CN107229031A - A kind of ammeter dynamic evaluation system and method analyzed based on paddy electricity - Google Patents

Abstract

The invention relates to a dynamic evaluation system and method for electric meters based on valley current analysis. It solves the problems in the prior art that manual meter reading has a long cycle, the result lags behind, and the general electric meter can only obtain the total electricity, but cannot know the electricity consumption of each appliance, and there is no energy-saving analysis of the electricity consumption of each appliance. The system includes a meter terminal, a cloud server and a user terminal, the meter terminal includes a collection unit and a meter unit, and the cloud service terminal includes a computing unit, a storage unit, and an identity verification unit. Methods Gray relational analysis was used to calculate the estimated value of energy consumption for each day, and the energy-saving analysis was carried out according to the estimated value. The invention solves the problem that the general electric meter system can only simply display the energy consumption value, so that the user can know the specific energy consumption of each electrical equipment in detail; it can conduct energy-saving analysis on the electricity consumption situation of each day, so that the user can clearly understand the electricity consumption situation, and the user can according to Power consumption and energy saving situation Adjust the power consumption time or power consumption in time to achieve a better energy saving effect.

Description

A dynamic evaluation system and method for electric meters based on valley current analysis

technical field

The invention relates to the technical field of energy management, in particular to a dynamic evaluation system and method for electric meters based on valley electricity analysis.

Background technique

With the continuous improvement of the country's requirements for building energy saving and consumption reduction, electric energy consumption has become the focus of more and more attention of enterprises. In the regional power supply system, by comprehensively grasping the operation status of the power supply system, the operation reliability of the power supply system can be improved, and the power monitoring of important loads can be strengthened. At present, there are many problems in power management in many regions, such as low utilization rate of equipment, aging equipment and need to update, unable to grasp the current status of power consumption, unable to quantify energy-saving indicators of enterprises and other problems.

For a long time, the management of electric energy has generally adopted the manual regular meter reading method. Due to the long period of manual data reading, it is impossible to obtain the data of all electric loads at the same time, resulting in low reliability of the spatio-temporal distribution of electric power consumption, and man-made For statistics of meter reading data and calculation of power consumption rate, the report generation cycle is long, and the statistical results lag behind, which cannot provide effective reference data for people. In addition, there are also some electricity meters that use the Internet to obtain electricity online, but generally only a total electricity consumption can be obtained, and it is impossible to know the electricity consumption of each appliance in depth, and there is no energy-saving analysis of the electricity consumption of each appliance. Being able to simply know the total power consumption does not meet the user's needs, and the user cannot adjust the power consumption based on this information to achieve energy-saving effects.

Contents of the invention

The present invention mainly solves the problems in the prior art that the cycle of manual meter reading is long, the result lags behind, and the general electric meter can only obtain the total electricity, but cannot know the electricity consumption of each appliance, and there is no energy-saving analysis of the electricity consumption of each appliance, and provides A system and method for dynamic evaluation of electric meters based on valley current analysis are presented.

The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions: a dynamic evaluation system for electric meters based on valley electricity analysis, including electric meter end, cloud service end and user end; The meter units of each energy consumption node are connected to the collection unit respectively. The cloud server includes a computing unit, a storage unit, and an identity verification unit. The storage unit is connected to the computing unit, and the identity verification unit is connected to the storage unit and the computing unit. , the collection unit is connected to the computing unit through the network, and the client is respectively connected to the identity verification unit and the computing unit through the network. In the present invention, the electric meter unit detects the daily power consumption of energy-consuming nodes and the power consumption during valley power periods. The user of the collection unit receives the data of the meter unit, and then sends it to the computing unit of the cloud server. The calculation unit calculates the energy consumption of the measured place on the day according to the received data and evaluates the energy consumption of the day according to the historical data. The identity verification unit authenticates the user. After the verification is passed, the calculation unit receives the corresponding Energy consumption and influencing factor data are calculated, and then fed back to the user. The client side checks the energy consumption and evaluation of the measured place. The client side checks the energy consumption and evaluation of the measured place. The invention solves the problem that the general electric meter system can only simply display the energy consumption value and cannot meet the needs of users. The power consumption time or power consumption can be adjusted in time according to the power consumption and energy saving situation, so as to achieve the energy saving effect.

As a preferred solution of the above solution, each energy consumption node in the measured location includes lighting energy consumption nodes, air conditioning energy consumption nodes, and other energy consumption nodes of other electrical equipment.

A method for dynamic evaluation of electric meters based on valley electricity analysis, comprising the following steps:

S1. Perform identity verification on the client according to the application of the client;

S2. After the verification is passed, the calculation unit obtains the energy consumption data of each energy consumption node within the set number of days and the energy consumption data of each energy consumption node during the off-peak period;

S3. Calculate the correlation value between the energy consumption of each energy consumption node in each day and the energy consumption in the off-peak period;

S4. According to the degree of correlation, gray correlation analysis is used to calculate the energy-saving evaluation coefficient of the energy consumption of each energy-consuming node in each day;

S5. Determine the weight value of the energy consumption of each energy-consuming node;

S6. According to the energy-saving evaluation coefficient and the energy consumption weight value of each energy-consuming node, the gray correlation model is used to calculate the energy-saving evaluation value of the total energy consumption of each day;

S7. Judging the energy saving level of each day according to the energy saving evaluation value.

The invention enables the user to know in detail the specific energy consumption value of each electrical equipment and the energy saving situation of the current electricity consumption, and the user can timely adjust the electricity consumption time or the amount of electricity consumption according to the electricity consumption and energy saving situation, so as to achieve the energy saving effect.

As a preferred solution of the above solution, the identity verification process in step S1 includes:

S11. The client sends a viewing request with IP to the identity verification unit;

S12. The identity verification unit searches the user information of the IP from the storage unit, obtains the mobile phone number of the IP user, and sends verification information to the mobile phone number;

S13. The identity verification unit receives the verification information sent by the client, if the verification information is inconsistent, then end, if the verification information is consistent, enter the next step;

S14. According to the user information of the IP, the electric meter data corresponding to the user is retrieved in the storage unit.

As a preferred solution of the above solution, the calculation process of the associated value in step S3 includes:

S31. Establish an energy consumption matrix according to the energy consumption data of each energy consumption node in each day:

Where m is the set number of days, k represents the kth energy consumption node, and the order corresponding to k is lighting, air conditioning, and other energy consumption nodes;

S32. Obtain the energy consumption data of each energy consumption node in the valley power period of each day to establish a valley power consumption matrix:

S33. Calculate the correlation value between the energy consumption of each energy consumption node and the energy consumption of each energy consumption node during the off-peak period of each day, and obtain the correlation value matrix

in

As a preferred solution of the above-mentioned solution, the calculation process of the energy-saving evaluation coefficient of the energy consumption of each energy-consuming node in each day in step S4 includes:

S41. Select the optimal correlation degree in each day to form a reference matrix

The reference matrix is normalized by vector normalization The correlation matrix X ^* is standardized, and the formula is:

Obtain the standardized reference matrix X ₀ and the standardized correlation matrix X;

S42. Establish a reference sequence x ₀ , recorded as:

x ₀ (k)=[x ₀ (1),...,x ₀ (k)], k=1,2,3,

Establish a comparison sequence x _m , which is recorded as:

x _m (k)=[x _m (1),...,x _m (k)], m=1,2,...,i;

S43. Obtain the absolute difference between the reference sequence and the comparison sequence, the difference includes a maximum value and a minimum value, then it is expressed as:

Δmax＝max _m max _k |x ₀ (k)-x _m (k)|

Δmin＝min _m min _k |x ₀ (k)-x _m (k)|;

S44. According to the gray relational analysis, the energy-saving evaluation coefficient of the kth energy consumption node on the mth day is obtained as:

Where ρ is the resolution coefficient;

Get the evaluation coefficient matrix E,

E=[ξ _m (k)] _m×k .

As a preferred solution of the above solution, the calculation process of the weight value of the energy consumption of each energy consumption node in step 5 includes:

S51. According to the associated value matrix X ^* of the energy consumption of each energy consumption node and the energy consumption of each energy consumption node during the off-peak period, standardize the associated value of each energy consumption node:

Get the standardized correlation matrix

S52. Obtaining the entropy of the standardized correlation degree value is:

Among them, the order of S _k corresponding to k is the entropy of the correlation degree value after standardization of lighting, air conditioning, and other energy consumption, a=-(lni) ^-1 ,

S53. When season Obtain the entropy weight of energy consumption of each node:

ω _k is the entropy weight of lighting, air conditioning, and other energy consumption in the order corresponding to k, and finally obtains the weight W=[ω ₁ ...ω _k ] of energy consumption of energy consumption nodes. This scheme uses the entropy weight method to determine the energy consumption weight of each energy consumption node.

As a preferred solution of the above-mentioned solution, the calculation process of the energy-saving evaluation value of the total energy consumption of each day in step 6 includes:

S61. Acquire evaluation coefficient matrix

E=[ξ _m (k)] _m×k

and the weight of energy consumption node energy consumption W=[ω ₁ ...ω _k ];

S62. According to the gray relational model P=4×W, calculate the estimated value of energy consumption for each day P=[p _m ], m=1,2,...,i, where

As a preferred solution of the above-mentioned solution, the process of judging the energy-saving level of each day in step 7 includes:

S71. Select the optimal value P _max from the evaluation values of energy consumption in each day;

S72. According to the optimal value P _max , convert the energy consumption evaluation values of other days into percentage conversion to obtain the energy saving evaluation score F _m , and the conversion formula is:

Therefore, the advantages of the present invention are: it solves the problem that the general electric meter system can only simply display the energy consumption value and cannot meet the needs of users, so that the user can know the specific energy consumption value of each electrical equipment in detail; Energy-saving analysis of the situation, so that the user knows the power consumption situation, and the user can adjust the power consumption time or power consumption in time according to the power consumption and energy saving situation, so as to achieve a better energy-saving effect.

Description of drawings

Accompanying drawing 1 is a kind of structural block diagram of the present invention

1- Electric meter terminal 2- Cloud server terminal 3- User terminal 4- Electric meter unit 5- Collection unit 6- Calculation unit 7- Storage unit 8- Identity verification unit.

detailed description

The technical solutions of the present invention will be further specifically described below through the embodiments and in conjunction with the accompanying drawings.

Example:

In this embodiment, a dynamic evaluation system for electric meters based on valley power analysis, as shown in Figure 1, includes a meter end 1, a cloud server end 2, and a user end 3. The electric meter unit 4 of the node, each electric meter unit is connected to the collection unit respectively, the cloud server includes a calculation unit 6, a storage unit 7, and an identity verification unit 8, the storage unit is connected to the calculation unit, and the identity verification unit is connected to the storage unit and the calculation unit respectively , the collection unit is connected to the computing unit through the network, and the client is respectively connected to the identity verification unit and the computing unit through the network. The energy consumption nodes in the measured place include lighting energy consumption nodes, air conditioning energy consumption nodes, and other energy consumption nodes of other electrical equipment.

A method for dynamic evaluation of electric meters based on valley electricity analysis, comprising the following steps:

S1. Verify the identity of the client according to the application of the client; the verification process includes:

S11. The client sends a viewing request with IP to the identity verification unit;

S12. The identity verification unit searches the user information of the IP from the storage unit, obtains the mobile phone number of the IP user, and sends verification information to the mobile phone number;

S13. The identity verification unit receives the verification information sent by the client, if the verification information is inconsistent, then end, if the verification information is consistent, enter the next step;

S14. According to the user information of the IP, the electric meter data corresponding to the user is retrieved in the storage unit.

S2. After the verification is passed, the calculation unit obtains the energy consumption data of each energy consumption node within the set number of days and the energy consumption data of each energy consumption node during the off-peak period;

S3. Calculate the correlation value between the energy consumption of each energy consumption node of each day and the energy consumption during the off-peak period; the calculation process includes:

S31. Establish an energy consumption matrix according to the energy consumption data of each energy consumption node in each day:

Wherein, m is the set number of days, and this embodiment takes 10 days as an example. k represents the kth energy consumption node, and the order corresponding to k is lighting, air conditioning, and other energy consumption nodes; then the energy consumption matrix is:

S32. Obtain the energy consumption data of each energy consumption node in the valley power period of each day to establish a valley power consumption matrix:

which is

S33. Calculate the correlation value between the energy consumption of each energy consumption node and the energy consumption of each energy consumption node during the off-peak period of each day, and obtain the correlation value matrix

in Based on the 10-day example, then

S4. According to the degree of correlation, gray correlation analysis is used to calculate the energy-saving evaluation coefficient of the energy consumption of each energy-consuming node in each day; the calculation process includes:

S41. Select the optimal correlation degree in each day to form a reference matrix Here, the value with the largest value in each energy consumption node is selected as the optimal correlation degree of the energy consumption node.

The reference matrix is normalized by vector normalization The correlation matrix X ^* is standardized, and the formula is:

Obtain a standardized reference matrix X ₀ , a standardized correlation matrix X; x _m (k) is the standardized energy consumption value of the kth energy consumption node on the mth day, respectively representing lighting energy consumption nodes and air conditioning energy consumption according to the order of k nodes, other energy-consuming nodes.

S42. Establish a reference sequence x ₀ , recorded as:

x ₀ (k)=[x ₀ (1),...,x ₀ (k)], k=1,2,3,

Establish a comparison sequence x _m , which is recorded as:

x _m (k)=[x _m (1),...,x _m (k)], m=1,2,...,i;

S43. Obtain the absolute difference between the reference sequence and the comparison sequence, the difference includes a maximum value and a minimum value, then it is expressed as:

Δmax＝max _m max _k |x ₀ (k)-x _m (k)|

Δmin＝min _m min _k |x ₀ (k)-x _m (k)|;

S44. According to the gray relational analysis, the energy-saving evaluation coefficient of the kth energy consumption node on the mth day is obtained as:

Among them, ρ is the resolution coefficient, and the value range of ρ is 0.1～0.8 according to the actual situation. In this embodiment, ρ is 0.5, and m=1,2,...,i;

The evaluation coefficient matrix E=[ξ _m (k)] _m×k is obtained, which is:

S5. Determine the weight value of the energy consumption of each energy-consuming node; the calculation process includes:

S51. According to the associated value matrix X ^* of the energy consumption of each energy consumption node and the energy consumption of each energy consumption node during the off-peak period, standardize the associated value of each energy consumption node:

Get the standardized correlation matrix

Take 10 days as an example:

S52. Obtaining the entropy of the standardized correlation degree value is:

Among them, the order of S _k corresponding to k is the entropy of the correlation degree value after standardization of lighting, air conditioning, and other energy consumption, a=-(lni) ^-1 ,

S53. When season Obtain the entropy weight of energy consumption of each node:

ω _k is the entropy weight of lighting, air conditioning, and other energy consumption in the order corresponding to k, and finally obtains the weight W=[ω ₁ ,ω ₂ ,ω ₃ ] of the energy consumption of energy-consuming nodes.

S6. According to the energy-saving evaluation coefficient and the energy consumption weight value of each energy-consuming node, the gray correlation model is used to calculate the energy-saving evaluation value of the total energy consumption of each day; the calculation process includes:

S61. Acquire evaluation coefficient matrix

and energy consumption weight of node energy consumption W=[ω ₁ ,ω ₂ ,ω ₃ ];

S62. According to the gray relational model P=E×W, calculate the estimated value of energy consumption for each day P=[p _m ], m=1,2,...,i, where

S7. Judging the energy saving level of each day according to the energy saving evaluation value. The process includes:

S71. Select the optimal value P _max from the evaluation values of energy consumption in each day;

S72. According to the optimal value P _max , convert the energy consumption evaluation values of other days into percentage conversion to obtain the energy saving evaluation score F _m , and the conversion formula is:

The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which the present invention belongs can make various modifications or supplements to the described specific embodiments or adopt similar methods to replace them, but they will not deviate from the spirit of the present invention or go beyond the definition of the appended claims range.

Although terms such as meter end, cloud server end, user end, meter unit, and collection unit are frequently used in this article, the possibility of using other terms is not excluded. These terms are used only for the purpose of describing and explaining the essence of the present invention more conveniently; interpreting them as any kind of additional limitation is against the spirit of the present invention.

Claims (9)

1. A dynamic evaluation system for ammeters based on valley electricity analysis, characterized in that: it includes an ammeter end, a cloud service end and a user end, and the ammeter end includes a collection unit, an ammeter unit arranged at each energy consumption node of the measured place, and each ammeter The units are respectively connected to the collection unit. The cloud server includes a calculation unit, a storage unit, and an identity verification unit. The storage unit is connected to the calculation unit. The identity verification unit is connected to the storage unit and the calculation unit. The terminals are respectively connected to the identity verification unit and the calculation unit through the network. 2. A dynamic evaluation system for electric meters based on valley electricity analysis according to claim 1, wherein each energy consumption node at the measured place includes lighting energy consumption nodes, air conditioning energy consumption nodes, and other energy consumption nodes of other electrical equipment node. 3. A dynamic evaluation method for electric meters based on valley electricity analysis, adopting the system in claim 1, is characterized in that comprising the following steps: S1. Perform identity verification on the client according to the application of the client; S2. After the verification is passed, the calculation unit obtains the energy consumption data of each energy consumption node within the set number of days and the energy consumption data of each energy consumption node during the off-peak period; S3. Calculate the correlation value between the energy consumption of each energy consumption node in each day and the energy consumption in the off-peak period; S4. According to the degree of correlation, gray correlation analysis is used to calculate the energy-saving evaluation coefficient of the energy consumption of each energy-consuming node in each day; S5. Determine the weight value of the energy consumption of each energy-consuming node; S6. According to the energy-saving evaluation coefficient and the energy consumption weight value of each energy-consuming node, the gray correlation model is used to calculate the energy-saving evaluation value of the total energy consumption of each day; S7. Judging the energy saving level of each day according to the energy saving evaluation value. 4. A kind of ammeter dynamic evaluation method based on valley electricity analysis according to claim 3, it is characterized in that the process of identity verification in step S1 comprises: S11. The client sends a viewing request with IP to the identity verification unit; S12. The identity verification unit searches the user information of the IP from the storage unit, obtains the mobile phone number of the IP user, and sends verification information to the mobile phone number; S13. The identity verification unit receives the verification information sent by the client, if the verification information is inconsistent, then end, if the verification information is consistent, enter the next step; S14. According to the user information of the IP, the electric meter data corresponding to the user is retrieved in the storage unit. 5. A kind of ammeter dynamic evaluation method based on valley electricity analysis according to claim 3, it is characterized in that the calculation process of associated value in the step S3 comprises: S31. Establish an energy consumption matrix according to the energy consumption data of each energy consumption node in each day: <mrow> <mi>C</mi> <mo>=</mo> <msub> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>c</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>k</mi> </mrow> </msub> <mo>,</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>i</mi> <mo>;</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> </mrow> Where m is the set number of days, k represents the kth energy consumption node, and the order corresponding to k is lighting, air conditioning, and other energy consumption nodes; S32. Obtain the energy consumption data of each energy consumption node in the valley power period of each day to establish a valley power consumption matrix: <mrow> <mi>G</mi> <mo>=</mo> <msub> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>g</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>k</mi> </mrow> </msub> <mo>,</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>i</mi> <mo>;</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>;</mo> </mrow> S33. Calculate the correlation value between the energy consumption of each energy consumption node and the energy consumption of each energy consumption node during the off-peak period of each day, and obtain the correlation value matrix in 6. A kind of dynamic evaluation method of ammeter based on valley electricity analysis according to claim 5, it is characterized in that the energy-saving evaluation coefficient calculation process of each energy-consumption node energy consumption of each day in step S4 comprises: S41. Select the optimal correlation degree in each day to form a reference matrix The reference matrix is normalized by vector normalization The correlation matrix X ^* is standardized, and the formula is: <mrow> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msubsup> <mi>x</mi> <mi>m</mi> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msubsup> <mi>x</mi> <mi>m</mi> <mo>*</mo> </msubsup> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>,</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>i</mi> <mo>;</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> </mrow> Obtain the standardized reference matrix X ₀ and the standardized correlation matrix X; S42. Establish a reference sequence x ₀ , recorded as: x ₀ (k)=[x ₀ (1),...,x ₀ (k)], k=1,2,3, Establish a comparison sequence x _m , which is recorded as: x _m (k)=[x _m (1),...,x _m (k)], m=1,2,...,i; S43. Obtain the absolute difference between the reference sequence and the comparison sequence, the difference includes a maximum value and a minimum value, then it is expressed as: Δmax＝max _m max _k |x ₀ (k)-x _m (k)| Δmin＝min _m min _k |x ₀ (k)-x _m (k)|; S44. According to the gray relational analysis, the energy-saving evaluation coefficient of the kth energy consumption node on the mth day is obtained as: <mrow> <msub> <mi>&amp;xi;</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <munder> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <mi>m</mi> </munder> <munder> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <mi>k</mi> </munder> <mo>|</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>+</mo> <mi>&amp;rho;</mi> <munder> <mi>max</mi> <mi>m</mi> </munder> <munder> <mi>max</mi> <mi>k</mi> </munder> <mo>|</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mrow> <mo>|</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>+</mo> <mi>&amp;rho;</mi> <munder> <mi>max</mi> <mi>m</mi> </munder> <munder> <mi>max</mi> <mi>k</mi> </munder> <mo>|</mo> <msub> <mi>x</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>x</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> </mfrac> </mrow> 2 is the resolution factor; Get the evaluation coefficient matrix E, E=[ξ _m (k)] _m×k . 7. According to claim 5 or 6, a kind of dynamic evaluation method of ammeter based on valley electricity analysis, it is characterized in that the calculation process of the weight value of each energy consumption node energy consumption in step 5 comprises: S51. According to the associated value matrix X ^* of the energy consumption of each energy consumption node and the energy consumption of each energy consumption node during the off-peak period, standardize the associated value of each energy consumption node: <mrow> <msup> <msub> <mi>r</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mo>&amp;prime;</mo> </msup> <mo>=</mo> <mfrac> <msub> <mi>r</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mrow> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <msub> <mi>r</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> </mrow> </mfrac> <mo>,</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> </mrow> Get the standardized correlation matrix <mrow> <msup> <mi>X</mi> <mrow> <mo>*</mo> <mo>&amp;prime;</mo> </mrow> </msup> <mo>=</mo> <msub> <mrow> <mo>&amp;lsqb;</mo> <msup> <msub> <mi>r</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mo>&amp;prime;</mo> </msup> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>k</mi> </mrow> </msub> <mo>,</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>i</mi> <mo>;</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>;</mo> </mrow> S52. Obtaining the entropy of the standardized correlation degree value is: <mrow> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>=</mo> <mi>a</mi> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>i</mi> </msubsup> <msub> <mi>b</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mo>&amp;CenterDot;</mo> <mi>l</mi> <mi>n</mi> <mi> </mi> <msub> <mi>b</mi> <msub> <mi>k</mi> <mi>m</mi> </msub> </msub> <mo>,</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> </mrow> Among them, the order of S _k corresponding to k is the entropy of the correlation degree value after standardization of lighting, air conditioning, and other energy consumption, a=-(lni) ^-1 , S53. When season Obtain the entropy weight of energy consumption of each node: <mrow> <msub> <mi>&amp;omega;</mi> <mi>k</mi> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mrow> <mn>1</mn> <mo>-</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> </mrow> <mrow> <mi>k</mi> <mo>-</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>3</mn> </msubsup> <msub> <mi>S</mi> <mi>k</mi> </msub> </mrow> </mfrac> <mo>,</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>k</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> ω _k is the entropy weight of lighting, air conditioning, and other energy consumption in the order corresponding to k, and finally obtains the weight W=[ω ₁ ...ω _k ] of energy consumption of energy consumption nodes. 8. A kind of ammeter dynamic evaluation method based on valley electricity analysis according to claim 6, it is characterized in that the calculation process of the energy-saving evaluation value of each day's total energy consumption in step 6 comprises: S61. Acquire evaluation coefficient matrix E=[ξ _m (k)] _m×k and the weight of energy consumption node energy consumption W=[ω ₁ ...ω _k ]; S62. According to the gray relational model P=E×W, calculate the estimated value of energy consumption for each day P=[p _m ], m=1,2,...,i, where <mrow> <msub> <mi>p</mi> <mi>m</mi> </msub> <mo>=</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>3</mn> </msubsup> <msub> <mi>&amp;omega;</mi> <mi>k</mi> </msub> <mo>&amp;times;</mo> <msub> <mi>&amp;xi;</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>.</mo> </mrow> 9. A kind of ammeter dynamic evaluation method based on valley electricity analysis according to claim 8, it is characterized in that judging the energy-saving level process of each day in step 7 comprises: S71. Select the optimal value P _max from the evaluation values of energy consumption in each day; S72. According to the optimal value P _max , convert the energy consumption evaluation values of other days into percentage conversion to obtain the energy saving evaluation score F _m , and the conversion formula is: <mrow> <msub> <mi>F</mi> <mi>m</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>p</mi> <mi>m</mi> </msub> <msub> <mi>P</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mfrac> <mo>&amp;times;</mo> <mn>100.</mn> </mrow> 3