Industrial Internet of Things energy management system and method based on management cloud platform

Abstract

The invention provides an industrial Internet of Things energy management system and method based on a management cloud platform, the system comprises a management cloud platform and an industrial Internet of Things system, and the industrial Internet of Things system comprises an industrial Internet of Things management platform, an industrial Internet of Things sensing network platform and an industrial Internet of Things sensing control platform; the method comprises the steps that an industrial Internet of Things management platform generates energy information according to energy monitoring data and equipment operation parameters and sends the energy information to a management cloud platform; the management cloud platform receives the energy information; determining energy characteristics of the target factory according to the energy information; determining first energy distribution of the target factory according to the energy characteristics; for target factories which do not meet a first preset condition in the first energy distribution, obtaining a standard factory; generating standard management parameters according to the energy information of the standard factory; and according to the standard management parameters, generating a regulation and control instruction and sending the regulation and control instruction to an industrial Internet of Things management platform corresponding to the target factory. Through the system and the method, better energy management can be realized.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology, and in particular to an industrial Internet of Things energy management system and method based on a management cloud platform. Background Art

[0002] In the context of the rapid development of modern industry, energy management has become a key link for enterprises to improve operational efficiency and reduce costs. Traditional energy management methods have problems such as data islands, delayed response, and low management efficiency, which are difficult to meet the requirements of modern industry for refined energy management. The cloud platform can use big data analysis technology to deeply mine and process massive energy consumption data, providing enterprises with accurate energy consumption statistics, forecasting analysis, energy efficiency evaluation and other functions.

[0003] Therefore, it is necessary to provide an industrial Internet of Things energy management system and method based on a management cloud platform to realize the monitoring and management of various energy uses in industrial production. Summary of the invention

[0004] One aspect of an embodiment of the present specification provides an industrial Internet of Things energy management system based on a management cloud platform, the system includes a management cloud platform and an industrial Internet of Things system, the industrial Internet of Things system includes an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform and an industrial Internet of Things perception control platform; the industrial Internet of Things management platform is communicatively connected with the management cloud platform; the industrial Internet of Things management platform is configured to: generate energy information based on energy monitoring data and equipment operating parameters obtained from the industrial Internet of Things perception control platform, and send it to the management cloud platform, the energy information includes at least one of equipment management information, production scheduling information and energy scheduling information; the management cloud platform is configured to: receive the industrial Internet of Things management information The energy information is sent by the management platform; based on the energy information, the energy characteristics of the target factory are determined, and the energy characteristics include at least one of the energy type, the corresponding energy usage and the energy utilization rate; based on the energy characteristics, a first energy distribution of the target factory is determined; for the target factory that does not meet the first preset condition in the first energy distribution: a standard factory is obtained, and the standard factory and the target factory belong to the same factory type; based on the energy information of the standard factory, standard management parameters are generated; based on the standard management parameters, a control instruction is generated and sent to the industrial Internet of Things management platform corresponding to the target factory, and the control instruction includes at least one of an equipment control instruction, an energy control instruction and a monitoring control instruction.

[0005] Another aspect of an embodiment of the present specification provides an industrial Internet of Things energy management method based on a management cloud platform, the method being executed by an industrial Internet of Things energy management system, including: the industrial Internet of Things management platform generates energy information based on energy monitoring data and equipment operation parameters obtained from the industrial Internet of Things perception control platform, and sends the energy information to the management cloud platform, the energy information including at least one of equipment management information, production scheduling information and energy scheduling information; the management cloud platform receives the energy information sent by the industrial Internet of Things management platform; the management cloud platform determines the energy characteristics of the target factory based on the energy information, the energy characteristics including at least one of energy type, corresponding energy usage and energy utilization rate; the management cloud platform determines a first energy distribution of the target factory based on the energy characteristics; for the target factory that does not meet the first preset condition in the first energy distribution: the management cloud platform obtains a standard factory, the standard factory and the target factory belong to the same factory type; the management cloud platform generates standard management parameters based on the energy information of the standard factory; the management cloud platform generates control instructions based on the standard management parameters and sends them to the industrial Internet of Things management platform corresponding to the target factory, the control instructions including at least one of equipment control instructions, energy control instructions and monitoring control instructions.

[0006] In some embodiments of the present specification, by generating control instructions to control the target factory based on the energy information of the target factory and the energy information of the standard factory, combined with the big data analysis capabilities of the management cloud platform, it is possible to reduce energy consumption while ensuring stable equipment operation and achieve more targeted energy management and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a system structure diagram of an industrial Internet of Things energy management system based on a management cloud platform according to some embodiments of this specification; Figure 2 is an exemplary flow chart of an industrial Internet of Things energy management method based on a management cloud platform according to some embodiments of this specification; Figure 3 is an exemplary flow chart of generating and sending control instructions according to some embodiments of this specification; Figure 4 is an exemplary schematic diagram of adjusting the division of production time periods according to some embodiments of this specification; Figure 5is an exemplary schematic diagram of a parameter adjustment model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0008] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. The drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. The same reference numerals in the figures represent the same structure or operation.

[0009] It should be understood that the "system" and / or "device" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0010] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been explicitly identified, and these steps and elements do not constitute an exclusive list.

[0011] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0012] Some embodiments of this specification provide an industrial Internet of Things energy management system and method based on a management cloud platform, which can be applied to multiple industrial production scenarios, such as electricity, chemical industry, metallurgy, machinery manufacturing, etc. For example, in a power production plant, the system can monitor the consumption of energy such as electricity and gas in real time, and communicate closely with the management cloud platform to achieve refined energy management; for another example, in a machinery manufacturing plant, the system can monitor the consumption of energy such as mechanical energy and electricity, and provide customized energy management solutions in combination with the big data analysis capabilities of the management cloud platform to ensure stable equipment operation while achieving the goal of energy conservation and emission reduction.

[0013] Figure 1 It is a system structure diagram of an industrial Internet of Things energy management system based on a management cloud platform as shown in some embodiments of this specification.

[0014] like Figure 1As shown, the industrial Internet of Things energy management system 100 based on the management cloud platform may include a management cloud platform 110, an industrial Internet of Things management platform 120, an industrial Internet of Things sensor network platform 130, and an industrial Internet of Things perception control platform 140.

[0015] The management cloud platform 110 is a virtual management platform for providing computing, network and storage capabilities. In some embodiments, the management cloud platform 110 can facilitate the provision of remote services by reasonably and effectively utilizing server resources.

[0016] In some embodiments, the management cloud platform 110 can be configured as a cloud server. The cloud refers to a server facility that can be remotely accessed and / or processed through a network. For example, the cloud can include a private cloud, a public cloud, a hybrid cloud, and an industry cloud. In some embodiments, the cloud can include a cloud processor. A cloud processor refers to a processor based on a cloud computing platform. For example, a cloud processor can include a central processing unit (CPU), an application-specific integrated circuit (ASIC), a microcontroller (MCU), etc., or any combination thereof.

[0017] The industrial Internet of Things management platform 120 is a platform configured in each factory for Internet of Things management.

[0018] In some embodiments, the industrial Internet of Things management platform 120 can be configured as a single server or as a server group. The server group can be centralized or distributed, for example, the server can be a distributed system. In some embodiments, the server can be local or remote.

[0019] In some embodiments, the industrial Internet of Things management platform 120 can be configured to include a processor, such as a central processing unit, a microcontroller, an embedded processor (Embedded Processor), a graphics processing unit (Graphics Processing Unit, GPU), etc. or any combination thereof.

[0020] In some embodiments, the industrial Internet of Things management platform 120 is communicatively connected to the management cloud platform 110, and data interaction with the management cloud platform 110 can be achieved through the network.

[0021] The IIoT sensor network platform 130 refers to a platform for controlling information sensor communication. For example, the IIoT sensor network platform 130 can be configured as a communication base station, a router, a wireless device, and the like.

[0022] The industrial Internet of Things perception control platform 140 refers to a functional platform for obtaining perception information and executing control instructions. In some embodiments, the industrial Internet of Things perception control platform 140 can be configured in a factory production line, a factory storage and logistics center, or a factory equipment monitoring room. In some embodiments, the industrial Internet of Things perception control platform 140 can include a sensor device for monitoring energy usage. For example, the sensor device can include a current sensor, a voltage sensor, a gas metering device, etc.

[0023] In some embodiments, the industrial Internet of Things sensor network platform 130 can exchange data with the industrial Internet of Things management platform 120 and the industrial Internet of Things perception control platform 140.

[0024] In some embodiments, the IIoT management platform 120 can obtain energy monitoring data and equipment operating parameters from the IIoT perception control platform 140 through the IIoT sensor network platform 130, generate energy information based on the obtained energy monitoring data and equipment operating parameters, and send it to the management cloud platform 110. For more information about this section, please refer to Figure 2 and its related description.

[0025] In some embodiments, the management cloud platform 110 can receive energy information sent by the industrial Internet of Things management platform 120; determine the energy characteristics of the target factory based on the energy information; determine the first energy distribution of the target factory based on the energy characteristics; for the target factory that does not meet the first preset condition in the first energy distribution: obtain a standard factory; generate standard management parameters based on the energy information of the standard factory; generate control instructions based on the standard management parameters and send them to the industrial Internet of Things management platform 120 corresponding to the target factory. For more information about this part, please refer to Figure 2 and its related description.

[0026] It should be noted that the above description of the industrial Internet of Things energy management system based on the management cloud platform is only for the convenience of description and cannot limit this specification to the scope of the embodiments. For those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various platforms without deviating from this principle, or form a subsystem to connect with other platforms.

[0027] Figure 2 This is an exemplary flow chart of an industrial Internet of Things energy management method based on a management cloud platform according to some embodiments of this specification.

[0028] In some embodiments, the industrial Internet of Things management platform can generate energy information based on the energy monitoring data and equipment operating parameters obtained from the industrial Internet of Things perception and control platform, and send it to the management cloud platform.

[0029] In some embodiments, the management cloud platform can receive energy information sent by the industrial Internet of Things management platform; determine the energy characteristics of the target factory based on the energy information; determine the first energy distribution of the target factory based on the energy characteristics; for the target factory that does not meet the first preset condition in the first energy distribution: obtain a standard factory; generate standard management parameters based on the energy information of the standard factory; generate control instructions based on the standard management parameters and send them to the industrial Internet of Things management platform corresponding to the target factory.

[0030] like Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 can be performed jointly by the industrial Internet of Things energy management platform and the management cloud platform. Among them, step 210 is performed by the industrial Internet of Things energy management platform, and steps 220 to 250 (including steps 251 to 253) are performed by the management cloud platform. For relevant descriptions of the industrial Internet of Things energy management platform, the industrial Internet of Things perception control platform, and the management cloud platform, please refer to Figure 1 The corresponding description.

[0031] Step 210, generating energy information based on the energy monitoring data and equipment operating parameters obtained from the industrial Internet of Things perception control platform, and sending it to the management cloud platform.

[0032] Energy monitoring data refers to monitoring data related to the energy used by the production equipment of the factory. In some embodiments, the energy monitoring data may include the type of energy used by the production equipment and the corresponding usage. Among them, the production equipment refers to the equipment used by the factory to produce products, and the energy type may include electrical energy, sound energy, mechanical energy, coal, gas, etc.

[0033] In some embodiments, the industrial Internet of Things energy management platform can obtain energy monitoring data based on the sensor devices of the industrial Internet of Things perception and control platform, for example, obtaining the electricity usage of production equipment through current / voltage sensors, obtaining the gas usage of production equipment through gas metering devices, etc.

[0034] Equipment operating parameters refer to the operating parameters of relevant equipment in the factory.

[0035] In some embodiments, the equipment operation parameters may include production equipment parameters and energy equipment parameters. For example, the production equipment parameters may include the number of production equipment allocated to each production process and the corresponding working parameters (such as working power, working time, etc.), and the energy equipment parameters may include the energy supply power of the energy equipment corresponding to each energy type, etc. Among them, the energy equipment may include motors, boilers, etc. The energy supply power of the energy equipment may be represented by the amount of energy provided by the energy equipment per unit time.

[0036] Energy information is relevant information reflecting the energy management situation within the factory.

[0037] In some embodiments, the energy information may include equipment management information, production scheduling information, and energy scheduling information.

[0038] Device management information refers to the relevant parameters of each device. For example, the device management information may include the working power and corresponding working time of each device.

[0039] The production scheduling information reflects the relevant information of the production equipment allocated in different time periods during the production process. For example, the production scheduling information may include the number of production equipment.

[0040] Energy scheduling information refers to the scheduling information related to each energy type. For example, energy scheduling information may include the number of energy devices of each energy type, power supply and duration of energy supply, as well as the energy types used by production equipment in different periods and the corresponding energy usage.

[0041] In some embodiments, the industrial Internet of Things management platform can generate energy information in a variety of ways based on energy monitoring data and equipment operating parameters, and send it to the management cloud platform. For example, the industrial Internet of Things management platform can classify and integrate energy monitoring data and equipment operating parameters, and generate equipment management information, production scheduling information, and energy scheduling information in the energy information, and send them to the management cloud platform.

[0042] Step 220: Receive energy information sent by the industrial Internet of Things management platform.

[0043] In some embodiments, the management cloud platform can receive the energy information sent by the industrial Internet of Things management platform in a variety of ways. For example, the management cloud platform can receive the energy information sent by the industrial Internet of Things management platform through the network.

[0044] Step 230, determining the energy characteristics of the target plant based on the energy information.

[0045] The target factory refers to a factory that needs to perform energy management. In some embodiments, there may be multiple target factories.

[0046] Energy characteristics are data related to the energy usage characteristics of a factory. In some embodiments, energy characteristics may include energy type, corresponding energy usage, and energy utilization rate. Energy utilization rate is an indicator reflecting the production efficiency of energy.

[0047] In some embodiments, the management cloud platform can determine the energy characteristics of the target plant according to the energy information in a variety of ways. For example, the management cloud platform can directly obtain the energy type and the corresponding energy usage according to the energy scheduling information in the energy information.

[0048] The energy utilization rate can be expressed by the number of products produced per unit of energy. For example, the energy utilization rate can be determined by the ratio of the number of products produced by the production equipment to the energy usage of the production equipment within a preset time period. The preset time period can be manually set, such as the past hour, day, week, etc., calculated from the current time.

[0049] Step 240: Determine a first energy distribution of the target plant according to the energy characteristics.

[0050] The first energy distribution is data reflecting the distribution of energy composition of each target plant. In some embodiments, the first energy distribution may include energy composition ratios of each target plant, which may be represented by a matrix.

[0051] In some embodiments, the management cloud platform can determine the first energy distribution of the target plant in a variety of ways based on the energy characteristics. For example, the management cloud platform can determine the first energy distribution based on the energy characteristics by calculating the proportion of energy usage of different energy types of each target plant to the total energy consumption of the corresponding target plant. Exemplarily, each element in the first energy distribution P can be calculated by the following formula (1): (1) in, , Respectively represent the target factory and energy type , n is the total number of target factories, m is the number of energy types, Indicates the target factory Medium Energy Type The proportion of the first energy distribution P Line The elements of the column, Indicates the target factory Energy Type of energy usage, Indicates the target factory The total energy consumption of the first energy distribution P Row elements can form target factories The energy distribution vector P i , that is, P i ={ }.

[0052] Step 250, for the target factory that does not meet the first preset condition in the first energy distribution, execute steps 251 to 253.

[0053] The first preset condition is a condition used to determine whether the target factory needs to be regulated.

[0054] In some embodiments, the first preset condition may be that, for a target plant, based on matching the energy distribution vector of the target plant with the energy distribution vectors of other target plants, the target plant and other target plants with similarities greater than a similarity threshold are sorted in descending order according to the mean of energy utilization rates of each energy type, and the sorting position of the target plant is within the previous preset ratio, which means that the first preset condition is met. The similarity threshold and the preset ratio may be preset by technicians based on experience.

[0055] Step 251, obtaining a standard factory.

[0056] A standard factory refers to a factory used as a reference for energy regulation. In some embodiments, a standard factory may be one or more factories designated manually. In some embodiments, a standard factory may also be one or more factories that meet preset standards. For example, the preset standard may be: a factory whose energy utilization rate is higher than a first preset value and whose energy usage is lower than a second preset value. The first preset value and the second preset value are manually preset.

[0057] In some embodiments, the standard plant and the target plant are of the same plant type.

[0058] The factory type is a type classified according to the energy-related characteristics of the factory. In some embodiments, the management cloud platform can classify the factory according to the production scale, product type, and energy type used, etc., to obtain multiple factory types, see the relevant description below for details.

[0059] Step 252, generating standard management parameters based on the energy information of the standard factory.

[0060] Standard management parameters refer to relevant parameters that serve as standards for energy regulation.

[0061] In some embodiments, standard management parameters may include standard equipment parameters, standard production parameters, and standard energy parameters. Among them, standard equipment parameters may include standard working parameters of each production equipment, standard production parameters may include the number of standard production equipment allocated to each production process, and standard energy parameters may include the number of standard equipment used by energy equipment of each energy type, standard energy supply power, and standard energy supply duration.

[0062] In some embodiments, the management cloud platform can generate standard management parameters in a variety of ways based on the energy information of the standard factory. For example, the management cloud platform can determine the standard management parameters based on the weighted sum of energy information of multiple standard factories.

[0063] Exemplarily, the management cloud platform can determine the standard equipment parameters by weighted summing up the working power and working hours of each device in multiple standard factories according to the factory weight coefficients of multiple standard factories; determine the standard production parameters by weighted summing up the number of production equipment allocated in each production process in multiple standard factories according to the factory weight coefficients of multiple standard factories; determine the standard energy parameters by weighted summing up the energy supply power of energy equipment of each energy type in multiple standard factories according to the factory weight coefficients of multiple standard factories. Among them, the factory weight coefficients of each standard factory can be preset manually.

[0064] Step 253, based on the standard management parameters, a control instruction is generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

[0065] Control instructions refer to the relevant instructions for energy control of the target factory.

[0066] In some embodiments, the control instructions may include at least one of equipment control instructions, energy control instructions, and monitoring control instructions.

[0067] Equipment control instructions refer to control instructions related to production equipment. Energy control instructions refer to control instructions related to energy supply and use. Monitoring control instructions are control instructions related to the monitoring status of the target factory.

[0068] In some embodiments, the management cloud platform can generate control instructions in a variety of ways based on standard management parameters and send them to the industrial Internet of Things management platform corresponding to the target factory. For example, the management cloud platform can determine the equipment control instructions based on standard equipment parameters and standard production parameters, such as adjusting the target factory according to the standard production equipment quantity, standard working power and standard working hours.

[0069] For another example, the management cloud platform can determine energy control instructions based on standard energy parameters, such as adjusting the target factory according to the standard number of equipment used by energy equipment of each energy type, standard energy supply power and standard energy supply duration.

[0070] For another example, the management cloud platform can determine the monitoring frequency of the sensor devices in the factory based on the difference between the energy utilization rate of each energy type between the target factory and the standard factory, thereby determining the monitoring and control instructions. For example, the management cloud platform can determine the target factory according to the following formula (2): The monitoring frequency of the sensor device : (2) in, Indicates the preset monitoring frequency, which is the original monitoring frequency preset manually. Indicates standard factory The mean value of energy utilization rate of each energy type, is the number of standard factories, Indicates the target factory The mean value of energy utilization rate for each energy type.

[0071] In some embodiments, after receiving the control instructions, the industrial Internet of Things management platform can issue the control instructions to the industrial Internet of Things perception control platform to adjust the parameters of the corresponding equipment.

[0072] In some embodiments of the present specification, energy information is sent to a management cloud platform through an industrial Internet of Things management platform. The management cloud platform uses the energy information to determine the target factories that need energy regulation, and regulates the energy usage of the factories accordingly based on standard management parameters of standard factories of the same type. The type of factory can be taken into consideration to achieve more targeted energy regulation, making energy regulation more efficient and accurate.

[0073] In some embodiments, the management cloud platform is further configured to determine multiple factory types based on the production scales, product types produced, and energy types used by the multiple target factories and standard factories so as to classify the target factories and standard factories.

[0074] The production scale is an indicator that reflects the size of the factory's output. For example, the factory's production scale can be represented by the weighted sum of the total number of products produced per unit time and the number of production equipment in the factory, and the weight can be set manually. In some embodiments, the production scale can be divided into levels according to different stages of the data, and the higher the level, the larger the production scale. The division of different stages of the data can be preset manually.

[0075] Product type can refer to the general category that a product belongs to. For example, products such as shoes, clothes, bags, etc. can be classified as clothing, and products such as mobile phones and tablets can be classified as mobile devices, etc.

[0076] In some embodiments, the management cloud platform can determine multiple factory types in a variety of ways based on the production scale of the target factory, the type of products produced, and the type of energy used. For example, the management cloud platform can determine the factory type by querying a first preset table based on the level of production scale, the type of products produced, and the type of energy used by different target factories. The first preset table includes the correspondence between the above-mentioned related data and the factory type, which can be determined based on historical data or prior experience.

[0077] For another example, the management cloud platform can cluster multiple target factories and standard factories according to their production scales, product types, and energy types used. Based on the clustering results, different clusters are regarded as different factory types to determine multiple factory types. For one of the target factories or standard factories, the factory type corresponding to the cluster to which the target factory or standard factory belongs is the factory type of the target factory.

[0078] In some embodiments of the present specification, by classifying the target factories according to their production scale, the types of products produced, and the types of energy used, target factories of different factory types can be more effectively regulated in a more targeted manner.

[0079] Figure 3 FIG. 1 is an exemplary flow chart of generating and sending control instructions according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps. In some embodiments, process 300 can be performed by the industrial Internet of Things management platform and the management cloud platform. Among them, steps 311 to 312 are performed by the industrial Internet of Things management platform, and step 320 (including steps 321 to 323) is performed by the management cloud platform.

[0080] In some embodiments, the industrial Internet of Things management platform is further configured to: divide the production time periods into multiple time periods; generate energy information sets for multiple production time periods based on energy monitoring data and equipment operating parameters within the multiple production time periods, and send them to the management cloud platform.

[0081] In some embodiments, the management cloud platform is further configured to: for a target factory: determine an energy feature set for multiple production periods based on an energy information set; generate a second energy distribution for the target factory based on the energy feature set; in response to the second energy distribution not satisfying a second preset condition, generate a control instruction and send it to the industrial Internet of Things management platform corresponding to the target factory.

[0082] Step 311, dividing into multiple production time periods.

[0083] A production period is a period of time during which production occurs in a factory.

[0084] In some embodiments, the industrial Internet of Things management platform can divide multiple production time periods in a variety of ways. For example, the industrial Internet of Things management platform can divide multiple production time periods according to the production plan of the factory, such as dividing different production time periods according to the production time corresponding to different production batches.

[0085] Step 312, generating energy information sets for multiple production periods based on energy monitoring data and equipment operating parameters in multiple production periods, and sending the energy information sets to the management cloud platform.

[0086] The energy information set refers to the set of energy information corresponding to multiple production periods.

[0087] In some embodiments, the industrial Internet of Things management platform can generate energy information sets for multiple production periods in a variety of ways based on energy monitoring data and equipment operating parameters in multiple production periods, and send them to the management cloud platform.

[0088] For example, the industrial Internet of Things management platform can use the energy monitoring data at the end of each production period as the energy monitoring data corresponding to the production period, and the average of the equipment operating parameters within each production period as the equipment operating parameters corresponding to the production period. Based on the energy monitoring data and equipment operating parameters corresponding to the production period, the energy information corresponding to the production period is generated, and then a collection of energy information for multiple production periods is generated and sent to the management cloud platform through the network.

[0089] Each production period contains energy monitoring data at multiple points in time, forming a monitoring data sequence, and each production period contains equipment operating parameters at multiple points in time, forming an operating parameter sequence. For information on how to generate energy information based on energy monitoring data and equipment operating parameters, see Figure 2 Related description.

[0090] Step 320: for a target factory, execute steps 321 to 323.

[0091] Step 321, determining energy feature sets of multiple production periods based on the energy information set.

[0092] The energy feature set refers to the set of energy features corresponding to multiple production periods.

[0093] In some embodiments, the management cloud platform can determine the energy feature sets of multiple production periods in a variety of ways based on the energy information set. For example, the management cloud platform can determine the energy features of the corresponding production period based on the energy information of each production period in the energy information set, and use the set of energy features corresponding to the multiple production periods as the energy feature set. The method of determining the energy features of the corresponding production period based on the energy information of a production period is similar to the method of determining the energy features based on the energy information, which can be seen in Figure 2 Related description.

[0094] Step 322: Generate a second energy distribution of the target plant according to the energy feature set.

[0095] The second energy distribution reflects the distribution of energy usage of the factory in each production period. For example, the second energy distribution may include the average energy usage of each energy type in each production period. The average energy usage refers to the energy usage of a certain energy type in a factory per unit time.

[0096] In some embodiments, the management cloud platform can generate the second energy distribution of the target plant in a variety of ways based on the energy feature set. For example, the management cloud platform can determine the average energy usage of each energy type in the corresponding production period based on the energy usage and usage duration of each energy type in different production periods of the target plant, and then generate the second energy distribution of the target plant. Exemplarily, the management cloud platform can determine each element of the second energy distribution W according to the following formula (3): (3) in, , Respectively indicate the production period and energy type , q is the total number of production periods, m is the number of energy types, Indicates production period Medium Energy Type The average energy usage of Line The elements of the column, Indicates that during the production period Energy Type of energy usage, Indicates production period Length of time.

[0097] Step 323, in response to the second energy distribution not satisfying the second preset condition, generating a control instruction and sending it to the industrial Internet of Things management platform corresponding to the target factory.

[0098] The second preset condition is another condition for determining whether the target plant needs to be regulated.

[0099] In some embodiments, the second preset condition may be that the mean of the standard deviation of the usage of each energy type does not exceed the preset standard deviation threshold. The standard deviation of usage refers to the standard deviation of the average energy usage of a certain energy type in different production periods. For example, taking the second energy distribution W calculated above as an example, the first The standard deviation of the column elements is the energy type The standard deviation of usage.

[0100] In some embodiments, the management cloud platform is further configured to: determine a second preset condition based on energy information of the standard factory.

[0101] For example, the management cloud platform can calculate the standard deviation of the usage of each energy type for multiple standard factories, and calculate the standard deviation mean of the standard deviation of the usage of each energy type, and weight the mean of the standard deviation of the multiple standard factories according to the factory weight coefficient as the preset standard deviation threshold in the second preset condition, thereby determining the second preset condition. For an explanation of the factory weight coefficient, see Figure 2 The corresponding description of step 252 in FIG.

[0102] In some embodiments of the present specification, by determining the second preset condition based on the energy information of the standard factory, a second preset condition that is more in line with the actual situation can be determined, making the judgment on whether to perform energy regulation more accurate.

[0103] In some embodiments, the management cloud platform can generate control instructions in a variety of ways and send them to the industrial Internet of Things management platform corresponding to the target factory. For example, the management cloud platform can generate standard management parameters corresponding to each production period based on the energy information of each production period of the standard factory, and generate control instructions corresponding to each production period based on the standard management parameters of each production period, and send them to the industrial Internet of Things management platform corresponding to the target factory.

[0104] The method of generating the standard management parameters corresponding to each production period according to the energy information of each production period of the standard factory is similar to the method of generating the standard management parameters according to the energy information of the standard factory. Figure 2 The relevant description of step 252 in the above; the method of generating the control instructions corresponding to each production period according to the standard management parameters of each production period is similar to the method of generating the control instructions according to the standard management parameters, which can be referred to Figure 2 See step 253 in the description.

[0105] In some embodiments of the present specification, by dividing the production time periods, judging whether to adjust or not according to the energy usage of the target factory in different production time periods, and generating corresponding instructions, the energy management of the target factory can be carried out more meticulously and the accuracy of energy management can be improved.

[0106] In some embodiments, the target factory corresponds to different factory types in different production periods.

[0107] In some embodiments, the management cloud platform is further configured to determine the factory type corresponding to the target factory in different production periods based on the production scale, product type and energy type used by the target factory in different production periods.

[0108] In some embodiments, the management cloud platform can determine the factory type corresponding to the target factory in the production period in a variety of ways based on the production scale of the target factory in different production periods, the type of products produced, and the type of energy used. For example, the management cloud platform can determine the factory type by querying the second preset table based on the level of production scale, the type of products produced, and the type of energy used by the target factory in different production periods. The second preset table includes the correspondence between the above-mentioned related data and the factory type. The second preset table can be determined based on historical data or prior experience. For an explanation of the level of production scale, please refer to Figure 2 The corresponding description.

[0109] For another example, the management cloud platform can cluster multiple target factories in different production time periods based on the production scales, product types produced, and energy types used by multiple target factories and standard factories in different production time periods. Based on the clustering results, different clusters are regarded as different factory types to determine multiple factory types. For a certain production time period of one of the target factories, the factory type corresponding to the cluster to which the target factory belongs in the production time period is the factory type of the target factory in the production time period.

[0110] In some embodiments of the present specification, by making the target factory correspond to different factory types in different production periods, a more accurate classification can be performed according to the different characteristics of each production period, making the subsequent energy regulation more targeted.

[0111] It should be noted that the above description of the process 200 and the process 300 is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 200 and the process 300 under the guidance of this specification. These modifications and changes are still within the scope of this specification.

[0112] Figure 4 It is an exemplary schematic diagram of adjusting the division of production time periods according to some embodiments of the present specification.

[0113] In some embodiments, Figure 4 As shown, the industrial Internet of Things management platform is further configured to: in response to the fluctuation value 411 of the energy monitoring data and the equipment operation parameter exceeding the preset fluctuation threshold 412, adjust the division 420 of multiple production time periods.

[0114] The fluctuation value of energy monitoring data and equipment operating parameters is a value reflecting the fluctuation of energy monitoring data and equipment operating parameters at the current time point relative to the previous time point. In some embodiments, the fluctuation value of energy monitoring data and equipment operating parameters may include the fluctuation value of energy monitoring data and the fluctuation value of equipment operating parameters.

[0115] In some embodiments, the fluctuation value of the energy monitoring data may be a change in the energy usage of each energy type in the energy monitoring data at the current time point compared with the previous time point in the monitoring data sequence of a production period. For example, it may be expressed by the following formula (4): (4) in, Indicates energy monitoring data during the production period The current time The fluctuation value of , Respectively indicate the production period The current time and the previous time point Energy Type The energy usage of m is the number of energy types.

[0116] In some embodiments, the fluctuation value of the equipment operating parameter may be the degree of change of the working power of each production equipment and the energy supply power of each energy equipment in the equipment operating parameter at the current time point in the operating parameter sequence of a production period compared with the previous time point, which can be represented by the following formula (5) for example: (5) in, Indicates the equipment operating parameters during the production period The current time The total number of production equipment and energy equipment is , is the number of production equipment, is the number of energy devices, when hour, Indicates production equipment The weight coefficient of the working power, , Respectively indicate the production period The current time and the previous time point When production equipment Working power; when hour, Indicates energy equipment The weight coefficient of the energy supply power, , Respectively indicate the production period The current time and the previous time point When, energy equipment The energy supply power. Can be preset manually.

[0117] The preset fluctuation threshold value may be set by a technician based on experience. In some embodiments, the preset fluctuation threshold value may include a fluctuation value threshold value of a monitoring data sequence and a fluctuation value threshold value of an operating parameter sequence.

[0118] In some embodiments, in response to the fluctuation value of energy monitoring data and equipment operation parameters exceeding a preset fluctuation threshold, the industrial Internet of Things management platform can adjust the division of multiple production time periods in a variety of ways. For example, the industrial Internet of Things management platform can use the time point when the current exceeds the preset fluctuation threshold as a division point, divide the corresponding production period into two sub-periods, and determine the energy information of the previous sub-period in a similar way to determine the energy information within the production period, and send it to the management cloud platform. For the method of determining the energy information within the production period, please refer to Figure 3 Related instructions.

[0119] In some embodiments of the present specification, by adjusting the division of production time periods when the fluctuation values ​​of energy monitoring data and equipment operating parameters exceed preset fluctuation thresholds, the production time periods can be divided more finely according to the data, making subsequent analysis and energy regulation more in line with actual conditions and more reasonable.

[0120] In some embodiments, the industrial Internet of Things management platform is further configured to: obtain the factory type to which the target factory belongs during the current production period, and determine a preset fluctuation threshold based on the monitoring data sequence and operating parameter sequence of the standard factory.

[0121] In some embodiments, the industrial Internet of Things management platform can determine the corresponding preset fluctuation threshold value in a variety of ways based on the monitoring data sequence and operating parameter sequence of the standard factory.

[0122] For example, the industrial Internet of Things management platform can calculate the standard deviation of energy usage of each energy type in the energy detection data at the current point in time in the monitoring data sequence of the standard factory, and multiply the mean of the standard deviations calculated by multiple standard factories by the first coefficient as the fluctuation value threshold of the monitoring data sequence; calculate the standard deviation and value corresponding to the operating parameter sequence of the standard factory. The standard deviation and value refers to the standard deviation of the working power of each production equipment in the equipment operating parameters at the current point in time, and the sum of the standard deviation of the energy supply power of each energy equipment. The mean of the standard deviation and value calculated by multiple standard factories is multiplied by the second coefficient as the fluctuation value threshold of the operating parameter sequence.

[0123] Based on the triple standard deviation principle, the first coefficient and the second coefficient can be preset numbers within the range of 1 to 3.

[0124] When the number of standard factories involved in the calculation is small, the probability of the existence of accidental factors is high, and the standard deviation used to determine the preset fluctuation value threshold may have a certain deviation. At this time, the values ​​of the first coefficient and the second coefficient can be increased to relax the judgment conditions. Therefore, in some embodiments, the first coefficient and the second coefficient may be negatively correlated with the number of standard factories involved in the calculation.

[0125] In some embodiments of the present specification, by determining a preset fluctuation threshold based on a monitoring data sequence and an operating parameter sequence of a standard factory, the preset fluctuation threshold that meets the current data can be determined in real time, making the judgment of whether the fluctuation value is normal more accurate, thereby making a more reasonable division of production time periods.

[0126] In some embodiments, Figure 4 As shown, the management cloud platform is further configured to: determine the monitoring frequency 430 of the sensor device of the target factory according to the adjusted multiple production time periods; based on the monitoring frequency 430, generate a monitoring and control instruction 440 and send it to the industrial Internet of Things management platform corresponding to the target factory.

[0127] In some embodiments, the management cloud platform can determine the monitoring frequency of the sensor device of the target factory in a variety of ways based on the adjusted multiple production time periods. For example, for the sub-periods that have not been monitored in the adjusted multiple production time periods, the management cloud platform can increase the monitoring frequency of the sub-period according to the length of the sub-period, so that the number of data points in the sub-period is not less than the number of data points before the corresponding production time period is adjusted, and generate corresponding monitoring and control instructions to send to the industrial Internet of Things management platform corresponding to the target factory. The data point refers to the time point when the sensor device monitors and obtains energy monitoring data. It can be understood that the shorter the duration of the sub-period that has not been monitored, the greater the increase in the monitoring frequency of the sub-period.

[0128] For example, assuming that the duration of production period A is T1 and is divided into two sub-periods a and b, according to the original monitoring frequency n / T1, there should be n data points in production period A. After adjustment and division, the industrial Internet of Things management platform has generated the energy information of sub-period a and sent it to the management cloud platform. At this time, based on the duration T2 of sub-period b, the monitoring frequency of the sensor device can be increased to n / T2, and the number of data points monitored in sub-period b can be increased to n.

[0129] In some embodiments of the present specification, the monitoring frequency is adjusted according to the adjusted production time periods to ensure that there is sufficient data in the subsequent analysis of the adjusted production time periods, so that the analysis results are more accurate, and more reasonable energy management and control can be carried out.

[0130] Figure 5is an exemplary schematic diagram of a parameter adjustment model according to some embodiments of the present specification.

[0131] In some embodiments, Figure 5 As shown, for the target factory that does not meet the first preset condition in the first energy distribution: the management cloud platform 110 can obtain the regulated energy information 510 of the target factory; determine the regulated energy characteristics 522 of the target factory based on the regulated energy information 510; adjust the standard management parameters 530 based on the regulated energy characteristics 522 and the energy characteristics 521 before regulation; generate new regulation instructions 560 based on the adjusted standard management parameters 550 and send them to the industrial Internet of Things management platform corresponding to the target factory.

[0132] The regulated energy information refers to the energy information of the target factory adjusted according to the regulation instruction. In some embodiments, the regulated energy information refers to the energy information of the current production cycle after the target factory is adjusted according to the regulation instruction of the previous production cycle.

[0133] In some embodiments, the industrial Internet of Things management platform can determine the regulated energy information based on the energy monitoring data and equipment operation parameters of the current production cycle obtained from the industrial Internet of Things perception control platform.

[0134] The energy characteristics after regulation refer to the energy characteristics after the target plant executes the regulation instructions.

[0135] In some embodiments, the management cloud platform can determine the regulated energy characteristics of the target plant based on the regulated energy information. The determination method can be similar to the determination method of the energy characteristics. Figure 2 The relevant description of step 230 in FIG.

[0136] In some embodiments, the management cloud platform can determine the usage optimization coefficient based on the energy characteristics after regulation and the energy characteristics before regulation, and then adjust the standard management parameters. The energy characteristics before regulation refer to the energy characteristics of the target factory in the previous production cycle, and the determination method is the same as the determination method of the energy characteristics, see Figure 2 The relevant description of step 230 in FIG.

[0137] The usage optimization coefficient is a coefficient used to indicate the degree of optimization of energy usage. For example, the usage optimization coefficient may include optimization coefficients of energy usage of each energy type.

[0138] In some embodiments, the management cloud platform can determine the usage optimization coefficient in a variety of ways based on the energy characteristics after regulation and the energy characteristics before regulation. For example, the usage optimization coefficient can be related to the adjustment amount of energy usage. For example, the usage optimization coefficient can be calculated according to the following formula (6): (6) in, represents the energy type, m is the number of energy types, Indicates the energy type The corresponding usage optimization coefficient, Indicates the energy type Energy usage before regulation, Indicates the energy type Energy usage after regulation. The energy usage before regulation can be obtained from the energy characteristics before regulation, and the energy usage after regulation can be obtained from the energy characteristics after regulation. Here, taking the regulation of non-clean energy as an example, the energy usage after regulation should be less than the energy usage before regulation, so Should be a positive number, and The larger the value, the better the control effect.

[0139] In some embodiments, the management cloud platform can adjust the standard management parameters in a variety of ways according to the usage optimization coefficient. For example, the management cloud platform obtains the energy composition ratio of multiple standard factories, takes the energy type with the smallest usage optimization coefficient of the target factory as the target energy type, increases the factory weight coefficient of the standard factory with the largest energy usage ratio of the target energy type by a preset adjustment amount, and correspondingly reduces the factory weight coefficient of the standard factory with the smallest energy usage ratio of the target energy type, and determines the adjusted standard management parameters based on the new factory weight coefficient.

[0140] For example, the current target factory has three energy types: a, b, and c. The corresponding usage optimization coefficients are calculated respectively. , , , assuming that the minimum usage optimization coefficient is , the management cloud platform will increase the factory weight coefficient of the standard factory D with the largest proportion of energy type b by a preset adjustment amount, and reduce the factory weight coefficient of the standard factory E with the smallest proportion of energy type b by a preset adjustment amount, and determine the adjusted standard management parameters based on the new weight coefficients. For more information about the factory weight coefficients of standard factories and how to determine standard management parameters based on factory weight coefficients, see Figure 2 The relevant description of step 252 in FIG.

[0141] In some embodiments, Figure 5 As shown, different production periods correspond to different standard management parameters. The management cloud platform can determine the adjusted standard management parameters 550 through the parameter adjustment model 540 based on the target factory's energy characteristics 522 after regulation, the energy characteristics 521 before regulation, and the standard management parameters 530.

[0142] The parameter adjustment model is a model for determining the adjusted standard management parameters. In some embodiments, the parameter adjustment model may be a machine learning model. For example, the parameter adjustment model may be one or a combination of a neural network model (NN), a graph neural network model (GNN).

[0143] In some embodiments, the input of the parameter adjustment model may include the energy characteristics after adjustment, the energy characteristics before adjustment, and the standard management parameters, and the output is the adjusted standard management parameters. For the description of the energy characteristics after adjustment, the energy characteristics before adjustment, and the adjusted standard management parameters, please refer to the relevant description above, and for the description of the standard management parameters, please refer to Figure 2 The corresponding description of step 252 in FIG.

[0144] In some embodiments, the parameter adjustment model can be trained in various ways based on a large number of first training samples with first labels. For example, the parameters can be updated based on the gradient descent method. An exemplary training process includes: obtaining multiple first training samples with first labels; inputting multiple first training samples with first labels into the initial parameter adjustment model, constructing a loss function through the results of the labels and the initial parameter adjustment model, and iteratively updating the parameters of the initial parameter adjustment model based on the loss function through gradient descent or other methods. When the preset conditions are met, the model training is completed and a trained parameter adjustment model is obtained. The preset conditions may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0145] In some embodiments, the first training sample may include sample energy characteristics after regulation, sample energy characteristics before regulation, and sample standard management parameters of multiple standard factories of the factory type to which the factory belongs within the production period. The first training sample may be determined based on historical data.

[0146] In some embodiments, the first label may be the adjusted standard management parameter corresponding to the first training sample. In some embodiments, the first label may be the adjusted standard management parameter with the best subsequent optimization effect among multiple adjustments of the first training sample. The optimization effect may be characterized by the reduction in energy usage of each energy type, and the greater the reduction, the better the optimization effect.

[0147] In some embodiments of the present specification, the adjusted standard management parameters are determined by a parameter adjustment model, and the adjusted standard management parameters corresponding to different production time periods can be obtained, which helps to adjust the standard management parameters in real time according to changes in actual production conditions and improve production efficiency.

[0148] In some embodiments, after a preset number of training rounds, the management cloud platform can adjust the learning rate of the model by adjusting the parameters based on the decay factor.

[0149] The production characteristics refer to characteristics that reflect the production situation of the factory. For example, the production characteristics may include at least one of the length of the production period, the number of devices used, and the type of energy used.

[0150] The preset number of rounds refers to the preset number of model training rounds. In some embodiments, the preset number of rounds can be determined based on the production characteristics of the production period. For example, the larger the weighted sum of the length of the production period, the number of devices used, and the number of energy types, the larger the preset number of rounds.

[0151] In some embodiments, the industrial Internet of Things management platform can obtain production characteristics from the industrial Internet of Things perception control platform.

[0152] The decay factor is a parameter used to adjust the number of model training rounds. For example, the decay factor can be in the range of 0.0 to 1.0. The decay factor can be determined based on experience.

[0153] The learning rate is a parameter used in machine learning algorithms to control the magnitude of weight updates. In some embodiments, the learning rate is a configurable parameter used in neural network training, for example, the learning rate can be in the range of 0.0 to 1.0. Among them, weights are parameters used to calculate and estimate the relationship between input samples and output samples.

[0154] In order to enable the model to better learn the data features of the training set, when the weighted sum of the production features is large, it is necessary to increase the preset number of rounds and delay the time for the learning rate to decay. In some embodiments of this specification, adjusting the learning rate of the model according to the production characteristics of the production period can help the parameter adjustment model better converge to the optimal solution and avoid oscillation or failure to converge during the training process.

[0155] In some embodiments, the management cloud platform can generate new control instructions according to the adjusted standard management parameters. Exemplarily, after obtaining the adjusted standard management parameters, the management cloud platform can increase the supply priority of energy type B, and then determine new control instructions.

[0156] In some embodiments, different production time periods correspond to different control instructions. For different production time periods, the management cloud platform can generate corresponding control instructions according to the standard management parameters corresponding to the production time period. The method of generating corresponding control instructions according to the standard management parameters corresponding to the production time period is similar to the method of generating control instructions according to the standard management parameters, which can be referred to Figure 2 The relevant description of step 253 in FIG.

[0157] In some embodiments of the present specification, corresponding control instructions are generated based on each production time period, so that each production time period can be accurately controlled to ensure that production activities are carried out according to the preferred plan, thereby improving production efficiency.

[0158] In some embodiments, the management cloud platform can send new control instructions to the industrial Internet of Things management platform corresponding to the target factory.

[0159] In some embodiments of this specification, by dynamically adjusting the standard management parameters of the target factory, the energy utilization efficiency can be improved. At the same time, the control instructions are generated and sent in real time to ensure that the factory operation complies with the preferred energy configuration and reduce energy consumption costs, which can enhance the competitiveness of the enterprise and promote green production.

[0160] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and amendments to this specification. Such modifications, improvements and amendments still fall within the spirit and scope of the exemplary embodiments of this specification.

[0161] “Some embodiments” refers to a certain feature, structure or characteristic associated with at least one embodiment of this specification. In addition, certain features, structures or characteristics in one or more embodiments of this specification may be appropriately combined.

[0162] It should be noted that in order to simplify the description disclosed in this specification, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.

[0163] In some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0164] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. As an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification.

Claims

1. An industrial Internet of Things energy management system based on a management cloud platform, characterized in that: It includes a management cloud platform and an industrial Internet of Things system, wherein the industrial Internet of Things system includes an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform and an industrial Internet of Things perception control platform; the industrial Internet of Things management platform is communicatively connected with the management cloud platform; The industrial Internet of Things management platform is configured to: Generate energy information based on energy monitoring data and equipment operation parameters obtained from the industrial Internet of Things perception control platform, and send it to the management cloud platform, wherein the energy information includes at least one of equipment management information, production scheduling information, and energy scheduling information; The management cloud platform is configured as follows: Receiving the energy information sent by the industrial Internet of Things management platform; Determining energy characteristics of the target plant according to the energy information, the energy characteristics including at least one of energy type, corresponding energy usage, and energy utilization rate; Determining a first energy distribution of the target plant according to the energy characteristics; For the target factory that does not meet the first preset condition in the first energy distribution: Acquire a standard factory, where the standard factory and the target factory belong to the same factory type; Generating standard management parameters according to the energy information of the standard factory; According to the standard management parameters, a control instruction is generated and sent to the industrial Internet of Things management platform corresponding to the target factory, and the control instruction includes at least one of an equipment control instruction, an energy control instruction and a monitoring control instruction.

2. The industrial Internet of Things energy management system according to claim 1, characterized in that: The industrial Internet of Things management platform is further configured to: Divide into multiple production periods; Generate energy information sets for the multiple production periods based on the energy monitoring data and the equipment operating parameters in the multiple production periods, and send the energy information sets to the management cloud platform; The management cloud platform is further configured as follows: For one of the target factories: Determining energy feature sets for the multiple production time periods according to the energy information set; generating a second energy distribution of the target plant according to the energy feature set; In response to the second energy distribution not satisfying the second preset condition, the control instruction is generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

3. The industrial Internet of Things energy management system according to claim 2, characterized in that: The industrial Internet of Things management platform is further configured to: in response to fluctuation values ​​of the energy monitoring data and the equipment operation parameters exceeding a preset fluctuation threshold, adjust the division of the multiple production time periods.

4. The industrial Internet of Things energy management system according to claim 3, characterized in that: The management cloud platform is further configured as follows: Determining a monitoring frequency of a sensor device of the target factory according to the adjusted multiple production time periods; Based on the monitoring frequency, the monitoring and control instructions are generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

5. The industrial Internet of Things energy management system according to claim 1, characterized in that: The management cloud platform is further configured as follows: For the target factory that does not meet the first preset condition in the first energy distribution: Obtaining regulated energy information of the target plant; Determining the regulated energy characteristics of the target plant according to the regulated energy information; adjusting the standard management parameters according to the energy characteristics after the regulation and the energy characteristics before the regulation; According to the adjusted standard management parameters, new control instructions are generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

6. An industrial Internet of Things energy management method based on a management cloud platform, characterized in that: The method is performed by an industrial Internet of Things energy management system, including: The industrial Internet of Things management platform generates energy information based on the energy monitoring data and equipment operation parameters obtained from the industrial Internet of Things perception and control platform, and sends it to the management cloud platform, wherein the energy information includes at least one of equipment management information, production scheduling information and energy scheduling information; The management cloud platform receives the energy information sent by the industrial Internet of Things management platform; The management cloud platform determines the energy characteristics of the target plant according to the energy information, wherein the energy characteristics include at least one of energy type, corresponding energy usage and energy utilization rate; The management cloud platform determines a first energy distribution of the target plant according to the energy characteristics; For the target factory that does not meet the first preset condition in the first energy distribution: The management cloud platform acquires a standard factory, where the standard factory and the target factory belong to the same factory type; The management cloud platform generates standard management parameters according to the energy information of the standard factory; The management cloud platform generates control instructions based on the standard management parameters and sends them to the industrial Internet of Things management platform corresponding to the target factory. The control instructions include at least one of equipment control instructions, energy control instructions and monitoring control instructions.

7. The industrial Internet of Things energy management method according to claim 6, characterized in that: The energy information is generated according to the energy monitoring data and equipment operation parameters obtained from the industrial Internet of Things perception control platform, and sent to the management cloud platform, including: Divide into multiple production periods; Generate energy information sets for the multiple production periods based on the energy monitoring data and the equipment operating parameters in the multiple production periods, and send the energy information sets to the management cloud platform; The step of generating a control instruction according to the standard management parameter and sending the control instruction to the industrial Internet of Things management platform corresponding to the target factory includes: For one of the target factories: Determining energy feature sets for the multiple production time periods according to the energy information set; generating a second energy distribution of the target plant according to the energy feature set; In response to the second energy distribution not satisfying the second preset condition, the control instruction is generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

8. The industrial Internet of Things energy management method according to claim 7, characterized in that: The method further includes: in response to fluctuation values ​​of the energy monitoring data and the equipment operation parameters exceeding a preset fluctuation threshold, adjusting the division of the multiple production time periods.

9. The industrial Internet of Things energy management method according to claim 8, characterized in that: The method further comprises: Determining a monitoring frequency of a sensor device of the target factory according to the adjusted multiple production time periods; Based on the monitoring frequency, the monitoring and control instructions are generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

10. The industrial Internet of Things energy management method according to claim 6, characterized in that: The method further comprises: For the target factory that does not meet the first preset condition in the first energy distribution: Obtaining regulated energy information of the target plant; Determining the regulated energy characteristics of the target plant according to the regulated energy information; adjusting the standard management parameters according to the energy characteristics after the regulation and the energy characteristics before the regulation; According to the adjusted standard management parameters, new control instructions are generated and sent to the industrial Internet of Things management platform corresponding to the target factory.

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