Smart park energy consumption management system and method based on the Internet of Things

Abstract

The present invention relates to the field of energy consumption management, specifically to an energy consumption management system and method for a smart park based on the Internet of Things; the method comprises the following steps: obtaining electricity consumption behavior data, user data, and electricity consumption time data of the smart park, and constructing a park electricity consumption portrait model and a factory electricity consumption portrait model; obtaining behavioral characteristic data, a park management value, and a production group electricity consumption portrait model based on the park electricity consumption portrait model and the factory electricity consumption portrait model; obtaining behavioral characteristic data and production equipment data through the production group electricity consumption portrait model, and constructing a production process line; obtaining production slices and production management values ​​based on the production group electricity consumption portrait model and the behavioral characteristic data; obtaining an equipment management coefficient based on the production process line, the park management value, and the production management value; and performing energy consumption management on factory areas within the park based on the production slices and the production management coefficient. The present invention can improve energy utilization.

Description

Technical Field

[0001] The present invention relates to the field of energy consumption management, and in particular to an energy consumption management system and method for a smart park based on the Internet of Things. Background Art

[0002] With the acceleration of industrialization and urbanization, industrial parks (especially industrial parks, science and technology parks, etc.) have become major energy consumers. The world is currently facing problems such as energy shortages and environmental pollution. Energy conservation, emission reduction and green development have become important directions. The Internet of Things, as a key technology for realizing the interconnection of all things, is centered on the perception, identification and management of the physical world through various sensors, communication networks and platforms. In smart parks, the Internet of Things provides technical support for the real-time collection, remote monitoring and intelligent control of energy consumption data, becoming the foundation for building energy efficiency management systems.

[0003] In the existing technology, the factory areas within the park have failed to carry out refined energy consumption management, and the overall energy consumption management of the park often ignores the impact of the energy consumption of the factory areas on the energy consumption of the park; the equipment production situation and production process in the factory area have an impact on the energy consumption of the factory area. How to manage the energy consumption of the factory areas within the park is a problem we need to solve. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the background technology and propose an energy consumption management method for a smart park based on the Internet of Things.

[0005] The technical solution of the present invention: a method for managing energy consumption in a smart park based on the Internet of Things, comprising the following steps:

[0006] S1. Obtain electricity usage behavior data, user data, and electricity usage time data of the smart park, and build a park electricity usage profile model and a factory electricity usage profile model based on the electricity usage behavior data and electricity usage time data;

[0007] S2. Analyze the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models;

[0008] S3. Obtain behavioral characteristic data and production equipment data through the production group electricity usage profile model, build a production process line, obtain production slices and production management values ​​based on the production group electricity usage profile model and behavioral characteristic data, and obtain the equipment management coefficient based on the production process line, park management value, and production management value;

[0009] S4. Based on the production slices and the production management coefficient, obtain high production management slices and low production management slices, perform energy consumption management on the factory areas within the park, and generate an energy consumption management strategy.

[0010] Preferably, the process of obtaining the electricity consumption behavior data, user data, and electricity consumption time data of the smart park, and constructing the park electricity consumption profile model and the factory electricity consumption profile model based on the electricity consumption behavior data and the electricity consumption time data includes:

[0011] The smart campus includes factory areas and non-factory areas. Power monitoring points are set up in each power circuit in the power distribution room of the smart campus and in the user-side distribution box to obtain power consumption behavior data, user data, and power consumption time data within the smart sub-campus. Power consumption behavior data includes circuit voltage, circuit current, power load, and power consumption. Power consumption time data includes power consumption time and area tags. Area tags include factory tags and non-factory tags. User data includes user information, user-to-distribution box relationships, and distribution box-to-distribution box relationships.

[0012] Set the loop load label for each power circuit; construct the power voltage change curve, power current change curve, power load change curve, and power consumption change curve respectively based on the loop power voltage, loop power current, power load, power consumption, and power consumption time data of the power consumption behavior data;

[0013] According to the relationship between users' electrical boxes and the relationship between electrical box circuits, the power voltage change curve, power current change curve, power load change curve and power consumption change curve are associated with the user information of the end user to build a campus power consumption portrait model. The campus power consumption portrait model containing the factory label is recorded as the factory power consumption portrait model.

[0014] Preferably, the process of analyzing the park electricity consumption profile model to obtain behavioral characteristic data and park management values ​​includes:

[0015] Based on time series analysis, according to the power load change curve and power consumption change curve of the park power consumption portrait model, the periodic component and peak-valley component are obtained, and the power load period value of the periodic component and the power load peak value, power load valley value, power consumption peak value, power consumption valley value and power consumption time corresponding to the power consumption period value and the peak-valley component are obtained, and recorded as behavioral characteristic data; the behavioral characteristic data of the park power consumption portrait model is obtained according to the loop load label, and the obtained behavioral characteristic data is processed by the clustering method to classify the park power consumption portrait model to obtain a park-type power consumption portrait model. According to the park area corresponding to the park-type power consumption portrait model, the park group area is obtained;

[0016] Based on the behavioral characteristic data of the park-type electricity consumption profile model, the park management value of the park group area is obtained.

[0017] Preferably, the process of analyzing the factory electricity consumption profile model to obtain the production group electricity consumption profile model is as follows:

[0018] The power consumption change curves of all factory power consumption portrait models of each park-type power consumption portrait model are subjected to feature extraction and classification. The process of feature extraction and classification is as follows: a feature window is set, and the feature window is used to identify and extract the power features of the input power consumption change curve. The power features include power consumption cycles and power consumption change points, and according to the power consumption cycles and power consumption change points, the change rate is obtained, and the classification change rate is set; the change rate and the classification change rate are analyzed to obtain the power frequency points; according to the power consumption time of the power frequency points and the total power consumption time of the power consumption change curve, the frequency value is obtained; the frequency classification interval is set, and the frequency classification interval and the frequency value are analyzed to obtain the factory group-type power consumption portrait model; according to the power consumption of the power consumption change curve of each factory group-type power consumption portrait model, the total power consumption value of the factory is obtained, and the factory group-type power consumption portrait model corresponding to the maximum total power consumption value of the factory group is recorded as the production group power consumption portrait model.

[0019] Preferably, the process of obtaining behavioral characteristic data and production equipment data through the production group electricity consumption profile model and constructing a production process line includes:

[0020] Obtain behavioral characteristic data of the production group's electricity consumption profile model, recorded as equipment behavior data. The equipment behavior data includes equipment power load cycle value, equipment power consumption cycle value, equipment power load peak value, equipment power load valley value, equipment power consumption peak value, equipment power consumption valley value, and equipment power consumption time;

[0021] Obtain production equipment data corresponding to electricity usage time; production equipment data includes production process, operation cycle and material demand; production process includes production tasks, production time intervals and production equipment; based on production tasks and production equipment, obtain production nodes and process edges, and based on production nodes, process edges and material demand, obtain production process lines;

[0022] When the equipment power consumption cycle value or the equipment power load cycle value is equal to the operation cycle, the power consumption change curve corresponding to the equipment power consumption cycle value or the power load change curve corresponding to the equipment power load cycle value is associated with the production node corresponding to the operation cycle; and the load evaluation interval is set.

[0023] Preferably, the process of obtaining the production slice and the production management value based on the production group power consumption profile model and the behavioral characteristic data, and obtaining the equipment management coefficient based on the production process line, the park management value and the production management value includes:

[0024] Performing production energy consumption analysis on the power load change curve and the power consumption change curve of each production node, the production energy consumption analysis process is as follows: based on the cycle of the power load change curve, analyzing the power load and the load evaluation interval to obtain normal production slices, low production slices, low abnormal production slices, high production slices, and high abnormal production slices; obtaining normal production power, low production power, low abnormal power, high production power, and high abnormal power through normal production slices, low production slices, low abnormal production slices, high production slices, and high abnormal production slices, and recording the normal production slices, low production slices, low abnormal production slices, high production slices, and high abnormal production slices as production slices;

[0025] Analyze the production process line to obtain production management value of the factory area;

[0026] The equipment management value is obtained based on the production process line, park management value and production management value; the equipment management coefficient is obtained based on each equipment management value.

[0027] Preferably, according to the production slices and the production management coefficient, high production management slices and low production management slices are obtained, and energy consumption management is performed on the factory areas within the park. The process of generating the energy consumption management strategy includes:

[0028] The maximum operating cycle in the factory area is set as the energy consumption management cycle. The production management cycle of the production equipment is obtained based on the production management coefficient and the energy consumption management cycle. The production ratio is obtained based on the low production slice and high production slice of the production equipment.

[0029] According to the production management cycle, production ratio, low production slice and high production slice, high production management slice and low production management slice are obtained, and the low production management slices of multiple production equipment are staggered aligned with the high production management slices of multiple production equipment. The staggered alignment refers to the alignment of the time period of the low production management slices of each different production equipment with the time of the high production management slices of each different production equipment, and setting the standard total power value; ensure that the time period of the low production management slice is consistent with the time period of the high production management slice, and the sum of the low production power value of the low production management slice and the high production power value of the high production management slice is consistent with the standard total power value. The production time of the production equipment is managed through the high production management slice and the low production management slice to obtain the energy consumption management strategy.

[0030] The present invention also discloses an energy consumption management system for a smart park based on the Internet of Things, including a management center, wherein the management center is communicatively connected to a data acquisition module, a data analysis module, a data processing module, and an energy consumption management module:

[0031] The data acquisition module is used to obtain the electricity consumption behavior data, user data, and electricity consumption time data of the smart park. Based on the electricity consumption behavior data and electricity consumption time data, the park electricity consumption profile model and the factory electricity consumption profile model are constructed;

[0032] The data analysis module is used to analyze the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models;

[0033] The data processing module is used to obtain behavioral characteristic data and production equipment data through the production group electricity consumption profile model, build a production process line, obtain production slices and production management values ​​based on the production group electricity consumption profile model and behavioral characteristic data, and obtain the equipment management coefficient based on the production process line, park management value and production management value;

[0034] The energy consumption management module is used to obtain high production management slices and low production management slices based on production slices and production management coefficients, and to manage energy consumption in factory areas within the park and generate energy consumption management strategies.

[0035] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0036] By building park and factory electricity usage profile models, we can gain a more comprehensive understanding of electricity usage in the park as a whole and in factory areas. These models can be used to obtain behavioral characteristic data, park management values, and production group electricity usage profile models, improving our ability to conduct refined park analysis and facilitating segmented management and analysis of each park area.

[0037] Through the production process line, the relationship between production and energy consumption is analyzed to improve energy efficiency. With the help of production slices, the production time of production equipment is managed and controlled, and the equipment management coefficient helps to manage the energy consumption of production equipment in a combined manner. With the help of high production management slices and low production management slices, energy consumption management is carried out in factory areas within the park to improve the precision and flexibility of energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Example 1, as Figure 1 As shown, the energy consumption management method of a smart park based on the Internet of Things proposed by the present invention includes the following steps:

[0040] S1. Obtain electricity usage behavior data, user data, and electricity usage time data of the smart park, and build a park electricity usage profile model and a factory electricity usage profile model based on the electricity usage behavior data and electricity usage time data;

[0041] S2. Analyze the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models;

[0042] S3. Obtain behavioral characteristic data and production equipment data through the production group electricity usage profile model, build a production process line, obtain production slices and production management values ​​based on the production group electricity usage profile model and behavioral characteristic data, and obtain the equipment management coefficient based on the production process line, park management value, and production management value;

[0043] S4. Based on the production slices and the production management coefficient, obtain high production management slices and low production management slices, perform energy consumption management on the factory areas within the park, and generate an energy consumption management strategy.

[0044] It should be further explained that, in the specific implementation process, the process of obtaining the electricity consumption behavior data, user data, and electricity consumption time data of the smart park, and constructing the park electricity consumption profile model and the factory electricity consumption profile model based on the electricity consumption behavior data and electricity consumption time data is as follows:

[0045] The smart park includes factory areas and non-factory areas; power monitoring points are set up. The power monitoring points are set up in each power circuit in the power distribution room of the smart park and in the user-side distribution box to obtain power consumption behavior data, user data and power consumption time data in the smart sub-park. The power monitoring points are equipped with Internet of Things devices, including meters, multi-function instruments, sensors, etc.

[0046] The electricity usage behavior data includes loop power voltage, loop power current, power load and power consumption;

[0047] The power usage time data includes power usage time and area tags; the area tags include factory tags and non-factory tags. When the power monitoring point obtains data from the factory area or non-factory area, the factory tag or non-factory tag is marked in the power usage time;

[0048] The user data includes user information, user-distribution box relationship, and distribution box loop relationship; the user information includes basic information such as user name, address, and electricity account number; the user-distribution box relationship refers to the power distribution relationship between the end user and the user-side distribution box; the distribution box loop relationship refers to the transmission relationship between the user-side distribution box and the loop;

[0049] Set up an IoT center; upload the acquired electricity usage behavior data, user data, and electricity usage time data to the IoT center, and clean, convert, and aggregate the electricity usage behavior data, user data, and electricity usage time data through data processing technology;

[0050] Set a circuit load tag for each power circuit, and store the power load and power consumption time of each power circuit in the corresponding load tag;

[0051] Based on the loop power voltage, loop power current, power load, power consumption and power consumption time data of the power consumption behavior data, a power voltage change curve, a power current change curve, a power load change curve and a power consumption change curve are constructed respectively;

[0052] According to the relationship between users' electrical boxes and the relationship between electrical box circuits, the electricity voltage change curve, electricity current change curve, electricity load change curve and electricity consumption change curve corresponding to the electricity consumption behavior data and the electricity consumption time data are associated with the user information of the end user to build a campus electricity consumption portrait model, and the campus electricity consumption portrait model containing the factory label is recorded as the factory electricity consumption portrait model.

[0053] It should be further explained that during the specific implementation process, the process of analyzing the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models is as follows:

[0054] Based on time series analysis, the power load change curve and the power consumption change curve of the park power consumption portrait model are processed to obtain periodic components and peak-valley components, and the power load period value of the periodic component and the power load peak value, power load valley value, power consumption peak value, power consumption valley value and power consumption time corresponding to the power consumption period value and the peak-valley component are obtained, and recorded as behavioral characteristic data; the behavioral characteristic data of the park power consumption portrait model is obtained through the loop load label, and the obtained behavioral characteristic data is processed through the clustering method to classify the park power consumption portrait model to obtain a park-type power consumption portrait model, and the park areas corresponding to the park-type power consumption portrait model are divided into a group, recorded as park group areas;

[0055] Based on the behavioral characteristic data of the park-type electricity consumption profile model, the park management value of the park group area is obtained;

[0056] ;

[0057] in, is the park management value of the park group area numbered u; a is the number of park power consumption profile models in the park group area; The number of peak power consumption of the power consumption portrait model of each park; The number of electricity consumption valleys in the electricity consumption profile model of each park; The power consumption cycle value of the power consumption portrait model of each park;

[0058] The power consumption change curves of all factory power consumption portrait models of each park-type power consumption portrait model are subjected to feature extraction and classification. The process of feature extraction and classification is as follows: setting a feature window, the feature window is used to identify and extract the power features of the input power consumption change curve, the power features include power consumption cycles and power consumption change points, and calculating the change rate between adjacent power consumption change points of each power consumption cycle, and setting the classification change rate; when the change rate between adjacent power consumption change points is less than or equal to the classification change rate, the adjacent power consumption change points are recorded as power consumption same-frequency points; the power consumption time corresponding interval of the power consumption same-frequency point is calculated with the ratio of the total power consumption time corresponding interval of the power consumption change curve to obtain the same-frequency value; setting the same-frequency classification interval, the same-frequency classification interval includes the classification upper limit interval and the classification lower limit interval. If the maximum difference and the minimum difference between multiple same-frequency values ​​belong to the same-frequency classification interval, the power consumption portraits corresponding to the same-frequency values ​​are grouped together and recorded as the factory group power consumption portrait model;

[0059] The electricity consumption of the electricity consumption change curve of each factory group electricity consumption portrait model is summed up and recorded as the total factory electricity consumption value. The factory group electricity consumption portrait model corresponding to the maximum total factory group electricity consumption value is recorded as the production group electricity consumption portrait model.

[0060] It should be further explained that in the specific implementation process, the production group electricity consumption profile model is used to obtain behavioral characteristic data and production equipment data, and the production process line is constructed. Based on the production group electricity consumption profile model and behavioral characteristic data, the production slice and production management value are obtained. Based on the production process line, park management value and production management value, the equipment management coefficient is obtained as follows:

[0061] Obtain behavioral characteristic data of the production group's electricity consumption profile model, recorded as equipment behavior data, wherein the equipment behavior data includes equipment electricity load cycle value, equipment electricity consumption cycle value, equipment electricity load peak value, equipment electricity load valley value, equipment electricity consumption peak value, equipment electricity consumption valley value, and equipment electricity consumption time;

[0062] Obtain production equipment data corresponding to electricity usage time; the production equipment data includes production processes, operating cycles, and material requirements; the production processes include production tasks, production time intervals, and production equipment; divide the factory areas within the smart park, and based on production tasks and production equipment, record the production equipment as production nodes, record the production tasks as process edges, link the production nodes corresponding to the production equipment in the production process through the process edges corresponding to the production tasks, store the material requirements in the corresponding production nodes, and obtain the production process line;

[0063] When the equipment power consumption cycle value or the equipment power load cycle value is equal to the operation cycle, the power consumption change curve corresponding to the equipment power consumption cycle value or the power load change curve corresponding to the equipment power load cycle value is associated with the production node corresponding to the operation cycle; a load assessment interval is set, and the load assessment interval includes a load assessment upper limit value and a load assessment lower limit value; the load assessment upper limit value is the peak value of the equipment power load, and the load assessment lower limit value is the valley value of the equipment power load;

[0064] The power load change curve and power consumption change curve of each production node are used for production energy consumption analysis. The production energy consumption analysis process is as follows: based on the cycle of the power load change curve, when the power load belongs to the load evaluation interval, the power consumption time period corresponding to the power load is recorded as a normal production slice; when the power load is equal to the load evaluation lower limit, the power consumption time period corresponding to the power load is recorded as a low production slice; when the power load is less than the load evaluation lower limit, the power consumption time period corresponding to the power load is recorded as a low abnormal production slice; when the power load is equal to the load evaluation upper limit, the power consumption time period corresponding to the power load is recorded as a low abnormal production slice. The segment is recorded as a high production slice; when the power load is greater than the upper limit of the load assessment, the power consumption time period corresponding to the power load is recorded as a high abnormal production slice; through the normal production slice, low production slice, low abnormal production slice, high production slice and high abnormal production slice, the cumulative power consumption of each production slice in the power consumption change curve is calculated to obtain the normal production power, low production power, low abnormal power, high production power and high abnormal power, and store them in the corresponding production slice, the production slice includes the normal production slice, low production slice, low abnormal production slice, high production slice and high abnormal production slice;

[0065] It should be further explained that, in the specific implementation process, in particular, production slices containing flexible loads are identified by deep learning technology based on the flexible load's "load adjustment according to grid demand or price signals" feature. These production slices corresponding to the load that can be adjusted according to grid demand or price signals are recorded as flexible load production slices.

[0066] Analyze the production process line to obtain production management value of the factory area;

[0067] ;

[0068] in, The production management value of the production equipment in factory area number h; The operating cycle of the production equipment; The maximum operating cycle within the factory area; The time value corresponding to the high production slice of the production equipment; The maximum time value corresponding to the high production slice of the production equipment in the factory area; The time value corresponding to the low production slice of the production equipment; The maximum time value corresponding to the low production slice of the production equipment in the factory area;

[0069] The park management value of the park group area is multiplied by the production management value of the factory area of ​​the corresponding park group area to obtain the equipment management value; the equipment management value is proportionally calculated to obtain the production ratio corresponding to the equipment, which is recorded as the equipment management coefficient.

[0070] It should be further explained that in the specific implementation process, high production management slices and low production management slices are obtained based on the production slices and production management coefficients, and energy consumption management is performed on the factory areas within the park. The process of generating the energy consumption management strategy is as follows:

[0071] Set the maximum operating cycle in the factory area as the energy consumption management cycle, multiply the production management coefficient by the energy consumption management cycle to obtain the production management cycle of the production equipment, and calculate the ratio of the time intervals corresponding to the low production slice and the high production slice of the production equipment to obtain the production ratio;

[0072] The production ratio is allocated through the production management cycle, and the time intervals within the low production slice and the high production slice are changed according to the obtained allocation results to obtain high production management slices and low production management slices. The low production management slices of multiple production equipment are staggered and aligned with the high production management slices of multiple production equipment. The staggered alignment refers to the alignment of the time periods of the low production management slices of different production equipment with the time periods of the high production management slices of different production equipment, and setting the standard total power value; ensuring that the time period of the low production management slice is consistent with the time period of the high production management slice, and the total low production power value of the low production management slice is consistent with the high production management slice. The sum of the total high production power values ​​of the production management slice is consistent with the total standard power value. The production time of the production equipment is managed through the high production management slice and the low production management slice to obtain the energy consumption management strategy. The low abnormal production slice and the high abnormal production slice are sent to the production equipment repair personnel to check the production equipment failure, repair the production equipment, and monitor the real-time production process of the factory area through the production process line. If it is inconsistent with the production equipment data in the production process line, the production process line is updated, energy consumption management is re-performed, and the energy consumption management strategy is uploaded to the Internet of Things to regulate the production time of the production equipment through the Internet of Things.

[0073] It should be further explained that, in the specific implementation process, the above process is particularly applicable to flexible load production slices, and through the above process, energy consumption management of flexible loads can be achieved.

[0074] In the second embodiment, the energy consumption management system for a smart park based on the Internet of Things proposed in the present invention is applied to the energy consumption management method for a smart park based on the Internet of Things described in the first embodiment, and specifically includes a management center, which is communicatively connected to a data acquisition module, a data analysis module, a data processing module, and an energy consumption management module:

[0075] The data acquisition module is used to obtain the electricity consumption behavior data, user data, and electricity consumption time data of the smart park. Based on the electricity consumption behavior data and electricity consumption time data, the park electricity consumption profile model and the factory electricity consumption profile model are constructed;

[0076] The data analysis module is used to analyze the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models;

[0077] The data processing module is used to obtain behavioral characteristic data and production equipment data through the production group electricity consumption profile model, build a production process line, obtain production slices and production management values ​​based on the production group electricity consumption profile model and behavioral characteristic data, and obtain the equipment management coefficient based on the production process line, park management value and production management value;

[0078] The energy consumption management module is used to obtain high production management slices and low production management slices based on production slices and production management coefficients, and to manage energy consumption in factory areas within the park and generate energy consumption management strategies.

[0079] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. The energy consumption management method of a smart park based on the Internet of Things is characterized by: The following steps are involved: S1. Obtain electricity usage behavior data, user data, and electricity usage time data of the smart park, and build a park electricity usage profile model and a factory electricity usage profile model based on the electricity usage behavior data and electricity usage time data; S2. Analyze the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models; S3. Obtain behavioral characteristic data and production equipment data through the production group electricity usage profile model, build a production process line, obtain production slices and production management values ​​based on the production group electricity usage profile model and behavioral characteristic data, and obtain the equipment management coefficient based on the production process line, park management value, and production management value; S4. Based on the production slices and production management coefficients, obtain high production management slices and low production management slices, manage energy consumption in the factory areas within the park, and generate energy consumption management strategies. The process of obtaining the smart park's electricity usage behavior data, user data, and electricity usage time data and building the park electricity usage profile model and the factory electricity usage profile model based on the electricity usage behavior data and electricity usage time data includes: The smart campus includes factory areas and non-factory areas. Power monitoring points are set up in each power circuit in the power distribution room of the smart campus and in the user-side distribution box to obtain power consumption behavior data, user data, and power consumption time data within the smart sub-campus. Power consumption behavior data includes circuit power voltage, circuit power current, power load, and power consumption; power consumption time data includes power consumption time and area tags; area tags include factory tags and non-factory tags; user data includes user information, user-to-box relationship, and box-to-circuit relationship. Set the loop load label for each power circuit; construct the power voltage change curve, power current change curve, power load change curve, and power consumption change curve respectively based on the loop power voltage, loop power current, power load, power consumption, and power consumption time data of the power consumption behavior data; Based on the relationships between users' electrical boxes and electrical box circuits, the voltage, current, load, and power consumption curves are associated with the end-user's information to build a campus electricity usage profile model. The campus electricity usage profile model containing the factory tag is recorded as the factory electricity usage profile model. The process of analyzing the campus electricity usage profile model to obtain behavioral characteristic data and campus management values ​​includes: Based on time series analysis, according to the power load change curve and power consumption change curve of the park power consumption portrait model, the periodic component and peak-valley component are obtained, and the power load period value of the periodic component and the power load peak value, power load valley value, power consumption peak value, power consumption valley value and power consumption time corresponding to the power consumption period value and the peak-valley component are obtained, and recorded as behavioral characteristic data; the behavioral characteristic data of the park power consumption portrait model is obtained according to the loop load label, and the obtained behavioral characteristic data is processed by the clustering method to classify the park power consumption portrait model to obtain a park-type power consumption portrait model. According to the park area corresponding to the park-type power consumption portrait model, the park group area is obtained; Based on the behavioral characteristic data of the park-type electricity consumption profile model, the park management value of the park group area is obtained; The process of obtaining behavioral characteristic data and production equipment data through the production team electricity consumption profile model and building a production process line includes: Obtain behavioral characteristic data of the production group's electricity consumption profile model, recorded as equipment behavior data. The equipment behavior data includes equipment power load cycle value, equipment power consumption cycle value, equipment power load peak value, equipment power load valley value, equipment power consumption peak value, equipment power consumption valley value, and equipment power consumption time; Obtain production equipment data corresponding to electricity usage time; production equipment data includes production process, operation cycle and material demand; production process includes production tasks, production time intervals and production equipment; based on production tasks and production equipment, obtain production nodes and process edges, and based on production nodes, process edges and material demand, obtain production process lines; When the equipment power consumption cycle value or the equipment power load cycle value is equal to the operation cycle, the power consumption change curve corresponding to the equipment power consumption cycle value or the power load change curve corresponding to the equipment power load cycle value is associated with the production node corresponding to the operation cycle; set the load evaluation interval; Based on the production group's electricity consumption profile model and behavioral characteristic data, the production slice and production management value are obtained. Based on the production process line, park management value, and production management value, the equipment management coefficient is obtained. The process includes: Performing production energy consumption analysis on the power load change curve and the power consumption change curve of each production node, the production energy consumption analysis process is as follows: based on the cycle of the power load change curve, analyzing the power load and the load evaluation interval to obtain normal production slices, low production slices, low abnormal production slices, high production slices, and high abnormal production slices; obtaining normal production power, low production power, low abnormal power, high production power, and high abnormal power through normal production slices, low production slices, low abnormal production slices, high production slices, and high abnormal production slices, and recording the normal production slices, low production slices, low abnormal production slices, high production slices, and high abnormal production slices as production slices; Analyze the production process line to obtain production management value of the factory area; The equipment management value is obtained based on the production process line, park management value and production management value; the equipment management coefficient is obtained based on each equipment management value.

2. The energy consumption management method for a smart park based on the Internet of Things according to claim 1 is characterized in that: The process of analyzing the factory electricity consumption profile model and obtaining the production group electricity consumption profile model is as follows: Perform feature extraction and classification on the power consumption change curves of all factory power consumption profile models of each park-type power consumption profile model. The feature extraction and classification process is as follows: set a feature window, which is used to identify and extract the power features of the input power consumption change curve. The power features include the power consumption cycle and the power consumption change point. Based on the power consumption cycle and the power consumption change point, the change rate is obtained and the classification change rate is set; Analyze the change rate and classification change rate to obtain the power consumption frequency point; Obtain the same frequency value based on the electricity consumption time at the same frequency point and the total electricity consumption time of the electricity consumption change curve; Set the same-frequency classification interval, analyze the same-frequency classification interval and the same-frequency value, and obtain the factory group electricity consumption portrait model; obtain the total factory electricity consumption value based on the electricity consumption change curve of each factory group electricity consumption portrait model, and record the factory group electricity consumption portrait model corresponding to the maximum total factory group electricity consumption value as the production group electricity consumption portrait model.

3. The energy consumption management method for a smart park based on the Internet of Things according to claim 2 is characterized in that: Based on the production slices and production management coefficients, high-production management slices and low-production management slices are obtained, and energy consumption management is performed on the factory areas within the park. The process of generating an energy consumption management strategy includes: The maximum operating cycle in the factory area is set as the energy consumption management cycle. The production management cycle of the production equipment is obtained based on the production management coefficient and the energy consumption management cycle. The production ratio is obtained based on the low production slice and high production slice of the production equipment. According to the production management cycle, production ratio, low production slice and high production slice, high production management slice and low production management slice are obtained, and the low production management slices of multiple production equipment are staggered aligned with the high production management slices of multiple production equipment. The staggered alignment refers to the alignment of the time period of the low production management slices of each different production equipment with the time of the high production management slices of each different production equipment, and setting the standard total power value; ensure that the time period of the low production management slice is consistent with the time period of the high production management slice, and the sum of the low production power value of the low production management slice and the high production power value of the high production management slice is consistent with the standard total power value. The production time of the production equipment is managed through the high production management slice and the low production management slice to obtain the energy consumption management strategy.

4. A smart park energy consumption management system based on the Internet of Things, specifically applied to the smart park energy consumption management method based on the Internet of Things according to any one of claims 1 to 3, comprising a management center, characterized in that: The management center is connected to the data acquisition module, data analysis module, data processing module and energy consumption management module: The data acquisition module is used to obtain the electricity consumption behavior data, user data, and electricity consumption time data of the smart park. Based on the electricity consumption behavior data and electricity consumption time data, the park electricity consumption profile model and the factory electricity consumption profile model are constructed; The data analysis module is used to analyze the park electricity consumption profile model and the factory electricity consumption profile model to obtain behavioral characteristic data, park management values, and production group electricity consumption profile models; The data processing module is used to obtain behavioral characteristic data and production equipment data through the production group electricity consumption profile model, build a production process line, obtain production slices and production management values ​​based on the production group electricity consumption profile model and behavioral characteristic data, and obtain the equipment management coefficient based on the production process line, park management value and production management value; The energy consumption management module is used to obtain high production management slices and low production management slices based on production slices and production management coefficients, and to manage energy consumption in factory areas within the park and generate energy consumption management strategies.

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