Power consumption information monitoring method and system based on hardware industry power big data

Abstract

The invention discloses an electricity consumption information monitoring method and system based on hardware industry electric power big data, and belongs to the technical field of electric power information monitoring, and the method comprises the steps: S1, calculating segmented energy consumption data and classified energy consumption data based on electricity consumption data; s2, correcting the classified energy consumption data based on the working duration and rated power of the production equipment, and detecting whether the classified energy consumption data is abnormal or not; s3, constructing a fitting function by using the classified energy consumption data, the raw material feeding data and the finished product data, and judging whether the fluctuation information is abnormal or not; s4, judging the relationship between the power consumption data of the hardware feeding equipment and the threshold value to determine whether to adjust the feeding speed; s5, calculating the number of theoretical products based on the power consumption of the heating equipment, and adjusting the feeding speed if the actual number is smaller than a theoretical value; and S6, judging whether the heating equipment is abnormal or not according to the load current of the die forging equipment, and sending warning information. According to the scheme, multi-level power fault analysis is carried out on the hardware industry, so that the coverage range of power fault detection can be remarkably expanded.

Description

Technical Field

[0001] The present invention belongs to the technical field of power information monitoring, and in particular to a method and system for monitoring power consumption information based on power big data of the hardware industry. Background Art

[0002] The hardware industry is closely linked to human production and life, and is undergoing digital transformation. However, current hardware production faces problems such as insufficient intelligence and excessive power consumption. To ensure production, power consumption information needs to be monitored in real time, but the traditional energy consumption threshold setting is unreasonable, which can easily lead to misjudgment. Hardware production adopts a flexible approach, and each production target leads to differences in power consumption information. It is necessary to comprehensively consider multiple factors to monitor and judge power consumption information. In addition, hardware production includes multiple continuous links, such as feeding, heating, die forging, cutting, sandblasting, annealing, and CNC machining. The power consumption information of each link needs to be coordinated, otherwise frequent startup of equipment will lead to abnormal power consumption. The existing technology does not fully consider the matching problem between the various production links. Therefore, the hardware industry needs a comprehensive intelligent monitoring solution to improve production efficiency, reduce power consumption, and achieve sustainable development.

[0003] The existing technology of hardware industry power monitoring is mainly based on traditional power meters and monitoring equipment. These monitoring methods usually focus on the measurement of power parameters such as voltage, current, and power factor. Although these monitoring methods are effective for monitoring basic power parameters, they are usually relatively simple and limited, and have the following problems: Limited fault detection range: Traditional monitoring methods mainly focus on the measurement of power parameters and cannot cover other potential faults and problems in the production process, such as the operating status of equipment, production efficiency, and power waste. Lack of intelligent diagnosis function: Traditional monitoring methods lack intelligent fault diagnosis capabilities and are usually unable to actively identify and report different types of faults, requiring manual intervention and analysis. Local data collection: Traditional methods usually monitor at a few key points, ignoring the global nature of the entire production process, and therefore may miss early signs of potential problems. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of small fault monitoring range of the power monitoring method of the hardware industry in the prior art, and provide a power information monitoring method and system based on the power big data of the hardware industry. The segmented energy consumption data and the classified energy consumption data are calculated by the power consumption data of the hardware enterprise, the classified energy consumption data is corrected based on the accumulated working hours and rated power of the production equipment, and it is determined whether the classified power consumption is abnormal. A fitting function is constructed based on the classified energy consumption data, the raw material feeding data corresponding to the hardware products and the finished product data to judge whether the corresponding fluctuation information is abnormal. If it is abnormal, it is further judged whether the power consumption data of the hardware feeding equipment exceeds the threshold value. If it exceeds, the heating and heating speed is calculated and the feeding speed is adjusted to match the heating and heating speed, and the matching of the power consumption data of the heating and heating equipment with the number of hardware products is judged. If the number of hardware products is less than the theoretical number, the feeding speed is adjusted, otherwise, it is further judged whether the heating and heating equipment is abnormal based on the load current of the die forging equipment corresponding to the hardware products, and a warning message is issued according to the abnormal situation. By adopting a multi-level fault monitoring method, this solution can more comprehensively capture different types of power consumption information failures in the hardware industry and significantly improve the coverage of power fault detection in the hardware industry.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for monitoring electricity consumption information based on power big data of the hardware industry, comprising the following steps: S1, collecting electricity consumption data of hardware enterprises, and calculating segmented energy consumption data and classified energy consumption data based on the electricity consumption data; S2, based on the accumulated working hours and rated power of the production equipment, the classified energy consumption data is corrected to obtain classified corrected energy consumption data, and whether the classified energy consumption data is abnormal is determined based on the classified corrected energy consumption data, if so, S3 is executed, otherwise S1 is executed; S3, based on the classified energy consumption data, and the raw material feeding data and finished product data corresponding to the hardware products, a corresponding fitting function is constructed to determine whether the corresponding fluctuation information is abnormal, if so, execute S4, if not, execute S1; S4, determine whether the power consumption data of the hardware feeding equipment exceeds the threshold value, if so, calculate the heating temperature rise rate, and adjust the feeding speed to match the heating temperature rise rate, if not, execute S5; S5. Calculate the theoretical quantity of hardware products based on the power consumption data of the heating equipment, and determine whether the quantity of hardware products is less than the theoretical quantity. If so, adjust the feeding speed, if not, execute S6; S6. Determine whether the heating equipment is abnormal based on the load current of the die forging equipment corresponding to the hardware product, and send a warning message if it is abnormal.

[0006] In the above technical scheme, by collecting the electricity consumption data of the hardware enterprises, the segmented energy consumption data and the classified energy consumption data are calculated based on the electricity consumption data, and the real-time collection of electricity consumption data is helpful to understand the electricity consumption situation, provide data support for energy management and cost control, and help identify the peaks and troughs of energy consumption, so as to help enterprises optimize energy use; the classified energy consumption data is corrected by the accumulated working hours and rated power of the production equipment, and whether the classified energy consumption data is abnormal is determined based on the corrected classified energy consumption data E, which can more accurately evaluate the actual energy consumption of different production links, help to detect equipment failures or energy consumption anomalies as early as possible, and reduce energy consumption costs; by constructing a corresponding fitting function through the classified energy consumption data, the raw material feeding data corresponding to the hardware products and the finished product data to determine whether the corresponding fluctuation information is abnormal, the system can better understand the correlation between different production links, so as to better Controlling fluctuations in the production process can help identify problems in the production process and reduce the occurrence of quality problems; by judging whether the power consumption data of the hardware feeding equipment exceeds the threshold to determine whether to adjust the feeding speed, it can reduce equipment failures and improve production efficiency. Matching the heating and heating speed with the feeding speed can help ensure the coordination of the production process and improve production efficiency; by comparing the number of hardware products with the theoretical number to determine whether to adjust the feeding speed, problems in the production process can be detected in advance, ensuring that the number of hardware products is consistent with expectations and reducing the defective rate; monitoring the load current of the die forging equipment can help detect abnormal conditions of the heating and heating equipment, and can detect problems in advance and send warning information, reducing failures and maintenance time in production; by adopting a multi-level fault monitoring method, different types of faults can be captured more comprehensively, significantly improving the coverage of power fault detection in the hardware industry.

[0007] Preferably, S1 comprises the following steps: S11. Collect the power consumption data Y of the hardware enterprise based on the preset interval time i , 1≤i≤n, n is the number of electricity consumption data collected from hardware enterprises; S12, calculate the segmented energy consumption data U within each fixed time window i , where U i =Y i+1 -Y i ; S13, obtaining the model switching time point of the hardware product of the production target, and converting the segmented energy consumption data U into i Divided into several categories; S14. For each type of segmented energy consumption data U i Merge to obtain classified energy consumption data corresponding to the model information of hardware products.

[0008] In the above technical scheme, by regularly collecting electricity consumption data, it is helpful to understand the energy consumption of hardware companies in real time, and help companies to manage energy and control costs; by dividing the electricity consumption data into segmented energy consumption data according to time windows, it is helpful to analyze the energy consumption in different time periods, and can help companies identify the peak and trough periods of energy use, thereby optimizing energy utilization; by obtaining the time point of switching hardware product models, it is helpful to classify electricity consumption data by hardware product models, analyze the energy consumption of different models of hardware products, and optimize production processes and resource allocation; by merging and classifying energy consumption data, the energy consumption of each hardware product model can be better determined, which helps companies analyze the energy consumption of hardware product models, formulate corresponding management strategies, and improve energy utilization efficiency.

[0009] Preferably, S2 comprises the following steps: S21. Collect the accumulated working hours and rated power of production equipment in each production link; S22, correcting the classified energy consumption data according to the accumulated working time and the rated power to obtain corrected classified corrected energy consumption data E; Where: E 0 is the classified energy consumption data before correction, t is the number of production equipment, T j is the cumulative working time of the jth production equipment, T j0 is the theoretical working time of the jth production equipment, α j is the power factor, P j0 is the rated power of the jth production equipment, t k is the duration of the kth segment contained in the classified energy consumption data, and n is the total number of segments in the classified energy consumption data; S23. Calculate the theoretical energy consumption threshold of the classification based on the model information and product quantity of the hardware products; Where: E' is the theoretical energy consumption threshold of classification, E' 0 is the unit standard theoretical energy consumption threshold, M 1 is the quantity of raw materials, M 2 is the number of finished products, η is the defect rate, β 1 , β 2 is the weight coefficient; S24. By comparing the classified corrected energy consumption data E with the classified theoretical energy consumption threshold E', determine whether the classified energy consumption data corresponding to the hardware enterprise is abnormal. If so, execute S3; if not, execute S1.

[0010] In the above technical scheme, by collecting the cumulative working hours and rated power of the equipment, the actual operation status of the equipment can be obtained, which is helpful to identify the energy consumption and performance of the equipment and provide basic data for the correction of electricity consumption information; by classifying and correcting the energy consumption data, the actual energy consumption of each production link can be evaluated more accurately. Taking into account the operating time and rated power of the equipment, it is helpful for enterprises to better understand the electricity consumption and identify potential energy waste or anomalies; by calculating the classified theoretical energy consumption threshold, it is helpful to determine the theoretical energy consumption range of each product model, which can provide a benchmark for energy management for hardware companies and help evaluate whether the electricity consumption information meets expectations; by comparing the classified corrected energy consumption data and the theoretical energy consumption threshold, it is possible to detect whether there are abnormal conditions, and discover energy waste, equipment problems or reduced production efficiency in the production process as early as possible, thereby reducing costs and improving efficiency.

[0011] Preferably, S3 comprises the following steps: S31. Obtaining the raw material loading data M corresponding to the hardware product 1 、The number of finished products M 2 and the corresponding defect rate η, based on the number of finished products M 2 and defect rate η, obtain the yield correction quantity S32, normalizing the segmented energy consumption data, raw material loading data, and output correction quantity; S33, generating a corresponding fitting function based on the normalized segmented energy consumption data, raw material feeding data and output correction quantity; S33 includes the following sub-steps: S331, performing fitting based on the normalized segmented energy consumption data to obtain a fitting function U(t); S332, fitting is performed based on the normalized raw material feeding data to obtain a fitting function M 1 (t); S333, based on the normalized output correction quantity Perform fitting to obtain the fitting function M 2 (t); S34. Determine U(t) and M 1 (t), M 2 (t) whether the fluctuation trend matches, and whether the fluctuation information is abnormal according to the matching result. If abnormal, execute S4, otherwise execute S1; The S34 comprises the following sub-steps: S341, select m preset time points t 1 ,t 2 ,…,t m ; S342, determine whether the fluctuation deviation P is greater than the threshold, if it is greater, determine whether U(t) and M 1(t), M 2 (t) The fluctuation trend does not match, and the segmented power consumption fluctuation information is abnormal, and S4 is executed; otherwise, S1 is executed; in Where: 1 , σ 2 is the preset weight coefficient.

[0012] In the above technical solution, normalizing the segmented energy consumption data, raw material loading data and finished product quantity can help convert different types of data into similar scales so that they can be compared and analyzed, which helps to improve data quality and makes it easier to apply to the generation of fitting functions; generating fitting functions helps to establish associations between production data, so that the mutual influence of different factors in the production process can be better understood, and fluctuations in the production process can be better controlled; by judging whether the fluctuation trends match, abnormal situations in the production process can be quickly detected, which helps to discover problems in the production process early and reduce quality problems and production risks.

[0013] Preferably, S4 comprises the following steps: S41, determine whether the power consumption data of the hardware feeding equipment in the segment exceeds the threshold, if so, execute S42, if not, execute S5; S42, collect power data P of hardware feeding equipment 1 , according to the power data P 1 Determine whether the hardware feeding equipment is frequently started, if so, execute S43, if not, send an abnormal power consumption warning message; S43, obtaining power data P of the heating and temperature raising equipment 2 and the corresponding hardware product model size, based on the power data P of the heating equipment 2 The heating temperature rise time is calculated based on the model size, and the heating temperature rise rate v is calculated based on the heating temperature rise time; the expression of the heating temperature rise rate v is: Where: m 0 is the number of hardware products that need to be heated, T 0 is the standard heating time, P 20 is the theoretical power of the heating equipment, S is the model size of the hardware product, S 0 It is the standard reference size; S44, adjusting the feeding speed to match the heating temperature rise speed v.

[0014] In the above technical scheme, by judging whether the electricity consumption data exceeds the threshold, potential energy waste or equipment abnormalities can be discovered early, thereby reducing operating costs; frequent startup of hardware feeding equipment may lead to energy waste and shortened equipment life, so by judging the startup frequency, these problems can be discovered early, thereby reducing energy waste and maintenance costs; by calculating the heating and heating speed, the performance of the heating and heating equipment can be determined, and whether it matches the feeding speed, which helps to improve the consistency and efficiency of the production process; by adjusting the feeding speed to match the heating and heating speed, the coordination of the production process can be ensured, production bottlenecks can be avoided, and product quality can be improved, which helps to improve production efficiency and reduce resource waste.

[0015] Preferably, S5 comprises the following steps: S51, collecting power consumption data of heating and temperature raising equipment; S52, calculating the theoretical number of hardware products heated by the heating and temperature raising device based on the power consumption data; S53. If the quantity of hardware products is less than the theoretical quantity, adjust the feeding speed; S54. If the quantity of hardware products is greater than or equal to the theoretical quantity, execute S6.

[0016] In the above technical scheme, the theoretical number of heating hardware products is calculated through power consumption data, so that the performance of the heating and heating equipment and its relationship with the number of hardware products can be obtained, which helps to ensure the stability of the production process and the quality of hardware products; production adjustments are made through the relationship between the number of hardware products and the theoretical number, thereby improving the stability of the production process, the quality of hardware products and the efficiency of resource utilization.

[0017] In a second aspect, the present invention provides an electricity consumption information monitoring system, including an electricity big data monitoring platform, a production and manufacturing system, an order management system, a hardware feeding device, a first detection module, a heating and temperature raising device, a second detection module, a die forging device, and a third detection module; the electricity big data monitoring platform includes a classified energy consumption monitoring module and a segmented electricity consumption fluctuation monitoring module; the classified energy consumption monitoring module is used to collect electricity consumption data of target production products of hardware enterprises, calculate segmented electricity consumption data according to a preset time window, obtain model switching time points, divide segmented data into multiple categories, and obtain classified electricity consumption data corresponding to product models; the segmented electricity consumption fluctuation monitoring module is used to analyze the production fluctuation information of the hardware industry, including electricity consumption fluctuations, raw material feeding fluctuations, and output fluctuations of each type of product; judge whether the segmented electricity consumption fluctuations match the raw material feeding and output fluctuation trends, so as to gain insight into the correlation and efficiency of the production process; The first detection module, the second detection module, and the third detection module are respectively used to monitor the power consumption information data of hardware feeding equipment, heating equipment, and die forging equipment.

[0018] In the above technical scheme, by monitoring and analyzing electricity consumption data through the classified energy consumption monitoring module and the segmented electricity consumption fluctuation monitoring module in the power big data monitoring platform, the system can help hardware companies gain insights into the correlation and efficiency in the production process, which helps to improve production efficiency; through the use of the monitoring module, the system can help hardware companies implement quality control to ensure that the products produced meet the specified standards; through the power big data monitoring platform, hardware companies can better manage electricity consumption, identify energy-saving opportunities, and reduce energy consumption costs; the system can analyze the production fluctuation information of hardware products, and can help hardware companies monitor the production fluctuations of different products, so as to better respond to changes in market demand; through the first, second and third detection modules, the electricity consumption information data of hardware feeding equipment, heating and temperature raising equipment and die forging equipment can be monitored to ensure the normal operation of these equipment, which helps to reduce downtime and maintenance costs.

[0019] Preferably, the power big data monitoring platform also includes a classified energy consumption correction module; The classified energy consumption correction module is used to obtain the accumulated working hours and rated power of the production equipment in each production link, and correct the classified energy consumption data according to the accumulated working hours and rated power to obtain the corrected classified energy consumption data.

[0020] In the above technical solution, the classified energy consumption correction module can take into account the actual working time and rated power of the production equipment in each production link. The system can calculate and record electricity consumption data more accurately, which helps to eliminate errors in electricity consumption data caused by changes in equipment working time and rated power. By correcting electricity consumption data, enterprises can more easily discover energy-saving opportunities and optimization potential, which can help reduce electricity costs and improve energy efficiency.

[0021] Preferably, the electricity consumption information monitoring system further includes an energy consumption detection module for collecting electricity consumption data of hardware enterprises; The electric power big data monitoring platform also includes a material preparation equipment monitoring module, a heating and temperature raising equipment monitoring module and a die forging equipment monitoring module; The material preparation equipment monitoring module is used to monitor and determine whether the power consumption information of the hardware feeding equipment is abnormal; The heating and heating equipment monitoring module is used to determine whether the power consumption data of the heating and heating equipment matches the number of hardware products heated by the heating and heating equipment; The die forging equipment monitoring module is used to determine whether the die forging pressure of the hardware product is abnormal according to the load current of the die forging equipment; The order management system stores the model information of the hardware products that the hardware enterprise targets to produce.

[0022] In the above technical scheme, the energy consumption data of the hardware enterprise is collected through the energy consumption monitoring module, which can help the enterprise understand the overall energy consumption situation and provide data support for energy management; the power consumption information of the hardware loading equipment is monitored through the material preparation equipment monitoring module to determine whether there is an abnormality, which can reduce equipment failures and downtime in production and improve production efficiency; the heating and heating equipment monitoring module determines whether the power consumption data of the heating and heating equipment matches the number of heated hardware products, which helps to ensure the effectiveness and energy utilization of the heating process; the die forging equipment monitoring module determines whether the die forging pressure of the hardware product is abnormal based on the load current of the die forging equipment, which can ensure the quality of the product and the stability of the production process; the order management system stores the model information of the hardware products that the hardware enterprise targets to produce, which helps to track orders and production needs.

[0023] Preferably, the electricity consumption information monitoring system also includes a defect detection module, which includes a visual monitoring module and a defective product rejection module; the visual monitoring module is used to collect images of hardware products through visual sensors, and determine whether they are defective products after analysis and processing; the defective product rejection module is used to clamp the defective products and screen them out of the production line when the hardware products are determined to be defective products.

[0024] In the above technical solution, the images of hardware products are monitored in real time by the visual monitoring module, and these images are analyzed and processed to determine whether the products have defects, which helps to improve quality control and ensure that the products produced meet the specified standards; once the visual monitoring module detects defective products, the defective rejection module will quickly clamp and screen these defective products out of the production line, which can reduce the defective rate and reduce the production and flow of unqualified products into the market; the defective rejection module is automated and can quickly and accurately remove defective products from the production line, reducing the need for manual intervention and handling of defective products and improving production efficiency; by detecting and removing defective products, hardware companies can better ensure the consistency and reliability of produced products.

[0025] Beneficial effects of the present invention: By obtaining the target hardware product model information and regularly collecting electricity consumption data, enterprises can understand the energy consumption in real time, thereby carrying out effective energy management and cost control. This helps to reduce electricity costs, improve profitability, and is also beneficial to sustainable operations. Dividing electricity consumption data into segmented energy consumption data according to time windows helps to analyze energy consumption in different time periods. This enables enterprises to identify peak and trough periods of energy use and optimize energy utilization in a targeted manner. For example, taking energy-saving measures during peak periods can reduce electricity costs and reduce the burden on the power grid. By obtaining the time point when the hardware product model is switched, electricity consumption data can be classified by hardware product model. This helps to deeply analyze the energy consumption of hardware products of different models, identify potential improvement points, and optimize production processes and resource allocation. Enterprises can formulate more effective management strategies and improve energy utilization efficiency based on the differences in energy consumption of different products. By merging and classifying energy consumption data, enterprises can clearly understand the energy consumption of each hardware product model. This enables enterprises to deeply study the energy consumption of various products, helps to determine the energy management strategy of product models, reduce energy costs, and improve efficiency. By collecting the accumulated working hours and rated power of the equipment, the actual operation data is provided, which provides a basis for the correction of electricity consumption information. By classifying and correcting the energy consumption data, the actual energy consumption of each production link can be more accurately evaluated. This helps enterprises better understand the electricity consumption and identify potential energy waste or abnormalities as early as possible. This also provides data support for further improvements. By calculating the classified theoretical energy consumption threshold, enterprises can determine the theoretical energy consumption range of each product model, providing a benchmark for energy management. By comparing the corrected classified energy consumption data and the theoretical energy consumption threshold, enterprises can detect whether there are abnormal conditions and discover energy waste, equipment problems or reduced production efficiency in the production process as early as possible. This helps to reduce costs and improve efficiency. By normalizing the segmented energy consumption data, raw material feeding data and finished product quantity, the data is converted to a similar scale, which improves the data quality and makes it easier to apply to the generation of fitting functions. Generating fitting functions helps to establish associations between production data, so that the mutual influence between different factors in the production process can be better understood, and the fluctuations in the production process can be better controlled. By judging whether the fluctuation trends match, abnormal conditions in the production process can be quickly detected, which helps to discover problems in the production process as early as possible and reduce quality problems and production risks. By judging whether the power consumption exceeds the threshold, potential energy waste or equipment abnormalities can be discovered early, thereby reducing operating costs. Frequent startup of hardware feeding equipment may lead to energy waste and shortened equipment life, so by judging the startup frequency, these problems can be discovered early, thereby reducing energy waste and maintenance costs.Through the classified energy consumption monitoring module and segmented electricity fluctuation monitoring module in the power big data monitoring platform, the system can help hardware companies gain insight into the correlation and efficiency in the production process, which helps to improve production efficiency. Through the use of the detection module, the system can help hardware companies implement quality control to ensure that the products produced meet the specified standards. Through the power big data monitoring platform, hardware companies can better manage electricity consumption, identify energy-saving opportunities, and reduce energy consumption costs. The system can analyze the production fluctuation information of hardware products, and can help hardware companies monitor the production fluctuations of different products, so as to better respond to changes in market demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0027] Figure 1 A flow chart of a method for monitoring electricity consumption information based on hardware industry power big data provided in Example 1 of the present invention; Figure 2 A schematic diagram of the structure of an electricity consumption information monitoring system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. A process can be terminated when its operations are completed, but can also have additional steps not included in the accompanying drawings; a process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0030] Embodiment 1: like Figure 1 As shown, the power consumption information monitoring method based on hardware industry power big data provided in Example 1 of the present invention includes the following steps: S1. Collect electricity consumption data of hardware enterprises, and calculate segmented energy consumption data and classified energy consumption data based on the electricity consumption data.

[0031] Specifically, S1 includes the following steps: S11. Collect the power consumption data Y of hardware enterprises based on the preset interval time i , 1≤i≤n, n is the number of electricity consumption data collected from hardware enterprises; S12, calculate the segmented energy consumption data U within each fixed time window i , where U i =Y i+1 -Y i ; S13, obtaining the model switching time point of the hardware product of the production target, and converting the segmented energy consumption data U according to the model switching time point i Divided into several categories; S14. For each type of segmented energy consumption data U i Merge to obtain classified energy consumption data corresponding to the model information of hardware products.

[0032] Specifically, the model information of hardware products in the production process of the hardware enterprise is recorded, and the power consumption data is collected every hour. In the hourly time window, the power consumption data is segmented and processed to obtain the segmented energy consumption data of each hourly time window. The time point of each model switching of the hardware products corresponding to the production target of the hardware enterprise is recorded, and the segmented energy consumption data is divided into different model categories according to the obtained time points. The segmented energy consumption data of the same model are merged to obtain the classified energy consumption data of each model.

[0033] In this embodiment, by obtaining the target hardware product model information, the production data of a specific hardware product model can be tracked and analyzed. Regularly collecting electricity consumption data helps to understand the energy consumption of hardware companies in real time, helping companies to manage energy and control costs; by dividing the electricity consumption data into segmented energy consumption data according to time windows, it helps to analyze the energy consumption in different time periods, and can help companies identify peak and trough periods of energy use, thereby optimizing energy utilization; by obtaining the time point when the hardware product model is switched, it helps to classify the electricity consumption data by hardware product model, and can analyze the energy consumption of different models of hardware products, optimize production processes and resource allocation; by merging and classifying energy consumption data, it helps to clearly understand the energy consumption of each hardware product model and better determine it, which helps companies to analyze the energy consumption of hardware product models, formulate corresponding management strategies, and improve energy utilization efficiency.

[0034] S2. Based on the accumulated working hours and rated power of the production equipment, the classified energy consumption data is corrected to obtain the classified corrected energy consumption data. Based on the classified corrected energy consumption data, it is determined whether the classified energy consumption data is abnormal. If so, S3 is executed; otherwise, S1 is executed.

[0035] Specifically, S2 includes the following steps: S21. Collect the accumulated working hours and rated power of production equipment in each production link; S22, correcting the classified energy consumption data according to the accumulated working hours and the rated power to obtain corrected classified corrected energy consumption data E; Where: E 0 is the classified energy consumption data before correction, t is the number of production equipment, T j is the cumulative working time of the jth production equipment, T j0 is the theoretical working time of the jth production equipment, α j is the power factor, P j0 is the rated power of the jth production equipment, t k is the duration of the kth segment contained in the classified energy consumption data, and n is the total number of segments in the classified energy consumption data; S23. Calculate the theoretical energy consumption threshold of the classification based on the model information and product quantity of the hardware products; Where: E' is the theoretical energy consumption threshold of classification, E' 0 is the unit standard theoretical energy consumption threshold, M 1 is the quantity of raw materials, M 2 is the number of finished products, η is the defect rate, β 1 , β 2 is the weight coefficient; S24. By comparing the classified corrected energy consumption data E with the classified theoretical energy consumption threshold E', determine whether the classified energy consumption data corresponding to the hardware enterprise is abnormal. If so, execute S3; if not, execute S1.

[0036] Specifically, the cumulative working hours and rated power of production equipment in each production link are collected regularly to understand the operation status of the equipment. According to the collected cumulative working hours and rated power, the classified energy consumption data is corrected using the formula to obtain the classified corrected energy consumption data E. Based on the model information and actual product quantity of hardware products, the classified theoretical energy consumption threshold of each model is calculated to determine the expected energy consumption range of each product model. By comparing the classified corrected energy consumption data and the theoretical energy consumption threshold, it is determined whether there is any abnormality in the electricity consumption information. If the electricity consumption information exceeds the theoretical range, the company can be alerted to conduct further investigation and optimization.

[0037] In this embodiment, by collecting the cumulative working hours and rated power of the equipment, the actual operating conditions of the equipment can be obtained, which helps to identify the energy consumption and performance of the equipment and provide basic data for the correction of electricity usage information; by classifying and correcting the energy consumption data, the actual energy consumption of each production link can be evaluated more accurately. Taking into account the operating time and rated power of the equipment, it helps companies to better understand the electricity usage and identify potential energy waste or anomalies; by calculating the classified theoretical energy consumption threshold, it helps to determine the theoretical energy consumption range of each product model, which can provide a benchmark for energy management for hardware companies and help evaluate whether the electricity usage information meets expectations; by comparing the corrected classified energy consumption data and the theoretical energy consumption threshold, it can be detected whether there are abnormal conditions, and energy waste, equipment problems or reduced production efficiency in the production process can be discovered early, thereby reducing costs and improving efficiency.

[0038] S3. Based on the classified energy consumption data, and the raw material feeding data and finished product data corresponding to the hardware products, a corresponding fitting function is constructed to determine whether the corresponding fluctuation information is abnormal. If so, execute S4; if not, execute S1.

[0039] Specifically, S3 includes the following steps: S31. Obtaining the raw material loading data M corresponding to the hardware product 1 、The number of finished products M 2 and the corresponding defect rate η, based on the number of finished products M 2 and defect rate η, obtain the yield correction quantity S32, normalizing the segmented energy consumption data, raw material loading data, and output correction quantity; S33, generating a corresponding fitting function based on the normalized segmented energy consumption data, raw material feeding data and output correction quantity; S33 includes the following sub-steps: S331, performing fitting based on the normalized segmented energy consumption data to obtain a fitting function U(t); S332, fitting is performed based on the normalized raw material feeding data to obtain a fitting function M 1 (t); S333, based on the normalized output correction quantity Perform fitting to obtain the fitting function M 2 (t); S34. Determine U(t) and M 1 (t), M 2 (t) whether the fluctuation trend matches, and whether the fluctuation information is abnormal according to the matching result. If abnormal, execute S4, otherwise execute S1; S34 includes the following sub-steps: S341, select m preset time points t 1 ,t 2 ,…,t m ; S342, determine whether the fluctuation deviation P is greater than the threshold, if it is greater, determine whether U(t) and M 1 (t), M 2 (t) The fluctuation trend does not match, and the segmented power consumption fluctuation information is abnormal, and S4 is executed; otherwise, S1 is executed; in Where: 1 , σ 2 is the preset weight coefficient.

[0040] Specifically, obtain the segmented energy consumption data corresponding to each category of hardware products calculated through the electricity consumption information of the hardware enterprise, obtain the raw material feeding data of each segment corresponding to each category of hardware products, and obtain the finished product quantity M of each segment corresponding to each category of hardware products. 2 and the defect rate η, through the number of finished products M 2 The yield correction quantity is calculated by using the defect rate η Correction quantity for raw material loading data and output The segmented energy consumption data obtained by the previous calculation is normalized to eliminate the scale differences between them so that they can be compared on the same scale. The normalized raw material feeding data is fitted to obtain the fitting function M of the normalized raw material feeding data relative to time. 1 (t); Fit the normalized yield correction quantity to obtain the fitting function M of the normalized yield correction quantity relative to time 2 (t); Fit the normalized data to obtain the fitting function U(t) of the normalized segmented energy consumption data relative to time.

[0041] Specifically, m preset time points are set, for example, m is 5, then the time points are t 1 ,t 2 ,t 3 ,t 4 ,t5 , get the corresponding preset weight σ 1 , σ 2 , calculate the fitting function U(t), M 1 (t) and M 2 (t) The corresponding fluctuation deviation P, the value of P is: Get the corresponding fluctuation deviation threshold P 0 , the calculated fluctuation deviation P and the fluctuation deviation threshold P 0 Perform a size comparison. If the fluctuation deviation P is greater than the fluctuation deviation threshold P 0 , then U(t) and M 1 (t), M 2 The fluctuation trend of (t) does not match, indicating that the power fluctuation information is abnormal, and S4 is executed to further determine whether the power consumption information of the hardware feeding equipment matches normally. 0 , then return to execute S1 to reacquire data for detection.

[0042] In this embodiment, normalizing the segmented energy consumption data, raw material loading data and finished product quantity can help convert different types of data into similar scales so that they can be compared and analyzed, which helps to improve data quality and makes it easier to apply to the generation of fitting functions; generating fitting functions helps to establish associations between production data, so that the mutual influence between different factors in the production process can be better understood, and fluctuations in the production process can be better controlled; by judging whether the fluctuation trends match, abnormal situations in the production process can be quickly detected, which helps to discover problems in the production process early and reduce quality problems and production risks.

[0043] S4. Determine whether the power consumption data of the hardware feeding equipment exceeds the threshold. If so, calculate the heating temperature rise rate and adjust the feeding speed to match the heating temperature rise rate. If not, execute S5.

[0044] Specifically, S4 includes the following steps: S41, determine whether the power consumption data of the hardware feeding equipment in the segment exceeds the threshold, if so, execute S42, if not, execute S5; S42, collect power data P of hardware feeding equipment 1 , according to the power data P 1 Determine whether the hardware feeding equipment is frequently started, if so, execute S43, if not, send an abnormal power consumption warning message; S43, obtaining power data P of the heating and temperature raising equipment 2 And the corresponding hardware product model size, based on the power data P of the heating equipment 2Calculate the heating time based on the model size, and calculate the heating speed v based on the heating time; The expression of heating temperature rise rate v is: Where: m 0 is the number of hardware products that need to be heated, T 0 is the standard heating time, P 20 is the theoretical power of the heating equipment, S is the model size of the hardware product, S 0 It is the standard reference size; S44, adjusting the feeding speed to match the heating temperature rise speed v.

[0045] Specifically, the corresponding power consumption threshold is set according to the fixed parameter information of the hardware equipment, and the relationship between the power consumption data and the power consumption threshold is determined. If the power consumption data does not exceed the power consumption threshold, S5 is directly executed to determine the matching between the power consumption data of the heating equipment and the number of hardware products. If the power consumption data exceeds the power consumption threshold, the power data P of the hardware feeding equipment is first collected. 1 , according to the power data P 1 Determine whether the corresponding hardware feeding equipment is frequently started. During the process of starting or shutting down the hardware feeding equipment, the corresponding power data P 1 There will be changes, specifically the power data P when the hardware feeding equipment is started 1 Increase, power data P when hardware feeding equipment is closed 1 In this embodiment, according to the change of power data P 1 , set the start mark event and stop mark event of the hardware feeding equipment, and set the time interval threshold T between the two start mark events based on the normal operation time of the hardware feeding equipment m , real-time detection of the interval time T between two adjacent start-up events of hardware feeding equipment n , if the interval time T n Less than the time interval threshold T m , it is determined that the hardware equipment is frequently started; if the interval time T n Not less than the time interval threshold T m , determine that the hardware equipment is started normally, and directly send the corresponding abnormal power consumption warning information.

[0046] In this embodiment, by judging whether the electricity consumption data exceeds the threshold, potential energy waste or equipment abnormalities can be discovered early, thereby reducing operating costs; frequent startup of hardware feeding equipment may lead to energy waste and shortened equipment life, so by judging the startup frequency, these problems can be discovered early, thereby reducing energy waste and maintenance costs; by calculating the heating speed and the heating temperature rise speed, the performance of the heating and heating equipment can be determined, and whether it matches the feeding speed, which helps to improve the consistency and efficiency of the production process; by adjusting the feeding speed to match the heating temperature rise speed, the coordination of the production process can be ensured, production bottlenecks can be avoided, and product quality can be improved, which helps to improve production efficiency and reduce resource waste.

[0047] S5. Calculate the theoretical quantity of hardware products based on the power consumption data of the heating equipment, and determine whether the quantity of hardware products is less than the theoretical quantity. If so, adjust the feeding speed. If not, execute S6.

[0048] Specifically, S5 includes the following steps: S51, collecting power consumption data of heating and temperature raising equipment; S52. Calculate the theoretical number of hardware products heated by the heating and temperature raising equipment based on the power consumption data; S53. If the quantity of hardware products is less than the theoretical quantity, adjust the feeding speed; S54. If the quantity of hardware products is greater than or equal to the theoretical quantity, execute S6.

[0049] In this embodiment, by calculating the theoretical number of heating hardware products through power consumption data, the performance of the heating and temperature raising equipment and its relationship with the number of hardware products can be obtained, which helps to ensure the stability of the production process and the quality of hardware products; production adjustments are made through the relationship between the number of hardware products and the theoretical number, thereby improving the stability of the production process, the quality of hardware products and the efficiency of resource utilization.

[0050] S6. Determine whether the heating equipment is abnormal based on the load current of the die forging equipment corresponding to the hardware product, and send a warning message if it is abnormal.

[0051] Beneficial effects of this embodiment: By obtaining the target hardware product model information and regularly collecting power consumption data, enterprises can achieve real-time energy consumption monitoring, which helps effective energy management and cost control. This helps to reduce electricity costs, improve profitability, and is also beneficial to sustainable operations. The collection and analysis of segmented energy consumption data, as well as the division of time windows, enable enterprises to more accurately identify peak and trough periods of energy use. This provides enterprises with opportunities to optimize energy utilization, especially by taking energy-saving measures during peak hours, which can reduce energy costs, reduce the burden on the power grid, and improve electricity efficiency. By obtaining the time point of hardware product model switching and classifying the power consumption data, enterprises can deeply analyze the energy consumption of hardware products of different models. This provides valuable information for optimizing production processes and resource allocation. By merging and classifying energy consumption data, enterprises can clearly understand the energy consumption of each hardware product model, thereby formulating corresponding management strategies and improving energy utilization efficiency. The accumulated working hours and rated power of the collected equipment provide basic data for the correction of power consumption information, which helps to identify the energy consumption and performance of the equipment. By classifying and correcting energy consumption data, enterprises can more accurately evaluate the actual energy consumption of each production link, identify potential energy waste or abnormal situations, and provide support for improvement. By calculating the classified theoretical energy consumption threshold, enterprises can determine the theoretical energy consumption range of each product model and provide a benchmark for energy management. By comparing the corrected classified energy consumption data and the theoretical energy consumption threshold, enterprises can detect whether there are abnormal situations, and find energy waste, equipment problems or reduced production efficiency in the production process as early as possible, thereby reducing costs and improving efficiency. By normalizing the segmented energy consumption data, raw material feeding data and the number of finished products, the data becomes a similar scale, improving data quality and making it easier to use for the generation of fitting functions. Generating fitting functions helps to establish associations between production data, better understand the mutual influence of different factors in the production process, and better control the fluctuations of the production process. By judging whether the fluctuation trends match, abnormal situations in the production process can be quickly detected, problems in the production process can be found early, and quality problems and production risks can be reduced. Judging whether the power consumption exceeds the threshold helps to find potential energy waste or equipment abnormalities early and reduce operating costs. Frequent startup of hardware loading equipment may lead to energy waste and shorten the life of the equipment. By judging the startup frequency, these problems can be discovered early, reducing energy waste and maintenance costs.

[0052] Embodiment 2: Corresponding to the power consumption information monitoring method in Example 1, a power consumption information monitoring system provided in Example 2 of the present invention has a structural schematic diagram as shown in FIG. Figure 2As shown, it includes a power big data monitoring platform 1, a production and manufacturing system 2, an order management system 3, a hardware feeding device 4, a first detection module 5, a heating and temperature raising device 6, a second detection module 7, a die forging device 8, and a third detection module 9; the power big data monitoring platform includes a classified energy consumption monitoring module and a segmented power consumption fluctuation monitoring module; specifically, the classified energy consumption monitoring module is used to collect the power consumption data of the target production products of the hardware enterprise, calculate the segmented power consumption data according to the preset time window, obtain the model switching time point, divide the segmented data into multiple categories, and obtain the classified power consumption data corresponding to the product model; the segmented power consumption fluctuation monitoring module is used to analyze the production fluctuation information of the hardware industry, including the power consumption fluctuation of each type of product, the raw material feeding fluctuation, and the output fluctuation; judge whether the segmented power consumption fluctuation matches the raw material feeding and output fluctuation trend, so as to gain insight into the correlation and efficiency of the production process; The first detection module, the second detection module and the third detection module are respectively used to monitor the power consumption information data of hardware feeding equipment, heating equipment and die forging equipment.

[0053] In this embodiment, by monitoring and analyzing electricity consumption data through the classified energy consumption monitoring module and the segmented electricity consumption fluctuation monitoring module in the electric power big data monitoring platform, the system can help hardware companies gain insight into the correlation and efficiency in the production process, which helps to improve production efficiency; through the use of the detection module, the system can help hardware companies implement quality control to ensure that the products produced meet the specified standards; through the electric power big data monitoring platform, hardware companies can better manage electricity consumption, identify energy-saving opportunities, and reduce energy consumption costs; the system can analyze the production fluctuation information of hardware products, and can help hardware companies monitor the production fluctuations of different products, so as to better respond to changes in market demand; through the first, second and third detection modules, the electricity consumption information data of hardware feeding equipment, heating equipment and die forging equipment can be monitored to ensure the normal operation of these equipment, which helps to reduce downtime and maintenance costs.

[0054] Specifically, the electric power big data monitoring platform also includes a classified energy consumption correction module; the classified energy consumption correction module is used to obtain the cumulative working hours and rated power of the production equipment in each production link collected, and correct the classified energy consumption data according to the cumulative working hours and rated power to obtain the corrected classified energy consumption data.

[0055] In this embodiment, the classified energy consumption correction module can take into account the actual working time and rated power of the production equipment in each production link. The system can calculate and record electricity consumption data more accurately, which helps to eliminate errors in electricity consumption data caused by changes in equipment working time and rated power; by correcting electricity consumption data, enterprises can more easily discover energy-saving opportunities and optimization potential, which can help reduce electricity costs and improve energy efficiency.

[0056] Specifically, the electricity consumption information monitoring system also includes an energy consumption detection module 10, which is used to collect electricity consumption data of hardware enterprises; the power big data monitoring platform also includes a material preparation equipment monitoring module, a heating and heating equipment monitoring module and a die forging equipment monitoring module. The material preparation equipment monitoring module is used to monitor and determine whether the electricity consumption information of the hardware feeding equipment is abnormal, the heating and heating equipment monitoring module is used to determine whether the power consumption data of the heating and heating equipment matches the number of hardware products heated by the heating and heating equipment, and the die forging equipment monitoring module is used to determine whether the die forging pressure of the hardware products is abnormal according to the load current of the die forging equipment; the order management system stores the model information of the hardware products that the hardware enterprises target to produce.

[0057] In this embodiment, the energy consumption data of the hardware enterprise is collected through the energy consumption monitoring module, which can help the enterprise understand the overall energy consumption situation and provide data support for energy management; the power consumption information of the hardware loading equipment is monitored through the material preparation equipment monitoring module to determine whether there is an abnormality, which can reduce equipment failures and downtime in production and improve production efficiency; the heating and heating equipment monitoring module is used to determine whether the power consumption data of the heating and heating equipment matches the number of heated hardware products, which helps to ensure the effectiveness and energy utilization of the heating process; the die forging equipment monitoring module is used to determine whether the die forging pressure of the hardware products is abnormal based on the load current of the die forging equipment, which can ensure the quality of the product and the stability of the production process; the order management system is used to store the model information of the hardware products that the hardware enterprise targets to produce, which helps to track orders and production needs.

[0058] Specifically, the electricity consumption information monitoring system also includes a defect detection module, which includes a visual monitoring module and a defective product rejection module; the visual monitoring module is used to collect images of hardware products through visual sensors, and determine whether they are defective products after analysis and processing; the defective product rejection module is used to clamp the defective products and screen them out of the production line when the hardware products are determined to be defective products.

[0059] In this embodiment, the images of hardware products are monitored in real time by the visual monitoring module, and these images are analyzed and processed to determine whether the products have defects, which helps to improve quality control and ensure that the products produced meet the specified standards; once the visual monitoring module detects defective products, the defective rejection module will quickly clamp and screen these defective products out of the production line, which can reduce the defective rate and reduce the production and flow of unqualified products into the market; the defective rejection module is automated and can quickly and accurately remove defective products from the production line, reducing the need for manual intervention and handling of defective products and improving production efficiency; by detecting and removing defective products, hardware companies can better ensure the consistency and reliability of produced products.

[0060] The beneficial effects of this embodiment are as follows: through the electric power big data monitoring platform, classified energy consumption monitoring and segmented electricity fluctuation monitoring are carried out to improve production efficiency, help implement quality control, identify energy-saving opportunities, reduce energy consumption costs, and monitor production fluctuations to adapt to market demand. The classified energy consumption correction module eliminates data errors, discovers energy-saving potential, reduces costs, and improves energy efficiency. The monitoring modules for material preparation equipment, heating and temperature raising equipment, and die forging equipment ensure the normal operation of equipment, reduce downtime and maintenance costs. The order management system tracks orders and production requirements, the visual monitoring module improves quality control and ensures that products meet standards, and the defective rejection module reduces the defective rate and improves production efficiency. These benefits together improve the competitiveness and sustainability of hardware companies.

[0061] The above specific implementation is a preferred implementation of the electricity consumption information monitoring method and system based on the hardware industry power big data of the present invention, and it is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. The power consumption information monitoring method based on the hardware industry power big data is characterized by: The steps include: S1. Collecting electricity consumption data of hardware enterprises, and calculating segmented energy consumption data and classified energy consumption data based on the electricity consumption data; S2, based on the accumulated working hours and rated power of the production equipment, the classified energy consumption data is corrected to obtain classified corrected energy consumption data, and whether the classified energy consumption data is abnormal is determined based on the classified corrected energy consumption data, if so, S3 is executed, otherwise S1 is executed; S3, based on the classified energy consumption data, and the raw material feeding data and finished product data corresponding to the hardware products, a corresponding fitting function is constructed to determine whether the corresponding fluctuation information is abnormal, if so, execute S4, if not, execute S1; S4, determine whether the power consumption data of the hardware feeding equipment exceeds the threshold value, if so, calculate the heating temperature rise rate, and adjust the feeding speed to match the heating temperature rise rate, if not, execute S5; S5. Calculate the theoretical quantity of hardware products based on the power consumption data of the heating equipment, and determine whether the quantity of hardware products is less than the theoretical quantity. If so, adjust the feeding speed, if not, execute S6; S6. Determine whether the heating equipment is abnormal based on the load current of the die forging equipment corresponding to the hardware product, and send a warning message if it is abnormal.

2. The method for monitoring electricity consumption information based on hardware industry power big data according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Collect the power consumption data Y of the hardware enterprise based on the preset interval time i , 1≤i≤n, n is the number of electricity consumption data collected from hardware enterprises; S12, calculate the segmented energy consumption data U within each fixed time window i , where U i =Y i+1 -Y i ; S13, obtaining the model switching time point of the hardware product of the production target, and converting the segmented energy consumption data U into i Divided into several categories; S14. For each type of segmented energy consumption data U i Merge to obtain classified energy consumption data corresponding to the model information of hardware products.

3. The method for monitoring electricity consumption information based on power big data of the hardware industry according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Collect the accumulated working hours and rated power of production equipment in each production link; S22, correcting the classified energy consumption data according to the accumulated working time and the rated power to obtain corrected classified corrected energy consumption data E; Where: E0 is the classified energy consumption data before correction, t is the number of production equipment, T j is the cumulative working time of the jth production equipment, T j0 is the theoretical working time of the jth production equipment, α j is the power factor, P j0 is the rated power of the jth production equipment, t k is the duration of the kth segment contained in the classified energy consumption data, and n is the total number of segments in the classified energy consumption data; S23. Calculate the theoretical energy consumption threshold of the classification based on the model information and product quantity of the hardware products; Where: E' is the classification theoretical energy consumption threshold, E'0 is the unit standard theoretical energy consumption threshold, M1 is the number of raw materials, M2 is the number of finished products, η is the defect rate, β1 and β2 are weight coefficients; S24. By comparing the classified corrected energy consumption data E with the classified theoretical energy consumption threshold E', determine whether the classified energy consumption data corresponding to the hardware enterprise is abnormal. If so, execute S3; if not, execute S1.

4. The method for monitoring electricity consumption information based on power big data of the hardware industry according to claim 1 is characterized in that: The S3 comprises the following steps: S31. Obtain the raw material feeding data M1, the finished product quantity M2 and the corresponding defect rate η corresponding to the hardware product, and obtain the output correction quantity based on the finished product quantity M2 and the defect rate η. S32, normalizing the segmented energy consumption data, raw material loading data, and output correction quantity; S33, generating a corresponding fitting function based on the normalized segmented energy consumption data, raw material feeding data and output correction quantity; S33 includes the following sub-steps: S331, performing fitting based on the normalized segmented energy consumption data to obtain a fitting function U(t); S332, performing fitting based on the normalized raw material feeding data to obtain a fitting function M1(t); S333, based on the normalized output correction quantity Perform fitting to obtain the fitting function M2(t); S34, determine whether the fluctuation trends of U(t) match those of M1(t) and M2(t), and determine whether the fluctuation information is abnormal based on the matching result. If abnormal, execute S4, otherwise execute S1; The S34 comprises the following sub-steps: S341, select m preset time points t1, t2, ..., t m ; S342, determine whether the fluctuation deviation P is greater than the threshold value. If so, determine that the fluctuation trend of U(t) does not match that of M1(t) and M2(t), and the segmented power consumption fluctuation information is abnormal, and execute S4; otherwise, execute S1; in Where: σ1, σ2 are preset weight coefficients.

5. The method for monitoring electricity consumption information based on power big data of the hardware industry according to claim 1 is characterized in that: The S4 comprises the following steps: S41, determine whether the power consumption data of the hardware feeding equipment in the segment exceeds the threshold, if so, execute S42, if not, execute S5; S42, collecting power data P1 of the hardware feeding equipment, judging whether the hardware feeding equipment is frequently started according to the power data P1, and if so, executing S43, if not, sending abnormal power consumption warning information; S43, obtaining power data P2 of the heating and heating equipment and the corresponding model size of the hardware product, calculating the heating and heating time based on the power data P2 and the model size of the heating and heating equipment, and calculating the heating and heating speed v based on the heating and heating time; The expression of the heating temperature rise rate v is: Where: m0 is the number of hardware products that need to be heated, T0 is the standard heating time, P 20 is the theoretical power of the heating equipment, S is the model size of the hardware product, and S0 is the standard reference size; S44, adjusting the feeding speed to match the heating temperature rise speed v.

6. The method for monitoring electricity consumption information based on power big data of the hardware industry according to claim 1 is characterized in that: The S5 comprises the following steps: S51, collecting power consumption data of heating and temperature raising equipment; S52, calculating the theoretical number of hardware products heated by the heating and temperature raising device based on the power consumption data; S53. If the quantity of hardware products is less than the theoretical quantity, adjust the feeding speed; S54. If the quantity of hardware products is greater than or equal to the theoretical quantity, execute S6.

7. An electricity consumption information monitoring system, applicable to the electricity consumption information monitoring method based on hardware industry power big data as described in any one of claims 1 to 6, characterized in that: It includes a power big data monitoring platform, a production and manufacturing system, an order management system, hardware feeding equipment, a first detection module, a heating and temperature raising equipment, a second detection module, a die forging equipment, and a third detection module; the power big data monitoring platform includes a classified energy consumption monitoring module and a segmented power consumption fluctuation monitoring module; the classified energy consumption data monitoring module is used to collect the power consumption data of the target production products of the hardware enterprise, calculate the segmented power consumption data according to the preset time window, obtain the model switching time point, divide the segmented data into multiple categories, and obtain the classified power consumption data corresponding to the product model; the segmented power consumption fluctuation monitoring module is used to analyze the production fluctuation information of the hardware industry, including the power consumption fluctuation of each type of product, the raw material feeding fluctuation, and the output fluctuation; judge whether the segmented power consumption fluctuation matches the raw material feeding and output fluctuation trend, so as to gain insight into the correlation and efficiency of the production process; The first detection module, the second detection module, and the third detection module are respectively used to monitor the power consumption information data of hardware feeding equipment, heating equipment, and die forging equipment.

8. The power consumption information monitoring system according to claim 7, characterized in that: The electric power big data monitoring platform also includes a classification energy consumption correction module; The classified energy consumption correction module is used to obtain the accumulated working hours and rated power of the production equipment in each production link, and correct the classified energy consumption data according to the accumulated working hours and rated power to obtain the corrected classified energy consumption data.

9. The power consumption information monitoring system according to claim 7, characterized in that: The electricity consumption information monitoring system also includes an energy consumption detection module for collecting electricity consumption data of hardware enterprises; The electric power big data monitoring platform also includes a material preparation equipment monitoring module, a heating and temperature raising equipment monitoring module and a die forging equipment monitoring module; The material preparation equipment monitoring module is used to monitor and determine whether the power consumption information of the hardware feeding equipment is abnormal; The heating and heating equipment monitoring module is used to determine whether the power consumption data of the heating and heating equipment matches the number of hardware products heated by the heating and heating equipment; The die forging equipment monitoring module is used to determine whether the die forging pressure of the hardware product is abnormal according to the load current of the die forging equipment; The order management system stores the model information of the hardware products that the hardware enterprise targets to produce.

10. The power consumption information monitoring system according to claim 7, characterized in that: The electricity consumption information monitoring system also includes a defect detection module; the defect detection module includes a visual monitoring module and a defective product rejection module; the visual monitoring module is used to collect images of hardware products through visual sensors, and determine whether they are defective products after analysis and processing; the defective product rejection module is used to clamp the defective products and screen them out of the production line when the hardware products are determined to be defective products.

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