical temperature production capacity utilization rate at the historical i-th temperature value after scalar processing, LwD i is the historical temperature power consumption value at the historical i-th temperature value after scalar processing, α i1 is the set proportionality coefficient of the historical temperature working efficiency value at the historical i-th temperature value after scalar processing, α i2 is the set proportionality coefficient of the historical temperature production capacity utilization rate at the historical i-th temperature value after scalar processing, α i3 is the set proportionality coefficient of the historical temperature power consumption value at the historical i-th temperature value after scalar processing, α i1 +α i2 +α i3 =1, i = 1, 2, 3, …, N, where N is the total number of selected temperature values, Sds j is the humidity production equipment performance indicator at the historical j-th humidity value, LsGj LsC is the historical humidity working efficiency value at the j-th historical humidity value after scalar processing j LsD is the historical humidity production capacity utilization rate at the j-th historical humidity value after scalar processing j β is the historical humidity power consumption value at the j-th historical humidity value after scalar processing j1 β is the proportionality coefficient of the historical humidity working efficiency value at the j-th historical humidity value after scalar processing j2 β is the proportionality coefficient of the historical humidity production capacity utilization rate at the j-th historical humidity value after scalar processing j3 β is the proportionality coefficient of the historical humidity power consumption value at the j-th historical humidity value after scalar processing j1 +β j2 +β j3 = 1, j = 1, 2, 3, …, M, where M is the total number of selected humidity values

[0035] Among them, an example of the calculation data of the temperature production equipment performance index at the 1st historical temperature value is as follows in the table

[0036] Table 1 Example of Calculation Data of Temperature Production Equipment Performance Index

[0037] <![CDATA[LwG1]]> <![CDATA[LwC1]]> <![CDATA[LwD1]]> <![CDATA[α 11 > <![CDATA[α 12 > <![CDATA[α 12 > <![CDATA[Wds1]]> 1 0.41 0.39 0.052 0.265 0.644 0.092 2.123 2 0.70 0.63 0.482 0.250 0.169 0.581 1.489 3 0.73 0.023 0.393 0.575 0.366 0.059 0.582 4 0.50 0.18 0.036 0.192 0.611 0.196 5.703 5 0.19 0.25 0.679 0.030 0.675 0.296 0.613

[0038] In Table 1, Wds1 is the temperature production equipment performance index at the 1st historical temperature value, LwG1 is the historical temperature working efficiency value at the 1st historical temperature value, LwC1 is the historical temperature production capacity utilization rate at the 1st historical temperature value, LwD1 is the historical temperature power consumption value at the 1st historical temperature value, and α 11 α is the proportionality coefficient of the historical temperature working efficiency value at the 1st historical temperature value 12 α is the proportionality coefficient of the historical temperature production capacity utilization rate at the 1st historical temperature value 13 is the historical temperature power consumption value at the 1st historical temperature value

[0039] The first group of data: the historical temperature working efficiency value at the 1st historical temperature value: 0.41; the historical temperature production capacity utilization rate at the 1st historical temperature value: 0.39; the historical temperature power consumption value at the 1st historical temperature value: 0.052; the proportionality coefficient of the historical temperature working efficiency value at the 1st historical temperature value: 0.265; the proportionality coefficient of the historical production capacity utilization rate at the 1st historical temperature value: 0.644; the proportionality coefficient of the historical temperature power consumption value at the 1st historical temperature value: 0.092; the temperature production equipment performance index at the 1st historical temperature value: 2.123

[0040] Second set of data: Historical temperature working efficiency value at the 1st historical temperature value: 0.70; Historical temperature production capacity utilization rate at the 1st historical temperature value: 0.63; Historical temperature power consumption value at the 1st historical temperature value: 0.482; Proportion coefficient of the historical temperature working efficiency value at the 1st historical temperature value: 0.250; Proportion coefficient of the historical production capacity utilization rate at the 1st historical temperature value: 0.169; Proportion coefficient of the historical temperature power consumption value at the 1st historical temperature value: 0.581; Temperature production equipment performance index at the 1st historical temperature value: 1.489.

[0041] Third set of data: Historical temperature working efficiency value at the 1st historical temperature value: 0.73; Historical temperature production capacity utilization rate at the 1st historical temperature value: 0.023; Historical temperature power consumption value at the 1st historical temperature value: 0.393; Proportion coefficient of the historical temperature working efficiency value at the 1st historical temperature value: 0.575; Proportion coefficient of the historical production capacity utilization rate at the 1st historical temperature value: 0.366; Proportion coefficient of the historical temperature power consumption value at the 1st historical temperature value: 0.059; Temperature production equipment performance index at the 1st historical temperature value: 0.582.

[0042] Fourth set of data: Historical temperature working efficiency value at the 1st historical temperature value: 0.50; Historical temperature production capacity utilization rate at the 1st historical temperature value: 0.18; Historical temperature power consumption value at the 1st historical temperature value: 0.036; Proportion coefficient of the historical temperature working efficiency value at the 1st historical temperature value: 0.192; Proportion coefficient of the historical production capacity utilization rate at the 1st historical temperature value: 0.611; Proportion coefficient of the historical temperature power consumption value at the 1st historical temperature value: 0.196; Temperature production equipment performance index at the 1st historical temperature value: 5.703.

[0043] Fifth set of data: Historical temperature working efficiency value at the 1st historical temperature value: 0.19; Historical temperature production capacity utilization rate at the 1st historical temperature value: 0.25; Historical temperature power consumption value at the 1st historical temperature value: 0.679; Proportion coefficient of the historical temperature working efficiency value at the 1st historical temperature value: 0.030; Proportion coefficient of the historical production capacity utilization rate at the 1st historical temperature value: 0.675; Proportion coefficient of the historical temperature power consumption value at the 1st historical temperature value: 0.296; Temperature production equipment performance index at the 1st historical temperature value: 0.613, and the three-dimensional scatter plot formed according to Table 1 is as Figure 2 shown.

[0044] Among them, the calculation data examples of the humidity production equipment performance index at the 1st historical humidity value are as follows in the table:

[0045] Table 2 Example of Calculation Data for Performance Indicators of Humidity Production Equipment

[0046] <![CDATA[LsG1]]> <![CDATA[LsC1]]> <![CDATA[LsD1]]> <![CDATA[β 11 > <![CDATA[β 12 > <![CDATA[β 13 > <![CDATA[Sds1]]> 1 0.90 0.31 0.288 0.147 0.067 0.786 2.882 2 0.15 0.25 0.739 0.144 0.396 0.460 0.742 3 0.84 0.91 0.624 0.256 0.544 0.200 1.031 4 0.30 0.97 0.521 0.128 0.680 0.192 1.068 5 0.07 0.32 0.815 0.317 0.185 0.498 0.692

[0047] In Table 1, Sds1 is the performance indicator of the humidity production equipment at the 1st historical humidity value, LsG1 is the historical humidity working efficiency value at the 1st historical humidity value, LsC1 is the historical humidity production capacity utilization rate at the 1st historical humidity value, LsD1 is the historical humidity power consumption value at the 1st historical humidity value, and β 11 is the proportionality coefficient of the historical humidity working efficiency value at the 1st historical humidity value, and β 12 is the proportionality coefficient of the historical humidity production capacity utilization rate at the 1st historical humidity value, and β 13 is the historical humidity power consumption value at the 1st historical humidity value.

[0048] First set of data: Historical humidity working efficiency value at the 1st historical humidity value: 0.90; Historical humidity production capacity utilization rate at the 1st historical humidity value: 0.31; Historical humidity power consumption value at the 1st historical humidity value: 0.288; Proportionality coefficient of the historical humidity working efficiency value at the 1st historical humidity value: 0.147; Proportionality coefficient of the historical production capacity utilization rate at the 1st historical humidity value: 0.067; Proportionality coefficient of the historical humidity power consumption value at the 1st historical humidity value: 0.786; Performance indicator of the humidity production equipment at the 1st historical humidity value: 2.882.

[0049] Second set of data: Historical humidity working efficiency value at the 1st historical humidity value: 0.15; Historical humidity production capacity utilization rate at the 1st historical humidity value: 0.25; Historical humidity power consumption value at the 1st historical humidity value: 0.739; Proportionality coefficient of the historical humidity working efficiency value at the 1st historical humidity value: 0.144; Proportionality coefficient of the historical production capacity utilization rate at the 1st historical humidity value: 0.396; Proportionality coefficient of the historical humidity power consumption value at the 1st historical humidity value: 0.460; Performance indicator of the humidity production equipment at the 1st historical humidity value: 0.742.

[0050] Third set of data: Historical humidity working efficiency value at the 1st historical humidity value: 0.84; Historical humidity production capacity utilization rate at the 1st historical humidity value: 0.91; Historical humidity power consumption value at the 1st historical humidity value: 0.624; Proportionality coefficient of the historical humidity working efficiency value at the 1st historical humidity value: 0.256; Proportionality coefficient of the historical production capacity utilization rate at the 1st historical humidity value: 0.544; Proportionality coefficient of the historical humidity power consumption value at the 1st historical humidity value: 0.200; Performance indicator of the humidity production equipment at the 1st historical humidity value: 1.031.

[0051] The fourth set of data: The historical humidity working efficiency value at the 1st historical humidity value: 0.30; The historical humidity production capacity utilization rate at the 1st historical humidity value: 0.97; The historical humidity power consumption value at the 1st historical humidity value: 0.521; The proportionality coefficient of the historical humidity working efficiency value at the 1st historical humidity value: 0.128; The proportionality coefficient of the historical production capacity utilization rate at the 1st historical humidity value: 0.680; The proportionality coefficient of the historical humidity power consumption value at the 1st historical humidity value: 0.192; The performance index of the humidity production equipment at the 1st historical humidity value: 1.068.

[0052] The fifth set of data: The historical humidity working efficiency value at the 1st historical humidity value: 0.07; The historical humidity production capacity utilization rate at the 1st historical humidity value: 0.32; The historical humidity power consumption value at the 1st historical humidity value: 0.815; The proportionality coefficient of the historical humidity working efficiency value at the 1st historical humidity value: 0.317; The proportionality coefficient of the historical production capacity utilization rate at the 1st historical humidity value: 0.185; The proportionality coefficient of the historical humidity power consumption value at the 1st historical humidity value: 0.498; The performance index of the humidity production equipment at the 1st historical humidity value: 0.692, and the three-dimensional scatter plot formed according to Table 2 is as Figure 3 shown.

[0053] In this implementation plan, by precisely adjusting the production environment to adapt to the optimal working conditions of each device at specific temperatures and humidities, the working efficiency and production capacity utilization rate of the devices can be significantly improved. At the same time, this method also helps to reduce the wear of the devices caused by unsuitable environments, thereby extending the service life of the devices and reducing maintenance costs. By optimizing the temperature and humidity settings of each device to make it operate in the state of the highest energy efficiency, the energy consumption can be significantly reduced. For example, reducing the situations of overheating or overcooling to ensure the optimization of power consumption. This not only helps to reduce production costs but also supports environmental sustainability. Ensuring that the devices in each area operate under the environmental conditions with the best performance helps to improve the quality and consistency of the final products. Precise control of temperature and humidity, especially in the production process sensitive to environmental conditions, is crucial and can effectively avoid product quality problems caused by environmental fluctuations. By systematically collecting and analyzing the data on the impact of historical temperature and humidity on device performance, managers can make more well-founded decisions, such as device purchase, maintenance plans, and production scheduling. This data-based method helps to precisely adjust production strategies, thereby maximizing production efficiency and economic benefits.

[0054] Specifically, it is to determine whether the resource consumption data of each region respectively conforms to the set regional resource consumption data, and the first resource scheduling measure taken for the region corresponding to the resource consumption data that does not conform to the set regional resource consumption data is as follows: read the human resource consumption value, power consumption value, and raw material consumption value of each region and compare and judge them with the set human resource consumption value, power consumption value, and raw material consumption value respectively; for the region where the human resource consumption value is higher than the set human resource consumption value, first mark it as a human resource high-consumption abnormal region, conduct a high-consumption difference analysis based on the human resource consumption value and the set human resource consumption value, and at the same time send the high-consumption difference analysis result and the human resource high-consumption abnormal region to the relevant staff for human resource scheduling. For the region where the human resource consumption value is lower than the set human resource consumption value, first mark it as a human resource low-consumption abnormal region, conduct a low-consumption difference analysis based on the human resource consumption value and the set human resource consumption value, and at the same time send the low-consumption difference analysis result and the human resource low-consumption abnormal region to the relevant staff for human resource scheduling; for the region where the power consumption value is higher than the set power consumption value, first mark it as a power consumption high-consumption abnormal region, conduct a high-consumption difference analysis based on the power consumption value and the set power consumption value, and at the same time send the high-consumption difference analysis result to the relevant staff to supplement the power consumption high-consumption abnormal region with power. For the region where the power consumption value is lower than the set power consumption value, first mark it as a power consumption low-consumption abnormal region, conduct a low-consumption difference analysis based on the power consumption value and the set power consumption value, and at the same time send the low-consumption difference analysis result to the relevant staff to store the excess power in the power consumption low-consumption abnormal region. If there is a power consumption high-consumption abnormal region, retrieve the stored power to supplement the power consumption high-consumption abnormal region with power; for the region where the raw material consumption value is higher than the set raw material consumption value, first mark it as a raw material consumption high-consumption abnormal region, conduct a high-consumption difference analysis based on the raw material consumption value and the set raw material consumption value, and at the same time send the high-consumption difference analysis result to the relevant staff to conduct raw material replenishment scheduling for the raw material consumption high-consumption abnormal region, and at the same time check the abnormal situation. For the region where the raw material consumption value is lower than the set raw material consumption value, first mark it as a raw material consumption low-consumption abnormal region, conduct a low-consumption difference analysis based on the raw material consumption value and the set raw material consumption value, and at the same time send the low-consumption difference analysis result to the relevant staff to store the excess raw materials in the raw material consumption low-consumption abnormal region. If there is a raw material consumption high-consumption abnormal region, retrieve the stored raw materials to conduct raw material replenishment scheduling for the raw material consumption high-consumption abnormal region.

[0055] In this implementation plan, by monitoring and comparing the actual resource consumption, including manpower, electricity, and raw materials, in each area in real time with the preset standards, deviations in resource usage can be detected in a timely manner. This method allows for precise management of resource consumption, ensuring that resources are allocated and used reasonably, preventing resource waste. By identifying abnormal areas where resource consumption is higher or lower than the preset values and making corresponding adjustments, such as replenishing or conserving resources, it helps control operating costs. For example, effective management of electricity and raw materials not only reduces the costs of excessive consumption but also may mitigate the risk of production interruptions caused by resource shortages. By continuously monitoring resource consumption and making timely adjustments, the stability and reliability of resource supply during the production process can be ensured. This strategy reduces production delays or stoppages caused by resource mismatches and guarantees the smooth operation of the production line. Reasonable resource scheduling not only optimizes resource usage but also supports environmental protection goals. For example, by adjusting the consumption of electricity and raw materials, the negative impact of excessive consumption on the environment is reduced, contributing to the green development of the industrial park. The system provides data-driven insights by collecting and analyzing resource consumption data, enabling managers to make more precise strategic adjustments based on actual data. This data-supported decision-making process enhances the ability to respond to market and internal operation changes.

[0056] Specifically, based on the total output value of the park, the output difference is analyzed by comparing it with the expected total output value of the park. And based on the environmental data, the output difference, and the optimal environmental production equipment performance indicators, the second resource scheduling measures are taken for each area. Specifically: read the actual temperature value and actual humidity value at the current moment for each area; based on the temperature values and humidity values corresponding to the optimal temperature production equipment performance indicators and optimal humidity production equipment performance indicators for each area; determine whether the actual temperature value and actual humidity value at the current moment for each area respectively meet the temperature values and humidity values corresponding to the optimal temperature production equipment performance indicators and optimal humidity production equipment performance indicators for the corresponding area, and take the second resource scheduling measures for the area based on the judgment results.

[0057] Based on the judgment result, the specific second resource scheduling measures for the area are as follows: If the actual temperature value at the current moment in the area does not meet the temperature value corresponding to the optimal temperature production equipment performance index, then it is judged whether the actual temperature value is lower than the temperature value corresponding to the optimal temperature production equipment performance index. If the actual temperature value is lower than the temperature value corresponding to the optimal temperature production equipment performance index, then heating treatment is carried out on the area, and the power value required for the heating treatment is sent to the relevant staff for power supplement. If the actual temperature value is higher than the temperature value corresponding to the optimal temperature production equipment performance index, then cooling treatment is carried out on the area, and the power value required for the cooling treatment is sent to the relevant staff for power supplement; If the actual humidity value at the current moment in the area does not meet the humidity value corresponding to the optimal humidity production equipment performance index, then it is judged whether the actual humidity value is lower than the humidity value corresponding to the optimal humidity production equipment performance index. If the actual humidity value is lower than the humidity value corresponding to the optimal humidity production equipment performance index, then humidification treatment is carried out on the area, and the power value required for the humidification treatment is sent to the relevant staff for power supplement. If the actual humidity value is higher than the humidity value corresponding to the optimal humidity production equipment performance index, then drying treatment is carried out on the area, and the power value required for the drying treatment is sent to the relevant staff for power supplement.

[0058] In this implementation plan, by ensuring that the actual temperature and humidity in each area are consistent with the optimal operating conditions of the equipment, the working efficiency and production output of the equipment can be significantly improved. This precise environmental control helps to reduce equipment failure rates, extend the service life of the equipment, and improve the overall production quality. Achieving precise control of temperature and humidity can significantly reduce unnecessary energy consumption. For example, avoiding overheating or overcooling can reduce power consumption, thereby reducing energy costs. Reasonable energy management also helps to reduce carbon emissions, meeting the goals of sustainable development. Maintaining the production environment under optimal conditions helps to ensure that the products produced are of high quality and consistent, which is particularly important for production processes sensitive to temperature and humidity, such as the electronic component manufacturing, food processing and other industries. By monitoring environmental conditions in real time and making rapid adjustments, it is possible to better respond to sudden changes in the production process, such as the impact of climate change on temperature and humidity. This flexibility is the key to improving production scheduling capabilities and market adaptability. By collecting and analyzing environmental data and production data, managers can gain valuable insights, thereby making more data-driven decisions. For example, analyzing production efficiency under different environmental conditions can help optimize production plans and resource allocation.

[0059] In summary, this application has at least the following effects:

[0060] By real-time monitoring and adjusting the production environment to meet the optimal production equipment performance indicators, the working efficiency and capacity utilization of production equipment can be significantly improved. This refined management reduces energy waste, while optimizing the use of raw materials and human resources, reducing production costs and improving overall resource utilization.

[0061] The application of edge computing makes data processing faster, reducing the amount of data transmitted to the central server and the processing time. This near real-time data processing capability enables park management to respond quickly to production changes, achieve instant resource adjustments, and enhance the system's ability to adapt to changes in market demand.

[0062] By precisely controlling production environment conditions and dynamically scheduling resources based on real-time data, this method effectively reduces energy consumption and environmental impact, especially in abnormal areas with high or low energy consumption. Through precise difference analysis and resource scheduling, it can effectively balance energy supply, reduce energy waste, and support sustainable development goals.

[0063] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A resource scheduling method for an industrial park based on edge computing, characterized in that: The following steps are involved: Obtain environmental data, historical environmental production equipment performance data, regional output total value and resource consumption data for each area pre-divided in the industrial park; Preprocess the historical environmental production equipment performance data based on the preset edge computing nodes in each area to obtain the optimal environmental production equipment performance indicators in each area; Send the optimal environment production equipment performance indicators, the regional output value and resource consumption data of each area pre-divided in the industrial park to the central server, and determine whether the park output value meets the expected park output value; If the total output value of the park meets the expected total output value of the park, then determine whether the resource consumption data of each area meets the set regional resource consumption data, and take the first resource scheduling measure for the area corresponding to the resource consumption data that does not meet the set regional resource consumption data; If the total output value of the park does not meet the expected total output value of the park, the output difference is analyzed based on the total output value of the park and the expected total output value of the park, and the second resource scheduling measures are taken for each area based on the environmental data, the output difference, and the performance index of the production equipment in the best environment; The environmental data specifically include the actual temperature value and the actual humidity value at the current moment in the pre-divided area of ​​the industrial park; the historical environmental production equipment performance data specifically include the historical temperature work efficiency value, the historical temperature capacity utilization rate, the historical temperature power consumption value at each temperature value of each production equipment in the pre-divided area of ​​the industrial park, and the historical humidity work efficiency value, the historical humidity capacity utilization rate, and the historical humidity power consumption value at each humidity value; the resource consumption data specifically include the human resource consumption value, the power consumption value, and the raw material consumption value; the optimal environmental production equipment performance index includes the optimal temperature production equipment performance index and the optimal humidity production equipment performance index; The specific steps for pre-dividing the industrial park are as follows: Obtain the layout plan of the production area of ​​the industrial park, including the number of roads, buildings, equipment and equipment layout; Based on the set number of regional equipment, the layout planning map is divided into regions by minimizing the resource scheduling cost to obtain a number of regions, wherein the minimizing resource scheduling cost specifically refers to the shortest distance when scheduling human resources and raw materials, and the shortest transmission path when scheduling electricity; And mark the divided areas separately; Based on the preset edge computing nodes in each area, the historical environmental production equipment performance data is preprocessed to obtain the optimal environmental production equipment performance indicators for each area: Based on the edge computing nodes preset in each area, a temperature constraint model and a humidity constraint model are established for each area to perform constraint analysis, and the optimal temperature production equipment performance index and the optimal humidity production equipment performance index of each area are obtained; For the temperature constraint model, the temperature working efficiency and temperature capacity utilization are maximized, and the temperature power consumption is minimized as the temperature constraint; For the humidity constraint model, the humidity working efficiency and humidity capacity utilization are maximized, and the humidity power consumption is minimized, which is the humidity constraint condition; The specific process of obtaining the optimal temperature production equipment performance index and the optimal humidity production equipment performance index for each area is as follows: Read the historical temperature work efficiency value, historical temperature capacity utilization rate, historical temperature power consumption value at each temperature value of each production equipment in each area, and the historical humidity work efficiency value, historical humidity capacity utilization rate, historical humidity power consumption value at each humidity value, and perform scalar processing respectively; Based on the results after scalar processing, a weighted analysis is performed to obtain the temperature production equipment performance index at each temperature value and the humidity production equipment performance index at each humidity value of each production equipment in each area; Based on the temperature constraint model, a constraint analysis is performed on the temperature production equipment performance index of each production equipment in each area at each temperature value, and the temperature production equipment performance index of all equipment in each area at the optimal temperature value is obtained and marked as the optimal temperature production equipment performance index, and the optimal temperature value is recorded at the same time; Based on the humidity constraint model, a constraint analysis is performed on the humidity production equipment performance index at each humidity value of each production equipment in each area, and the humidity production equipment performance index of all equipment in each area at the optimal humidity value is obtained and marked as the optimal humidity production equipment performance index, and the optimal humidity value is recorded at the same time; The calculation formulas for the temperature production equipment performance index and the humidity production equipment performance index are as follows: ； in, For the history Temperature production equipment performance indicators at temperature values, The history after scalar processing The historical temperature working efficiency value under the temperature value, The history after scalar processing The historical temperature capacity utilization rate under the temperature value, The history after scalar processing The historical temperature power consumption value under the temperature value, The history after the set scalar processing The proportional coefficient of the historical temperature working efficiency value under the temperature value, The history after the set scalar processing The proportional coefficient of the historical temperature capacity utilization rate under the temperature value, The history after the set scalar processing The proportionality coefficient of the historical temperature power consumption value under the temperature value, , , is the total number of selected temperature values, For the history Humidity production equipment performance indicators under humidity values, The history after scalar processing The historical humidity working efficiency value under humidity value, The history after scalar processing The historical humidity capacity utilization rate at humidity values, The history after scalar processing The historical humidity power consumption values ​​at humidity values, The history after the set scalar processing The proportional coefficient of the historical humidity working efficiency value under the humidity value, The history after the set scalar processing The proportional coefficient of the historical humidity capacity utilization rate under humidity values, The history after the set scalar processing The proportionality coefficient of the historical humidity power consumption value under the humidity value is , , The total number of humidity values ​​selected.

2. According to the edge computing-based industrial park resource scheduling method of claim 1, it is characterized in that: The calculation formula for the total output value of the park is as follows: ； in, is the total output value of the park, The pre-divided area numbers for the industrial park, is a positive integer, , ,......, It is the total regional output value corresponding to each area pre-divided in the industrial park.

3. According to the edge computing-based industrial park resource scheduling method of claim 1, it is characterized in that: Determine whether the resource consumption data of each region meets the set regional resource consumption data, and take the first resource scheduling measure for the region corresponding to the resource consumption data that does not meet the set regional resource consumption data: Read the human resource consumption value, power consumption value and raw material consumption value of each area and compare and judge with the set human resource consumption value, power consumption value and raw material consumption value respectively; For areas where the human resource consumption value is higher than the set human resource consumption value, they are first marked as human resource high consumption abnormal areas, and a high consumption difference analysis is performed based on the human resource consumption value and the set human resource consumption value. At the same time, the high consumption difference analysis results and the human resource high consumption abnormal areas are sent to relevant staff for human resource scheduling. For areas where the human resource consumption value is lower than the set human resource consumption value, they are first marked as human resource low consumption abnormal areas, and a low consumption difference analysis is performed based on the human resource consumption value and the set human resource consumption value. At the same time, the low consumption difference analysis results and the human resource low consumption abnormal areas are sent to relevant staff for human resource scheduling; For areas with power consumption values ​​higher than the set power consumption value, they are first marked as abnormal high power consumption areas, and a high consumption difference analysis is performed based on the power consumption value and the set power consumption value, and the high consumption difference analysis results are sent to relevant staff to supplement power for the abnormal high power consumption areas. For areas with power consumption values ​​lower than the set power consumption value, they are first marked as abnormal low power consumption areas, and a low consumption difference analysis is performed based on the power consumption value and the set power consumption value, and the low consumption difference analysis results are sent to relevant staff to store excess power in the abnormal low power consumption areas. If there is an abnormal high power consumption area, the stored power is retrieved to supplement power for the abnormal high power consumption area. For areas where the raw material consumption value is higher than the set raw material consumption value, they are first marked as abnormal high-consumption raw material consumption areas, and a high-consumption difference analysis is performed based on the raw material consumption value and the set raw material consumption value. At the same time, the high-consumption difference analysis results are sent to relevant staff to perform raw material replenishment scheduling for the abnormal high-consumption raw material consumption area, and check for abnormal situations at the same time. For areas where the raw material consumption value is lower than the set raw material consumption value, they are first marked as abnormal low-consumption raw material consumption areas, and a low-consumption difference analysis is performed based on the raw material consumption value and the set raw material consumption value. At the same time, the low-consumption difference analysis results are sent to relevant staff to store excess raw materials in the abnormal low-consumption raw material consumption area. If there is an abnormal high-consumption raw material consumption area, the stored raw materials are retrieved to perform raw material replenishment scheduling for the abnormal high-consumption raw material consumption area.

4. The method for scheduling industrial park resources based on edge computing according to claim 1 is characterized in that: The output difference is analyzed based on the total output value of the park and the expected total output value of the park, and the second resource scheduling measures are taken for each area based on environmental data, output difference, and optimal environment production equipment performance indicators. Specifically: Read the actual temperature and humidity values ​​of each area at the current moment; Based on the temperature value and humidity value corresponding to the optimal temperature production equipment performance index and the optimal humidity production equipment performance index of each area; Determine whether the actual temperature value and actual humidity value of each area at the current moment respectively meet the temperature value and humidity value corresponding to the optimal temperature production equipment performance index and the optimal humidity production equipment performance index of the corresponding area, and take the second resource scheduling measure for the area based on the judgment result.

5. The method for scheduling industrial park resources based on edge computing according to claim 4 is characterized in that: The second resource scheduling measures taken for the region based on the judgment result are as follows: If the actual temperature value in the area at the current moment does not meet the temperature value corresponding to the optimal temperature production equipment performance index, then determine whether the actual temperature value is lower than the temperature value corresponding to the optimal temperature production equipment performance index. If the actual temperature value is lower than the temperature value corresponding to the optimal temperature production equipment performance index, then take a heating treatment for the area, and send the power value required for the heating treatment to the relevant staff for power replenishment. If the actual temperature value is higher than the temperature value corresponding to the optimal temperature production equipment performance index, then take a cooling treatment for the area, and send the power value required for the cooling treatment to the relevant staff for power replenishment. If the actual humidity value in the area at the current moment does not meet the humidity value corresponding to the performance index of the optimal humidity production equipment, determine whether the actual humidity value is lower than the humidity value corresponding to the performance index of the optimal humidity production equipment. If the actual humidity value is lower than the humidity value corresponding to the performance index of the optimal humidity production equipment, humidification treatment is adopted for the area, and the power value required for humidification treatment is sent to relevant staff for power replenishment. If the actual humidity value is higher than the humidity value corresponding to the performance index of the optimal humidity production equipment, drying treatment is adopted for the area, and the power value required for drying treatment is sent to relevant staff for power replenishment.

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