An intelligent cooling regulation control method and system combined with fire water tank cold storage

Abstract

The present application relates to the field of intelligent cooling technology, and in particular to an intelligent cooling regulation and control method and system combined with fire water tank cold storage, including the following steps: collecting and preprocessing temperature data and electricity price data of several spaces in a building, and flow data of a fire water tank; calculating the preprocessed temperature data; classifying the time periods of the preprocessed electricity price data; calculating the preprocessed flow data; outputting a comfortable temperature range threshold; outputting a flow threshold; real-time monitoring of the temperature data outside the building, the temperature data and electricity price data of each space in the building, the flow data and cold storage capacity of the fire water tank; establishing a mode judgment model; performing different operations on the electric refrigeration equipment and the water tank cold storage system based on different modes; after performing the operation, sending a feedback report to the user end. The present application has the effect of improving the comprehensive utilization rate of the fire water tank, and can also significantly reduce the peak demand of the cooling system on the power grid.

Description

Technical Field

[0001] The present application relates to the field of intelligent cooling technology, and in particular to an intelligent cooling regulation and control method and system combined with fire water tank cold storage. Background Art

[0002] In existing building cooling systems, cold storage technology has been used to reduce peak electricity demand. This method usually involves using refrigeration equipment to make ice or cool water at night or during low electricity price periods, and then using the stored cold to provide cooling during peak daytime hours. However, these cold storage systems are often designed without considering the integration with other functions of the building, especially fire water pools. Fire water pools are an integral part of building safety design, and their main function is to provide necessary water for firefighting when a fire occurs. In non-firefighting conditions, these pools are usually idle, and their huge water storage capacity and good thermal insulation performance are not used for daily cooling needs.

[0003] Specifically, the potential heat exchange capacity of existing fire water pools is not being developed and utilized in non-emergency situations. The water in the pool can absorb heat and heat up in the summer, and cool down at night through natural cooling or auxiliary cooling methods, thereby achieving heat storage and release. If this process can be combined with the building cooling system, it will not only improve the comprehensive utilization rate of the fire water pool, but also significantly reduce the peak demand of the cooling system on the power grid, and reduce overall energy consumption and operating costs. However, there is currently a lack of an effective integration method and technology to achieve the collaborative work between the fire water pool and the building cooling system. Therefore, improvements are needed. Summary of the invention

[0004] In order to solve the above problems, the present application provides an intelligent cooling regulation and control method and system combined with fire water tank cold storage.

[0005] The first object of the invention of this application is achieved through the following technical solutions:

[0006] An intelligent cooling regulation and control method combined with cold storage in a fire water tank, characterized in that it comprises the following steps:

[0007] Collect and pre-process temperature data and electricity price data of several spaces in the building, and flow data of the fire water pool;

[0008] Calculate the preprocessed temperature data, including the temperature change rate and average temperature;

[0009] The pre-processed electricity price data is classified into time periods, including peak, flat and valley periods;

[0010] Calculate the pre-processed flow data, including the average flow and flow fluctuation range;

[0011] Based on the calculation results of temperature data, the comfortable temperature range threshold is output;

[0012] Based on the flow data calculation results, output the flow threshold;

[0013] Real-time monitoring of temperature data outside the building, temperature data and electricity price data of each space in the building, flow data and cold storage capacity of the fire water tank;

[0014] Based on the temperature data outside the building, the comfortable temperature range threshold, the flow threshold, the time period classification and the cold storage capacity, a mode judgment model is established, wherein the modes include a natural cooling mode, an electric cooling mode and a water pool cold storage mode;

[0015] Based on different modes, different operations are performed on the electric refrigeration equipment and the water pool cold storage system;

[0016] After the operation is completed, a feedback report is sent to the user end.

[0017] In a preferred embodiment, the step of calculating the pre-processed temperature data, including the temperature change rate and the average temperature, comprises the steps of:

[0018] Preset data points, For the Temperature data for data points, For the Temperature data for data points, It is The time of the data point, It is The time of each data point;

[0019] Based on preset formula , calculate the average temperature ;

[0020] Based on preset formula , calculate the temperature change rate .

[0021] In a preferred embodiment, the pre-processed electricity price data is classified into time periods, wherein the time period classification includes peak, flat and valley time periods, and the steps include:

[0022] Preset data points, For the Electricity price data for data points, is the minimum electricity price data, is the maximum electricity price data;

[0023] when When the time is low, it is judged to be a low period;

[0024] when When , the period is judged to be a flat valley period;

[0025] when , it is judged that the period is the peak period.

[0026] In a more specific embodiment, the step of calculating the pre-processed flow data, including the average flow and the flow fluctuation range, comprises the following steps:

[0027] Preset data points, For the Traffic data of data points, is the maximum flow data, is the minimum flow data;

[0028] Based on preset formula , calculate the average flow ;

[0029] Based on preset formula , calculate the flow fluctuation range .

[0030] In a preferred embodiment, the step of outputting a comfortable temperature range threshold based on the temperature data calculation result comprises the steps of:

[0031] Based on preset formula , is the preset minimum average temperature coefficient, is the preset minimum temperature change rate coefficient, It is the preset minimum constant term, which calculates the minimum threshold of the comfortable temperature range ;

[0032] Based on preset formula , is the preset maximum average temperature coefficient, is the preset maximum temperature change rate coefficient, is the preset maximum constant term, which is used to calculate the maximum threshold of the comfortable temperature range. .

[0033] In a preferred embodiment, the step of outputting the flow threshold value based on the flow data calculation result comprises the steps of:

[0034] Based on preset formula , is the preset flow coefficient, calculates the flow threshold .

[0035] In a preferred embodiment, the mode judgment model is established based on the temperature data outside the building, the comfortable temperature range threshold, the flow threshold, the time period classification and the cold storage capacity, and the mode includes the steps of natural cooling mode, electric cooling mode and water pool cold storage mode, including the steps of:

[0036] when , , , when the period is the low period, It is the temperature data outside the building, judging that it is in natural cooling mode;

[0037] when , , When , it is judged to be electric cooling mode;

[0038] when , , , when the period is the low period, is the cold storage capacity, It is the preset maximum cooling capacity of the water pool, and it is judged to be the water pool cooling mode.

[0039] In a preferred embodiment, the steps of performing different operations on the electric refrigeration equipment and the water pool cold storage system based on different modes include the steps of:

[0040] When the natural cooling mode is triggered, the electric refrigeration equipment is turned off and the natural ventilation system is turned on;

[0041] When the electric refrigeration mode is triggered, the electric refrigeration equipment is started and the operating parameters of the refrigeration equipment are adjusted;

[0042] When the water pool cold storage mode is triggered, the water pool cold storage system is started and the operating parameters of the water pump are adjusted.

[0043] The above-mentioned second objective of the present application is achieved through the following technical solutions:

[0044] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned intelligent cooling regulation and control method combined with cold storage in a fire water tank are implemented.

[0045] The third objective of the present application is achieved through the following technical solutions:

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned intelligent cooling regulation and control method combined with cold storage in a fire water tank.

[0047] In summary, the present application includes at least one of the following beneficial technical effects:

[0048] Collect data from temperature sensors, electricity price information, and fire pool flow sensors from different areas in the building. Preprocessing includes data cleaning (removing invalid or erroneous data) and normalization (converting data into a unified format or range). Perform mathematical calculations on the preprocessed temperature data. The temperature change rate refers to the amount of change in temperature per unit time, which can reflect the speed of temperature fluctuation. The average temperature refers to the arithmetic mean of all temperature measurements within a certain time range, which provides the overall level of temperature during that time period. Divide time into different categories based on the level of electricity prices, usually including peak hours (highest electricity prices), flat hours (medium electricity prices), and valley hours (lowest electricity prices). Such classification helps to optimize energy use according to different electricity prices in subsequent steps. Calculate statistics on the flow of the fire pool, including average flow (the average flow rate within a certain period of time) and flow fluctuation range (the maximum difference in flow values, that is, the difference between the maximum and minimum values) to understand the stability and changes of the flow. Determine a comfortable temperature threshold. This threshold defines the range in which the indoor temperature should be to ensure the comfort of the occupants. Set a flow threshold to monitor whether the flow of the fire pool is within the normal range. When the flow rate is greater than this flow threshold, it may indicate a problem. Continuously monitor and collect temperature data outside the building, as well as the temperature of each space inside the building, electricity price, fire pool flow and cold storage data for real-time analysis and decision-making. Use all collected data to build a pattern judgment model to judge and select the most appropriate cooling mode. The pattern judgment model will take into account external temperature, comfort temperature threshold, flow threshold, electricity price time period classification and cold storage. Perform appropriate actions, such as turning on or off electric refrigeration equipment and adjusting the operating status of the water pool cold storage system. Generate a feedback report that includes information such as operation results, energy usage, system performance, etc., and send it to users so that users can understand the operating status of the system and any necessary follow-up actions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flowchart of an implementation of an embodiment of an intelligent cooling regulation and control method combined with fire water tank cold storage in the present application;

[0050] Figure 2 This is a flowchart of the implementation of step S20 in an embodiment of an intelligent cooling regulation and control method combined with fire water tank cold storage in the present application;

[0051] Figure 3 This is a flowchart of the implementation of step S30 in an embodiment of an intelligent cooling regulation and control method combined with fire water tank cold storage according to the present application;

[0052] Figure 4This is a flowchart of the implementation of step S40 in an embodiment of an intelligent cooling regulation and control method combined with fire water tank cold storage in the present application;

[0053] Figure 5 It is another implementation flow chart of step S50 in an embodiment of an intelligent cooling regulation and control method combined with cold storage in a fire water tank;

[0054] Figure 6 It is a flowchart for implementing step S60 in an embodiment of an intelligent cooling regulation and control method combined with cold storage in a fire water tank;

[0055] Figure 7 It is another implementation flow chart of step S80 in an embodiment of an intelligent cooling regulation and control method combined with cold storage in a fire water tank;

[0056] Figure 8 It is another implementation flow chart of step S90 in an embodiment of an intelligent cooling regulation and control method combined with cold storage in a fire water tank;

[0057] Fig. 9 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0058] The following is combined with Figure 1-9 This application is described in further detail.

[0059] In one embodiment, if Figure 1 As shown, the present application discloses an intelligent cooling regulation and control method combined with fire water tank cold storage, which specifically includes the following steps:

[0060] S10: Collect and pre-process temperature data and electricity price data of several spaces in the building, and flow data of the fire water pool;

[0061] In this example, this step involves collecting data from temperature sensors, electricity price information, and fire pool flow sensors from different areas within the building. Preprocessing includes data cleaning (removing invalid or erroneous data) and normalization (converting data into a uniform format or range).

[0062] S20: Calculate the pre-processed temperature data, including the temperature change rate and the average temperature;

[0063] In this embodiment, mathematical calculations are performed on the preprocessed temperature data. The temperature change rate refers to the amount of change in temperature per unit time, which can reflect the temperature fluctuation rate. The average temperature refers to the arithmetic mean of all temperature measurements within a certain time range, which provides the overall level of temperature within that time period.

[0064] S30: classifying the pre-processed electricity price data into time periods, where the time period classification includes peak, flat and valley periods;

[0065] In this embodiment, the time is divided into different categories according to the electricity price, usually including peak time (highest electricity price), off-peak time (medium electricity price) and off-peak time (lowest electricity price). Such classification helps to optimize energy use according to different electricity prices in subsequent steps.

[0066] S40: Calculate the preprocessed flow data, including the average flow and flow fluctuation range;

[0067] In this embodiment, the statistical data of the fire water tank flow rate will be calculated, including the average flow rate (the average value of the flow rate within a certain period of time) and the flow fluctuation range (the maximum difference in the flow value, that is, the difference between the maximum and minimum values) to understand the stability and change of the flow rate.

[0068] S50: Outputting a comfortable temperature range threshold based on the temperature data calculation result;

[0069] In this embodiment, a comfortable temperature threshold is determined, which defines the range of indoor temperature to ensure the comfort of the occupants.

[0070] S60: outputting a flow threshold value based on the flow data calculation result;

[0071] In this embodiment, a flow rate threshold is set to monitor whether the flow rate of the fire water pool is within a normal range. When the flow rate is greater than the flow rate threshold, it may indicate a problem.

[0072] S70: Real-time monitoring of temperature data outside the building, temperature data and electricity price data of each space in the building, flow data and cold storage capacity of the fire water tank;

[0073] In this embodiment, the temperature data outside the building and the temperature of each space inside the building, electricity price, fire pool flow and cold storage data are continuously monitored and collected for real-time analysis and decision-making.

[0074] S80: Based on the temperature data outside the building, the comfortable temperature range threshold, the flow threshold, the time period classification and the cold storage capacity, a mode judgment model is established, wherein the modes include a natural cooling mode, an electric cooling mode and a water pool cold storage mode;

[0075] In this embodiment, all collected data will be used to establish a mode judgment model for judging and selecting the most appropriate cooling mode. The mode judgment model will take into account the external temperature, comfort temperature threshold, flow threshold, electricity price time classification and cold storage capacity.

[0076] S90: performing different operations on the electric refrigeration equipment and the water pool cold storage system based on different modes;

[0077] In this embodiment, corresponding operations are performed, such as turning on or off the electric refrigeration equipment and adjusting the operating state of the water pool cold storage system.

[0078] S100: After the operation is performed, a feedback report is sent to the user end.

[0079] In this embodiment, a feedback report including information such as operation results, energy usage, system performance, etc. is generated and sent to the user so that the user can understand the operating status of the system and any necessary follow-up actions.

[0080] Figure 2 , step S20, comprising the steps of:

[0081] S201: Preset data points, For the Temperature data for data points, For the Temperature data for data points, It is The time of the data point, It is The time of each data point;

[0082] S202: Based on preset formula , calculate the average temperature ;

[0083] S203: Based on preset formula , calculate the temperature change rate .

[0084] In this embodiment, a series of data points are first preset (S201), each of which contains temperature data and a corresponding timestamp. Indicates The temperature of the data point is Indicates The temperature of the data points is and Respectively represent the time of these two data points. Then, based on the preset formula Calculate the average temperature (S202), that is, add the temperature values ​​of all data points and then divide by the total number of data points to obtain the average temperature of the entire data set. Finally, to calculate the temperature change rate (S203), another preset formula is used: , which considers the temperature difference between two different time points divided by the corresponding time difference to obtain the temperature change rate Thus, through these steps, useful statistical information can be obtained from the collected temperature data for subsequent analysis and decision making.

[0085] Figure 3 , step S30, comprising the steps of:

[0086] S301: Preset data points, For the Electricity price data for data points, is the minimum electricity price data, is the maximum electricity price data;

[0087] S302: When When the time is low, it is judged to be a low period;

[0088] S303: When When , the period is judged to be a flat valley period;

[0089] S304: When , it is judged that the period is the peak period.

[0090] In this embodiment, a series of data points are first preset (S301), each of which represents the electricity price at a specific time point. Indicates The electricity price of each data point is recorded, and the minimum electricity price data in the entire data set is recorded. and maximum electricity price data Then, the time periods are classified according to the electricity price level: When (S302), that is, when the current electricity price is equal to the minimum electricity price, the period is judged to be a valley period, which usually means that the power supply is sufficient and the electricity price is low; when When (S303), that is, when the current electricity price is between the minimum electricity price and the maximum electricity price, it is judged that the period is a flat period and the electricity price is at a medium level; when When (S304), that is, when the current electricity price is greater than the maximum electricity price, the period is judged to be a peak period, at which time the electricity demand is high and the electricity price is relatively high. Through these steps, the system can divide different time periods into valley, flat and peak periods according to the electricity price data, thereby providing a basis for electricity management and consumption strategies.

[0091] Figure 4 , step S40, comprising the steps of:

[0092] S401: Preset data points, For the Traffic data of data points, is the maximum flow data, is the minimum flow data;

[0093] S402: Based on the preset formula , calculate the average flow ;

[0094] S403: Based on the preset formula , calculate the flow fluctuation range .

[0095] In this embodiment, a series of data points are first preset (S401), each of which contains flow data. Indicates The flow data of each data point is recorded, and the maximum flow data in the entire data set is recorded at the same time. and minimum flow data Then, based on the preset formula Calculate average flow (S402), that is, the flow values ​​of all data points are added together and then divided by the total number of data points to obtain the average flow of the entire data set. Next, in order to calculate the flow fluctuation range (S403), another preset formula is used , the formula is usually the difference between the maximum flow and the minimum flow, that is, the flow fluctuation range ,This can quantify the variation of flow data and provide important reference information for monitoring and managing the flow of fire water tanks. Through these steps, a statistical description of flow conditions can be obtained from flow data, which is helpful for evaluating the stability and performance of the system.

[0096] Figure 5 , step S50, comprising the steps of:

[0097] S501: Based on preset formula , is the preset minimum average temperature coefficient, is the preset minimum temperature change rate coefficient, It is the preset minimum constant term, which calculates the minimum threshold of the comfortable temperature range ;

[0098] S502: Based on the preset formula , is the preset maximum average temperature coefficient, is the preset maximum temperature change rate coefficient, is the preset maximum constant term, which is used to calculate the maximum threshold of the comfortable temperature range. .

[0099] In this embodiment, S501: according to a preset formula Calculate the minimum threshold for the comfortable temperature range This formula contains three key parameters: the preset minimum average temperature coefficient ( ), preset minimum temperature change rate coefficient ( ) and the preset minimum constant term ( ). These parameters are pre-set based on comfort research and actual needs.

[0100] S502: Similarly, using another preset formula To calculate the maximum threshold of the comfort temperature range This formula also contains three parameters: the preset maximum average temperature coefficient ( ), preset maximum temperature change rate coefficient ( ) and the preset maximum constant term ( ). These parameters determine the upper limit of the comfortable temperature range.

[0101] Figure 6 , step S60, comprising the steps of:

[0102] S601: Based on preset formula , is the preset flow coefficient, calculates the flow threshold .

[0103] In this embodiment, S601: using a preset formula To calculate the flow threshold The formula contains a key parameter, namely the preset flow coefficient ( ). This factor is pre-set based on the system design requirements, safety standards and expected flow range.

[0104] Figure 7 , step S80, comprising the steps of:

[0105] S801: When , , , when the period is the low period, It is the temperature data outside the building, judging that it is in natural cooling mode;

[0106] S802: When , , When , it is judged to be electric cooling mode;

[0107] S803: When , , , when the period is the low period, is the cold storage capacity, It is the preset maximum cooling capacity of the water pool, and it is judged to be the water pool cooling mode.

[0108] In this embodiment, in step S801, when , , , when the period is the low period, is the temperature data outside the building, indicating that the external ambient temperature is low enough to effectively reduce the indoor temperature through natural ventilation or other natural cooling methods, then the natural cooling mode will be judged and enabled. , , When the temperature is low, the system will judge and switch to electric cooling mode, that is, start electric cooling equipment, such as air conditioner or refrigerator, to meet the indoor cooling demand. Then, in step S803, when , , , when the period is the low period, is the cold storage capacity, is the preset maximum cooling capacity of the water pool, which means that the water pool has the ability to store more cooling capacity. In this case, the water pool cooling mode will be judged and selected, that is, when the electricity price is low, the cooling capacity is generated by the refrigeration equipment and stored in the water pool, so that the stored cooling capacity can be used for cooling during the period of high electricity price, thereby saving energy costs. These three steps together constitute an intelligent decision-making process to ensure that the cooling system can operate in the most economical and efficient way according to real-time data and preset conditions.

[0109] Figure 8 , step S90, comprising the steps of:

[0110] S901: When the natural cooling mode is triggered, the electric cooling equipment is turned off and the natural ventilation system is turned on;

[0111] S902: When the electric refrigeration mode is triggered, the electric refrigeration equipment is started and the operating parameters of the refrigeration equipment are adjusted;

[0112] S903: When the water pool cold storage mode is triggered, the water pool cold storage system is started and the operating parameters of the water pump are adjusted.

[0113] In this embodiment, when entering step S901 and triggering the natural cooling mode, in order to maximize energy conservation, all electric refrigeration equipment will be turned off to avoid unnecessary power consumption. At the same time, the natural ventilation system will be turned on to use the colder air outside to reduce the indoor temperature, and the cooling effect will be achieved through natural convection or mechanical ventilation. In step S902, when it is determined that the electric refrigeration mode is needed to meet the cooling demand, the electric refrigeration equipment will be started, and the operating parameters of the refrigeration equipment, such as the set temperature, fan speed, etc., will be adjusted according to the indoor and outdoor environmental conditions and the preset comfort standards to ensure effective refrigeration. Finally, in step S903, when the water pool cold storage mode is triggered, that is, the cold capacity is prepared and stored by the refrigeration equipment during the low electricity price period, the water pool cold storage system will be started, and the operating parameters of the water pump, such as the water flow rate and the cycle, will be adjusted to optimize the storage and subsequent use of the cold capacity. These three steps together ensure that the cooling system can automatically perform the most appropriate operation according to different cooling needs and environmental conditions to achieve efficient and energy-saving cooling purposes.

[0114] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0115] In one embodiment, an intelligent cooling regulation and control system combined with fire water tank cold storage is provided, and the intelligent cooling regulation and control system combined with fire water tank cold storage corresponds to the intelligent cooling regulation and control method combined with fire water tank cold storage in the above embodiment. The intelligent cooling regulation and control system combined with fire water tank cold storage includes:

[0116] Collection module: collects and pre-processes temperature data and electricity price data of several spaces in the building, and flow data of the fire water pool;

[0117] The first calculation module is used to calculate the pre-processed temperature data, including the temperature change rate and the average temperature;

[0118] Classification module: classify the pre-processed electricity price data into time periods, including peak, flat and valley periods;

[0119] The second calculation module is used to calculate the pre-processed flow data, including the average flow and flow fluctuation range;

[0120] The first output module: outputs the comfortable temperature range threshold based on the temperature data calculation result;

[0121] The second output module: outputs the flow threshold value based on the flow data calculation result;

[0122] Real-time monitoring module: real-time monitoring of temperature data outside the building, temperature data and electricity price data of each space in the building, flow data and cold storage capacity of the fire water pool;

[0123] Establishing a model module: establishing a mode judgment model based on the temperature data outside the building, the comfortable temperature range threshold, the flow threshold, the time period classification and the cold storage capacity, wherein the modes include a natural cooling mode, an electric cooling mode and a water pool cold storage mode;

[0124] Execution operation module: based on different modes, perform different operations on the electric refrigeration equipment and the water pool cold storage system;

[0125] Feedback module: After the operation is completed, a feedback report is sent to the user end.

[0126] Optionally, also include:

[0127] The first preset module: preset data points, For the Temperature data for data points, For the Temperature data for data points, It is The time of the data point, It is The time of each data point;

[0128] Average temperature calculation module: based on preset formula , calculate the average temperature ;

[0129] Temperature change rate module: based on preset formula , calculate the temperature change rate .

[0130] Optionally, also include:

[0131] The second preset module: preset data points, For the Electricity price data for data points, is the minimum electricity price data, is the maximum electricity price data;

[0132] Valley module: When When the time is low, it is judged to be a low period;

[0133] Pinggu module: When , the period is judged to be a flat valley period;

[0134] Trough module: when , it is judged that the period is the peak period.

[0135] Optionally, also include:

[0136] The third preset module: preset data points, For the Traffic data of data points, is the maximum flow data, is the minimum flow data;

[0137] Average flow calculation module: based on preset formula , calculate the average flow ;

[0138] Fluctuation range calculation module: based on preset formula , calculate the flow fluctuation range .

[0139] Optionally, also include:

[0140] Minimum threshold calculation module: based on preset formula , is the preset minimum average temperature coefficient, is the preset minimum temperature change rate coefficient, It is the preset minimum constant term, which calculates the minimum threshold of the comfortable temperature range ;

[0141] Maximum threshold calculation module: based on preset formula , is the preset maximum average temperature coefficient, is the preset maximum temperature change rate coefficient, is the preset maximum constant term, which is used to calculate the maximum threshold of the comfortable temperature range. .

[0142] Optionally, also include:

[0143] Traffic threshold calculation module: based on preset formula , is the preset flow coefficient, calculates the flow threshold .

[0144] Optionally, also include:

[0145] Natural cooling mode module: , , , when the period is the low period, It is the temperature data outside the building, judging that it is in natural cooling mode;

[0146] Electric cooling mode module: , , When , it is judged to be electric cooling mode;

[0147] Pool cooling mode module: , , , when the period is the low period, is the cold storage capacity, It is the preset maximum cooling capacity of the water pool, and it is judged to be the water pool cooling mode.

[0148] Optionally, also include:

[0149] Triggering the natural cooling mode module: When the natural cooling mode is triggered, the electric refrigeration equipment is turned off and the natural ventilation system is turned on;

[0150] Triggering electric cooling mode module: when the electric cooling mode is triggered, the electric cooling equipment is started and the operating parameters of the cooling equipment are adjusted;

[0151] Triggering the water pool cold storage mode module: When the water pool cold storage mode is triggered, the water pool cold storage system is started and the operating parameters of the water pump are adjusted.

[0152] For the specific definition of an intelligent cooling regulation and control system combined with fire water tank cold storage, please refer to the definition of an intelligent cooling regulation and control method combined with fire water tank cold storage in the above text, which will not be repeated here. Each module in the above-mentioned intelligent cooling regulation and control system combined with fire water tank cold storage can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0153] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store different modes. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent cooling regulation and control method combined with fire water tank cold storage is implemented.

[0154] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, an intelligent cooling regulation and control method combined with cold storage in a fire water tank is implemented.

[0155] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, an intelligent cooling regulation and control method combined with cold storage in a fire water tank is implemented.

[0156] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0157] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. An intelligent cooling regulation and control method combined with fire water tank cold storage, characterized in that: Includes steps: Collect and pre-process temperature data and electricity price data of several spaces in the building, and flow data of the fire water tank; Calculate the preprocessed temperature data, including the temperature change rate and average temperature; Preset data points, For the Temperature data for data points, For the Temperature data for data points, It is The time of the data point, It is The time of each data point; Based on preset formula , calculate the average temperature ; Based on preset formula , calculate the temperature change rate ; The pre-processed electricity price data is classified into time periods, including high-valley, flat-valley and low-valley periods; Calculate the pre-processed flow data, including the average flow and flow fluctuation range; Based on the calculation results of temperature data, the comfortable temperature range threshold is output; Based on preset formula , is the preset minimum average temperature coefficient, is the preset minimum temperature change rate coefficient, It is the preset minimum constant term, which calculates the minimum threshold of the comfortable temperature range ; Based on preset formula , is the preset maximum average temperature coefficient, is the preset maximum temperature change rate coefficient, is the preset maximum constant term, which is used to calculate the maximum threshold of the comfortable temperature range ; Based on the flow data calculation results, output the flow threshold; Real-time monitoring of temperature data outside the building, temperature data and electricity price data of each space in the building, flow data and cold storage capacity of the fire water tank; Based on the temperature data outside the building, the comfortable temperature range threshold, the flow threshold, the time period classification and the cold storage capacity, a mode judgment model is established, wherein the modes include a natural cooling mode, an electric cooling mode and a water pool cold storage mode; Based on different modes, different operations are performed on the electric refrigeration equipment and the water pool cold storage system; After the operation is completed, a feedback report is sent to the user end.

2. According to claim 1, the intelligent cooling regulation and control method combined with the cold storage of the fire water tank is characterized by: The step of classifying the pre-processed electricity price data into time periods, wherein the time period classification includes high-valley, flat-valley, and low-valley time periods, includes the following steps: Preset data points, For the Electricity price data for data points, is the minimum electricity price data, is the maximum electricity price data; when When the time is low, it is judged to be a low period; when When , the period is judged to be a flat valley period; when , it is judged that the period is the peak period.

3. The intelligent cooling regulation and control method combined with the cold storage of the fire water tank according to claim 1 is characterized in that: The step of calculating the pre-processed flow data, including the average flow and the flow fluctuation range, comprises the following steps: Preset data points, For the Traffic data of data points, is the maximum flow data, is the minimum flow data; Based on preset formula , calculate the average flow ; Based on preset formula , calculate the flow fluctuation range .

4. The intelligent cooling regulation and control method combined with the cold storage of the fire water tank according to claim 3 is characterized by: The step of outputting the flow threshold value based on the flow data calculation result comprises the steps of: Based on preset formula , is the preset flow coefficient, calculates the flow threshold .

5. The intelligent cooling regulation and control method combined with the cold storage of the fire water tank according to claim 4 is characterized in that: The method of establishing a mode judgment model based on the temperature data outside the building, the comfortable temperature range threshold, the flow threshold, the time period classification and the cold storage capacity, wherein the mode includes the steps of natural cooling mode, electric cooling mode and water pool cold storage mode, comprises the following steps: when , , , when the period is the low period, It is the temperature data outside the building, judging that it is in natural cooling mode; when , , When , it is judged to be electric cooling mode; when , , , when the period is the low period, is the cold storage capacity, It is the preset maximum cooling capacity of the water pool, and it is judged to be the water pool cooling mode.

6. The intelligent cooling regulation and control method combined with the cold storage of the fire water tank according to claim 1 is characterized in that: The steps of performing different operations on the electric refrigeration equipment and the water pool cold storage system based on different modes include the following steps: When the natural cooling mode is triggered, the electric refrigeration equipment is turned off and the natural ventilation system is turned on; When the electric refrigeration mode is triggered, the electric refrigeration equipment is started and the operating parameters of the refrigeration equipment are adjusted; When the water pool cold storage mode is triggered, the water pool cold storage system is started and the operating parameters of the water pump are adjusted.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of an intelligent cooling regulation and control method combined with fire water tank cold storage as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of an intelligent cooling regulation and control method combined with fire water tank cold storage as claimed in any one of claims 1 to 6.

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