CN114517963B - Air conditioner control method and system for intelligent resource allocation - Google Patents
Air conditioner control method and system for intelligent resource allocation Download PDFInfo
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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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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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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
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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Abstract
The invention provides an air conditioner control method and system for intelligent resource allocation, wherein the method comprises the following steps: the control sensor acquires control parameters of a corresponding loop aiming at control equipment in real time, the variable frequency controller fuzzifies the control parameters to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters, and maps the fuzzy subsets of the input quantity and the membership vector values into alternative control sets according to a rule cluster; the alternative control set comprises a plurality of control quantity fuzzy subsets, the control quantity fuzzy subsets are in one-to-one correspondence with the input quantity fuzzy subsets, and the membership vector value is mapped into a weight parameter of each control quantity fuzzy subset; and feeding back the expected control quantity in the alternative control set to each variable frequency controller, and controlling the control equipment in the corresponding loop by the variable frequency controller according to the fed-back expected control quantity. The method has the advantages of sustainable optimization control equipment control, real-time feedback control parameter, predictable control parameter, energy consumption reduction and the like.
Description
Technical Field
The invention relates to the technical field of air conditioners, in particular to an air conditioner control method and system for intelligent resource allocation.
Background
With the high-speed development of the economy in China and the increasing of the living standard of people, the application of the central air conditioning system is becoming common. The central air conditioning system of the large building comprises a water system and a tail end fan system, wherein the water system comprises a water chilling unit (main machine), a chilled water loop and a cooling water loop, and the tail end fan system comprises a fan, a coil heat exchanger and the like. Early air conditioners are operated for a long time under the power frequency operation condition, but the coincidence of general users is low, great waste is easily caused, and the energy consumption proportion of the air conditioner in a building can reach 40% -60% according to data statistics. Currently, in order to save energy, most central air conditioners use a variable frequency control technology, so that the central air conditioner maintains a dynamic workload according to actual needs, rather than a constant workload.
In a central air conditioning system, main control objects of frequency conversion control comprise compressor power of a water chilling unit, rotation speed of a chilled water pump, rotation speed of a cooling water pump, cooling fan air quantity of a cooling tower, tail end fan air quantity and the like. In the variable frequency control, a variable frequency controller collects control parameters such as temperature and humidity, water supply temperature, backwater temperature, flow, pressure difference and the like in a building through a sensor, and generates a variable frequency instruction by applying a control strategy so as to realize variable frequency control on a control object. The control strategy comprises optimal control, PID control, fuzzy control, neural network control, adaptive control and the like.
The existing frequency conversion control method of the central air conditioner has the following defects:
1. in the existing air conditioner variable frequency control system, a variable frequency controller, a sensor matched with the variable frequency controller and equipment controlled by the variable frequency controller form a set of closed loop, and the large-scale central air conditioner system comprises a plurality of variable frequency controllers corresponding to a plurality of closed loop, and the loops are mutually closed and isolated. However, the whole central air conditioning system is a system which is mutually integrated and influenced, and the water chilling unit, the chilled water loop, the cooling water loop and the tail end are mutually influenced, so that each closed loop is only optimized for respective control equipment by utilizing a control strategy according to control parameters, the overall optimization of the central air conditioning system cannot be realized, the sustainable optimization of the control equipment cannot be truly realized, frequent switching of the working state of the central air conditioner is easy to cause, and the energy consumption is high.
2. Under the condition that the loops are mutually closed and isolated, the specific heat of the chilled water loop and the cooling water loop of the central air conditioning system is large, the pipelines are distributed in a complex manner, so that the time lag is strong, the response to the variable frequency control is slow, the influence factors of the response are many, the variable frequency control is in a nonlinear characteristic, the predictability is low, and therefore the influence of the variable frequency control on the integral optimization of the central air conditioner is not easy to determine and the feedback adjustment cannot be performed quickly.
Disclosure of Invention
In view of the above-mentioned shortcomings in the prior art, the present invention aims to: the control parameters are fuzzified, the fuzzified data are mapped into alternative control sets, and weight parameters are mapped for each fuzzy subset of the control quantity of the alternative control sets, so that the calculated expected control quantity of the alternative control sets is adopted to control the control equipment. The control loops can be mutually blended, and the control strategy can be optimized according to the control parameters of different control loops. The method has the advantages of sustainable optimization control equipment control, real-time feedback control parameter, predictable control parameter, energy consumption reduction and the like.
The intelligent resource allocation air conditioner control method comprises the following specific steps of:
acquiring control parameters of a corresponding loop for control equipment in real time through a control sensor, and transmitting the control parameters to a corresponding variable frequency controller in real time;
fuzzifying the control parameters through a variable frequency controller to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters, and mapping the fuzzy subsets of the input quantity and the membership vector values into alternative control sets according to a rule cluster;
the alternative control set comprises a plurality of control quantity fuzzy subsets, the control quantity fuzzy subsets correspond to the input quantity fuzzy subsets one by one, and the membership vector value is mapped into a weight parameter of each control quantity fuzzy subset; the expected control amount in the alternative control set corresponds to the expected control amount of all control devices in the whole air conditioner;
and feeding back the expected control quantity in the alternative control set to each variable frequency controller, and controlling the control equipment in the corresponding loop by the variable frequency controller according to the fed-back expected control quantity.
Further, the method comprises the steps of controlling a coordination pool, and specifically comprises the following steps:
each frequency conversion controller sends the alternative control set generated in the preset time period to the control coordination pool, and the control coordination pool executes conflict judgment and applicability judgment on all the alternative control sets in the preset time period;
analyzing the expected control quantity of the whole air conditioner according to the execution conflict judging result and the applicability result among the alternative control sets;
the control coordination pool feeds back the obtained expected control quantity to each variable frequency controller, and the variable frequency controllers control the control equipment in the corresponding loop according to the fed-back expected control quantity.
Further, the method for controlling the coordination pool to execute conflict resolution judgment comprises the following steps:
judging whether the expected control quantity of each alternative control set aiming at the same control equipment has conflict or not according to the alternative control sets uploaded by all the variable frequency controllers;
if yes, the control coordination pool judges that the current expected control of the control equipment has conflict, and the control parameters of the control equipment are not modified; if not, the control coordination pool calculates the expected control quantity according to the alternative control set with conflict, and modifies the control parameters of the control equipment according to the obtained expected control quantity.
Further, the calculating the expected control amount according to the alternative control set where the conflict occurs includes:
respectively selecting control quantity fuzzy subsets with the expected control quantity overlapping from the alternative control sets with the conflict through a control coordination pool; and calculating the expected control quantity according to the superposition range between the expected control quantities of the fuzzy subset of the alternative control centralized control quantity which generates the conflict and the weight parameters corresponding to the superposition range.
Further, before the expected control quantity in the alternative control set is fed back to each variable frequency controller, the deblurring is needed, and the specific method is as follows:
the membership function of the alternative control set has only one peak value, and the maximum value of the membership function is used as a clear value, namely:
μ c (u c )＝max(μ c (u))
wherein C is a set of alternative control sets, and u is a member in the output membership vector value range.
Further, before the expected control quantity in the alternative control set is fed back to each variable frequency controller, the deblurring is needed, and the specific method is as follows:
the set of alternative controls for the output is represented by a continuous function, i.e
for a set of fuzzy alternative control sets expressed in discrete terms, the output is obtained by weighted averaging, i.e
An intelligent resource allocation air conditioner control system comprises a plurality of variable frequency controllers, a plurality of control sensors and a plurality of control devices, wherein the variable frequency controllers, the control sensors and the control devices are in one-to-one correspondence with each other and form a control loop; the variable frequency controller comprises a fuzzification module, a fuzzy reasoning module and a rule cluster;
the control sensor is used for acquiring control parameters of the corresponding loop for the control equipment in real time; the blurring module is used for blurring the control parameters to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters; the fuzzy reasoning module is used for mapping each input quantity fuzzy subset and membership vector value into an alternative control set according to the rule cluster, wherein the alternative control set comprises a plurality of control quantity fuzzy subsets, and the control quantity fuzzy subsets are in one-to-one correspondence with the input quantity fuzzy subsets; the fuzzy inference module is also used for mapping the membership vector value into a weight parameter of each control quantity fuzzy subset; the expected control quantity in the alternative control set corresponds to the expected control quantity of all control devices in the whole air conditioner, and the variable frequency controller controls the control devices in the corresponding loop according to the feedback expected control quantity.
Further, the system also comprises a control coordination pool, wherein the control coordination pool specifically comprises:
each frequency conversion controller sends the alternative control set generated in the preset time period to the control coordination pool, and the control coordination pool executes conflict judgment and applicability judgment on all the alternative control sets in the preset time period;
analyzing the expected control quantity of the whole air conditioner according to the execution conflict judging result and the applicability result among the alternative control sets;
the control coordination pool feeds back the obtained expected control quantity to each variable frequency controller, and the variable frequency controllers control the control equipment in the corresponding loop according to the fed-back expected control quantity.
Further, the controlling the coordination pool to execute conflict resolution judgment includes:
judging whether the expected control quantity of each alternative control set aiming at the same control equipment has conflict or not according to the alternative control sets uploaded by all the variable frequency controllers;
if yes, the control coordination pool judges that the current expected control of the control equipment has conflict, and the control parameters of the control equipment are not modified; if not, the control coordination pool calculates the expected control quantity according to the alternative control set with conflict, and modifies the control parameters of the control equipment according to the obtained expected control quantity.
The fuzzy control system further comprises a defuzzification module, wherein the defuzzification module is used for converting the fuzzy expected control quantity into the clear expected control quantity, the fuzzification module, the fuzzy reasoning module and the defuzzification module are sequentially connected, and the fuzzification module, the fuzzy reasoning module and the defuzzification module are respectively connected with the rule clusters.
Compared with the prior art, the invention has the following advantages:
before the control parameters are utilized to formulate equipment control strategies for optimization, fuzzy control is carried out on the control parameters, the control parameters are fuzzified, fuzzy data are mapped into alternative control sets, the alternative control sets comprise a plurality of control quantity fuzzy subsets, weight parameters of each control quantity fuzzy subset are mapped, accordingly, the calculated expected control quantity of the alternative control sets corresponds to the calculated expected control quantity, and finally corresponding equipment is controlled according to the fed-back expected control quantity. In this way, different control loops can be mutually blended, and the optimal control strategy can be calculated according to the control parameters of the different control loops. The method has the advantages of sustainable optimization control equipment control, real-time feedback control parameter, predictable control parameter, energy consumption reduction and the like.
Drawings
FIG. 1 is a control flow chart of an air conditioner control method for intelligent resource allocation in a first embodiment of the invention;
FIG. 2 is a control coordination flow chart of a control coordination pool according to a first embodiment of the invention;
FIG. 3 is a control flow diagram of a first embodiment of the present invention for controlling a coordination pool to perform conflict resolution;
FIG. 4 is a block diagram of an air conditioning control system for intelligent resource allocation in a second embodiment of the present invention;
FIG. 5 is a fuzzy schematic block diagram of a variable frequency controller in a second embodiment of the present invention;
fig. 6 is a system block diagram of a variable frequency controller employing an adaptive fuzzy-PID controller in accordance with a second embodiment of the present invention.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, which should not be construed as limiting the scope of the present invention.
Embodiment one:
referring to fig. 1, an air conditioner control method for intelligent resource allocation, a system for applying the method comprises a plurality of variable frequency controllers, a plurality of control sensors and a plurality of control devices, wherein the variable frequency controllers, the control sensors and the control devices are in one-to-one correspondence with each other and form a control loop, and the specific steps are as follows:
and acquiring control parameters of the corresponding loop for the control equipment in real time through the control sensor, and transmitting the control parameters to the corresponding variable frequency controller in real time. Specifically, the control equipment comprises a compressor of a water chiller, a chilled water pump, a cooling fan of a cooling tower, a tail end fan and the like, and each control equipment corresponds to one control sensor and is used for collecting control parameters of the compressor, the water pump, the cooling air tower, the tail end fan and the like; for example, for the control equipment of the chilled water pump, control parameters such as return water temperature deviation (deviation value of actual return water temperature and set return water temperature), chilled water flow deviation and the like are collected and calculated by using a control sensor.
Fuzzifying the control parameters through a variable frequency controller to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters, and mapping the fuzzy subsets of the input quantity and the membership vector values into alternative control sets according to a rule cluster; the resulting alternative control set is for the entire air conditioning system.
The alternative control set comprises a plurality of control quantity fuzzy subsets, the control quantity fuzzy subsets correspond to the input quantity fuzzy subsets one by one, and the membership vector value is mapped into a weight parameter of each control quantity fuzzy subset; the expected control amount in the alternative control set corresponds to the expected control amount of all control devices in the entire air conditioner. Specifically, the expected control amount of the alternative control set may be calculated according to the weight parameter of each control amount fuzzy subset in the alternative control set, the expected control amount in the alternative control set being the expected control amount for all control devices in the entire air conditioning system, not just for the control device of the variable frequency controller (such as the chilled water pump in the above example).
And feeding back the expected control quantity in the alternative control set to each variable frequency controller, and controlling the control equipment in the corresponding loop by the variable frequency controller according to the fed-back expected control quantity.
In the air conditioner control method for intelligent resource allocation, before the control parameters are utilized to formulate the equipment control strategy optimization, fuzzy control is carried out on the control parameters, the control parameters are fuzzified, fuzzy data are mapped into alternative control sets according to fuzzy rule clusters, the alternative control sets comprise a plurality of control quantity fuzzy subsets, weight parameters of each control quantity fuzzy subset are mapped, accordingly, the calculated expected control quantity of the alternative control sets corresponds to the calculated expected control quantity, and finally corresponding equipment is controlled according to the fed-back expected control quantity. In this way, different control loops can be mutually blended, and the optimal control strategy can be calculated according to the control parameters of the different control loops. The method has the advantages of sustainable optimization control equipment control, real-time feedback control parameter, predictable control parameter, energy consumption reduction and the like.
Referring to fig. 2 and 3, the above-mentioned air conditioner control method for intelligent resource allocation includes a control coordination pool, and a specific control coordination method is as follows:
each variable frequency controller sends the alternative control set generated in the preset time period to the control coordination pool, and the control coordination pool executes conflict judgment and applicability judgment on all the alternative control sets in the preset time period. Specifically, each variable frequency controller sends the generated alternative control set to the control coordination pool, and does not immediately execute control by utilizing the control instruction conforming to the alternative control set; the control coordination pool collects alternative control sets uploaded by each variable frequency controller according to a time interval with a certain time length in the future, and the preset time period can be set according to a specific scene.
And analyzing the expected control quantity of the whole air conditioner according to the execution conflict judging result and the applicability result among the alternative control sets. Specifically, the expected control amount of the alternative control set may be calculated according to the weight parameter of each control amount fuzzy subset in the alternative control set, the execution conflict resolution result and the applicability result; conflict resolution is to upload alternative control sets to all the variable frequency controllers, wherein each alternative control set contains expected control quantity aiming at the same control equipment and judges whether the expected control quantity aiming at the same control equipment has conflict or not. For example, if the expected control quantity for the control device in all the fuzzy subsets of control quantities in the alternative control set a does not overlap with the expected control quantity for the control device in all the fuzzy subsets of control quantities in the alternative control set B, the collision is represented, and the current collision of the expected control for the control device is judged, and the control parameters of the control device are not modified. If the control quantity fuzzy subset is overlapped, the control quantity fuzzy subset with the overlapped expected control quantity is selected from the alternative control set A and the alternative control set B, and then the expected control quantity of the control quantity fuzzy subset in the alternative control set A and the expected control quantity of the control quantity fuzzy subset in the alternative control set B are calculated according to the weight parameters corresponding to the overlapping range of the control quantity fuzzy subset A and the control quantity fuzzy subset B and the weight parameters corresponding to the control quantity fuzzy subset B, and the control parameters of the control equipment are modified according to the obtained expected control quantity.
The control coordination pool feeds back the obtained expected control quantity to each variable frequency controller, and the variable frequency controllers control the control equipment in the corresponding loop according to the fed-back expected control quantity.
The applicability of the air conditioner control method for intelligent resource allocation refers to the applicability of parameter types and instruction types, for example, the sensor collects actual backwater temperature aiming at a chilled water pump, and the control parameter is the rotating speed of the water pump so as to enable the chilled water to be pressurized or depressurized; aiming at the cooling fan, the sensor collects the air flow quantity, and the control parameter is the rotating speed of the fan, so that the air flow quantity is increased or decreased; for the compressor, the sensor collects the power of the compressor, and the control parameter is the power of the compressor to enable air to be pressurized or depressurized. It can be seen that although each alternative control set contains control parameters of all devices in the air conditioning system, the types and instructions are different, and the suitability of the control parameters needs to be determined. In particular implementations, the alternative control sets are coordinated in conjunction with performing conflict resolution and fitness. In this way, a sustainable optimal control of the control device can be further ensured.
According to the air conditioner control method for intelligent resource allocation, before the expected control quantity in the alternative control set is fed back to each variable frequency controller, the ambiguity is required to be solved, and a direct method can be adopted, and the specific method is as follows:
the membership function of the alternative control set has only one peak value, and the maximum value of the membership function is used as a clear value, namely:
μ c (u c )＝max(μ c (u))
wherein C is a set of alternative control sets, and u is a member in the output membership vector value range.
Besides the direct method, a regional center method can be adopted, and the specific method is as follows:
the set of alternative controls for the output is represented by a continuous function, i.e
for a set of fuzzy alternative control sets expressed in discrete terms, the output is obtained by weighted averaging, i.e
In specific implementation, the variable frequency controller further comprises a database for storing all the fuzzy subsets of the input quantity, the fuzzy subsets of the output quantity and the membership vector values, and provides data like a fuzzy reasoning module. The database and the fuzzy rule clusters commonly support fuzzy processing of control parameters. The rule clusters are a set of fuzzy condition semantics obtained by summarizing the control experience and process knowledge of an operator or expert in practice. Fuzzy rule clusters are typically described in terms of a cluster of rules or a matrix table, such as: if A then B and if A and B then C. The membership function can be a basic function such as a Z function, a II function, an S function and the like, and can also be a linear function such as a trapezoidal function, a trigonometric function, a single linear function and the like.
Embodiment two:
referring to fig. 4 and 5, an air conditioner control system for intelligent resource allocation comprises a plurality of variable frequency controllers, a plurality of control sensors and a plurality of control devices, wherein the variable frequency controllers, the control sensors and the control devices are in one-to-one correspondence with each other and form a control loop; the variable frequency controller comprises a fuzzification module, a fuzzy reasoning module, a defuzzification module and a rule cluster;
the control sensor is used for acquiring control parameters of the corresponding loop for the control equipment in real time; specifically, the control equipment comprises a compressor of a water chiller, a chilled water pump, a cooling fan of a cooling tower, a tail end fan and the like, and each control equipment corresponds to one control sensor and is used for collecting control parameters of the compressor, the water pump, the cooling air tower, the tail end fan and the like; for example, for the control equipment of the chilled water pump, control parameters such as return water temperature deviation (deviation value of actual return water temperature and set return water temperature), chilled water flow deviation and the like are collected and calculated by using a control sensor.
The blurring module is used for blurring the control parameters to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters.
The fuzzy reasoning module is used for mapping each input quantity fuzzy subset and membership vector value into an alternative control set according to a rule cluster, the alternative control set comprises a plurality of control quantity fuzzy subsets, the control quantity fuzzy subsets are in one-to-one correspondence with the input quantity fuzzy subsets, and the fuzzy reasoning module is also used for mapping the membership vector value into a weight parameter of each control quantity fuzzy subset.
The expected control quantity in the alternative control set corresponds to the expected control quantity of all control devices in the whole air conditioner, and the variable frequency controller controls the control devices in the corresponding loop according to the feedback expected control quantity.
The defuzzification module is used for converting the fuzzy expected control quantity into the clear expected control quantity, and the defuzzification module, the fuzzy reasoning module and the defuzzification module are sequentially connected and respectively connected with the rule clusters.
In the air conditioner control system for intelligent resource allocation, before the control strategy optimization of equipment is formulated by using the control parameters, fuzzy control is carried out on the control parameters, the control parameters are fuzzified, fuzzy data are mapped into alternative control sets according to fuzzy rule clusters, the alternative control sets comprise a plurality of control quantity fuzzy subsets, weight parameters of each control quantity fuzzy subset are mapped, accordingly, the calculated expected control quantity of the alternative control sets corresponds to the calculated expected control quantity, and finally corresponding equipment is controlled according to the fed-back expected control quantity. In this way, different control loops can be mutually blended, and the optimal control strategy can be calculated according to the control parameters of the different control loops. The method has the advantages of sustainable optimization control equipment control, real-time feedback control parameter, predictable control parameter, energy consumption reduction and the like.
The air conditioner control system for intelligent resource allocation further comprises a control coordination pool for executing conflict judgment and applicability judgment on all the alternative control sets in a preset time period, and the first embodiment can be specifically referred.
In specific implementation, the variable frequency controller further comprises a database for storing all the fuzzy subsets of the input quantity, the fuzzy subsets of the output quantity and the membership vector values, and provides data like a fuzzy reasoning module. The database and the fuzzy rule clusters commonly support fuzzy processing of control parameters. The rule clusters are a set of fuzzy condition semantics obtained by summarizing the control experience and process knowledge of an operator or expert in practice. Fuzzy rule clusters are typically described in terms of a cluster of rules or a matrix table, such as: if A then B and if A and B then C. The membership function can be a basic function such as a Z function, a II function, an S function and the like, and can also be a linear function such as a trapezoidal function, a trigonometric function, a single linear function and the like.
In specific implementation, referring to fig. 6, the variable frequency controller may adopt an adaptive fuzzy-PID controller, find out the fuzzy relation between three parameters of the PID and the input of the controller according to expert knowledge and experience of an operator, store the obtained fuzzy subset membership function vector value and fuzzy rule into a knowledge base, and enable the PID parameters to be dynamically adjusted on line through fuzzy reasoning according to real-time input by the controller. The system deviation e and the deviation change rate ec are used as inputs of a PID controller, the deviation e and the deviation change rate ec are detected in real time in the running process of the system, and PID parameters are dynamically set through fuzzy reasoning, so that the requirements of the deviation e and the deviation change rate ec at different moments on the PID parameters are met, and the whole system is in an optimal state. Thus, the control parameters can be adaptively fuzzy, so that the analysis and prediction accuracy of the expected control quantity is improved.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, and the present invention is intended to be covered in the scope of the present invention.
Claims (5)
1. The air conditioner control method for intelligent resource allocation is characterized by presetting a plurality of variable frequency controllers, a plurality of control sensors, a plurality of control devices and a control coordination pool, wherein the variable frequency controllers, the control sensors and the control devices are in one-to-one correspondence with each other and form a control loop, and the specific steps are as follows:
acquiring control parameters of a corresponding loop for control equipment in real time through a control sensor, and transmitting the control parameters to a corresponding variable frequency controller in real time;
fuzzifying the control parameters through a variable frequency controller to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters, and mapping the fuzzy subsets of the input quantity and the membership vector values into alternative control sets according to a rule cluster; each frequency conversion controller sends the alternative control set generated in the preset time period to the control coordination pool, and the control coordination pool executes conflict judgment and applicability judgment on all the alternative control sets in the preset time period; the conflict judgment is that the pointers judge whether the expected control quantity of the same control equipment has conflict or not for the alternative control sets uploaded by all the variable frequency controllers; the applicability judgment is to judge whether the parameter types and the instruction types of the alternative control set are applicable or not; analyzing the expected control quantity of the alternative control sets according to the execution conflict judging result and the applicability result among the alternative control sets; the control coordination pool feeds back the obtained expected control quantity to each variable frequency controller, and the variable frequency controllers control the control equipment in the corresponding loop according to the fed-back expected control quantity;
the method for controlling the coordination pool to execute conflict resolution judgment comprises the following steps: judging whether the expected control quantity of each alternative control set aiming at the same control equipment has conflict or not according to the alternative control sets uploaded by all the variable frequency controllers; if yes, the control coordination pool judges that the current expected control of the control equipment has conflict, and the control parameters of the control equipment are not modified; if not, the control coordination pool calculates the expected control quantity according to the alternative control set with conflict, and modifies the control parameters of the control equipment according to the obtained expected control quantity;
wherein the calculating the expected control amount according to the alternative control set with collision comprises: respectively selecting control quantity fuzzy subsets with the expected control quantity overlapping from the alternative control sets with the conflict through a control coordination pool; calculating the expected control quantity according to the coincidence range between the expected control quantity of the fuzzy subset of the alternative control centralized control quantity which generates the conflict and the weight parameters corresponding to the expected control quantity;
the alternative control set comprises a plurality of control quantity fuzzy subsets, the control quantity fuzzy subsets correspond to the input quantity fuzzy subsets one by one, and the membership vector value is mapped into a weight parameter of each control quantity fuzzy subset; the expected control amount in the alternative control set corresponds to the expected control amount of all control devices;
and feeding back the expected control quantity in the alternative control set to each variable frequency controller, and controlling the control equipment in the corresponding loop by the variable frequency controller according to the fed-back expected control quantity.
2. The intelligent resource allocation air conditioner control method according to claim 1, wherein the method for performing the defuzzification is as follows before feeding back the expected control amount in the alternative control set to each variable frequency controller:
the membership function of the alternative control set has only one peak value, and the maximum value of the membership function is used as a clear value, namely:
μ c (u c )＝max(μ c (u))
wherein C is a set of alternative control sets, and u is a member in the output membership vector value range.
3. The intelligent resource allocation air conditioner control method according to claim 1, wherein the method for performing the defuzzification is as follows before feeding back the expected control amount in the alternative control set to each variable frequency controller:
the set of alternative controls for the output is represented by a continuous function, i.e
for a set of fuzzy alternative control sets expressed in discrete terms, the output is obtained by weighted averaging, i.e
4. The air conditioner control system for intelligent resource allocation is characterized by comprising a plurality of variable frequency controllers, a plurality of control sensors, a plurality of control devices and a control coordination pool, wherein the variable frequency controllers, the control sensors and the control devices are in one-to-one correspondence with each other and form a control loop; the variable frequency controller comprises a fuzzification module, a fuzzy reasoning module and a rule cluster;
the control sensor is used for acquiring control parameters of the corresponding loop for the control equipment in real time; the blurring module is used for blurring the control parameters to obtain fuzzy subsets of the input quantity and membership vector values corresponding to the control parameters; the fuzzy reasoning module is used for mapping each input quantity fuzzy subset and membership vector value into an alternative control set according to the rule cluster; each frequency conversion controller sends the alternative control set generated in the preset time period to the control coordination pool, and the control coordination pool executes conflict judgment and applicability judgment on all the alternative control sets in the preset time period; the conflict judgment is that the pointers judge whether the expected control quantity of the same control equipment has conflict or not for the alternative control sets uploaded by all the variable frequency controllers; the applicability judgment is to judge whether the parameter types and the instruction types of the alternative control set are applicable or not; analyzing the expected control quantity of the alternative control sets according to the execution conflict judging result and the applicability result among the alternative control sets; the control coordination pool feeds back the obtained expected control quantity to each variable frequency controller, and the variable frequency controllers control the control equipment in the corresponding loop according to the fed-back expected control quantity; the method for controlling the coordination pool to execute conflict resolution judgment comprises the following steps: judging whether the expected control quantity of each alternative control set aiming at the same control equipment has conflict or not according to the alternative control sets uploaded by all the variable frequency controllers; if yes, the control coordination pool judges that the current expected control of the control equipment has conflict, and the control parameters of the control equipment are not modified; if not, the control coordination pool calculates the expected control quantity according to the alternative control set with conflict, and modifies the control parameters of the control equipment according to the obtained expected control quantity; wherein the calculating the expected control amount according to the alternative control set with collision comprises: respectively selecting control quantity fuzzy subsets with the expected control quantity overlapping from the alternative control sets with the conflict through a control coordination pool; calculating the expected control quantity according to the coincidence range between the expected control quantity of the fuzzy subset of the alternative control centralized control quantity which generates the conflict and the weight parameters corresponding to the expected control quantity;
the alternative control set comprises a plurality of control quantity fuzzy subsets, and the control quantity fuzzy subsets are in one-to-one correspondence with the input quantity fuzzy subsets;
the fuzzy inference module is also used for mapping the membership vector value into a weight parameter of each control quantity fuzzy subset; the expected control quantity in the alternative control set corresponds to the expected control quantity of all control devices, and the variable frequency controller controls the control devices in the corresponding loops according to the feedback expected control quantity.
5. The intelligent resource allocation air conditioner control system according to claim 4, further comprising a defuzzification module, wherein the defuzzification module is used for converting the fuzzy expected control quantity into a clear expected control quantity, and the defuzzification module, the fuzzy inference module and the defuzzification module are sequentially connected and respectively connected with the rule clusters.
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