Energy management method and device based on Internet of Things, and electronic equipment

Abstract

The invention discloses an energy management method and device based on the Internet of Things and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring power consumption data of each enterprise, the power consumption data comprising power consumption, load rate and power consumption time distribution; the enterprises are clustered according to the power consumption data, a plurality of enterprise groups are obtained, and one enterprise group comprises one or more enterprises; obtaining the average power consumption and the average load rate of each enterprise group; according to a preset weight database, weighted summation is carried out on the average electricity consumption and the average load rate, the electricity consumption index value of each enterprise group is obtained, and the preset weight database comprises the weight corresponding to the average electricity consumption and the weight corresponding to the average load rate; and in a preset strategy database, matching a corresponding power utilization strategy for the enterprise group according to the power utilization index value, and sending the power utilization strategy to a corresponding enterprise in the enterprise group. By implementing the technical scheme provided by the invention, different power consumption strategies are allocated for different enterprise groups, and the accuracy of power consumption management is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to an energy management method, device and electronic equipment based on the Internet of Things. Background Art

[0002] With the acceleration of industrialization and the increase in the number of enterprises in industrial parks, it is becoming increasingly important to effectively monitor and manage enterprise energy. Among them, the electricity consumption of enterprises in industrial parks usually accounts for a large proportion of the entire city's electricity consumption, which not only affects the stable operation of the power grid, but is also directly related to the production costs and energy efficiency of enterprises. Therefore, power managers in industrial parks are increasingly focusing on intelligent management and strategic allocation of enterprise electricity consumption to improve energy efficiency, reduce waste, and optimize the allocation of power resources.

[0003] At present, existing management often adopts a unified electricity consumption planning strategy, fails to carry out personalized electricity consumption management based on the actual electricity consumption behavior and needs of each enterprise, and there is a problem of inaccurate electricity consumption management.

[0004] Therefore, there is an urgent need for an energy management method, device and electronic equipment based on the Internet of Things. Summary of the invention

[0005] The present application provides an energy management method, device and electronic equipment based on the Internet of Things, which allocates different power consumption strategies to different enterprise groups, thereby improving the accuracy of power consumption management.

[0006] In a first aspect of the present application, an energy management method based on the Internet of Things is provided, the method comprising: obtaining electricity consumption data of each enterprise, the electricity consumption data including electricity consumption, load rate and electricity consumption time distribution; clustering the enterprises according to the electricity consumption data to obtain multiple enterprise groups, one enterprise group including one or more enterprises; obtaining the average electricity consumption and average load rate of each enterprise group; performing weighted summation of the average electricity consumption and the average load rate according to a preset weight database to obtain electricity consumption index values ​​of each enterprise group, the preset weight database including the weight corresponding to the average electricity consumption and the weight corresponding to the average load rate; in a preset strategy database, matching the corresponding electricity consumption strategy for the enterprise group according to the electricity consumption index value, and sending the electricity consumption strategy to the corresponding enterprise in the enterprise group.

[0007] By adopting the above technical solution, by obtaining the power consumption data such as the power consumption, load rate and power consumption time distribution of the enterprise, the power consumption behavior and power consumption characteristics of the enterprise can be fully understood. According to these power consumption data, cluster analysis of enterprises can be carried out, and enterprises with similar power consumption patterns can be divided into the same enterprise group, so as to realize the classification management of different types of enterprises. By calculating the average power consumption and average load rate of each enterprise group, the overall power consumption level of each enterprise group can be quantitatively evaluated. The average power consumption and the average load rate are weighted and summed using the preset weight database to obtain the power consumption index value that comprehensively considers the power consumption and load rate, which can more comprehensively and accurately reflect the power demand of the enterprise group. Finally, according to the power consumption index value, the optimal power consumption strategy is matched from the preset strategy database and issued to each enterprise in the corresponding enterprise group. Through this precise and differentiated power consumption strategy guidance, enterprises can be effectively guided to optimize power consumption behavior and improve the efficiency of power resource utilization, thereby realizing intelligent power consumption management. Different power consumption strategies are assigned to different enterprise groups, which improves the accuracy of power consumption management.

[0008] Optionally, in the preset policy database, matching the corresponding power consumption strategy for the enterprise group according to the power consumption index value specifically includes: determining a first enterprise group and a second enterprise group, the first enterprise group being an enterprise group among multiple enterprise groups, whose power consumption index value is higher than a preset threshold, and the second enterprise group being an enterprise group among multiple enterprise groups, whose power consumption index value is lower than or equal to the preset threshold; matching a peak-shifting power consumption strategy for the first enterprise group according to the preset policy database, the peak-shifting power consumption strategy including shifting the power consumption time from the peak power consumption period to the valley power consumption period; matching an energy-saving power consumption strategy for the second enterprise group according to the preset policy database, the energy-saving power consumption strategy including reducing an adjustable power load.

[0009] By adopting the above technical solution, multiple enterprise groups are divided into the first enterprise group and the second enterprise group according to the power consumption index value, and classified policies can be implemented for enterprises with different power consumption levels. For the first enterprise group with a higher power consumption index value, it means that its power demand is higher, and there may be a problem of tight power supply during peak power consumption periods. Therefore, a targeted peak-shifting power consumption strategy is matched from the preset strategy database to guide the enterprises in the first enterprise group to transfer part of their power demand from the peak power consumption period to the low power consumption period, while ensuring the normal production of the enterprise, and relieving the power supply pressure during the peak power consumption period. For the second enterprise group with a lower power consumption index value, it means that its power demand is lower. Matching the energy-saving power consumption strategy for the second enterprise group, by guiding the enterprise to reduce the adjustable load in non-essential periods and reduce the ineffective consumption of electricity, thereby achieving energy saving and consumption reduction for the enterprise. By distinguishing the enterprise groups and adopting two differentiated strategies of peak shifting and energy saving, the energy-saving potential of different enterprises can be more accurately tapped, and power optimization tailored to the enterprise can be achieved.

[0010] Optionally, after determining the first enterprise group and the second enterprise group, the method also includes: obtaining historical electricity consumption data of the first enterprise, where the first enterprise is any one of the enterprises in the first enterprise group; analyzing the historical electricity consumption data to obtain electricity consumption patterns corresponding to the first enterprise, wherein the electricity consumption patterns include peak electricity consumption periods and valley electricity consumption periods.

[0011] By adopting the above technical solution, the historical electricity consumption data of enterprises in the first enterprise group is obtained, and the electricity consumption pattern of the enterprise is summarized by analyzing the historical electricity consumption data, including identifying the peak and valley periods of electricity consumption. By deeply mining the historical electricity consumption data of the enterprise itself, the peak-shifting electricity consumption strategy can be made to better match the actual situation of the enterprise, and the operability and effectiveness of the peak-shifting measures can be improved.

[0012] Optionally, the historical electricity consumption data is analyzed to obtain the electricity consumption pattern corresponding to the first enterprise, specifically including: performing time series decomposition on the historical electricity consumption data to obtain trend items, cycle items and random items; determining the typical electricity consumption cycle of the first enterprise based on the cycle items, the typical electricity consumption cycle including multiple time periods; counting the total electricity consumption of each time period, and determining the time period when the total electricity consumption is higher than a preset electricity consumption threshold as the peak electricity consumption period, and determining the time period when the total electricity consumption is lower than or equal to the preset electricity consumption threshold as the valley electricity consumption period.

[0013] By adopting the above technical solution, after obtaining the historical electricity consumption data of the first enterprise, the time series decomposition method is used to decompose the electricity consumption data into three parts: long-term trend, periodic fluctuation and random fluctuation. By analyzing the periodic items, the typical electricity consumption cycle of the enterprise can be accurately identified. By counting the total electricity consumption in different time periods of each typical electricity consumption cycle and setting a preset electricity consumption threshold, the peak electricity consumption period and the valley electricity consumption period can be quantitatively divided. This division method based on the data characteristics of the enterprise itself can be more in line with the actual situation of the enterprise, making the identified peak electricity consumption period and valley electricity consumption period more targeted and representative. If the subsequent staggered electricity consumption strategy is formulated based on this peak electricity consumption period and valley electricity consumption period, it can be more targeted to guide the enterprise to reduce the peak and fill the valley during the peak period and improve the staggered effect.

[0014] Optionally, after matching the peak-shaving power consumption strategy for the first enterprise group according to the preset strategy database, the method also includes: obtaining a production plan and equipment list of the first enterprise; determining, based on the production plan and equipment list, critical equipment and non-critical equipment of the first enterprise during the peak-shaving power consumption strategy; determining to shut down the non-critical equipment during the peak power consumption period, and to operate the critical equipment and non-critical equipment during the low power consumption period.

[0015] By adopting the above technical solutions, the production plan and equipment list of the enterprise can be obtained, and the electricity demand and main electricity-consuming equipment of the enterprise in each period can be fully understood. Based on this, it is possible to distinguish between key equipment that must be operated during the peak shift period and non-key equipment that can adjust the operating period. By reasonably scheduling non-key equipment, the unnecessary electricity consumption during the peak shift period can be minimized while ensuring the normal operation of key equipment and unaffected production tasks. A detailed peak shift production plan can be issued to the enterprise, clearly suggesting which non-key equipment should be shut down during the peak power consumption period, and arranging these equipment to be supplemented during the valley period to achieve the optimal configuration of power load during high and low peak periods. This peak shift measure that goes deep into the equipment level is more operational, can more accurately control the actual electricity consumption of the enterprise during the peak shift period, can also minimize the impact of the peak shift measures on the production of the enterprise, and balance the relationship between peak shift scheduling and stable production and supply. Enterprises can refer to the given equipment peak shift scheduling suggestions and make flexible arrangements in combination with their own production schedules, so as to maximize the benefits of the enterprise while responding to the needs of the power grid.

[0016] Optionally, clustering the enterprises according to the electricity consumption data to obtain multiple enterprise groups specifically includes: extracting features from the electricity consumption, the load rate and the time distribution of electricity consumption to obtain corresponding feature vectors; clustering the feature vectors using a clustering algorithm to obtain multiple cluster clusters, and one cluster cluster corresponds to one enterprise group.

[0017] By adopting the above technical solution, the cluster analysis method is used to mine the enterprise electricity consumption data. First, the key features in the electricity consumption data, such as electricity consumption, load rate and electricity consumption time distribution, are extracted to generate feature vectors that reflect the characteristics of each enterprise's electricity consumption behavior. Then, the clustering algorithm is applied to cluster similar enterprises in the feature space to form several clusters. The electricity consumption characteristics of enterprises in each cluster are relatively close, and the differences between clusters are large. Each cluster corresponds to an enterprise group. Compared with simple grouping statistics, clustering analysis can automatically discover the inherent differences in enterprise electricity consumption patterns from massive data, making the grouping results objective and accurate, and can retain the inherent characteristics of different enterprise groups to the maximum extent. Based on the refined grouping management of clustering results, the subsequent electricity consumption strategy formulation can be more targeted, and the electricity consumption behavior of each type of enterprise can be optimized separately according to local conditions, so as to better match the energy-saving needs and scheduling potential of different enterprise groups, thereby improving the overall level of power demand side management.

[0018] Optionally, before obtaining the electricity consumption data of each enterprise, the method also includes: sending an electricity consumption data request to an electricity consumption monitoring device, the electricity consumption data request including the requested data type and time range; receiving original electricity consumption data of the enterprise returned by the electricity consumption monitoring device; performing preprocessing operations on the original electricity consumption data to obtain the electricity consumption data, the preprocessing operations including data cleaning, data normalization and data conversion.

[0019] By adopting the above technical solution, before obtaining the enterprise's electricity consumption data, data is interacted with the electricity consumption monitoring device, and an electricity consumption data request is sent to the electricity consumption monitoring device to clarify the required data type and the start and end time (time range) of the data. This active data request method can avoid the electricity consumption monitoring device from blindly uploading massive data and reduce unnecessary data transmission and storage overhead. According to the electricity consumption data request, the electricity consumption monitoring device returns the collected original electricity consumption data. After obtaining the original electricity consumption data, a series of preprocessing operations are performed, including data cleaning, data normalization, and data conversion, to process the coarse-grained original electricity consumption data into structured and standardized electricity consumption data, laying the foundation for subsequent data analysis and mining.

[0020] In a second aspect of the present application, an energy management device based on the Internet of Things is provided, the device comprising: an acquisition module and a processing module, wherein: the acquisition module is used to acquire the electricity consumption data of each enterprise, the electricity consumption data including electricity consumption, load rate and electricity consumption time distribution; the processing module is used to cluster the enterprises according to the electricity consumption data to obtain multiple enterprise groups, one of the enterprise groups including one or more of the enterprises; the acquisition module is also used to acquire the average electricity consumption and average load rate of each of the enterprise groups; the processing module is also used to perform weighted summation of the average electricity consumption and the average load rate according to a preset weight database to obtain the electricity consumption index value of each of the enterprise groups, the preset weight database including the weight corresponding to the average electricity consumption and the weight corresponding to the average load rate; the processing module is also used to match the corresponding electricity consumption strategy for the enterprise group according to the electricity consumption index value in the preset strategy database, and send the electricity consumption strategy to the corresponding enterprise in the enterprise group.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the electricity consumption data of enterprises, such as electricity consumption, load rate and electricity consumption time distribution, we can fully understand the electricity consumption behavior and electricity consumption characteristics of enterprises. According to these electricity consumption data, cluster analysis of enterprises can be carried out, and enterprises with similar electricity consumption patterns can be divided into the same enterprise group, so as to realize the classification management of different types of enterprises. By calculating the average electricity consumption and average load rate of each enterprise group, the overall electricity consumption level of each enterprise group can be quantitatively evaluated. The average electricity consumption and average load rate are weighted and summed using the preset weight database to obtain the electricity consumption index value that comprehensively considers the electricity consumption and load rate, which can more comprehensively and accurately reflect the electricity demand of the enterprise group. Finally, according to the electricity consumption index value, the optimal electricity consumption strategy is matched from the preset strategy database and issued to each enterprise in the corresponding enterprise group. Through this precise and differentiated electricity consumption strategy guidance, enterprises can be effectively guided to optimize electricity consumption behavior and improve the efficiency of power resource utilization, thereby realizing smart electricity consumption management.

[0024] 2. Dividing multiple enterprise groups into the first enterprise group and the second enterprise group according to the power consumption index value can realize the classification and implementation of policies for enterprises with different power consumption levels. For the first enterprise group with a higher power consumption index value, it means that its power demand is higher, and there may be a problem of tight power supply during peak power consumption periods. Therefore, a targeted peak-shifting power consumption strategy is matched from the preset strategy database to guide the enterprises in the first enterprise group to transfer part of their power demand from the peak power consumption period to the low power consumption period, while ensuring the normal production of the enterprise, and relieving the power supply pressure during the peak power consumption period. For the second enterprise group with a lower power consumption index value, it means that its power demand is lower. Matching the energy-saving power consumption strategy for the second enterprise group, by guiding enterprises to reduce the adjustable load in non-essential periods and reduce ineffective power consumption, thereby achieving energy saving and consumption reduction for enterprises. By distinguishing enterprise groups and adopting two differentiated strategies of peak shifting and energy saving, the energy-saving potential of different enterprises can be more accurately tapped, and power consumption optimization can be achieved according to the needs of enterprises.

[0025] 3. Obtain the historical electricity consumption data of enterprises in the first enterprise group, and summarize the electricity consumption patterns of the enterprise by analyzing the historical electricity consumption data, including identifying the peak and valley periods of electricity consumption. By deeply mining the historical electricity consumption data of the enterprise itself, the peak-shifting electricity consumption strategy can be made more in line with the actual situation of the enterprise, and the operability and effectiveness of the peak-shifting measures can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of an energy management method based on the Internet of Things disclosed in an embodiment of the present application; Figure 2 It is a module schematic diagram of an energy management device based on the Internet of Things disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0027] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0028] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0029] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0030] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple means two or more, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0031] This application provides an energy management method based on the Internet of Things. Figure 1 , Figure 1 1 is a flow chart of an energy management method based on the Internet of Things provided in an embodiment of the present application. The method is applied to a server and includes steps S101 to S106, which are as follows: Step S101: Obtain electricity consumption data of each enterprise, where the electricity consumption data includes electricity consumption, load rate, and electricity consumption time distribution.

[0032] Before step S101, the method also includes: sending an electricity consumption data request to the electricity consumption monitoring device; receiving the original electricity consumption data of the enterprise returned by the electricity consumption monitoring device; performing a preprocessing operation on the original electricity consumption data to obtain the electricity consumption data, the preprocessing operation including data cleaning, data normalization and data conversion.

[0033] Specifically, the server sends a power consumption data request to the power consumption monitoring device through the Internet of Things communication protocol. The power consumption monitoring device is usually installed on the power consumption equipment of the enterprise to collect and record the power consumption of the enterprise in real time. The server can establish a communication connection with the power consumption monitoring device through the network and send a data request instruction. The data request instruction should include the requested data type (such as power consumption, load rate, power consumption time, etc.) and time range (such as data for the most recent month). The server receives the original power consumption data returned by the power consumption monitoring device. After receiving the data request instruction, the power consumption monitoring device will package the corresponding original power consumption data according to the requested data type and time range and send it to the server. The original data is usually stored in formats such as CSV and JSON, containing a large amount of unprocessed power consumption information. Each piece of original data contains information such as the acquisition timestamp, power consumption, and load rate. Finally, the server performs preprocessing operations on the received original power consumption data for subsequent data analysis and mining. The preprocessing operation usually includes data cleaning, data normalization, and data conversion steps. Data cleaning refers to detecting and correcting errors, inconsistencies, or incomplete information in the original data. For example, the server can identify and filter abnormal data points (such as negative power consumption, current exceeding the rated value, etc.) by setting the value range; and handle data missing problems caused by equipment failure or communication interruption by filling missing values. Data normalization refers to the unification of data of different dimensions or orders of magnitude to the same scale for comparison and analysis. Data conversion refers to the conversion of raw data from one format or type to another format or type to meet specific analysis requirements. After preprocessing operations, the raw power consumption data will be converted into structured and standardized power consumption data.

[0034] In step S101, each enterprise in the industrial park is equipped with a power consumption monitoring device, which can record the power consumption, load rate, and power consumption time data of the enterprise in real time. The server can establish a communication connection with the power consumption monitoring device and other IoT devices to collect these power consumption data regularly or in real time.

[0035] Step S102: clustering enterprises according to the electricity consumption data to obtain multiple enterprise groups, where one enterprise group includes one or more enterprises.

[0036] In step S102, the power consumption, load rate and power consumption time distribution are feature extracted to obtain corresponding feature vectors; the feature vectors are clustered using a clustering algorithm to obtain multiple clusters, and one cluster corresponds to one enterprise group.

[0037] Specifically, the server needs to extract features from three types of data: power consumption, load rate, and power consumption time distribution. Power consumption data can be used to extract the average power consumption, peak power consumption, and standard deviation of power consumption; load rate data can be used to extract the average load rate, maximum load rate, and minimum load rate; power consumption time distribution data can be used to extract the peak power consumption period, valley power consumption period, and peak-valley difference.

[0038] For example, for company A, the server collected the electricity consumption data of the past month from its smart meter. Through feature extraction, the following feature vector is obtained: [average electricity consumption: 5000kWh / day, peak electricity consumption: 7500kWh / day, standard deviation of electricity consumption: 800kWh / day, average load rate: 0.7, maximum load rate: 0.9, minimum load rate: 0.4, peak hours: 10:00-12:00, 14:00-16:00, valley hours: 00:00-06:00, peak-valley difference: 4000kWh / day]. Similarly, the server also extracts features from the electricity consumption data of companies B and C and obtains the corresponding feature vectors.

[0039] After obtaining the feature vectors of the electricity consumption data of each enterprise, the server inputs the feature vectors of each enterprise into the K-means clustering algorithm to cluster the enterprises. The server first determines the number of clusters K, and the K value can be determined by the electricity consumption management platform in advance. After determining the K value, the server randomly selects K feature vectors as the initial cluster centers, and then performs the following iterative process: For each enterprise's feature vector, the server calculates its distance to each cluster center and assigns it to the cluster with the closest distance. For each cluster, recalculate the mean vector of all feature vectors within it, and use the mean vector as the new cluster center. The server repeats the above steps until the cluster center no longer changes or the maximum number of iterations is reached. After the iteration, the server obtains K clusters, each of which corresponds to an enterprise group. Enterprises in the same cluster have similar electricity consumption characteristics and can adopt similar electricity consumption strategies.

[0040] Step S103: Obtain the average power consumption and average load rate of each enterprise group.

[0041] In step S103, the server reads the power consumption and load rate of all enterprises in each enterprise group. The power consumption and load rate are stored in the form of time series, such as one data point every 15 minutes, every hour, or every day. Taking enterprise group 1 as an example, assuming that the group includes enterprise A and enterprise B, the server reads the following data: Company A: Power consumption: [1000, 1200, 1500, ..., 1800] kWh, a total of 30 data points, representing the power consumption for 30 consecutive days. Load rate: [0.6, 0.7, 0.8, ..., 0.75], a total of 30 data points, representing the load rate for 30 consecutive days.

[0042] Company B: Power consumption: [800, 900, 1100, ..., 1300] kWh, a total of 30 data points. Load rate: [0.55, 0.6, 0.7, ..., 0.65], a total of 30 data points.

[0043] Next, the server calculates the average power consumption and average load rate of Enterprise A and Enterprise B respectively. The average power consumption can be obtained by summing the power consumption in a period of time and then dividing it by the length of the period. The average load rate can be obtained by summing the load rate in a period of time and then dividing it by the length of the period. For Enterprise A, its average power consumption is: (1000+1200+...+1800) / 30=1450kWh / day; its average load rate is: (0.6+0.7+...+0.75) / 30=0.725. For Enterprise B, its average power consumption is: (800+900+...+1300) / 30=1050kWh / day; its average load rate is: (0.55+0.6+...+0.65) / 30=0.625. The server averages the average power consumption and average load rate of Enterprise A and Enterprise B to obtain the average power consumption and average load rate of the entire enterprise group 1.

[0044] Step S104: According to a preset weight database, average power consumption and average load rate are weighted and summed to obtain power consumption index values ​​for each enterprise group. The preset weight database includes weights corresponding to average power consumption and weights corresponding to average load rate.

[0045] In step S104, the server reads the weight corresponding to the average power consumption and the weight corresponding to the average load rate from the preset weight database. These weights reflect the degree of influence of different indicators on power efficiency, and can be predetermined based on expert experience and historical data analysis. The specific value of the weight is not limited in this application. The server performs a weighted summation of the average power consumption and average load rate of each enterprise group. The formula for weighted summation is: Power consumption index value = average power consumption * average power consumption weight + average load rate * average load rate weight. The higher the power consumption index value, the higher the power demand of the enterprise group; conversely, the lower the power consumption index value, the lower the power demand of the enterprise group.

[0046] Step S105: In the preset policy database, a corresponding power usage policy is matched for the enterprise group according to the power usage index value, and the power usage policy is sent to the corresponding enterprise in the enterprise group.

[0047] In step S105, a first enterprise group and a second enterprise group are determined, the first enterprise group being an enterprise group among multiple enterprise groups whose electricity consumption index value is higher than a preset threshold, and the second enterprise group being an enterprise group among multiple enterprise groups whose electricity consumption index value is lower than or equal to the preset threshold; according to the preset strategy database, a peak-shifting electricity consumption strategy is matched for the first enterprise group, and the peak-shifting electricity consumption strategy includes shifting the electricity consumption time from the peak electricity consumption period to the valley electricity consumption period; according to the preset strategy database, an energy-saving electricity consumption strategy is matched for the second enterprise group, and the energy-saving electricity consumption strategy includes reducing the adjustable electricity load.

[0048] Specifically, the server divides the enterprise group into a first enterprise group and a second enterprise group according to the power consumption index value. The first enterprise group includes enterprise groups whose power consumption index values ​​are higher than the preset threshold value. The power consumption and load of these enterprise groups are high and need to be focused on and optimized. The second enterprise group includes enterprise groups whose power consumption index values ​​are lower than or equal to the preset threshold value. The power consumption and load of these enterprise groups are relatively low and the optimization potential is relatively small. The preset threshold value can be determined based on historical data analysis, industry standards or management objectives, and this application does not limit this. For example, assuming that the preset threshold value is 1000, the enterprise group with a power consumption index value higher than 1000 is divided into the first enterprise group, and the enterprise group with a power consumption index value lower than or equal to 1000 is divided into the second enterprise group. Then, the server matches the corresponding power consumption strategy for the first enterprise group and the second enterprise group according to the preset strategy database. For the first enterprise group, the server matches the "peak power consumption strategy" from the preset strategy database. The purpose of the peak power consumption strategy is to transfer part of the power consumption during the peak power consumption period to the power consumption valley period to reduce the pressure on the power grid and improve the power efficiency. Specific measures may include: adjusting the production plan and arranging some production tasks during the off-peak period. Arrange equipment inspection and maintenance reasonably to avoid repairing high-power equipment during peak periods. The server sends this strategy to the energy management of enterprise A, and the enterprise managers implement it. For the second enterprise group, the server matches the energy-saving power strategy from the preset strategy database. The purpose of the energy-saving power strategy is to reduce unnecessary power consumption and improve power efficiency by reducing the adjustable power load. Specific measures may include: updating old high-energy-consuming equipment and adopting energy-saving equipment and technology. The server sends this strategy to the energy management of enterprise B, and the enterprise managers implement it. Both the peak-shifting power strategy and the energy-saving power strategy are typical power optimization strategies that are pre-designed based on past experience and expert knowledge and stored in the preset strategy database. When enterprises are divided into different enterprise groups according to power consumption index values, the server can match the corresponding strategy from the preset strategy database and send it to the corresponding enterprise group.

[0049] Through the above steps, the server matches and issues targeted power consumption strategies according to the power consumption index values ​​of the enterprise group. This differentiated strategy formulation method can better meet the actual needs of different enterprise groups and improve the level of refinement of power consumption management. At the same time, through the direct connection between the server and the enterprise energy management, the strategy can be quickly transmitted and executed, improving the timeliness and operability of power consumption management. After receiving the power consumption strategy issued by the server, the enterprise can further refine and adjust the strategy according to its own production characteristics and management practices to ensure the effective implementation of the strategy.

[0050] In a possible implementation, after determining the first enterprise group and the second enterprise group, the method further includes: obtaining historical electricity consumption data of the first enterprise, where the first enterprise is any one of the enterprises in the first enterprise group; analyzing the historical electricity consumption data to obtain the electricity consumption pattern corresponding to the first enterprise, the electricity consumption pattern including peak electricity consumption periods and valley electricity consumption periods.

[0051] Specifically, after determining the first enterprise group and the second enterprise group, the server further analyzes the historical power consumption data of any enterprise in the first enterprise group (referred to as the first enterprise) to obtain its power consumption pattern. The server obtains the historical power consumption data of the first enterprise. The historical power consumption data generally includes power consumption and power consumption time. The server analyzes the historical power consumption data to obtain the power consumption pattern of the first enterprise. The server uses a statistical analysis method to mine the power consumption pattern of the first enterprise.

[0052] In one possible implementation, historical electricity consumption data is analyzed to obtain electricity consumption patterns corresponding to the first enterprise, specifically including: performing time series decomposition on the historical electricity consumption data to obtain trend items, cycle items, and random items; determining a typical electricity consumption cycle of the first enterprise based on the cycle items, the typical electricity consumption cycle including multiple time periods; counting the total electricity consumption of each time period, and determining a time period in which the total electricity consumption is higher than a preset electricity consumption threshold as a peak electricity consumption period, and determining a time period in which the total electricity consumption is lower than or equal to the preset electricity consumption threshold as a valley electricity consumption period.

[0053] Specifically, the server performs time series decomposition on the historical electricity consumption data of the first enterprise. Time series decomposition is a commonly used time series analysis method, which can decompose time series data into three parts: trend item / cycle item and random item; wherein, the trend item reflects the long-term trend and change direction of the time series data, such as the year-on-year increase or decrease in electricity consumption. The cycle item reflects the periodic changes of the time series data, such as the daily, weekly, monthly and quarterly periodic fluctuations of electricity consumption. The random item reflects the random disturbance and uncertainty factors in the time series data, such as emergencies, measurement errors, etc. The server can use a variety of time series decomposition algorithms, such as additive decomposition, multiplicative decomposition, and STL decomposition. In the embodiment of the present application, the server adopts additive decomposition. Through time series decomposition, the server can more clearly observe the long-term trend and periodic changes of the electricity consumption of the first enterprise. For example, the decomposition result may show that the electricity consumption of the first enterprise shows a trend of increasing year by year, and there are obvious weekly cycles (higher electricity consumption on weekdays than on weekends) and annual cycles (higher electricity consumption in summer than in winter). Next, the server determines the typical electricity consumption cycle of the first enterprise based on the cycle item. The typical electricity consumption cycle reflects the periodic law of the enterprise's electricity consumption, which can be a daily cycle, a weekly cycle, or a monthly cycle. For example, by analyzing the cycle item, the server finds that the first enterprise has an obvious daily cycle, that is, the daily electricity consumption changes are basically similar. Therefore, the server divides a daily cycle (24 hours) into multiple time periods, such as one time period per hour, for a total of 24 time periods. The server then counts the total electricity consumption of each time period, and divides the time period into peak electricity consumption periods and trough electricity consumption periods based on the preset electricity consumption threshold. The preset electricity consumption threshold can be set based on historical data, industry standards, or management objectives. This application For example, the server calculates the total power consumption of the first enterprise in 24 time periods, namely 0:00-1:00, 1:00-2:00, ..., 23:00-24:00, among which the total power consumption from 0:00-1:00 is 500kWh; the total power consumption from 1:00-2:00 is 450kWh; ...; the total power consumption from 10:00-11:00 is 1200kWh; the total power consumption from 11:00-12:00 is 1300kW h;......; The total power consumption from 23:00 to 24:00 is 600kWh; Assuming that the preset power consumption threshold is 1000kWh, the server will determine the time period when the total power consumption is higher than 1000kWh (such as 10:00-11:00, 11:00-12:00) as the peak power consumption period, and the time period when the total power consumption is lower than or equal to 1000kWh (such as 0:00-1:00, 1:00-2:00) as the valley power consumption period.

[0054] Through the above steps, the server obtains the peak power consumption period (such as 10:00-12:00) and the low power consumption period (such as 0:00-6:00) of the first enterprise. For any enterprise in the first enterprise group, the server takes the above steps to obtain the peak power consumption period and low power consumption period of all enterprises in the first enterprise group.

[0055] In a possible implementation, after matching the peak-shifting power consumption strategy for the first enterprise group according to a preset strategy database, the method also includes: obtaining a production plan and equipment list for the first enterprise; determining the key equipment and non-key equipment of the first enterprise during the peak-shifting power consumption strategy period according to the production plan and the equipment list; and determining to shut down non-key equipment during peak power consumption periods and to operate key equipment and non-key equipment during low power consumption periods.

[0056] Specifically, the server obtains the production plan and equipment list of the first enterprise. The production plan reflects the production tasks and schedules of the enterprise in the future, including product types and quantity information. The equipment list lists all the electrical equipment of the enterprise, including equipment names and power information. For example, the first enterprise provides a production plan for the next week, planning to produce three products A, B, and C. At the same time, the first enterprise also provides an equipment list, including production equipment (such as injection molding machines, CNC machine tools, assembly lines, etc.) and auxiliary equipment (such as air compressors, chillers, lighting, etc.).

[0057] The server determines the key equipment and non-key equipment of the first enterprise during the peak power consumption strategy period based on the production plan and equipment list. Key equipment refers to equipment that is closely related to key production tasks and must be operated within the specified time, while non-key equipment is equipment that can be flexibly scheduled and has less impact on production tasks. The server can use a variety of methods to divide key equipment and non-key equipment, such as based on the critical path method, priority sorting method, etc. Taking the critical path method as an example, the server can follow the following steps: The server converts the production plan into a process network diagram, where each process corresponds to a node, and the lines between nodes represent the dependencies between processes. The earliest start time, latest start time, and time margin parameters of each process are calculated, and the critical path (i.e., the process chain with zero time margin) is determined. The equipment corresponding to the process on the critical path is determined as critical equipment, and other equipment is non-critical equipment.

[0058] For example, based on the production plan of the first enterprise, the server determines the following critical path: Product A: Raw material preparation -> Injection molding -> Surface treatment -> Assembly test Product B: Raw material preparation -> CNC machining -> Surface treatment -> Assembly test Injection molding machines, CNC machine tools, surface treatment equipment, and assembly and testing equipment on the critical path are identified as critical equipment, while auxiliary equipment such as air compressors, chillers, and lighting are identified as non-critical equipment. The server formulates a peak-shifting power consumption plan at the equipment level based on peak power consumption periods and low power consumption periods. Specifically, during peak power consumption periods (such as 10:00-12:00), the server sends a peak-shifting power consumption strategy to recommend that the first company shut down non-critical equipment and only keep critical equipment running to ensure the completion of critical production tasks. During low power consumption periods (such as 0:00-6:00), the server's peak-shifting power consumption strategy recommends that the first company can operate all equipment, including critical and non-critical equipment.

[0059] Reference Figure 2 The present application also provides an energy management device based on the Internet of Things, which is a server. The server includes an acquisition module 201 and a processing module 202, wherein: the acquisition module 201 is used to obtain the electricity consumption data of each enterprise, and the electricity consumption data includes electricity consumption, load rate and electricity consumption time distribution; the processing module 202 is used to cluster the enterprises according to the electricity consumption data to obtain multiple enterprise groups, and one enterprise group includes one or more enterprises; the acquisition module 201 is also used to obtain the average electricity consumption and average load rate of each enterprise group; the processing module 202 is also used to perform weighted summation of the average electricity consumption and the average load rate according to a preset weight database to obtain the electricity consumption index value of each enterprise group, and the preset weight database includes the weight corresponding to the average electricity consumption and the weight corresponding to the average load rate; the processing module 202 is also used to match the corresponding electricity consumption strategy for the enterprise group according to the electricity consumption index value in the preset strategy database, and send the electricity consumption strategy to the corresponding enterprise in the enterprise group.

[0060] In a possible implementation, in a preset policy database, the processing module 202 matches corresponding power consumption strategies for enterprise groups according to power consumption index values, specifically including: the processing module 202 determines a first enterprise group and a second enterprise group, the first enterprise group being an enterprise group among multiple enterprise groups whose power consumption index values ​​are higher than a preset threshold, and the second enterprise group being an enterprise group among multiple enterprise groups whose power consumption index values ​​are lower than or equal to the preset threshold; the processing module 202 matches a peak power consumption strategy for the first enterprise group according to the preset policy database, the peak power consumption strategy includes shifting the power consumption time from a peak power consumption period to a valley power consumption period; the processing module 202 matches an energy-saving power consumption strategy for the second enterprise group according to the preset policy database, the energy-saving power consumption strategy includes reducing an adjustable power load.

[0061] In a possible implementation, after the processing module 202 determines the first enterprise group and the second enterprise group, the method further includes: the acquisition module 201 acquires the historical electricity consumption data of the first enterprise, where the first enterprise is any one of the enterprises in the first enterprise group; the processing module 202 analyzes the historical electricity consumption data to obtain the electricity consumption pattern corresponding to the first enterprise, and the electricity consumption pattern includes peak electricity consumption periods and valley electricity consumption periods.

[0062] In one possible implementation, the processing module 202 analyzes the historical electricity consumption data to obtain the electricity consumption pattern corresponding to the first enterprise, specifically including: the processing module 202 performs time series decomposition on the historical electricity consumption data to obtain trend items, cycle items and random items; the processing module 202 determines the typical electricity consumption cycle of the first enterprise based on the cycle items, and the typical electricity consumption cycle includes multiple time periods; the processing module 202 counts the total electricity consumption of each time period, and determines the time period when the total electricity consumption is higher than the preset electricity consumption threshold as the peak electricity consumption period, and determines the time period when the total electricity consumption is lower than or equal to the preset electricity consumption threshold as the valley electricity consumption period.

[0063] In a possible implementation, after the processing module 202 matches the peak-shaving power consumption strategy for the first enterprise group according to a preset strategy database, the method further includes: the acquisition module 201 acquires the production plan and equipment list of the first enterprise; the processing module 202 determines the key equipment and non-key equipment of the first enterprise during the peak-shaving power consumption strategy period according to the production plan and the equipment list; the processing module 202 determines to shut down non-key equipment during peak power consumption periods and to operate key equipment and non-key equipment during low power consumption periods.

[0064] In a possible implementation, the processing module 202 clusters the enterprises according to the electricity consumption data to obtain multiple enterprise groups, specifically including: the processing module 202 extracts features of electricity consumption, load rate and electricity consumption time distribution to obtain corresponding feature vectors; the processing module 202 uses a clustering algorithm to cluster the feature vectors to obtain multiple cluster clusters, and one cluster cluster corresponds to one enterprise group.

[0065] In a possible implementation, before the acquisition module 201 acquires the electricity consumption data of each enterprise, the method further includes: the processing module 202 sends an electricity consumption data request to the electricity consumption monitoring device, the electricity consumption data request includes the requested data type and time range; the processing module 202 receives the original electricity consumption data of the enterprise returned by the electricity consumption monitoring device; the processing module 202 performs a preprocessing operation on the original electricity consumption data to obtain the electricity consumption data, the preprocessing operation includes data cleaning, data normalization and data conversion.

[0066] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0067] The present application also provides an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0068] The communication bus 302 is used to realize the connection and communication between these components.

[0069] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0070] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0071] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes operations, user interfaces and applications; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0072] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing operations, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operation, a network communication module, a user interface module and an application program of an energy management method based on the Internet of Things.

[0073] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of an energy management method based on the Internet of Things stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0074] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into one another, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0080] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0081] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An energy management method based on the Internet of Things, characterized in that: The method comprises: Obtaining electricity consumption data of each enterprise, wherein the electricity consumption data includes electricity consumption, load rate, and electricity consumption time distribution; Clustering the enterprises according to the electricity consumption data to obtain a plurality of enterprise groups, wherein one enterprise group includes one or more enterprises; Obtaining the average power consumption and average load rate of each of the enterprise groups; According to a preset weight database, the average power consumption and the average load rate are weighted and summed to obtain the power consumption index value of each enterprise group, wherein the preset weight database includes the weight corresponding to the average power consumption and the weight corresponding to the average load rate; In the preset strategy database, a corresponding power consumption strategy is matched for the enterprise group according to the power consumption index value, and the power consumption strategy is sent to the corresponding enterprise in the enterprise group.

2. The method according to claim 1, characterized in that The matching of the corresponding power consumption strategy for the enterprise group according to the power consumption index value in the preset strategy database specifically includes: Determine a first enterprise group and a second enterprise group, wherein the first enterprise group is an enterprise group among the plurality of enterprise groups whose power consumption index value is higher than a preset threshold, and the second enterprise group is an enterprise group among the plurality of enterprise groups whose power consumption index value is lower than or equal to the preset threshold; According to the preset strategy database, a peak-shifting power consumption strategy is matched for the first enterprise group, wherein the peak-shifting power consumption strategy includes shifting the power consumption time from the peak power consumption period to the off-peak power consumption period; According to the preset strategy database, an energy-saving electricity strategy is matched for the second enterprise group, where the energy-saving electricity strategy includes reducing an adjustable electricity load.

3. The method according to claim 2, characterized in that After determining the first enterprise group and the second enterprise group, the method further includes: Acquire historical electricity consumption data of a first enterprise, where the first enterprise is any enterprise in the first enterprise group; The historical electricity consumption data is analyzed to obtain the electricity consumption pattern corresponding to the first enterprise, where the electricity consumption pattern includes a peak electricity consumption period and a valley electricity consumption period.

4. The method according to claim 3, characterized in that The analyzing the historical electricity consumption data to obtain the electricity consumption pattern corresponding to the first enterprise specifically includes: Decomposing the historical electricity consumption data into time series to obtain trend items, period items and random items; Determine a typical electricity consumption cycle of the first enterprise according to the cycle item, where the typical electricity consumption cycle includes multiple time periods; The total power consumption in each of the time periods is counted, and the time period when the total power consumption is higher than the preset power consumption threshold is determined as the peak power consumption period, and the time period when the total power consumption is lower than or equal to the preset power consumption threshold is determined as the valley power consumption period.

5. The method according to claim 3, characterized in that: After matching the peak-shifting power consumption strategy for the first enterprise group according to the preset strategy database, the method further includes: Obtaining the production plan and equipment list of the first enterprise; According to the production plan and equipment list, determine the key equipment and non-key equipment of the first enterprise during the period of the peak power consumption strategy; Determine to shut down the non-critical equipment during the peak power consumption period, and operate the critical equipment and the non-critical equipment during the valley power consumption period.

6. The method according to claim 1, characterized in that The clustering of the enterprises according to the electricity consumption data to obtain a plurality of enterprise groups specifically includes: Extracting features of the power consumption, the load rate, and the power consumption time distribution to obtain corresponding feature vectors; A clustering algorithm is used to cluster the feature vectors to obtain a plurality of clusters, and one cluster corresponds to one enterprise group.

7. The method according to claim 1, characterized in that Before obtaining the electricity consumption data of each enterprise, the method further includes: Sending a power consumption data request to the power consumption monitoring device, wherein the power consumption data request includes the requested data type and time range; Receiving the original electricity consumption data of the enterprise returned by the electricity consumption monitoring device; The original power consumption data is preprocessed to obtain the power consumption data, wherein the preprocessing operation includes data cleaning, data normalization and data conversion.

8. An energy management device based on the Internet of Things, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire the electricity consumption data of each enterprise, wherein the electricity consumption data includes electricity consumption, load rate and electricity consumption time distribution; The processing module (202) is used to cluster the enterprises according to the electricity consumption data to obtain a plurality of enterprise groups, wherein one of the enterprise groups includes one or more of the enterprises; The acquisition module (201) is further used to acquire the average power consumption and average load rate of each enterprise group; The processing module (202) is further configured to perform weighted summation of the average power consumption and the average load rate according to a preset weight database to obtain the power consumption index value of each enterprise group, wherein the preset weight database includes a weight corresponding to the average power consumption and a weight corresponding to the average load rate; The processing module (202) is further configured to match a corresponding power usage strategy for the enterprise group according to the power usage index value in a preset strategy database, and send the power usage strategy to a corresponding enterprise in the enterprise group.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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