CN116624918A - Intelligent heat supply energy-saving control method based on neural network - Google Patents
Intelligent heat supply energy-saving control method based on neural network 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
- F24D19/00—Details
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- F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
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- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D19/00—Details
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
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- F24—HEATING; RANGES; VENTILATING
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Abstract
The invention relates to the technical field of heating control, and particularly discloses an intelligent heating energy-saving control method based on a neural network, which comprises the steps of acquiring the temperature of each unit according to a preset temperature sensor, and creating a temperature map layer according to the height of the unit; overlapping the temperature map layers to obtain a heat distribution map, and determining a heat supply area based on the heat distribution map and weather information; determining a heat supply network according to the heat supply area, and determining a heat supply demand meter based on the heat supply network; and when the heat supply characteristics input by the user are received, inquiring the heat supply path based on the heat supply demand table. The invention updates the heat supply area in real time according to the temperature sensor, determines a heat supply network on the basis of the heat supply area on an established energy source transmission network, determines heat supply paths in the heat supply network under different heat supply demands in simulation software in advance, directly inquires the heat supply paths when the heat supply demands are received, and synchronously outputs the regulation process; the staff only needs to verify the adjusting process, so that the adjusting efficiency is greatly optimized.
Description
Technical Field
The invention relates to the technical field of heating control, in particular to an intelligent heating energy-saving control method based on a neural network.
Background
Along with the improvement of living standard, the heat supply process gradually becomes an infrastructure in production and living; it greatly improves the quality of life, and is also an indispensable link in some production activities, and it can be provided by air conditioner or heating water.
The existing heat supply process is mainly to lay a heat supply network in advance, wherein the heat supply network comprises a power grid and a pipe network, each region is heated based on the power grid and the pipe network, and a plurality of valves are arranged in the heat supply network, so that the valves are very complicated, and the one-time adjustment process is very troublesome; only when the heat supply area is changed greatly, the energy supply party can organize personnel to adjust; in fact, when the heating area is large, small changes in the heating temperature often occur, and these small changes can cause a certain energy waste, and the adjustment efficiency becomes an important factor restricting the adjustment process.
Disclosure of Invention
The invention aims to provide an intelligent heat supply energy-saving control method based on a neural network, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent heat supply energy-saving control method based on a neural network, comprising the following steps:
acquiring the temperature of each unit according to a preset temperature sensor, and creating a temperature map layer according to the height of the unit;
overlapping the temperature map layers to obtain a heat distribution map, and determining a heat supply area based on the heat distribution map and weather information;
determining a heat supply network according to the heat supply area, and determining a heat supply demand meter based on the heat supply network; the heat supply demand table comprises a heat supply characteristic item and a heat supply path item;
inquiring a heating path based on a heating demand table when receiving the heating characteristics input by a user;
wherein the heating feature is used to characterize the location of the heating zone.
As a further scheme of the invention: the step of obtaining the temperature of each unit according to a preset temperature sensor and creating a temperature map layer according to the height of the unit comprises the following steps:
building data in a preset zone range are read, and closed spaces in the building data are numbered;
determining the installation parameters of the temperature sensor according to the size of the closed space; the installation parameters comprise the number and the positions;
acquiring temperature information of each closed space based on a temperature sensor;
splicing temperature information according to the height relation of each closed space to obtain a temperature map layer; the temperature map layer contains height information;
wherein the temperature map layer corresponds to a zone.
As a further scheme of the invention: the step of superposing the temperature map layers to obtain a thermal distribution map, and determining the heat supply area based on the thermal distribution map and weather information comprises the following steps:
reading a map of the area range, and establishing a reference layer;
reading a temperature map layer, and adding the temperature map layer into a reference map layer;
transmitting a reference map layer containing a temperature map layer to a preset identification model, and outputting a heat distribution map;
inquiring weather information, and determining a reference distribution diagram according to the weather information;
and comparing the heat distribution map with a reference distribution map to determine a heat supply area containing energy.
As a further scheme of the invention: the step of reading the temperature map layer and adding the temperature map layer to the reference map layer comprises the following steps:
reading all the temperature layers, and carrying out normalization treatment on the temperature layers;
sequentially reading normalized values at each position in each temperature layer based on the height sequence, and accumulating the normalized values to a reference layer;
in the accumulation process, calculating the value difference of adjacent layers in real time, determining a correction coefficient according to the value difference, and correcting the normalized value according to the correction coefficient; the correction factor is proportional to the value difference.
As a further scheme of the invention: the step of determining a heat supply network according to the heat supply area and determining a heat supply demand meter based on the heat supply network comprises the following steps:
acquiring a heat supply source in a regional range, and determining a heat supply network according to the heat supply source and the heat supply region;
counting a temperature layer, and determining the selection probability of each heat supply area based on the temperature layer;
randomly determining a heat supply combination based on the selection probability, and determining heat supply characteristics of the heat supply combination;
and determining a heat supply path according to the heat supply characteristics, and establishing a heat supply demand table.
As a further scheme of the invention: the step of determining a heating path according to the heating characteristics and establishing a heating demand table comprises the following steps:
determining heat supply points according to the heat supply characteristics, and calculating the distance between adjacent heat supply points;
calculating the probability that each heating point reaches the adjacent heating point based on a preset probability formula;
and determining a heat supply path according to the probability, and establishing a heat supply demand table.
As a further scheme of the invention: the step of calculating the probability that each heating point reaches the adjacent heating point based on the preset probability formula comprises the following steps:
sequentially acquiring paths of each heating point and adjacent heating points;
obtaining heat supply distances of all paths, and calculating visibility according to the heat supply distances;
calculating the reserved energy of each path according to a preset loss coefficient;
calculating the probability of each path in real time based on the visibility and the reserved energy;
and determining heat supply paths from the heat supply sources to all heat supply points based on the probability and updating the heat supply paths regularly.
As a further scheme of the invention: the probability formula is as follows:
wherein i and j respectively represent two heat supply points, P ij Probability between two heating points; η (eta) ij For visibility, η ij ＝/d ij ，d ij The heating distance between two nodes is provided; τ ij The energy retained for the path between i and j, the energy retained being used to characterize the residual heat in the path; k is the k adjacent heating point of the heating point, and k is epsilon allowed k Representing the heating pointAll adjacent heating points; alpha and beta are preset coefficients; the path is a subsection of the heat supply energy network, and the heat supply distance is the total length of the path.
As a further scheme of the invention: the retention energy is as follows:
wherein m is the number of paths passing through two heat supply points, and l is the path passing through the two heat supply points;the influence quantity of the path passing through the two heat supply points on the path is the first path; the amount of impact is predetermined by the staff, generally related to the total length of the path; τ c Is an initial energy value; ρ is the loss factor;
when the monitoring equipment exists, acquiring data of the monitoring equipment in real time, judging the accuracy of the reserved energy according to the acquired data, and correcting the initial value according to the accuracy obtained by judgment.
As a further scheme of the invention: the step of determining the heating paths from the heating source to all the heating points based on the probability and updating at regular time comprises the following steps:
sequentially selecting the maximum probability value to determine a main heating path, and generating a node record table;
determining a secondary heating path based on the node record table;
updating a node record table based on the secondary heat supply path and circularly executing until all heat supply points belong to a certain heat supply path;
and circulating the determining process of the heat supply path according to the preset time interval.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the heat supply area is updated in real time according to the temperature sensor, a heat supply network is determined on the basis of the heat supply area on an established energy source conveying network, heat supply paths in the heat supply network are determined in simulation software in advance under different heat supply demands, and when the heat supply demands are received, the heat supply paths can be inquired directly by means of database reading operation, and then the regulation process is output; the staff only needs to verify the adjusting process, so that the adjusting efficiency is greatly optimized.
When the heat supply network is determined, a worker performs first adjustment on the existing energy supply network, and performs second adjustment after the heat supply path is determined, so that the energy waste caused by the change of a heat supply area is greatly reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a flow chart diagram of an intelligent heating energy-saving control method based on a neural network.
Fig. 2 is a first sub-flowchart of a neural network-based intelligent heating energy-saving control method.
FIG. 3 is a second sub-flowchart of a neural network-based intelligent heating energy-saving control method.
Fig. 4 is a third sub-flowchart of the intelligent heating energy-saving control method based on the neural network.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flow chart of an intelligent heat supply energy-saving control method based on a neural network, and in an embodiment of the invention, the intelligent heat supply energy-saving control method based on the neural network includes:
step S100: acquiring the temperature of each unit according to a preset temperature sensor, and creating a temperature map layer according to the height of the unit;
the temperature sensor is a basic device in the heating field, and is pre-installed in each unit with a heating system; some are installed in the unit, some are installed in the heating system, the purpose of which is to obtain the temperature in the unit; the temperature sensor may be understood as a thermometer, and the type of thermometer is not limited.
Step S200: overlapping the temperature map layers to obtain a heat distribution map, and determining a heat supply area based on the heat distribution map and weather information;
under the existing building system, the heights of different units are different, colloquially speaking, the floors are different, and the units can be understood as a certain room; a temperature map layer is built by units of the same floor, and a plurality of temperature map layers are overlapped to obtain a heat distribution condition, which is called a heat distribution map; the thermal profile is a two-dimensional graph, but the values of each pixel represent the temperatures at all elevations at each location, the temperatures at all elevations being related to energy, the higher the temperature.
On the basis, by combining weather information, the areas in the energy supply state can be determined; it should be noted that only the energy supply condition exists, and the energy supply condition is regarded as the heat supply area.
Step S300: determining a heat supply network according to the heat supply area, and determining a heat supply demand meter based on the heat supply network; the heat supply demand table comprises a heat supply characteristic item and a heat supply path item;
the heat supply area represents areas which need to be supplied with energy, a heat supply network is determined according to the areas which need to be supplied with energy, and the heat supply network is analyzed to determine a heat supply demand meter; the heat supply demand table comprises a heat supply characteristic item and a heat supply path item; the heating characteristic is a two-dimensional matrix, and the heating characteristic is used for representing the position of a heating area; the heating paths are subsets of the borrowing thermal energy network.
The heat supply energy network includes a heat supply circuit (for adjusting a heat supply air conditioner) and a heat supply pipe.
Step S400: inquiring a heating path based on a heating demand table when receiving the heating characteristics input by a user;
when the heat supply characteristics input by the user are received, the execution main body of the method can determine which areas need to be supplied with energy, and at the moment, the heat supply path can be inquired and selected by means of the generated heat supply demand table.
Fig. 2 is a first sub-flowchart of a neural network-based intelligent heat supply energy saving control method, wherein the steps of obtaining the temperature of each unit according to a preset temperature sensor and creating a temperature map layer according to the height of the unit include:
step S101: building data in a preset zone range are read, and closed spaces in the building data are numbered;
the energy supply areas are pre-defined areas, and each energy supply source has a corresponding energy supply area; inquiring building data in a regional range, acquiring a closed space in the building data, and numbering, wherein the closed space is a unit in the technical scheme of the invention; the closed space is predefined by a worker, and in general, a room with a door is a closed space.
Step S102: determining the installation parameters of the temperature sensor according to the size of the closed space; the installation parameters comprise the number and the positions;
the air density at different temperatures is different, so that the temperature at the upper part in the closed space is higher than the temperature at the lower part, and therefore, the installation parameters are determined according to the size of the closed space and are used for more accurately determining the measured temperature; the installation parameters represent the number of temperature sensors and their installation locations.
Step S103: acquiring temperature information of each closed space based on a temperature sensor;
according to the data acquired by the temperature sensor, more accurate temperature information in each closed space can be acquired.
Step S104: splicing temperature information according to the height relation of each closed space to obtain a temperature map layer; the temperature map layer contains height information;
the temperature information of the closed space under the same height is spliced together, so that a temperature map layer can be obtained; the temperature map layer is indexed by height.
The temperature map layer is the same as the boundary of the division area.
FIG. 3 is a second sub-flowchart of a neural network-based intelligent heating energy-saving control method, wherein the steps of superposing temperature layers to obtain a thermal distribution diagram and determining a heating area based on the thermal distribution diagram and weather information include:
step S201: reading a map of the area range, and establishing a reference layer;
step S202: reading a temperature map layer, and adding the temperature map layer into a reference map layer;
step S203: transmitting a reference map layer containing a temperature map layer to a preset identification model, and outputting a heat distribution map;
step S204: inquiring weather information, and determining a reference distribution diagram according to the weather information;
step S205: and comparing the heat distribution map with a reference distribution map to determine a heat supply area containing energy.
Step S201 to step S205 limit the determination process of the heating area, firstly, a map within the area is obtained, the energy supply party generally measures a map with extremely high precision in advance, and a reference layer can be established based on the map; then, sequentially superposing the temperature layers on each height into the reference layer to obtain a superposition graph; identifying the superposition value of each pixel point based on the trained neural network identification model, and obtaining the heat at each pixel point, which is called a heat distribution map; the corresponding relation between the added value and the heat is generated by training a training set pre-established by a worker.
On the basis of generating a heat distribution map, inquiring weather information, acquiring the temperature of each place under the weather information, and representing the corresponding heat quantity by a reference distribution map, wherein the reference distribution map and the heat distribution map have the same format, and comparing the reference distribution map with the heat distribution map to obtain a heat supply area, and determining the energy of the heat supply area according to the difference degree of the reference distribution map and the heat distribution map.
It should be noted that, in the technical scheme of the present invention, the condition of time is hidden, in fact, the precondition of all the processing procedures is time correspondence; for example, the thermal profile at a time may need to be compared to a reference profile determined from weather information at that time.
As a preferred embodiment of the present invention, the step of reading the temperature map layer and adding the temperature map layer to the reference map layer includes:
reading all the temperature layers, and carrying out normalization treatment on the temperature layers;
the values in the temperature layers are generally referred to as gray values, the range of the gray values is [0,255], if a plurality of temperature layers exist, the superimposed values are more likely to exceed 255, therefore, normalization processing is needed to be performed on each temperature layer in advance, so that the values of all the temperature layers are in a certain range, the superimposed values are ensured not to exceed 255, and if the superimposed values exceed 255, the process of referring to an image processing algorithm is more difficult.
Sequentially reading normalized values at each position in each temperature layer based on the height sequence, and accumulating the normalized values to a reference layer;
and sequentially reading the temperature layers from the descending order or the ascending order of the heights, and superposing the temperature layers into the reference layer.
As a preferred embodiment of the technical scheme of the invention, in the accumulation process, the value difference of the adjacent layers is calculated in real time, a correction coefficient is determined according to the value difference, and the normalized value is corrected according to the correction coefficient; the correction coefficient is in direct proportion to the value difference;
in real life, the temperature between the adjacent floors can be transferred, if the upper unit and the lower unit are in a heat supply state, the heat transfer process between the upper unit and the lower unit can reduce energy (less heat loss), therefore, when a heat analysis chart is generated, a correction coefficient is required to be introduced to represent the heat loss condition, if the temperature difference between the two units is large (the difference between the normalized values is large), the heat loss is large, the energy supply requirement is large, at the moment, the corresponding heat is large, and the heat is large in a mode of introducing a large correction coefficient, so that the correction coefficient is in direct proportion to the temperature difference.
It should be noted that the thermal analysis chart indicates the energy supply condition at each position, not the existing condition.
FIG. 4 is a third sub-flowchart of a neural network-based intelligent heating energy-saving control method, wherein the steps of determining a heating energy network according to the heating area and determining a heating demand table based on the heating energy network include:
step S301: acquiring a heat supply source in a regional range, and determining a heat supply network according to the heat supply source and the heat supply region;
and acquiring a heat supply source in the region, wherein the heat supply source refers to a source point for providing heat, a heat supply energy network is built between the heat supply source and the heat supply region, and a heat supply process occurs in the heat supply energy network.
Step S302: counting a temperature layer, and determining the selection probability of each heat supply area based on the temperature layer;
and reading each temperature layer again, inquiring the frequency of occurrence of the determined heat supply areas in each temperature layer, and determining the selection probability of each heat supply area according to the frequency of occurrence.
Step S303: randomly determining a heat supply combination based on the selection probability, and determining heat supply characteristics of the heat supply combination;
randomly determining a heat supply combination according to the selection probability, and inquiring the positions of all heat supply areas in the heat supply combination to obtain heat supply characteristics; in practice, the number of heating zones is limited, as is the number of heating combinations, but its number is large, in many cases the exhaustion time is too long, so that a random selection based on the selection probability is used.
Step S304: determining a heat supply path according to the heat supply characteristics, and establishing a heat supply demand table;
and establishing a heat supply path every time a heat supply characteristic is determined, and counting the heat supply characteristic and the corresponding heat supply path to obtain a heat supply demand table.
Further, the step of determining a heating path according to the heating characteristics, and establishing a heating demand table includes:
determining heat supply points according to the heat supply characteristics, and calculating the distance between adjacent heat supply points;
calculating the probability that each heating point reaches the adjacent heating point based on a preset probability formula;
and determining a heat supply path according to the probability, and establishing a heat supply demand table.
In one example of the technical solution of the present invention, the heating characteristic reflects the position of the heating area, and the heating point is determined from the position of the heating area, where the heating point may be the center point of the heating area, and after determining the heating point, the distance of the heating point is synchronously calculated.
And then, analyzing each heating point in turn, calculating the path selection probability between adjacent heating points, and determining the heating path according to the path selection probability after the path selection probabilities of all the heating points and the adjacent heating points are determined.
And finally, taking the heat supply characteristics as an index, and establishing a heat supply demand table.
Specifically, the step of calculating the probability that each heating point reaches its adjacent heating point based on the preset probability formula includes:
sequentially acquiring paths of each heating point and adjacent heating points;
obtaining heat supply distances of all paths, and calculating visibility according to the heat supply distances;
calculating the reserved energy of each path according to a preset loss coefficient;
calculating the probability of each path in real time based on the visibility and the reserved energy;
and determining heat supply paths from the heat supply sources to all heat supply points based on the probability and updating the heat supply paths regularly.
The above is defined on a probability formula and an application process thereof, wherein the probability formula is as follows:
wherein i and j respectively represent two heat supply points, P ij Probability between two heating points; η (eta) ij For visibility, η ij ＝1/d ij ，d ij The heating distance between two nodes is provided; τ ij The energy retained for the path between i and j, the energy retained being used to characterize the residual heat in the path; k is the k adjacent heating point of the heating point, and k is epsilon allowed k All adjacent heating points representing the heating point; alpha and beta are preset coefficients; the path is a subsection of the heat supply energy network, and the heat supply distance is the total length of the path; τ ik For the path between i and k, η ik Is the visibility between i and k.
The principle of the calculation process is that the ratio of the adjacent heat supply nodes is determined by the reserved energy and the visibility together, and the larger the product of the reserved energy and the visibility is, the larger the selection probability is; wherein the loss coefficient is pre-set by a worker, represents the temperature drop process of a certain path, and reflects the consumption in a heating path; in general, the higher the temperature, the slower the cooling process, and the path that the subject of the method wishes to choose is the path with the least loss, the nearest path, and when the loss is the least, the probability of the path being the greatest.
It is worth mentioning that the probability can be understood as a weight representing the relation between different adjacent heating points and the heating points to be analyzed.
As a preferred embodiment of the present invention, the retention energy is:
in an example of the technical scheme of the invention, on the basis of original reserved energy, the influence of other paths on the probability of two heat supply points is introduced; wherein m is the number of paths passing through two heat supply points, and l is the path passing through the two heat supply points;the influence quantity of the path passing through the two heat supply points on the path is the first path; the amount of influence being predetermined by the staff, generally the roadThe total length of the diameter is related; τ c Is an initial energy value; ρ is the loss factor.
Further, the determination process about the path is as follows:
the step of determining the heating paths from the heating source to all the heating points based on the probability and updating at regular time comprises the following steps:
sequentially selecting the maximum probability value to determine a main heating path, and generating a node record table;
determining a secondary heating path based on the node record table;
updating a node record table based on the secondary heat supply path and circularly executing until all heat supply points belong to a certain heat supply path;
and circulating the determining process of the heat supply path according to the preset time interval.
In a preferred embodiment of the present invention, the path selection process refers to the existing tree diagram generation process, and first, a main heating path is determined by probability, and in most cases, the main heating path does not include all heating nodes, and at this time, additional other paths called secondary heating paths are required.
The primary path determination is generally fixed under the condition of initial probability determination, and the secondary path determination is determined by staff according to the situation, wherein one way is to reject the heat supply points in the node record table in the heat supply points, determine the secondary heat supply path based on the reserved heat supply points, and the paths between the heat supply points can be repeated although the arriving heat supply points are different due to the fact that the traffic situation between the heat supply points is limited by a heat supply energy network,changes τ ij Also, the probability changes, and the path selection condition changes; the process is circularly executed, and a proper path with dynamic balance can be finally obtained.
It is important to say that the paths of the two nodes are generated based on the heat supply network and are subsections of the heat supply network; by way of example, a heating power grid is understood a rectangular grid, the path being made up of individual segments of the rectangular grid.
As a preferred embodiment of the technical scheme of the invention, when the monitoring equipment exists, the data of the monitoring equipment are acquired in real time, the accuracy judgment is carried out on the reserved energy according to the acquired data, and the initial value is corrected according to the accuracy obtained by judgment.
In the calculation process, τ c The initial energy value is preset by a worker, but the initial energy value can be adjusted, the influence on the reserved energy is large, and the worker can adjust the initial energy value in such a way that the temperature of each section in the heat supply network is acquired through monitoring equipment, so that the temperature is used as a reference, the initial energy value is adjusted, the probability is changed along with the adjustment, and the final path is possibly changed.
The above path is a theoretical path, which occurs in simulation software, and when the path is determined, a worker is required to adjust the relevant network, so as to realize the path, and the implementation process is not limited herein.
The functions which can be realized by the intelligent heat supply energy-saving control method based on the neural network are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to realize the functions of the intelligent heat supply energy-saving control method based on the neural network.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The intelligent heat supply energy-saving control method based on the neural network is characterized by comprising the following steps of:
acquiring the temperature of each unit according to a preset temperature sensor, and creating a temperature map layer according to the height of the unit;
overlapping the temperature map layers to obtain a heat distribution map, and determining a heat supply area based on the heat distribution map and weather information;
determining a heat supply network according to the heat supply area, and determining a heat supply demand meter based on the heat supply network; the heat supply demand table comprises a heat supply characteristic item and a heat supply path item;
inquiring a heating path based on a heating demand table when receiving the heating characteristics input by a user;
wherein the heating feature is used to characterize the location of the heating zone.
2. The intelligent heat supply energy saving control method based on a neural network according to claim 1, wherein the step of obtaining the temperature of each unit according to a preset temperature sensor and creating a temperature map layer according to the height of the unit comprises:
building data in a preset zone range are read, and closed spaces in the building data are numbered;
determining the installation parameters of the temperature sensor according to the size of the closed space; the installation parameters comprise the number and the positions;
acquiring temperature information of each closed space based on a temperature sensor;
splicing temperature information according to the height relation of each closed space to obtain a temperature map layer; the temperature map layer contains height information;
wherein the temperature map layer corresponds to a zone.
3. The intelligent heat supply energy saving control method based on the neural network according to claim 2, wherein the step of superposing the temperature map layers to obtain a heat distribution map, and determining the heat supply area based on the heat distribution map and weather information comprises:
reading a map of the area range, and establishing a reference layer;
reading a temperature map layer, and adding the temperature map layer into a reference map layer;
transmitting a reference map layer containing a temperature map layer to a preset identification model, and outputting a heat distribution map;
inquiring weather information, and determining a reference distribution diagram according to the weather information;
and comparing the heat distribution map with a reference distribution map to determine a heat supply area containing energy.
4. A neural network based intelligent heating energy saving control method according to claim 3, wherein the step of reading a temperature map layer and stacking the temperature map layer into a reference map layer comprises:
reading all the temperature layers, and carrying out normalization treatment on the temperature layers;
sequentially reading normalized values at each position in each temperature layer based on the height sequence, and accumulating the normalized values to a reference layer;
in the accumulation process, calculating the value difference of adjacent layers in real time, determining a correction coefficient according to the value difference, and correcting the normalized value according to the correction coefficient; the correction factor is proportional to the value difference.
5. The intelligent heating energy-saving control method based on the neural network according to claim 2, wherein the step of determining a heating power network according to the heating area and determining a heating demand table based on the heating power network comprises:
acquiring a heat supply source in a regional range, and determining a heat supply network according to the heat supply source and the heat supply region;
counting a temperature layer, and determining the selection probability of each heat supply area based on the temperature layer;
randomly determining a heat supply combination based on the selection probability, and determining heat supply characteristics of the heat supply combination;
and determining a heat supply path according to the heat supply characteristics, and establishing a heat supply demand table.
6. The intelligent heating energy-saving control method based on a neural network according to claim 5, wherein the step of determining a heating path according to the heating characteristics and establishing a heating demand table comprises:
determining heat supply points according to the heat supply characteristics, and calculating the distance between adjacent heat supply points;
calculating the probability that each heating point reaches the adjacent heating point based on a preset probability formula;
and determining a heat supply path according to the probability, and establishing a heat supply demand table.
7. The intelligent heat supply energy saving control method based on a neural network according to claim 6, wherein the step of calculating the probability that each heat supply point reaches its neighboring heat supply point based on a preset probability formula comprises:
sequentially acquiring paths of each heating point and adjacent heating points;
obtaining heat supply distances of all paths, and calculating visibility according to the heat supply distances;
calculating the reserved energy of each path according to a preset loss coefficient;
calculating the probability of each path in real time based on the visibility and the reserved energy;
and determining heat supply paths from the heat supply sources to all heat supply points based on the probability and updating the heat supply paths regularly.
8. The neural network-based intelligent heating energy-saving control method according to claim 7, wherein the probability formula is:
wherein i and j respectively represent two heat supply points, P ij Probability between two heating points; η (eta) ij For visibility, η ij ＝1/d ij ，d ij The heating distance between two nodes is provided; τ ij The energy retained for the path between i and j, the energy retained being used to characterize the residual heat in the path; k is the k adjacent heating point of the heating point, and k is epsilon allowed k All adjacent heating points representing the heating point; alpha and beta are preset coefficients; the path is a subsection of the heat supply energy network, and the heat supply distance is the total length of the path.
9. The neural network-based intelligent heating energy-saving control method of claim 8, wherein the retained energy is:
wherein m is the number of paths passing through two heat supply points, and l is the path passing through the two heat supply points;the influence quantity of the path passing through the two heat supply points on the path is the first path; the amount of impact is predetermined by the staff, generally related to the total length of the path; τ c Is an initial energy value; ρ is the loss factor;
when the monitoring equipment exists, acquiring data of the monitoring equipment in real time, judging the accuracy of the reserved energy according to the acquired data, and correcting the initial value according to the accuracy obtained by judgment.
10. The intelligent heating energy-saving control method based on a neural network according to claim 7, wherein the step of determining heating paths from a heating source to all heating points based on the probabilities and updating at a timing comprises:
sequentially selecting the maximum probability value to determine a main heating path, and generating a node record table;
determining a secondary heating path based on the node record table;
updating a node record table based on the secondary heat supply path and circularly executing until all heat supply points belong to a certain heat supply path;
and circulating the determining process of the heat supply path according to the preset time interval.
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| CN117252398B (en) * | 2023-11-15 | 2024-02-02 | 中国海洋大学 | Urban heat supply balance regulation and control method based on neural network |
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