CN114517963B - Air Conditioning Control Method and System for Intelligent Resource Allocation - Google Patents

Abstract

The present invention provides an air-conditioning control method and system for intelligent resource allocation. The method includes: the control sensor obtains the control parameters of the corresponding loop for the control equipment in real time, and the frequency conversion controller fuzzifies the control parameters to obtain the fuzzy input values corresponding to the control parameters. Subsets and membership degree vector values, and map each input fuzzy subset and membership degree vector value into an alternative control set according to the rule cluster; the alternative control set includes multiple fuzzy subsets of control quantities, fuzzy subsets of control quantities and The fuzzy subsets of input quantities correspond one-to-one, and the membership degree vector value is mapped to the weight parameter of each control quantity fuzzy subset; the expected control quantity in the candidate control set is fed back to each variable frequency controller, and the variable frequency controller is based on the feedback The expected control quantity controls the control equipment in the corresponding loop. It has the advantages of sustainable optimization of control equipment control, real-time feedback of control parameters, predictable control parameters and reduced energy consumption.

Description

Air Conditioning Control Method and System for Intelligent Resource Allocation

technical field

The invention relates to the technical field of air conditioning, in particular to an air conditioning control method and system for intelligent resource allocation.

Background technique

With the rapid development of my country's economy and the improvement of people's living standards, the application of central air-conditioning systems is becoming more and more common. The central air-conditioning system of a large building includes a water system and a terminal fan system. The water system includes a chiller (main unit), a chilled water circuit and a cooling water circuit, and the terminal fan system includes a fan and a coil heat exchanger. The early air conditioners were operated for a long time under the condition of power frequency operation, but the compliance of general users was low, which was easy to cause a lot of waste. According to statistics, the proportion of air conditioners in building energy consumption can reach 40%-60%. At present, in order to save energy, most central air conditioners adopt frequency conversion control technology, so that the central air conditioner maintains a dynamic workload according to actual needs, rather than a constant workload.

In the central air-conditioning system, the main control objects of the frequency conversion control include the compressor power of the chiller, the speed of the chilled water pump, the speed of the cooling water pump, the air volume of the cooling fan of the cooling tower, the air volume of the terminal fan, etc. In frequency conversion control, the frequency conversion controller collects control parameters such as temperature and humidity in the building, supply water temperature, return water temperature, flow rate, and pressure difference through sensors, and uses control strategies to generate frequency conversion commands to realize frequency conversion control of the control objects. Control strategies include optimal control, PID control, fuzzy control, neural network control and adaptive control.

The existing frequency conversion control method of central air conditioner has the following deficiencies:

1. In the existing air conditioner frequency conversion control system, the frequency conversion controller, the sensors supporting the frequency conversion controller, and the equipment controlled by the frequency conversion controller form a set of closed-loop loops. Large-scale central air-conditioning systems include multiple frequency conversion controllers. Corresponding to multiple closed closed loops, the loops are closed and isolated from each other. However, the entire central air-conditioning system is a system that integrates and interacts with each other. Chillers, chilled water loops, cooling water loops, and terminals interact with each other. Therefore, each closed loop is only for its own control equipment. The optimization of the strategy cannot realize the overall optimization of the central air-conditioning system, and it is impossible to truly realize the sustainable optimization of the control equipment, and it is easy to cause frequent switching of the central air-conditioning working state and high energy consumption.

2. In the case that the above loops are closed and isolated from each other, due to the large specific heat of the chilled water loop and the cooling water loop of the central air conditioning system and the complex distribution of the pipelines, the time lag is strong and the response to the frequency conversion control is slow. , the response has many influencing factors, is nonlinear, and has low predictability. Therefore, the influence of frequency conversion control on the overall optimization of the central air conditioner is not easy to determine, and feedback adjustment cannot be performed quickly.

Contents of the invention

In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide an air-conditioning control method and system for intelligent resource allocation, which fuzzifies the control parameters, and then maps the fuzzy data into an alternative control set, which is an alternative control set. Each control quantity fuzzy subset of the set is mapped to weight parameters, so that the calculated expected control quantity of the alternative control set is used to control the control equipment. The control loops can be integrated with each other, and the control strategy can be optimized according to the control parameters of different control loops. It has the advantages of sustainable optimization of control equipment control, real-time feedback of control parameters, predictable control parameters and reduced energy consumption.

An air-conditioning control method for intelligent resource allocation, in which multiple frequency conversion controllers, multiple control sensors, and multiple control devices are preset, and the frequency conversion controllers, control sensors, and control devices are in one-to-one correspondence with each other and form a control loop, specifically Proceed as follows:

Obtain the control parameters of the corresponding loop for the control equipment in real time through the control sensor, and transmit the control parameters to the corresponding frequency conversion controller in real time;

The control parameters are fuzzified through the frequency conversion controller to obtain the fuzzy subsets of the input quantities and the vector values of the membership degrees corresponding to the control parameters, and map the fuzzy subsets of the input quantities and the vector values of the membership degrees into alternative control sets according to the rule cluster;

The candidate control set includes a plurality of control quantity fuzzy subsets, and the control quantity fuzzy subsets correspond to the input quantity fuzzy subsets one by one, and the membership degree vector value is mapped to 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;

The expected control quantity in the alternative control set is fed back to each frequency conversion controller, and the frequency conversion controller controls the control equipment in the corresponding loop according to the feedback expected control quantity.

Further, it includes controlling the coordination pool, specifically including:

Each variable frequency controller sends the candidate control sets generated within the preset time period to the control coordination pool, and the control coordination pool performs conflict resolution judgment and resolution on all the candidate control sets within the preset time period. applicability judgment;

According to the execution conflict ruling results and applicability results among the alternative control sets, analyze the expected control amount of the entire air conditioner;

The control coordination pool feeds back the obtained expected control quantity to each frequency conversion controller, and the frequency conversion controller controls 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 includes:

According to the alternative control sets uploaded by all frequency conversion controllers, it is judged whether there is a conflict in the expected control quantity of each alternative control set for the same control device;

If yes, the control coordination pool judges that there is conflict in the current expected control of the control device, and does not modify the control parameters of the control device; if not, the control coordination pool calculates the expected control amount according to the conflicting alternative control set, and obtains The expected control amount modifies the control parameters of the control device.

Further, the calculating the expected control amount according to the conflicting alternative control set includes:

Select fuzzy subsets of control quantities with overlapping expected control quantities from the conflicting alternative control sets through the control coordination pool; according to the overlap range, And their corresponding weight parameters to calculate the expected control amount.

Furthermore, before the expected control quantity in the alternative control set is fed back to each variable frequency controller, it needs to be defuzzified, and the specific method is as follows:

The membership function of the candidate control set has only one peak value, and the maximum value of the membership function is taken as a clear value, namely:

μ _c (u _c )=max(μ _c (u))

Among them, C is the set of alternative control sets, and u is the element in the value range of the output membership degree vector.

Furthermore, before the expected control quantity in the alternative control set is fed back to each variable frequency controller, it needs to be defuzzified, and the specific method is as follows:

The alternative control set for the output is represented by a continuous function, namely

其中，T为连续数，u为输出隶属度矢量值范围中的元；

Wherein, T is a continuous number, and u is an element in the output membership vector value range;

For the set of fuzzy alternative control sets expressed discretely, the output is obtained by the weighted average method, namely

其中，i＝1，2，…，n，表示备选控制集的集合覆盖的元素个数。

Wherein, i=1, 2, . . . , n, represents the number of elements covered by the candidate control set.

An air-conditioning control system for intelligent resource allocation, including multiple frequency conversion controllers, multiple control sensors and multiple control devices, the frequency conversion controllers, control sensors and control devices are in one-to-one correspondence with each other and form a control loop; the Frequency conversion controller includes fuzzy module, fuzzy reasoning module and rule cluster;

The control sensor is used to obtain the control parameters of the corresponding loop for the control equipment in real time; the fuzzy module is used to fuzzify the control parameters to obtain fuzzy subsets of input quantities and membership vector values corresponding to the control parameters; the fuzzy The inference module is used to map each input variable fuzzy subset and membership vector value into an alternative control set according to the rule cluster, and the alternative control set includes a plurality of control variable fuzzy subsets, control variable fuzzy subsets and input variable fuzzy subsets One-to-one correspondence between subsets; the fuzzy reasoning module is also used to map the membership degree vector value to the weight parameter of each control quantity fuzzy subset; the expected control quantity in the candidate control set corresponds to all control devices in the entire air conditioner The expected control amount, the frequency conversion controller controls the control equipment in the corresponding loop according to the expected control amount fed back.

Further, the system also includes a control coordination pool, and the control coordination pool specifically includes:

Each variable frequency controller sends the candidate control sets generated within the preset time period to the control coordination pool, and the control coordination pool performs conflict resolution judgment and resolution on all the candidate control sets within the preset time period. applicability judgment;

According to the execution conflict ruling results and applicability results among the alternative control sets, analyze the expected control amount of the entire air conditioner;

The control coordination pool feeds back the obtained expected control quantity to each frequency conversion controller, and the frequency conversion controller controls the control equipment in the corresponding loop according to the fed back expected control quantity.

Further, the control and coordination pool performing conflict resolution judgment includes:

According to the alternative control sets uploaded by all frequency conversion controllers, it is judged whether there is a conflict in the expected control quantity of each alternative control set for the same control device;

If yes, the control coordination pool judges that there is conflict in the current expected control of the control device, and does not modify the control parameters of the control device; if not, the control coordination pool calculates the expected control amount according to the conflicting alternative control set, and obtains The expected control amount modifies the control parameters of the control device.

Further, it also includes a defuzzification module, the defuzzification module is used to convert the fuzzy expected control quantity into a clear expected expected control quantity, the fuzzification module, the fuzzy reasoning module and the defuzzification module are connected in sequence, the fuzzy The modules and defuzzification modules are respectively connected with the rule clusters.

Compared with the prior art, the present invention has the following advantages:

The present invention provides an air conditioner control method and system for intelligent resource allocation. Before using the control parameters to formulate equipment control strategy optimization, fuzzy control is performed on the control parameters. The control set, the alternative control set includes multiple control quantity fuzzy subsets, and the weight parameters of each control quantity fuzzy subset are mapped, so as to calculate the corresponding expected control quantity of the alternative control set, and finally according to the feedback expected control quantity Control the corresponding device. In this way, different control loops can be integrated with each other, and the optimal control strategy can be calculated according to the control parameters of different control loops. It has the advantages of sustainable optimization of control equipment control, real-time feedback of control parameters, predictable control parameters and reduced energy consumption.

Description of drawings

FIG. 1 is a control flowchart of an air-conditioning control method for intelligent resource allocation in Embodiment 1 of the present invention;

Fig. 2 is a control coordination flow chart of the control coordination pool in Embodiment 1 of the present invention;

FIG. 3 is a control flow diagram for controlling the coordination pool to execute conflict resolution in Embodiment 1 of the present invention;

4 is a system block diagram of an air-conditioning control system for intelligent resource allocation in Embodiment 2 of the present invention;

Fig. 5 is the fuzzy principle block diagram of frequency conversion controller in the second embodiment of the present invention;

FIG. 6 is a system block diagram of an adaptive fuzzy-PID controller used in the frequency conversion controller in Embodiment 2 of the present invention.

Detailed ways

Embodiments of the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, so they are only examples, and should not be used to limit the protection scope of the present invention.

Embodiment one:

Referring to Fig. 1 , an air-conditioning control method for intelligent resource allocation, the preset system for applying this method includes a plurality of frequency conversion controllers, a plurality of control sensors and a plurality of control devices, and the frequency conversion controllers, control sensors and control devices interact with each other One-to-one correspondence between them and form a control loop, the specific steps are as follows:

The control parameters of the corresponding loop for the control equipment are obtained in real time through the control sensor, and the control parameters are transmitted to the corresponding frequency conversion controller in real time. Specifically, the control equipment includes the compressor of the chiller, the chilled water pump, the cooling water pump, the cooling fan and the terminal fan of the cooling tower, etc., and each control equipment corresponds to a control sensor, which is used to collect data from the compressor, water pump, cooling tower, etc. , terminal fan and other control parameters; for example, for the control equipment of the chilled water pump, the control sensor is used to collect and calculate the return water temperature deviation (the deviation between the actual return water temperature and the set return water temperature), the chilled water flow deviation and other control parameters .

The control parameters are fuzzified through the frequency conversion controller to obtain the fuzzy subsets of the input quantities and the vector values of the membership degrees corresponding to the control parameters, and map the fuzzy subsets of the input quantities and the vector values of the membership degrees into alternative control sets according to the rule cluster; The resulting candidate control set targets the entire air conditioning system.

The candidate control set includes a plurality of control quantity fuzzy subsets, and the control quantity fuzzy subsets correspond to the input quantity fuzzy subsets one by one, and the membership degree vector value is mapped to a weight parameter of each control quantity fuzzy subset; The expected control quantities in the candidate control set correspond to the expected control quantities of all control devices in the entire air conditioner. Specifically, the expected control quantity of the candidate control set can be calculated according to the weight parameters of each control quantity fuzzy subset in the candidate control set, and the expected control quantity of the candidate control set is the expected control of all control devices in the entire air conditioning system The amount, not only the expected control amount of the control equipment of the frequency conversion controller (such as the chilled water pump in the above example).

The expected control quantity in the alternative control set is fed back to each frequency conversion controller, and the frequency conversion controller controls the control equipment in the corresponding loop according to the feedback expected control quantity.

In the above air-conditioning control method for intelligent resource allocation, before using the control parameters to formulate equipment control strategy optimization, fuzzy control is performed on the control parameters. First, the control parameters are fuzzy, and then the fuzzy data is mapped to an alternative control set according to the fuzzy rule cluster , the alternative control set includes multiple fuzzy subsets of control quantities, mapping the weight parameters of each fuzzy subset of control quantities, so as to calculate the corresponding expected control quantities of the alternative control sets, and finally control the corresponding control quantities according to the feedback expected control quantities device of. In this way, different control loops can be integrated with each other, and the optimal control strategy can be calculated according to the control parameters of different control loops. It has the advantages of sustainable optimization of control equipment control, real-time feedback of control parameters, predictable control parameters and reduced energy consumption.

Referring to Figure 2 and Figure 3, the above-mentioned air-conditioning control method for intelligent resource allocation includes the control coordination pool, and the specific control coordination method is as follows:

Each variable frequency controller sends the candidate control sets generated within the preset time period to the control coordination pool, and the control coordination pool performs conflict resolution judgment and resolution on all the candidate control sets within the preset time period. Judgment of applicability. Specifically, each variable frequency controller sends the candidate control set generated by itself to the control coordination pool, instead of immediately using the control instruction that conforms to the candidate control set to perform control; the control coordination pool is aimed at a time interval of a certain length of time in the future, Collect the candidate control sets uploaded by each frequency conversion controller, and the preset time period can be set according to specific scenarios.

According to the execution conflict ruling results and applicability results among the alternative control sets, the expected control amount of the whole air conditioner is analyzed. Specifically, the expected control quantity of the candidate control set can be calculated according to the weight parameters of each fuzzy subset of the control quantity in the candidate control set, the results of execution conflict arbitration and the results of applicability; the conflict arbitration refers to the The uploaded candidate control sets, each candidate control set contains the expected control quantity for the same control device, and it is judged whether there is a conflict in the expected control quantity for the same control device. For example, the expected control quantity for the control equipment in all the fuzzy subsets of control quantities in the alternative control set A and the expected control quantity for the control equipment in all the fuzzy subsets of control quantities in the alternative control set B are both If no coincidence occurs, it means that there is a conflict, and it is judged that there is a conflict in the current expected control for the control device, and the control parameters of the control device are not modified. If coincidence occurs, it means that there is no conflict, and the fuzzy subsets of control quantities with overlapping expected control quantities are selected from the conflicting candidate control set A and candidate control set B respectively, and then, for the candidate control set The expected control quantity of the fuzzy subset of the control quantity in A, and the expected control quantity of the fuzzy subset of the control quantity in B, according to the overlapping range of the two, and the corresponding weight parameters of the two, the expected control quantity is calculated, and according to the obtained The desired control quantity modifies the control parameters of the control device.

The control coordination pool feeds back the obtained expected control quantity to each frequency conversion controller, and the frequency conversion controller controls the control equipment in the corresponding loop according to the fed back expected control quantity.

The applicability of the above-mentioned air-conditioning control method for intelligent resource allocation refers to the applicability of parameter types and command types. For example, for chilled water pumps, the sensor collects the actual return water temperature, and the control parameter is the pump speed to increase the pressure of the chilled water. or decompression; for the cooling fan, the sensor collects the air flow, and the control parameter is the fan speed to increase or decrease the flow air; for the compressor, the sensor collects the power of the compressor, and the control parameter is the compressor The power to pressurize or depressurize the air. It can be seen that although each candidate control set contains the control parameters of all the equipment in the air conditioning system, the types and instructions are different, and the applicability of the control parameters needs to be judged. In specific implementation, the alternative control sets are coordinated in combination with the execution conflict resolution and the degree of applicability. In this way, a sustainable optimal control of the control device can be further ensured.

In the above-mentioned air-conditioning control method of intelligent resource deployment, defuzzification is required before the expected control quantity in the alternative control set is fed back to each variable frequency controller. The direct method can be used, and the specific method is as follows:

The membership function of the candidate control set has only one peak value, and the maximum value of the membership function is taken as a clear value, namely:

μ _c (u _c )=max(μ _c (u))

Among them, C is the set of alternative control sets, and u is the element in the value range of the output membership degree vector.

In addition to the direct method, the regional center method can also be used, the specific method is as follows:

The alternative control set for the output is represented by a continuous function, namely

其中，T为连续数，u为输出隶属度矢量值范围中的元；

Wherein, T is a continuous number, and u is an element in the output membership vector value range;

For the set of fuzzy alternative control sets expressed discretely, the output is obtained by the weighted average method, namely

其中，i＝1，2，…，n，表示备选控制集的集合覆盖的元素个数。

Wherein, i=1, 2, . . . , n, represents the number of elements covered by the candidate control set.

During specific implementation, the frequency conversion controller also includes a database for storing all input fuzzy subsets, output fuzzy subsets and membership vector values, and provides data to the fuzzy reasoning module. The database and fuzzy rule clusters commonly support the fuzzy processing of control parameters. A rule cluster is a collection of fuzzy conditional semantics obtained by summarizing the control experience and process knowledge of the operator or expert in practice. Fuzzy rule clusters are generally described in the form of a cluster of rules or matrix tables, for example: if A then B and if A and B then C. The membership function can be basic functions such as Z function, II function, and S function, and can also be linear functions such as trapezoidal functions, trigonometric functions, and single-line functions.

Embodiment two:

Referring to Fig. 4 and Fig. 5, an air-conditioning control system for intelligent resource allocation includes a plurality of frequency conversion controllers, a plurality of control sensors and a plurality of control devices, and the frequency conversion controllers, control sensors and control devices are in one-to-one correspondence with each other And constitute a control loop; the frequency conversion controller includes a fuzzy module, a fuzzy inference module, a defuzzification module and a rule cluster;

The control sensor is used to obtain the control parameters of the corresponding loop for the control equipment in real time; specifically, the control equipment includes the compressor of the chiller, the chilled water pump, the cooling water pump, the cooling fan of the cooling tower and the terminal fan, etc., each of which controls Each device corresponds to a control sensor, which is used to collect control parameters such as compressors, water pumps, cooling towers, and terminal fans; Set the return water temperature deviation value), chilled water flow deviation and other control parameters.

The fuzzy module is used to fuzzify the control parameters to obtain fuzzy subsets of each input quantity corresponding to the control parameters and membership degree vector values.

The fuzzy inference module is used to map each fuzzy subset of input quantities and the vector value of membership into an alternative control set according to the rule cluster, and the alternative control set includes a plurality of fuzzy subsets of control quantities, fuzzy subsets of control quantities and There is a one-to-one correspondence between the fuzzy subsets of the input quantities, and the fuzzy reasoning module is also used to map the membership degree vector value to a weight parameter of each fuzzy subset of the control quantities.

The expected control quantities in the alternative control set correspond to the expected control quantities of all control devices in the entire air conditioner, and the frequency conversion controller controls the control devices in the corresponding loops according to the feedback expected control quantities.

The defuzzification module is used to convert the fuzzy expected control quantity into a clear expected expected control quantity, the fuzzification module, the fuzzy reasoning module and the defuzzification module are connected in sequence, and the fuzzy module, the fuzzy reasoning module and the defuzzification module are respectively connected with the rule cluster .

In the above air-conditioning control system for intelligent resource allocation, before using the control parameters to formulate equipment control strategy optimization, fuzzy control is performed on the control parameters. First, the control parameters are fuzzy, and then the fuzzy data is mapped to an alternative control set according to the fuzzy rule cluster , the alternative control set includes multiple fuzzy subsets of control quantities, mapping the weight parameters of each fuzzy subset of control quantities, so as to calculate the corresponding expected control quantities of the alternative control sets, and finally control the corresponding control quantities according to the feedback expected control quantities device of. In this way, different control loops can be integrated with each other, and the optimal control strategy can be calculated according to the control parameters of different control loops. It has the advantages of sustainable optimization of control equipment control, real-time feedback of control parameters, predictable control parameters and reduced energy consumption.

The above-mentioned air-conditioning control system for intelligent resource allocation also includes a control coordination pool, which is used to perform conflict resolution judgment and applicability judgment for all candidate control sets within a preset time period, and refer to Embodiment 1 for details.

During specific implementation, the frequency conversion controller also includes a database for storing all input fuzzy subsets, output fuzzy subsets and membership vector values, and provides data to the fuzzy reasoning module. The database and fuzzy rule clusters commonly support the fuzzy processing of control parameters. A rule cluster is a collection of fuzzy conditional semantics obtained by summarizing the control experience and process knowledge of the operator or expert in practice. Fuzzy rule clusters are generally described in the form of a cluster of rules or matrix tables, for example: if A then B and if A and B then C. The membership function can be basic functions such as Z function, II function, and S function, and can also be linear functions such as trapezoidal functions, trigonometric functions, and single-line functions.

For specific implementation, referring to Fig. 6, the variable frequency controller can adopt an adaptive fuzzy-PID controller. According to expert knowledge and operator experience, find out the fuzzy relationship between the three parameters of PID and the input of the controller, and then get The fuzzy subset membership function vector value and fuzzy rules are stored in the knowledge base, and the controller can dynamically adjust the PID parameters online through fuzzy reasoning according to real-time input. Among them, the deviation e and the deviation change rate ec of the system are used as the input of the PID controller, and the deviation e and the deviation change rate ec are detected in real time during the operation of the system, and the PID parameters are dynamically adjusted through fuzzy reasoning to realize the deviation e at different times And the deviation change rate ec on the PID parameter requirements, so that the whole system is in the best state. In this way, control parameters can be adaptively fuzzy processed, thereby improving the accuracy of analysis and prediction of expected control quantities.

Finally, it is noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or Equivalent replacements without departing from the spirit and scope of the technical solution of the present invention shall fall within the protection 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

Wherein T is a continuous number, and u is a member in the output membership vector value range;

for a set of fuzzy alternative control sets expressed in discrete terms, the output is obtained by weighted averaging, i.e

Where i=1, 2, …, n represents the number of elements covered by the set of alternative control sets.

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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