An energy-saving optimization control system for water-cooled air-conditioning systems in data centers based on intelligence

Abstract

The present invention belongs to the technical field of water-cooled air-conditioning energy-saving control, and specifically is an energy-saving optimization control system for a water-cooled air-conditioning system in a data center based on intelligence. The energy-saving potential evaluation coefficient of the historical monitoring period of the target system is explored to determine whether there is room for energy saving by considering abnormal energy efficiency performance and abnormal load matching. The comprehensive evaluation of the early energy-saving potential of the water-cooled air-conditioning system and the lack of spatial judgment in the prior art are improved. After determining that there is room for energy saving, the energy-saving effectiveness coefficient of the candidate configuration set of each component of the target system relative to the component benchmark configuration set is examined based on a refrigeration simulation test, and the energy-saving effect of adjusting the configuration parameters of the target system components is fed back. If the effect is not good, the potential upgrade components of the target system are further identified, and the recommended sequence of component upgrade models of the target system is determined for feedback, so as to fully tap the energy-saving potential of the water-cooled control system of the data center of the target manufacturing plant, thereby significantly improving the energy-saving control performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of water-cooling air-conditioning energy-saving control, and specifically is an energy-saving optimization control system for a water-cooling air-conditioning system in a data center based on intelligence. Background Art

[0002] With the rapid development of information technology, data centers have become an indispensable core component of modern manufacturing. In manufacturing plants, data centers are not only responsible for the storage and processing of massive production data, but also the key infrastructure for promoting the implementation of advanced manufacturing models such as intelligent manufacturing and industrial Internet. However, since data centers need to operate for a long time and at a high intensity, their high energy consumption has gradually become the focus of attention, especially in terms of water-cooled air-conditioning systems, whose energy consumption accounts for a considerable proportion of the total energy consumption of data centers. In view of this, energy-saving and optimized management of water-cooled air-conditioning systems in manufacturing plant data centers has become particularly critical.

[0003] In the prior art, there are also solutions related to energy saving that can be applied to water-cooled air-conditioning systems in manufacturing plants. For example, the invention patent application for an energy-saving control method, system and equipment for a central air-conditioning water-cooling unit with patent number CN2021103734026 performs trial operations and / or fine-tuning operations on parameters including the number and gear positions of cooling tower fans, the number of cooling tower water pumps turned on, and the chilled water supply temperature setting within a safe range. After each trial operation and / or fine-tuning operation, the overall energy consumption of the chiller and the cooling tower is calculated, and the operating parameter combination with the lowest energy consumption and its corresponding basic operating conditions and energy consumption per unit time are used as operating knowledge and learning parameters, and recorded in a database , in order to find the most energy-saving control method, although it can improve the energy saving of water-cooled air conditioners, there are still common limitations of existing technologies. Specifically, the existing technology focuses on the adjustment strategy of the configuration parameters of each component in the water-cooled air-conditioning system to pursue the energy-saving goal. On the one hand, there is a lack of comprehensive evaluation and spatial judgment of the early energy-saving potential of the water-cooled air-conditioning system, which makes the formulation of energy-saving optimization plans lack of scientific basis and pertinence, and it is easy to fall into the misunderstanding of "blind adjustment", which often leads to unnecessary optimization measures for systems that already have a high energy efficiency level. Not only does it fail to effectively promote the efficient use of resources, but it may also cause resource mismatch, unclear energy-saving effects, and even reversely increase energy consumption due to improper adjustments.

[0004] On the other hand, the decisive influence of the performance and efficiency of the component structure itself on the overall energy consumption of the water cooling control system is ignored. When the component itself no longer has the potential to further improve energy efficiency through parameter adjustment due to design, material or technical limitations, continuing such adjustments will not only fail to bring substantial energy efficiency improvements, but may also waste resources. Energy-saving optimization measures only remain at a single level of component parameter adjustment and ignore the upgrade and update of component models. This is a manifestation of insufficient exploration of the energy-saving potential of the water cooling control system. Summary of the invention

[0005] In order to overcome the shortcomings of the background technology, the embodiment of the present invention provides an energy-saving optimization control system for a data center water-cooling air-conditioning system based on intelligence, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: an intelligent data center water-cooling air conditioning system energy-saving optimization control system, including: an energy-saving space judgment module, a component configuration module, a component configuration optimization module, an equipment upgrade recommendation module and a cloud database.

[0007] The energy-saving space judgment module is connected to the component configuration configuration module, the component configuration configuration module is connected to the component configuration optimization module, the component configuration optimization module is connected to the equipment upgrade recommendation module, and the cloud database is respectively connected to the energy-saving space judgment module, the component configuration configuration module, and the equipment upgrade recommendation module.

[0008] The energy-saving space judgment module is used to record the water-cooled air-conditioning system of the target manufacturing plant data center as the target system, retrieve the refrigeration performance data of the target system during the historical monitoring period, including the cooling capacity, power consumption and supply and return water temperature difference in each monitoring period during each working day, and judge whether there is energy-saving space in the target system.

[0009] The component configuration module is used to retrieve the fixed configuration parameter combinations of each component in the historical monitoring period of the target system to generate a component benchmark configuration set when it is determined that there is energy-saving space in the target system. Combined with the adjustable configuration parameter ranges of each component of the target system stored in the cloud database, several component configuration combinations of the target system are obtained through random configuration, which are recorded as candidate configuration sets of each component.

[0010] The component configuration optimization module is used to execute the refrigeration simulation test of the target system based on the component baseline configuration set and each component candidate configuration set respectively, and evaluate the energy-saving effectiveness coefficient of each component candidate configuration set of the target system relative to the component baseline configuration set. If the maximum value is less than the preset value, the equipment upgrade instruction is triggered. Otherwise, the component candidate configuration set corresponding to the maximum value is fed back as the optimal component configuration set of the target system.

[0011] The equipment upgrade recommendation module is used to respond to equipment upgrade instructions, retrieve the operation and maintenance performance data of each component in the historical monitoring period of the target system to identify the potential upgrade components of the target system, determine the recommended sequence of component upgrade models of the target system and provide feedback.

[0012] The cloud database is used to store the adjustable configuration ranges and replaceable model sequences of each component of the target system, store the preset reasonable energy efficiency ratio thresholds of the water-cooled air-conditioning system and the reasonable matching status of the cooling load corresponding to the preset supply and return water temperature difference ranges, and store the monitoring compliance value ranges of each specific operating indicator of each component of the water-cooled air-conditioning system.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention explores the energy-saving potential evaluation coefficient of the target system's historical monitoring period by considering abnormal energy efficiency performance and abnormal load matching, and judges whether there is room for energy saving based on this, thereby improving the lack of comprehensive evaluation and spatial judgment of the prior art on the early energy-saving potential of the water-cooled air-conditioning system, avoiding blind optimization, and ensuring the pertinence and effectiveness of the energy-saving measures of the water-cooled air-conditioning system.

[0014] (2) In the process of adjusting the configuration parameters of each component of the water-cooled air-conditioning system for energy-saving optimization, the present invention comprehensively considers the abnormal improvement in energy efficiency performance, abnormal improvement in load matching, and improvement in refrigeration efficiency of the candidate configuration sets of each component of the water-cooled air-conditioning system relative to the component benchmark configuration set, and introduces a detailed performance comparison mechanism to obtain accurate, objective, and reliable energy-saving effectiveness coefficient indicators.

[0015] (3) When the energy-saving effect of adjusting the configuration parameters of the components of the water-cooled air-conditioning system is not good, the present invention further identifies the potential upgrade components of the water-cooled air-conditioning system, determines the recommended sequence of component upgrade models of the water-cooled air-conditioning system for feedback, and fully explores the energy-saving potential of the water-cooled control system from the dimension of component model upgrade and update, and provides the staff with an intuitive and clear model update plan, thereby significantly improving the energy-saving control performance of the water-cooled air-conditioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 It is a schematic diagram of module connection of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1As shown, the present invention provides an energy-saving optimization control system for a water-cooled air-conditioning system in a data center based on intelligence, comprising: an energy-saving space judgment module, a component configuration module, a component configuration optimization module, an equipment upgrade recommendation module and a cloud database.

[0020] The energy-saving space judgment module is connected to the component configuration configuration module, the component configuration configuration module is connected to the component configuration optimization module, the component configuration optimization module is connected to the equipment upgrade recommendation module, and the cloud database is respectively connected to the energy-saving space judgment module, the component configuration configuration module, and the equipment upgrade recommendation module.

[0021] The energy-saving space judgment module is used to record the water-cooled air-conditioning system of the target manufacturing plant data center as the target system, retrieve the refrigeration performance data of the target system within the historical monitoring period, including the cooling capacity, power consumption and supply and return water temperature difference in each monitoring period during each working day, and judge whether there is energy-saving space in the target system.

[0022] It should be noted that the monitoring process of the refrigeration performance data within the historical monitoring period of the above-mentioned target system is as follows: temperature sensors are installed on the water inlet and outlet pipes of the evaporator to monitor and record the temperature difference when the cold water enters and leaves the evaporator in real time, and the cooling capacity is calculated using the temperature difference and the water flow through the evaporator through professional integrated software. The power consumption is monitored in real time by installing an electric energy meter on the power line of the air-conditioning host or by using a current transformer in conjunction with a voltage transmitter. The supply and return water temperature difference is obtained by installing a temperature sensor in the supply and return water pipelines, measuring the supply water temperature and the return water temperature respectively, and calculating the temperature difference between the two. The cooling capacity, power consumption and supply and return water temperature difference of each monitoring period on each working day are transmitted to the integrated control unit by wired or wireless means for collation.

[0023] Specifically, the method of judging whether there is room for energy saving in the target system includes: taking the ratio of cooling capacity to power consumption as the energy efficiency ratio, obtaining the energy efficiency ratio of each monitoring period in each working day during the historical monitoring period of the target system, substituting it into a rectangular coordinate system with time as the horizontal axis and energy efficiency ratio as the vertical axis to draw an energy efficiency monitoring curve, and extracting the preset reasonable energy efficiency ratio threshold of the water-cooled air-conditioning system stored in the cloud database. Draw the corresponding straight line in the rectangular coordinate system, mark it as the baseline, and calculate the energy efficiency abnormality performance of the target system in the historical monitoring period. .

[0024] Extract the supply and return water temperature difference of each monitoring period in each working day during the historical monitoring period of the target system, compare it with the preset supply and return water temperature difference range corresponding to the reasonable matching state of the refrigeration load of the water-cooled air-conditioning system stored in the cloud database, and calculate the load abnormal matching degree of the historical monitoring period of the target system .

[0025] The energy efficiency abnormality performance and load abnormality matching degree are respectively accumulated with the product of their corresponding preset weight ratios to obtain the energy-saving potential evaluation coefficient of the target system's historical monitoring period. If it is greater than or equal to the preset energy-saving potential evaluation coefficient reaching the standard threshold, it is judged that there is energy-saving space in the target system, otherwise it is judged that there is no energy-saving space.

[0026] Specifically, the The calculation process includes: sorting out the energy efficiency monitoring curve segments below the baseline in the rectangular coordinate system corresponding to the working days, monitoring periods, and deviation energy efficiency ratios, and counting the number of abnormal monitoring periods on each working day in the historical monitoring cycle of the target system. and maximum deviation energy efficiency ratio , is the number of each working day in the historical monitoring period, , according to the formula The energy efficiency abnormality performance of the target system in the historical monitoring period is obtained, where The number of monitoring periods within a preset unit working day.

[0027] It should be noted that the deviation energy efficiency ratio corresponding to each energy efficiency monitoring curve segment below the baseline refers to the absolute difference between the energy efficiency ratio corresponding to each energy efficiency monitoring curve segment below the baseline and the preset reasonable energy efficiency ratio threshold of the water-cooled air-conditioning system.

[0028] Specifically, the The calculation process includes: using the standard deviation formula to calculate the standard deviation of the supply and return water temperature difference on each working day in the historical monitoring period of the target system, and using the ratio of the standard deviation to the reference supply and return water temperature difference as the supply and return water temperature difference fluctuation ratio on each working day in the historical monitoring period of the target system.

[0029] It should be noted that the above-mentioned reference supply and return water temperature difference refers to the calculated average of the upper and lower limits of the preset supply and return water temperature difference range corresponding to the reasonable matching state of the refrigeration load of the water-cooled air-conditioning system.

[0030] Calculate the supply and return water offset temperature difference of each monitoring period outside the preset supply and return water temperature difference range when the supply and return water temperature difference is in a reasonable matching state of the water-cooled air-conditioning refrigeration load on each working day in the historical monitoring period of the target system, and add it to the ratio of the reference supply and return water temperature difference to obtain the supply and return water temperature difference offset ratio of each working day in the historical monitoring period of the target system.

[0031] It should be noted that the calculation process of the supply and return water offset temperature difference in each monitoring period outside the preset supply and return water temperature difference range when the supply and return water temperature difference in each working day within the historical monitoring period of the above-mentioned target system is as follows: the supply and return water temperature difference in each monitoring period outside the preset supply and return water temperature difference range when the supply and return water temperature difference in each working day within the historical monitoring period of the target system is in a reasonable matching state of the water-cooled air-conditioning refrigeration load and the supply and return water temperature difference in each monitoring period outside the preset supply and return water temperature difference range are compared with the upper limit value and the lower limit value of the preset supply and return water temperature difference range corresponding to the reasonable matching state of the water-cooled air-conditioning refrigeration load. If it is greater than the upper limit value, the supply and return water offset temperature difference is taken as the difference between the supply and return water temperature difference and the upper limit value; if it is less than the lower limit value, the supply and return water offset temperature difference is taken as the absolute difference between the supply and return water temperature difference and the lower limit value.

[0032] The product of the supply and return water temperature difference fluctuation ratio and the supply and return water temperature difference offset ratio is taken as the supply and return water temperature difference constant index. The supply and return water temperature difference constant index of each working day in the historical monitoring period of the target system is averaged to obtain the load anomaly matching degree of the historical monitoring period of the target system.

[0033] The embodiment of the present invention explores the energy-saving potential evaluation coefficient of the target system's historical monitoring period and judges whether there is room for energy saving by considering abnormal energy efficiency performance and abnormal load matching. It improves the lack of comprehensive evaluation and spatial judgment of the early energy-saving potential of the water-cooled air-conditioning system in the prior art, avoids blind optimization, and ensures the pertinence and effectiveness of the energy-saving measures of the water-cooled air-conditioning system.

[0034] The component configuration module is used to retrieve the fixed configuration parameter combination of each component in the historical monitoring period of the target system to generate a component benchmark configuration set when judging that there is energy saving space in the target system, and to obtain several component configuration combinations of the target system through random configuration in combination with the adjustable configuration parameter intervals of each component of the target system stored in the cloud database, which are recorded as candidate configuration sets of each component.

[0035] The component configuration optimization module is used to respectively perform refrigeration simulation tests of the target system based on the component baseline configuration set and each component candidate configuration set, evaluate the energy-saving effectiveness coefficient of each component candidate configuration set of the target system relative to the component baseline configuration set, and if the maximum value is less than a preset value, trigger an equipment upgrade instruction, otherwise the component candidate configuration set corresponding to the maximum value is fed back as the optimal component configuration set of the target system.

[0036] Specifically, the respectively executing refrigeration simulation tests of the target system based on the component benchmark configuration set and the candidate configuration sets of each component includes: obtaining a structural design drawing of a data center of a target manufacturing plant to build a refrigeration test simulation model.

[0037] The temperature and humidity values ​​of each partition of the target manufacturing plant data center before the start of each working day during the historical monitoring period of the target system are collected, and the reference temperature and humidity values ​​of each partition of the target manufacturing plant data center before the target system works are obtained by average calculation, so as to simulate the environment before the target system refrigeration simulation test.

[0038] It should be noted that the above-mentioned target manufacturing plant data center structural design drawing is manually uploaded to the system of the present invention by image capture, and the division of each partition of the target manufacturing plant data center can be obtained by referring to the partition annotation information in the target manufacturing plant data center structural design drawing.

[0039] The target system is placed in the component baseline configuration set and the component candidate configuration set states respectively, and the cooling mode is started. The reference temperature value and reference humidity value of each partition of the target manufacturing plant data center are required to be adjusted to the preset temperature value and preset humidity value of the target manufacturing plant data center working requirements and reasonable operating environment, so as to perform the cooling simulation test.

[0040] Specifically, the energy-saving effectiveness coefficient of the candidate configuration sets of each component of the target system is evaluated relative to the component benchmark configuration set, including: collecting the cooling capacity, output electric power, supply and return water temperature difference of the target system at each monitoring time point during the refrigeration simulation test based on the component benchmark configuration set and each component candidate configuration set, and the temperature and humidity values ​​of each partition of the target manufacturing plant data center.

[0041] The energy efficiency abnormality, load abnormality matching and cooling efficiency index of the target system based on the component benchmark configuration set and the cooling simulation test of each component candidate configuration set are calculated and recorded as , and ,in is the number of the candidate configuration set of each component, .

[0042] It should be noted that the calculation methods of the energy efficiency abnormality performance and load abnormality matching of the refrigeration simulation test of the above-mentioned target system based on the component benchmark configuration set and the candidate configuration set of each component are consistent with the calculation methods of the energy efficiency abnormality performance and load abnormality matching of the historical monitoring period of the above-mentioned target system, and will not be elaborated here.

[0043] By formula Evaluate the energy-saving effectiveness coefficient of the candidate configuration set of each component of the target system relative to the component baseline configuration set, is a natural constant.

[0044] Specifically, the The calculation formula is: ,in They are respectively the first , Monitoring time point target manufacturing plant data center The temperature and humidity values ​​of each partition, is the number of each partition of the target manufacturing plant data center, , is the number of each monitoring time point, , To monitor the number of time points, is the number of data center partitions for the target manufacturing plant, is a preset constant.

[0045] It should be noted that the above The calculation method is the same as The calculation method is the same as that of , and will not be elaborated here.

[0046] In the process of adjusting the configuration parameters of each component of the water-cooled air-conditioning system for energy-saving optimization, the embodiment of the present invention comprehensively considers the abnormal improvement in energy efficiency performance, abnormal improvement in load matching, and improvement in refrigeration efficiency of the candidate configuration sets of each component of the water-cooled air-conditioning system relative to the component benchmark configuration set, and introduces a detailed performance comparison mechanism to obtain accurate, objective and reliable energy-saving effectiveness coefficient indicators.

[0047] The device upgrade recommendation module is used to respond to the device upgrade instruction, retrieve the operation and maintenance performance data of each component in the historical monitoring period of the target system to identify the potential upgrade components of the target system, determine the recommended sequence of component upgrade models of the target system and provide feedback.

[0048] Specifically, the operation and maintenance performance data includes operation performance parameters and maintenance performance parameters, wherein the operation performance parameters include monitoring values ​​of specific operation indicators corresponding to each monitoring period in each working day, and the maintenance performance parameters include maintenance evaluation levels of each maintenance.

[0049] It should be noted that the operating performance coefficient of each component during the historical monitoring period of the above-mentioned target system is usually automatically collected by sensors or controllers installed at key locations of the components.

[0050] Exemplarily, the target system components include but are not limited to cooling towers, water pumps, etc., and the cooling tower corresponds to its specific operating indicators including water temperature, flow, pressure, motor current and voltage, and noise, wherein the water temperature and pressure are respectively collected in real time by temperature sensors and pressure gauges installed in the cooling water inlet and outlet pipes, the flow rate is collected in real time by a flow meter installed on the cooling water pipe, the motor current and voltage are collected in real time by electrical monitoring equipment arranged on the cooling tower motor, and the noise is collected in real time by a noise meter installed on the cooling tower body.

[0051] The specific operating indicators of the water pump include flow rate, head, vibration amplitude, temperature of the water pump bearing and the water pump motor. The flow rate is collected in real time by the flow meter installed at the outlet of the water pump, and the head is collected by the pressure gauge, flow rate meter and fluid height monitoring meter installed at the inlet and outlet of the water pump. The head calculation formula is used to calculate the head. Get it, are the fluid pressure value, fluid velocity value and fluid height at the pump outlet, respectively. are the pump inlet fluid pressure value, fluid velocity value and fluid height respectively. is the preset fluid density, To preset the gravity acceleration, the vibration amplitude is collected in real time by the vibration sensor installed on the water pump body, and the temperature of the water pump bearing and the water pump motor is collected in real time by the temperature sensors installed at the key parts of the water pump bearing and the water pump motor.

[0052] It should also be noted that the maintenance performance parameters of each component within the historical monitoring period of the above-mentioned target system, that is, the maintenance assessment level of each maintenance, are comprehensively evaluated by the professional maintenance team of the water-cooled air-conditioning system within the data center of the target manufacturing plant after each maintenance operation is completed, based on a series of established standards and professional judgments. The established standards include but are not limited to maintenance difficulty, maintenance time, maintenance amount, etc. The professional maintenance team then uploads or records the maintenance assessment level of each component within the historical monitoring period of the target system in the maintenance log, and the system of the present invention can extract it from it.

[0053] Specifically, the identification of potential upgrade components of the target system includes: extracting the monitoring compliance value intervals of each specific operating indicator corresponding to each component of the water-cooled air-conditioning system stored in the cloud database, combining the monitoring values ​​of each specific operating indicator corresponding to each monitoring period within each working day within the historical monitoring cycle of each component of the target system, and analyzing the operating status evaluation coefficients of the historical monitoring cycle of each component of the target system.

[0054] It should be noted that the analysis process of the operating status evaluation coefficient of the historical monitoring period of each component of the above-mentioned target system includes: if the monitoring value of a specific operating indicator is within its monitoring standard value interval, the specific operating indicator monitoring abnormality value is set to 0; conversely, if the monitoring value of a specific operating indicator is outside its monitoring standard value interval, the relationship between the monitoring value of the specific operating indicator and the upper and lower limits of its monitoring standard value interval is compared; if it is less than the lower limit, the absolute difference between the monitoring value of the specific operating indicator and the lower limit is taken as the specific operating indicator monitoring abnormality value; if it is greater than the upper limit, the absolute difference between the monitoring value of the specific operating indicator and the upper limit is taken as the specific operating indicator monitoring abnormality value; thereby obtaining the specific operating indicator monitoring abnormality values ​​corresponding to each monitoring period within each working day within the historical monitoring period of each component of the target system, and screening the maximum monitoring abnormality values ​​of each specific operating indicator within each working day within the historical monitoring period of each component of the target system. ,in is the number of each component of the target system, , is the number of each specific operating indicator, , according to the formula The operating status evaluation coefficients of the historical monitoring cycles of each component of the target system are obtained, where For the target system Component No. A specific operating indicator reference monitoring value, which is determined by the target system Component No. The upper and lower limits of the monitoring standard value interval of a specific operating indicator are calculated by averaging. For the preset target system Component No. The corresponding weight ratio of specific operating indicators is: It is the number of working days in the historical monitoring period.

[0055] According to the maintenance assessment level of each maintenance in the historical monitoring period of each component of the target system, the functional abnormality weight ratio corresponding to each maintenance assessment level of the water-cooled air-conditioning component is set, and the functional abnormality weight ratio corresponding to the maintenance assessment level of each maintenance in the historical monitoring period of each component of the target system is accumulated to obtain the maintenance status assessment coefficient of each component of the target system in the historical monitoring period.

[0056] It should be noted that the above-mentioned setting of the functional abnormality weight ratio corresponding to each maintenance assessment level of the water-cooled air-conditioning component is to assign the functional abnormality weight ratio value according to the order of the maintenance assessment level of the water-cooled air-conditioning component from high to low. Exemplarily, the maintenance assessment levels of the water-cooled air-conditioning components are in the order from high to low: Grade A, Grade B, Grade C, Grade D, and the corresponding functional abnormality weight ratios are specifically: 0.4, 0.3, 0.2, and 0.1.

[0057] The ratio of the maintenance status assessment coefficient to the operation status assessment coefficient is used as the upgrade potential evaluation coefficient of each component of the target system, and the components of the target system whose upgrade potential evaluation coefficient is greater than the preset threshold of the water-cooled air-conditioning system component are selected as the potential upgrade components of the target system.

[0058] Specifically, the method of determining a recommended sequence of component upgrade models of a target system includes: randomly selecting a potential upgrade component of the target system, extracting a sequence of replaceable models corresponding to the potential upgrade component stored in a cloud database, replacing the potential upgrade components one by one, performing a refrigeration simulation test of the target system with a component candidate configuration set corresponding to a maximum energy-saving effectiveness coefficient, collecting energy-saving effectiveness coefficient improvement values ​​of each replaceable model in the sequence of replaceable models corresponding to the potential upgrade component for the target system, and selecting a replaceable model corresponding to the maximum value as a reference replacement model for the potential upgrade component.

[0059] Similarly, obtain the reference replacement models of each potential upgrade component of the target system and their energy-saving effectiveness coefficient improvement values ​​for the target system, and arrange the reference replacement models of each potential upgrade component in descending order of energy-saving effectiveness coefficient improvement value to form a recommended sequence of target system component upgrade models.

[0060] When the energy-saving effect of adjusting the configuration parameters of the components of the water-cooled air-conditioning system is not good, the embodiment of the present invention further identifies the potential upgrade components of the water-cooled air-conditioning system, determines the recommended sequence of component upgrade models of the water-cooled air-conditioning system for feedback, fully explores the energy-saving potential of the water-cooled control system from the dimension of component model upgrade and update, and provides the staff with an intuitive and clear model update plan, thereby significantly improving the energy-saving control performance of the water-cooled air-conditioning system.

[0061] The cloud database is used to store the adjustable configuration intervals and replaceable model sequences of each component of the target system, store the preset reasonable energy efficiency ratio thresholds of the water-cooled air-conditioning system and the preset supply and return water temperature difference intervals corresponding to the reasonable matching status of the refrigeration load, and store the monitoring compliance value intervals of each specific operating indicator corresponding to each component of the water-cooled air-conditioning system.

[0062] The data sources in the cloud database of this embodiment are shown in Table 1 below.

[0063] Table 1 Detailed description of data sources in cloud database

[0064]

[0065] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent data center water-cooling air conditioning system energy-saving optimization control system, characterized in that: The system includes: The energy-saving space judgment module is used to record the water-cooled air-conditioning system of the target manufacturing plant data center as the target system, retrieve the cooling performance data of the target system in the historical monitoring period, including the cooling capacity, power consumption and supply and return water temperature difference in each monitoring period of each working day, and judge whether there is energy-saving space in the target system; The component configuration module is used to retrieve the fixed configuration parameter combination of each component in the historical monitoring period of the target system to generate a component benchmark configuration set when judging that there is energy saving space in the target system. Combined with the adjustable configuration parameter range of each component of the target system stored in the cloud database, several component configuration combinations of the target system are obtained through random configuration, which are recorded as candidate configuration sets of each component. The component configuration optimization module is used to respectively perform refrigeration simulation tests of the target system based on the component baseline configuration set and each component candidate configuration set, evaluate the energy-saving effectiveness coefficient of each component candidate configuration set of the target system relative to the component baseline configuration set, and trigger the equipment upgrade instruction if the maximum value is less than the preset value, otherwise the component candidate configuration set corresponding to the maximum value is fed back as the optimal component configuration set of the target system; The equipment upgrade recommendation module is used to respond to equipment upgrade instructions, retrieve the operation and maintenance performance data of each component in the historical monitoring period of the target system to identify each potential upgrade component of the target system, determine the recommended sequence of component upgrade models of the target system and provide feedback; The cloud database is used to store the adjustable configuration ranges and replaceable model sequences of each component of the target system, store the preset reasonable energy efficiency ratio thresholds of the water-cooled air-conditioning system and the reasonable matching status of the cooling load corresponding to the preset supply and return water temperature difference ranges, and store the monitoring compliance value ranges of each specific operating indicator of each component of the water-cooled air-conditioning system.

2. According to claim 1, an intelligent data center water-cooling air conditioning system energy-saving optimization control system is characterized by: The method of judging whether there is room for energy saving in the target system includes: taking the ratio of cooling capacity to electric power consumption as the energy efficiency ratio, obtaining the energy efficiency ratio of each monitoring period in each working day during the historical monitoring period of the target system, substituting it into a rectangular coordinate system with time as the horizontal axis and energy efficiency ratio as the vertical axis to draw an energy efficiency monitoring curve, and extracting the preset reasonable energy efficiency ratio threshold of the water-cooled air-conditioning system stored in the cloud database. Draw the corresponding straight line in the rectangular coordinate system, mark it as the baseline, and calculate the energy efficiency abnormality performance of the target system in the historical monitoring period. ; Extract the supply and return water temperature difference of each monitoring period in each working day during the historical monitoring period of the target system, compare it with the preset supply and return water temperature difference range corresponding to the reasonable matching state of the cooling load of the water-cooled air-conditioning system stored in the cloud database, and calculate the load abnormal matching degree of the historical monitoring period of the target system ; The energy efficiency abnormality performance and load abnormality matching degree are respectively accumulated with the product of their corresponding preset weight ratios to obtain the energy-saving potential evaluation coefficient of the target system's historical monitoring period. If it is greater than or equal to the preset energy-saving potential evaluation coefficient reaching the standard threshold, it is judged that there is energy-saving space in the target system, otherwise it is judged that there is no energy-saving space.

3. According to claim 2, an intelligent data center water-cooling air conditioning system energy-saving optimization control system is characterized by: Said The calculation process includes: sorting out the energy efficiency monitoring curve segments below the baseline in the rectangular coordinate system corresponding to the working days, monitoring periods, and deviation energy efficiency ratios, and counting the number of abnormal monitoring periods on each working day in the historical monitoring cycle of the target system. and maximum deviation energy efficiency ratio , is the number of each working day in the historical monitoring period, , according to the formula The energy efficiency abnormality performance of the target system in the historical monitoring period is obtained, where The number of monitoring periods within a preset unit working day.

4. According to claim 2, an intelligent data center water-cooling air conditioning system energy-saving optimization control system is characterized in that: Said The calculation process includes: using the standard deviation formula to calculate the standard deviation of the supply and return water temperature difference on each working day in the historical monitoring period of the target system, and using the ratio of the standard deviation to the reference supply and return water temperature difference as the supply and return water temperature difference fluctuation ratio on each working day in the historical monitoring period of the target system; Calculate the supply and return water offset temperature difference of each monitoring period outside the preset supply and return water temperature difference range when the supply and return water temperature difference is in a reasonable matching state of the water-cooled air conditioning refrigeration load on each working day in the historical monitoring period of the target system, and add it to the ratio of the reference supply and return water temperature difference to obtain the supply and return water temperature difference offset ratio of each working day in the historical monitoring period of the target system; The product of the supply and return water temperature difference fluctuation ratio and the supply and return water temperature difference offset ratio is taken as the supply and return water temperature difference constant index. The supply and return water temperature difference constant index of each working day in the historical monitoring period of the target system is averaged to obtain the load anomaly matching degree of the historical monitoring period of the target system.

5. According to claim 1, the intelligent data center water cooling air conditioning system energy-saving optimization control system is characterized by: The refrigeration simulation test of the target system based on the component benchmark configuration set and the candidate configuration set of each component is respectively performed, including: obtaining a structural design drawing of a data center of a target manufacturing plant to build a refrigeration test simulation model; Collect the temperature and humidity values ​​of each partition of the target manufacturing plant data center before the start of each working day in the historical monitoring period of the target system, and obtain the reference temperature and humidity values ​​of each partition of the target manufacturing plant data center before the target system works by averaging, so as to simulate the environment before the refrigeration simulation test of the target system; The target system is placed in the component baseline configuration set and the component candidate configuration set states respectively, and the cooling mode is started. The reference temperature value and reference humidity value of each partition of the target manufacturing plant data center are required to be adjusted to the preset temperature value and preset humidity value of the target manufacturing plant data center working requirements and reasonable operating environment, so as to perform the cooling simulation test.

6. According to claim 5, an intelligent data center water-cooling air conditioning system energy-saving optimization control system is characterized in that: The energy-saving effectiveness coefficient of the candidate configuration sets of each component of the target system is evaluated relative to the component benchmark configuration set, including: collecting the cooling capacity, output power, supply and return water temperature difference of the target system at each monitoring time point during the refrigeration simulation test based on the component benchmark configuration set and each candidate configuration set of the component, and the temperature and humidity values ​​of each partition of the target manufacturing plant data center; The energy efficiency abnormality, load abnormality matching and cooling efficiency index of the target system based on the component benchmark configuration set and the cooling simulation test of each component candidate configuration set are calculated and recorded as , and ,in is the number of the candidate configuration set of each component, ; By formula Evaluate the energy-saving effectiveness coefficient of the candidate configuration set of each component of the target system relative to the component baseline configuration set, is a natural constant.

7. The intelligent data center water cooling air conditioning system energy-saving optimization control system according to claim 6 is characterized by: Said The calculation formula is: ,in They are respectively the first , Monitoring time point target manufacturing plant data center The temperature and humidity values ​​of each partition, is the number of each partition of the target manufacturing plant data center, , is the number of each monitoring time point, , To monitor the number of time points, is the number of data center partitions for the target manufacturing plant, is a preset constant.

8. The intelligent data center water-cooling air conditioning system energy-saving optimization control system according to claim 1 is characterized by: The operation and maintenance performance data includes operation performance parameters and maintenance performance parameters, wherein the operation performance parameters include monitoring values ​​of specific operation indicators corresponding to each monitoring period in each working day, and the maintenance performance parameters include maintenance evaluation levels of each maintenance.

9. The intelligent data center water-cooling air conditioning system energy-saving optimization control system according to claim 8 is characterized in that: The identifying of each potential upgrade component of the target system includes: extracting the monitoring compliance value intervals of each specific operation indicator of each component of the water-cooled air-conditioning system stored in the cloud database, combining the monitoring values ​​of each specific operation indicator of each monitoring period of each working day in the historical monitoring cycle of each component of the target system, and analyzing the operating status evaluation coefficient of each component of the target system in the historical monitoring cycle; According to the maintenance assessment level of each maintenance in the historical monitoring period of each component of the target system, the functional abnormality weight ratio corresponding to each maintenance assessment level of the water-cooled air-conditioning component is set, and the functional abnormality weight ratio corresponding to the maintenance assessment level of each maintenance in the historical monitoring period of each component of the target system is accumulated to obtain the maintenance status assessment coefficient of the historical monitoring period of each component of the target system; The ratio of the maintenance status assessment coefficient to the operation status assessment coefficient is used as the upgrade potential evaluation coefficient of each component of the target system, and the components of the target system whose upgrade potential evaluation coefficient is greater than the preset threshold of the water-cooled air-conditioning system component are selected as the potential upgrade components of the target system.

10. The intelligent data center water cooling air conditioning system energy-saving optimization control system according to claim 9, characterized in that: The determining of the recommended sequence of component upgrade models of the target system includes: randomly selecting a potential upgrade component of the target system, extracting a sequence of replaceable models corresponding to the potential upgrade component stored in a cloud database, replacing the potential upgrade components one by one, performing a refrigeration simulation test of the target system with a component candidate configuration set corresponding to a maximum energy-saving effectiveness coefficient, collecting energy-saving effectiveness coefficient improvement values ​​of each replaceable model in the sequence of replaceable models corresponding to the potential upgrade component for the target system, and selecting a replaceable model corresponding to the maximum value as a reference replacement model for the potential upgrade component; Similarly, obtain the reference replacement models of each potential upgrade component of the target system and their energy-saving effectiveness coefficient improvement values ​​for the target system, and arrange the reference replacement models of each potential upgrade component in descending order of energy-saving effectiveness coefficient improvement value to form a recommended sequence of target system component upgrade models.

Images