CN112032800A - Intelligent pipe network balance system and regulation and control method thereof - Google Patents
Intelligent pipe network balance system and regulation and control method thereof Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
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- F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
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Abstract
The invention relates to an intelligent pipe network balance system, which comprises a pipe network balance system and a target pipe network system, wherein the target pipe network system comprises a parameter monitoring system, a control system and an execution system; the pipe network balance system comprises a first processor and a second processor, the first processor is connected with the input module, the output module and the calculation module, and the second processor is connected with the control system; the input module is used for inputting parameters of a target pipe network system under actual working conditions; the output module is used for outputting the adjustment parameters of the pipe network balance system; a first processor for data processing and communication control; the calculation module is used for predicting and judging the effect of pipe network balance according to the parameters of the target pipe network system and outputting state parameters achieving the expected effect; the communication module is used for transmitting implementation parameters of the parameter monitoring system, state parameters of the execution system and state adjustment parameters; and the second processor is used for sending the state adjustment parameters to the execution system. The invention also includes a method of regulation.
Description
Technical Field
The invention relates to the technical field of balance adjustment of a heating pipe network, in particular to an intelligent pipe network balance system and a regulation and control method thereof.
Background
In the urban heat supply pipe network, the defects of hydraulic unbalance, uneven heat distribution and the like exist inevitably, and long and complex adjustment work is needed when the heat supply season comes.
The prior art is based on an empirical method, rough adjustment is carried out according to pipe network design parameters, a theoretical primary net water and water temperature curve, a theoretical secondary net water and water temperature curve and the heat load condition before formal heat supply, and then follow-up adjustment is carried out in a follow-up mode according to user feedback and needs. Even if the state can be adjusted to a satisfactory state, with the scale fluctuation of hot users and the dynamic changes of scaling, new and old maintenance and the like of a pipe network, the adjustment work of the pipe network balance needs to be repeated, a large number of maintenance personnel can struggle to meet the requirements for many days continuously, and the energy waste, the manpower and material resource waste and the high user complaints caused by the conventional pipe network balance adjustment method become the largest pain points in the industry.
Disclosure of Invention
The invention provides an intelligent pipe network balance system and a regulation and control method thereof, aiming at the problems of time and labor waste in pipe network regulation and poor pipe network balance regulation effect.
The technical scheme for solving the technical problems is as follows: an intelligent pipe network balance system comprises a target pipe network system and a pipe network balance system;
the target pipe network system comprises: the control system is respectively in communication connection with the parameter monitoring system and the execution system;
the pipe network balance system comprises: the first processor and the second processor are connected through a communication module, the first processor is respectively connected with the input module, the output module and the calculation module, and the second processor is in communication connection with the control system;
the input module is used for inputting parameters of a target pipe network system under actual working conditions;
the output module is used for outputting the adjustment parameters of the pipe network balance system;
the first processor is used for data processing and communication control;
the calculation module is used for predicting and judging the effect of pipe network balance according to the parameters of the target pipe network system and outputting the state parameters of the execution system which achieves the expected effect;
the communication module is used for transmitting implementation parameters of the parameter monitoring system, state parameters of the execution system and state adjustment parameters;
and the second processor is used for sending the state adjustment parameters to the execution system so as to carry out pipe network balance adjustment.
On the basis of the technical scheme, in order to achieve the convenience of use and the stability of equipment, the invention can also make the following improvements on the technical scheme:
the system further comprises a storage module, wherein the storage module is respectively in communication connection with the input module and the first processor, and the storage module is used for storing input parameters of the target pipe network system.
Further, the communication module comprises a first communication module and a second communication module which are connected, the first communication module is in communication connection with the first processor, and the second communication module is in communication connection with the second processor;
the first communication module is used for transmitting the state adjustment parameters of the execution system to the second communication module;
and the second communication module is used for sending the implementation parameters of the parameter monitoring system and various types of state parameters of the execution system to the first communication module.
Further, the communication connection includes a wired connection and a wireless connection.
Compared with the prior art, the invention has the following beneficial effects: according to the method, the pipe network balance system and the target pipe network system are combined, and the processor and the calculation module of the pipe network balance system are used for automatically controlling the target pipe network, so that a large amount of manpower and material resources are saved; and forecasting and integrally leveling the whole pipe network balance system according to the parameters of the target pipe network system.
The invention also comprises a regulation and control method of the intelligent pipe network balance system, which comprises the following steps:
step S1: the input module receives the arrangement parameters of an execution system and a parameter monitoring system in the target pipe network system and generates a parameter matrix according to the arrangement parameters;
step S2: establishing an improved radial basis function neural network for balance prediction of each heat exchange station and heat user in a pipe network system by taking the parameter matrix as input and the pipe network balance state as output;
step S3: and establishing an integral balance prediction network of the target pipe network system based on the balance prediction network of the heat exchange station and the heat user.
On the basis of the above, the invention can be further improved as follows:
further, in the step S1, the layout parameters of the execution system include a regulating valve, a circulating pump and a water replenishing pump; the arrangement parameters of the parameter monitoring system comprise temperature monitoring, pressure monitoring and flow monitoring.
Further, in step S2, the parameter matrix includes a system parameter matrix, a monitoring system parameter matrix, and a pipe network efficiency parameter matrix; the pipe network balanced state output comprises a balanced state, an unbalanced state and a transfer state.
Further, in step S2, the modified radial basis function neural network includes an input layer, a computation layer, an implication layer and an output layer.
Further, in step S3, the overall balance prediction network includes a register, a balance prediction network of the heat exchange station and the heat user side, and a BP neural network.
Compared with the prior art, the method has the following beneficial effects:
(1) the method combines a temperature, flow and other parameter monitoring system and an automatic control valve body and other execution systems in the pipe network to realize the automatic balance effect of the pipe network, and saves a large amount of field trial-and-reference work.
(2) The method establishes a balance prediction network for all heat exchange stations and heat user ends in the pipe network system, and also establishes a balance prediction network for the whole pipe network system based on the balance prediction network, thereby solving the linkage influence of local leveling on the balance of the whole pipe network in the prior art.
(3) The method introduces the adjustable pipe network efficiency parameter matrix, and can flexibly and accurately reduce the influence of the scale fluctuation of the heat user and the dynamic changes of scaling, new and old maintenance and the like of the pipe network on the balance of the pipe network.
Drawings
FIG. 1 is a diagram of the operation architecture of an intelligent pipe network balance system according to the present invention;
FIG. 2 is a diagram of an overall model framework of a heat supply pipe network system;
FIG. 3 is a block diagram of an improved radial basis balance prediction network;
fig. 4 is a diagram of a balance prediction network structure of the whole pipe network system.
The reference numbers are recorded as follows: the system comprises an input module 10, an output module 20, a first processor 30, a storage module 40, a first communication module 50, a calculation module 60, a second communication module 70 and a second processor 80.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, an intelligent pipe network balancing system includes a target pipe network system and a pipe network balancing system;
the target pipe network system comprises: the control system is respectively in communication connection with the parameter monitoring system and the execution system;
the pipe network balance system comprises: a first processor 30 and a second processor 80 connected through a communication module, wherein the first processor 30 is respectively connected with the input module 10, the output module 20 and the calculation module 60, and the second processor 80 is connected with the control system in a communication way;
the input module 10 is used for inputting parameters of a target pipe network system under actual working conditions;
the output module 20 is configured to output an adjustment parameter of the pipe network balance system;
the first processor 30 is used for data processing and communication control;
the calculation module 60, in which a pipe network balance parameter adjustment program is run in the calculation module 60, is used for predicting and judging the effect of pipe network balance according to the parameters of the target pipe network system, and outputting the state parameters of the execution system which achieve the expected effect;
the communication module is used for transmitting implementation parameters of the parameter monitoring system, state parameters of the execution system and state adjustment parameters;
the second processor 80 is configured to send the state adjustment parameter to the execution system, so as to perform pipe network balance adjustment.
The system further comprises a storage module 40, wherein the storage module 40 is in communication connection with the input module 10 and the first processor 30, and the storage module 40 is used for storing input parameters of the target pipe network system.
The communication module comprises a first communication module 50 and a second communication module 70 which are connected, wherein the first communication module 50 is connected with the first processor 30 in a communication way, and the second communication module 70 is connected with the second processor 80 in a communication way;
the first communication module 50 is configured to transmit the status adjustment parameter of the execution system to the second communication module 70;
the second communication module 70 is configured to send implementation parameters of the parameter monitoring system and various types of status parameters of the execution system to the first communication module 50.
The communication connection comprises a wired connection and a wireless connection. Wired connection includes net twine, optic fibre, cable etc. wireless connection includes Wifi, Zigbee, bluetooth, infrared, Lora etc. still can include other modes according to the service condition communication connection mode, and no longer repeated here.
A regulation and control method of an intelligent pipe network balance system comprises the following steps:
step S1: the input module 10 receives the arrangement parameters of the execution system and the monitoring system in the target pipe network system, and generates a parameter matrix according to the arrangement parameters.
Referring to fig. 2, heat is generated from a heat source plant and then transmitted to a heat exchange station through media such as water or high-temperature steam, and after heat energy conversion and treatment in the heat exchange station, heat is supplied to heat users, and the heat users supply heat to regions and communities. The heat exchange station is provided with a temperature measuring point, a pressure measuring point, a flow measuring point and other parameter monitoring systems, and also provided with an adjusting valve, a circulating pump, a water replenishing pump and other execution systems; in the hot useThe client also has a parameter monitoring system and an execution system. The heating pipeline network system of the invention is provided with Z1A heat exchange station, Z2And (4) a hot user.
The input module 10 firstly receives the arrangement parameters of the execution system and the monitoring system in the target pipe network system, and the arrangement parameters of the execution system of the heat exchange station are recorded as
(1≤i≤Z1I is an integer representing the count of heat exchange stations), the monitoring system layout parameters of the heat exchange stations are recorded as
The execution system layout parameters of the hot user are recorded as
(1≤j≤Z2J is an integer representing the count of hot users), the monitoring system layout parameters of the hot users are recorded as
In addition, the heat user scale fluctuation and the scaling, new and old maintenance and other dynamic changes of the pipe network greatly influence the balance of the pipe network, so that the pipe network efficiency parameter matrix is introduced into the method
And
respectively representing the pipe network efficiency parameter within the range of the heat exchange station i and the pipe network efficiency parameter of the heat user j, wherein the pipe network efficiency parameter reflects the integrity state of each component element of a pipe network system including a pipeline, a joint, a valve and a pump, and the parameter is set to be [0,1 ] according to the integrity of each component element]In the interval, the integrity value is 1, the abnormal working value is 0, the integrity parameter value is adjusted according to the state of each element, and a matrix formed by all the element integrity parameter values in the range of one heat exchange station or heat user is a pipe network efficiency parameter matrix.
As an embodiment of the present invention, the arrangement parameters of the input execution system and the monitoring system in the management direction input module 10 of the heat exchange station i are: the execution system types comprise three types of regulating valves, circulating pumps and water replenishing pumps, wherein the quantity of the regulating valves is e, the quantity of the circulating pumps is f, and the quantity of the water replenishing pumps is g. The monitoring system types comprise three types of temperature monitoring, pressure monitoring and flow monitoring, wherein the number of temperature monitoring points is a, the number of pressure monitoring points is b and the number of flow monitoring points is c.
Then there is an execution system layout parameter matrix and a monitoring system layout parameter matrix of the heat exchange station i
Is generated as follows:
wherein the content of the first and second substances,
(where 1. ltoreq. d. ltoreq. e) represents the opening state of the d-th regulating valve of the heat exchange station i,
(where 1. ltoreq. d. ltoreq. f) represents the operating state of the d-th circulation pump of the heat exchange station i,
(where 1. ltoreq. d. ltoreq.g) represents the opening state of the d-th water replenishing pump of the heat exchange station i.
Under the actual working condition, the quantities e, f and g of various execution systems are often unequal, the maximum value max (e, f and g) of the quantities is taken as the column dimension, and the vacancy is supplemented by 1, so that the number of the execution systems is equal to the maximum value max (e, f and g), and the number of the execution systems is equal to the maximum value max (e
The matrix is regular, i.e. all row vectors have the same dimension.
Wherein the content of the first and second substances,
a monitoring parameter representing an a-th temperature monitoring point of the heat exchange station i, a monitoring parameter representing a b-th pressure monitoring point of the heat exchange station i,
the monitoring parameter of the c monitoring point of the heat exchange station i is represented, the column dimension with the maximum value of the number max (a, b, c) is taken, and the vacancy is supplemented by 1, so that
The matrix is regular, i.e. all row vectors have the same dimension.
Similarly, the arrangement parameters of the execution system and the monitoring system of the hot user side input in the management direction input module 10 of the hot user j are as follows: the execution system types comprise three types of regulating valves, circulating pumps and water replenishing pumps, wherein the quantity of the regulating valves is e, the quantity of the circulating pumps is f, and the quantity of the water replenishing pumps is g. The monitoring system types comprise three types of temperature monitoring, pressure monitoring and flow monitoring, wherein the number of temperature monitoring points is a, the number of pressure monitoring points is b and the number of flow monitoring points is c. The execution system layout parameter matrix P for the hot user jj uAnd monitoring system placement parameter matrix
Is generated as follows:
wherein the content of the first and second substances,
(where 1. ltoreq. d. ltoreq. e) represents the opening state of the d-th regulating valve of the heat consumer j,
(where 1. ltoreq. df) The operation state of the d-th circulation pump representing the hot user j,
(where 1. ltoreq. d. ltoreq.g) represents the opening state of the d-th water replenishing pump of the heat consumer j.
Under the actual working condition, the quantities e, f and g of various execution systems are often unequal, the maximum value max (e, f and g) of the quantities is taken as the column dimension, and the vacancy is supplemented by 1, so that the number of the execution systems is equal to the maximum value max (e, f and g), and the number of the execution systems is equal to the maximum value max (e
Is a regular matrix, i.e. all the row vector dimensions are equal
The same is true.
Wherein the content of the first and second substances,
a monitored parameter representing the a-th temperature monitoring point of the hot user j,
the monitored parameter representing the pressure monitor point of the b-th hot user j,
the monitoring parameter of the c-th monitoring point representing the hot user j takes the column dimension with the maximum value of the number max (a, b, c) and the vacancy is supplemented by 1, so that
The matrix is regular, i.e. all row vectors have the same dimension.
Denote the elements in the range of the heat exchange station i as
A tth element representing a heat exchange station i to which a health parameter α is assigned according to the health statust(αt∈[0,1]) Integrity of all elements of Heat exchange station iParameters jointly form a pipe network efficiency parameter matrix of the heat exchange station i
To facilitate and improve the calculation efficiency, will
Expressed in a square matrix form, the conversion method of the square matrix is as follows:
if the heat exchange station i has q elements in total, the corresponding integrity parameter is alpha1，α2，···，αqGet
(rounded down) as part of the real matrix, and the other empty elements of the square matrix are filled with 1 s.
As an embodiment of the invention, if a heat exchange station has 10 total various pipeline, valve and pump elements, the integrity parameters of the corresponding elements are a1,a2，...，a10Then, then
Namely the pipeline efficiency parameter matrix of the heat exchange station.
Step S2: and establishing an improved radial basis function neural network for balance prediction of each heat exchange station and heat user in the pipe network system by taking the parameter matrix as input and the pipe network balance state as output.
The parameter matrix of each heat exchange station or heat user comprises an execution system parameter matrix P, a monitoring system parameter matrix M and a pipe network efficiency parameter matrix. The balance state output of the pipe network is three states: equilibrium state Y1Non-equilibrium state Y2And a neutral transition state Y3。
Referring to fig. 3, the improved radial basis function neural network has a 4-layer structure, an input layer, a computation layer, a hidden layer and an output layer. The number of the input layer is 3, the received input is 3 parameter matrixes of a heat exchange station or a heat user, the input layer is connected to the calculation layer in a single mode, and the connection weight is 1; the number of neurons in the computing layer is equal to that of the input layer, the activation function of the computing layer adopts judgment logic, and when the pressure difference between the inlet and the outlet of a heat exchange station or a heat user meets the requirement of pipe network balanced operation, the logic output is 0; and when the pressure difference between the inlet and the outlet does not meet the operation requirement of pipe network balance, the logic outputs 1 and the neuron of the computation layer outputs. The number of hidden layer neurons is determined by the following method:
l＝maX(colum(P)，colum(M)，colum(Δ))
colum () represents the column dimension of the matrix, i.e. the number of hidden layer neurons is the maximum value of the column dimension in the three parameter matrices of the heat exchange station or the hot user.
The base function of the hidden layer is Gaussian function
Wherein x represents a function argument, xiRepresenting the center of the selected gaussian function.
The hidden layer and the output layer are all connected, and the connection weight is wij(i is more than or equal to 1 and j is more than or equal to 1 and less than or equal to 3) wherein i and j represent the ith neuron of the hidden layer and the jth neuron of the output layer, and the number of the neurons of the output layer is 3.
Based on the improved radial basis function neural network model, the fitting result of an output layer is as follows:
the three state classification outputs for evaluating the pipe network equilibrium state are: y isj＝max(Y′1,Y′2,Y3′)。
When the network output result is Y1And Y3In time, corresponding execution system parameter matrixes P (1) and P (3) are recorded, and because the whole structure of the pipe network system is similar to a tree structure and time lag exists between the branch network and the main network, the method simultaneously records the balance stateAnd executing system parameter matrixes P (1) and P (3) in a transfer state, so that the concussion of the official network system in the balance adjustment process can be effectively reduced, and the balance of the network can be achieved more quickly.
Step S3: and establishing an integral balance prediction network of the target pipe network system based on the balance prediction network of the heat exchange station and the heat user.
For an integral pipe network system, the unbalance of a plurality of branch pipe networks can greatly influence the balance of the integral pipe network, and in the prior art, the interlocking adjustment of a backbone network is often caused by local leveling in the adjustment work of the pipe network balance, so that the time consumption is long, and the over-adjustment phenomenon sometimes occurs. In order to solve the problems, the invention also establishes an integral balance prediction network of the target pipe network system based on the balance prediction network of the heat exchange station and the heat user.
Referring to fig. 4, the overall balance prediction network is composed of a register, all heat exchange stations and a heat user end balance prediction network in a pipe network, and a BP neural network. The register is used for storing an execution system matrix when the balance prediction network of each heat exchange station and the heat user end reaches a balance state or a transit state, namely, the execution system state parameters when the heat exchange stations and the heat user end reach a pipe network balance or transit state. The output of the balance prediction network of all the heat exchange stations and the heat user terminals in the target pipe network system is used as the input of the BP neural network, so the structure of the BP network in the whole balance prediction network is as follows:
the number w of neuron of input layer is equal to the number Z of heat exchange stations in target pipe network system1And number of hot user nodes Z2Sum of (Z)1+Z2). Let the number of hidden layer neurons also be w, the number of output layer neurons be 3, and output as three states (balanced, unbalanced and neutral transfer states) of the overall pipe network balance.
The connection weight between the input layer vth neuron and the hidden layer h neuron of the BP neural network is W, and the connection weight between the hidden layer h neuron and the output layer t neuron is WhtThe input to the h-th neuron of the hidden layer is:
wherein, thetavhIs the threshold for the input layer to the hidden layer. The output of the h neuron of the hidden layer is:
wherein the content of the first and second substances,
for the S-type activation function, the value range is (0,1), and it can map a real number to the interval of (0, 1).
The output layer has 3 neurons, and the input of the tth neuron is:
wherein, thetahtIs the hidden layer to output layer threshold.
The output layer activation function of the BP network adopts a normalized exponential function, and the beneficial effect brought by the invention is that output quantity annihilation phenomenon caused by different parameter factor magnitudes can be avoided without separate normalization processing.
Wherein, yτ(τ ∈ {1, 23}) is the fitted output of the output layer.
At this point, the BP network establishment is completed, and the specific calculation process may be performed by using mathematical calculation tool software, such as matlab, etc., which is not described herein again.
Remember y1,y2,y3Respectively have mean square error errors of
Selecting the item with the minimum mean square error
The corresponding output is the final equilibrium state output of the whole network. When the output pipe network state is an equilibrium state, P is at the momentiAnd the state parameters of the execution system stored in the register are the pipe network balance adjustment parameters of the target pipe network.
Therefore, the adjustment processing program of the pipe network balance parameters is realized. After reading the pipe network balance adjustment parameter from the register, the calculation module 60 sends the balance adjustment parameter to the first processor 30, the first processor 30 sends the balance adjustment parameter to the second communication module 70 through the first communication module 50, the second communication module 70 sends the balance adjustment parameter to the second processor 80, and the second processor 80 controls elements such as an adjusting valve and a pump in the target pipe network system to be adjusted according to the balance adjustment parameter through a control system in the target pipe network system, so that the expected balance effect can be obtained.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. An intelligent pipe network balance system is characterized by comprising a target pipe network system and a pipe network balance system;
the target pipe network system comprises: the control system is respectively in communication connection with the parameter monitoring system and the execution system;
the pipe network balance system comprises: a first processor (30) and a second processor (80) connected through a communication module, wherein the first processor (30) is respectively connected with an input module (10), an output module (20) and a calculation module (60), and the second processor (80) is in communication connection with the control system;
the input module (10) is used for inputting parameters of the target pipe network system under the actual working condition;
the output module (20) is used for outputting the adjustment parameters of the pipe network balance system;
the first processor (30) is used for data processing and communication control;
the calculation module (60) is used for predicting and judging the effect of pipe network balance according to the parameters of the target pipe network system and outputting the state parameters of the execution system which achieves the expected effect;
the communication module is used for transmitting implementation parameters of the parameter monitoring system, state parameters of the execution system and state adjustment parameters;
and the second processor (80) is used for sending the state adjustment parameters to the execution system so as to carry out pipe network balance adjustment.
2. The intelligent pipe network balancing system according to claim 1, further comprising a storage module (40), wherein the storage module (40) is communicatively connected to the input module (10) and the first processor (30), respectively, and the storage module (40) is configured to store the input parameters of the target pipe network system.
3. The intelligent pipe network balancing system according to claim 1, wherein the communication module comprises a first communication module (50) and a second communication module (70) which are connected, the first communication module (50) is connected with the first processor (30) in a communication way, and the second communication module (70) is connected with the second processor (80) in a communication way;
the first communication module (50) is used for transmitting the state adjustment parameters of the execution system to the second communication module (70);
a second communication module (70) for sending implementation parameters of the parameter monitoring system and status parameters of the execution system to the first communication module (50).
4. The intelligent pipe network balancing system of claim 1, wherein the communication link comprises a wired link and a wireless link.
5. A regulation and control method of an intelligent pipe network balance system is applied to the intelligent pipe network balance system according to any one of claims 1 to 4, and the method comprises the following steps:
step S1: the input module (20) receives the arrangement parameters of an execution system and a parameter monitoring system in the target pipe network system and generates a parameter matrix according to the arrangement parameters;
step S2: establishing an improved radial basis function neural network for balance prediction of each heat exchange station and heat user in a pipe network system by taking the parameter matrix as input and the pipe network balance state as output;
step S3: and establishing an integral balance prediction network of the target pipe network system based on the balance prediction network of the heat exchange station and the heat user.
6. The method for regulating and controlling the intelligent pipe network balance system according to claim 5, wherein in the step S1, the layout parameters of the execution system comprise a regulating valve, a circulating pump and a water replenishing pump; the arrangement parameters of the parameter monitoring system comprise temperature monitoring, pressure monitoring and flow monitoring.
7. The method for regulating and controlling the intelligent pipe network balance system according to claim 5, wherein in the step S2, the parameter matrix comprises a system parameter matrix, a monitoring system parameter matrix and a pipe network efficiency parameter matrix; the pipe network balanced state output comprises a balanced state, an unbalanced state and a transfer state.
8. The method for controlling an intelligent pipe network balance system according to claim 5, wherein in step S2, the modified radial basis function neural network comprises an input layer, a computation layer, a hidden layer and an output layer.
9. The method for controlling an intelligent pipe network balance system according to claim 5, wherein in step S3, the overall balance prediction network comprises PiA register, a heat exchange station, a balance prediction network of a hot user end and a BP neural network.
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