Automatic control method and device for cold source equipment, equipment and medium

Abstract

The invention discloses an automatic control method, device and equipment for cold source equipment and a medium. The method comprises the following steps: acquiring a total demand target, a cold source equipment attribute and at least one space demand target of a target building; processing the total demand target and each space demand target by using a pre-trained first artificial intelligence model to obtain a target supply demand matched with each target space of the target building; calculating and obtaining an automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute; and applying the automatic control scheme to the cold source equipment of the target building so as to automatically control the cold source equipment. According to the technical scheme, automatic control over the cold source equipment can be achieved, the control efficiency and control precision of the cold source equipment are improved, the control effect is optimized, and human resources are saved.

Description

Technical Field

[0001] The present invention relates to the field of automation, and in particular to an automatic control method, device, equipment and medium for cold source equipment. Background Art

[0002] In building air-conditioning systems, efficient control of cold source equipment is crucial to ensuring indoor environmental comfort and reducing energy consumption.

[0003] Currently, classic feedback control schemes are widely used to control the operation of cooling units. While they can maintain system operation to a certain extent, they present several key challenges. First, in terms of long-term feedforward control, within a 4-6 hour control window, optimizing strategies for early startup, early shutdown, and cold and heat storage for cooling units are crucial for building energy management and comfort assurance. However, feedback control relies on system outputs to adjust inputs, and its inherent lag makes it difficult to plan these strategies for cooling units in advance, making it ineffective for effectively addressing complex operating conditions over long periods. Second, in terms of global optimal control of transient cooling plants, within a 1-hour control window, the coordination of chillers, pumps, and cooling towers within the cooling unit to achieve maximum efficiency requires multi-device coordination and complex energy flow relationships. Feedback control typically relies on local information and historical data, making it difficult to comprehensively analyze and derive optimal operating strategies for each cooling unit component, resulting in suboptimal global operation during transient conditions. Finally, in terms of displaying panoramic parameters, due to its own limitations, the feedback control logic cannot fully display all the detailed conditions that may occur during the operation of the cold source equipment. When providing countermeasures and review analysis of the operation of the cold source equipment, there is a lack of comprehensive data support, making it difficult for operation and maintenance personnel to gain an in-depth understanding of the overall operation of the equipment and unable to accurately optimize the control strategy, which limits the refined management and performance improvement of the cold source equipment and even the entire building air-conditioning system.

[0004] In summary, the existing control method for cold source equipment has the problems of low control efficiency and control accuracy of the cold source equipment, poor control effect, and high human resource consumption. Summary of the Invention

[0005] The present invention provides an automatic control method, device, equipment and medium for cold source equipment, which can solve the problems of low control efficiency and control accuracy of cold source equipment, poor control effect and high human resource consumption in existing control methods for cold source equipment.

[0006] In a first aspect, an embodiment of the present invention provides an automatic control method for a cold source device, the method comprising:

[0007] acquire a total demand target of a target building, a cold source equipment attribute, and at least one space demand target, wherein the target building is composed of the cold source equipment and at least one target space;

[0008] process the total demand target and the space demand targets by using a pre-trained first artificial intelligence model to obtain target supply demand amounts respectively matched with the target spaces of the target building;

[0009] obtain an automatic control scheme matched with the target building based on the target supply demand amounts of the target spaces and the cold source equipment attribute;

[0010] apply the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment.

[0011] In a second aspect, an embodiment of the present application provides an automatic control device of a cold source equipment, and the device comprises:

[0012] a data acquisition module configured to acquire a total demand target of a target building, a cold source equipment attribute, and at least one space demand target, wherein the target building is composed of the cold source equipment and at least one target space;

[0013] a supply demand amount calculation module configured to process the total demand target and the space demand targets by using a pre-trained first artificial intelligence model to obtain target supply demand amounts respectively matched with the target spaces of the target building;

[0014] a scheme calculation module configured to obtain an automatic control scheme matched with the target building based on the target supply demand amounts of the target spaces and the cold source equipment attribute;

[0015] a scheme application module configured to apply the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, and the electronic device comprises:

[0017] at least one processor; and

[0018] a memory in communication connection with the at least one processor; wherein

[0019] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute an automatic control method of a cold source equipment according to any one of the embodiments of the present application.

[0020] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions for causing a processor to implement the automatic control method of the cold source equipment according to any of the embodiments of the present application when executed.

[0021] The technical scheme of the embodiment of the present application obtains the total demand target of the target building, the cold source equipment attribute and at least one space demand target, then processes the total demand target and each space demand target using the first artificial intelligence model trained in advance to obtain the target supply demand quantity matched with each target space of the target building, then calculates the automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute, and finally applies the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment, thereby solving the problems of low control efficiency and control precision, poor control effect and large consumption of human resources of the existing control method of the cold source equipment, realizing the automatic control of the cold source equipment, improving the control efficiency and control precision of the cold source equipment, optimizing the control effect and saving human resources.

[0022] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is a flow chart of an automatic control method of a cold source equipment according to an embodiment of the present application;

[0025] Figure 2 is a flow chart of an automatic control method of a cold source equipment according to an embodiment of the present application;

[0026] Figure 3 is a structural schematic diagram of an automatic control device of a cold source equipment according to an embodiment of the present application;

[0027] Figure 4 is a structural schematic diagram of an electronic device for implementing an automatic control method of a cold source equipment according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a method for automatically controlling a cold source device provided in Example 1 of the present invention. This embodiment is applicable to situations where a cold source device is automatically controlled. The method can be executed by an automatic control device of the cold source device. The automatic control device of the cold source device can be implemented in the form of hardware and / or software. The automatic control device of the cold source device can be configured in a terminal or server with an automatic control function of the cold source device.

[0032] like Figure 1 As shown, the method includes:

[0033] S110: Obtain a total demand target, cooling source equipment attributes, and at least one space demand target of a target building.

[0034] Among them, the target building is composed of cold source equipment and at least one target space; further, the target building refers to a building that requires air-conditioning system control, which is composed of cold source equipment and at least one target space. The cold source equipment is a combination of equipment that provides cooling for the building, including a chiller, a chilled water pump, a cooling tower, and a cooling water pump. The target space is an area inside the building that has independent air-conditioning requirements.

[0035] The total demand target comprises a target temperature and a target carbon dioxide concentration, and the cold source equipment attribute comprises a chilled water pump attribute, a cooling tower attribute, a cooling water pump attribute, a rated refrigerating capacity, a part load range, and an optimal part load of the cold source equipment.

[0036] Further, the cold source equipment attribute is a parameter set describing the characteristics of the cold source equipment, and specifically comprises a chilled water pump attribute, a cooling tower attribute, a cooling water pump attribute, a rated refrigerating capacity, a part load range, and an optimal part load. 3 Further, the cold source equipment attribute is a parameter set describing the characteristics of the cold source equipment, and specifically comprises a chilled water pump attribute, a cooling tower attribute, a cooling water pump attribute, a rated refrigerating capacity, a part load range, and an optimal part load. 3 Further, the cold source equipment attribute is a parameter set describing the characteristics of the cold source equipment, and specifically comprises a chilled water pump attribute, a cooling tower attribute, a cooling water pump attribute, a rated refrigerating capacity, a part load range, and an optimal part load.

[0037] It should be noted that the cold source equipment attribute can be directly obtained by relevant staff according to the specification information or data of the cold source equipment, and the embodiment does not limit the obtaining method of the cold source equipment attribute.

[0038] Further, the space demand target is a standard of temperature and carbon dioxide concentration set for a single target space, for example, a conference room, and the space demand target thereof can be 26°C of temperature and 1000ppm of carbon dioxide concentration; and a corridor, and the space demand target thereof can be 27°C of temperature and 1500ppm of carbon dioxide concentration.

[0039] Optionally, before obtaining the total demand target of the target building, the method further comprises: in response to an input operation of a user, obtaining a demand satisfaction rate, a comfortable temperature upper limit, a temperature overheating threshold, a carbon dioxide upper limit, and a carbon dioxide threshold; calculating the target temperature of the target building based on a formula: target temperature = demand satisfaction rate × comfortable temperature upper limit + (1-demand satisfaction rate) × temperature overheating threshold; calculating the carbon dioxide concentration of the target building based on a formula: carbon dioxide concentration = demand satisfaction rate × carbon dioxide upper limit + (1-demand satisfaction rate) × carbon dioxide threshold; and aggregating the target temperature and the carbon dioxide concentration to obtain the total demand target of the target building.

[0040] S120, processing the total demand target and each space demand target using the pre-trained first artificial intelligence model to obtain a target supply demand quantity matched with each target space of the target building.

[0041] The target supply demand quantity refers to the cooling or heating load required by each target space in the target building to meet its own set space demand target.

[0042] The pre-trained first artificial intelligence model is an AI large model. Further, the AI large model has a large number of model parameters (e.g., more than 1 billion) and is a pre-trained model based on a deep neural network architecture (e.g., a recurrent neural network, a convolutional neural network, etc.) for building air conditioning system operation. The AI large model can be a predictive AI system. That is, after being trained by a large amount of building operation history data (such as past temperature, humidity, energy consumption data, etc.), weather forecast data, and equipment control action data, the AI large model can predict the cooling / heating load required by the building for the total demand target or each space demand target.

[0043] S130, calculating an automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute.

[0044] Specifically, calculating the automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute includes: summing the target supply demand quantity of each target space to obtain a building demand quantity matched with the target building; constructing at least one to-be-verified control scheme based on the building demand quantity and the cold source equipment attribute; processing each to-be-verified control scheme by the pre-trained first artificial intelligence model to obtain a scheme energy consumption result matched with each to-be-verified control scheme; and setting the to-be-verified control scheme corresponding to the scheme energy consumption result with the smallest value as the automatic control scheme matched with the target building.

[0045] In one specific implementation scenario of the embodiment, the process of calculating the automatic control scheme matched with the target building based on the target supply demand of each target space and the cold source equipment attributes can be specifically as follows: first, the target supply demands of each target space are summed to obtain the building demand matched with the target building. For example, if a target building contains three target spaces with target supply demands of 50 kW, 80 kW and 70 kW respectively, the building demand obtained after summation is 200 kW, which represents the total cooling / heating load required by the entire building to meet the demand of all target spaces. Secondly, at least one control scheme to be verified is constructed based on the building demand and the cold source equipment attributes. Taking the cold source equipment containing two large chillers (with rated cooling capacities of 150 kW each, PLR ranges of 30%-100%, and optimal PLRs of 70%) and one small chiller (with a rated cooling capacity of 100 kW, a PLR range of 30%-100%, and an optimal PLR of 60%) as an example, combined with the building demand of 200 kW, multiple chiller opening schemes can be generated through exhaustive combination, such as “one large chiller + one small chiller” (total cooling capacity 150+100=250 kW, which can meet the demand of 200 kW), “two large chillers” (total cooling capacity 300 kW), etc. For each chiller scheme, the corresponding opening scheme is matched according to the attributes of the chilled water pump, cooling tower and cooling water pump (for example, the chiller scheme of “one large chiller + one small chiller” is matched with the chilled water pump scheme of “one large pump + one small pump”, the cooling tower scheme with a rated cooling water flow close to 1.5 times the total rated cooling water flow of the chiller, etc.), thereby forming multiple control schemes to be verified. Then, the first artificial intelligence model trained in advance is used to process each control scheme to be verified to obtain the scheme energy consumption result matched with each scheme. For example, the opening scheme of “one large chiller + one small chiller” and the matching water pump and cooling tower is input into the AI model, which will predict the total energy consumption of the scheme during operation based on historical data and equipment characteristics, and the result is assumed to be 150 kWh; the scheme of “two large chillers” and the matching scheme is input into the model, and the predicted energy consumption is 180 kWh. Finally, the control scheme to be verified corresponding to the minimum scheme energy consumption result is set as the automatic control scheme matched with the target building. For example, in the above example, the energy consumption of “one large chiller + one small chiller” and the matching scheme is 150 kWh, which is the minimum, so this scheme is determined as the automatic control scheme of the target building.

[0046] S140, applying the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment.

[0047] Specifically, applying the automatic control scheme to the cold source equipment of the target building is to convert each parameter in the automatic control scheme into an actual operation instruction of the equipment through a control system, such as automatically starting the corresponding number and type of cooling machines according to the opening combination of the cooling machines in the automatic control scheme, and adjusting the load rate to make it in the optimal partial load range; according to the setting of the chilled water pump in the automatic control scheme, automatically starting the corresponding number of water pumps and adjusting the opening degree to match the required flow; according to the opening scheme of the cooling tower, controlling the cooling tower to start and adjusting the cooling water flow to make the rated cooling water total flow close to 1.5 times of the rated cooling water total flow of the cooling machine; at the same time, the cooling water pump will also be automatically started and the operating parameters will be adjusted according to the automatic control scheme setting. Through such automatic control, the cold source equipment can dynamically adjust the operating state according to the real-time demand of the building without manual intervention, which not only ensures that the cooling / heating demand of each target space is met, but also maintains the system in a low energy consumption operating state.

[0048] The technical scheme of the embodiment of the present application obtains the total demand target of the target building, the cold source equipment attribute and at least one space demand target, then processes the total demand target and each space demand target using the pre-trained first artificial intelligence model to obtain the target supply demand quantity matched with each target space of the target building, then calculates the automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute, and finally applies the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment, which realizes the automatic control of the cold source equipment, improves the control efficiency and control precision of the cold source equipment, optimizes the control effect and saves human resources.

[0049] Embodiment two

[0050] Figure 2 A flowchart of an automatic control method of a cold source equipment provided by the embodiment two of the present application is provided, and the embodiment is refined based on the above-mentioned embodiment. In the embodiment, the processing of the total demand target and each space demand target using the pre-trained first artificial intelligence model to obtain the target supply demand quantity matched with each target space of the target building is refined.

[0051] As Figure 2 shown, the method comprises:

[0052] S210, obtaining the total demand target of the target building, the cold source equipment attribute and at least one space demand target.

[0053] The target building is composed of the cold source equipment and at least one target space.

[0054] S220, inputting the total demand target into a pre-trained second artificial intelligence model to obtain a building adjustment strategy.

[0055] The pre-trained second artificial intelligence model is used to predict the building state based on the configuration of the building, and the building state includes the actual temperature and the actual carbon dioxide concentration of the target building under the current building configuration, and the building configuration is a control action parameter of the terminal air conditioning equipment of the target building.

[0056] Further, inputting the total demand target into the pre-trained second artificial intelligence model to obtain a building adjustment strategy includes: obtaining a pre-set initial building configuration; inputting the initial building configuration into the pre-trained second artificial intelligence model to obtain a building test result matched with the initial building configuration; performing similarity calculation on the building test result and the total demand target to obtain a first similarity calculation result; when the first similarity calculation result is not less than a pre-set first similarity threshold, setting the initial building configuration as the building adjustment strategy of the target building; when the first similarity calculation result is less than the pre-set first similarity threshold, updating the initial building configuration based on a pre-set building adjustment range, and returning to perform the operation of inputting the initial building configuration into the pre-trained second artificial intelligence model based on the updated initial building configuration.

[0057] For example, in one specific implementation scenario of the embodiment, an initial building configuration is first obtained, which is an initial setting of the end air conditioning equipment control action parameters, for example, setting the opening of all air valves and water valves to 70%, and setting the air conditioning to start 1 hour in advance according to the default rule. Then, the initial building configuration is input into the second artificial intelligence model, and the model outputs the corresponding building test results, i.e., the predicted average temperature and carbon dioxide concentration of the building. Then, the first similarity calculation result (such as temperature deviation and carbon dioxide concentration deviation) between the building test result and the total demand target is calculated. If the result is not less than the preset first similarity threshold (such as temperature deviation ≤ 0.3℃ and carbon dioxide concentration deviation ≤ 50ppm), it means that the initial building configuration can meet the total demand target, and it is set as the building adjustment strategy; if the result is less than the threshold, for example, in the above example, the temperature deviation is 0.6℃ and the carbon dioxide concentration deviation is 100ppm, both of which exceed the threshold, then the initial building configuration is updated based on the preset building adjustment range, for example, the air valve opening is adjusted from 70% to 75%, and the start-up time is adjusted from 1 hour to 1.25 hours, and then the updated configuration is input into the second artificial intelligence model for re-prediction until the deviation between the building test result and the total demand target is within the threshold range, at which time the configuration is the building adjustment strategy. For example, after multiple adjustments, the air valve opening is set to 78% and the start-up time is 1.5 hours, the second artificial intelligence model predicts that the average temperature is 26.3℃ and the carbon dioxide concentration is 1120ppm, with deviations of 0.1℃ and 20ppm, respectively, which meet the threshold requirement, and the configuration becomes the final building adjustment strategy.

[0058] It should be noted that the specific values of the first similarity threshold and the building adjustment range can be set and modified by relevant personnel according to the actual implementation scenario, and the embodiment does not limit the specific values thereof.

[0059] S230, configuring each target space based on the building adjustment strategy to obtain a space test result respectively matched with each target space.

[0060] Among them, the space test result includes: test success result and test failure result.

[0061] Specifically, after obtaining the building adjustment strategy, the control parameters of the end devices in the strategy are applied to the air conditioning equipment corresponding to each target space, for example, the air valve of each independent area such as the office and the conference room is set to 75% opening, the water valve is set to 70% opening, and the operation is performed according to the rule of starting 1.5 hours in advance. Then, the running state of each target space is predicted separately by calling the building AI large model, and the predicted temperature and carbon dioxide concentration of each space are obtained. Compare these prediction results with the demand standard of the space: if the predicted temperature of office A is 26.3℃ (between 24℃-27℃), and the carbon dioxide concentration is 1200ppm (between 1000ppm-1500ppm), it is determined that the test is successful; if the predicted temperature of conference room B is 27.6℃ (exceeding the overheating limit of 27℃), it is determined that the test fails.

[0062] S240, set the building adjustment strategy as the space adjustment strategy of each target space whose space test result is a test success result.

[0063] On the basis of the above steps, it is easy to understand that for the target space whose space test result is a test success, it means that the building adjustment strategy can make its running state meet the demand, and no additional adjustment is needed, so the building adjustment strategy is directly used as its space adjustment strategy. For example, the test result of the above-mentioned office A is successful, so the space adjustment strategy of office A is the building adjustment strategy of air valve opening 75%, water valve opening 70%, and starting 1.5 hours in advance. Subsequently, the air conditioning equipment of office A will be controlled according to this strategy to maintain its running state meeting the demand.

[0064] S250, input the space demand target of each target space whose space test result is a test failure result into the pre-trained second artificial intelligence model to obtain a space adjustment strategy matched with each target space.

[0065] On the basis of the above steps, the pre-trained second artificial intelligence model is further used to predict the space state based on the configuration of the space, and the space state includes: the actual temperature and the actual carbon dioxide concentration of the target space under the current space configuration, and the space configuration is the control action parameter of the end air conditioning equipment of the target space.

[0066] The space requirement target of each target space with a space test result as a test failure result is input into a pre-trained second artificial intelligence model to obtain a space adjustment strategy matched with each target space, including: setting the building adjustment strategy as an initial space configuration of the target space; updating the initial space configuration based on a preset space adjustment range, and inputting the updated initial space configuration into the pre-trained second artificial intelligence model to obtain a space test result matched with the updated initial space configuration; performing similarity calculation on the space test result and the space requirement target of the target space to obtain a second similarity calculation result; when the second similarity calculation result is not less than a preset second similarity threshold, setting the initial space configuration as the space adjustment strategy of the target space; when the second similarity calculation result is less than the preset second similarity threshold, updating the initial space configuration based on the preset space adjustment range, and returning to execute the operation of inputting the updated initial space configuration into the pre-trained second artificial intelligence model based on the updated initial space configuration.

[0067] The target space with a space test result as a test failure result is a region whose predicted operating state exceeds the range of its own space requirement target after application of the building adjustment strategy. For example, a conference room is a target space, and its space requirement target is that the temperature is not higher than 27°C (temperature overheating limit) and the carbon dioxide concentration is not higher than 1500 ppm (high limit), but after application of the building adjustment strategy, the AI model predicts that the temperature is 28°C and the carbon dioxide concentration is 1600 ppm, both of which exceed the limits, i.e., the target space with a test failure.

[0068] Specifically, on the basis of the above steps, first, the building adjustment strategy is set to the initial space configuration of the test failure target space, for example, the initial space configuration of a test failure conference room is that the air valve is 75% and the water valve is 70%. Then, the initial space configuration is updated based on the preset space adjustment amplitude, for example, the air valve opening is adjusted from 75% to 80%, and the updated configuration is input into the building AI large model, and the model outputs the corresponding space test result, that is, the predicted temperature and carbon dioxide concentration of the conference room. Then, the second similarity calculation result of the space test result and the conference room space demand target is calculated. If the result is not less than the preset second similarity threshold, it means that the current initial space configuration can meet the demand of the target space, and it is set as the space adjustment strategy of the target space; if the result is less than the threshold, for example, the predicted temperature is still 27.8℃ (deviation of 0.8℃ from the 27℃ overheating limit, exceeding the 0.5℃ threshold), the initial space configuration is updated again based on the space adjustment amplitude, for example, the air valve opening is adjusted from 80% to 85%, and the AI model is input again for prediction. After several iterations, assuming that the air valve opening is adjusted to 88%, the AI model predicts that the temperature of the conference room is 26.8℃ (deviation of 0.2℃ from the 27℃ overheating limit) and the carbon dioxide concentration is 1450ppm (deviation of 50ppm from the 1500ppm upper limit), both of which meet the second similarity threshold requirement, then the configuration (air valve 88%, water valve 70%) at this time is set as the space adjustment strategy of the conference room.

[0069] It should be noted that the specific values of the above-mentioned second similarity threshold and space adjustment amplitude can be set and modified by relevant personnel according to the actual implementation scene, and the specific values thereof are not limited in the present embodiment.

[0070] S260, based on the space adjustment strategy of each target space, a target supply demand quantity respectively matched with each target space is calculated through a first artificial intelligence model.

[0071] Among them, the target supply demand quantity refers to the cooling or heating load required by each target space in the target building to meet its own space demand target after determining the space adjustment strategy of the target space.

[0072] Specifically, on the basis of the above steps, the space adjustment strategy of the target space is obtained, and the actual temperature and actual carbon dioxide concentration of the target space under the space adjustment strategy are obtained by processing the space adjustment strategy through a second artificial intelligence model. Then, the first artificial intelligence model calculates the cooling or heating load matched with the target space based on the actual temperature and actual carbon dioxide concentration, that is, the target supply demand.

[0073] S270, calculating an automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute.

[0074] S280, applying the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment.

[0075] The technical scheme of the embodiment of the present application, by obtaining the total demand target of the target building, the cold source equipment attribute and at least one space demand target, then inputting the total demand target into the pre-trained second artificial intelligence model to obtain the building adjustment strategy, and configuring each target space based on the building adjustment strategy to obtain the space test result matched with each target space respectively, then setting the building adjustment strategy as the space adjustment strategy of each target space with the space test result as the test success result, and inputting the space demand target of each target space with the space test result as the test failure result into the pre-trained second artificial intelligence model to obtain the space adjustment strategy matched with each target space respectively, then calculating the target supply demand quantity matched with each target space respectively based on the space adjustment strategy of each target space through the first artificial intelligence model, then calculating the automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute, and finally applying the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment, realizes the automatic control of the cold source equipment, improves the control efficiency and control precision of the cold source equipment, optimizes the control effect, and saves human resources.

[0076] Embodiment three

[0077] Figure 3 A structural schematic diagram of an automatic control device of a cold source equipment provided by the embodiment three of the present application. As shown in the figure, Figure 3 The device comprises:

[0078] The data acquisition module 310 is configured to obtain the total demand target of the target building, the cold source equipment attribute and at least one space demand target, wherein the target building is composed of the cold source equipment and at least one target space;

[0079] The supply demand quantity calculation module 320 is configured to process the total demand target and each space demand target using the pre-trained first artificial intelligence model to obtain the target supply demand quantity matched with each target space of the target building respectively;

[0080] The scheme calculation module 330 is configured to calculate an automatic control scheme matched with the target building based on the target supply demand quantity of each target space and the cold source equipment attribute;

[0081] The scheme application module 340 is configured to apply the automatic control scheme to the cold source equipment of the target building to automatically control the cold source equipment.

[0082] The technical scheme of the embodiment of the present application realizes automatic control of the cold source equipment, improves the control efficiency and control accuracy of the cold source equipment, optimizes the control effect, and saves human resources.

[0083] On the basis of the above embodiment, the supply demand quantity calculation module 320 comprises:

[0084] The building strategy generation unit is configured to input the total demand target into a pre-trained second artificial intelligence model to obtain a building adjustment strategy.

[0085] The space test unit is configured to configure each target space based on the building adjustment strategy to obtain a space test result matched with each target space, wherein the space test result comprises a test success result and a test failure result.

[0086] The first strategy setting unit is configured to set the building adjustment strategy as a space adjustment strategy of each target space with a space test result being the test success result.

[0087] The second strategy setting unit is configured to input a space demand target of each target space with a space test result being the test failure result into the pre-trained second artificial intelligence model to obtain a space adjustment strategy matched with each target space.

[0088] The demand quantity calculation unit is configured to calculate a target supply demand quantity matched with each target space based on the space adjustment strategy of each target space through the first artificial intelligence model.

[0089] On the basis of the above embodiment, the building strategy generation unit comprises:

[0090] The initial configuration acquisition unit is configured to acquire an initial building configuration set in advance.

[0091] The building test result generation unit is configured to input the initial building configuration into a second pre-trained artificial intelligence model to obtain a building test result matched with the initial building configuration.

[0092] The first similarity calculation unit is configured to perform similarity calculation on the building test result and the total demand target to obtain a first similarity calculation result.

[0093] The first similarity comparison unit is configured to set the initial building configuration as a building adjustment strategy of the target building when the first similarity calculation result is not less than a preset first similarity threshold.

[0094] The building configuration updating unit is configured to update the initial building configuration based on a preset building adjustment range when the first similarity calculation result is less than the preset first similarity threshold, and return to perform the operation of inputting the initial building configuration into the second pre-trained artificial intelligence model based on the updated initial building configuration.

[0095] On the basis of the above embodiment, the second strategy setting unit comprises:

[0096] The initial space configuration acquisition unit is configured to set the building adjustment strategy as an initial space configuration of the target space.

[0097] The first space configuration updating unit is configured to update the initial space configuration based on a preset space adjustment range, and input the updated initial space configuration into the second pre-trained artificial intelligence model to obtain a space test result matched with the updated initial space configuration.

[0098] The second similarity calculation unit is configured to perform similarity calculation on the space test result and a space demand target of the target space to obtain a second similarity calculation result.

[0099] The second similarity comparison unit is configured to set the initial space configuration as a space adjustment strategy of the target space when the second similarity calculation result is not less than a preset second similarity threshold.

[0100] The second space configuration updating unit is configured to update the initial space configuration based on a preset space adjustment range when the second similarity calculation result is less than the preset second similarity threshold, and return to perform the operation of inputting the updated initial space configuration into the second pre-trained artificial intelligence model based on the updated initial space configuration.

[0101] On the basis of the above embodiment, the scheme calculation module 330 comprises:

[0102] The building demand quantity calculation unit is configured to sum up the target supply demand quantities of the target spaces to obtain a building demand quantity matched with the target building.

[0103] The to-be-verified scheme generation unit is configured to construct at least one to-be-verified control scheme based on the building demand quantity and the cold source equipment attribute.

[0104] The to-be-verified scheme processing unit is configured to process each to-be-verified control scheme by using a pre-trained first artificial intelligence model to obtain a scheme energy consumption result matched with each to-be-verified control scheme.

[0105] The scheme determination unit is configured to set the to-be-verified control scheme corresponding to the scheme energy consumption result with the minimum value as an automatic control scheme matched with the target building.

[0106] On the basis of the above-mentioned embodiments, the data acquisition module 310 is further configured to, before acquiring the total demand target of the target building, acquire, in response to an input operation of a user, a demand satisfaction rate, a comfortable temperature upper limit, a temperature overheating threshold, a carbon dioxide upper limit, and a carbon dioxide threshold; calculate a target temperature of the target building based on a formula: target temperature = demand satisfaction rate * comfortable temperature upper limit + (1-demand satisfaction rate) * temperature overheating threshold; calculate a carbon dioxide concentration of the target building based on a formula: carbon dioxide concentration = demand satisfaction rate * carbon dioxide upper limit + (1-demand satisfaction rate) * carbon dioxide threshold; and aggregate the target temperature and the carbon dioxide concentration to obtain the total demand target of the target building.

[0107] The automatic control device of the cold source equipment provided in the embodiments of the present application can execute the automatic control method of the cold source equipment provided in any of the embodiments of the present application, and has the function modules and beneficial effects corresponding to the execution method.

[0108] Embodiment Four

[0109] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices such as personal digital assistants, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0110] As Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a loudspeaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0112] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as an automatic control method of a cold source device.

[0113] Correspondingly, the method includes:

[0114] obtaining a total demand target of a target building, a cold source device attribute, and at least one space demand target, wherein the target building is composed of the cold source device and at least one target space;

[0115] processing the total demand target and each space demand target using a pre-trained first artificial intelligence model to obtain a target supply demand amount respectively matched with each target space of the target building;

[0116] calculating an automatic control scheme matched with the target building based on the target supply demand amount of each target space and the cold source device attribute;

[0117] applying the automatic control scheme to the cold source device of the target building to automatically control the cold source device.

[0118] In some embodiments, the automatic control method of a cold source device can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the automatic control method of a cold source device described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the automatic control method of a cold source device by any other suitable means, e.g., by means of firmware.

[0119] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0120] Computer programs used to implement the processes of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a separate software package, or entirely on a remote machine or server.

[0121] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0123] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0124] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0125] It should be understood that the various forms of flow shown above can be reordered, additional steps added, or steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

Claims

1. An automatic control method for a cold source device, characterized in that: include: Obtaining a total demand target, a cooling source equipment attribute, and at least one space demand target of a target building, wherein the target building is composed of cooling source equipment and at least one target space; Using a pre-trained first artificial intelligence model to process the total demand target and the demand targets for each space, to obtain target supply demand quantities that match each target space of the target building; Calculating an automatic control solution that matches the target building based on the target supply demand of each target space and the properties of the cooling source equipment; The automatic control scheme is applied to the cold source equipment of the target building to automatically control the cold source equipment.

2. The method according to claim 1, characterized in that The total demand target includes: target temperature and target carbon dioxide concentration; And / or, the cold source equipment attributes include: chilled water pump attributes, cooling tower attributes, cooling water pump attributes, rated cooling capacity, partial load range, and optimal partial load of the cold source equipment.

3. The method according to claim 1, characterized in that The total demand target and each space demand target are processed using a pre-trained first artificial intelligence model to obtain target supply demand quantities that match each target space of the target building, including: Inputting the total demand target into a pre-trained second artificial intelligence model to obtain a building adjustment strategy; Configure each target space based on the building adjustment strategy to obtain a space test result that matches each target space, wherein the space test result includes: a test success result and a test failure result; Setting the building adjustment strategy as the space adjustment strategy for each target space whose space test result is a successful test result; Inputting the spatial demand target of each target space whose spatial test result is a test failure result into a pre-trained second artificial intelligence model to obtain a spatial adjustment strategy matching each target space; Based on the spatial adjustment strategy of each target space, the target supply demand that matches each target space is calculated respectively through the first artificial intelligence model.

4. The method according to claim 3, characterized in that The total demand target is input into a pre-trained second artificial intelligence model to obtain a building adjustment strategy, including: Get the pre-set initial building configuration; Inputting the initial building configuration into a pre-trained second artificial intelligence model to obtain a building test result that matches the initial building configuration; Performing a similarity calculation on the building test result and the total demand target to obtain a first similarity calculation result; When the first similarity calculation result is not less than a preset first similarity threshold, setting the initial building configuration as the building adjustment strategy of the target building; When the first similarity calculation result is less than a preset first similarity threshold, the initial building configuration is updated based on a preset building adjustment amplitude, and based on the updated initial building configuration, the operation of inputting the initial building configuration into the pre-trained second artificial intelligence model is returned.

5. The method according to claim 3, characterized in that The spatial demand target of each target space whose spatial test result is a test failure result is input into the pre-trained second artificial intelligence model to obtain a spatial adjustment strategy matching each target space, including: Setting the building adjustment strategy as the initial spatial configuration of the target space; Updating the initial spatial configuration based on a preset spatial adjustment amplitude, and inputting the updated initial spatial configuration into a pre-trained second artificial intelligence model to obtain a spatial test result that matches the updated initial spatial configuration; Performing similarity calculation on the spatial test result and the spatial demand target of the target space to obtain a second similarity calculation result; When the second similarity calculation result is not less than a preset second similarity threshold, setting the initial space configuration as the space adjustment strategy of the target space; When the second similarity calculation result is less than a preset second similarity threshold, the initial spatial configuration is updated based on a preset spatial adjustment amplitude, and based on the updated initial spatial configuration, the operation of inputting the updated initial spatial configuration into the pre-trained second artificial intelligence model is returned.

6. The method according to claim 1, characterized in that An automatic control solution matching the target building is calculated based on the target supply demand of each target space and the properties of the cooling source equipment, including: The target supply demand of each target space is summed up to obtain the building demand that matches the target building; Constructing at least one control solution to be verified based on the building demand and the properties of the cooling source equipment; Processing each control scheme to be verified by a pre-trained first artificial intelligence model to obtain energy consumption results matching each control scheme to be verified; The control scheme to be verified corresponding to the energy consumption result of the scheme with the smallest numerical value is set as the automatic control scheme matching the target building.

7. The method according to any one of claims 1-2, characterized in that Before obtaining the total demand target of the target building, it also includes: In response to a user input operation, obtaining a demand satisfaction rate, a comfort temperature upper limit, an overheating temperature lower limit, a carbon dioxide upper limit, and a carbon dioxide threshold; The target temperature of the target building is calculated based on the formula: target temperature = demand satisfaction rate × comfort temperature upper limit + (1-demand satisfaction rate) × overheating temperature upper limit; The carbon dioxide concentration of the target building is calculated based on the formula carbon dioxide concentration = demand satisfaction rate × carbon dioxide upper limit + (1-demand satisfaction rate) × carbon dioxide threshold; The target temperature is aggregated with the carbon dioxide concentration to obtain a total demand target for the target building.

8. An automatic control device for a cold source device, characterized in that: include: A data acquisition module is used to obtain a total demand target, a cooling source equipment attribute, and at least one space demand target of a target building, wherein the target building is composed of the cooling source equipment and the at least one target space; a supply demand calculation module, configured to process the total demand target and each space demand target using a pre-trained first artificial intelligence model to obtain target supply demand quantities that match each target space of the target building; A solution calculation module is used to calculate an automatic control solution that matches the target building based on the target supply demand of each target space and the properties of the cooling source equipment; The solution application module is used to apply the automatic control solution to the cold source equipment of the target building to automatically control the cold source equipment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the automatic control method for a cold source device according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement an automatic control method for a cold source device according to any one of claims 1 to 7 when executed.

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