CN117329581A - Big data analysis management and control system and method for heat supply secondary pipe network - Google Patents
Big data analysis management and control system and method for heat supply secondary pipe network Download PDFInfo
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Abstract
The invention discloses a big data analysis management and control system and method for a heat supply secondary pipe network, and belongs to the technical field of heat supply data regulation and control. The system comprises: the system comprises a heat supply data acquisition module, a secondary network water supply and return regulation module, an on-demand heat supply analysis module, a node judgment module and an early warning module; the output end of the heat supply data acquisition module is connected with the input end of the secondary network water supply and return regulation module; the output end of the secondary network water supply and return regulation module is connected with the input end of the on-demand heat supply analysis module; the output end of the on-demand heat supply analysis module is connected with the input end of the node judgment module; the output end of the node judging module is connected with the input end of the early warning module. The dynamic regulation and control of the balance regulation and control module solves the substantial problems of heat imbalance, uneven cold and heat of users, low heat energy utilization rate and the like in the heat supply process; based on big data analysis and artificial intelligence assistance, the whole network can supply heat according to needs, and energy conservation and consumption reduction are realized.
Description
Technical Field
The invention relates to the technical field of heat supply data regulation and control, in particular to a big data analysis management and control system and method for a heat supply secondary pipe network.
Background
In the indoor central heating field, there are two concepts of a primary network and a secondary network, wherein the primary network is used for ensuring long-distance heat transfer, and a working state of high temperature and high pressure is kept under the effect of the primary network, generally, the design temperature of the primary network is 130/70 ℃, that is, the design temperature of water supply is 130 ℃, the design temperature of water return is 70 ℃, when the temperature of water is high, the stress of the network is high due to expansion and contraction, and the network is possibly broken due to expansion and contraction; and the polyurethane heat-insulating layer can be carbonized and blackened, so that the heat-insulating effect is deteriorated and the like. Therefore, a secondary network concept is provided, in the current heating design, the water supply temperature of the secondary network is designed to be 60-65 ℃, the backwater temperature is designed to be 45-50 ℃, and the temperature difference is 15-20 ℃. However, so far, there are still a few areas, the maximum temperature difference of the water supply and return is less than 15 ℃, the maximum temperature difference of the water supply and return is only about 12 ℃, and the temperature difference of the water supply and return is only about 7 ℃ in the initial and final cold periods of heat supply. The reason for this is that the high flow rate operation makes the hot water from the heat source stay too short in the user radiator, i.e. the flow rate is too fast, and the heat is forcedly pulled back by the circulation pump without being dissipated. However, if the flow rate of the circulating pump is reduced, two cases occur in which the flow rate of the circulating water is reduced: firstly, when the temperature of a front end user of a heating system reaches the standard, the temperature of a tail end user of the heating system does not reach the standard; secondly, when the heating temperature of the end user is met, the temperature of the near-end user is too high, so that a plurality of households open windows, and a large amount of heat is wasted.
Therefore, how to realize artificial intelligent heat supply adjustment according to multiple source data parameters such as different communities, heat preservation types, living conditions, heat consumption requirements and the like in the heat supply process and how to realize whole-network heat supply according to requirements, energy conservation and consumption reduction is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a big data analysis management and control system and method for a heat supply secondary pipe network, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a big data analysis management and control method for a heat supply secondary pipe network comprises the following steps:
s1, acquiring working condition parameters and environmental temperature of a heating system, wherein the working condition parameters comprise an operation working condition and a design working condition, and the environmental temperature comprises an indoor temperature and an outdoor temperature;
s2, constructing an average temperature regulation model of the water supply and return of the secondary network based on working condition parameters and environmental temperature of the heating system, generating an average temperature of the water supply and return of the secondary network, and automatically issuing an instruction of an electric regulating valve at a user end by taking the average temperature of the water supply and return of the secondary network as a control target, so that uniform heat supply among all users is realized;
s3, acquiring a heating area of a user in a heating season in a heating area, forming a prediction analysis model of the heating area based on change data of the heating area of the user in the heating season in the history data, and outputting a high-amplitude change point moment of the heating area based on the prediction analysis model;
and S4, acquiring weather temperature difference high change point time based on weather prediction information, and generating warning information to an administrator port if the time length between the weather temperature difference high change point time and the high-amplitude change point time of the heating area is lower than a set threshold value.
According to the technical scheme, the operation condition refers to the condition information of the heating system in actual operation; the design working condition refers to working condition information of the heating system in a design stage;
in the design stage, the problem of imbalance of the primary network and the secondary network is considered, in the primary network, if the temperature difference of the primary network is increased, the circulation flow required by the unit heating area is reduced, because the primary network has no vertical imbalance, only horizontal imbalance (namely uneven flow distribution among heat exchange stations) is not needed, and the circulation flow is not worry about reduction, so that the smaller the flow is, the better the smaller the flow is, the heat is transmitted, and as long as the heat is equal, the larger the temperature difference is, the smaller the flow is, and the lower the electricity consumption is. However, the secondary net has not only horizontal misalignment but also vertical misalignment, and because of different water densities at different temperatures, the secondary net has low high-temperature water density and low-temperature water density. Based on this, a situation is formed in which the temperature of the top floor is high and the temperature of the bottom floor is low in the building. Under the condition that the room temperature control of each household is not performed at present, the vertical offset of the secondary network is very difficult to overcome, so that the smaller the flow of the secondary network is, the better the flow is, and the smaller the flow is, the more the vertical offset is caused. Based on the current situation, the temperature control of the water supply and return of the secondary network is performed.
Constructing an average temperature regulation model of the water supply and return of the secondary network:
wherein T is pj Refers to the average temperature of the water supply and return of the generated secondary network; t (T) n Refers to the outdoor temperature in actual working conditions; t is t 2g 、t 2h Respectively refers to the water supply and return temperatures of the secondary network under the design working condition; t (T) w Refers to the indoor temperature in actual working conditions; t is t n 、t w Respectively refers to the outdoor temperature and the indoor temperature of the secondary network under the design working condition; b denotes the emissivity;
the primary network regulating valve of the intelligent regulation heat exchange station takes the average temperature of secondary network water supply and return as a control target, and adjusts the electric regulating valve of each user end so as to realize uniform heat supply among the user ends.
According to the technical scheme, a heating area under the control target of the average temperature of the secondary network supply backwater is obtained, and the heating area in a heating season of a user in the heating area is obtained and is recorded as a marking data set;
building a unit time period, acquiring historical data in a heat supply area, wherein the change data of the heat supply area of a user in every unit time period in a heating season, and building a prediction analysis model of the heat supply area:
the change data of the heating area is recorded as a set { x } 1 、x 2 、…、x i Setting a change threshold, and marking change data exceeding the change threshold in the set as a high-amplitude change point;
acquiring a heating area proportion corresponding to each high-amplitude change point, wherein the heating area proportion refers to a proportion value of a heating area to a heating area on the current high-amplitude change point; calculating the time length between every two adjacent high-amplitude change points, and forming a data column by the current heating area proportion and the time length between the current heating area proportion and the preamble high-amplitude change points, wherein any group of data in the data column is recorded as [ y ] j 、Δs j ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein y is j Refers to the heating area ratio, deltas, in the j-th group of data j Refers to the time length between two high-amplitude change points in the j-th group of data;
selecting M groups of data samples from the data columns, extracting k groups of characteristics from the M groups of data samples, and setting an input layer by using a multi-layer perceptron mode, wherein the initial input layer U is expressed as U epsilon R M*k If the hidden layer has h neurons, the weight W of the hidden layer h Deviation b h Represented as W h ∈R k*h 、b h ∈R 1*h The method comprises the steps of carrying out a first treatment on the surface of the If the corresponding output layer label value is set to q, the weight W of the output layer o Deviation b o Represented as W o ∈R q*h 、b o ∈R 1*q ；
Calculating the output H of the hidden layer and the output O of the output layer:
H＝U*W h +b h
O＝H*W o +b o
introducing an activation function to perform nonlinear transformation on each layer, wherein the input layer does not perform nonlinear transformation, and a sigmoid function is used as the activation function:
the sigmoid function can map the output result to be between 0 and 1, and the expression form is as follows:
wherein x refers to an input argument in a sigmoid function;
the output of the L layer before passing through the activation function is denoted as Z L Representation of sigZ after activation L The method comprises the steps of carrying out a first treatment on the surface of the The presence is:
Z L ＝W L *sigZ L-1 +b L
sigZ L ＝sigmoid(Z L )
wherein W is L 、b L Respectively representing the weight and the deviation of the output layer of the L layer;
the forward propagation mode is adopted, the weight and the bias of each layer are utilized to calculate the output value, the final group of corresponding output values of the data column are calculated and recorded as [ y ] j0 、Δs j0 ]And outputting the value result as a prediction result of a prediction analysis model of the heating area.
According to the technical scheme, the time length between the high-amplitude change points is obtained under the prediction result, the time of the high-amplitude change points is obtained, weather prediction information is obtained, a temperature threshold is set, and if the temperature difference between the lowest temperature and the highest temperature of two adjacent days exceeds the temperature threshold, the time of the high-amplitude change points is recorded as the time of the high-amplitude change points;
and calculating the interval time between the weather temperature difference high change point time and the weather temperature difference high change point time, judging whether the interval time is lower than a set threshold value, and if the interval time is lower than the set threshold value, generating warning information to an administrator port to remind an administrator of carrying out one-time network management control.
In a heating system, weather is one of main influence reasons for adjusting the temperature of the heating system, generally, under the condition that the temperature difference of the weather suddenly changes, the heating system needs to adjust the flow of a primary network, so as to change the temperature of water supplied and returned by a secondary network and maintain the heat supply of households as required, but the adjustment can greatly influence the heating system, after primary amplitude adjustment, the heating system needs to be stabilized for a period of time so as to maintain the operation of the whole heating pipeline, and meanwhile, the pipeline is overhauled and checked, so that the deterioration of materials caused by adjustment is prevented. Because of the principles of thermal expansion and cold contraction, each adjustment brings about the expansion of the steel pipe and the expansion of the compensator, which is one of the main problems of the current heating system pipe network. Another important factor for the regulation of the heating system temperature is a large change of the heating area, such as sudden user entry, delivery and pipe network variation, if such a situation is in a range of shorter variation with the weather temperature difference, the failure condition of the pipe network is easy to be caused, and the insulation layer is broken, so that the heat-insulating layer is monitored by adopting a certain prediction mode to prevent the failure.
A big data analysis management and control system for heat supply secondary pipe network, the system includes: the system comprises a heat supply data acquisition module, a secondary network water supply and return regulation module, an on-demand heat supply analysis module, a node judgment module and an early warning module;
the heat supply data acquisition module is used for acquiring working condition parameters and environmental temperature of the heat supply system, wherein the working condition parameters comprise an operation working condition and a design working condition, and the environmental temperature comprises an indoor temperature and an outdoor temperature; the secondary network water supply and return regulation module builds an average temperature regulation model of water supply and return of the secondary network based on working condition parameters and environmental temperature of the heating system, generates average temperature of water supply and return of the secondary network, takes the average temperature of water supply and return of the secondary network as a control target, automatically issues an instruction of an electric regulating valve at a user end, and realizes uniform heat supply among all users; the on-demand heating analysis module is used for acquiring the heating area of the user in the heating season in the heating area, forming a prediction analysis model of the heating area based on the change data of the heating area of the user in the heating season in the history data, and outputting the moment of a high-amplitude change point of the heating area based on the prediction analysis model; the node judging module acquires weather temperature difference high change point time based on weather prediction information and judges whether the duration between the weather temperature difference high change point time and the high amplitude change point time of the heating area is lower than a set threshold value; the early warning module is used for generating warning information to an administrator port when the time length between the time when the weather temperature difference high change point exists and the time when the temperature difference high change point exists in the heating area is lower than a set threshold value;
the output end of the heat supply data acquisition module is connected with the input end of the secondary network water supply and return regulation module; the output end of the secondary network water supply and return regulation module is connected with the input end of the on-demand heat supply analysis module; the output end of the on-demand heat supply analysis module is connected with the input end of the node judgment module; the output end of the node judging module is connected with the input end of the early warning module.
According to the technical scheme, the secondary network water supply and return regulation module comprises an initial design unit and an average temperature regulation unit;
the initial design unit comprises the steps of acquiring operation conditions and working condition information under the design conditions, storing the operation conditions and the working condition information in a data port, and constructing an average temperature regulation model of water supply and return of the secondary network; the average temperature regulation unit forms the average temperature of the water supply and return of the secondary network based on an average temperature regulation model of the water supply and return of the secondary network, and the average temperature of the water supply and return of the secondary network is used as a control target to regulate the electric regulating valves of all the clients so as to realize uniform heat supply among all the clients;
the output end of the initial design unit is connected with the input end of the average temperature regulating unit.
According to the technical scheme, the on-demand heat supply analysis module comprises a heat supply area analysis unit and a prediction unit;
the heat supply area analysis unit is used for acquiring the heat supply area of the user in the heat supply season in the heat supply area, and forming a prediction analysis model of the heat supply area based on the change data of the heat supply area of the user in the heat supply season in the history data; the prediction unit outputs the moment of the high-amplitude change point of the heating area based on the prediction analysis model, and sends the moment to the node judgment module;
the output end of the heat supply area analysis unit is connected with the input end of the prediction unit.
According to the technical scheme, the node judging module comprises a weather analysis unit and a judging unit;
the weather analysis unit acquires the moment of a weather temperature difference high change point based on weather prediction information; the judging unit is used for judging whether the time length between the weather temperature difference high change point time and the heating area high amplitude change point time is lower than a set threshold value;
the output end of the weather analysis unit is connected with the input end of the judging unit.
According to the technical scheme, the early warning module is connected with the manager end, generates warning information in the form of a signal lamp or a system issuing message, and reminds the manager to regulate and control the heating system.
Compared with the prior art, the invention has the following beneficial effects: based on the coupling property and hysteresis of the heat supply network regulation, the dynamic regulation and control of the balance regulation and control module solves the substantial problems of heat imbalance, uneven heat and cold of users, low heat energy utilization rate and the like in the heat supply process, and simultaneously establishes a foundation for building an intelligent heat supply system by combining auxiliary functions of monitoring, alarming, load prediction and the like of the system; based on big data analysis and artificial intelligence assistance, the heat balance deviation caused by manual adjustment of personnel is avoided, the heating of the whole network according to needs is realized, energy is saved, and consumption is reduced.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic flow chart of a system and method for analyzing and controlling big data for a heat supply secondary pipe network according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in a first embodiment: the big data analysis and control method for the heat supply secondary pipe network comprises the following steps: acquiring working condition parameters and environmental temperature of a heating system, wherein the working condition parameters comprise an operation working condition and a design working condition, and the environmental temperature comprises an indoor temperature and an outdoor temperature;
constructing an average temperature regulation model of the water supply and return of the secondary network based on working condition parameters and environmental temperature of the heating system, generating an average temperature of the water supply and return of the secondary network, taking the average temperature of the water supply and return of the secondary network as a control target, and automatically issuing an instruction of an electric regulating valve at a user end to realize uniform heat supply among all users;
the operation condition refers to the condition information of the heating system in actual operation; the design working condition refers to working condition information of the heating system in a design stage;
constructing an average temperature regulation model of the water supply and return of the secondary network:
wherein T is pj Refers to the average temperature of the water supply and return of the generated secondary network; t (T) n Refers to the outdoor temperature in actual working conditions; t is t 2g 、t 2h Respectively refers to the water supply and return temperatures of the secondary network under the design working condition; t (T) w Refers to the indoor temperature in actual working conditions; t is t n 、t w Respectively refers to the outdoor temperature and the indoor temperature of the secondary network under the design working condition; b denotes the emissivity;
the primary network regulating valve of the intelligent regulation heat exchange station takes the average temperature of secondary network water supply and return as a control target, and adjusts the electric regulating valve of each user end so as to realize uniform heat supply among the user ends.
Acquiring a heating area under the control target of the average temperature of the secondary network supply backwater, acquiring the heating area of a user in a heating season in the heating area, and marking the heating area as a marking data set;
building a unit time period, acquiring historical data in a heat supply area, wherein the change data of the heat supply area of a user in every unit time period in a heating season, and building a prediction analysis model of the heat supply area:
the change data of the heating area is recorded as a set { x } 1 、x 2 Setting a change threshold, marking change data exceeding the change threshold in the set as a high-amplitude change point;
acquiring a heating area proportion corresponding to each high-amplitude change point, wherein the heating area proportion refers to a proportion value of a heating area to a heating area on the current high-amplitude change point; calculating the time length between every two adjacent high-amplitude change points, and forming a data column by the current heating area proportion and the time length between the current heating area proportion and the preamble high-amplitude change points, wherein any group of data in the data column is recorded as [ y ] j 、Δs j ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein y is j Refers to the heating area ratio, deltas, in the j-th group of data j Refers to the time length between two high-amplitude change points in the j-th group of data;
selecting M groups of data samples from the data columns, extracting k groups of characteristics from the M groups of data samples, and setting an input layer by using a multi-layer perceptron mode, wherein the initial input layer U is expressed as U epsilon R M*k If the hidden layer has h neurons, the weight W of the hidden layer h Deviation b h Represented as W h ∈R k*h 、b h ∈R 1*h The method comprises the steps of carrying out a first treatment on the surface of the If the corresponding output layer label value is set to q, the weight W of the output layer o Deviation b o Represented as W o ∈R q*h 、b o ∈R 1*q ；
Calculating the output H of the hidden layer and the output O of the output layer:
H＝U*W h +b h
O＝H*W o +b o
introducing an activation function to perform nonlinear transformation on each layer, wherein the input layer does not perform nonlinear transformation, and a sigmoid function is used as the activation function:
the sigmoid function can map the output result to be between 0 and 1, and the expression form is as follows:
wherein x refers to an input argument in a sigmoid function;
the output of the L layer before passing through the activation function is denoted as Z L Representation of sigZ after activation L The method comprises the steps of carrying out a first treatment on the surface of the The presence is:
Z L ＝W L *sigZ L-1 +b L
sigZ L ＝sigmoid(Z L )
wherein W is L 、b L Respectively representing the weight and the deviation of the output layer of the L layer;
the forward propagation mode is adopted, the weight and the bias of each layer are utilized to calculate the output value, the final group of corresponding output values of the data column are calculated and recorded as [ y ] j0 、Δs j0 ]And outputting the value result as a prediction result of a prediction analysis model of the heating area.
Acquiring the time length between high-amplitude change points under the prediction result, obtaining the time of the high-amplitude change points, acquiring weather prediction information (in the form of weather forecast and the like), setting a temperature threshold, and if the temperature difference between the lowest temperature and the highest temperature of two adjacent days exceeds the temperature threshold, recording the time of the high-amplitude change points of the weather temperature difference;
and calculating the interval time between the weather temperature difference high change point time and the weather temperature difference high change point time, judging whether the interval time is lower than a set threshold value, and if the interval time is lower than the set threshold value, generating warning information to an administrator port to remind an administrator of carrying out one-time network management control.
In a second embodiment, a big data analysis management and control system for a heat supply secondary pipe network is provided, the system including: the system comprises a heat supply data acquisition module, a secondary network water supply and return regulation module, an on-demand heat supply analysis module, a node judgment module and an early warning module;
the heat supply data acquisition module is used for acquiring working condition parameters and environmental temperature of the heat supply system, wherein the working condition parameters comprise an operation working condition and a design working condition, and the environmental temperature comprises an indoor temperature and an outdoor temperature; the secondary network water supply and return regulation module builds an average temperature regulation model of water supply and return of the secondary network based on working condition parameters and environmental temperature of the heating system, generates average temperature of water supply and return of the secondary network, takes the average temperature of water supply and return of the secondary network as a control target, automatically issues an instruction of an electric regulating valve at a user end, and realizes uniform heat supply among all users; the on-demand heating analysis module is used for acquiring the heating area of the user in the heating season in the heating area, forming a prediction analysis model of the heating area based on the change data of the heating area of the user in the heating season in the history data, and outputting the moment of a high-amplitude change point of the heating area based on the prediction analysis model; the node judging module acquires weather temperature difference high change point time based on weather prediction information and judges whether the duration between the weather temperature difference high change point time and the high amplitude change point time of the heating area is lower than a set threshold value; the early warning module is used for generating warning information to an administrator port when the time length between the time when the weather temperature difference high change point exists and the time when the temperature difference high change point exists in the heating area is lower than a set threshold value;
the output end of the heat supply data acquisition module is connected with the input end of the secondary network water supply and return regulation module; the output end of the secondary network water supply and return regulation module is connected with the input end of the on-demand heat supply analysis module; the output end of the on-demand heat supply analysis module is connected with the input end of the node judgment module; the output end of the node judging module is connected with the input end of the early warning module.
The secondary network water supply and return regulation module comprises an initial design unit and an average temperature regulation unit;
the initial design unit comprises the steps of acquiring operation conditions and working condition information under the design conditions, storing the operation conditions and the working condition information in a data port, and constructing an average temperature regulation model of water supply and return of the secondary network; the average temperature regulation unit forms the average temperature of the water supply and return of the secondary network based on an average temperature regulation model of the water supply and return of the secondary network, and the average temperature of the water supply and return of the secondary network is used as a control target to regulate the electric regulating valves of all the clients so as to realize uniform heat supply among all the clients;
the output end of the initial design unit is connected with the input end of the average temperature regulating unit.
The on-demand heat supply analysis module comprises a heat supply area analysis unit and a prediction unit;
the heat supply area analysis unit is used for acquiring the heat supply area of the user in the heat supply season in the heat supply area, and forming a prediction analysis model of the heat supply area based on the change data of the heat supply area of the user in the heat supply season in the history data; the prediction unit outputs the moment of the high-amplitude change point of the heating area based on the prediction analysis model, and sends the moment to the node judgment module;
the output end of the heat supply area analysis unit is connected with the input end of the prediction unit.
The node judging module comprises a weather analysis unit and a judging unit;
the weather analysis unit acquires the moment of a weather temperature difference high change point based on weather prediction information; the judging unit is used for judging whether the time length between the weather temperature difference high change point time and the heating area high amplitude change point time is lower than a set threshold value;
the output end of the weather analysis unit is connected with the input end of the judging unit.
The early warning module is connected with the manager end and generates warning information in the form of a signal lamp or a system issuing message to remind the manager of regulating and controlling the heating system.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A big data analysis management and control method for a heat supply secondary pipe network is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring working condition parameters and environmental temperature of a heating system, wherein the working condition parameters comprise an operation working condition and a design working condition, and the environmental temperature comprises an indoor temperature and an outdoor temperature;
s2, constructing an average temperature regulation model of the water supply and return of the secondary network based on working condition parameters and environmental temperature of the heating system, generating an average temperature of the water supply and return of the secondary network, and automatically issuing an instruction of an electric regulating valve at a user end by taking the average temperature of the water supply and return of the secondary network as a control target, so that uniform heat supply among all users is realized;
s3, acquiring a heating area of a user in a heating season in a heating area, forming a prediction analysis model of the heating area based on change data of the heating area of the user in the heating season in the history data, and outputting a high-amplitude change point moment of the heating area based on the prediction analysis model;
and S4, acquiring weather temperature difference high change point time based on weather prediction information, and generating warning information to an administrator port if the time length between the weather temperature difference high change point time and the high-amplitude change point time of the heating area is lower than a set threshold value.
2. The big data analysis and control method for a heat supply secondary pipe network according to claim 1, wherein the big data analysis and control method comprises the following steps: the operation condition refers to the condition information of the heating system in actual operation; the design working condition refers to working condition information of the heating system in a design stage;
constructing an average temperature regulation model of the water supply and return of the secondary network:
wherein T is pj Refers to the average temperature of the water supply and return of the generated secondary network; t (T) n Refers to the outdoor temperature in actual working conditions; t is t 2g 、t 2h Respectively refers to the water supply and return temperatures of the secondary network under the design working condition; t (T) w Refers to the indoor temperature in actual working conditions; t is t n 、t w Respectively refers to the outdoor temperature and the indoor temperature of the secondary network under the design working condition; b denotes the emissivity;
the primary network regulating valve of the intelligent regulation heat exchange station takes the average temperature of secondary network water supply and return as a control target, and adjusts the electric regulating valve of each user end so as to realize uniform heat supply among the user ends.
3. The big data analysis and control method for the heat supply secondary pipe network according to claim 2, wherein the big data analysis and control method is characterized in that:
acquiring a heating area under the control target of the average temperature of the secondary network supply backwater, acquiring the heating area of a user in a heating season in the heating area, and marking the heating area as a marking data set;
building a unit time period, acquiring historical data in a heat supply area, wherein the change data of the heat supply area of a user in every unit time period in a heating season, and building a prediction analysis model of the heat supply area:
the change data of the heating area is recorded as a set { x } 1 、x 2 、…、x i Setting a change threshold value, and winning a bid in a setRecording the change data exceeding the change threshold value as a high-amplitude change point;
acquiring a heating area proportion corresponding to each high-amplitude change point, wherein the heating area proportion refers to a proportion value of a heating area to a heating area on the current high-amplitude change point; calculating the time length between every two adjacent high-amplitude change points, and forming a data column by the current heating area proportion and the time length between the current heating area proportion and the preamble high-amplitude change points, wherein any group of data in the data column is recorded as [ y ] j 、Δs j ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein y is j Refers to the heating area ratio, deltas, in the j-th group of data j Refers to the time length between two high-amplitude change points in the j-th group of data;
selecting M groups of data samples from the data columns, extracting k groups of characteristics from the M groups of data samples, and setting an input layer by using a multi-layer perceptron mode, wherein the initial input layer U is expressed as U epsilon R M*k If the hidden layer has h neurons, the weight W of the hidden layer h Deviation b h Represented as W h ∈R k*h 、b h ∈R 1*h The method comprises the steps of carrying out a first treatment on the surface of the If the corresponding output layer label value is set to q, the weight W of the output layer o Deviation b o Represented as W o ∈R q*h 、b o ∈R 1*q ；
Calculating the output H of the hidden layer and the output O of the output layer:
H＝U*W h +b h
O＝H*W o +b o
introducing an activation function to perform nonlinear transformation on each layer, wherein the input layer does not perform nonlinear transformation, and a sigmoid function is used as the activation function:
the sigmoid function can map the output result to be between 0 and 1, and the expression form is as follows:
wherein x refers to an input argument in a sigmoid function;
before passing the L layer through the activation functionThe output of (2) is denoted as Z L Representation of sigZ after activation L The method comprises the steps of carrying out a first treatment on the surface of the The presence is:
Z L ＝W L *sigZ L-1 +b L
sigZ L ＝sigmoid(Z L )
wherein W is L 、b L Respectively representing the weight and the deviation of the output layer of the L layer;
the forward propagation mode is adopted, the weight and the bias of each layer are utilized to calculate the output value, the final group of corresponding output values of the data column are calculated and recorded as [ y ] j0 、Δs j0 ]And outputting the value result as a prediction result of a prediction analysis model of the heating area.
4. A method for big data analysis and management of a heat supply secondary pipe network according to claim 3, wherein:
acquiring the time length between high-amplitude change points under the prediction result, obtaining the time of the high-amplitude change points, acquiring weather prediction information, setting a temperature threshold, and recording the time of the high-amplitude change points of the weather temperature difference if the temperature difference between the lowest temperature and the highest temperature of two adjacent days exceeds the temperature threshold;
and calculating the interval time between the weather temperature difference high change point time and the weather temperature difference high change point time, judging whether the interval time is lower than a set threshold value, and if the interval time is lower than the set threshold value, generating warning information to an administrator port to remind an administrator of carrying out one-time network management control.
5. A big data analysis management and control system for heat supply secondary pipe network, its characterized in that: the system comprises: the system comprises a heat supply data acquisition module, a secondary network water supply and return regulation module, an on-demand heat supply analysis module, a node judgment module and an early warning module;
the heat supply data acquisition module is used for acquiring working condition parameters and environmental temperature of the heat supply system, wherein the working condition parameters comprise an operation working condition and a design working condition, and the environmental temperature comprises an indoor temperature and an outdoor temperature; the secondary network water supply and return regulation module builds an average temperature regulation model of water supply and return of the secondary network based on working condition parameters and environmental temperature of the heating system, generates average temperature of water supply and return of the secondary network, takes the average temperature of water supply and return of the secondary network as a control target, automatically issues an instruction of an electric regulating valve at a user end, and realizes uniform heat supply among all users; the on-demand heating analysis module is used for acquiring the heating area of the user in the heating season in the heating area, forming a prediction analysis model of the heating area based on the change data of the heating area of the user in the heating season in the history data, and outputting the moment of a high-amplitude change point of the heating area based on the prediction analysis model; the node judging module acquires weather temperature difference high change point time based on weather prediction information and judges whether the duration between the weather temperature difference high change point time and the high amplitude change point time of the heating area is lower than a set threshold value; the early warning module is used for generating warning information to an administrator port when the time length between the time when the weather temperature difference high change point exists and the time when the temperature difference high change point exists in the heating area is lower than a set threshold value;
the output end of the heat supply data acquisition module is connected with the input end of the secondary network water supply and return regulation module; the output end of the secondary network water supply and return regulation module is connected with the input end of the on-demand heat supply analysis module; the output end of the on-demand heat supply analysis module is connected with the input end of the node judgment module; the output end of the node judging module is connected with the input end of the early warning module.
6. The big data analysis management and control system for a heat supply secondary pipe network according to claim 5, wherein: the secondary network water supply and return regulation module comprises an initial design unit and an average temperature regulation unit;
the initial design unit comprises the steps of acquiring operation conditions and working condition information under the design conditions, storing the operation conditions and the working condition information in a data port, and constructing an average temperature regulation model of water supply and return of the secondary network; the average temperature regulation unit forms the average temperature of the water supply and return of the secondary network based on an average temperature regulation model of the water supply and return of the secondary network, and the average temperature of the water supply and return of the secondary network is used as a control target to regulate the electric regulating valves of all the clients so as to realize uniform heat supply among all the clients;
the output end of the initial design unit is connected with the input end of the average temperature regulating unit.
7. The big data analysis management and control system for a heat supply secondary pipe network according to claim 5, wherein: the on-demand heat supply analysis module comprises a heat supply area analysis unit and a prediction unit;
the heat supply area analysis unit is used for acquiring the heat supply area of the user in the heat supply season in the heat supply area, and forming a prediction analysis model of the heat supply area based on the change data of the heat supply area of the user in the heat supply season in the history data; the prediction unit outputs the moment of the high-amplitude change point of the heating area based on the prediction analysis model, and sends the moment to the node judgment module;
the output end of the heat supply area analysis unit is connected with the input end of the prediction unit.
8. The big data analysis management and control system for a heat supply secondary pipe network according to claim 5, wherein: the node judging module comprises a weather analysis unit and a judging unit;
the weather analysis unit acquires the moment of a weather temperature difference high change point based on weather prediction information; the judging unit is used for judging whether the time length between the weather temperature difference high change point time and the heating area high amplitude change point time is lower than a set threshold value;
the output end of the weather analysis unit is connected with the input end of the judging unit.
9. The big data analysis management and control system for a heat supply secondary pipe network according to claim 5, wherein: the early warning module is connected with the manager end and generates warning information in the form of a signal lamp or a system issuing message to remind the manager of regulating and controlling the heating system.
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