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

Abstract

The present application provides an industrial energy conservation management method, system and medium based on big data, which belongs to the field of energy management and big data technology. The method includes: obtaining summary information of energy-consuming equipment and classifying it, obtaining characteristic information of energy-consuming equipment and processing it through a preset energy configuration model, obtaining an energy configuration distribution portrait of energy-consuming equipment, extracting energy configuration distribution characteristic information, dividing the energy sub-region grid through a preset energy region division model, obtaining a cognitive map of energy configuration distribution characteristics of the sub-region, and then extracting energy configuration distribution characteristic data of each sub-region to obtain the energy supply index of each sub-region; obtaining operation monitoring information of energy-consuming equipment in each sub-region, obtaining equipment energy consumption index and then combining it with energy supply index to obtain energy utilization index and judge whether it meets the requirements. The present application can intuitively display the overall situation of energy configuration in the region, and realize the improvement of energy utilization efficiency and intelligent level of equipment management.

Description

Technical Field

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

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

[0003] In response to 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 summary information of energy-consuming equipment and classify equipment types, obtain characteristic information of energy-consuming equipment and process it through a preset energy configuration model, obtain energy configuration distribution portraits of energy-consuming equipment, extract energy configuration distribution characteristic information and perform energy sub-region grid division through a preset energy region division model, obtain sub-region energy configuration distribution characteristic cognitive maps, and then extract energy configuration distribution characteristic data of each sub-region, process to obtain energy supply index of each sub-region; obtain energy-consuming equipment operation monitoring information of each sub-region, process to obtain equipment energy consumption index and then combine it with energy supply index to obtain 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 intelligent level of equipment management.

[0005] This application provides an industrial energy conservation management method based on big data, comprising the following steps:

[0006] Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the equipment types to obtain characteristic information of the energy-consuming devices;

[0007] 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;

[0008] 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 cognitive map of sub-region energy configuration distribution features;

[0009] 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 the energy supply index of each sub-region;

[0010] 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;

[0011] 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.

[0012] Among them, in the industrial energy conservation management method based on big data described in this 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:

[0013] 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;

[0014] The energy-consuming device type is classified according to the energy-consuming device type information, energy-consuming device state information, energy consumption information of the energy-consuming device, energy-consuming device use cycle information and fault record information to obtain energy-consuming device feature information.

[0015] Among them, in the industrial energy conservation management method based on big data described in this application, the energy-consuming equipment characteristic information is processed through a preset energy configuration model to obtain an energy configuration distribution portrait of the energy-consuming equipment, specifically:

[0016] Extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy-consuming equipment characteristic information;

[0017] The energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information;

[0018] The equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information;

[0019] The energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity 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.

[0020] Among them, in the industrial energy conservation management method based on big data described in this application, 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 into energy sub-regions through a preset energy region division model to obtain a cognitive map of the sub-region energy configuration distribution features, specifically:

[0021] Extracting energy configuration distribution feature information based on 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;

[0022] 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;

[0023] 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;

[0024] 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.

[0025] Among them, in the industrial energy conservation management method based on big data described in this 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:

[0026] Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data;

[0027] 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.

[0028] Among them, in the industrial energy conservation management method based on big data described in this 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:

[0029] Obtaining operation monitoring information of energy-consuming equipment in each sub-area, extracting total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data, and total operation duration data;

[0030] 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.

[0031] Among them, in the industrial energy conservation management method based on big data described in this application, 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:

[0032] The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization evaluation model to obtain an energy utilization index;

[0033] Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate;

[0034] comparing the utilization index deviation rate with a preset utilization index deviation rate threshold;

[0035] 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;

[0036] If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization information is sent.

[0037] 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 for an industrial energy conservation management method based on big data, the program for an industrial energy conservation management method based on big data, when executed by the processor, implementing the following steps:

[0038] Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the equipment types to obtain characteristic information of the energy-consuming devices;

[0039] 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;

[0040] 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 cognitive map of sub-region energy configuration distribution features;

[0041] 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 the energy supply index of each sub-region;

[0042] 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;

[0043] 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.

[0044] In the big data-based industrial energy conservation management system described in this 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:

[0045] 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;

[0046] The energy-consuming device type is classified according to the energy-consuming device type information, energy-consuming device state information, energy consumption information of the energy-consuming device, energy-consuming device use cycle information and fault record information to obtain energy-consuming device feature information.

[0047] On the third aspect, the present application also provides a computer-readable storage medium, which 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, it implements the steps of an industrial energy saving management method based on big data as described in any one of the above items.

[0048] As can be seen from the above, the embodiment of the present application provides 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 the energy-consuming equipment, extracts the energy configuration distribution characteristic information and divides the energy sub-region grid through a preset energy region division model, obtains the sub-region energy configuration distribution characteristic cognitive map, and then extracts the energy configuration distribution characteristic data of each sub-region, processes and obtains the energy supply index of each sub-region; obtains the energy consumption equipment operation monitoring information of 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 to determine whether the energy utilization index meets the requirements. The present application scientifically subdivides a large area into multiple sub-regions, which can intuitively display the overall situation of energy configuration in the region, accurately reflect the energy supply and demand status of each sub-region and the energy consumption level of the equipment, and realizes the improvement of energy utilization efficiency and the level of intelligent equipment management, which is of great significance to promoting sustainable development and energy conservation and emission reduction.

[0049] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. 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 relevant drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A flowchart of an industrial energy conservation management method based on big data provided in an embodiment of the present application;

[0052] Figure 2 A flowchart of obtaining characteristic information of energy-consuming equipment in an industrial energy conservation management method based on big data provided in an embodiment of the present application;

[0053] Figure 3 A flowchart of obtaining an energy configuration distribution portrait of energy-consuming equipment in an industrial energy conservation management method based on big data provided in an embodiment of the present application;

[0054] Figure 4 A flowchart of obtaining a cognitive map of sub-region energy configuration distribution characteristics of an industrial energy conservation management method based on big data provided in an embodiment of the present application;

[0055] 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

[0056] 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 generally 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 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 fall within the scope of protection of the present application.

[0057] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, 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 must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0058] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for managing industrial energy conservation based on big data in some embodiments of the present application. This method for managing industrial energy conservation based on big data is used in terminal devices, such as computers and mobile terminals. This method for managing industrial energy conservation based on big data includes the following steps:

[0059] S101. Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the devices by type to obtain characteristic information of the energy-consuming devices;

[0060] S102: Process the energy-consuming device characteristic information using a preset energy configuration model to obtain an energy configuration distribution profile of the energy-consuming device;

[0061] S103, 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 cognitive map of sub-region energy configuration distribution features;

[0062] S104, extracting energy configuration distribution characteristic data of each sub-region based on the sub-region energy configuration distribution characteristic cognitive map, and processing to obtain an energy supply index of each sub-region;

[0063] S105: Obtaining the energy-consuming 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;

[0064] S106 , processing the energy supply index in combination with the equipment energy consumption index to obtain an energy utilization index, and comparing the energy utilization index with a preset utilization index to determine whether the energy utilization index meets the requirements.

[0065] 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 energy configuration distribution characteristic information and divides the energy sub-region grid through a preset energy region division model, obtains the sub-region energy configuration distribution characteristic cognitive map, and then extracts the energy configuration distribution characteristic data of each sub-region, processes and obtains the energy supply index of each sub-region; obtains the energy consumption equipment operation monitoring information of 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 to determine whether the energy utilization index meets the requirements. The present application scientifically subdivides a large area into multiple sub-regions, which can intuitively display the overall situation of energy configuration in the region, accurately reflect the energy supply and demand status of each sub-region and the energy consumption level of the equipment, help to timely discover and solve energy waste problems, achieve the improvement of energy utilization efficiency and the level of intelligent equipment management, help enterprises reduce energy consumption costs and improve economic benefits, and 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.

[0066] 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 method of obtaining summary information of energy-consuming equipment in a preset area within a preset time period and classifying the equipment types to obtain characteristic information of energy-consuming equipment is specifically as follows:

[0067] S201. 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;

[0068] S202: Classify the energy-consuming device type according to the energy-consuming device type information, energy-consuming device status information, energy consumption information, energy-consuming device usage cycle information, and fault record information to obtain energy-consuming device feature information.

[0069] To obtain characteristic information about energy-consuming devices, aggregated information about energy-consuming devices in a preset area within a preset time period is obtained, including device type information, device status information, energy consumption information, device lifecycle information, and fault history information. Device type information includes the device type (e.g., motor, lighting, air conditioning system, etc.), model, and manufacturer. Device status information refers to the device's current operating status (e.g., running, standby, off), as well as historical status change records. Energy consumption information refers to the device's energy consumption within a preset time period, which may be expressed in various forms, such as electricity, water, and gas. Lifecycle information refers to the device's operating time, number of starts, and downtime, used to assess the device's frequency of use and lifespan. Fault history information refers to faults that occurred within the preset time period, including fault time, fault type, repair measures, and repair results. Device type classification is then performed to obtain characteristic information about the energy-consuming devices.

[0070] Please refer to Figure 3 , Figure 3 This is a flowchart of obtaining an energy configuration distribution profile 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 configuration distribution profile of energy-consuming equipment is obtained by processing the characteristic information of the energy-consuming equipment through a preset energy configuration model, specifically:

[0071] S301, extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy-consuming equipment characteristic information;

[0072] S302, the energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information;

[0073] S303, the equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information;

[0074] S304: Process the energy consumption change trend information, energy consumption peak pattern information and seasonal energy consumption pattern information in combination 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.

[0075] To better understand energy distribution and usage and generate a profile of the energy configuration of energy-consuming devices, energy consumption parameter characteristics and device operating status characteristics are extracted based on the device's characteristic information. These include energy consumption trend information, peak energy consumption patterns, seasonal energy consumption patterns, and device start / stop frequency information and operating cycle information. Energy consumption trend information reflects the changing trend of a device's energy consumption over a preset time period, which may show an upward, downward, or fluctuating pattern. Peak energy consumption patterns identify peak periods of energy consumption, such as daily, weekly, or monthly peaks. Seasonal energy consumption patterns include seasonal variations in energy consumption, such as increased air conditioning energy consumption in summer and increased heating energy consumption in winter. Device start / stop frequency information records the number of times a device starts and stops within a preset time period, reflecting its operating frequency. Operating cycle information refers to the device's operating and downtime, as well as the periodic patterns of operation and downtime. The energy consumption trend information, peak energy consumption patterns, and seasonal energy consumption patterns, combined with the device start / stop frequency information and operating cycle information, are processed using a preset energy configuration model to generate a profile of the energy configuration of the energy-consuming devices. The portrait shows the equipment's energy consumption distribution, operating rules, and optimization suggestions for energy configuration.

[0076] Please refer to Figure 4 , Figure 4 This is a flowchart of obtaining a cognitive map of sub-region energy configuration distribution characteristics in a big data-based industrial energy conservation management method in some embodiments of the present application. According to an embodiment of the present invention, the energy configuration distribution characteristic information is extracted based on the energy configuration distribution profile of the energy-consuming equipment and the energy sub-region grid is divided using a preset energy region division model to obtain a cognitive map of sub-region energy configuration distribution characteristics, specifically:

[0077] S401. Extracting energy configuration distribution feature information based on 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;

[0078] S402: Process the energy type distribution information, energy distribution density information, and energy distribution geographic location information using a preset energy region division model to obtain sub-region grid energy distribution data;

[0079] 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;

[0080] S404: Process 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.

[0081] To more scientifically analyze energy distribution and obtain a cognitive map of sub-regional energy configuration distribution characteristics, energy configuration distribution characteristic information is extracted based on the energy configuration distribution profile of energy-consuming equipment. This includes energy type distribution information, energy distribution density information, and energy distribution location information. Energy type distribution information refers to the energy types (such as electricity, water, and gas) used by energy-consuming equipment and their distribution ratios, understanding the contribution of different energy types to overall energy consumption. Energy distribution density information refers to the energy distribution density within a region, that is, the amount of energy per unit area or volume, reflecting the concentration of energy in that region. Energy distribution location information records and analyzes the geographical location of energy distribution, including latitude, longitude, and altitude, providing a basis for subsequent energy zoning. This is then processed using a pre-set energy zoning model to obtain sub-regional grid energy distribution data, including grid energy capacity data, grid energy density data, sub-region energy distribution data, and sub-regional total energy capacity data. This energy region partitioning model divides a pre-defined region into several subregions and divides each subregion into grids. Grid energy capacity data reflects the total energy capacity or storage capacity within each grid. Grid energy density data refers to the energy distribution density within each grid, namely, the ratio of the amount of energy within the grid to its area or volume. Subregion energy distribution data records the distribution and characteristics of energy within each subregion, including energy type and distribution ratio. Subregion total energy capacity data summarizes the energy capacity of all grids within each subregion, reflecting the subregion's overall energy storage capacity. A pre-defined energy distribution feature cognitive model processes the grid energy capacity data, grid energy density data, subregion energy distribution data, and subregion total energy capacity data to generate a cognitive map of subregion energy configuration distribution features. This energy distribution feature cognitive model processes and analyzes the subregion grid energy distribution data, extracts energy configuration distribution features, and generates a cognitive map. This map displays the distribution, capacity, density, and relationships and trends of energy within the subregion.

[0082] Please refer to Figure 5 , Figure 5 This is a flowchart of obtaining the energy supply index of each sub-region 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 configuration distribution characteristic data of each sub-region is extracted based on 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:

[0083] S501. Extracting energy configuration distribution characteristic data of each sub-region based on the sub-region energy configuration distribution characteristic cognitive map, including supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data;

[0084] S502: Process the supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data using a preset energy supply model to obtain an energy supply index for each sub-region.

[0085] To obtain the energy supply index of each subregion, the energy configuration distribution characteristic data of each subregion is extracted based on the cognitive map of the energy configuration distribution characteristics of the subregion, including 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 subregion, such as electricity, water, gas, wind, solar energy, etc., as well as the proportion of these energy types in the subregion's energy supply; the energy type distribution characteristic data analyzes the spatial distribution characteristics of different energy types in each subregion, including the agglomeration areas and diffusion trends of energy types, to understand the geographical distribution patterns of energy types; the total energy supply data counts the total amount of energy supply in each subregion, including the supply of each energy type and the total supply, to assess the energy supply capacity of the subregion; the energy supply frequency data records the frequency of energy supply in each subregion, that is, the periodicity and stability of energy supply, including the variation characteristics of energy supply on time scales such as daily, weekly, monthly, and annual. The energy supply type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data are processed using a preset energy supply model to obtain the energy supply index of each subregion.

[0086] The calculation formula of the energy supply model is:

[0087] ;

[0088] 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. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0089] 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:

[0090] Obtaining operation monitoring information of energy-consuming equipment in each sub-area, extracting total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data, and total operation duration data;

[0091] 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.

[0092] 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 energy consumption peak 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 starts and stops 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, the preset energy consumption evaluation model is used for processing 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;

[0093] The calculation formula of the energy consumption evaluation model is:

[0094] ;

[0095] 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. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0096] 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 compared with a preset utilization index to determine whether the energy utilization index meets the requirements, specifically:

[0097] The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization evaluation model to obtain an energy utilization index;

[0098] Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate;

[0099] comparing the utilization index deviation rate with a preset utilization index deviation rate threshold;

[0100] 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;

[0101] If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization rate message is sent;

[0102] The calculation formula of the utilization evaluation model is:

[0103] ;

[0104] in, is the energy utilization index, is the energy supply index, is the equipment energy consumption index, is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0105] 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 to obtain an energy utilization index, and compared with the preset utilization index to obtain a utilization index deviation rate, and then compared 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, it means that the energy utilization rate is low, and there may be problems of energy waste or low equipment efficiency. A low utilization rate message 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 energy is effectively utilized. A high utilization rate message is sent to encourage relevant personnel to continue to maintain or further improve energy utilization efficiency.

[0106] According to an embodiment of the present invention, the further embodiment includes:

[0107] Acquiring temperature data of a preset area within the preset time period;

[0108] Comparing the temperature data with a first temperature threshold and a second temperature threshold respectively;

[0109] The first temperature threshold is less than the second temperature threshold;

[0110] If the temperature data is less than the first temperature threshold, a low temperature message is sent;

[0111] 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;

[0112] If the temperature data is greater than the second temperature threshold, high temperature information is sent.

[0113] 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 the 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.

[0114] According to an embodiment of the present invention, the further embodiment includes:

[0115] Obtaining air pressure change rate data for a preset area within the preset time period;

[0116] Comparing the air pressure change rate data with a preset air pressure change rate threshold to obtain an air pressure change rate deviation rate;

[0117] comparing the pressure change rate deviation rate with a preset pressure change rate deviation rate threshold;

[0118] If the pressure change rate deviation rate is greater than or equal to the pressure change rate deviation rate threshold, an air pressure abnormality message is sent;

[0119] If the pressure change rate deviation rate is less than the pressure change rate deviation rate threshold, the pressure normal information is sent.

[0120] Among them, in order to reduce the impact of air pressure changes on energy-consuming equipment, the air pressure change rate data of the preset area within the preset time period is obtained, and compared with the preset air pressure change rate threshold, the air pressure change rate deviation rate is obtained, 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 the air pressure abnormality information needs to be sent; 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 the normal air pressure information can be sent to relevant personnel, or no special notification is made.

[0121] The present invention also discloses an industrial energy conservation management system based on big data, comprising a memory and a processor. The memory includes a program for an industrial energy conservation management method based on big data. When the program is executed by the processor, the following steps are implemented:

[0122] Obtain summary information of energy-consuming devices in a preset area within a preset time period and classify the equipment types to obtain characteristic information of the energy-consuming devices;

[0123] 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;

[0124] 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 cognitive map of sub-region energy configuration distribution features;

[0125] 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 the energy supply index of each sub-region;

[0126] 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;

[0127] 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.

[0128] 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 energy configuration distribution characteristic information and divides the energy sub-region grid through a preset energy region division model, obtains the sub-region energy configuration distribution characteristic cognitive map, and then extracts the energy configuration distribution characteristic data of each sub-region, processes and obtains the energy supply index of each sub-region; obtains the energy consumption equipment operation monitoring information of 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 to determine whether the energy utilization index meets the requirements. The present application scientifically subdivides a large area into multiple sub-regions, which can intuitively display the overall situation of energy configuration in the region, accurately reflect the energy supply and demand status of each sub-region and the energy consumption level of the equipment, help to timely discover and solve energy waste problems, achieve the improvement of energy utilization efficiency and the level of intelligent equipment management, help enterprises reduce energy consumption costs and improve economic benefits, and 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.

[0129] 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:

[0130] 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;

[0131] The energy-consuming device type is classified according to the energy-consuming device type information, energy-consuming device state information, energy consumption information of the energy-consuming device, energy-consuming device use cycle information and fault record information to obtain energy-consuming device feature information.

[0132] To obtain characteristic information about energy-consuming devices, aggregated information about energy-consuming devices in a preset area within a preset time period is obtained, including device type information, device status information, energy consumption information, device lifecycle information, and fault history information. Device type information includes the device type (e.g., motor, lighting, air conditioning system, etc.), model, and manufacturer. Device status information refers to the device's current operating status (e.g., running, standby, off), as well as historical status change records. Energy consumption information refers to the device's energy consumption within a preset time period, which may be expressed in various forms, such as electricity, water, and gas. Lifecycle information refers to the device's operating time, number of starts, and downtime, used to assess the device's frequency of use and lifespan. Fault history information refers to faults that occurred within the preset time period, including fault time, fault type, repair measures, and repair results. Device type classification is then performed to obtain characteristic information about the energy-consuming devices.

[0133] 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:

[0134] Extracting energy consumption parameter characteristic information and equipment operation status characteristic information according to the energy-consuming equipment characteristic information;

[0135] The energy consumption parameter characteristic information includes energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity information;

[0136] The equipment operation status characteristic information includes equipment start and stop frequency information and operation cycle information;

[0137] The energy consumption change trend information, energy consumption peak regularity information and seasonal energy consumption regularity 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.

[0138] To better understand energy distribution and usage and generate a profile of the energy configuration of energy-consuming devices, energy consumption parameter characteristics and device operating status characteristics are extracted based on the device's characteristic information. These include energy consumption trend information, peak energy consumption patterns, seasonal energy consumption patterns, and device start / stop frequency information and operating cycle information. Energy consumption trend information reflects the changing trend of a device's energy consumption over a preset time period, which may show an upward, downward, or fluctuating pattern. Peak energy consumption patterns identify peak periods of energy consumption, such as daily, weekly, or monthly peaks. Seasonal energy consumption patterns include seasonal variations in energy consumption, such as increased air conditioning energy consumption in summer and increased heating energy consumption in winter. Device start / stop frequency information records the number of times a device starts and stops within a preset time period, reflecting its operating frequency. Operating cycle information refers to the device's operating and downtime, as well as the periodic patterns of operation and downtime. The energy consumption trend information, peak energy consumption patterns, and seasonal energy consumption patterns, combined with the device start / stop frequency information and operating cycle information, are processed using a preset energy configuration model to generate a profile of the energy configuration of the energy-consuming devices. The portrait shows the equipment's energy consumption distribution, operating rules, and optimization suggestions for energy configuration.

[0139] According to an embodiment of the present invention, the energy configuration distribution feature information is extracted based on the energy configuration distribution portrait of the energy-consuming equipment, and energy sub-region grid division is performed using a preset energy region division model to obtain a sub-region energy configuration distribution feature cognitive map, specifically:

[0140] Extracting energy configuration distribution feature information based on 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;

[0141] 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;

[0142] 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;

[0143] 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.

[0144] To more scientifically analyze energy distribution and obtain a cognitive map of sub-regional energy configuration distribution characteristics, energy configuration distribution characteristic information is extracted based on the energy configuration distribution profile of energy-consuming equipment. This includes energy type distribution information, energy distribution density information, and energy distribution location information. Energy type distribution information refers to the energy types (such as electricity, water, and gas) used by energy-consuming equipment and their distribution ratios, understanding the contribution of different energy types to overall energy consumption. Energy distribution density information refers to the energy distribution density within a region, that is, the amount of energy per unit area or volume, reflecting the concentration of energy in that region. Energy distribution location information records and analyzes the geographical location of energy distribution, including latitude, longitude, and altitude, providing a basis for subsequent energy zoning. This is then processed using a pre-set energy zoning model to obtain sub-regional grid energy distribution data, including grid energy capacity data, grid energy density data, sub-region energy distribution data, and sub-regional total energy capacity data. This energy region partitioning model divides a pre-defined region into several subregions and divides each subregion into grids. Grid energy capacity data reflects the total energy capacity or storage capacity within each grid. Grid energy density data refers to the energy distribution density within each grid, namely, the ratio of the amount of energy within the grid to its area or volume. Subregion energy distribution data records the distribution and characteristics of energy within each subregion, including energy type and distribution ratio. Subregion total energy capacity data summarizes the energy capacity of all grids within each subregion, reflecting the subregion's overall energy storage capacity. A pre-defined energy distribution feature cognitive model processes the grid energy capacity data, grid energy density data, subregion energy distribution data, and subregion total energy capacity data to generate a cognitive map of subregion energy configuration distribution features. This energy distribution feature cognitive model processes and analyzes the subregion grid energy distribution data, extracts energy configuration distribution features, and generates a cognitive map. This map displays the distribution, capacity, density, and relationships and trends of energy within the subregion.

[0145] According to an embodiment of the present invention, the energy configuration distribution characteristic data of each sub-region is extracted based on 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:

[0146] Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data;

[0147] 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.

[0148] To obtain the energy supply index of each subregion, the energy configuration distribution characteristic data of each subregion is extracted based on the cognitive map of the energy configuration distribution characteristics of the subregion, including 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 subregion, such as electricity, water, gas, wind, solar energy, etc., as well as the proportion of these energy types in the subregion's energy supply; the energy type distribution characteristic data analyzes the spatial distribution characteristics of different energy types in each subregion, including the agglomeration areas and diffusion trends of energy types, to understand the geographical distribution patterns of energy types; the total energy supply data counts the total amount of energy supply in each subregion, including the supply of each energy type and the total supply, to assess the energy supply capacity of the subregion; the energy supply frequency data records the frequency of energy supply in each subregion, that is, the periodicity and stability of energy supply, including the variation characteristics of energy supply on time scales such as daily, weekly, monthly, and annual. The energy supply type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data are processed using a preset energy supply model to obtain the energy supply index of each subregion.

[0149] The calculation formula of the energy supply model is:

[0150] ;

[0151] 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. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0152] 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:

[0153] Obtaining operation monitoring information of energy-consuming equipment in each sub-area, extracting total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data, and total operation duration data;

[0154] 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.

[0155] 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 energy consumption peak 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 starts and stops 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, the preset energy consumption evaluation model is used for processing 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;

[0156] The calculation formula of the energy consumption evaluation model is:

[0157] ;

[0158] 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. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0159] 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 compared with a preset utilization index to determine whether the energy utilization index meets the requirements, specifically:

[0160] The energy supply index is combined with the equipment energy consumption index and processed by a preset utilization evaluation model to obtain an energy utilization index;

[0161] Comparing the energy utilization index with a preset utilization index to obtain a utilization index deviation rate;

[0162] comparing the utilization index deviation rate with a preset utilization index deviation rate threshold;

[0163] 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;

[0164] If the utilization index deviation rate is less than the utilization index deviation rate threshold, a high utilization rate message is sent;

[0165] The calculation formula of the utilization evaluation model is:

[0166] ;

[0167] in, is the energy utilization index, is the energy supply index, is the equipment energy consumption index, is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy monitoring database).

[0168] 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 to obtain an energy utilization index, and compared with the preset utilization index to obtain a utilization index deviation rate, and then compared 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, it means that the energy utilization rate is low, and there may be problems of energy waste or low equipment efficiency. A low utilization rate message 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 energy is effectively utilized. A high utilization rate message is sent to encourage relevant personnel to continue to maintain or further improve energy utilization efficiency.

[0169] According to an embodiment of the present invention, the further embodiment includes:

[0170] Acquiring temperature data of a preset area within the preset time period;

[0171] comparing the temperature data with a preset first temperature threshold and a preset second temperature threshold respectively;

[0172] The first temperature threshold is less than the second temperature threshold;

[0173] If the temperature data is less than the first temperature threshold, a low temperature message is sent;

[0174] 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;

[0175] If the temperature data is greater than the second temperature threshold, high temperature information is sent.

[0176] 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 the 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.

[0177] According to an embodiment of the present invention, the further embodiment includes:

[0178] Obtaining air pressure change rate data for a preset area within the preset time period;

[0179] Comparing the air pressure change rate data with a preset air pressure change rate threshold to obtain an air pressure change rate deviation rate;

[0180] comparing the pressure change rate deviation rate with a preset pressure change rate deviation rate threshold;

[0181] If the pressure change rate deviation rate is greater than or equal to the pressure change rate deviation rate threshold, an air pressure abnormality message is sent;

[0182] If the pressure change rate deviation rate is less than the pressure change rate deviation rate threshold, the pressure normal information is sent.

[0183] Among them, in order to reduce the impact of air pressure changes on energy-consuming equipment, the air pressure change rate data of the preset area within the preset time period is obtained, and compared with the preset air pressure change rate threshold, the air pressure change rate deviation rate is obtained, 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 the air pressure abnormality information needs to be sent; 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 the normal air pressure information can be sent to relevant personnel, or no special notification is made.

[0184] The third aspect of the present invention provides a computer-readable storage medium, which 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, it implements the steps of an industrial energy saving management method based on big data as described in any one of the above items.

[0185] The present invention discloses a method, system and medium for industrial energy conservation management based on big data. The method comprises the following steps: obtaining summary information of energy-consuming equipment and classifying equipment types; obtaining characteristic information of energy-consuming equipment and processing it through a preset energy configuration model; obtaining an energy configuration distribution portrait of energy-consuming equipment; extracting characteristic information of energy configuration distribution and performing grid division of energy sub-regions through a preset energy region division model; obtaining a cognitive map of energy configuration distribution characteristics of sub-regions; extracting characteristic data of energy configuration distribution of each sub-region, and processing to obtain an energy supply index of each sub-region; obtaining operation monitoring information of energy-consuming equipment in each sub-region and processing it through a preset energy consumption evaluation model; obtaining an equipment energy consumption index combined with an energy supply index; obtaining an energy utilization index and comparing it with a preset utilization index to determine 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 reflect the energy supply and demand status of each sub-region and the energy consumption level of the equipment, thereby improving energy utilization efficiency and the level of intelligent equipment management, and is of great significance for promoting sustainable development and energy conservation and emission reduction.

[0186] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0187] 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 across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0188] 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.

[0189] Those skilled in the art will 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. When the program is executed, it 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.

[0190] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable 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 equipment types to obtain characteristic information of the 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 cognitive map of sub-region energy configuration distribution features; 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 the 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; Processing the energy supply index in combination with the equipment energy consumption index to obtain an energy utilization index and comparing it with a preset utilization index to determine whether the energy utilization index meets the requirements; The energy configuration distribution feature information is extracted based on 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 based on 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; 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; The energy configuration distribution characteristic data of each sub-region is extracted based on the sub-region energy configuration distribution characteristic cognitive map, and the energy supply index of each sub-region is obtained by processing the data. Specifically, Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data; Obtaining an energy supply index for each sub-region by processing the supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data using a 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. is the preset characteristic coefficient; Also includes: Obtaining air pressure change rate data for a preset area within the preset time period; Comparing the air pressure change rate data with a preset air pressure change rate threshold to obtain an air pressure change rate deviation rate; comparing the pressure change rate deviation rate with a preset 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 air pressure abnormality message is sent; If the pressure change rate deviation rate is less than the pressure change rate deviation rate threshold, the pressure normal information is sent.

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 the 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 energy-consuming device type is classified according to the energy-consuming device type information, energy-consuming device state information, energy consumption information of the energy-consuming device, energy-consuming device use cycle information and fault record information to obtain energy-consuming device feature 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 regularity information and seasonal energy consumption regularity 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 1 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 operation monitoring information of energy-consuming equipment in each sub-area, extracting total energy consumption data, unit energy consumption data, energy consumption peak data, operation frequency data, and total operation duration 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.

5. The industrial energy saving management method based on big data according to claim 4 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 compared 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 evaluation model to obtain an energy utilization 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 information is sent.

6. An industrial energy conservation management system based on big data, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a program for industrial energy conservation management method based on big data, and when the program for industrial energy conservation management method 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 equipment types to obtain characteristic information of the 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 cognitive map of sub-region energy configuration distribution features; 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 the 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; Processing the energy supply index in combination with the equipment energy consumption index to obtain an energy utilization index and comparing it with a preset utilization index to determine whether the energy utilization index meets the requirements; The energy configuration distribution feature information is extracted based on 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 based on 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; 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; The energy configuration distribution characteristic data of each sub-region is extracted based on the sub-region energy configuration distribution characteristic cognitive map, and the energy supply index of each sub-region is obtained by processing the data. Specifically, Extracting energy configuration distribution characteristic data of each sub-region according to the sub-region energy configuration distribution characteristic cognitive map, including supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data; Obtaining an energy supply index for each sub-region by processing the supplied energy type data, energy type distribution characteristic data, total energy supply data, and energy supply frequency data using a 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. is the preset characteristic coefficient; Also includes: Obtaining air pressure change rate data for a preset area within the preset time period; Comparing the air pressure change rate data with a preset air pressure change rate threshold to obtain an air pressure change rate deviation rate; comparing the pressure change rate deviation rate with a preset 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 air pressure abnormality message is sent; If the pressure change rate deviation rate is less than the pressure change rate deviation rate threshold, the pressure normal information is sent.

7. The industrial energy conservation management system based on big data according to claim 6 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 the 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 energy-consuming device type is classified according to the energy-consuming device type information, energy-consuming device state information, energy consumption information of the energy-consuming device, energy-consuming device use cycle information and fault record information to obtain energy-consuming device feature information.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a big data-based industrial energy energy-saving management method program. When the big data-based industrial energy energy-saving management method program is executed by a processor, it implements the steps of a big data-based industrial energy energy-saving management method as described in any one of claims 1 to 5.

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