Modular energy-saving control method and system for central air conditioning

Abstract

The present invention discloses a method and system for modular energy-saving control of central air-conditioning, and relates to the relevant field of intelligent control. The method comprises: establishing a data set of a target building, collecting the temperature in the building through a wireless temperature sensor network to construct a spatial temperature field, performing heat source labeling of the spatial temperature field, generating a spatial cooling demand with the target control temperature and the spatial temperature field, inputting the data set and the spatial cooling demand into a module coordination layer, performing collaborative call control optimization of N sub-modules, constructing a first and a second continuous influence based on heat source labeling and ambient temperature prediction, compensating the control optimization result with the first and the second continuous influence, obtaining the compensated control result, and performing collaborative cooling control of the N sub-modules of the central air-conditioning in the target building, thereby solving the technical problem of lack of energy-saving control over the central air-conditioning, resulting in high energy consumption, and achieving the technical effect of reasonable and accurate modular energy-saving control over the central air-conditioning.

Description

Technical Field

[0001] The present application relates to the field of intelligent control, and in particular to a method and system for modular energy-saving control of central air conditioners. Background Art

[0002] With the development of economy and society, the demand for energy is also increasing. Energy conservation and consumption reduction have become the focus of attention of the whole society, and the market demand for central air-conditioning shows a strong growth trend. The application of energy-saving technology in central air-conditioning systems is of great significance to improving the automation level of central air-conditioning, reducing energy consumption, reducing the impact on the power grid, and extending the service life of machinery and pipelines. However, there is a lack of energy-saving control of central air-conditioning nowadays, which leads to technical problems of high energy consumption. Summary of the invention

[0003] The present application solves the technical problem of high energy consumption caused by lack of energy-saving control over central air conditioners by providing a method and system for modular energy-saving control over central air conditioners, and achieves the technical effect of reasonable and accurate modular energy-saving control over central air conditioners.

[0004] The present application provides a method for modular energy-saving control of central air conditioning, the method is applied to a system for modular energy-saving control of central air conditioning, the system includes a module coordination layer, the module coordination layer is communicatively connected with N sub-modules, and includes: establishing a data set of a target building, the data set includes building structure data, module distribution data of N sub-modules, establishing module associations of N sub-modules with the data set, initializing the module coordination layer with the module associations, N is an integer greater than 1; collecting the temperature in the building of the target building through a wireless temperature sensor network, constructing a spatial temperature field, and performing heat source labeling of the spatial temperature field; obtaining a target control temperature of the target building, and generating a spatial cooling demand with the target control temperature and the spatial temperature field, and inputting the data set and the spatial cooling demand into the module coordination layer, executing collaborative call control optimization of N sub-modules; constructing a first continuous influence based on the heat source labeling, constructing a second continuous influence based on the ambient temperature prediction, compensating the control optimization result with the first continuous influence and the second continuous influence, obtaining a compensation control result, and performing cooling collaborative control of N sub-modules of the central air conditioning in the target building with the compensation control result.

[0005] In a possible implementation, the following processing is performed: a reference spatial temperature is established, and the temperature of the spatial temperature field is evaluated by the reference spatial temperature to generate a temperature difference mark; time-series monitoring of the temperature difference of the spatial position is performed, and the heat source distribution is predicted based on the time-series monitoring result of the temperature difference to generate a heat source location probability; the heat source temperature rise is evaluated based on the time-series monitoring result of the temperature difference to generate a heat source temperature rise value; and heat source labeling is completed based on the heat source location probability and the heat source temperature rise value.

[0006] In a possible implementation, the collaborative call control optimization of N sub-modules is performed, and the following processing is performed: A1: cooling space is divided through module distribution data to generate space division results; A2: load evaluation of corresponding sub-modules is performed based on the space division results and space cooling requirements, and N load evaluation results are input into the collaborative processing model to generate associated excitation signals for the sub-modules; A3: collaborative control optimization is completed based on the associated excitation signals.

[0007] In a possible implementation, the following processing is performed: A4: Establishing a hierarchical trigger level threshold for temperature, wherein each level in the hierarchical trigger level threshold corresponds to a basic frequency conversion strategy, wherein the frequency of the basic frequency conversion strategy of a low hierarchical trigger level is lower than that of the basic frequency conversion strategy of a high hierarchical trigger level; A5: Based on the associated excitation signal, perform full-load power cooling of the linkage space of the sub-modules to generate a fitting result; A6: When the fitting result meets the hierarchical trigger level threshold, record the full-load time, call the corresponding basic frequency conversion strategy, and perform re-fitting; A7: Repeat the fitting process in step A6 to trigger the hierarchical trigger level threshold, and record the corresponding strategy time; complete the collaborative control optimization according to the recorded results and the associated excitation signal.

[0008] In a possible implementation, the following processing is performed: fitting the continuous influence of the heat source based on the heat source annotation, generating a continuous influence value with a location identifier, and using the continuous influence value as the first continuous influence; obtaining weather forecast data, and using the weather forecast data as the first environmental prediction data; inputting the weather forecast data and the data set into a heat transfer fitting model to generate a heat transfer fitting result, and using the heat transfer fitting result as the second environmental prediction data; constructing a second continuous influence based on the first environmental prediction data and the second environmental prediction data, and completing the control optimization result compensation with the first continuous influence and the second continuous influence.

[0009] In a possible implementation, the following processing is performed: fitting compensation for full-load power cooling is performed based on the first continuous influence and the second continuous influence, and a fitting compensation result is generated, which is used as the first-stage compensation result; timing compensation parameters are constructed according to the first continuous influence and the second continuous influence, and the timing compensation parameters are used as the second-stage compensation result; and control optimization result compensation is completed through the first-stage compensation result and the second-stage compensation result.

[0010] In a possible implementation, the following processing is performed: configuring an approach threshold of temperature; when executing cooling collaborative control, if the predetermined space of the target building meets the approach threshold, triggering continuous monitoring feedback; and continuously correcting the cooling collaborative control through the continuous monitoring feedback.

[0011] The present application also provides a system for modular energy-saving control of central air conditioning, comprising:

[0012] An establishment module, the establishment module is used to establish a data set of a target building, the data set includes building structure data, module distribution data of N submodules, establish module associations of the N submodules with the data set, and initialize the module coordination layer with the module associations, where N is an integer greater than 1;

[0013] A labeling module, the labeling module is used to collect the temperature inside the target building through a wireless temperature sensor network, construct a spatial temperature field, and perform heat source labeling of the spatial temperature field;

[0014] An optimization module, the optimization module is used to obtain the target control temperature of the target building, and generate a space cooling demand based on the target control temperature and the space temperature field, and input the data set and the space cooling demand into the module coordination layer to perform collaborative call control optimization of N submodules;

[0015] A control module, wherein the control module is used to construct a first continuous influence based on the heat source labeling, construct a second continuous influence based on the ambient temperature prediction, use the first continuous influence and the second continuous influence to compensate the control optimization result, obtain a compensation control result, and use the compensation control result to perform coordinated cooling control of N sub-modules of the central air-conditioning in the target building.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] The method and system for modular energy-saving control of central air conditioners provided in the present application relate to the field of intelligent control technology, solve the technical problem in the prior art of lack of energy-saving control over central air conditioners, resulting in high energy consumption, and achieve the technical effect of reasonable and accurate modular energy-saving control of central air conditioners. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0019] Figure 1 A flow chart of a modular energy-saving control method for a central air conditioner provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a system for modular energy-saving control of central air conditioning provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0023] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0024] The embodiment of the present application provides a method for modular energy-saving control of a central air conditioner, the method is applied to a system for modular energy-saving control of a central air conditioner, the system includes a module coordination layer, the module coordination layer is communicatively connected with N submodules, such as Figure 1 As shown, the method includes:

[0025] Step A100, establishing a data set of a target building, the data set including building structure data and module distribution data of N submodules, establishing module associations of the N submodules with the data set, and initializing the module coordination layer with the module associations, where N is an integer greater than 1;

[0026] Based on the building structure data of the target building, namely the building plan, building stereogram and building material specifications as the construction basis of the data set, the functions of N sub-modules are further clarified in turn, wherein N is an integer greater than 1, and the module distribution data of the N sub-modules are determined after the specific position of each module in the target building is located, and then the dependency and interaction relationship between the sub-modules are analyzed, and the module association of the N sub-modules is identified and constructed using graph theory or other data structures. The module coordination layer is further initialized with the module association, which is to define a unified interface specification for each sub-module in the N sub-modules to ensure communication and data exchange between modules, and set an initial state for each sub-module according to the module distribution data, which serves as an important reference for the later realization of modular energy-saving control of central air-conditioning.

[0027] Execute step A200, collect the temperature inside the building of the target building through the wireless temperature sensor network, build a spatial temperature field, and perform heat source marking of the spatial temperature field; determine the number and layout positions of the wireless temperature sensors according to the building structure, space size and temperature distribution requirements of the target building, build a data acquisition system of the wireless sensor network through sensor nodes, gateways and servers, which is responsible for collecting temperature data and transmitting the data to the system through wireless communication, and further use spatial interpolation, gridding and other methods to convert discrete temperature data into a continuous spatial temperature field. In a possible implementation, step A200 further includes step A210, establishing a reference spatial temperature, and using the reference spatial temperature The temperature evaluation of the spatial temperature field is performed to generate a temperature difference mark. First, one or more reference space temperature values ​​are set based on the use purpose, functional areas and personnel comfort requirements of the building to establish a reference space temperature. Further, the temperature evaluation of the spatial temperature field is performed through the reference space temperature, which means comparing the temperature data of each measuring point in the spatial temperature field with the reference space temperature. For example, the area with a temperature deviation within ±1°C is regarded as a "comfort zone", the area with a deviation between ±1°C and ±2°C is regarded as an "acceptable zone", and the area with a deviation exceeding ±2°C is regarded as an "area requiring attention". Then, in the visualization interface of the spatial temperature field, the temperature difference mark of each measuring point is superimposed on the temperature distribution map to output the temperature difference mark.

[0028] Execute step A220, perform time-series monitoring of temperature differences at spatial locations, and predict heat source distribution based on the results of time-series monitoring of temperature differences to generate heat source location probabilities. Time-series monitoring of temperature differences at spatial locations refers to time-series analysis of temperature data at each spatial location, extracting trends and periodicity of temperature changes, and using the results of time-series monitoring of temperature differences as basic reference data to extract key features. Then, a neural network can be used to predict heat source distribution. Historical time-series data of temperature differences can be used to train a neural network model, and a validation data set can be used to evaluate the performance of the neural network model to predict future temperature difference time series. Further, based on the prediction results, the probability of each spatial location becoming a heat source is calculated and output as a heat source location probability. Step A230 is executed to evaluate the heat source temperature rise based on the temperature difference time-series monitoring results to generate a heat source temperature rise value. Step A240 is executed to complete heat source labeling based on the heat source location probability and the heat source temperature rise value. Firstly, the temperature difference time series data of the spatial position in the target building is extracted, and the evaluation time period of the heat source temperature rise is determined based on the use demand of the target building. The average temperature rise value of each spatial position in the period is further calculated by comparing the difference between the highest temperature and the lowest temperature in the period. According to the temperature rise threshold and the temperature rise value after standardization, different evaluation levels are set to divide the target building into different heat source areas. The temperature rise threshold is defined according to the purpose of use, material characteristics, design requirements and other factors of the building. On this basis, the areas with higher temperature rise in the target area are identified, and the heat source temperature rise value is generated. Finally, the heat source is marked in the target building according to the heat source location probability and heat source temperature rise value determined above, which lays a solid foundation for the subsequent modular energy-saving control of central air-conditioning.

[0029] Execute step A300, obtain the target control temperature of the target building, and generate a space cooling demand with the target control temperature and the space temperature field, and input the data set and the space cooling demand into the module coordination layer to execute the coordinated call control optimization of N sub-modules; firstly, based on the use purpose, design standard and comfort requirement of the target building, the target control temperature of the target building is correspondingly obtained, and the space temperature field is further compared with the target control temperature, that is, the temperature of each measuring point in the space temperature field is compared with the target control temperature, and it is determined which areas in the target building have a temperature higher than the target control temperature, and then for the areas with a temperature higher than the target control temperature, the required cooling amount is calculated and the space cooling demand is output, and further under the control of the module coordination layer, the operation mode and parameter setting of the central air conditioner are optimized by comprehensively considering the capabilities, current states and cooling demands of each sub-module, and the N sub-modules are coordinated based on the input data set and space cooling demand, and finally the optimal control strategy can be found in the coordinated call process by using optimization algorithms such as genetic algorithms and particle swarm optimization to execute the coordinated call control optimization of N sub-modules.

[0030] In a possible implementation, step A300 further includes step A310, A1: divide the cooling space through module distribution data to generate a space division result, and divide the cooling interval according to the type and location of each cooling module determined in the target building and the space cooling demand in the target building, and then divide the cooling interval according to the module distribution data, cooling demand and priority. The division can be based on the coverage of the module, or the internal space of the building can be divided into different cooling areas based on the cooling demand of the space area, so as to ensure that each area has a corresponding module responsible for cooling and generate a corresponding space division result, and then execute step A320, A2: based on the space division result and the space cooling demand, the load of the corresponding sub-module is evaluated, and N load evaluation results are input into the collaborative processing model to generate an associated excitation signal for the sub-module; execute step A330, A3: complete the collaborative control optimization based on the associated excitation signal, and use the above-mentioned space division result and space cooling demand as basic reference data to load the corresponding sub-module. Evaluation, first determine the load evaluation index according to the type, performance parameters and cooling demand of the sub-module, and further correspond each sub-module to the cooling area it is responsible for according to the space division result, and then calculate the load evaluation value of the sub-module according to the cooling demand of the area and the performance parameters of the sub-module. The evaluation value is the duration of the full load of the central air-conditioning in the target building. If there is an area with a full load duration that is too long, the associated modules need to collaborate. Therefore, N load evaluation results are input into the collaborative processing model. The collaborative processing model can be constructed based on collaborative processing algorithms such as neural networks, fuzzy logic, and optimization algorithms. At the same time, the input of the collaborative processing model is determined to be N load evaluation results, and the output is the associated excitation signal of the sub-module. The excitation signal can be a control parameter, an operating mode, a power output, etc. Finally, the generated associated excitation signal is output to the system for the actual control of the operation of the sub-module. By continuously adjusting and optimizing the excitation signal, the collaborative work between the sub-modules can be achieved to complete the collaborative control optimization and improve the performance and efficiency of the entire cooling system.

[0031] The collaborative control optimization process can also be implemented in a possible way. Step A400 further includes step A340, A4: establishing a hierarchical trigger level threshold of temperature, wherein each level in the hierarchical trigger level threshold corresponds to a basic frequency conversion strategy, wherein the frequency of the basic frequency conversion strategy of the low hierarchical trigger level is lower than that of the basic frequency conversion strategy of the high hierarchical trigger level. In order to better and more accurately collaboratively control the central air-conditioning in the target building, it is first necessary to define the temperature range of the target building based on the entire temperature controllable range of the internal temperature of the target building, and set the hierarchical trigger level threshold based on the defined temperature range. Since each level in the hierarchical trigger level threshold corresponds to a basic frequency conversion strategy, the hierarchical trigger level threshold contains multiple intervals, which can be low level, medium level, and high level of temperature, and each interval corresponds to a trigger level. When the temperature reaches or exceeds the threshold, the corresponding basic frequency conversion strategy will be triggered. Further, a basic frequency conversion strategy is formulated for each trigger level based on energy efficiency, comfort level, equipment performance, etc. The basic frequency conversion strategy can be a low-level trigger level, a medium-level trigger level, or a high-level trigger level corresponding to the low-level threshold. At the same time, the frequency of the basic frequency conversion strategy of the low-level trigger level is lower than that of the basic frequency conversion strategy of the high-level trigger level. Then, steps A350 and A5 are executed: based on the associated excitation signal, the sub-modules are linked to cool the space at full load power to generate a fitting result; the sub-modules are linked and coordinated according to the control parameters, operation modes, or power output data for different sub-modules contained in the associated excitation signal, and then full-load power cooling is performed based on the linked space sub-modules according to the space cooling requirements, that is, the sub-modules work together and operate at maximum capacity to reduce the space temperature as quickly as possible. The fitting result is generated and output by describing the relationship between the operation of the sub-modules and the space temperature.

[0032] Execute steps A360 and A6: when the fitting result meets the graded trigger level threshold, record the full load duration, call the corresponding basic frequency conversion strategy, and perform re-fitting; execute steps A370 and A7: repeatedly execute the fitting process in step A6, trigger the graded trigger level threshold, and record the corresponding strategy duration; execute step A380, complete the collaborative control optimization according to the recorded results and the associated excitation signal, first judge the above-generated fitting result and the trigger level threshold, when the fitting result meets the graded trigger level threshold, the system immediately records the time from the start of full load power cooling to the reaching of the trigger threshold, that is, the full load duration, further according to the currently triggered graded trigger level threshold, the system calls the corresponding basic frequency conversion strategy, and adjusts the operating status of the submodule according to the preset strategy. , power output or operation mode. Since the change of the basic frequency conversion strategy may affect the trend of the change of the space temperature, it is necessary to update it through re-fitting to more accurately predict the future temperature change. Further, the fitting process in step A6 is repeatedly performed, and the fitting result is compared with the graded trigger level threshold to determine whether the fitting result meets the trigger condition. If the fitting result meets the trigger condition, according to the current trigger level, the basic frequency conversion strategy of the submodule is adjusted and the timer is started to record the execution time of the current strategy. When the strategy adjustment or re-fitting occurs, the timer is stopped and the execution time of the current strategy is recorded. Finally, the strategy duration is output together with the corresponding trigger level and strategy type. According to the recorded results and the associated excitation signal, the collaborative control optimization is completed to continuously optimize the operation strategy and energy efficiency performance of the system.

[0033] Execute step A400, construct a first continuous influence based on the heat source annotation, construct a second continuous influence based on the ambient temperature prediction, compensate the control optimization result with the first continuous influence and the second continuous influence, obtain the compensation control result, and use the compensation control result to perform the cooling collaborative control of the N submodules of the central air conditioner in the target building. In a possible implementation, step A400 further includes step A410, fitting the continuous influence of the heat source based on the heat source annotation, generating a continuous influence value with a position identifier, and using the continuous influence value as the first continuous influence, firstly by collecting the heat source annotation data such as the location, intensity, and type of the heat source, and at the same time performing an impact analysis on the heat source annotation data based on factors such as the type, intensity, and distance of the heat source, determining the potential impact of the heat source on the surrounding environment, and further calculating the continuous influence value of each heat source annotation position by fitting, which is used to reflect the relationship between the heat source location, intensity, and continuous influence value, and then associating the location information of each heat source annotation with the corresponding continuous influence value, and outputting the continuous influence value with the location identifier as the first continuous influence.

[0034] Further, step A420 is executed to obtain weather forecast data, and the weather forecast data is used as the first environmental forecast data; step A430 is executed to input the weather forecast data and the data set into the heat transfer fitting model to generate a heat transfer fitting result, and the heat transfer fitting result is used as the second environmental forecast data. The source of the weather data is determined based on the weather forecast service provider, and data is screened and extracted according to the required weather forecast data, and then the weather forecast data is captured from the data source, and only used as the first environmental forecast data. In order to obtain environmental data more accurately, the heat transfer is firstly A fitting model is constructed, and the heat transfer fitting model can simulate the heat transfer process inside and outside the building based on weather conditions, building characteristics, etc., and the weather forecast data (such as temperature, wind speed, humidity, etc.) and other relevant information in the data set (such as thermal conductivity and specific heat capacity of building materials) are used as input parameters of the model. Then the weather forecast data and the data set are input into the heat transfer fitting model, and the heat transfer process inside and outside the building is simulated by the heat transfer fitting model, and the heat transfer fitting result is generated, the generated heat transfer fitting result is verified, and the verified heat transfer fitting result is output as the second environment prediction data.

[0035] Execute step A440, construct a second continuous influence based on the first environmental prediction data and the second environmental prediction data, and complete the control optimization result compensation with the first continuous influence and the second continuous influence. In one possible implementation, step A440 further includes step A441, based on the first continuous influence and the second continuous influence, the full-load power cooling fitting compensation is generated, and it is used as the first stage compensation result; analyze the first continuous influence obtained based on the continuous influence of the heat source labeling and the second continuous influence obtained based on the heat transfer fitting result of the weather forecast data, obtain the first continuous influence and the second continuous influence acting on the full-load power cooling process, and identify and determine the nodes that need to be compensated. Further, according to system requirements and performance standards, set the compensation target of the full-load power cooling process. The compensation target may be to optimize energy consumption, improve cooling efficiency or maintain indoor temperature stability. The full-load power cooling fitting compensation based on the first continuous influence and the second continuous influence refers to predicting the compensation amount that may be required for the full-load power cooling process under different influencing factors, and the corresponding compensation is obtained. Take the compensation amount corresponding to each influencing factor to generate a fitting compensation result as the first stage compensation result for output, further execute step A442, construct a timing compensation parameter according to the first continuous influence and the second continuous influence, use the timing compensation parameter as the second stage compensation result, execute step A443, complete the control optimization result compensation through the first stage compensation result and the second stage compensation result, first construct the timing compensation parameter according to the properties of the first continuous influence and the second continuous influence, the timing compensation parameter includes data such as the compensation amount, compensation rate, compensation time window at different time points, and then output the compensation parameter value based on each time point as the second stage compensation result, finally complete the control optimization result compensation of the target building according to the first stage compensation result and the second stage compensation result, which has a promoting effect on the modular energy-saving control of central air conditioning.

[0036] In one possible implementation, step A400 further includes step A450, configuring an approach threshold of the temperature; executing step A460, after executing the cooling collaborative control, if the predetermined space of the target building meets the approach threshold, triggering continuous monitoring feedback; executing step A470, continuously correcting the cooling collaborative control through the continuous monitoring feedback. In order to better perform collaborative control on the central air-conditioning during operation, it is also necessary to continuously correct the data of the cooling collaborative control. First, the approach threshold of the temperature in the target building is configured based on the use requirements, indoor environmental standards and energy efficiency requirements of the target building. When the central air-conditioning in the target building performs cooling collaborative control After that, after the cooling collaborative control strategy is implemented, the environmental parameters of the predetermined space of the target building are monitored, and it is judged whether the predetermined space in the target building meets the configured approximation threshold. If it meets the threshold, continuous monitoring feedback is triggered. Continuous monitoring feedback is data monitoring of the temperature of the predetermined space in the target building for a continuous period of time. Continuous monitoring feedback can include regular inspection of changes in internal environmental parameters of the building, recording energy usage, evaluating system performance, etc. Finally, according to the continuous monitoring feedback, the environmental parameters of the predetermined space in the target building are continuously monitored and the system performance is evaluated, thereby completing the continuous correction of the cooling collaborative control and improving the accuracy of modular energy-saving control of central air-conditioning in the later stage.

[0037] The embodiments of the present application solve the technical problem of high energy consumption caused by lack of energy-saving control over central air conditioners, and achieve the technical effect of reasonable and accurate modular energy-saving control over central air conditioners.

[0038] In the above, refer to Figure 1 The modular energy-saving control method for central air conditioning according to an embodiment of the present invention is described in detail. Figure 2 A system for modular energy-saving control of a central air conditioner according to an embodiment of the present invention is described.

[0039] The modular energy-saving control system for central air conditioners according to the embodiment of the present invention is used to solve the technical problem of high energy consumption caused by lack of energy-saving control of central air conditioners, and achieve the technical effect of reasonable and accurate modular energy-saving control of central air conditioners. The modular energy-saving control system for central air conditioners includes: an establishment module 10, a labeling module 20, an optimization module 30, and a control module 40.

[0040] Establishing module 10, the establishing module 10 is used to establish a data set of a target building, the data set includes building structure data, module distribution data of N submodules, establish module associations of N submodules with the data set, and initialize the module coordination layer with the module associations, where N is an integer greater than 1;

[0041] A labeling module 20, the labeling module 20 is used to collect the temperature inside the target building through a wireless temperature sensor network, construct a spatial temperature field, and perform heat source labeling of the spatial temperature field;

[0042] The optimization module 30 is used to obtain the target control temperature of the target building, generate a space cooling demand based on the target control temperature and the space temperature field, input the data set and the space cooling demand into the module coordination layer, and perform collaborative call control optimization of N submodules;

[0043] A control module 40 is used to construct a first continuous influence based on the heat source labeling, and to construct a second continuous influence based on the ambient temperature prediction, to compensate the control optimization result with the first continuous influence and the second continuous influence, to obtain a compensation control result, and to perform a coordinated cooling control of N sub-modules of the central air-conditioning in the target building with the compensation control result.

[0044] The specific configuration of the item labeling module 20 will be described in detail below. As described above, the labeling module 20 may further include: a temperature evaluation unit for establishing a reference space temperature, and performing a temperature evaluation of the space temperature field through the reference space temperature to generate a temperature difference mark; a heat source distribution prediction unit for performing a time series monitoring of the temperature difference of the space position, and performing a heat source distribution prediction based on the time series monitoring result of the temperature difference to generate a heat source position probability; a heat source temperature rise evaluation unit for performing a heat source temperature rise evaluation based on the time series monitoring result of the temperature difference to generate a heat source temperature rise value; and a labeling unit for completing the heat source labeling based on the heat source position probability and the heat source temperature rise value.

[0045] The specific configuration of the optimization module 30 will be described in detail below. As described above, the optimization module 30 may further include: a cooling space division unit for A1: performing cooling space division through module distribution data to generate a space division result; a load evaluation unit for A2: performing load evaluation of the corresponding submodule based on the space division result and the space cooling demand, and inputting the N load evaluation results into the collaborative processing model to generate an associated excitation signal for the submodule; and a first collaborative control optimization unit for A3: completing collaborative control optimization based on the associated excitation signal.

[0046] Below, the specific configuration of the optimization module 30 will be described in detail. As described above, the optimization module 30 may further include: a threshold establishment unit for A4: establishing a hierarchical trigger level threshold of temperature, wherein each level in the hierarchical trigger level threshold corresponds to a basic frequency conversion strategy, wherein the frequency of the basic frequency conversion strategy of the low hierarchical trigger level is lower than that of the basic frequency conversion strategy of the high hierarchical trigger level; a result generation unit for A5: performing full-load power cooling of the linkage space of the sub-module based on the associated excitation signal to generate a fitting result; a first judgment unit for A6: when the fitting result meets the hierarchical trigger level threshold, recording the full-load duration, calling the corresponding basic frequency conversion strategy, and performing re-fitting; a trigger unit for A7: repeatedly executing the fitting process in step A6, triggering the hierarchical trigger level threshold, and recording the corresponding strategy duration; a first collaborative control optimization unit for completing collaborative control optimization according to the recorded results and the associated excitation signal.

[0047] The specific configuration of the control module 40 will be described in detail below. As described above, the control module 40 may further include: a fitting unit for fitting the continuous influence of the heat source based on the heat source label, generating a continuous influence value with a position identifier, and using the continuous influence value as the first continuous influence; an environmental prediction data unit for obtaining weather prediction data, using the weather prediction data as the first environmental prediction data; an input unit for inputting the weather prediction data and the data set into a heat transfer fitting model, generating a heat transfer fitting result, and using the heat transfer fitting result as the second environmental prediction data; a first control optimization result compensation unit for constructing a second continuous influence based on the first environmental prediction data and the second environmental prediction data, and completing the control optimization result compensation with the first continuous influence and the second continuous influence.

[0048] The specific configuration of the first control optimization result compensation unit will be described in detail below. As described above, the first control optimization result compensation unit may further include: a compensation unit for fitting compensation for full-load power cooling based on the first continuous influence and the second continuous influence, generating a fitting compensation result, and using it as the first stage compensation result; a parameter construction unit for constructing a timing compensation parameter according to the first continuous influence and the second continuous influence, and using the timing compensation parameter as the second stage compensation result; and a second control optimization result compensation unit for completing the control optimization result compensation through the first stage compensation result and the second stage compensation result.

[0049] The specific configuration of the control module 40 will be described in detail below. As described above, the control module 40 may further include: a threshold configuration unit for configuring an approaching threshold of a temperature; a feedback trigger unit for triggering continuous monitoring feedback when the temperature-lowering collaborative control is executed and if the predetermined space of the target building meets the approaching threshold; and a continuous correction unit for continuously correcting the temperature-lowering collaborative control through the continuous monitoring feedback.

[0050] The system for modular energy-saving control of central air conditioners provided in the embodiments of the present invention can execute the method for modular energy-saving control of central air conditioners provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0051] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0052] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for modular energy-saving control of a central air conditioner, the method being applied to a system for modular energy-saving control of a central air conditioner, the system comprising a module coordination layer, the module coordination layer being communicatively connected with N submodules, characterized in that: The method comprises: Establishing a data set of a target building, the data set includes building structure data and module distribution data of N submodules, establishing module associations of the N submodules with the data set, and initializing the module coordination layer with the module associations, where N is an integer greater than 1; The temperature of the target building is collected through a wireless temperature sensor network, a spatial temperature field is constructed, and heat source marking of the spatial temperature field is performed; Obtaining the target control temperature of the target building, and generating a space cooling demand with the target control temperature and the space temperature field, and inputting the data set and the space cooling demand into the module coordination layer to perform collaborative call control optimization of N submodules; A first continuous influence is constructed based on the heat source labeling, a second continuous influence is constructed based on the ambient temperature prediction, the first continuous influence and the second continuous influence are used to compensate the control optimization result, and a compensation control result is obtained, and the compensation control result is used to perform a temperature reduction collaborative control of N submodules of the central air conditioner in the target building; The method comprises: Establishing a reference space temperature, and using the reference space temperature to perform a temperature evaluation of the space temperature field, and generating a temperature difference mark; Perform time-series monitoring of temperature differences at spatial locations, and use the results of time-series monitoring to predict heat source distribution and generate heat source location probabilities; Performing a heat source temperature rise evaluation on the temperature difference time series monitoring result to generate a heat source temperature rise value; The heat source is marked by using the heat source location probability and the heat source temperature rise value; The execution of the coordinated call control optimization of N submodules includes: A1: Perform cooling space division based on module distribution data to generate space division results; A2: Based on the space division result and the space cooling demand, load evaluation of the corresponding submodule is performed, and N load evaluation results are input into the collaborative processing model to generate associated excitation signals of the submodules; A3: completing the collaborative control optimization based on the associated excitation signal; A4: Establishing a hierarchical trigger level threshold of temperature, wherein each level in the hierarchical trigger level threshold corresponds to a basic frequency conversion strategy, wherein the frequency of the basic frequency conversion strategy of a low hierarchical trigger level is lower than that of the basic frequency conversion strategy of a high hierarchical trigger level; A5: performing linkage space full load power cooling of the submodules based on the associated excitation signal to generate a fitting result; A6: When the fitting result meets the hierarchical trigger level threshold, the full load duration is recorded, and the corresponding basic frequency conversion strategy is called to perform re-fitting; A7: Repeat the fitting process in step A6 to trigger the hierarchical trigger level threshold and record the corresponding strategy duration; Complete collaborative control optimization according to the recorded results and the associated excitation signal; Fitting the continuous influence of the heat source based on the heat source annotation to generate a continuous influence value with a position identifier, and using the continuous influence value as the first continuous influence; Acquire weather forecast data, and use the weather forecast data as first environmental forecast data; Inputting the weather forecast data and the data set into a heat transfer fitting model to generate a heat transfer fitting result, and using the heat transfer fitting result as the second environment forecast data; Constructing a second continuous influence based on the first environmental prediction data and the second environmental prediction data, and completing control optimization result compensation with the first continuous influence and the second continuous influence; Based on the first continuous influence and the second continuous influence, a full-load power cooling fitting compensation is performed to generate a fitting compensation result as a first-stage compensation result; constructing a timing compensation parameter according to the first continuous influence and the second continuous influence, and using the timing compensation parameter as a second-stage compensation result; The control optimization result compensation is completed through the first stage compensation result and the second stage compensation result.

2. The method according to claim 1, characterized in that The method comprises: Configure the approaching threshold of temperature; When the cooling collaborative control is executed, if the predetermined space of the target building meets the approach threshold, continuous monitoring feedback is triggered; The continuous monitoring feedback is used to continuously correct the cooling coordinated control.

3. A system for modular energy-saving control of a central air conditioner, the system being used to implement the method for modular energy-saving control of a central air conditioner according to any one of claims 1 to 2, the system comprising a module coordination layer, the module coordination layer being communicatively connected with N submodules, characterized in that: The system comprises: An establishment module, the establishment module is used to establish a data set of a target building, the data set includes building structure data, module distribution data of N submodules, establish module associations of the N submodules with the data set, and initialize the module coordination layer with the module associations, where N is an integer greater than 1; A labeling module, the labeling module is used to collect the temperature inside the target building through a wireless temperature sensor network, construct a spatial temperature field, and perform heat source labeling of the spatial temperature field; An optimization module, the optimization module is used to obtain the target control temperature of the target building, and generate a space cooling demand based on the target control temperature and the space temperature field, and input the data set and the space cooling demand into the module coordination layer to perform collaborative call control optimization of N submodules; A control module, wherein the control module is used to construct a first continuous influence based on the heat source labeling, construct a second continuous influence based on the ambient temperature prediction, use the first continuous influence and the second continuous influence to compensate the control optimization result, obtain a compensation control result, and use the compensation control result to perform coordinated cooling control of N sub-modules of the central air-conditioning in the target building.

Images