Equipment power consumption control method and system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent decision, and discloses an equipment power consumption control method based on the Internet of Things, which comprises the following steps: calculating the equipment energy consumption of equipment to be controlled, and calculating the uncontrolled power consumption of the equipment to be controlled according to the equipment category and the equipment energy consumption; based on the input business volume, analyzing the business volume to be processed of the to-be-controlled equipment in the preset next time period, identifying the parameter necessity of the historical operation parameters in the historical data for the historical business volume in the historical data, and setting the parameter reduction number of the to-be-controlled equipment according to the parameter necessity; setting a device reduction number of the to-be-controlled device according to the to-be-processed service volume, distributing a processable service volume of the to-be-controlled device according to the to-be-processed service volume and the device reduction number, and generating a reduced operation parameter of the to-be-controlled device according to the processable service volume and the parameter reduction number; controlled power consumption of the device to be controlled is calculated using the processable traffic and the reduced operating parameters. According to the invention, the data processing amount of equipment power consumption control can be reduced.

Description

Technical Field

[0001] The present invention relates to a device power consumption control method and system based on the Internet of Things, belonging to the technical field of intelligent decision making. Background Art

[0002] With the continuous growth of global energy demand, the energy crisis has become increasingly prominent. For various factory equipment, power consumption is an important part of energy consumption. For example, there are a large number of disinfection equipment in the factory. If the power consumption of each disinfection equipment can be reduced by a certain proportion, then the energy consumption of the entire factory will be significantly reduced. From the perspective of the enterprise, reducing equipment power consumption can directly reduce electricity bills. For some industrial equipment with high power consumption, such as large motors, furnaces, etc., the cost savings brought by power consumption control are even more considerable.

[0003] At present, power consumption control is usually carried out at the micro level, that is, control of the internal parts and structures of the device. For example, power consumption control of the device is achieved by optimizing the circuit of the device. However, power consumption control of the device itself is rarely carried out at the macro level. In addition, power consumption control is often carried out by putting the device in a sleep state, a shutdown state, a functional sleep state in the device, a functional shutdown state, etc. As mentioned above, whether the device itself is shut down or the internal functions of the device are shut down, power consumption control is carried out at the level of the internal circuit structure of the device. Such detailed power consumption control requires analysis of the complex structural data of each device, resulting in excessive data processing.

[0004] Therefore, the amount of data processing for device power consumption control is large. Summary of the invention

[0005] The present invention provides a device power consumption control method and system based on the Internet of Things, the main purpose of which is to reduce the data processing amount of device power consumption control.

[0006] To achieve the above object, the present invention provides a method for controlling device power consumption based on the Internet of Things, comprising:

[0007] Classify the devices to be controlled to obtain device categories, and collect input business volume, operation data and historical data of the devices to be controlled;

[0008] Calculating the device energy consumption of the device to be controlled according to the device category, the input service volume and the operation data, and calculating the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption;

[0009] Based on the input traffic volume, analyzing the traffic volume to be processed by the device to be controlled in a preset next time period, identifying the parameter necessity of the historical operating parameters in the historical data to the historical traffic volume in the historical data, and setting the parameter reduction number of the device to be controlled according to the parameter necessity;

[0010] According to the to-be-processed traffic, setting the number of devices to be controlled to be reduced, allocating the processable traffic of the to-be-controlled device according to the to-be-processed traffic and the number of devices to be reduced, and generating the reduced operating parameters of the to-be-controlled device according to the processable traffic and the number of parameters to be reduced;

[0011] The controlled power consumption of the device to be controlled is calculated by using the processable business volume and the reduced operating parameters, and the uncontrolled power consumption is compared with the controlled power consumption to obtain a power consumption comparison result. Based on the power consumption comparison result, the device power consumption of the device to be controlled is controlled by using the device reduction number and the parameter reduction number to obtain a power consumption control result.

[0012] Optionally, calculating the device energy consumption of the device to be controlled according to the device category, the input service volume and the operation data includes:

[0013] According to the equipment type, the input business volume and the operation data, the equipment energy consumption of the equipment to be controlled is calculated using the following formula:

[0014]

[0015] Among them, p k Represents the energy consumption of the equipment, P 1 represents the thermal energy corresponding to the first equipment category in the equipment category, c represents the specific heat capacity, m represents the mass of the input business volume, Δt represents the temperature change of the input business volume under the action of the operating data, P 2 represents the mechanical energy corresponding to the second equipment category in the equipment category, E represents the mechanical energy consumption of the second equipment category, F represents the friction energy consumption of the second equipment category, V represents the moving speed of the second equipment category under the action of the operating data, a represents the acceleration of the second equipment category under the action of the operating data, s represents the displacement of the second equipment category under the action of the operating data, μ represents the surface friction coefficient of the second equipment category under the action of the operating data, P 3 represents the radiation energy corresponding to the third equipment category in the equipment category, α represents the activity of the radioactive element of the third equipment category under the action of the operating data, β represents the radiation energy of the third equipment category under the action of the operating data, γ represents the attenuation coefficient of the third equipment category under the action of the operating data, t represents time, and S represents the radiation distance of the third equipment category under the action of the operating data.

[0016] Optionally, calculating the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption includes:

[0017] According to the device category, determining whether there is a data transmission relationship between each of the devices to be controlled;

[0018] When a data transmission relationship exists between each of the devices to be controlled, identifying input devices and output devices in the devices to be controlled according to the data transmission relationship;

[0019] constructing a device transmission path between the input device and the output device;

[0020] The transmission power consumption on the transmission path of the device is calculated using the following formula:

[0021] q l (u, v) = q lT (u)-q lR (u)-q l (v)

[0022] Among them, q l (u, v) represents the transmission power consumption, q lT (u) represents the transmission power of the first pair of input device and output device for input traffic u, q lR (u) represents the receiving power of the first pair of input device and output device for input traffic u, q l (v) represents the loss caused by the obstructing device v between the first pair of input devices and output devices;

[0023] According to the energy consumption of the device, the power consumption of the device to be controlled is calculated using the following formula:

[0024]

[0025] Where P represents the power consumption of the device, P 1 Indicates the thermal energy corresponding to the first equipment category in the equipment category, P 2 Indicates the mechanical energy corresponding to the second equipment category in the equipment category, P 3 represents the radiation energy corresponding to the third equipment category in the equipment category, t represents time, and t′ represents the time limit of t;

[0026] According to the transmission power consumption and the device power consumption, the uncontrolled power consumption of the device to be controlled is calculated using the following formula:

[0027]

[0028] Among them, P all Indicates that power consumption is not controlled, ql (u, v) represents the transmission power consumption, l represents the pairing sequence number between the input device and the output device, L represents the number of pairs between the input device and the output device, and P represents the device power consumption.

[0029] Optionally, the analyzing, based on the input business volume, the business volume to be processed by the device to be controlled within a preset next time period includes:

[0030] Inputting the input business volume into a preset long short-term memory network;

[0031] In the long short-term memory network, based on the input traffic volume, the following formula is used to analyze the traffic volume to be processed by the device to be controlled in the next preset period of time:

[0032] f t =σ(W f [h t-1 , x t ]+b f )

[0033] i t =σ(W i [h t-1 , x t ]+b i )

[0034]

[0035]

[0036] o t =σ(W o [h t-1 , x t ]+b o )

[0037] h t =o t tanh(c t )

[0038] Among them, h t represents the volume of business to be processed, W f , W i , W c , W o represents the weight matrix, b f 、b i 、b c 、b o represents the bias vector, c t Indicates the cell state, represents the candidate vector at the current moment, c t-1represents the cell state of the storage unit at the previous moment, σ and tanh represent the activation function, i t represents the output of the input gate, o t represents the output of the output gate, f t represents the output of the forget gate, h t-1 Indicates the output volume at the previous moment, x t Indicates the input business volume.

[0039] Optionally, the identifying the necessity of the historical operating parameters in the historical data for the parameters of the historical business volume in the historical data includes:

[0040] Obtaining parameter values ​​of the historical operating parameters;

[0041] Calculating the grey correlation coefficient between the parameter value and the historical business volume;

[0042] Calculating the grey correlation degree of the historical operating parameter to the historical business volume in the historical data by using the grey correlation coefficient;

[0043] The grey correlation degree is used as a historical operation parameter for the parameter necessity of the historical business volume in the historical data.

[0044] Optionally, setting the number of parameter reductions of the device to be controlled according to the parameter necessity includes:

[0045] According to the necessity of the parameters, non-essential parameters of the device to be controlled are selected;

[0046] Selecting a target to be reduced from the non-essential parameters;

[0047] The number of the targets to be reduced is counted to obtain the number of parameter reductions.

[0048] Optionally, the step of setting a reduction number of devices to be controlled according to the traffic to be processed includes:

[0049] Aggregating devices of the same type among the devices to be controlled to obtain a device set;

[0050] Sorting the input devices and output devices in the device to be controlled to obtain a device sequence;

[0051] According to the device set and the device combination, the device combination of different types of devices in the device to be controlled is constructed using the following formula:

[0052] {y1, y2, ....y m}

[0053] Among them, {y1, y2, ....y m} represents a combination of different types of devices, y1 represents the device set of the first type of devices, y2 represents the device set of the second type of devices, m represents the device set of the mth type of devices of the same type, y1 and y2 are the relationship between the input device and the output device in the device sequence, y m Represents an output device;

[0054] Using the device combination to generate an edge between each of the devices to be controlled;

[0055] The processing time of the device to be controlled is used as the edge weight, and the device to be controlled is used as the vertex;

[0056] Constructing a directed acyclic graph between each of the devices to be controlled by using the edges, the edge weights and the vertices;

[0057] After inputting the to-be-processed traffic into the directed acyclic graph, generating edge weight parameters of the to-be-processed traffic in the directed acyclic graph;

[0058] According to the edge weight parameter, converting the directed acyclic graph into a weighted directed graph;

[0059] The following formula is used to identify the vertices on the critical path in the weighted directed graph:

[0060] v e (Source) = 0

[0061] V e (k) = max{v e (j)+cost(j,k)}

[0062] v d (Sinking point) = v e (Meeting Point)

[0063] V d (k) = min{V d (j)-cost(k,j)}

[0064] e(i)=v e (k)

[0065] d(i)=v d (j)-cost(k, j)

[0066] v i , 0 = e(i) - d(i)

[0067] Among them, v i represents the vertex on the critical path, v e (source point) represents the earliest occurrence time of the source point in the weighted directed graph, ve (k) represents the earliest occurrence time of the k-th vertex, cost(j, k) represents the edge weight of the edge (j, k) in the weighted directed graph, and v e (j) represents the earliest occurrence time of the jth vertex, v d (Sinking point) indicates the latest occurrence time of the sinking point, v e (Sinking point) indicates the earliest occurrence time of the sinking point, v d (k) represents the latest occurrence time of the k-th vertex, cost(k, j) represents the edge weight of the edge (k, j) in the weighted directed graph, and v e (j) represents the latest occurrence time of the j-th vertex, e(i) represents the earliest occurrence time of the ith edge, and d(i) represents the latest occurrence time of the ith edge;

[0068] Determine a critical path corresponding to a vertex on the critical path;

[0069] Query the time length between the earliest start time and the latest end time of the critical path;

[0070] Determine whether the time length exceeds the time limit corresponding to the business volume to be processed;

[0071] When the time length does not exceed the time length limit corresponding to the to-be-processed traffic, the number of devices to be controlled is reduced by using the to-be-controlled devices in the weighted directed graph.

[0072] Optionally, allocating the processable traffic volume of the device to be controlled according to the traffic volume to be processed and the reduced number of devices includes:

[0073] Determining controllable devices among the devices to be controlled according to the reduced number of devices;

[0074] Obtaining a weighted directed graph corresponding to the controllable device;

[0075] Distribute the to-be-processed traffic equally to the same type of devices in the weighted directed graph, and determine whether the same type of devices are the last devices in the weighted directed graph;

[0076] When the device of the same type is not the last device in the weighted directed graph, allocating the to-be-processed traffic to a subsequent device of the device of the same type according to an input-output relationship of the device of the same type;

[0077] When the successor device is the last device in the weighted directed graph, the processable traffic volume of the controllable device is determined.

[0078] Optionally, generating the reduced operating parameters of the device to be controlled according to the processable business volume and the parameter reduction number includes:

[0079] Determining controllable parameters in the device to be controlled according to the parameter reduction number;

[0080] Obtaining a parameter directed graph corresponding to the controllable parameters;

[0081] Distribute the to-be-processed traffic equally to the parameters of the same type in the parameter directed graph, and determine whether the parameters of the same type are the end parameters in the parameter directed graph;

[0082] When the parameter of the same type is not the last parameter in the parameter directed graph, allocating the to-be-processed traffic to a subsequent parameter of the parameter of the same type through an input-output relationship of the parameter of the same type;

[0083] When the successor parameter is the last parameter in the parameter directed graph, the reduced operating parameter of the controllable parameter is determined.

[0084] In order to solve the above problems, the present invention also provides a device power consumption control system based on the Internet of Things, the system comprising:

[0085] A data collection module is used to classify the devices to be controlled, obtain the device category, and collect the input business volume, operation data and historical data of the devices to be controlled;

[0086] A power consumption calculation module, used to calculate the device energy consumption of the device to be controlled according to the device category, the input service volume and the operation data, and calculate the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption;

[0087] A number setting module, used for analyzing the to-be-controlled device's to-be-controlled traffic volume in a preset next time period based on the input traffic volume, identifying the parameter necessity of the historical operating parameters in the historical data to the historical traffic volume in the historical data, and setting the parameter reduction number of the to-be-controlled device according to the parameter necessity;

[0088] a parameter reduction module, configured to set the number of devices to be controlled to be reduced according to the amount of business to be processed, allocate the processable business volume of the device to be controlled according to the amount of business to be processed and the number of devices to be reduced, and generate the reduced operating parameters of the device to be controlled according to the processable business volume and the number of parameters to be reduced;

[0089] The power consumption control module is used to calculate the controlled power consumption of the device to be controlled by using the processable business volume and the reduced operating parameters, compare the uncontrolled power consumption with the controlled power consumption, and obtain a power consumption comparison result; based on the power consumption comparison result, the device power consumption of the device to be controlled is controlled by using the device reduction number and the parameter reduction number to obtain a power consumption control result.

[0090] Compared with the problem described in the background technology, the embodiment of the present invention calculates the device energy consumption of the device to be controlled according to the device category, the input business volume and the operating data, so as to analyze the energy consumption of the device to be controlled when power consumption control is not performed from a macro level such as the device level, thereby facilitating subsequent power consumption calculations. Furthermore, the embodiment of the present invention calculates the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption, so as to compare the uncontrolled power consumption with the power consumption after subsequent power consumption control, thereby selecting a method that can reduce the uncontrolled power consumption. The embodiment of the present invention analyzes the pending business volume of the device to be controlled in a preset next time period based on the input business volume, so as to analyze the business volume of factory orders in future time periods, thereby controlling the power consumption of the device distribution in future time periods, thereby achieving macro power consumption control with low data processing volume. Furthermore The embodiment of the present invention identifies the parameter necessity of the historical operating parameters in the historical data to the historical business volume in the historical data, so as to identify the importance of the historical operating parameters in the historical data to the historical business volume in the historical data. If the historical operating parameters are not important to the historical business volume, the operation of the historical operating parameters can be reduced, thereby reducing the power consumption of the device. Furthermore, the embodiment of the present invention sets the parameter reduction number of the device to be controlled according to the parameter necessity, so as to use the parameter reduction number to shut down and sleep some useless functions of the device in the future, thereby reducing power consumption. The embodiment of the present invention sets the device reduction number of the device to be controlled according to the business volume to be processed, so as to use the device reduction number to sleep and shut down the running device to be controlled, thereby controlling the power consumption of the device at a macro level and reducing the data processing amount of power consumption control. Therefore, the device power consumption control method based on the Internet of Things proposed by the present invention can reduce the data processing amount of device power consumption control. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A schematic diagram of a flow chart of a method for controlling power consumption of an IoT-based device provided by an embodiment of the present invention;

[0092] Figure 2 A schematic diagram of a module for implementing the method for controlling device power consumption based on the Internet of Things provided in one embodiment of the present invention.

[0093] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0094] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0095] The embodiment of the present application provides a device power consumption control method based on the Internet of Things. The execution subject of the device power consumption control method based on the Internet of Things includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the device power consumption control method based on the Internet of Things can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0096] Embodiment 1:

[0097] Reference Figure 1 FIG. 1 is a flow chart of a method for controlling power consumption of a device based on the Internet of Things according to an embodiment of the present invention. In this embodiment, the method for controlling power consumption of a device based on the Internet of Things includes:

[0098] S1. Classify the devices to be controlled to obtain device categories, and collect input business volume, operation data and historical data of the devices to be controlled.

[0099] In an embodiment of the present invention, the equipment categories include heating equipment categories, radiation equipment categories, and mechanical equipment categories. The heating equipment is used to heat the products to be processed, the radiation equipment is used to disinfect and sterilize the products to be processed, and the mechanical equipment is used to rotate and displace the products to be processed.

[0100] Furthermore, in an embodiment of the present invention, the input business volume refers to the amount of products received by the factory that need to be processed by the device to be controlled, and the operating data refers to the operating data of the device to be controlled in the current time period, such as the heating temperature and heating time set by the heating equipment, and the historical data includes historical business volume and historical operating data, and the historical data refers to data within a historical time period, that is, data earlier than the operating data.

[0101] S2. Calculate the device energy consumption of the device to be controlled according to the device category, the input business volume and the operation data, and calculate the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption.

[0102] The embodiment of the present invention calculates the device energy consumption of the device to be controlled according to the device category, the input business volume and the operating data, so as to analyze the energy consumption of the device to be controlled when power consumption control is not performed from a macro level such as the device level, thereby facilitating subsequent power consumption calculations.

[0103] The device energy consumption refers to the energy consumption of the device to be controlled.

[0104] In one embodiment of the present invention, the calculating the device energy consumption of the device to be controlled according to the device category, the input business volume and the operating data includes: calculating the device energy consumption of the device to be controlled according to the device category, the input business volume and the operating data using the following formula:

[0105]

[0106] Among them, p k Represents the energy consumption of the equipment, P 1 represents the thermal energy corresponding to the first equipment category in the equipment category, c represents the specific heat capacity, m represents the mass of the input business volume, Δt represents the temperature change of the input business volume under the action of the operating data, P 2 represents the mechanical energy corresponding to the second equipment category in the equipment category, E represents the mechanical energy consumption of the second equipment category, F represents the friction energy consumption of the second equipment category, V represents the moving speed of the second equipment category under the action of the operating data, a represents the acceleration of the second equipment category under the action of the operating data, s represents the displacement of the second equipment category under the action of the operating data, μ represents the surface friction coefficient of the second equipment category under the action of the operating data, P 3 represents the radiation energy corresponding to the third equipment category in the equipment category, α represents the activity of the radioactive element of the third equipment category under the action of the operating data, β represents the radiation energy of the third equipment category under the action of the operating data, γ represents the attenuation coefficient of the third equipment category under the action of the operating data, t represents time, and S represents the radiation distance of the third equipment category under the action of the operating data.

[0107] Furthermore, an embodiment of the present invention calculates the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption, so as to compare the uncontrolled power consumption with the power consumption after subsequent power consumption control, thereby selecting a method that can reduce the uncontrolled power consumption.

[0108] The uncontrolled power consumption refers to the power consumption when the power consumption of the device to be controlled is not controlled.

[0109] In one embodiment of the present invention, the uncontrolled power consumption of the device to be controlled is calculated according to the device category and the device energy consumption, including: judging whether there is a data transmission relationship between each of the devices to be controlled according to the device category; when there is a data transmission relationship between each of the devices to be controlled, identifying the input device and the output device in the device to be controlled according to the data transmission relationship; constructing a device transmission path between the input device and the output device; and calculating the transmission power consumption on the device transmission path using the following formula:

[0110] q l (u, v) = q lT (u)-q lR (u)-q l (v)

[0111] Among them, q l (u, v) represents the transmission power consumption, q lT (u) represents the transmission power of the first pair of input device and output device for input traffic u, q lR (u) represents the receiving power of the first pair of input device and output device for input traffic u, q l (v) represents the loss caused by the blocking device v between the first pair of input device and output device;

[0112] According to the energy consumption of the device, the power consumption of the device to be controlled is calculated using the following formula:

[0113]

[0114] Where P represents the power consumption of the device, P 1 Indicates the thermal energy corresponding to the first equipment category in the equipment category, P 2 Indicates the mechanical energy corresponding to the second equipment category in the equipment category, P 3 represents the radiation energy corresponding to the third equipment category in the equipment category, t represents time, and t′ represents the time limit of t;

[0115] According to the transmission power consumption and the device power consumption, the uncontrolled power consumption of the device to be controlled is calculated using the following formula:

[0116]

[0117] Among them, P all Indicates that power consumption is not controlled, q l (u, v) represents the transmission power consumption, l represents the pairing sequence number between the input device and the output device, L represents the number of pairs between the input device and the output device, and P represents the device power consumption.

[0118] Among them, the data transmission relationship refers to the relationship in which a certain device to be controlled processes the received data and sends it to other devices for the next step. Different equipment categories correspond to different processing steps. The order between different devices to be controlled can be determined according to the process of the processing steps, and the order represents the data transmission relationship from the previous device outputting data to the next device. The input device is relative to the output device, that is, the input device refers to the direct predecessor of the output device, and the output device refers to the direct successor of the input device. The device transmission path refers to the shortest path from the input device to the output device. This path needs to be determined based on the actual factory environment. For example, in a factory, each device is distributed in a matrix, and the row and shortest path between the second device in the first row of the matrix and the tenth device in the first row are the device transmission paths.

[0119] It should be noted that q lT (u), q l (v), q lR (u) are all parameters that need to be determined by measuring the distribution characteristics of values ​​in actual scenarios.

[0120] S3. Based on the input business volume, analyze the business volume to be processed by the device to be controlled in the next preset time period, identify the parameter necessity of the historical operating parameters in the historical data to the historical business volume in the historical data, and set the parameter reduction number of the device to be controlled according to the parameter necessity.

[0121] The embodiment of the present invention analyzes the business volume to be processed by the controlled device in the preset next time period based on the input business volume, so as to analyze the business volume of factory orders in the future time period, thereby controlling the power consumption of the equipment distribution in the future time period, thereby realizing macro-level and low data processing power consumption control.

[0122] The business volume to be processed refers to the amount of products to be processed that the factory will receive in the future period analyzed by the neural network model.

[0123] In one embodiment of the present invention, the analyzing the traffic volume to be processed by the device to be controlled in a preset next time period based on the input traffic volume includes: inputting the input traffic volume into a preset long short-term memory network; in the long short-term memory network, based on the input traffic volume, analyzing the traffic volume to be processed by the device to be controlled in a preset next time period using the following formula:

[0124] f t =σ(W f [h t-1 , x t ]+b f )

[0125] i t =σ(W i [h t-1 , x t ]+b i )

[0126]

[0127]

[0128] o t =σ(W o [h t-1 , x t ]+b o )

[0129] h t =o t tanh(c t )

[0130] Among them, h t represents the volume of business to be processed, W f , W i , W c , W o represents the weight matrix, b f 、b i 、b c 、b o represents the bias vector, c t Indicates the cell state, represents the candidate vector at the current moment, c t-1 represents the cell state of the storage unit at the previous moment, σ and tanh represent the activation function, i t represents the output of the input gate, o t represents the output of the output gate, f t represents the output of the forget gate, h t-1 Indicates the output volume at the previous moment, x t Indicates the input business volume.

[0131] Furthermore, an embodiment of the present invention is used to identify the importance of the historical operating parameters in the historical data to the historical business volume in the historical data by identifying the parameter necessity of the historical operating parameters in the historical data to the historical business volume in the historical data. If the historical operating parameters are not important to the historical business volume, the operation of the historical operating parameters can be reduced, thereby reducing the power consumption of the device.

[0132] In one embodiment of the present invention, the identifying of the parameter necessity of the historical operating parameters in the historical data to the historical business volume in the historical data includes: obtaining the parameter value of the historical operating parameters; calculating the grey correlation coefficient between the parameter value and the historical business volume; using the grey correlation coefficient to calculate the grey correlation degree of the historical operating parameters to the historical business volume in the historical data; and using the grey correlation degree as the parameter necessity of the historical operating parameters to the historical business volume in the historical data.

[0133] Furthermore, the embodiment of the present invention sets the parameter reduction number of the device to be controlled according to the parameter necessity, so as to subsequently use the parameter reduction number to shut down or put some useless functions of the device into sleep mode, thereby reducing power consumption.

[0134] The reduced number of parameters refers to the number of parameters that are turned off in the device to be controlled.

[0135] In one embodiment of the present invention, setting the number of parameter reductions for the device to be controlled according to the parameter necessity includes: selecting non-essential parameters for the device to be controlled according to the parameter necessity; selecting targets to be reduced from the non-essential parameters; and counting the number of targets to be reduced to obtain the number of parameter reductions.

[0136] Among them, the target to be reduced refers to a part of the parameters that need to be reduced or turned off, which are randomly selected from the non-essential parameters. The non-essential parameters refer to parameters with low parameter necessity selected from the operating parameters of the equipment to be controlled. For example, the parameters of a heating equipment are heating temperature, heating humidity, and heating time, among which heating humidity is a parameter with low necessity.

[0137] It should be noted that each parameter reduction number corresponds to a different distribution of targets to be reduced, that is, when the parameter reduction number is subsequently used to reduce, shut down and put the device to sleep, it is necessary to reduce it according to the distribution of the targets to be reduced. The non-essential parameters and the targets to be reduced only represent parameter categories and do not contain actual numerical values. For example, the heating time parameter does not include the specific value of the time. Therefore, it can be seen that when the specific value of the heating time parameter is needed later, the minimum to maximum range of the heating time can be used.

[0138] S4. According to the amount of business to be processed, the number of reduced devices of the device to be controlled is set; according to the amount of business to be processed and the number of reduced devices, the processable business volume of the device to be controlled is allocated; according to the processable business volume and the number of parameter reductions, the reduced operating parameters of the device to be controlled are generated.

[0139] The embodiment of the present invention sets the device reduction number of the device to be controlled according to the amount of business to be processed, so as to use the device reduction number to put the running device to be controlled to sleep and shut down, thereby controlling the device power consumption at a macro level and reducing the data processing amount of power consumption control.

[0140] In one embodiment of the present invention, setting the number of devices to be controlled to be reduced according to the to-be-processed traffic volume includes: aggregating devices of the same type in the devices to be controlled to obtain a device set; sorting input devices and output devices in the devices to be controlled to obtain a device sequence; and constructing a device combination of different types of devices in the devices to be controlled according to the device set and the device combination using the following formula:

[0141] {y1, y2, ....y m}

[0142] Among them, {y1, y2, ....y m} represents a combination of different types of devices, y1 represents the device set of the first type of devices, y2 represents the device set of the second type of devices, m represents the device set of the mth type of devices of the same type, y1 and y2 are the relationship between the input device and the output device in the device sequence, y m Represents an output device;

[0143] The device combination is used to generate an edge between each device to be controlled in the devices to be controlled; the processing time of the device to be controlled is used as the edge weight, and the device to be controlled is used as the vertex; the edge, the edge weight and the vertex are used to construct a directed acyclic graph between each device to be controlled in the devices to be controlled; after the business volume to be processed is input into the directed acyclic graph, an edge weight parameter of the business volume to be processed is generated in the directed acyclic graph; according to the edge weight parameter, the directed acyclic graph is converted into a weighted directed graph; and the vertices on the critical path in the weighted directed graph are identified using the following formula:

[0144] v e (Source) = 0

[0145] V e (k) = max{v e (j)+cost(j,k)}

[0146] v d (Sinking point) = v e (Meeting Point)

[0147] V d (k) = min{V d (j)-cost(k,j)}

[0148] e(i)=v e (k)

[0149] d(i)=V d (j)-cost(k, j)

[0150] v i , 0 = e(i) - d(i)

[0151] Among them, v i represents the vertex on the critical path, v e (source point) represents the earliest occurrence time of the source point in the weighted directed graph, v e (k) represents the earliest occurrence time of the k-th vertex, cost(j, k) represents the edge weight of the edge (j, k) in the weighted directed graph, and v e (j) represents the earliest occurrence time of the jth vertex, v d (Sinking point) indicates the latest occurrence time of the sinking point, v e (Sinking point) indicates the earliest occurrence time of the sinking point, v d (k) represents the latest occurrence time of the k-th vertex, cost(k, j) represents the edge weight of the edge (k, j) in the weighted directed graph, and v e (j) represents the latest occurrence time of the j-th vertex, e(i) represents the earliest occurrence time of the ith edge, and d(i) represents the latest occurrence time of the ith edge;

[0152] Determine the critical path corresponding to the vertex on the critical path; query the time length between the earliest start time and the latest end time of the critical path; determine whether the time length exceeds the time limit corresponding to the pending business volume; when the time length does not exceed the time limit corresponding to the pending business volume, use the to-be-controlled devices in the weighted directed graph to set the to-be-controlled devices to reduce the number of devices.

[0153] It should be noted that there are a total of m device sets, and all devices in each device set are devices that can receive the same data and process it at the same time. For example, there are 3 first-level heating devices and 5 second-level heating devices in the heating device type. The first-level heating device and the second-level heating device are in the relationship of input device and output device. The 3 first-level heating devices can receive all business volumes at the same time and process them at the same time. After processing, they are transmitted to the second-level heating devices. The 5 second-level heating devices receive all business volumes at the same time and process them at the same time. Furthermore, the edge between each device to be controlled in the device to be controlled is a directed edge between different device sets. The edge weight refers to the processing time of the device set corresponding to the starting point of the edge. The weight parameter refers to the actual size of the edge weight calculated based on the size of the input business volume to be processed. In the process of calculating the edge weight parameter, it is necessary to first construct a functional relationship between the edge weight parameter and the business volume based on historical experience, and use the functional relationship to determine the actual edge weight parameter. The edge weight of the directed acyclic graph has no actual value, and the edge weight of the weighted directed graph has an actual value. The earliest start time of the critical path refers to the earliest occurrence time of the source point of the critical path, and the latest end time of the critical path refers to the latest occurrence time of the sink point. The time length refers to the difference between the latest occurrence time and the earliest start time. The time limit refers to the factory user, that is, the order time limit required by the user who initiates the business volume to be processed.

[0154] It should be noted that the number of devices in the device set is a random value that does not exceed the data owned by the factory.

[0155] Optionally, the process of setting the reduced number of devices to be controlled by using the devices to be controlled in the weighted directed graph refers to: querying the number of devices in the device set and the actual number of devices in the factory. If the number is less than the actual number of devices in the factory, the smaller number of devices is used as the reduced number of devices, and it is necessary to record which types of equipment have been reduced, that is, the same size of reduced number of devices corresponds to reduced devices of different distributions.

[0156] In one embodiment of the present invention, the allocation of the processable business volume of the device to be controlled based on the business volume to be processed and the reduced number of devices includes: determining the controllable devices among the devices to be controlled based on the reduced number of devices; obtaining a weighted directed graph corresponding to the controllable devices; evenly distributing the business volume to be processed to the same type of devices in the weighted directed graph, and determining whether the same type of devices are the last devices in the weighted directed graph; when the same type of devices are not the last devices in the weighted directed graph, allocating the business volume to be processed to the successor devices of the same type of devices through the input-output relationship of the same type of devices; until the successor device is the last device in the weighted directed graph, the processable business volume of the controllable devices is determined.

[0157] The controllable device refers to a device that has not been reduced, the tail device refers to a sink, and the input-output relationship refers to a predecessor and successor relationship between devices.

[0158] Optionally, the process of evenly distributing the pending business volume to the same type of devices in the weighted directed graph means: for example, there are 3 first-level heating devices and 5 second-level heating devices in the heating device type, and the pending business volume is 9, 9 / 3=3, and a pending business volume of 3 is allocated to each of the 3 first-level heating devices.

[0159] In one embodiment of the present invention, generating the reduced operating parameters of the device to be controlled based on the processable business volume and the number of parameter reductions includes: determining the controllable parameters in the device to be controlled based on the number of parameter reductions; obtaining a parameter directed graph corresponding to the controllable parameters; evenly distributing the business volume to be processed to the same type of parameters in the parameter directed graph, and determining whether the same type of parameters are the end parameters in the parameter directed graph; when the same type of parameters are not the end parameters in the parameter directed graph, allocating the business volume to be processed to the successor parameters of the same type of parameters through the input-output relationship of the same type of parameters; until the successor parameters are the end parameters in the parameter directed graph, determining the reduced operating parameters of the controllable parameters.

[0160] It should be noted that the construction of the parameter directed graph is similar to the construction principle of the aforementioned weighted directed graph, and will not be further elaborated here. It should be noted that each reduced operating parameter has a corresponding business volume, such as the heating temperature of the heating equipment. If the heating equipment can heat a business volume of 9 at a time, then the business volume corresponding to the heating temperature is 9.

[0161] S5. Calculate the controlled power consumption of the device to be controlled by using the processable business volume and the reduced operating parameters, compare the uncontrolled power consumption with the controlled power consumption to obtain a power consumption comparison result, and based on the power consumption comparison result, control the power consumption of the device to be controlled by using the device reduction number and the parameter reduction number to obtain a power consumption control result.

[0162] Optionally, the process of calculating the controlled power consumption of the device to be controlled by using the processable business volume and the reduced operating parameters: using the processable business volume and the reduced operating parameters to determine the business volume allocated to the device to be controlled and the operating parameters of the device, thereby processing the business volume, and then using the same principle as the aforementioned calculation of uncontrolled power consumption to calculate the controlled power consumption of the device to be controlled.

[0163] The power consumption comparison result refers to the comparison result of power consumption size.

[0164] Optionally, based on the power consumption comparison result, the device reduction number and the parameter reduction number are used to control the device power consumption of the device to be controlled to obtain the power consumption control result: when the controlled power consumption is less than the uncontrolled power consumption, and the controlled power consumption is the minimum power consumption (different sizes of parameter reduction numbers and device reduction numbers will produce different sizes of controlled power consumption), the device reduction number and parameter reduction number corresponding to the controlled power consumption are used to reduce the device and reduce the parameters (reduction means shutting down or putting them to sleep).

[0165] Compared with the problem described in the background technology, the embodiment of the present invention calculates the device energy consumption of the device to be controlled according to the device category, the input business volume and the operating data, so as to analyze the energy consumption of the device to be controlled when power consumption control is not performed from a macro level such as the device level, thereby facilitating subsequent power consumption calculations. Furthermore, the embodiment of the present invention calculates the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption, so as to compare the uncontrolled power consumption with the power consumption after subsequent power consumption control, thereby selecting a method that can reduce the uncontrolled power consumption. The embodiment of the present invention analyzes the pending business volume of the device to be controlled in a preset next time period based on the input business volume, so as to analyze the business volume of factory orders in future time periods, thereby controlling the power consumption of the device distribution in future time periods, thereby achieving macro power consumption control with low data processing volume. Furthermore The embodiment of the present invention identifies the parameter necessity of the historical operating parameters in the historical data to the historical business volume in the historical data, so as to identify the importance of the historical operating parameters in the historical data to the historical business volume in the historical data. If the historical operating parameters are not important to the historical business volume, the operation of the historical operating parameters can be reduced, thereby reducing the power consumption of the device. Furthermore, the embodiment of the present invention sets the parameter reduction number of the device to be controlled according to the parameter necessity, so as to use the parameter reduction number to shut down and sleep some useless functions of the device in the future, thereby reducing power consumption. The embodiment of the present invention sets the device reduction number of the device to be controlled according to the business volume to be processed, so as to use the device reduction number to sleep and shut down the running device to be controlled, thereby controlling the power consumption of the device at a macro level and reducing the data processing amount of power consumption control. Therefore, the device power consumption control method based on the Internet of Things proposed by the present invention can reduce the data processing amount of device power consumption control.

[0166] Embodiment 2:

[0167] like Figure 2 The figure shows a functional module diagram of a device power consumption control system based on the Internet of Things according to the present invention.

[0168] The device power consumption control system 200 based on the Internet of Things described in the present invention can be installed in an electronic device. According to the functions implemented, the device power consumption control system based on the Internet of Things can include a data acquisition module 201, a power consumption calculation module 202, a number setting module 203, a parameter reduction module 204 and a power consumption control module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0169] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0170] The data collection module 201 is used to classify the devices to be controlled, obtain the device category, and collect the input business volume, operation data and historical data of the devices to be controlled;

[0171] The power consumption calculation module 202 is used to calculate the device energy consumption of the device to be controlled according to the device category, the input service volume and the operation data, and calculate the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption;

[0172] The number setting module 203 is used to analyze the to-be-controlled device's to-be-controlled traffic volume in a preset next time period based on the input traffic volume, identify the parameter necessity of the historical operating parameters in the historical data to the historical traffic volume in the historical data, and set the parameter reduction number of the to-be-controlled device according to the parameter necessity;

[0173] The parameter reduction module 204 is used to set the number of devices to be controlled to be reduced according to the amount of business to be processed, allocate the processable business volume of the device to be controlled according to the amount of business to be processed and the number of devices to be reduced, and generate the reduced operating parameters of the device to be controlled according to the processable business volume and the number of parameters to be reduced;

[0174] The power consumption control module 205 is used to calculate the controlled power consumption of the device to be controlled by using the processable business volume and the reduced operating parameters, compare the uncontrolled power consumption with the controlled power consumption, and obtain a power consumption comparison result. Based on the power consumption comparison result, the device power consumption of the device to be controlled is controlled by using the device reduction number and the parameter reduction number to obtain a power consumption control result.

[0175] In detail, each module in the device power consumption control system 200 based on the Internet of Things in the embodiment of the present invention adopts the same method as above when in use. Figure 1The same technical means as the device power consumption control method based on the Internet of Things described in the text and can produce the same technical effects are used, and will not be repeated here.

[0176] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for controlling power consumption of a device based on the Internet of Things, characterized in that: The method comprises: Classify the devices to be controlled to obtain device categories, and collect input business volume, operation data and historical data of the devices to be controlled; Calculating the device energy consumption of the device to be controlled according to the device category, the input service volume and the operation data, and calculating the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption; Based on the input traffic volume, analyzing the traffic volume to be processed by the device to be controlled in a preset next time period, identifying the parameter necessity of the historical operating parameters in the historical data to the historical traffic volume in the historical data, and setting the parameter reduction number of the device to be controlled according to the parameter necessity; According to the to-be-processed traffic, setting the number of devices to be controlled to be reduced, allocating the processable traffic of the to-be-controlled device according to the to-be-processed traffic and the number of devices to be reduced, and generating the reduced operating parameters of the to-be-controlled device according to the processable traffic and the number of parameters to be reduced; The controlled power consumption of the device to be controlled is calculated by using the processable business volume and the reduced operating parameters, and the uncontrolled power consumption is compared with the controlled power consumption to obtain a power consumption comparison result. Based on the power consumption comparison result, the device power consumption of the device to be controlled is controlled by using the device reduction number and the parameter reduction number to obtain a power consumption control result.

2. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The calculating the device energy consumption of the device to be controlled according to the device category, the input business volume and the operation data includes: According to the equipment type, the input business volume and the operation data, the equipment energy consumption of the equipment to be controlled is calculated using the following formula: Among them, p k Represents the energy consumption of the equipment, P 1 represents the thermal energy corresponding to the first equipment category in the equipment category, c represents the specific heat capacity, m represents the mass of the input business volume, Δt represents the temperature change of the input business volume under the action of the operating data, P 2 represents the mechanical energy corresponding to the second equipment category in the equipment category, E represents the mechanical energy consumption of the second equipment category, F represents the friction energy consumption of the second equipment category, V represents the moving speed of the second equipment category under the action of the operating data, a represents the acceleration of the second equipment category under the action of the operating data, s represents the displacement of the second equipment category under the action of the operating data, μ represents the surface friction coefficient of the second equipment category under the action of the operating data, P 3 represents the radiation energy corresponding to the third equipment category in the equipment category, α represents the activity of the radioactive element of the third equipment category under the action of the operating data, β represents the radiation energy of the third equipment category under the action of the operating data, γ represents the attenuation coefficient of the third equipment category under the action of the operating data, t represents time, and S represents the radiation distance of the third equipment category under the action of the operating data.

3. The device power consumption control method based on the Internet of Things according to claim 1, characterized in that: The calculating the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption includes: According to the device category, determining whether there is a data transmission relationship between each of the devices to be controlled; When a data transmission relationship exists between each of the devices to be controlled, identifying input devices and output devices in the devices to be controlled according to the data transmission relationship; constructing a device transmission path between the input device and the output device; The transmission power consumption on the transmission path of the device is calculated using the following formula: q l (u,v)=q lT (u)-q lR (u)-q l (v) Among them, q l (u,v) represents the transmission power consumption, q lT (u) represents the transmission power of the first pair of input device and output device for input traffic u, q lR (u) represents the receiving power of the first pair of input device and output device for input traffic u, q l (v) represents the loss caused by the obstructing device v between the first pair of input devices and output devices; According to the energy consumption of the device, the power consumption of the device to be controlled is calculated using the following formula: Where P represents the power consumption of the device, P 1 Indicates the thermal energy corresponding to the first equipment category in the equipment category, P 2 Indicates the mechanical energy corresponding to the second equipment category in the equipment category, P 3 represents the radiation energy corresponding to the third equipment category in the equipment category, t represents time, and t′ represents the time limit of t; According to the transmission power consumption and the device power consumption, the uncontrolled power consumption of the device to be controlled is calculated using the following formula: Among them, P all Indicates that power consumption is not controlled, q l (u,v) represents the transmission power consumption, l represents the pairing sequence number between the input device and the output device, L represents the number of pairs between the input device and the output device, and P represents the device power consumption.

4. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The analyzing, based on the input traffic volume, the traffic volume to be processed by the device to be controlled within a preset next time period includes: Inputting the input business volume into a preset long short-term memory network; In the long short-term memory network, based on the input traffic volume, the following formula is used to analyze the traffic volume to be processed by the device to be controlled in the next preset period of time: f t =σ(W f [h t-1 ,x t ]+b f ) i t =σ(W i [h t-1 ,x t ]+b i ) the t =σ(W o [h t-1 ,x t ]+b o ) h t =o t fishy t ) Among them, h t represents the volume of business to be processed, W f , W i , W c , W o represents the weight matrix, b f , b i , b c , b o represents the bias vector, c t Indicates the cell state, represents the candidate vector at the current moment, c t-1 represents the cell state of the storage unit at the previous moment, σ and tanh represent the activation function, i t represents the output of the input gate, o t represents the output of the output gate, f t represents the output of the forget gate, h t-1 Indicates the output volume at the previous moment, x t Indicates the input business volume.

5. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The identifying the necessity of the historical operation parameters in the historical data to the parameters of the historical business volume in the historical data includes: Obtaining parameter values ​​of the historical operating parameters; Calculating the grey correlation coefficient between the parameter value and the historical business volume; Calculating the grey correlation degree of the historical operating parameter to the historical business volume in the historical data by using the grey correlation coefficient; The grey correlation degree is used as a historical operation parameter for the parameter necessity of the historical business volume in the historical data.

6. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The step of setting the number of parameter reductions of the device to be controlled according to the parameter necessity includes: According to the necessity of the parameters, non-essential parameters of the device to be controlled are selected; Selecting a target to be reduced from the non-essential parameters; The number of the targets to be reduced is counted to obtain the number of parameter reductions.

7. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The step of setting the number of devices to be controlled to be reduced according to the traffic to be processed includes: Aggregating devices of the same type among the devices to be controlled to obtain a device set; Sorting the input devices and output devices in the device to be controlled to obtain a device sequence; According to the device set and the device combination, the device combination of different types of devices in the device to be controlled is constructed using the following formula: {y1,y2,....y m } Among them, {y1,y2,....y m } represents a combination of different types of devices, y1 represents the device set of the first type of devices, y2 represents the device set of the second type of devices, m represents the device set of the mth type of devices of the same type, y1 and y2 are the relationship between the input device and the output device in the device sequence, y m Represents an output device; Using the device combination to generate an edge between each of the devices to be controlled; The processing time of the device to be controlled is used as the edge weight, and the device to be controlled is used as the vertex; Constructing a directed acyclic graph between each of the devices to be controlled by using the edges, the edge weights and the vertices; After inputting the to-be-processed traffic into the directed acyclic graph, generating edge weight parameters of the to-be-processed traffic in the directed acyclic graph; According to the edge weight parameter, converting the directed acyclic graph into a weighted directed graph; The following formula is used to identify the vertices on the critical path in the weighted directed graph: v e (Source point) = 0 v e (k)=max{v e (j)+cost(j,k)} v d (Sinking point) = v e (Meeting Point) v d (k)=min{v d (j)-cost(k,j)} e(i)=v e (k) d(i)=v d (j)-cost(k,j) v i ,0=e(i)-d(i) Among them, v i represents the vertex on the critical path, v e (source point) represents the earliest occurrence time of the source point in the weighted directed graph, v e (k) represents the earliest occurrence time of the k-th vertex, cost(j,k) represents the edge weight on the (j,k) edge in the weighted directed graph, and v e (j) represents the earliest occurrence time of the jth vertex, v d (Sinking point) indicates the latest occurrence time of the sinking point, v e (Sinking point) indicates the earliest occurrence time of the sinking point, v d (k) represents the latest occurrence time of the k-th vertex, cost(k,j) represents the edge weight of the (k,j) edge in the weighted directed graph, and v e (j) represents the latest occurrence time of the j-th vertex, e(i) represents the earliest occurrence time of the ith edge, and d(i) represents the latest occurrence time of the ith edge; Determine a critical path corresponding to a vertex on the critical path; Query the time length between the earliest start time and the latest end time of the critical path; Determine whether the time length exceeds the time limit corresponding to the business volume to be processed; When the time length does not exceed the time length limit corresponding to the to-be-processed traffic, the number of devices to be controlled is reduced by using the to-be-controlled devices in the weighted directed graph.

8. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The allocating the processable traffic volume of the device to be controlled according to the traffic volume to be processed and the reduced number of devices includes: Determining controllable devices among the devices to be controlled according to the reduced number of devices; Obtaining a weighted directed graph corresponding to the controllable device; Distribute the to-be-processed traffic equally to the same type of devices in the weighted directed graph, and determine whether the same type of devices are the last devices in the weighted directed graph; When the device of the same type is not the last device in the weighted directed graph, allocating the to-be-processed traffic to a subsequent device of the device of the same type according to an input-output relationship of the device of the same type; When the successor device is the last device in the weighted directed graph, the processable traffic volume of the controllable device is determined.

9. The method for controlling power consumption of an IoT-based device according to claim 1, wherein: The generating, according to the processable business volume and the parameter reduction number, the reduced operating parameters of the device to be controlled includes: Determining controllable parameters in the device to be controlled according to the parameter reduction number; Obtaining a parameter directed graph corresponding to the controllable parameters; Distribute the to-be-processed traffic equally to the parameters of the same type in the parameter directed graph, and determine whether the parameters of the same type are the end parameters in the parameter directed graph; When the parameter of the same type is not the last parameter in the parameter directed graph, allocating the to-be-processed traffic to a subsequent parameter of the parameter of the same type through an input-output relationship of the parameter of the same type; When the successor parameter is the last parameter in the parameter directed graph, the reduced operating parameter of the controllable parameter is determined.

10. A device power consumption control system based on the Internet of Things, characterized in that: The system comprises: A data collection module is used to classify the devices to be controlled, obtain the device category, and collect the input business volume, operation data and historical data of the devices to be controlled; A power consumption calculation module, used to calculate the device energy consumption of the device to be controlled according to the device category, the input service volume and the operation data, and calculate the uncontrolled power consumption of the device to be controlled according to the device category and the device energy consumption; A number setting module, used for analyzing the to-be-controlled device's to-be-controlled traffic volume in a preset next time period based on the input traffic volume, identifying the parameter necessity of the historical operating parameters in the historical data to the historical traffic volume in the historical data, and setting the parameter reduction number of the to-be-controlled device according to the parameter necessity; a parameter reduction module, configured to set the number of devices to be controlled to be reduced according to the amount of business to be processed, allocate the processable business volume of the device to be controlled according to the amount of business to be processed and the number of devices to be reduced, and generate the reduced operating parameters of the device to be controlled according to the processable business volume and the number of parameters to be reduced; The power consumption control module is used to calculate the controlled power consumption of the device to be controlled by using the processable business volume and the reduced operating parameters, compare the uncontrolled power consumption with the controlled power consumption, and obtain a power consumption comparison result; based on the power consumption comparison result, the device power consumption of the device to be controlled is controlled by using the device reduction number and the parameter reduction number to obtain a power consumption control result.

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