Industrial energy saving management method and system based on big data and medium

Abstract

The invention provides an industrial energy saving management method and system based on big data and a medium, and belongs to the technical field of energy management and big data. The method comprises the steps of obtaining energy consumption equipment summary information and classifying the information, obtaining energy consumption equipment feature information and processing the information through a preset energy configuration model, obtaining an energy consumption equipment energy configuration distribution portrait, extracting energy configuration distribution feature information and performing energy sub-region grid division through a preset energy region division model, obtaining a sub-region energy configuration distribution feature cognitive map, extracting energy configuration distribution feature data of each sub-region, and obtaining an energy supply index of each sub-region; energy consumption equipment operation monitoring information of each sub-region is obtained, an equipment energy consumption index is obtained, an energy utilization rate index is obtained in combination with an energy supply index, and whether the requirement is met or not is judged. According to the invention, the overall condition of energy configuration in the area can be visually displayed, and the energy utilization efficiency and the intelligent level of equipment management are improved.

Description

Technical Field

[0001] The present application relates to the field of energy management and big data technology, and more specifically, to an industrial energy saving management method, system and medium based on big data. Background Art

[0002] With the rapid development of industrialization, energy consumption has increased dramatically, and energy conservation and emission reduction have become a global urgent task. Traditional industrial energy-saving management systems mostly focus on the energy consumption monitoring of a single device, lacking comprehensive analysis and optimization strategies for overall energy consumption. This leads to blindness in energy management for high-energy-consuming enterprises or parks, making it difficult to achieve accurate energy conservation and efficient management. Therefore, it is particularly important to develop a management system that can monitor and analyze equipment energy consumption and energy utilization in real time and comprehensively, while providing targeted energy-saving solutions.

[0003] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0004] The purpose of this application is to provide an industrial energy conservation management method, system and medium based on big data, which can obtain the summary information of energy-consuming equipment and classify the equipment types, obtain the characteristic information of energy-consuming equipment and process it through a preset energy configuration model, obtain the energy configuration distribution portrait of energy-consuming equipment, extract the characteristic information of energy configuration distribution, and divide the energy sub-region grid through a preset energy area division model, obtain the cognitive map of the energy configuration distribution characteristics of the sub-region, and then extract the energy configuration distribution characteristic data of each sub-region, process to obtain the energy supply index of each sub-region; obtain the operation monitoring information of energy-consuming equipment in each sub-region, process to obtain the equipment energy consumption index and then combine it with the energy supply index, obtain the energy utilization index and judge whether it meets the requirements. This application can intuitively display the overall situation of energy configuration in the region, and realize the improvement of energy utilization efficiency and the level of intelligent equipment management.

[0005] This application provides an industrial energy saving management method based on big data, comprising the following steps: Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the device types to obtain characteristic information of energy-consuming devices; Processing the energy-consuming equipment characteristic information through a preset energy configuration model to obtain an energy configuration distribution profile of the energy-consuming equipment; Extracting energy configuration distribution feature information based on the energy configuration distribution portrait of the energy-consuming equipment and performing energy sub-region grid division using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map; Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain energy supply index of each sub-region; Obtaining the energy consumption equipment operation monitoring information of each sub-area and processing it through a preset energy consumption evaluation model to obtain an equipment energy consumption index; The energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index, which is compared with a preset utilization index to determine whether the energy utilization index meets the requirements.

[0006] Among them, in the industrial energy saving management method based on big data described in the present application, the acquisition of summary information of energy-consuming equipment in a preset area within a preset time period and classification of equipment types to obtain characteristic information of energy-consuming equipment is specifically as follows: Obtain summary information of energy-consuming devices in a preset area within a preset time period, including energy-consuming device type information, energy-consuming device status information, energy consumption information of energy-consuming devices, energy-consuming device usage cycle information and fault record information; The equipment type is classified according to the energy-consuming equipment type information, energy-consuming equipment state information, energy-consuming equipment energy consumption information, energy-consuming equipment use cycle information and fault record information to obtain energy-consuming equipment characteristic information.

[0007] Among them, in the industrial energy saving management method based on big data described in the present application, the energy consumption equipment characteristic information is processed by a preset energy configuration model to obtain the energy configuration distribution portrait of the energy consumption equipment, specifically: Extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy consuming equipment characteristic information; The energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information; The equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information; The energy consumption change trend information, energy consumption peak pattern information and seasonal energy consumption pattern information are combined with the equipment start and stop frequency information and operation cycle information through a preset energy configuration model to obtain an energy configuration distribution portrait of the energy-consuming equipment.

[0008] Among them, in the industrial energy saving management method based on big data described in the present application, the energy configuration distribution characteristic information is extracted according to the energy configuration distribution portrait of the energy-consuming equipment, and the energy sub-region grid is divided through a preset energy area division model to obtain a sub-region energy configuration distribution characteristic cognitive map, specifically: Extracting energy configuration distribution feature information according to the energy configuration distribution portrait of the energy-consuming equipment, including energy type distribution information, energy distribution density information and energy distribution geographical location information; The energy type distribution information, energy distribution density information and energy distribution geographical location information are processed by a preset energy area division model to obtain sub-region grid energy distribution data; The sub-region grid energy distribution data includes grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data; The grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data are processed through a preset energy distribution feature cognitive model to obtain a sub-region energy configuration distribution feature cognitive map.

[0009] Among them, in the industrial energy saving management method based on big data described in the present application, the energy configuration distribution characteristic data of each sub-region is extracted according to the cognitive map of the energy configuration distribution characteristics of the sub-region, and the energy supply index of each sub-region is obtained by processing, specifically: Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data; The energy supply index of each sub-region is obtained by processing the supplied energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data through a preset energy supply model.

[0010] Among them, in the industrial energy saving management method based on big data described in the present application, the energy-consuming equipment operation monitoring information of each sub-area is obtained and processed by a preset energy consumption evaluation model to obtain the equipment energy consumption index, specifically: Obtaining the operation monitoring information of the energy-consuming equipment in each sub-area, extracting the total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data; The total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data are processed through a preset energy consumption evaluation model to obtain an equipment energy consumption index.

[0011] Among them, in the industrial energy saving management method based on big data described in the present application, the energy supply index is processed in combination with the equipment energy consumption index to obtain the energy utilization index and compare it with the preset utilization index to determine whether the energy utilization index meets the requirements, specifically: The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization rate evaluation model to obtain an energy utilization rate index; Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate; comparing the utilization index deviation rate with a preset utilization index deviation rate threshold; If the utilization index deviation rate is greater than or equal to the utilization index deviation rate threshold, a low utilization rate message is sent; If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization rate message is sent.

[0012] In a second aspect, the present application provides an industrial energy conservation management system based on big data, the system comprising: a memory and a processor, the memory comprising a program of an industrial energy conservation management method based on big data, the program of the industrial energy conservation management method based on big data being executed by the processor to implement the following steps: Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the device types to obtain characteristic information of energy-consuming devices; Processing the energy-consuming equipment characteristic information through a preset energy configuration model to obtain an energy configuration distribution profile of the energy-consuming equipment; Extracting energy configuration distribution feature information based on the energy configuration distribution portrait of the energy-consuming equipment and performing energy sub-region grid division using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map; Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain energy supply index of each sub-region; Obtaining the energy consumption equipment operation monitoring information of each sub-area and processing it through a preset energy consumption evaluation model to obtain an equipment energy consumption index; The energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index, which is compared with a preset utilization index to determine whether the energy utilization index meets the requirements.

[0013] In the industrial energy saving management system based on big data described in the present application, the summary information of energy-consuming equipment in a preset area within a preset time period is obtained and the equipment type is classified to obtain the characteristic information of energy-consuming equipment, specifically: Obtain summary information of energy-consuming devices in a preset area within a preset time period, including energy-consuming device type information, energy-consuming device status information, energy consumption information of energy-consuming devices, energy-consuming device usage cycle information and fault record information; The equipment type is classified according to the energy-consuming equipment type information, energy-consuming equipment state information, energy-consuming equipment energy consumption information, energy-consuming equipment use cycle information and fault record information to obtain energy-consuming equipment characteristic information.

[0014] In the third aspect, the present application also provides a computer-readable storage medium, which includes an industrial energy energy-saving management method program based on big data. When the industrial energy energy-saving management method program based on big data is executed by a processor, it implements the steps of an industrial energy energy-saving management method based on big data as described in any one of the above items.

[0015] As can be seen from the above, the industrial energy saving management method, system and medium based on big data provided by the embodiment of the present application obtains the summary information of energy-consuming equipment and classifies the equipment types, obtains the characteristic information of energy-consuming equipment and processes it through a preset energy configuration model, obtains the energy configuration distribution portrait of energy-consuming equipment, extracts the characteristic information of energy configuration distribution and divides the energy sub-region grid through a preset energy region division model, obtains the cognitive map of the energy configuration distribution characteristics of the sub-region, and then extracts the energy configuration distribution characteristic data of each sub-region, and processes to obtain the energy supply index of each sub-region; obtains the operation monitoring information of energy-consuming equipment in each sub-region and processes it through a preset energy consumption evaluation model, obtains the equipment energy consumption index combined with the energy supply index, obtains the energy utilization index and compares it with the preset utilization index, and judges whether the energy utilization index meets the requirements. The present application scientifically subdivides a large area into multiple sub-regions, can intuitively display the overall situation of energy configuration in the region, accurately reflects the energy supply and demand status of each sub-region and the energy consumption level of the equipment, realizes the improvement of energy utilization efficiency and the intelligent level of equipment management, and is of great significance to promoting sustainable development and energy conservation and emission reduction.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of an industrial energy saving management method based on big data provided in an embodiment of the present application; Figure 2 A flowchart of obtaining characteristic information of energy-consuming equipment in an industrial energy saving management method based on big data provided in an embodiment of the present application; Figure 3A flowchart of obtaining an energy configuration distribution portrait of energy-consuming equipment in an industrial energy saving management method based on big data provided in an embodiment of the present application; Figure 4 A flowchart of obtaining a cognitive map of sub-region energy configuration distribution characteristics of an industrial energy saving management method based on big data provided in an embodiment of the present application; Figure 5 A flowchart of obtaining the energy supply index of each sub-region in an industrial energy saving management method based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart of an industrial energy saving management method based on big data in some embodiments of the present application. The industrial energy saving management method based on big data is used in terminal devices, such as computers, mobile terminals, etc. The industrial energy saving management method based on big data includes the following steps: S101, obtaining summary information of energy-consuming devices in a preset area within a preset time period and classifying the device types to obtain characteristic information of the energy-consuming devices; S102, processing the energy-consuming equipment characteristic information through a preset energy configuration model to obtain an energy configuration distribution portrait of the energy-consuming equipment; S103, extracting energy configuration distribution feature information according to the energy configuration distribution portrait of the energy-consuming equipment and performing energy sub-region grid division using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map; S104, extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain energy supply index of each sub-region; S105, obtaining the energy consumption equipment operation monitoring information of each sub-area and processing it through a preset energy consumption evaluation model to obtain an equipment energy consumption index; S106: Process the energy supply index in combination with the equipment energy consumption index to obtain an energy utilization index and compare it with a preset utilization index to determine whether the energy utilization index meets the requirements.

[0022] Among them, the present application obtains the summary information of energy-consuming equipment and classifies the equipment types, obtains the characteristic information of energy-consuming equipment and processes it through a preset energy configuration model, obtains the energy configuration distribution portrait of energy-consuming equipment, extracts the characteristic information of energy configuration distribution and divides the energy sub-region grid through a preset energy area division model, obtains the cognitive map of the energy configuration distribution characteristics of the sub-region, and then extracts the energy configuration distribution characteristic data of each sub-region, and processes to obtain the energy supply index of each sub-region; obtains the operation monitoring information of energy-consuming equipment in each sub-region and processes it through a preset energy consumption evaluation model, obtains the equipment energy consumption index combined with the energy supply index, obtains the energy utilization index and compares it with the preset utilization index, and judges whether the energy utilization index meets the requirements. The present application can intuitively display the overall situation of energy configuration in the region by scientifically subdividing a large area into multiple sub-regions, accurately reflects the energy supply and demand status of each sub-region and the energy consumption level of the equipment, helps to timely discover and solve the problem of energy waste, realizes the improvement of energy utilization efficiency and the level of intelligent equipment management, helps enterprises reduce energy consumption costs and improve economic benefits, is of great significance to promoting sustainable development and energy conservation and emission reduction, and is in line with the current green and low-carbon development trend. The preset model of this application solution is relied upon and obtained through a third-party platform.

[0023] Please refer to Figure 2 , Figure 2 This is a flowchart of obtaining characteristic information of energy-consuming equipment in a method for industrial energy conservation management based on big data in some embodiments of the present application. According to an embodiment of the present invention, the energy-consuming equipment summary information of a preset area within a preset time period is obtained and the equipment type is classified to obtain characteristic information of energy-consuming equipment, specifically: S201, obtaining summary information of energy-consuming devices in a preset area within a preset time period, including energy-consuming device type information, energy-consuming device status information, energy consumption information of energy-consuming devices, energy-consuming device use cycle information and fault record information; S202: classify the energy-consuming device type according to the energy-consuming device type information, energy-consuming device state information, energy consumption information, energy-consuming device use cycle information and fault record information to obtain energy-consuming device characteristic information.

[0024] Among them, in order to obtain the characteristic information of energy-consuming equipment, the summary information of energy-consuming equipment in the preset area within the preset time period is obtained, including the type information of energy-consuming equipment, the status information of energy-consuming equipment, the energy consumption information of energy-consuming equipment, the use cycle information of energy-consuming equipment and the fault record information. The type information of energy-consuming equipment includes the type of equipment (such as motors, lighting equipment, air conditioning systems, etc.), model and manufacturer, etc.; the status information of energy-consuming equipment refers to the current operating status of the equipment (such as running, standby, closed, etc.) and the historical status change record; the energy consumption information of energy-consuming equipment refers to the energy consumption of the equipment within the preset time period, which may be expressed in different forms such as electricity, water, gas, etc.; the use cycle information of energy-consuming equipment refers to the operating time, number of startups, downtime, etc. of the equipment, which is used to evaluate the use frequency and life of the equipment; the fault record information refers to the fault situation that occurred within the preset time period, including the fault time, fault type, maintenance measures and maintenance results, etc. Then classify the equipment type to obtain the characteristic information of energy-consuming equipment.

[0025] Please refer to Figure 3 , Figure 3 It is a flowchart of obtaining an energy configuration distribution portrait of energy-consuming equipment in an industrial energy saving management method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the energy configuration distribution portrait of energy-consuming equipment is obtained by processing the characteristic information of the energy-consuming equipment through a preset energy configuration model, specifically: S301, extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy consuming equipment characteristic information; S302, the energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information; S303, the equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information; S304, according to the energy consumption change trend information, energy consumption peak law information and seasonal energy consumption law information combined with the equipment start and stop frequency information and operation cycle information, a preset energy configuration model is used to process and obtain an energy configuration distribution portrait of energy-consuming equipment.

[0026] Among them, in order to better understand the energy distribution and usage and obtain the energy configuration distribution portrait of energy-consuming equipment, energy consumption parameter characteristic information and equipment operation status characteristic information are extracted according to the characteristic information of energy-consuming equipment, including energy consumption change trend information, energy consumption peak law information and seasonal energy consumption law information, as well as equipment start-stop frequency information and operation cycle information. Energy consumption change trend information reflects the change trend of energy consumption of equipment in a preset time period, which may present the form of rising, falling or fluctuating; energy consumption peak law information includes identifying the peak time period of equipment energy consumption, such as daily, weekly or monthly energy consumption peak; seasonal energy consumption law information includes the energy consumption difference of equipment in different seasons, such as increased air conditioning energy consumption in summer and increased heating energy consumption in winter; equipment start-stop frequency information records the number of times the equipment is started and stopped in a preset time period, reflecting the operation frequency of the equipment; operation cycle information refers to the operation time and shutdown time of the equipment, as well as the periodic law of operation and shutdown. According to the energy consumption change trend information, energy consumption peak law information and seasonal energy consumption law information combined with equipment start-stop frequency information and operation cycle information, the preset energy configuration model is used for processing to obtain the energy configuration distribution portrait of energy-consuming equipment. The portrait displays the equipment’s energy consumption distribution, operating rules, and optimization suggestions for energy configuration.

[0027] Please refer to Figure 4 , Figure 4 It is a flowchart of obtaining a cognitive map of sub-region energy configuration distribution characteristics of an industrial energy saving management method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the energy configuration distribution characteristic information is extracted according to the energy configuration distribution portrait of the energy-consuming equipment and the energy sub-region grid is divided through a preset energy region division model to obtain a cognitive map of sub-region energy configuration distribution characteristics, specifically: S401, extracting energy configuration distribution characteristic information according to the energy configuration distribution portrait of the energy-consuming equipment, including energy type distribution information, energy distribution density information and energy distribution geographical location information; S402, processing the energy type distribution information, energy distribution density information and energy distribution geographical location information through a preset energy area division model to obtain sub-region grid energy distribution data; S403, the sub-region grid energy distribution data includes grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data; S404, processing the grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data through a preset energy distribution feature cognitive model to obtain a sub-region energy configuration distribution feature cognitive map.

[0028] Among them, in order to more scientifically analyze the energy distribution and obtain the cognitive map of the sub-regional energy configuration distribution characteristics, the energy configuration distribution characteristic information is extracted according to the energy configuration distribution portrait of the energy-consuming equipment, including energy type distribution information, energy distribution density information and energy distribution geographical location information. Energy type distribution information refers to the energy types (such as electricity, water, gas, etc.) used by energy-consuming equipment and their distribution ratios, so as to understand the contribution of different energy types to the overall energy consumption; energy distribution density information refers to the distribution density of energy in the region, that is, the amount of energy per unit area or unit volume, reflecting the concentration of energy in the region; energy distribution geographical location information records and analyzes the geographical location of energy distribution, including longitude and latitude, altitude and other information, to provide a basis for the subsequent energy regional division. Then, the sub-region grid energy distribution data is obtained through the preset energy regional division model, including grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data. The energy area division model is used to divide the preset area into several sub-areas and divide grids in each sub-area; the grid energy capacity data reflects the total capacity or storage capacity of energy in each grid; the grid energy density data refers to the energy distribution density in each grid, that is, the ratio of the amount of energy in the grid to its area or volume; the sub-area energy distribution data records the distribution and characteristics of energy in each sub-area, including energy type, distribution ratio, etc.; the sub-area energy total capacity data summarizes the energy capacity of all grids in each sub-area, reflecting the overall energy storage capacity of the sub-area. According to the grid energy capacity data, grid energy density data, sub-area energy distribution data and sub-area energy total capacity data, the preset energy distribution feature cognitive model is used to process and analyze the sub-area grid energy distribution data, extract the energy configuration distribution features, and generate a cognitive map. The map shows the distribution, capacity, density, and relationship and trend of energy in the sub-area.

[0029] Please refer to Figure 5 , Figure 5 It is a flowchart of obtaining the energy supply index of each sub-region in an industrial energy saving management method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the energy configuration distribution characteristic data of each sub-region is extracted according to the cognitive map of the energy configuration distribution characteristics of the sub-region, and the energy supply index of each sub-region is obtained by processing, specifically: S501, extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data; S502: Process the supplied energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data through a preset energy supply model to obtain an energy supply index for each sub-region.

[0030] Among them, in order to obtain the energy supply index of each sub-region, the energy configuration distribution characteristic data of each sub-region are extracted according to the cognitive map of the energy configuration distribution characteristics of the sub-region, including the supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data. The supply energy type data records the main energy types supplied in each sub-region, such as electricity, water, gas, wind, solar energy, etc., and the proportion of these energy types in the energy supply of the sub-region; the energy type distribution characteristic data analyzes the spatial distribution characteristics of different energy types in each sub-region, including the agglomeration area and diffusion trend of energy types, so as to understand the geographical distribution law of energy types; the total energy supply data counts the total energy supply in each sub-region, including the supply of each energy type and the total supply, so as to evaluate the energy supply capacity of the sub-region; the energy supply frequency data records the frequency of energy supply in each sub-region, that is, the periodicity and stability of energy supply, including the change characteristics of energy supply on time scales such as day, week, month and year. According to the supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data, the energy supply index of each sub-region is obtained by processing through the preset energy supply model; The calculation formula of the energy supply model is: ; in, is the energy supply index, They are respectively the data of energy type supplied, the data of energy type distribution characteristics, the data of total energy supply and the data of energy supply frequency. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0031] According to an embodiment of the present invention, the energy consumption equipment operation monitoring information of each sub-area is obtained and processed by a preset energy consumption evaluation model to obtain an equipment energy consumption index, specifically: Obtaining the operation monitoring information of the energy-consuming equipment in each sub-area, extracting the total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data; The total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data are processed through a preset energy consumption evaluation model to obtain an equipment energy consumption index.

[0032] Among them, in order to grasp the energy consumption of equipment in each sub-area and obtain the equipment energy consumption index, the operation monitoring information of energy-consuming equipment in each sub-area is obtained, and the total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data are extracted. The total energy consumption data refers to the total energy consumed by the equipment; the unit energy consumption data refers to the energy consumption of the equipment per unit time (such as per hour, per day); the peak energy consumption data refers to the highest value of the energy consumption of the equipment in a specific time period; the operation frequency data refers to the number of times the equipment is started and stopped in a specific time period; the total operation time data refers to the total operation time of the equipment in a specific time period. Then, it is processed through the preset energy consumption evaluation model to obtain the equipment energy consumption index. The energy consumption evaluation model is used to process and analyze key energy consumption data extracted from the monitoring information. The equipment energy consumption index reflects the energy efficiency and performance of the equipment; The calculation formula of the energy consumption evaluation model is: ; in, is the equipment energy consumption index, They are total energy consumption data, unit energy consumption data, peak energy consumption data, operating frequency data and total operating time data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0033] According to an embodiment of the present invention, the energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index and compare it with a preset utilization index to determine whether the energy utilization index meets the requirements, specifically: The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization rate evaluation model to obtain an energy utilization rate index; Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate; comparing the utilization index deviation rate with a preset utilization index deviation rate threshold; If the utilization index deviation rate is greater than or equal to the utilization index deviation rate threshold, a low utilization rate message is sent; If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization rate message is sent; The calculation formula of the utilization evaluation model is: ; in, is the energy utilization index, is the energy supply index, is the equipment energy consumption index, It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0034] Among them, in order to improve the energy utilization rate in the region, the energy supply index is combined with the equipment energy consumption index through a preset utilization evaluation model for processing, and the energy utilization index is obtained, and compared with the preset utilization index to obtain the utilization index deviation rate, and then compared with the preset utilization index deviation rate threshold. If the utilization index deviation rate is greater than or equal to the utilization index deviation rate threshold, it means that the energy utilization rate is low, and there may be problems of energy waste or low equipment efficiency. Low utilization rate information is sent to remind relevant personnel to take energy-saving measures or optimize equipment operation; if the utilization index deviation rate is less than the utilization index deviation rate threshold, it means that the energy utilization rate is high and the energy is effectively utilized. High utilization rate information is sent to encourage relevant personnel to continue to maintain or further improve energy utilization efficiency.

[0035] According to an embodiment of the present invention, it also includes: Acquire temperature data of a preset area within the preset time period; Comparing the temperature data with a preset first temperature threshold and a preset second temperature threshold respectively; The first temperature threshold is less than the second temperature threshold; If the temperature data is less than the first temperature threshold, low temperature information is sent; If the temperature data is greater than or equal to the first temperature threshold and less than or equal to the second temperature threshold, a normal temperature message is sent; If the temperature data is greater than the second temperature threshold, high temperature information is sent.

[0036] Among them, in order to avoid the negative impact of extreme ambient temperature on energy-consuming equipment and energy utilization, it is necessary to monitor the temperature in the preset area in real time. The temperature data of the preset area in the preset time period can be obtained and compared with the preset first temperature threshold and second temperature threshold respectively. The first temperature threshold is less than the second temperature threshold. For example, the first temperature threshold is 6°C and the second temperature threshold is 27°C. If the temperature data is less than the first temperature threshold, a low temperature message is sent, which means that the heating equipment needs to be started or other warming measures need to be taken; if the temperature data is greater than or equal to the first temperature threshold and less than or equal to the second temperature threshold, a normal temperature message is sent, and the equipment is in the best working state and no additional intervention is required; if the temperature data is greater than the second temperature threshold, a high temperature message is sent, which means that the cooling equipment needs to be started, some energy-consuming equipment needs to be shut down, or other cooling measures need to be taken.

[0037] According to an embodiment of the present invention, it also includes: Acquire the air pressure change rate data of the preset area within the preset time period; Comparing the air pressure change rate data with a preset air pressure change rate threshold, obtaining an air pressure change rate deviation rate; comparing the air pressure change rate deviation rate with a preset air pressure change rate deviation rate threshold; If the pressure change rate deviation rate is greater than or equal to the pressure change rate deviation rate threshold, an abnormal pressure message is sent; If the air pressure change rate deviation rate is less than the air pressure change rate deviation rate threshold, the air pressure normal information is sent.

[0038] Among them, in order to reduce the impact of air pressure changes on energy-consuming equipment, the air pressure change rate data of a preset area within a preset time period is obtained, and compared with the preset air pressure change rate threshold, to obtain the air pressure change rate deviation rate, and then compared with the preset air pressure change rate deviation rate threshold. If the air pressure change rate deviation rate is greater than or equal to the air pressure change rate deviation rate threshold, it indicates that the air pressure change is abnormal and it is necessary to send abnormal air pressure information; if the air pressure change rate deviation rate is less than the air pressure change rate deviation rate threshold, it indicates that the air pressure change is normal, and normal air pressure information can be sent to relevant personnel, or no special notification is made.

[0039] The present invention also discloses an industrial energy conservation management system based on big data, comprising a memory and a processor, wherein the memory comprises an industrial energy conservation management method program based on big data, and when the industrial energy conservation management method program based on big data is executed by the processor, the following steps are implemented: Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the device types to obtain characteristic information of energy-consuming devices; Processing the energy-consuming equipment characteristic information through a preset energy configuration model to obtain an energy configuration distribution profile of the energy-consuming equipment; Extracting energy configuration distribution feature information based on the energy configuration distribution portrait of the energy-consuming equipment and performing energy sub-region grid division using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map; Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain energy supply index of each sub-region; Obtaining the energy consumption equipment operation monitoring information of each sub-area and processing it through a preset energy consumption evaluation model to obtain an equipment energy consumption index; The energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index, which is compared with a preset utilization index to determine whether the energy utilization index meets the requirements.

[0040] Among them, the present application obtains the summary information of energy-consuming equipment and classifies the equipment types, obtains the characteristic information of energy-consuming equipment and processes it through a preset energy configuration model, obtains the energy configuration distribution portrait of energy-consuming equipment, extracts the characteristic information of energy configuration distribution and divides the energy sub-region grid through a preset energy area division model, obtains the cognitive map of the energy configuration distribution characteristics of the sub-region, and then extracts the energy configuration distribution characteristic data of each sub-region, and processes to obtain the energy supply index of each sub-region; obtains the operation monitoring information of energy-consuming equipment in each sub-region and processes it through a preset energy consumption evaluation model, obtains the equipment energy consumption index combined with the energy supply index, obtains the energy utilization index and compares it with the preset utilization index, and judges whether the energy utilization index meets the requirements. The present application can intuitively display the overall situation of energy configuration in the region by scientifically subdividing a large area into multiple sub-regions, accurately reflects the energy supply and demand status of each sub-region and the energy consumption level of the equipment, helps to timely discover and solve the problem of energy waste, realizes the improvement of energy utilization efficiency and the level of intelligent equipment management, helps enterprises reduce energy consumption costs and improve economic benefits, is of great significance to promoting sustainable development and energy conservation and emission reduction, and is in line with the current green and low-carbon development trend. The preset model of this application solution is relied upon and obtained through a third-party platform.

[0041] According to an embodiment of the present invention, the acquisition of summary information of energy-consuming devices in a preset area within a preset time period and classification of device types to obtain characteristic information of energy-consuming devices is specifically as follows: Obtain summary information of energy-consuming devices in a preset area within a preset time period, including energy-consuming device type information, energy-consuming device status information, energy consumption information of energy-consuming devices, energy-consuming device usage cycle information and fault record information; The equipment type is classified according to the energy-consuming equipment type information, energy-consuming equipment state information, energy-consuming equipment energy consumption information, energy-consuming equipment use cycle information and fault record information to obtain energy-consuming equipment characteristic information.

[0042] Among them, in order to obtain the characteristic information of energy-consuming equipment, the summary information of energy-consuming equipment in the preset area within the preset time period is obtained, including the type information of energy-consuming equipment, the status information of energy-consuming equipment, the energy consumption information of energy-consuming equipment, the use cycle information of energy-consuming equipment and the fault record information. The type information of energy-consuming equipment includes the type of equipment (such as motors, lighting equipment, air conditioning systems, etc.), model and manufacturer, etc.; the status information of energy-consuming equipment refers to the current operating status of the equipment (such as running, standby, closed, etc.) and the historical status change record; the energy consumption information of energy-consuming equipment refers to the energy consumption of the equipment within the preset time period, which may be expressed in different forms such as electricity, water, gas, etc.; the use cycle information of energy-consuming equipment refers to the operating time, number of startups, downtime, etc. of the equipment, which is used to evaluate the use frequency and life of the equipment; the fault record information refers to the fault situation that occurred within the preset time period, including the fault time, fault type, maintenance measures and maintenance results, etc. Then classify the equipment type to obtain the characteristic information of energy-consuming equipment.

[0043] According to an embodiment of the present invention, the energy consumption equipment characteristic information is processed by a preset energy configuration model to obtain an energy configuration distribution portrait of the energy consumption equipment, specifically: Extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy consuming equipment characteristic information; The energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information; The equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information; The energy consumption change trend information, energy consumption peak pattern information and seasonal energy consumption pattern information are combined with the equipment start and stop frequency information and operation cycle information through a preset energy configuration model to obtain an energy configuration distribution portrait of the energy-consuming equipment.

[0044] Among them, in order to better understand the energy distribution and usage and obtain the energy configuration distribution portrait of energy-consuming equipment, energy consumption parameter characteristic information and equipment operation status characteristic information are extracted according to the characteristic information of energy-consuming equipment, including energy consumption change trend information, energy consumption peak law information and seasonal energy consumption law information, as well as equipment start-stop frequency information and operation cycle information. Energy consumption change trend information reflects the change trend of energy consumption of equipment in a preset time period, which may present the form of rising, falling or fluctuating; energy consumption peak law information includes identifying the peak time period of equipment energy consumption, such as daily, weekly or monthly energy consumption peak; seasonal energy consumption law information includes the energy consumption difference of equipment in different seasons, such as increased air conditioning energy consumption in summer and increased heating energy consumption in winter; equipment start-stop frequency information records the number of times the equipment is started and stopped in a preset time period, reflecting the operation frequency of the equipment; operation cycle information refers to the operation time and shutdown time of the equipment, as well as the periodic law of operation and shutdown. According to the energy consumption change trend information, energy consumption peak law information and seasonal energy consumption law information combined with equipment start-stop frequency information and operation cycle information, the preset energy configuration model is used for processing to obtain the energy configuration distribution portrait of energy-consuming equipment. The portrait displays the equipment’s energy consumption distribution, operating rules, and optimization suggestions for energy configuration.

[0045] According to an embodiment of the present invention, the energy configuration distribution feature information is extracted according to the energy configuration distribution portrait of the energy consuming equipment, and energy sub-region grid division is performed through a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map, specifically: Extracting energy configuration distribution feature information according to the energy configuration distribution portrait of the energy-consuming equipment, including energy type distribution information, energy distribution density information and energy distribution geographical location information; The energy type distribution information, energy distribution density information and energy distribution geographical location information are processed by a preset energy area division model to obtain sub-region grid energy distribution data; The sub-region grid energy distribution data includes grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data; The grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data are processed through a preset energy distribution feature cognitive model to obtain a sub-region energy configuration distribution feature cognitive map.

[0046] Among them, in order to more scientifically analyze the energy distribution and obtain the cognitive map of the sub-regional energy configuration distribution characteristics, the energy configuration distribution characteristic information is extracted according to the energy configuration distribution portrait of the energy-consuming equipment, including energy type distribution information, energy distribution density information and energy distribution geographical location information. Energy type distribution information refers to the energy types (such as electricity, water, gas, etc.) used by energy-consuming equipment and their distribution ratios, so as to understand the contribution of different energy types to the overall energy consumption; energy distribution density information refers to the distribution density of energy in the region, that is, the amount of energy per unit area or unit volume, reflecting the concentration of energy in the region; energy distribution geographical location information records and analyzes the geographical location of energy distribution, including longitude and latitude, altitude and other information, to provide a basis for the subsequent energy regional division. Then, the sub-region grid energy distribution data is obtained through the preset energy regional division model, including grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data. The energy area division model is used to divide the preset area into several sub-areas and divide grids in each sub-area; the grid energy capacity data reflects the total capacity or storage capacity of energy in each grid; the grid energy density data refers to the energy distribution density in each grid, that is, the ratio of the amount of energy in the grid to its area or volume; the sub-area energy distribution data records the distribution and characteristics of energy in each sub-area, including energy type, distribution ratio, etc.; the sub-area energy total capacity data summarizes the energy capacity of all grids in each sub-area, reflecting the overall energy storage capacity of the sub-area. According to the grid energy capacity data, grid energy density data, sub-area energy distribution data and sub-area energy total capacity data, the preset energy distribution feature cognitive model is used to process and analyze the sub-area grid energy distribution data, extract the energy configuration distribution features, and generate a cognitive map. The map shows the distribution, capacity, density, and relationship and trend of energy in the sub-area.

[0047] According to an embodiment of the present invention, the energy configuration distribution characteristic data of each sub-region is extracted according to the cognitive map of the energy configuration distribution characteristics of the sub-region, and the energy supply index of each sub-region is obtained by processing, specifically: Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data; The energy supply index of each sub-region is obtained by processing the supplied energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data through a preset energy supply model.

[0048] Among them, in order to obtain the energy supply index of each sub-region, the energy configuration distribution characteristic data of each sub-region are extracted according to the cognitive map of the energy configuration distribution characteristics of the sub-region, including the supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data. The supply energy type data records the main energy types supplied in each sub-region, such as electricity, water, gas, wind, solar energy, etc., and the proportion of these energy types in the energy supply of the sub-region; the energy type distribution characteristic data analyzes the spatial distribution characteristics of different energy types in each sub-region, including the agglomeration area and diffusion trend of energy types, so as to understand the geographical distribution law of energy types; the total energy supply data counts the total energy supply in each sub-region, including the supply of each energy type and the total supply, so as to evaluate the energy supply capacity of the sub-region; the energy supply frequency data records the frequency of energy supply in each sub-region, that is, the periodicity and stability of energy supply, including the change characteristics of energy supply on time scales such as day, week, month and year. According to the supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data, the energy supply index of each sub-region is obtained by processing through the preset energy supply model; The calculation formula of the energy supply model is: ; in, is the energy supply index, They are respectively the data of energy type supplied, the data of energy type distribution characteristics, the data of total energy supply and the data of energy supply frequency. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0049] According to an embodiment of the present invention, the energy consumption equipment operation monitoring information of each sub-area is obtained and processed by a preset energy consumption evaluation model to obtain an equipment energy consumption index, specifically: Obtaining the operation monitoring information of the energy-consuming equipment in each sub-area, extracting the total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data; The total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data are processed through a preset energy consumption evaluation model to obtain an equipment energy consumption index.

[0050] Among them, in order to grasp the energy consumption of equipment in each sub-area and obtain the equipment energy consumption index, the operation monitoring information of energy-consuming equipment in each sub-area is obtained, and the total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data are extracted. The total energy consumption data refers to the total energy consumed by the equipment; the unit energy consumption data refers to the energy consumption of the equipment per unit time (such as per hour, per day); the peak energy consumption data refers to the highest value of the energy consumption of the equipment in a specific time period; the operation frequency data refers to the number of times the equipment is started and stopped in a specific time period; the total operation time data refers to the total operation time of the equipment in a specific time period. Then, it is processed through the preset energy consumption evaluation model to obtain the equipment energy consumption index. The energy consumption evaluation model is used to process and analyze key energy consumption data extracted from the monitoring information. The equipment energy consumption index reflects the energy efficiency and performance of the equipment; The calculation formula of the energy consumption evaluation model is: ; in, is the equipment energy consumption index, They are total energy consumption data, unit energy consumption data, peak energy consumption data, operating frequency data and total operating time data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0051] According to an embodiment of the present invention, the energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index and compare it with a preset utilization index to determine whether the energy utilization index meets the requirements, specifically: The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization rate evaluation model to obtain an energy utilization rate index; Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate; comparing the utilization index deviation rate with a preset utilization index deviation rate threshold; If the utilization index deviation rate is greater than or equal to the utilization index deviation rate threshold, a low utilization rate message is sent; If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization rate message is sent; The calculation formula of the utilization evaluation model is: ; in, is the energy utilization index, is the energy supply index, is the equipment energy consumption index, It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0052] Among them, in order to improve the energy utilization rate in the region, the energy supply index is combined with the equipment energy consumption index through a preset utilization evaluation model for processing, and the energy utilization index is obtained, and compared with the preset utilization index to obtain the utilization index deviation rate, and then compared with the preset utilization index deviation rate threshold. If the utilization index deviation rate is greater than or equal to the utilization index deviation rate threshold, it means that the energy utilization rate is low, and there may be problems of energy waste or low equipment efficiency. Low utilization rate information is sent to remind relevant personnel to take energy-saving measures or optimize equipment operation; if the utilization index deviation rate is less than the utilization index deviation rate threshold, it means that the energy utilization rate is high and the energy is effectively utilized. High utilization rate information is sent to encourage relevant personnel to continue to maintain or further improve energy utilization efficiency.

[0053] According to an embodiment of the present invention, it also includes: Acquire temperature data of a preset area within the preset time period; Comparing the temperature data with a preset first temperature threshold and a preset second temperature threshold respectively; The first temperature threshold is less than the second temperature threshold; If the temperature data is less than the first temperature threshold, low temperature information is sent; If the temperature data is greater than or equal to the first temperature threshold and less than or equal to the second temperature threshold, a normal temperature message is sent; If the temperature data is greater than the second temperature threshold, high temperature information is sent.

[0054] Among them, in order to avoid the negative impact of extreme ambient temperature on energy-consuming equipment and energy utilization, it is necessary to monitor the temperature in the preset area in real time. The temperature data of the preset area in the preset time period can be obtained and compared with the preset first temperature threshold and second temperature threshold respectively. The first temperature threshold is less than the second temperature threshold. For example, the first temperature threshold is 6°C and the second temperature threshold is 27°C. If the temperature data is less than the first temperature threshold, a low temperature message is sent, which means that the heating equipment needs to be started or other warming measures need to be taken; if the temperature data is greater than or equal to the first temperature threshold and less than or equal to the second temperature threshold, a normal temperature message is sent, and the equipment is in the best working state and no additional intervention is required; if the temperature data is greater than the second temperature threshold, a high temperature message is sent, which means that the cooling equipment needs to be started, some energy-consuming equipment needs to be shut down, or other cooling measures need to be taken.

[0055] According to an embodiment of the present invention, it also includes: Acquire the air pressure change rate data of the preset area within the preset time period; Comparing the air pressure change rate data with a preset air pressure change rate threshold, obtaining an air pressure change rate deviation rate; comparing the air pressure change rate deviation rate with a preset air pressure change rate deviation rate threshold; If the pressure change rate deviation rate is greater than or equal to the pressure change rate deviation rate threshold, an abnormal pressure message is sent; If the air pressure change rate deviation rate is less than the air pressure change rate deviation rate threshold, the air pressure normal information is sent.

[0056] Among them, in order to reduce the impact of air pressure changes on energy-consuming equipment, the air pressure change rate data of a preset area within a preset time period is obtained, and compared with the preset air pressure change rate threshold, to obtain the air pressure change rate deviation rate, and then compared with the preset air pressure change rate deviation rate threshold. If the air pressure change rate deviation rate is greater than or equal to the air pressure change rate deviation rate threshold, it indicates that the air pressure change is abnormal and it is necessary to send abnormal air pressure information; if the air pressure change rate deviation rate is less than the air pressure change rate deviation rate threshold, it indicates that the air pressure change is normal, and normal air pressure information can be sent to relevant personnel, or no special notification is made.

[0057] The third aspect of the present invention provides a computer-readable storage medium, which includes an industrial energy energy-saving management method program based on big data. When the industrial energy energy-saving management method program based on big data is executed by a processor, it implements the steps of an industrial energy energy-saving management method based on big data as described in any one of the above items.

[0058] The present invention discloses an industrial energy conservation management method, system and medium based on big data, which obtains the summary information of energy-consuming equipment and classifies the equipment types, obtains the characteristic information of energy-consuming equipment and processes it through a preset energy configuration model, obtains the energy configuration distribution portrait of energy-consuming equipment, extracts the characteristic information of energy configuration distribution and divides the energy sub-region grid through a preset energy region division model, obtains the cognitive map of the energy configuration distribution characteristics of the sub-region, and then extracts the energy configuration distribution characteristic data of each sub-region, and processes to obtain the energy supply index of each sub-region; obtains the operation monitoring information of energy-consuming equipment in each sub-region and processes it through a preset energy consumption evaluation model, obtains the equipment energy consumption index combined with the energy supply index, obtains the energy utilization index and compares it with the preset utilization index, and judges whether the energy utilization index meets the requirements. The present application scientifically subdivides a large area into multiple sub-regions, can intuitively display the overall situation of energy configuration in the region, accurately reflects the energy supply and demand status of each sub-region and the energy consumption level of the equipment, realizes the improvement of energy utilization efficiency and the intelligent level of equipment management, and is of great significance to promoting sustainable development and energy conservation and emission reduction.

[0059] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0060] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0061] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0062] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0063] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An industrial energy saving management method based on big data, characterized in that: The following steps are involved: Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the device types to obtain characteristic information of energy-consuming devices; Processing the energy-consuming equipment characteristic information through a preset energy configuration model to obtain an energy configuration distribution profile of the energy-consuming equipment; Extracting energy configuration distribution feature information based on the energy configuration distribution portrait of the energy-consuming equipment and performing energy sub-region grid division using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map; Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain energy supply index of each sub-region; Obtaining the energy consumption equipment operation monitoring information of each sub-area and processing it through a preset energy consumption evaluation model to obtain an equipment energy consumption index; The energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index, which is compared with a preset utilization index to determine whether the energy utilization index meets the requirements.

2. The industrial energy saving management method based on big data according to claim 1 is characterized in that: The step of obtaining the summary information of energy-consuming devices in a preset area within a preset time period and classifying the types of devices to obtain characteristic information of energy-consuming devices is as follows: Obtain summary information of energy-consuming devices in a preset area within a preset time period, including energy-consuming device type information, energy-consuming device status information, energy consumption information of energy-consuming devices, energy-consuming device usage cycle information and fault record information; The equipment type is classified according to the energy-consuming equipment type information, energy-consuming equipment state information, energy-consuming equipment energy consumption information, energy-consuming equipment use cycle information and fault record information to obtain energy-consuming equipment characteristic information.

3. The industrial energy saving management method based on big data according to claim 2 is characterized in that: The energy consumption equipment characteristic information is processed by a preset energy configuration model to obtain an energy configuration distribution portrait of the energy consumption equipment, specifically: Extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy consuming equipment characteristic information; The energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information; The equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information; The energy consumption change trend information, energy consumption peak pattern information and seasonal energy consumption pattern information are combined with the equipment start and stop frequency information and operation cycle information through a preset energy configuration model to obtain an energy configuration distribution portrait of the energy-consuming equipment.

4. The industrial energy saving management method based on big data according to claim 3 is characterized in that: The energy configuration distribution feature information is extracted according to the energy configuration distribution portrait of the energy-consuming equipment, and the energy sub-region grid is divided by a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map, specifically: Extracting energy configuration distribution feature information according to the energy configuration distribution portrait of the energy-consuming equipment, including energy type distribution information, energy distribution density information and energy distribution geographical location information; The energy type distribution information, energy distribution density information and energy distribution geographical location information are processed by a preset energy area division model to obtain sub-region grid energy distribution data; The sub-region grid energy distribution data includes grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data; The grid energy capacity data, grid energy density data, sub-region energy distribution data and sub-region total energy capacity data are processed through a preset energy distribution feature cognitive model to obtain a sub-region energy configuration distribution feature cognitive map.

5. The industrial energy saving management method based on big data according to claim 4 is characterized in that: The energy configuration distribution characteristic data of each sub-region is extracted according to the sub-region energy configuration distribution characteristic cognitive map, and the energy supply index of each sub-region is obtained by processing, specifically: Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supply energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data; The energy supply index of each sub-region is obtained by processing the supplied energy type data, energy type distribution characteristic data, total energy supply data and energy supply frequency data through a preset energy supply model.

6. The industrial energy saving management method based on big data according to claim 5 is characterized in that: The energy consumption equipment operation monitoring information of each sub-area is obtained and processed by a preset energy consumption evaluation model to obtain an equipment energy consumption index, specifically: Obtaining the operation monitoring information of the energy-consuming equipment in each sub-area, extracting the total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data; The total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data and total operation time data are processed through a preset energy consumption evaluation model to obtain an equipment energy consumption index.

7. The industrial energy saving management method based on big data according to claim 6 is characterized in that: The energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index and compare it with a preset utilization index to determine whether the energy utilization index meets the requirements, specifically: The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization rate evaluation model to obtain an energy utilization rate index; Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate; comparing the utilization index deviation rate with a preset utilization index deviation rate threshold; If the utilization index deviation rate is greater than or equal to the utilization index deviation rate threshold, a low utilization rate message is sent; If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization rate message is sent.

8. An industrial energy saving management system based on big data, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises an industrial energy saving management method program based on big data, and the industrial energy saving management method program based on big data implements the following steps when executed by the processor: Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the device types to obtain characteristic information of energy-consuming devices; Processing the energy-consuming equipment characteristic information through a preset energy configuration model to obtain an energy configuration distribution profile of the energy-consuming equipment; Extracting energy configuration distribution feature information based on the energy configuration distribution portrait of the energy-consuming equipment and performing energy sub-region grid division using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map; Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain energy supply index of each sub-region; Obtaining the energy consumption equipment operation monitoring information of each sub-area and processing it through a preset energy consumption evaluation model to obtain an equipment energy consumption index; The energy supply index is processed in combination with the equipment energy consumption index to obtain an energy utilization index, which is compared with a preset utilization index to determine whether the energy utilization index meets the requirements.

9. The industrial energy saving management system based on big data according to claim 8 is characterized in that: The step of obtaining the summary information of energy-consuming devices in a preset area within a preset time period and classifying the types of devices to obtain characteristic information of energy-consuming devices is as follows: Obtain summary information of energy-consuming devices in a preset area within a preset time period, including energy-consuming device type information, energy-consuming device status information, energy consumption information of energy-consuming devices, energy-consuming device usage cycle information and fault record information; The equipment type is classified according to the energy-consuming equipment type information, energy-consuming equipment state information, energy-consuming equipment energy consumption information, energy-consuming equipment use cycle information and fault record information to obtain energy-consuming equipment characteristic information.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes an industrial energy saving management method program based on big data. When the industrial energy saving management method program based on big data is executed by a processor, the steps of an industrial energy saving management method based on big data as described in any one of claims 1 to 7 are implemented.

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