Power distribution and utilization energy efficiency management method and system based on Internet of Things

Abstract

The invention discloses a power distribution and utilization energy efficiency management method and system based on the Internet of Things, and belongs to the technical field of power distribution and utilization energy efficiency management, and the method comprises the steps: S1, obtaining the power distribution real-time data, equipment rated data and equipment connection relation of power distribution equipment in a power distribution region; s2, comparing the power distribution real-time data of the power distribution equipment with equipment rated data to obtain a normal load set and an ultra / low load set; s3, constructing a node matrix; s4, calculating a load balance degree; s5, calculating an energy efficiency matching degree; s6, forming a matching matrix; inputting the matching matrix into a KM algorithm to obtain a maximum weight matrix; s7, calculating the fitness of the system; comparing the system fitness with a fitness threshold, and judging whether the maximum weight matrix is reliable or not; if yes, outputting a maximum weight matrix; and if not, repeating the steps S4-S7 until the maximum weight matrix is reliable, and outputting the maximum weight matrix at the moment. The scheme and the system can reduce power distribution times and power distribution distance, and reduce loss.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution and consumption energy efficiency management, and particularly relates to a power distribution and consumption energy efficiency management method and system based on the Internet of Things. Background Art

[0002] The power distribution system is an important part of the power system. It is responsible for safely and efficiently distributing electric energy from the high-voltage transmission network to the user side. It is not only used to connect the upper-level transmission cable, the lower-level main transformer and the power distribution equipment, but also provides corresponding countermeasures in time when emergencies occur in the power distribution equipment.

[0003] The existing power distribution management methods mainly collect the power consumption of users and provide the best power distribution plan after analyzing the power consumption. This method conducts power distribution management at the user side, ignoring the line energy consumption problem caused by power distribution to each user end one by one. For example, the existing power distribution management methods will distribute power to multiple power distribution devices that are close to each other separately, resulting in multiple line losses, thereby affecting the power distribution efficiency.

[0004] Therefore, it is necessary to effectively supply power to multiple power distribution devices with connection relationships at the same time in combination with the connection relationships between the power distribution devices, reduce the number of power distribution times, thereby reducing the line losses caused by multiple power distributions, and improving the power distribution efficiency. Summary of the Invention

[0005] The present invention proposes a power distribution and consumption energy efficiency management method and system based on the Internet of Things to solve the above problems.

[0006] In order to solve the above technical problems, the present invention proposes the following solution: A power distribution and consumption energy efficiency management method based on the Internet of Things, including the following steps: S1: Obtain the real-time power distribution data, equipment rated data and equipment connection relationships of all power distribution devices in the power distribution area; S2: Compare the real-time power distribution data of the power distribution devices with the equipment rated data, and divide them into abnormal power distribution devices and non-abnormal power distribution devices according to the comparison results; assume that there are X power distribution devices in the power distribution area, including M non-abnormal power distribution devices and Y abnormal power distribution devices; divide the M non-abnormal power distribution devices into a normal load set and a super / low load set, and assume that there are M1 normal load devices and M2 super / low load devices; S3: Construct a node matrix according to the connection relationships between the normal load devices in the normal load set and the super / low load devices in the super / low load set; S4: Select Mx normal load devices from the normal load set, where 0 < Mx < M1; select My super / low load devices from the super / low load set; 0 < My < M2; calculate the load balance degree corresponding to the Mx normal load devices and the My super / low load devices; S5: Calculate the energy efficiency matching degree of Mx normal load devices and My over / under load devices according to the load balancing degree of Mx normal load devices and My over / under load devices; S6: placing the energy efficiency matching degree of the corresponding normal load equipment and the over / under load equipment at the corresponding position of the node matrix to form a matching matrix; inputting the matching matrix into the KM algorithm to obtain the maximum weight matrix; S7: Calculate the system fitness of the maximum weight matrix; set the fitness threshold, compare the system fitness with the fitness threshold, and determine whether the maximum weight matrix is ​​reliable based on the comparison result; if the system fitness is greater than or equal to the fitness threshold, the maximum weight matrix is ​​reliable, and the maximum weight matrix is ​​output; if the system fitness is less than the fitness threshold, the maximum weight matrix is ​​unreliable, and Mx normal load devices are reselected from the normal load set, and My ultra / low load devices are selected from the ultra / low load set; repeat S4-S7, and subsequently generate another maximum weight matrix, continuously calculate the system fitness of the subsequent maximum weight matrix, compare the system fitness of the subsequent maximum weight matrix with the fitness threshold, and determine whether the maximum weight matrix is ​​reliable based on the comparison result, until the maximum weight matrix is ​​reliable, S4-S7 will no longer be cycled, and the maximum weight matrix at this time will be output.

[0007] Further, step S2 includes the following steps: S2.1: For a certain power distribution equipment, its real-time power distribution data includes: real-time voltage Vt, real-time current It, real-time temperature value Tt and real-time ambient temperature value Te; its equipment rated data includes: rated voltage V rated , Rated current I rated , Equipment temperature threshold T th And the device ambient temperature threshold T m ; If Vt≤V rated , and at the same time It≤I rated , Tt≤T th and Te≤T m , then the power distribution equipment is a normal power distribution equipment; otherwise, the power distribution equipment is an abnormal power distribution equipment; S2.2: For any non-abnormal power distribution equipment, calculate its real-time power Pt, Pt=Vt*It; calculate its rated power P rated , P rated =V rated* I rated ;like , then the non-abnormal distribution equipment is a normal load equipment, and the normal load equipment is classified as a normal load set; if or , then, the non-abnormal power distribution equipment is an over / under load equipment, and the over / under load equipment is classified into the over / under load concentration.

[0008] Further, step S3 includes the following steps: S3.1: Number all normal load devices in the normal load group; number all over / under load devices in the over / under load group; Set the number of M1 normal load equipment to: N 1 、N 2 ,……,N k , ... N M1 ; k∈[1,M1], k is an integer; Set M2 over / under load equipment numbers to: H 1 , H 2 ,……,H f , ...H M2 ; f∈[1,M2], f is an integer; S3.2: Establish a node matrix based on the connection relationship between normal load equipment and over / under load equipment; The constructed node matrix is ​​expressed as:

[0009] matrix A middle Indicates the number is N k Normal load equipment, and number H f The connection relationship of the over / under load equipment; if N k With H f If there is a connection relationship, Recorded as "1"; if N k With H f If there is no connection relationship, Recorded as "0".

[0010] Furthermore, in step S4, the method of selecting Mx normal load devices from the normal device set and My overload / underload devices from the overload / underload set are both random selections; The load balancing formula corresponding to Mx normal load devices and My overload / underload devices is:

[0011] In formula (1), Indicates the number is N k Real-time power of normal load equipment; Indicates the number is H f Real-time power of over / under load equipment; Indicates the number is N k Normal load equipment and number H fReal-time power average of over / underload equipment; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded devices.

[0012] Furthermore, in step S5, the calculation formula of the energy efficiency matching degree is:

[0013] In formula (2), Indicates the number is N k The normal load equipment is H f Energy efficiency matching of over / under load equipment; All are preset coefficients; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded equipment; P loss Indicates that the number is N k Normal load equipment and number H f The sum of line losses caused by powering over / underloaded equipment; d Indicates that the number is N k Normal load equipment and number H f Total distances for over / under load equipment power supply.

[0014] Furthermore, in step S7, the formula for system fitness is:

[0015] In formula (3), F is the system fitness of the maximum weight matrix; Represents the real-time power of the i-th power distribution device among the selected power distribution devices; i∈[1,Mx+My]; represents the rated power of the i-th power distribution equipment among the selected power distribution equipment; Mx+My represents the number of power distribution devices selected in S4; Expressed as equipment power utilization factor; Expressed as power supply reliability coefficient; ; It is expressed as the power supply reliability index of the i-th distribution equipment, .

[0016] Furthermore, an Internet of Things-based power distribution and energy efficiency management system includes the following modules: A data acquisition module is used to obtain real-time power distribution data, equipment rating data and equipment connection relationships of power distribution equipment in the power distribution area; An equipment classification module is used to compare the real-time distribution data of the distribution equipment with its equipment rated data, and according to the comparison result, classify the distribution equipment in the distribution area into abnormal distribution equipment and non-abnormal distribution equipment, and classify the non-abnormal distribution equipment into a normal load set and an over / under load set; A matrix construction module is used to number the normal load centralized normal load equipment, number the super / low load centralized super / low load equipment, and construct a node matrix according to the connection relationship between the normal load centralized normal load equipment and the super / low load centralized super / low load equipment; An energy efficiency matching degree calculation module is used to select some normal load equipment from the normal load set, select some super / low load equipment from the super / low load set, and calculate the energy efficiency matching degree corresponding to the selected normal load equipment and the selected super / low load equipment; A matching matrix generation module is used to place the corresponding energy efficiency matching degree in the node matrix to generate a matching matrix; The maximum weight calculation module is used to perform the maximum weight allocation operation on the matching matrix through the KM algorithm to obtain the maximum weight matrix; The system optimization module is used to calculate the system fitness of the maximum weight matrix, compare the system fitness of the maximum weight matrix with the fitness threshold, and judge whether the current maximum weight matrix is ​​reliable based on the comparison result. If it is reliable, the maximum weight matrix is ​​output. If it is unreliable, the energy efficiency matching calculation module is used to reselect normal load equipment and over / under load equipment, and recalculate the energy efficiency matching.

[0017] By adopting the above scheme, this scheme can achieve the following beneficial effects: 1. The present invention first classifies the equipment according to its load conditions, and then constructs a node matrix based on the equipment connection relationship. Then, the energy efficiency matching degree between the devices is calculated to ensure that the matching between the devices is more reasonable. A highly matched equipment combination can reduce line loss, improve power supply efficiency, and avoid unnecessary energy consumption between devices. Next, the KM algorithm is used to perform maximum weight allocation to obtain the best matching solution. The method provided by the present invention can effectively improve the matching degree between devices and ensure that the energy efficiency of the entire power distribution system is optimized. By optimizing the power distribution relationship and matching solution between devices, the energy efficiency of the entire power distribution system can be improved, unnecessary energy consumption can be reduced, and thus the waste of electricity can be effectively reduced.

[0018] 2. The power distribution system supplies power to two distribution devices at the same time. Compared with the power distribution system supplying power to each distribution device individually, it reduces the number of power distribution times and thus reduces the loss on the distribution line.

[0019] 3. The two power distribution devices with high matching degree are matched. When the power distribution system distributes power to them, the distribution distance is effectively reduced, thereby reducing the loss in the distribution line. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The invention is a flow chart of a method for managing power distribution and utilization energy efficiency based on the Internet of Things. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1: See Figure 1 , a power distribution energy efficiency management method based on the Internet of Things, comprising the following steps: S1: Obtain the real-time distribution data, equipment rating data and equipment connection relationship of all distribution equipment in the distribution area.

[0023] For example, assuming that there are X distribution devices in the distribution area, the real-time distribution data, device rated data and device connection relationships of the X distribution devices can be obtained based on the Internet of Things.

[0024] Among them, the real-time data of power distribution of the power distribution equipment includes: real-time voltage, real-time current, real-time temperature value and real-time ambient temperature value.

[0025] The device rated data includes: rated voltage, rated current, temperature threshold, and ambient temperature threshold.

[0026] The equipment connection relationship includes: physical connection or communication connection between power distribution equipment. The real-time data of power distribution is obtained by different sensors through collecting real-time data, the equipment rated data is obtained by collecting the data of the power distribution equipment identification plate, and the equipment connection relationship is obtained by the topological structure of the power distribution equipment. The above data and connection relationships are all transmitted to the power distribution system based on the Internet of Things.

[0027] S2: Compare the real-time power distribution data of the power distribution equipment with the rated data of the equipment, and divide the data into abnormal power distribution equipment and non-abnormal power distribution equipment according to the comparison result; divide the non-abnormal power distribution equipment into a normal load set and an over / under load set.

[0028] S2 specifically includes the following steps: S2.1: The real-time voltage Vt, real-time current It, real-time temperature value Tt and real-time ambient temperature value Te of a certain power distribution equipment are respectively compared with the rated voltage V corresponding to the power distribution equipment. rated , Rated current I rated , Equipment temperature threshold T th And the device ambient temperature threshold T m Compare; For this power distribution equipment, if Vt≤V rated , and at the same time It≤I rated , Tt≤T th and Te≤T m , then the power distribution equipment is normal power distribution equipment. No, then the power distribution equipment is abnormal power distribution equipment.

[0029] S2.2: Abnormal distribution equipment is eliminated from the distribution area, and non-abnormal distribution equipment is retained; non-abnormal distribution equipment is divided into normal load set and over / under load set according to the classification standards.

[0030] Assume that there are Y abnormal power distribution devices and M normal power distribution devices among X power distribution devices, then M=XY.

[0031] For a certain non-abnormal power distribution equipment, calculate its real-time power Pt, Pt=Vt*It; calculate its rated power P rated , then P rated =V rated* I rated ; The classification criteria are: For a non-abnormal power distribution equipment, if , then it is a normal load device, and the normal load device is classified into the normal load set.

[0032] like or , it is an over / under load device and belongs to the over / under load concentration.

[0033] S3: Construct a node matrix according to the connection relationship between the normal load equipment in the normal load concentration and the super / low load equipment in the super / low load concentration.

[0034] S3 specifically includes the following steps: S3.1: Count the number of normal load devices in the normal load set, and number all normal load devices in the normal load set; count the number of super / low load devices in the super / low load set, and number all super / low load devices in the super / low load set.

[0035] Assume that the normal load is concentrated, there are M1 normal load devices in total; the over / under load is concentrated, there are M2 over / under load devices in total, M=M1+M2.

[0036] Number M1 normal load devices, and set the numbers as: N 1 , N 2 , ……, N k , ……N M1 ; k ∈ [1, M1], k is an integer.

[0037] Number M2 over / under load devices, and set the numbers as: H 1 , H 2 , ……, H f , ……H M2 ; f ∈ [1, M2], f is an integer.

[0038] S3.2: Based on the connection relationship between normal load devices and over / under load devices, establish a node matrix.

[0039] The constructed node matrix is represented as:

[0040] Matrix A in represents the connection relationship between the normal load device numbered N k and the over / under load device numbered H f ; if there is a connection relationship between N k and H f , then is recorded as "1"; if there is no connection relationship between N k and H f , then is recorded as "0".

[0041] S4: Select Mx normal load devices from the normal load set, 0 < Mx < M1; select My over / under load devices from the over / under load set; 0 < My < M2; calculate the load balance degree corresponding to the Mx normal load devices and the My over / under load devices.

[0042] In this embodiment, the method of selecting Mx normal load devices and selecting My over / under load devices is random selection.

[0043] The formula for the load balance degree is:

[0044] In formula (1), represents the real-time power of the normal load device numbered N k ; represents the real-time power of the over / under load device numbered H f ; Indicates the number is N k Normal load equipment and number H f Real-time power average of over / underload equipment; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded devices.

[0045] The load balance degree between any normal load device in Mx and any overload / underload device in My can be calculated by formula (1).

[0046] S5: According to the load balancing degree of any normal load device in Mx and any overload / underload device in My, the energy efficiency matching degree of any normal load device in Mx and any overload / underload device in My is calculated.

[0047] The energy efficiency matching formula is:

[0048] In formula (2), Indicates number N k Normal load equipment and number H f Energy efficiency matching of over / under load equipment; All are preset coefficients; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded equipment; P loss Indicates that the number is N k Normal load equipment and number H f The sum of line losses caused by powering over / underloaded equipment; Line loss is proportional to the square of line current, line resistance and line length, and is calculated by obtaining values ​​of line current, line resistance, line length, etc.

[0049] d Indicates that the number is N k Normal load equipment and number H f Total distances for over / under load equipment power supply.

[0050] The load balance degree between any normal load device in Mx and any overload / underload device in My can be calculated by formula (2).

[0051] S6: Place the energy efficiency matching degree of the corresponding normal load equipment and the over / under load equipment at the corresponding position of the node matrix to form a matching matrix; input the matching matrix into the KM algorithm to obtain the maximum weight matrix.

[0052] Because energy efficiency matching For number N k Normal load equipment and number H f Energy efficiency matching of over / under load equipment; So will Placed in the node matrix Position; when setting the matching matrix, the elements in the matching matrix must satisfy: If the node matrix If equal to "1", Placed in the node matrix At, instead of the node matrix "1" at the No, do not change the node matrix The numerical value at .

[0053] Steps S4-S6 are illustrated by an example: Assume that there are 5 normal load devices in the power distribution area, denoted as: N 1 、N 2 、N 3 、N 4 、N 5 ; 4 over / under load devices, denoted as: H 1 , H 2 , H 3 , H 4 ; Assume that the connection relationship between 5 normal load devices and 4 over / under load devices is: N 1 With H 1 and H 3 There is a connection relationship; N 2 With H 2 and H 4 There is a connection relationship; N 3 With H 1 There is a connection relationship; N 4 There is no connection with other power distribution equipment; N 5 With H 2 There is a connection relationship; Then, according to the above corresponding connection relationship, the node matrix constructed A 1 for:

[0054] Randomly select 4 normal load devices from 5 normal load devices. Assume that the selected normal load devices are: N 1 、N 2 、N 3 、N 4 ; Randomly select 4 normal load devices from 5 normal load devices. Assume that the selected normal load devices are: N 1 、N 2 、N 3 、N 4 ; Randomly select 3 overload / underload devices from 4 overload / underload devices. Assume that the selected overload / underload devices are: H 1 , H 2 , H 3 ; According to formula (1) and formula (2), the energy efficiency matching degree of the selected 4 normal load devices and the selected 3 overload / underload devices is calculated respectively. Assume that: N 1 With H 1 , H 2 , H 3 The energy efficiency matching degrees are 0.9, 0.7 and 0.5 respectively. 2 With H 1 , H 2 , H 3 The energy efficiency matching degrees are: 0.7, 0.6 and 0.3 respectively. 3 With H 1 , H 2 , H 3 The energy efficiency matching degrees are: 0.8, 0.5 and 0.3 respectively. 4 With H 1 , H 2 , H 3 The energy efficiency matching degree is 0.4, 0.3 and 0.2.

[0055] Place the corresponding energy efficiency matching degree in the node matrix A 1 The matching matrix obtained is E 1 for:

[0056] The matching matrix E 1 After input into the KM algorithm, the KM algorithm will match the matrix E 1 Perform the maximum weight allocation operation to obtain the maximum weight matrix.

[0057] Example: Matching the matrix E 1After input into the KM algorithm, KM obtains the maximum weight matrix based on the maximum weight allocation principle. G 1 for:

[0058] Brief description of allocation principle: Matching matrix E 1 Look at the first line, N 1 With H 1 The matching degree is compared with N 1 With H 2 , H 3 and H 4 are both high, so the maximum weight matrix G 1 The first element of the first row is "1", and the others are "0", indicating that N 1 With H 1 match.

[0059] To match the matrix E 1 Look at the second line, N 2 With H 4 The matching degree is better than N 2 With H 1 , H 2 , H 3 The matching degree is high, so the maximum weight matrix G 1 The fourth element in the second row is "1", and the others are "0".

[0060] To match the matrix E 1 The fourth row shows that all the values ​​are "0", which indicates that N 4 With H 1 , H 2 , H 3 and H 4 None of them match; Maximum weight matrix G 1 The fourth row in the table contains "0".

[0061] To match the matrix E 1 Look at the fifth line, N 5 With H 2 The matching degree is compared with N 5 With H 1 , H 3 and H 4 are both high, so the maximum weight matrix G 1 The second element of the fifth row is "1", and the others are "0".

[0062] To match the matrixE 1 Look at the third line, because N 1 、N 2 、N 4 All have been matched, so H 3 Can only be assigned to N 3 , so the maximum weight matrix G 1 The third element of the third row is "1", and the others are "0".

[0063] So far, the maximum weight matrix of the selected 4 normal load devices and the selected 3 over / under load devices is obtained G 1 , according to the maximum weight matrix G 1 According to the matching rules in the distribution system, the distribution system supplies power to two distribution devices with high matching degree at the same time, that is, the distribution system supplies power to N 1 With H 1 、N 2 With H 4 、N 3 With H 3 or N 5 With H 2 Any combination of N 4 Separate power supply.

[0064] S7: Calculate the system fitness of the maximum weight matrix; set a fitness threshold, compare the system fitness with the fitness threshold, and determine whether the maximum weight matrix is ​​reliable; If it is reliable, then output the maximum weight matrix; If it is unreliable, then reselect Mx normal load devices from the normal load set, and reselect My ultra / low load devices from the ultra / low load set; repeat S4-S7, and subsequently generate another maximum weight matrix, continuously calculate the system fitness of the subsequent maximum weight matrix, compare the system fitness of the subsequent maximum weight matrix with the fitness threshold, and judge whether the maximum weight matrix is ​​reliable based on the comparison result. After the maximum weight matrix is ​​reliable, S4-S7 will no longer be looped, and the maximum weight matrix at this time will be output.

[0065] S7 specifically includes the following steps: S7.1: Calculate the system fitness of the maximum weight matrix.

[0066] The formula for system fitness is:

[0067] In formula (3), F is the system fitness of the maximum weight matrix; Represents the real-time power of the ith distribution device among the selected distribution devices; i∈[1,Mx+My]. The ith distribution device may be a normal load device or an over / under load device.

[0068] Represents the rated power of the i-th distribution equipment among the selected distribution equipment; i∈[1,Mx+My].

[0069] Mx+My represents the number of power distribution devices selected in S4; Expressed as equipment power utilization factor; Expressed as power supply reliability coefficient; .

[0070] It is represented as the power supply reliability index of the selected ith power distribution equipment. In this embodiment, it is the ratio of the power supply duration of the ith power distribution equipment to the statistical time domain duration during the statistical time domain. ; The duration of the statistical time domain is selected by the power supply unit, such as: one month.

[0071] S7.2: Compare the system fitness of the maximum weight matrix with the fitness threshold. If the system fitness is greater than or equal to the set fitness threshold, D , then the maximum weight matrix is ​​reliable, and the maximum weight matrix is ​​output; If the system fitness is less than the set fitness threshold D , then the maximum weight matrix is ​​unreliable, re-select Mx normal load devices from the normal load set in S4, and re-select My ultra / low load devices from the ultra / low load set in S4; repeat S4-S7 until the maximum weight matrix is ​​reliable, and output the corresponding maximum weight matrix.

[0072] Embodiment 2: An Internet of Things-based power distribution and energy efficiency management system, comprising the following modules: A data acquisition module is used to obtain real-time power distribution data, equipment rating data and equipment connection relationships of power distribution equipment in the power distribution area; An equipment classification module is used to compare the real-time distribution data of the distribution equipment with its equipment rated data, and according to the comparison result, classify the distribution equipment in the distribution area into abnormal distribution equipment and non-abnormal distribution equipment, and classify the non-abnormal distribution equipment into a normal load set and an over / under load set; A matrix construction module is used to number the normal load centralized normal load equipment, number the super / low load centralized super / low load equipment, and construct a node matrix according to the connection relationship between the normal load centralized normal load equipment and the super / low load centralized super / low load equipment; An energy efficiency matching degree calculation module is used to select some normal load equipment from the normal load set, select some super / low load equipment from the super / low load set, and calculate the energy efficiency matching degree corresponding to the selected normal load equipment and the selected super / low load equipment; A matching matrix generation module is used to place the corresponding energy efficiency matching degree in the node matrix to generate a matching matrix; The maximum weight calculation module is used to perform the maximum weight allocation operation on the matching matrix through the KM algorithm to obtain the maximum weight matrix; The system optimization module is used to calculate the system fitness of the maximum weight matrix, compare the system fitness of the maximum weight matrix with the fitness threshold, and judge whether the current maximum weight matrix is ​​reliable based on the comparison result. If it is reliable, the maximum weight matrix is ​​output. If it is unreliable, the energy efficiency matching calculation module is used to reselect normal load equipment and over / under load equipment, and recalculate the energy efficiency matching.

[0073] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for managing power distribution efficiency based on the Internet of Things, characterized in that: It includes the following steps: S1: Obtain the real-time power distribution data, rated device data, and device connection relationships of all power distribution devices within the power distribution area; S2: Compare the real-time power distribution data of the power distribution devices with the rated device data, and classify them into abnormal power distribution devices and non-abnormal power distribution devices according to the comparison results; Assume there are X power distribution devices in the power distribution area, among which M are non-abnormal power distribution devices and Y are abnormal power distribution devices; Divide the M non-abnormal power distribution devices into a normal load set and an over / under load set, and assume there are M1 normal load devices and M2 over / under load devices; S3: Construct a node matrix based on the connection relationships between the normal load devices in the normal load set and the over / under load devices in the over / under load set; S4: Select Mx normal load devices from the normal load set, where 0 < Mx < M1; Select My over / under load devices from the over / under load set; 0 < My < M2; Calculate the load balance degree corresponding to the Mx normal load devices and the My over / under load devices; S5: Calculate the energy efficiency matching degree corresponding to the Mx normal load devices and the My over / under load devices according to the load balance degree corresponding to the Mx normal load devices and the My over / under load devices; S6: Place the energy efficiency matching degrees of the corresponding normal load devices and over / under load devices in the corresponding positions of the node matrix to form a matching matrix; Input the matching matrix into the KM algorithm to obtain the maximum weight matrix; S7: Calculate the system fitness of the maximum weight matrix; Set a fitness threshold, compare the system fitness with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison results; If the system fitness is greater than or equal to the fitness threshold, the maximum weight matrix is reliable, and output the maximum weight matrix; If the system fitness is less than the fitness threshold, the maximum weight matrix is unreliable. Re-select Mx normal load devices from the normal load set and My over / under load devices from the over / under load set; Repeat S4 - S7, and subsequently generate another maximum weight matrix. Continuously calculate the system fitness of the subsequent maximum weight matrices, compare the system fitness of the subsequent maximum weight matrices with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison results until the maximum weight matrix is reliable and then stop looping S4 - S7, and output the maximum weight matrix at this time.

2. The method for managing power distribution efficiency based on the Internet of Things according to claim 1, characterized in that: Step S2 includes the following steps: S2.1: For a certain power distribution equipment, its real-time power distribution data includes: real-time voltage Vt, real-time current It, real-time temperature value Tt and real-time ambient temperature value Te; its equipment rated data includes: rated voltage V rated , Rated current I rated , Equipment temperature threshold T th And the device ambient temperature threshold T m ; If Vt≤V rated , and at the same time It≤I rated , Tt≤T th and Te≤T m , then the power distribution equipment is a normal power distribution equipment; otherwise, the power distribution equipment is an abnormal power distribution equipment; S2.2: For any non-abnormal power distribution equipment, calculate its real-time power Pt, Pt=Vt*It; calculate its rated power P rated , P rated =V rated* I rated ;like , then the non-abnormal distribution equipment is a normal load equipment, and the normal load equipment is classified as a normal load set; if or , then, the non-abnormal power distribution equipment is an over / under load equipment, and the over / under load equipment is classified into the over / under load concentration.

3. The method for managing power distribution efficiency based on the Internet of Things according to claim 2, characterized in that: Step S3 includes the following steps: S3.1: Number all the normal load devices in the normal load set; Number all the over / under load devices in the over / under load set; Set the numbers of M1 normal load devices to: N1, N2, ..., N k , ... N M1 ; k∈[1,M1], k is an integer; Set the M2 over / under load equipment numbers to: H1, H2, ..., H f , ...H M2 ; f∈[1,M2], f is an integer; S3.2: Establish a node matrix based on the connection relationships between the normal load devices and the over / under load devices; The constructed node matrix is expressed as: ； matrix A middle Indicates the number is N k Normal load equipment, and number H f The connection relationship of the over / under load equipment; if N k With H f If there is a connection relationship, Recorded as "1"; if N k With H f If there is no connection relationship, Recorded as "0".

4. The method for managing power distribution efficiency based on the Internet of Things according to claim 3 is characterized in that: In step S4, the method of selecting Mx normal load devices from the normal device set and My over / under load devices from the over / under load set is random selection; The formula for the load balance degree corresponding to the Mx normal load devices and the My over / under load devices is: ； In formula (1), Indicates the number is N k Real-time power of normal load equipment; Indicates the number is H f Real-time power of over / under load equipment; Indicates the number is N k Normal load equipment and number H f Real-time power average of over / underload equipment; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded devices.

5. The method for managing power distribution efficiency based on the Internet of Things according to claim 4 is characterized in that: In step S5, the calculation formula for the energy efficiency matching degree is: ； In formula (2), Indicates the number is N k The normal load equipment is H f Energy efficiency matching of over / under load equipment; All are preset coefficients; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded equipment; P loss Indicates that the number is N k Normal load equipment and number H f The sum of line losses caused by powering over / underloaded equipment; d Indicates that the number is N k Normal load equipment and number H f Total distances for over / under load equipment power supply.

6. The method for managing power distribution efficiency based on the Internet of Things according to claim 5, characterized in that: In step S7, the formula for the system fitness is: ； In formula (3), F is the system fitness of the maximum weight matrix; Represents the real-time power of the i-th power distribution device among the selected power distribution devices; i∈[1,Mx+My]; represents the rated power of the i-th power distribution equipment among the selected power distribution equipment; Mx + My represents the number of power distribution devices selected in S4; Expressed as equipment power utilization factor; Expressed as power supply reliability coefficient; ; It is expressed as the power supply reliability index of the i-th distribution equipment, .

7. A power distribution energy efficiency management system based on the Internet of Things, based on the power distribution energy efficiency management method based on the Internet of Things according to claim 6, characterized in that: It includes the following modules: A data acquisition module is used to obtain real-time power distribution data, equipment rating data and equipment connection relationships of power distribution equipment in the power distribution area; An equipment classification module is used to compare the real-time distribution data of the distribution equipment with its equipment rated data, and according to the comparison result, classify the distribution equipment in the distribution area into abnormal distribution equipment and non-abnormal distribution equipment, and classify the non-abnormal distribution equipment into a normal load set and an over / under load set; A matrix construction module is used to number the normal load centralized normal load equipment, number the super / low load centralized super / low load equipment, and construct a node matrix according to the connection relationship between the normal load centralized normal load equipment and the super / low load centralized super / low load equipment; An energy efficiency matching degree calculation module is used to select some normal load equipment from the normal load set, select some super / low load equipment from the super / low load set, and calculate the energy efficiency matching degree corresponding to the selected normal load equipment and the selected super / low load equipment; A matching matrix generation module is used to place the corresponding energy efficiency matching degree in the node matrix to generate a matching matrix; The maximum weight calculation module is used to perform the maximum weight allocation operation on the matching matrix through the KM algorithm to obtain the maximum weight matrix; The system optimization module is used to calculate the system fitness of the maximum weight matrix, compare the system fitness of the maximum weight matrix with the fitness threshold, and judge whether the current maximum weight matrix is ​​reliable based on the comparison result. If it is reliable, the maximum weight matrix is ​​output. If it is unreliable, the energy efficiency matching calculation module is used to reselect normal load equipment and over / under load equipment, and recalculate the energy efficiency matching.

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