CN113757786B - Block chain edge temperature safety control method, system and electronic equipment - Google Patents
Block chain edge temperature safety control method, system and electronic equipment Download PDFInfo
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- CN113757786B CN113757786B CN202111057620.5A CN202111057620A CN113757786B CN 113757786 B CN113757786 B CN 113757786B CN 202111057620 A CN202111057620 A CN 202111057620A CN 113757786 B CN113757786 B CN 113757786B
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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
- F24D19/10—Arrangement or mounting of control or safety devices
- F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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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
- F24D2220/00—Components of central heating installations excluding heat sources
- F24D2220/02—Fluid distribution means
- F24D2220/0207—Pumps
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F2119/08—Thermal analysis or thermal optimisation
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Abstract
The application provides a block chain edge temperature safety control method, a system and electronic equipment, wherein the method comprises the following steps: calculating a first deviation and a first deviation change rate of a first actual temperature and a safe temperature of a heating system; fuzzifying the first deviation and the first deviation change rate according to a set fuzzy domain and a set fuzzy set respectively to obtain a first fuzzy linguistic variable value corresponding to the first deviation and a second fuzzy linguistic variable value corresponding to the first deviation change rate; determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table; performing deblurring processing on the third fuzzy language variable value to obtain a first control quantity, and calculating according to the first control quantity and a preset linear ratio to obtain a first rotating speed corresponding to the heat exchange pump; the heat exchange pump is adjusted to work at the first rotating speed, so that the sensitivity of a heating system to interference can be reduced, and the stability is improved.
Description
Technical Field
The application relates to the technical field of safety control, in particular to a block chain edge temperature safety control method, a block chain edge temperature safety control system and electronic equipment.
Background
The heating system supplies corresponding heat to the indoor in order to maintain the indoor required temperature, and is divided into electric ground heating and water ground heating. The water floor heating system adopts a mode that hot water is used as a heating medium, a floor or a heating radiator is heated in a circulating flow mode in a heating pipe, and heat is supplied to the indoor space through ground radiation heat transfer. The household water heater has a strong household heating function and can supply large-flow constant-temperature domestic hot water.
The final goal of any heating system is to achieve temperature control, but temperature control in a heating system is often affected by a variety of factors, such as ambient temperature, building insulation conditions, heat transfer pump power and operating efficiency, various sensor accuracies, and even the case of partial system failure. The control method of the heating system in the prior art has low accuracy and needs repeated regulation and control.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method, a system, and an electronic device for controlling a block chain edge temperature, which can reduce the sensitivity of a heating system to interference and improve stability.
In a first aspect, an embodiment of the present application provides a block chain edge temperature safety control method, which is applied to a heating system, where the heating system includes a heat exchanger, a heat exchange pump, and a heat exchange tube; the heat exchanger is connected with the heat exchange pump through a heat exchange pipe, and heat exchange liquid flows through the heat exchange pipe; the method comprises the following steps:
calculating to obtain a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of a heating system;
fuzzification processing is carried out on the first deviation and the first deviation change rate respectively according to a set fuzzy domain and a set fuzzy set, and a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate are obtained;
determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table;
performing deblurring processing on the third fuzzy language variable value to obtain a first control quantity, and calculating according to the first control quantity and a preset linear ratio to obtain a first rotating speed corresponding to the heat exchange pump;
and adjusting the heat exchange pump to work at the first rotating speed.
In a preferred embodiment of the present application, the method further includes:
acquiring a second actual temperature of the heating system when the heat exchange pump works at the first rotating speed;
if the difference value between the second actual temperature and the safe temperature is larger than a preset range, calculating to obtain a second deviation and a second deviation change rate of the second actual temperature and the safe temperature of the heating system;
fuzzification processing is carried out on the second deviation and the second deviation change rate respectively to obtain a fourth fuzzy language variable value corresponding to the second deviation and a fifth fuzzy language variable value corresponding to the second deviation change rate;
determining a corresponding sixth fuzzy language variable value according to a second corresponding relation of the fourth fuzzy language variable value and the fifth fuzzy language variable value in a preset dead zone fuzzy control rule table;
performing deblurring processing on the sixth fuzzy language variable value to obtain a second control quantity, and calculating according to the second control quantity to obtain a second rotating speed corresponding to the heat exchange pump;
and adjusting the heat exchange pump to work at the second rotating speed.
In a preferred technical solution of the present application, in the method, if a difference between the second actual temperature and the safe temperature is smaller than or equal to a preset range, the heat exchange pump continues to operate at the first rotation speed.
In a preferred technical solution of the present application, the second corresponding relationship is determined by a corresponding relationship between the first deviation and the first rotation speed in a preset dead zone fuzzy control rule table.
In a preferred embodiment of the present invention, the fuzzy sets of the first deviation and the first deviation change rate are all: { NB, NM, NS, NZ, Z, PZ, PS, PM, PB }, where the domains of correspondence are: { -3, -2, -1, -0.1,0,0.1,1,2,3}.
In a preferred embodiment of the present application, the deblurring processing the third fuzzy language variable value to obtain a first control quantity, and calculating a first rotation speed corresponding to the heat exchanger according to the first control quantity includes:
calculating to obtain a first control quantity according to a control component corresponding to each rule in a preset dead zone fuzzy control rule table and a weight corresponding to the control component of the first deviation and the first deviation change rate;
and calculating to obtain a first rotating speed corresponding to the heat exchange pump according to the first control quantity.
In a preferred technical solution of the present application, the calculating to obtain a first deviation and a first deviation change rate of a first actual temperature and a safe temperature of a heating system includes:
collecting a first actual temperature of the heating system;
taking the difference between the first actual temperature and the safe temperature as a first deviation;
and calculating to obtain a first deviation ratio through the first deviation after difference and filtering.
In a second aspect, an embodiment of the present application provides a block chain edge temperature safety control system, configured to control a heating system, where the heating system includes a heat exchanger, a heat exchange pump, and a heat exchange tube; the heat exchanger is connected with the heat exchange pump through a heat exchange pipe, and heat exchange liquid flows through the heat exchange pipe; the system comprises:
the client is used for sending an evaluation instruction to the Internet cloud platform;
the Internet of things cloud platform is used for receiving the evaluation instruction sent by the client, generating a corresponding control instruction according to the evaluation instruction, and sending the control instruction to the block chain edge computing Internet of things gateway;
the block chain edge computing Internet of things gateway is used for detecting the heating system according to the received control command;
the block chain edge computing internet of things gateway comprises:
the calculation module is used for calculating and obtaining a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of the heating system;
the fuzzy processing module is used for respectively carrying out fuzzification processing on the first deviation and the first deviation change rate according to a set fuzzy domain and a set fuzzy set to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate;
the determining module is used for determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table;
the deblurring module is used for deblurring the third fuzzy language variable value to obtain a first control quantity and calculating a first rotating speed corresponding to the heat exchange pump according to the first control quantity and a preset linear ratio;
and the adjusting module is used for adjusting the heat exchange pump to work at the first rotating speed.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the above-mentioned block chain edge temperature safety control method when executing the computer program.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
the method comprises the steps that a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of a heating system are obtained through calculation; then, fuzzifying the first deviation and the first deviation change rate according to a set fuzzy domain and a set fuzzy set respectively to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate; determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table; then, carrying out deblurring processing on the third fuzzy language variable value to obtain a first control quantity, and calculating according to the first control quantity and a preset linear ratio to obtain a first rotating speed corresponding to the heat exchange pump; and finally, the heat exchange pump is regulated to work at the first rotating speed, so that the sensitivity of a heating system to interference can be reduced, and the stability is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart illustrating a block chain edge temperature safety control method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a temperature multi-modal closed-loop dead zone fuzzy control model provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of a multi-modal dead zone fuzzy controller provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a block chain edge temperature safety control system according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a block chain edge temperature safety control method, a system and electronic equipment, which are described through embodiments below.
The method is used for controlling a heating system, wherein the heating system comprises a heat exchanger, a heat exchange pump and a heat exchange tube; the heat exchanger is connected with the heat exchange pump through a heat exchange pipe, and heat exchange liquid flows through the heat exchange pipe.
Fig. 1 is a schematic flowchart illustrating a method for controlling temperature safety at an edge of a blockchain according to an embodiment of the present disclosure, where the method includes steps S101-S105; specifically, the method comprises the following steps:
s101, calculating to obtain a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of a heating system;
s102, fuzzifying the first deviation and the first deviation change rate according to a set fuzzy domain and a set fuzzy set respectively to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate;
s103, determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table;
s104, deblurring processing is carried out on the third fuzzy language variable value to obtain a first control quantity, and a first rotating speed corresponding to the heat exchange pump is obtained through calculation according to the first control quantity and a preset linear ratio;
and S105, adjusting the heat exchange pump to work at a first rotating speed.
The method comprises the steps of testing a heating system, establishing a control model, and designing a fuzzy controller suitable for the control characteristic of the heating temperature to realize the intelligent control of the heating system temperature; the sensitivity of the heating system to interference can be reduced, and the stability is improved.
Some embodiments of the present application are described in detail below. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
S101, calculating to obtain a first deviation and a first deviation change rate of a first actual temperature and a safe temperature of a heating system.
This application passes through sensor acquisition heating system's first actual temperature, then is according to heating system's safe temperature, calculates and obtains the first deviation and the first deviation rate of change of the first actual temperature of heating system and safe temperature. The safe temperature of the heating system is the highest temperature which can be borne by the heating system when the heating system works normally. The safe temperature of the heating system may be provided by the manufacturer of the heating system.
Specifically, a first actual temperature of a heating system is collected; taking the difference value of the first actual temperature and the safety temperature as a first deviation; and the first deviation is subjected to difference and filtering, and a first deviation ratio is obtained through calculation.
And S102, fuzzifying the first deviation and the first deviation change rate according to the set fuzzy domain and the set fuzzy set respectively to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate.
The fuzzy sets of the first deviation and the first deviation change rate are set in the application as follows: { NB, NM, NS, NZ, Z, PZ, PS, PM, PB }, where the domains of correspondence are: { -3, -2, -1, -0.1,0,0.1,1,2,3}.
And respectively carrying out fuzzy processing on the first deviation and the first deviation change rate according to the set fuzzy domain and the set fuzzy set to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate.
S103, determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table.
Table 1: dead zone fuzzy control rule table
As shown in the above table, after the first fuzzy language variable value and the second fuzzy language variable value are determined, the third fuzzy language variable value is correspondingly determined in the above table.
And S104, performing deblurring processing on the third fuzzy language variable value to obtain a first control quantity, and calculating according to the first control quantity and a preset linear ratio to obtain a first rotating speed corresponding to the heat exchange pump.
When specifically handling, carry out the deblurring to the third fuzzy language variable value and handle, obtain first controlled quantity to according to first controlled quantity, calculate and obtain the first rotational speed that the heat exchanger pump corresponds, include:
calculating to obtain a first control quantity according to a control component corresponding to each rule in a preset dead zone fuzzy control rule table and a weight corresponding to the control component of the first deviation and the first deviation change rate;
and calculating to obtain a first rotating speed corresponding to the heat exchange pump according to the first control quantity.
And S105, adjusting the heat exchange pump to work at a first rotating speed.
This application can carry out a lot of to heating system and adjust in order to guarantee heating system's temperature at safe temperature range.
Acquiring a second actual temperature of the heating system when the heat exchange pump works at the first rotating speed;
if the difference value between the second actual temperature and the safety temperature is larger than the preset range, calculating to obtain a second deviation and a second deviation change rate of the second actual temperature and the safety temperature of the heating system;
fuzzification processing is carried out on the second deviation and the second deviation change rate respectively to obtain a fourth fuzzy language variable value corresponding to the second deviation and a fifth fuzzy language variable value corresponding to the second deviation change rate;
determining a corresponding sixth fuzzy language variable value according to a second corresponding relation between the fourth fuzzy language variable value and the fifth fuzzy language variable value in a preset dead zone fuzzy control rule table;
performing deblurring processing on the sixth fuzzy language variable value to obtain a second control quantity, and calculating a second rotating speed corresponding to the heat exchange pump according to the second control quantity;
and regulating the heat exchange pump to work at the second rotation speed regulation.
After the readjustment, it is determined whether the second actual temperature after the readjustment is within the range of the safe temperature.
And if the difference value between the second actual temperature and the safety temperature is smaller than or equal to the preset range, the heat exchange pump continues to work at the first rotating speed.
And the second corresponding relation is determined by the corresponding relation between the first deviation and the first rotating speed in a preset dead zone fuzzy control rule table.
In the implementation, the scheme of the application can be correspondingly transformed, and the specific scheme is as follows:
a transfer function from the heat exchange pump to the heating air temperature can be obtained by testing according to a classical control theory, and is recorded as F(s), and a temperature MultiMode closed-loop dead zone Fuzzy control model based on a MultiMode dead zone Fuzzy Controller (MMFC) is constructed as shown in fig. 2.
The stable intelligent control of the heating temperature can be realized through a multi-mode closed-loop dead zone fuzzy control system, wherein the design of a multi-mode dead zone fuzzy controller is emphasized. The input and the output of the multi-mode dead zone fuzzy controller are respectively a temperature error Tref and a rotating speed w of a heat exchange pump in a temperature closed-loop control model.
The designed multi-modal dead zone fuzzy controller mainly comprises a multi-modal knowledge base, a machine learning mode selector, an inference machine model, a fuzzification interface, a defuzzification interface, dead zone processing and the like, wherein the multi-modal knowledge base is composed of an experience database and a professional rule base, and the dead zone processing is performed on the multi-modal knowledge base.
1. Dead zone obfuscation interface
The error e and the error change rate ec of the input Terr are fuzzified, and the controlled variable u is fuzzified.
e. The fuzzy sets of ec and u are: { NB, NM, NS, NZ, Z, PZ, PS, PM, PB }, where the domains of correspondence are: { -3, -2, -1, -0.1,0,0.1,1,2,3}.
Different from the traditional fuzzification process, a dead zone fuzzy set and a dead zone domain are introduced, the addition of the dead zone is favorable for improving the stability of a heating temperature control system, when the error e or the error change rate ec is close to zero, the adjustment frequency of the system is reduced, the oscillation possibly generated by the repeated adjustment of the system in a near-zero state is avoided, and the service life of the system is prolonged.
2. Dead zone fuzzy control rule table
The dead zone fuzzy control rule table is the core content of the inference engine model, and is compiled according to a knowledge base formed by an experience database and a professional rule base under the condition of field debugging. Different from the traditional rule table, the dead zone interval is introduced, when the error e and the error change rate ec are close to zero, the control quantity u is also zero, and the dead zone rule is set, so that the stability of temperature system regulation is also facilitated. For the dead zone fuzzy control rule table of the heating system, fine adjustment is recommended on the basis of the following dead zone rule table.
As can be seen from the dead zone rule table, the fuzzy rule on the output u is generated from the inputs e and ec for a total of 81, and the rule operation in the ith rule generates ui, which results in a total of 81 control quantities u1 to u 81.
3. Multimodal knowledge base
And compiling different actual dead zone rule tables through the recommended dead zone rule table and manual experience to construct a multi-mode knowledge base which is used as a basic basis for multi-mode operation of the system.
4. Deblurring interface
And calculating the output control quantity u by adopting a weighted average algorithm, namely:
where ri is the weighting coefficient corresponding to ui.
Because dead zones are introduced into the fuzzification interface and the rule table part, the output control quantity u generated by the defuzzification interface also has the dead zone characteristic, the adjusting times of the temperature system can be reduced, and the running stability of the system is improved.
5. Dead zone fuzzy processing procedure
In an actual heating system, it is sufficient that the temperature control accuracy satisfies a certain error range, and stable operation in the aspects of temperature control stability, overall heating system operation stability, heating pipe network fluid fluctuation conditions, and the like is more important. By integrating dead zone processing into the fuzzy controller, the sensitivity of the system to the following interferences can be reduced, and the stability is improved:
a. self-measuring noise, external crosstalk and communication interference of a temperature sensor, a pressure sensor and a flow sensor;
b. the field sensors are numerous in type and quantity, and coupling interference among the sensor data processing of various types and quantities is avoided;
c. after fuzzy processing, the output inertia of the controller is increased, the adjusting frequency and the adjusting amplitude of the rotating speed of the heat exchange pump are reduced, the pipeline fluid fluctuation of a self heating loop can be reduced, and the pipeline fluid fluctuation of the whole pipe network loop is reduced.
The specific process of the dead zone fuzzy processing is as follows:
firstly, by introducing { NZ, PZ } in a fuzzy set, the corresponding domain is { -0.1,0.1}, the stability of the processing is increased for e, ec and u, and the specific process is as follows:
a. for the error e, when the absolute value of e is smaller than a certain degree, the control precision is considered to meet the requirement, the output of a controller is not needed, and the system stability is obviously improved;
b. for the error change rate ec, when the absolute value of ec is smaller than a certain degree, the controller does not output, and the error change rate ec is more sensitive to noise than the error e, so that the fuzzy processing effect is more obvious for the error change rate ec;
c. for the control quantity u, when the absolute value of u is smaller than a certain degree, the system control is considered to reach the requirement, the output control is not needed, and the system adjusting times are reduced.
Secondly, dead zone rules are introduced into the dead zone rule table to further reduce the output amplitude and frequency of the controlled variable u, namely the output of the dead zone rules surrounding e and ec fuzzy sets { NZ, Z and PZ } is Z, which means that when e and ec are relatively small, the controlled variable u is not output, the system does not need to be adjusted, the number of times of adjustment can be reduced, and system fluctuation caused by system adjustment is reduced.
Finally, the fuzzy processing stability of the input and the output of the controller can be fully ensured by combining the previous processing, and the deblurring interface is a mathematical continuous function and can further reserve and execute the stability brought by the fuzzy processing.
After through above blind area fuzzy processing, fuzzy controller has abundant stability, including controller internal stability and outside anti-interference stability, not only can stable control self return circuit, has also reduced the interference to system pipe network return circuit, consequently can promote the stability of whole heating system temperature control and operation, reduces the running loss, increases system life-span.
6. Machine learning multimodal knowledge base work process
Firstly, a multi-mode knowledge base is formed by a plurality of dead zone fuzzy control rule tables which are compiled in advance and obtained through artificial experience accumulation, and the multi-mode knowledge base comprises N experience databases and a professional rule base.
And then, the machine learning mode selector selects the optimal mode i according to the input temperature error Terr and the performance of the output heat exchange pump w, namely the fuzzy control rule table i and the corresponding experience database i and the professional rule base i.
Finally, the operation of the current fuzzy controller is guided by modality i.
Fig. 4 is a schematic structural diagram illustrating a block chain edge temperature safety control system according to an embodiment of the present application, where the system includes:
the client is used for sending an evaluation instruction to the network connection cloud platform;
the Internet of things cloud platform is used for receiving the evaluation instruction sent by the client, generating a corresponding control instruction according to the evaluation instruction, and sending the control instruction to the block chain edge computing Internet of things gateway;
the block chain edge computing Internet of things gateway is used for detecting the heating system according to the received control instruction; the mathematical computation in the application is completely completed in the gateway, and the block chain function of the gateway records the results of measurement and computation, so that the results of measurement and computation are safe, reliable and transparent, and the final result can be remotely checked.
The block chain edge computing internet of things gateway comprises:
the calculation module is used for calculating and obtaining a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of the heating system;
the fuzzy processing module is used for respectively carrying out fuzzification processing on the first deviation and the first deviation change rate according to a set fuzzy domain and a set fuzzy set to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate;
the determining module is used for determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table;
the deblurring module is used for deblurring the third fuzzy language variable value to obtain a first control quantity and calculating a first rotating speed corresponding to the heat exchange pump according to the first control quantity and a preset linear ratio;
and the adjusting module is used for adjusting the heat exchange pump to work at a first rotating speed.
As shown in fig. 5, an embodiment of the present application provides an electronic device for executing the method for block chain edge temperature safety control in the present application, where the device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for block chain edge temperature safety control when executing the computer program.
Specifically, the memory and the processor may be general-purpose memory and processor, which are not limited in particular, and when the processor runs a computer program stored in the memory, the above-mentioned block chain edge temperature safety control method can be executed.
Corresponding to the block chain edge temperature safety control method in the present application, an embodiment of the present application further provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to perform the steps of the block chain edge temperature safety control method.
Specifically, the storage medium can be a general-purpose storage medium, such as a removable disk, a hard disk, or the like, and when executed, the computer program on the storage medium can execute the above-mentioned block chain edge temperature safety control method.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions in actual implementation, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of systems or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit the technical solutions, and the scope of the present application is not limited to the above-mentioned embodiments, although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. A block chain edge temperature safety control method is characterized by being applied to a heating system, wherein the heating system comprises a heat exchanger, a heat exchange pump and a heat exchange tube; the heat exchanger is connected with the heat exchange pump through a heat exchange pipe, and heat exchange liquid flows through the heat exchange pipe; the method comprises the following steps:
calculating to obtain a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of a heating system;
fuzzification processing is carried out on the first deviation and the first deviation change rate respectively according to a set fuzzy domain and a set fuzzy set, and a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate are obtained; the first deviation and the fuzzy set of the first deviation rate are both: { NB, NM, NS, NZ, Z, PZ, PS, PM, PB }, where the domains of correspondence are: { -3, -2, -1, -0.1,0,0.1,1,2,3};
determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table; the dead zone fuzzy control rule table comprises a dead zone interval;
performing deblurring processing on the third fuzzy language variable value to obtain a first control quantity, and calculating according to the first control quantity and a preset linear ratio to obtain a first rotating speed corresponding to the heat exchange pump;
adjusting the heat exchange pump to work at the first rotating speed adjustment;
the deblurring processing is performed on the third fuzzy language variable value to obtain a first control quantity, and a first rotating speed corresponding to the heat exchange pump is obtained through calculation according to the first control quantity, and the method includes:
calculating to obtain a first control quantity according to a control component corresponding to each rule in a preset dead zone fuzzy control rule table and a weight corresponding to the control component of the first deviation and the first deviation change rate;
and calculating to obtain a first rotating speed corresponding to the heat exchange pump according to the first control quantity.
2. The method of claim 1, further comprising:
acquiring a second actual temperature of the heating system when the heat exchange pump works at the first rotating speed;
if the difference value between the second actual temperature and the safe temperature is larger than a preset range, calculating to obtain a second deviation and a second deviation change rate of the second actual temperature and the safe temperature of the heating system;
fuzzification processing is carried out on the second deviation and the second deviation change rate respectively to obtain a fourth fuzzy language variable value corresponding to the second deviation and a fifth fuzzy language variable value corresponding to the second deviation change rate;
determining a corresponding sixth fuzzy language variable value according to a second corresponding relation of the fourth fuzzy language variable value and the fifth fuzzy language variable value in a preset dead zone fuzzy control rule table;
performing deblurring processing on the sixth fuzzy language variable value to obtain a second control quantity, and calculating according to the second control quantity to obtain a second rotating speed corresponding to the heat exchange pump;
and adjusting the heat exchange pump to work at the second rotation speed adjustment.
3. The method of claim 2,
and if the difference value between the second actual temperature and the safe temperature is smaller than or equal to a preset range, the heat exchange pump continues to work at the first rotating speed.
4. The method according to claim 2, wherein the second correspondence is determined by a correspondence of the first deviation and the first rotation speed in a preset dead zone fuzzy control rule table.
5. The method of claim 1, wherein the calculating a first deviation and a first deviation rate of change of a first actual temperature of the heating system from a safe temperature comprises:
collecting a first actual temperature of the heating system;
taking the difference between the first actual temperature and the safe temperature as a first deviation;
and calculating to obtain a first deviation ratio through the first deviation after difference and filtering.
6. A block chain edge temperature safety control system is characterized by being used for controlling a heating system, wherein the heating system comprises a heat exchanger, a heat exchange pump and a heat exchange tube; the heat exchanger is connected with the heat exchange pump through a heat exchange pipe, and heat exchange liquid flows through the heat exchange pipe; the system comprises:
the client is used for sending an evaluation instruction to the network connection cloud platform;
the Internet of things cloud platform is used for receiving the evaluation instruction sent by the client, generating a corresponding control instruction according to the evaluation instruction, and sending the control instruction to the block chain edge computing Internet of things gateway;
the block chain edge calculation Internet of things gateway is used for detecting the heating system according to the received control instruction;
the block chain edge computing internet of things gateway comprises:
the calculation module is used for calculating and obtaining a first deviation and a first deviation change rate of a first actual temperature and a safety temperature of the heating system;
the fuzzy processing module is used for respectively carrying out fuzzification processing on the first deviation and the first deviation change rate according to a set fuzzy domain and a set fuzzy set to obtain a first fuzzy language variable value corresponding to the first deviation and a second fuzzy language variable value corresponding to the first deviation change rate;
the determining module is used for determining a corresponding third fuzzy language variable value according to a preset first corresponding relation of the first fuzzy language variable value and the second fuzzy language variable value in a preset dead zone fuzzy control rule table; the first deviation and the fuzzy set of first deviation rates are each: { NB, NM, NS, NZ, Z, PZ, PS, PM, PB }, where the domains of correspondence are: { -3, -2, -1, -0.1,0,0.1,1,2,3};
the deblurring module is used for deblurring the third fuzzy language variable value to obtain a first control quantity and calculating a first rotating speed corresponding to the heat exchange pump according to the first control quantity and a preset linear ratio; the dead zone fuzzy control rule table comprises a dead zone interval; the deblurring processing is performed on the third fuzzy language variable value to obtain a first control quantity, and a first rotating speed corresponding to the heat exchange pump is obtained through calculation according to the first control quantity, and the method comprises the following steps: calculating to obtain a first control quantity according to a control component corresponding to each rule in a preset dead zone fuzzy control rule table and a weight corresponding to the control component of the first deviation and the first deviation change rate; according to the first control quantity, calculating to obtain a first rotating speed corresponding to the heat exchange pump;
and the adjusting module is used for adjusting the heat exchange pump to work at the first rotating speed.
7. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of block chain edge temperature safety control as recited in any one of claims 1 to 5.
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