Building refrigeration machine room operation and maintenance management method and system based on internet of things platform

Abstract

This invention provides a method and system for operation and maintenance management of building refrigeration rooms based on an Internet of Things (IoT) platform. First, it generates an equipment-environment association sequence by associating equipment operating status sequences with environmental impact sequences. Then, it constructs an equipment operation collaboration mode based on this sequence. Next, it inputs the equipment operation collaboration mode into the IoT platform's operation adaptation system for verification, obtaining verification results including collaborative operation smoothness and environmental adaptability. Based on the verification results, it adjusts the equipment operation collaboration mode to obtain a target collaboration mode, which is then sent to the equipment linkage control system. Finally, it tracks the equipment operating status feedback sequence, updates the equipment-environment association sequence, and stores it. This improves the intelligence level of operation and maintenance management, reduces energy consumption and costs, and enhances building comfort and energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent building technology, and more specifically, to a method and system for operation and maintenance management of building refrigeration room based on an Internet of Things (IoT) platform. Background Technology

[0002] In the construction industry, refrigeration rooms are crucial facilities for ensuring a comfortable environment within buildings, making their operation and maintenance management paramount. Traditional methods for managing building refrigeration rooms primarily rely on manual, periodic inspections and experience-based judgment. Maintenance personnel need to regularly visit the room to check the equipment's operating status, such as the compressor's operation and the cooling tower's water level, and manually adjust the equipment's operating parameters based on indoor and outdoor environmental parameters such as temperature and humidity.

[0003] However, the above methods have many drawbacks. On the one hand, manual inspections are time-sensitive, making it impossible to monitor equipment operating status and environmental changes in real time, and difficult to detect potential equipment malfunctions and abnormal environmental fluctuations in a timely manner. For example, when a minor malfunction occurs in a piece of equipment in the computer room, causing a decrease in heat exchange efficiency, manual inspection may not detect it immediately. As the malfunction gradually worsens, it may affect the normal operation of the entire refrigeration system, or even lead to equipment damage, increasing maintenance costs and downtime. On the other hand, traditional management methods lack in-depth analysis and utilization of the complex relationship between equipment operating status and environmental factors. Equipment operation is not isolated; environmental factors such as heat exchange characteristics and airflow characteristics in the computer room directly affect equipment operating efficiency and performance, while the starting-up and shutdown transitions of the equipment also affect the environment. However, traditional methods fail to perform correlation analysis on these factors, failing to achieve optimized and coordinated equipment operation, resulting in high energy consumption and high operating costs in the refrigeration computer room.

[0004] With the development of IoT technology, although some simple IoT-based equipment monitoring systems have been applied to building refrigeration rooms, most of these systems can only remotely view equipment status and provide simple alarm functions. They do not fully consider the correlation between equipment operating status and environmental impact, and cannot build an effective equipment operation collaboration mode or carry out precise operation and maintenance management. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for operation and maintenance management of building refrigeration equipment rooms based on an Internet of Things (IoT) platform, the method comprising: The operating status sequence and environmental impact sequence of the building's refrigeration room equipment are correlated to generate an equipment-environment correlation sequence. The operating status sequence includes the equipment's start-up and shutdown transition characteristics, and the environmental impact sequence includes the heat exchange characteristics and airflow characteristics within the room. Based on the device environment association sequence, a device operation coordination mode is constructed, which includes device combination operation characteristics and state transition triggering conditions. The device operation collaboration mode is input into the operation adaptation system of the Internet of Things platform for compatibility testing to obtain the mode test results, which include the smoothness of collaborative operation and environmental adaptability. Based on the pattern verification results, the equipment operation coordination mode is adjusted to obtain the target coordination mode and sent to the equipment linkage control system; The system tracks the state feedback sequence of the device when it is running in the target collaborative mode, updates the device environment association sequence based on the state feedback sequence, generates the updated device environment association sequence, and stores it in the association sequence library of the IoT platform.

[0006] In another aspect, embodiments of the present invention also provide a building refrigeration room operation and maintenance management system based on an Internet of Things platform, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the machine-readable storage medium to implement the above-described method.

[0007] Based on the above, this embodiment of the invention generates an equipment-environment association sequence by associating the operating state sequence of the building's refrigeration room equipment with the environmental impact sequence. Based on this equipment-environment association sequence, a collaborative operation mode is constructed, clarifying the combined operating characteristics and state transition triggering conditions of the equipment. This achieves optimized collaborative operation between equipment, improving the overall operating efficiency of the refrigeration room. The collaborative operation mode is input into the IoT platform's operation adaptation system for compatibility testing. The resulting mode test results include the smoothness of collaborative operation and environmental adaptability, accurately assessing the feasibility and effectiveness of the collaborative mode. The collaborative operation mode is adjusted according to the test results to obtain the target collaborative mode, which is then sent to the equipment linkage control system, ensuring that the equipment operates in the optimal mode. Tracking the state feedback sequence of the equipment running in the target collaborative mode and updating the equipment-environment association sequence enables dynamic data updates and closed-loop management. This allows the operation and maintenance management system to continuously adapt to changes in equipment and the environment, continuously optimizing operation and maintenance strategies. This improves the intelligence level of the building's refrigeration room operation and maintenance management, reduces energy consumption and operating costs, extends equipment lifespan, and enhances building comfort and energy efficiency. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the execution flow of the building refrigeration room operation and maintenance management method based on the Internet of Things platform provided in the embodiments of the present invention.

[0009] Figure 2This is a schematic diagram of exemplary hardware and software components of the building refrigeration room operation and maintenance management system based on the Internet of Things platform provided in an embodiment of the present invention. Detailed Implementation

[0010] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for the operation and maintenance management of a building refrigeration room based on an Internet of Things (IoT) platform, according to an embodiment of the present invention. The following is a detailed description of this method for the operation and maintenance management of a building refrigeration room based on an IoT platform.

[0011] Step S110: Associate the operating status sequence and environmental impact sequence of the building refrigeration room equipment to generate an equipment environment association sequence. The operating status sequence includes the equipment's start-up and shutdown transition characteristics, and the environmental impact sequence includes the heat exchange characteristics and airflow characteristics within the room.

[0012] In this embodiment, a refrigeration room in a commercial complex is used as the application scenario. This room contains multiple chiller units, cooling water pumps, chilled water pumps, and other equipment. The IoT platform uses sensors deployed on each device and environmental sensors within the room to collect real-time data on equipment operating status and environmental parameters. The operating status sequence refers to the continuous set of status data collected by sensors over a period of time, from startup to operation and then to shutdown. Startup and operation characteristics cover various state changes from the initial startup to reaching a stable operating state, while shutdown transition characteristics cover various state decay information from the issuance of the stop command to complete shutdown. The environmental impact sequence refers to the continuous set of environmental parameters related to equipment operation within the room over the same time period. Heat exchange characteristics include information on heat transfer between different areas within the room, and airflow characteristics include information on the air circulation path and flow speed within the room.

[0013] In this scenario, the process of linking the operational status sequence and environmental impact sequence of the building's refrigeration equipment is achieved through the data processing module of the IoT platform. The data processing module first receives operational status data from various equipment sensors and environmental impact data from environmental sensors. Then, it performs initial alignment of this data according to timestamps to ensure that each time point has corresponding equipment operational status and environmental impact data. Next, the data processing module filters this data, removing obvious anomalies, such as data exceeding reasonable ranges due to sensor malfunctions. Then, it searches for the intrinsic relationships between startup and operation characteristics in the operational status sequence and heat exchange characteristics in the environmental impact sequence. Similarly, it performs similar correlation analysis on shutdown transition characteristics in the operational status sequence and airflow characteristics in the environmental impact sequence. Finally, these correlations are integrated chronologically to form an equipment-environment correlation sequence. Each data point in this sequence simultaneously contains equipment operational status information and environmental impact information for the corresponding time point.

[0014] Step S111: Extract the startup operation features and shutdown transition features from the operating state sequence of the building refrigeration room equipment. The startup operation features include the state change trend during startup and the transition time before stable operation. The shutdown transition features include the state decay trend during shutdown and the preparation time before complete shutdown.

[0015] In this embodiment, the operating state sequence can be analyzed when extracting startup and operation features. The state change trend during startup refers to the changes in various operating parameters (such as the compressor speed of the chiller unit, the flow rate of the cooling water pump, etc.) over time after the equipment is given a startup command. A trend curve reflecting these changes can be generated by continuously monitoring these parameters. The transition time before stable operation refers to the time elapsed from the start of equipment startup until the various operating parameters reach the stable operating range. The time difference between the start time and the parameter stabilization time can be recorded as the transition time before stable operation.

[0016] For extracting shutdown transition characteristics, the state decay trend during shutdown refers to the gradual decrease of various operating parameters of the equipment until they reach zero after receiving a shutdown command. This decay process of these parameters can be monitored to generate corresponding decay trend curves. The preparation time before complete shutdown refers to the time elapsed from the issuance of the shutdown command to the complete reduction of all operating parameters of the equipment to zero. This preparation time before complete shutdown can be calculated by recording the time the shutdown command was issued and the time the parameters returned to zero.

[0017] Step S112: Obtain the heat exchange characteristics and airflow characteristics in the computer room from the environmental impact sequence. The heat exchange characteristics include the heat transfer direction and the distribution of heat exchange areas. The airflow characteristics include the airflow circulation path and flow velocity characteristics.

[0018] In this embodiment, the data in the environmental impact sequence is collected by devices such as temperature sensors and airflow sensors within the computer room. This data can be processed to obtain heat exchange characteristics and airflow characteristics. The direction of heat transfer refers to the direction in which heat is transferred from high-temperature areas to low-temperature areas within the computer room. This can be determined by analyzing temperature change data collected by temperature sensors at different locations, such as heat transfer from the chiller unit to the surrounding environment, or heat transfer from outside the computer room to the inside.

[0019] Heat exchange zone distribution refers to the area within the computer room where heat exchange occurs. Different heat exchange zones can be defined based on the distribution of temperature sensors and the collected temperature data, such as the high-temperature heat exchange zone around the chiller unit and the medium-temperature heat exchange zone near the cooling water pump, and the boundary range of these zones can be recorded.

[0020] Airflow circulation path refers to the route of airflow within a computer room. This can be mapped by analyzing airflow direction and velocity data collected by airflow sensors. For example, the path of air entering from the air inlet, passing through equipment such as chillers and cooling water pumps, and then exiting from the air outlet. Flow velocity characteristics refer to the airflow velocity at different locations along the circulation path. This data can be used to record the changes in airflow velocity at each location over time, forming flow velocity characteristic data.

[0021] Step S113: Associate and map the startup and operation features and heat exchange features, align the state change trend and heat transfer direction during startup with the time axis, determine the heat transfer direction change corresponding to each state change node, and associate the transition time before stable operation with the expansion time of the heat exchange area distribution, and mark the change nodes of the heat exchange area within the transition time.

[0022] Step S1131: Decompose the startup process state change trend in the startup running characteristics into multiple continuous state change nodes over time, with each state change node corresponding to a running parameter value.

[0023] In this embodiment, the trend curve of the startup process state changes can be analyzed and decomposed into multiple continuous state change nodes according to the changes in operating parameters. For example, for the startup process of a chiller unit, when the compressor speed starts from the initial value, each time point that increases by a certain amount is taken as a state change node. Each node corresponds to the compressor speed value at that time. The above state change nodes are arranged in chronological order, which completely reflects the changes in operating parameters during the startup process.

[0024] Step S1132: Decompose the heat transfer direction in the heat exchange feature into multiple continuous direction change nodes over time, with each direction change node corresponding to a heat transfer direction.

[0025] In this embodiment, the change of heat transfer direction over time can be analyzed. When the heat transfer direction changes, that point in time is taken as a direction change node. Each direction change node corresponds to the heat transfer direction at that time. For example, the heat transfer direction corresponding to one node is from the chiller unit to the cooling water pump, and the heat transfer direction corresponding to another node is from the cooling water pump to the chiller unit, etc. The above-mentioned direction change nodes are arranged in chronological order to form a sequence of heat transfer direction changes.

[0026] Step S1133: Align the state change node and the direction change node along the time axis, calculate the time difference between each state change node and the nearest direction change node, and if the time difference is within a preset range, determine that the state change node and the direction change node are associated, and record the association relationship.

[0027] In this embodiment, the state change node sequence and the direction change node sequence can be placed on the same time axis to ensure their starting points are consistent. Then, for each state change node, the direction change node that is closest in time to it is found in the direction change node sequence, and the time difference between them is calculated. The preset range is set according to the actual operation of the equipment in the computer room, such as within a certain time range. If the calculated time difference is within the preset range, the two nodes are considered to be related, and the above-mentioned relationship can be recorded, such as state change node A being associated with direction change node B, and stored in the association mapping result.

[0028] Step S1134: Extract the transition duration before stable operation from the startup and running features, and determine the start and end times of the transition duration.

[0029] In this embodiment, the transition time before stable operation can be extracted from the startup and operation feature data. The start time of the transition time is the time when the equipment startup command is issued. For example, when a chiller unit receives a startup command at a certain time, that time is the start time of the transition time. The end time is the time when the various operating parameters of the equipment reach the stable operating range. This can be achieved by monitoring the operating parameters, and recording the time when the parameters no longer change significantly and are within a stable range as the end time of the transition time.

[0030] Step S1135: Extract the expansion duration of the heat exchange region distribution in the heat exchange features, and determine the start and end times of the expansion duration, wherein the expansion duration is the time for the heat exchange region to expand from the initial range to the stable range.

[0031] In this embodiment, the expansion duration of the heat exchange region distribution can be extracted from heat exchange characteristic data. The start time of the expansion duration is the point in time when the heat exchange region begins to expand outward from its initial range. This can be determined by analyzing historical data on the heat exchange region distribution to find the moment when the region begins to expand. The end time is the point in time when the heat exchange region expands to a stable range and stops expanding. This is also determined by monitoring changes in the heat exchange region.

[0032] Step S1136: Align the transition duration and the extension duration along the time axis. If the difference between their start and end times is within a preset range, then the transition duration and the extension duration are considered to be related.

[0033] In this embodiment, the time axes of the transition duration and the extension duration can be aligned to ensure consistent time scales. Then, the difference between the start time of the transition duration and the start time of the extension duration, as well as the difference between the end time of the transition duration and the end time of the extension duration, are calculated. The preset range is set based on the actual correlation between data center heat exchange and equipment startup. If both differences are within the preset range, then a correlation is considered to exist between the transition duration and the extension duration.

[0034] Step S1137: During the transition period, extract the state of the heat exchange area distribution at fixed time intervals and mark the change nodes of the heat exchange area. The change nodes are the time points when the range of the heat exchange area changes significantly.

[0035] In this embodiment, within the time period corresponding to the transition duration, the regional state at that moment can be extracted from the heat exchange area distribution data at fixed time intervals, such as every certain period of time. When the boundary change of the heat exchange area extracted at two adjacent time points exceeds a certain level, the latter time point is marked as a change node of the heat exchange area. For example, if the boundary of the heat exchange area extracted at a certain time point is within one meter around the chiller unit, and the boundary extracted at the next time point expands to within one and a half meters, and the above change exceeds a preset significant change standard, then the next time point is marked as a change node.

[0036] Step S1138: Integrate the direction change nodes and heat exchange area change nodes associated with each state change node and the transition time to generate an association mapping result of startup operation characteristics and heat exchange characteristics. The association mapping result includes timestamps, corresponding state change nodes, direction change nodes, and heat exchange area change nodes.

[0037] In this embodiment, the direction change nodes associated with each previously determined state change node, as well as all heat exchange area change nodes marked within the transition time, can be summarized according to timestamps. For each timestamp, the state change node information, direction change node information, and heat exchange area change node information (if any) at that time point are recorded. This forms a correlation mapping result between startup and operation characteristics and heat exchange characteristics, which fully reflects the temporal correlation between changes in equipment operating state and changes in heat exchange characteristics during startup.

[0038] Step S114: Associate and map the shutdown transition features and airflow features, align the state decay trend during shutdown with the contraction trend of the airflow circulation path along the time axis, determine the airflow path change corresponding to each decay node, and associate the preparation time before complete shutdown with the decay time of the flow velocity feature, marking the nodes of flow velocity change within the preparation time.

[0039] Step S1141: Decompose the shutdown process state decay trend in the shutdown transition feature into multiple consecutive decay nodes over time, with each decay node corresponding to a decay value of an operating parameter.

[0040] In this embodiment, the shutdown process state decay trend curve can be analyzed, and it can be decomposed into multiple consecutive decay nodes according to the decay of operating parameters. For example, during the shutdown process of a chiller unit, the compressor speed starts to decrease from the stable operating value. Each time the speed decreases by a certain amount, that time point is taken as a decay node. Each node corresponds to the compressor speed decay value at that time, that is, the difference from the stable operating value. The above decay nodes are arranged in chronological order, reflecting the decay of operating parameters during the shutdown process.

[0041] Step S1142: Decompose the contraction trend of the airflow circulation path in the airflow characteristics into multiple continuous path change nodes over time, with each path change node corresponding to an airflow circulation path range.

[0042] In this embodiment, the contraction trend of the airflow circulation path can be analyzed. When the range of the airflow circulation path contracts significantly, that time point is taken as the path change node. Each path change node corresponds to the airflow circulation path range at that time. For example, the path range corresponding to one node is the entire area covering the chiller unit and cooling water pump, while the path range corresponding to another node is a small area surrounding only the chiller unit. The above path change nodes are arranged in chronological order to form a sequence of changes in the contraction of the airflow circulation path.

[0043] Step S1143: Align the attenuation node and the path change node along the time axis, calculate the time difference between each attenuation node and the nearest path change node, and if the time difference is within a preset range, determine that the attenuation node is associated with the path change node and record the association relationship.

[0044] In this embodiment, the decay node sequence and the path change node sequence can be placed on the same time axis to ensure that the starting points are consistent. For each decay node, the path change node with the closest time in the path change node sequence is found, and the time difference between the two is calculated. If the time difference is within a preset range, such as within a certain period of time, the two nodes are determined to be associated, and the above association relationship is recorded, such as decay node C being associated with path change node D.

[0045] Step S1144: Extract the preparation time before complete shutdown from the shutdown transition features, and determine the start and end times of the preparation time.

[0046] In this embodiment, the preparation time before complete shutdown can be extracted from the shutdown transition feature data. The start time of the preparation time is the time when the equipment receives the shutdown command, and the end time is the time when all operating parameters of the equipment drop to zero. By recording these two time points, the time range of the preparation time can be determined.

[0047] Step S1145: Extract the decay time of the flow velocity feature in the air flow characteristics, and determine the start and end times of the decay time, wherein the decay time is the time it takes for the flow velocity to decay from a stable value to zero.

[0048] In this embodiment, the decay time of flow velocity characteristics can be extracted from airflow characteristic data. The start time of the decay time is the point at which the airflow velocity begins to decrease from its stable operating value, and the end time is the point at which the airflow velocity completely drops to zero. These two time points can be determined by monitoring changes in flow velocity, thereby obtaining the time range of the decay time.

[0049] Step S1146: Align the preparation time and decay time along the time axis. If the difference between their start and end times is within a preset range, the preparation time and decay time are considered to be related.

[0050] In this embodiment, the time axes of preparation time and decay time can be aligned to ensure consistent time scales. Then, the difference between the start time of the preparation time and the start time of the decay time, as well as the difference between the end time of the preparation time and the end time of the decay time, are calculated. If both differences are within a preset range, the preparation time and decay time are considered to be correlated.

[0051] Step S1147: During the preparation period, extract the state of the flow velocity characteristics at fixed time intervals and mark the nodes of change in flow velocity, wherein the nodes of change are the time points when the flow velocity changes significantly.

[0052] In this embodiment, within the time period corresponding to the preparation time, the flow velocity status at a given moment can be extracted from the flow velocity feature data at fixed time intervals, such as every certain period of time. This flow velocity status includes airflow velocity values ​​at different locations within the machine room, such as the flow velocity near the chiller unit or around the cooling water pump. When the change in flow velocity at the same location extracted from two adjacent time points exceeds a preset significant change standard, the latter time point is marked as a flow velocity change node. For example, if the flow velocity near the chiller unit is a certain value extracted at a certain time point, and the flow velocity at that location extracted at the next time point decreases by a certain percentage, and the decrease exceeds a preset standard, then the next time point is marked as a flow velocity change node.

[0053] Step S1148: Integrate the path change nodes associated with each decay node and the flow velocity change nodes within the preparation time to generate a correlation mapping result between the shutdown transition characteristics and the airflow characteristics. The correlation mapping result includes a timestamp, the corresponding decay node, the path change node, and the flow velocity change node.

[0054] In this embodiment, the path change nodes associated with each attenuation node, as well as all flow velocity change nodes marked within the preparation time, can be summarized according to timestamps. For each timestamp, the attenuation node information, path change node information, and flow velocity change node information (if any) at that time point are recorded. This forms a correlation mapping result between shutdown transition characteristics and airflow characteristics, which comprehensively reflects the temporal correlation between equipment operating state attenuation and airflow characteristic changes during shutdown.

[0055] Step S115: Arrange the association mapping results of the startup operation feature and the heat exchange feature in chronological order to generate a first association chain; arrange the association mapping results of the shutdown transition feature and the air flow feature in chronological order to generate a second association chain; merge the first association chain and the second association chain to generate an equipment environment association sequence. Each time node of the equipment environment association sequence contains the specific state of the corresponding startup operation feature or shutdown transition feature, heat exchange feature or air flow feature.

[0056] In this embodiment, the IoT platform first processes the association mapping result between startup and operation features and heat exchange features. For example, it can arrange the information of each node in the association mapping result in chronological order according to the timestamps to form a first association chain. Each link in the first association chain contains the specific state of the startup and operation features at the corresponding time point, such as the device operating parameter values ​​and state change trends, as well as the specific state of the heat exchange features, such as the heat transfer direction and heat exchange area distribution.

[0057] Next, the correlation mapping results between the shutdown transition characteristics and airflow characteristics are processed in the same way, arranged in chronological order according to timestamps, to generate a second correlation chain. Each link in the second correlation chain contains the specific state of the shutdown transition characteristics at the corresponding time point, such as the attenuation value of operating parameters and the state attenuation trend, as well as the specific state of the airflow characteristics, such as the airflow circulation path range and flow velocity.

[0058] When merging the first and second association chains, information with the same timestamp in both chains is merged based on the timestamp. Information with different timestamps is inserted sequentially according to chronological order, forming a continuous sequence. The resulting equipment-environment association sequence accurately presents the specific state of equipment startup / operation characteristics or shutdown transition characteristics, as well as heat exchange or airflow characteristics within the computer room at each time point, achieving a comprehensive correlation between equipment operating status and environmental impact.

[0059] Step S120: Construct a device operation coordination mode based on the device environment association sequence. The device operation coordination mode includes device combination operation characteristics and state transition triggering conditions.

[0060] In this embodiment, the device environment association sequence can be analyzed in depth to construct a device operation collaboration mode. The device environment association sequence contains the operating status of multiple devices at different points in time and the corresponding environmental characteristics. First, this data is classified and organized, and the status data of multiple devices operating together in the same time period are grouped together to analyze the collaboration relationship between them.

[0061] Equipment combination operation characteristics refer to the overall characteristics exhibited by multiple devices when operating in combination. These characteristics can be determined by calculating the startup synchronization, shutdown synchronization, and load distribution of each device within the combination. State transition triggering conditions refer to the conditions that cause the combined equipment operation state to transition from one stage to another. These triggering conditions are determined by analyzing changes in environmental characteristics before and after state transitions and device runtime in the equipment environment correlation sequence. By correlating equipment combination operation characteristics with state transition triggering conditions, a collaborative equipment operation model is ultimately constructed. This collaborative model can guide the collaborative operation of multiple devices under different environmental conditions.

[0062] Step S121: Parse the device environment association sequence, extract the combination of operating states of different devices at the same time node, and mark them as collaborative state nodes. The collaborative state nodes include the combination of device start-up and operation features or the combination of shutdown and transition features.

[0063] In this embodiment, the device environment association sequence can be parsed node by node. For each node, the operating status of all devices at that time point can be extracted, and these operating statuses can be combined to form the operating status combination for that time point. If the device operating status at that time point mainly exhibits state changes during the startup process, then the operating status combination is a startup operation feature combination; if it mainly exhibits state decay during the shutdown process, then it is a shutdown transition feature combination.

[0064] Furthermore, the operational states at each point in time can be combined and marked as a collaborative state node, and each collaborative state node contains the specific operational characteristics of each device at that point in time. For example, at a certain point in time, chiller unit 1 is in a certain state of startup operation characteristics, chiller unit 2 is also in a certain state of startup operation characteristics, and the cooling water pump and chilled water pump are also in their respective startup operation characteristic states. The combination of these states forms a collaborative state node that contains the combination of startup operation characteristics.

[0065] Step S122: Extract the equipment combination operation characteristics from the collaborative state node. The equipment combination operation characteristics include the start-up synchronization degree, shutdown synchronization degree, and load distribution ratio of each equipment in the combination. The start-up synchronization degree is the degree of consistency of the state change trend of each equipment during the start-up process, and the shutdown synchronization degree is the degree of consistency of the state decay trend of each equipment during the shutdown process.

[0066] Step S1221: Select nodes in the startup phase from the collaborative status nodes and extract the status change trend data of each device during the startup process.

[0067] In this embodiment, nodes in the startup phase can be filtered based on the startup and operation feature combinations contained in the collaborative state nodes. For these filtered nodes, the state change trend data of each device during the startup process can be extracted. The above data includes the changes of various operating parameters of each device over time from startup to stable operation, such as the compressor speed increase curve of the chiller unit over time, the flow rate of the cooling water pump increase curve over time, etc.

[0068] These state change trend data will be organized into a structured form for use in subsequent calculations of startup synchronization. For example, for the equipment combination of chiller unit 1, chiller unit 2, cooling water pump, and chilled water pump, the state change trend data of each of their coordinated state nodes during the startup phase can be extracted to form their respective trend datasets.

[0069] Step S1222: Calculate the similarity of state change trend data of any two devices by the degree of agreement of the trend curves.

[0070] In this embodiment, the state change trend data of each device can be converted into a trend curve. For any two devices, the degree of agreement between their trend curves can be calculated to determine the similarity of their state change trend data. When calculating the degree of agreement, the difference in parameter values ​​of the two curves at the same time point can be compared. The smaller the difference, the higher the degree of agreement and the higher the similarity.

[0071] For example, when calculating the similarity of the state change trend data of chiller unit 1 and chiller unit 2, the compressor speed trend curves of the two can be compared, the difference in speed values ​​at multiple identical time points can be calculated, and the degree of agreement between the two curves can be determined based on the overall situation of these differences, thereby obtaining the similarity between the two.

[0072] Step S1223: Calculate the startup synchronization degree based on the similarity between all devices, where the startup synchronization degree is the average of the similarity between all devices.

[0073] In this embodiment, after calculating the similarity of the state change trend data between any two devices, all these similarity values ​​can be summarized. Then, the average of these similarity values ​​can be calculated, and this average value is the startup synchronization degree of the device combination.

[0074] For example, for a combination of equipment including chiller unit 1, chiller unit 2, cooling water pump and chilled water pump, there are six pairs of similarity between the equipment. These six similarity values ​​can be added together and then divided by six to get the startup synchronization degree of the equipment combination.

[0075] Step S1224: Filter out the nodes in the shutdown phase from the collaborative status nodes and extract the status decay trend data of each device during the shutdown process.

[0076] In this embodiment, nodes in the shutdown phase can be selected based on the combination of shutdown transition features contained in the collaborative state nodes. For these nodes, state decay trend data of each device during the shutdown process can be extracted. The above data includes the decay of various operating parameters of each device over time from the issuance of the shutdown command to the complete shutdown, such as the decrease curve of the compressor speed of the chiller unit over time, the decrease curve of the flow rate of the cooling water pump over time, etc.

[0077] These state decay trend data will also be organized into a structured form to prepare for calculating shutdown synchronization. For example, for the above-mentioned equipment combination, the state decay trend data of each coordinated state node during the shutdown phase can be extracted to form their respective decay trend datasets.

[0078] Step S1225: Calculate the similarity of state decay trend data between any two devices. Based on the similarity between all devices, calculate the shutdown synchronization degree, which is the average of the similarity between all devices.

[0079] In this embodiment, the state decay trend data of each device can be converted into a decay trend curve. For any two devices, the degree of agreement between their decay trend curves is calculated to determine the similarity of their state decay trend data. Similar to the startup synchronization calculation, the smaller the difference in parameter values ​​of the curves at the same time point, the higher the degree of agreement and the higher the similarity.

[0080] After obtaining the similarity between all devices, the average of these similarities can be calculated, which is the shutdown synchronization degree of the device combination. For example, for the above device combination, after calculating the similarity of the state decay trend data between six pairs of devices, the similarity values ​​are added together and then divided by six. The result is the shutdown synchronization degree of the combination.

[0081] Step S1226: Select nodes in the stable operation phase from the collaborative status nodes and extract the load data of each device.

[0082] In this embodiment, nodes in a coordinated state where the device operating parameters are within a stable range can be selected; these nodes are those in a stable operating phase. For these nodes, load data for each device can be extracted. The load data can be parameters reflecting the device's load, such as power consumption and processing flow.

[0083] For example, for a coordinated state node in a stable operation phase, load data such as the power of chiller unit 1, the power of chiller unit 2, the flow rate of cooling water pump, and the flow rate of chilled water pump can be extracted. This data will be used to calculate the load distribution ratio.

[0084] Step S1227: Calculate the proportion of the load data of each device to the total combined load data to obtain the load distribution ratio.

[0085] In this embodiment, the load data of each device in the collaborative state node that is in a stable operating phase can be added together to obtain the combined total load data. Then, the load data of each device is divided by the combined total load data to obtain the load allocation ratio of that device in the combination.

[0086] For example, the total load data of the equipment combination is the sum of the power of chiller unit 1, the power of chiller unit 2, the flow rate of cooling water pump and the flow rate of chilled water pump (normalized before calculation to ensure uniformity of dimensions). The load allocation ratio of chiller unit 1 is the ratio of its power to the total load data, and the load allocation ratio of other equipment is calculated in the same way.

[0087] Step S1228: Classify the start-up synchronization, shutdown synchronization, and load distribution ratio according to the equipment combination category. Each equipment combination category corresponds to a set of equipment combination operation characteristics.

[0088] In this embodiment, the equipment combinations can be classified according to the type and quantity of the equipment. For example, a combination containing two chillers, two cooling water pumps and two chilled water pumps is one type, and a combination containing one chiller, one cooling water pump and one chilled water pump is another type, etc.

[0089] For each type of equipment combination, the previously calculated startup synchronization, shutdown synchronization, and load distribution ratio can be grouped together as the operational characteristics of that equipment combination category. In this way, different types of equipment combinations have corresponding operational characteristics, facilitating the subsequent construction of collaborative modes.

[0090] Step S1229: Perform statistical analysis on the operating characteristics of multiple equipment combinations of the same equipment combination category, and calculate the average value as the typical operating characteristics of the equipment combination category. The typical operating characteristics include average start-up synchronization, average shutdown synchronization, and average load distribution ratio.

[0091] In this embodiment, multiple sets of equipment combination operation characteristics can be collected for the same equipment combination category. By statistically analyzing these characteristics, the average value of start-up synchronization, the average value of shutdown synchronization, and the average value of load distribution ratio of each equipment are calculated. These average values ​​together constitute the typical operation characteristics of this equipment combination category.

[0092] For example, for a certain equipment combination category, if five sets of equipment combination operation characteristics are collected, the start-up synchronization of these five characteristics can be added together and then divided by five to obtain the average start-up synchronization. The average shutdown synchronization and average load distribution ratio can be calculated in the same way to form the typical operation characteristics of this category.

[0093] Step S123: Analyze the conditions for the transition of collaborative state nodes in the equipment environment association sequence, and determine the state transition trigger conditions. The state transition trigger conditions include the change threshold of heat exchange characteristics, the change threshold of air flow characteristics, and the equipment's own running time threshold.

[0094] In this embodiment, the transition of cooperative state nodes in the device environment association sequence can be tracked, that is, the process of transitioning from one cooperative state node to another. During this process, changes in heat exchange characteristics, changes in airflow characteristics, and the device's own runtime before and after the transition can be analyzed.

[0095] To determine the threshold for changes in heat exchange characteristics, we can calculate the change in heat transfer direction and the range of change in heat exchange region distribution before and after the transition, thus establishing a critical value. When the change in heat exchange characteristics exceeds this critical value, the transition of the cooperative state node can be triggered. The determination of the threshold for changes in airflow characteristics is similar; we can analyze the degree of change in airflow circulation path and the change in flow velocity before and after the transition to determine the corresponding critical value.

[0096] The device's own runtime threshold refers to the maximum continuous operating time of the device at a certain collaborative state node. When the runtime reaches this threshold, a transition of the collaborative state node will be triggered regardless of changes in environmental characteristics. By combining these analyses, the triggering conditions for state transitions can be determined.

[0097] Step S124: Classify the combined operation characteristics of the equipment into startup stage, stable operation stage, and shutdown stage, with each stage corresponding to a set of combined operation characteristics of the equipment.

[0098] In this embodiment, the combined operation characteristics of the equipment can be classified according to the stage of equipment operation. The combined operation characteristics of the equipment in the startup stage refer to the combined operation characteristics of the equipment from the start of startup to the point of reaching a stable operating state, including the startup synchronization degree, load distribution ratio, etc. in this stage.

[0099] The combined operation characteristics of equipment during the stable operation phase refer to the combined operation characteristics of equipment when it is in a stable operating state, mainly including the load distribution ratio during this phase. The combined operation characteristics of equipment during the shutdown phase refer to the combined operation characteristics of equipment from the start of shutdown to complete cessation of operation, including the shutdown synchronization degree during this phase.

[0100] Step S125: Associate the equipment combination operation characteristics of each stage with the corresponding state transition trigger conditions to generate the stage transition logic of the equipment operation coordination mode. The stage transition logic stipulates that when any state transition trigger condition is met, the equipment combination operation characteristics will transition from the current stage to the next stage.

[0101] In this embodiment, the combined operation characteristics of the equipment during the startup phase can be associated with the state transition triggering conditions from the startup phase to the stable operation phase, and the combined operation characteristics of the equipment during the stable operation phase can be associated with the state transition triggering conditions from the stable operation phase to the shutdown phase.

[0102] The generated phase transition logic explicitly stipulates that when any one of the following conditions is met: a change in heat exchange characteristics reaches its change threshold, a change in airflow characteristics reaches its change threshold, or the equipment's operating time in the current phase reaches its own operating time threshold, the equipment's combined operating characteristics will transition from the current phase to the next phase. For example, when the equipment's operating time in the startup phase reaches the startup phase's operating time threshold, the equipment's combined operating characteristics will transition from the startup phase to the stable operation phase.

[0103] Step S126: Integrate the stage transition logic and the equipment combination operation characteristics of each stage to generate a complete equipment operation coordination mode. The equipment operation coordination mode includes startup stage coordination characteristics, stable operation stage coordination characteristics, shutdown stage coordination characteristics and corresponding state transition triggering conditions.

[0104] In this embodiment, the stage transition logic can be integrated with the equipment combination operation characteristics of each stage. The startup stage collaborative characteristics are the equipment combination operation characteristics of the startup stage, the stable operation stage collaborative characteristics are the equipment combination operation characteristics of the stable operation stage, and the shutdown stage collaborative characteristics are the equipment combination operation characteristics of the shutdown stage. The collaborative characteristics of each stage correspond to the corresponding state transition triggering conditions.

[0105] Through the above integration, a complete equipment operation coordination mode is formed, which accurately describes the operating characteristics of the equipment combination at different stages and under what conditions it will switch from one stage to another.

[0106] Step S130: Input the device operation collaboration mode into the operation adaptation system of the Internet of Things platform for adaptation test, and obtain the mode test result, which includes the smoothness of collaborative operation and environmental adaptability.

[0107] Step S131: Import the device operation collaboration mode into the operation adaptation system of the Internet of Things platform. The operation adaptation system includes a virtual device model that is consistent with the actual data center equipment model and quantity, as well as a virtual environment model that can simulate different heat exchange characteristics and air flow characteristics.

[0108] In this embodiment, the process of importing the device operation collaboration mode is implemented through the communication module within the IoT platform. The communication module can first encapsulate the data of the device operation collaboration mode, and then send the encapsulated data to the data receiving interface of the running adaptation system according to a preset communication protocol.

[0109] After receiving data, the data receiving interface of the running adaptation system can decapsulate and verify the format of the data. If the verification passes, the device operation collaboration mode is stored in the system's mode library and prepared for simulation operation.

[0110] The virtual device models in this operating adaptation system are constructed based on the equipment parameters in the actual chiller room, including chillers, cooling water pumps, and chilled water pumps. The operating characteristics and parameter ranges of each virtual device model are completely consistent with the corresponding actual equipment. For example, the cooling capacity range and power consumption curve of the virtual chiller are the same as those of the actual installed chiller.

[0111] Virtual environment models can simulate different heat exchange and airflow characteristics by adjusting internal parameters. When simulating different heat exchange characteristics, parameters such as temperature gradients and heat transfer coefficients in different areas of the virtual environment can be changed; when simulating different airflow characteristics, parameters such as airflow velocity distribution and direction changes in the virtual environment can be changed.

[0112] Step S132: Set multiple sets of environmental parameters in the virtual environment model. Each set of environmental parameters includes heat transfer direction, heat exchange area distribution, airflow circulation path, and flow velocity characteristics.

[0113] In this embodiment, the environment parameters of the virtual environment model are set through the environment parameter configuration interface of the adapted system. Operators can manually input or select preset combinations of environment parameters on this interface, or the system can automatically generate multiple sets of different environment parameters.

[0114] The set of multiple environmental parameters needs to cover various typical environmental conditions that may occur in an actual chiller room. For example, the first set of environmental parameters can be set so that the heat transfer direction mainly diffuses from the chiller unit to the surrounding area of ​​the room, the heat exchange area is distributed with the chiller unit as the center and extends outward within a three-meter radius, the airflow circulation path is to enter from the air inlet at the top of the room, pass through the equipment area and exit from the air outlet at the bottom, and the flow velocity characteristics are that the flow velocity is faster in the area around the equipment and slower in the area away from the equipment.

[0115] The second set of environmental parameters can be set so that the heat transfer direction is mainly from the outside of the computer room to the inside, the heat exchange area is concentrated in the area of ​​the computer room near the outer wall, the airflow circulation path is to enter from the air inlet on one side of the computer room, flow along the direction of equipment arrangement and then be discharged from the air outlet on the other side, and the flow velocity characteristic is that the overall flow velocity is relatively uniform.

[0116] By setting multiple sets of different environmental parameters, the adaptability of the equipment operation collaboration mode under various environmental conditions can be comprehensively tested.

[0117] Step S133: Under each set of environmental parameters, control the operation of the virtual device model according to the device operation collaboration mode, and record the collaborative operation data of the virtual device model in the startup phase, stable operation phase, and shutdown phase.

[0118] In this embodiment, after each set of environmental parameters is set, the adaptation system can automatically start the virtual device model and control the virtual device startup according to the startup sequence, startup time interval, and other requirements specified in the device operation coordination mode. During the startup phase, the startup time of each virtual device and the parameter changes during the startup process (such as the compressor speed rise curve of the chiller unit, the flow rate rise curve of the water pump, etc.) can be recorded.

[0119] Once the virtual devices enter a stable operating phase, the operating parameters of each virtual device (such as the outlet water temperature of the chiller unit, the pressure of the cooling water pump, the flow rate of the chilled water pump, etc.), the coordination parameters between devices (such as the real-time changes in the load distribution ratio), and the real-time changes in the virtual environment parameters can be continuously recorded.

[0120] During the shutdown phase, virtual devices can be shut down according to the shutdown sequence and shutdown time interval specified in the equipment operation coordination mode, and the shutdown time of each virtual device and the parameter changes during the shutdown process (such as compressor speed decrease curve, water pump flow decrease curve, etc.) can be recorded.

[0121] The collaborative operation data under each set of environmental parameters will be stored in the database of the running adaptation system, and the data will be classified and labeled according to the startup phase, stable operation phase, and shutdown phase, so as to extract relevant indicators later.

[0122] Step S134: Extract the smoothness index of collaborative operation from the collaborative operation data. The smoothness index of collaborative operation includes the start-up synchronization compliance rate, the shutdown synchronization compliance rate, and the load distribution deviation rate. The start-up synchronization compliance rate is the percentage of time during the start-up process when the synchronization meets the preset standard. The shutdown synchronization compliance rate is the percentage of time during the shutdown process when the synchronization meets the preset standard. The load distribution deviation rate is the degree of deviation between the actual load distribution ratio and the load distribution ratio in the equipment combination operation characteristics.

[0123] In this embodiment, the process of extracting the smoothness index of collaborative operation from the collaborative operation data is completed by the index extraction module of the running adaptation system. For the extraction of the startup synchronization compliance rate, the real-time startup synchronization data of each virtual device can be extracted from the collaborative operation data of the startup phase. Then, these data are compared with the preset startup synchronization standard, the duration of synchronization meeting the standard is calculated, and then divided by the total duration of the startup phase to obtain the startup synchronization compliance rate.

[0124] For example, under a certain set of environmental parameters, the total startup time is ten minutes. If the startup synchronization of each device meets the preset standard for eight minutes, then the startup synchronization rate is the ratio of eight minutes to ten minutes.

[0125] The process for extracting the shutdown synchronization compliance rate is similar to that for the startup synchronization compliance rate. The indicator extraction module extracts real-time shutdown synchronization data of each virtual device from the collaborative operation data during the shutdown phase, compares it with the preset shutdown synchronization standard, counts the duration that meets the standard, and then divides it by the total duration of the shutdown phase to obtain the shutdown synchronization compliance rate.

[0126] To extract the load distribution deviation rate, the actual load distribution ratio of each virtual device is extracted from the collaborative operation data during the stable operation phase. Then, it is compared with the load distribution ratio specified in the device combination operation characteristics to calculate the deviation value at each time point. Finally, the deviation values ​​at all time points are averaged to obtain the load distribution deviation rate.

[0127] Step S135: Extract environmental adaptability indicators from collaborative operation data. The environmental adaptability indicators include stable operating time under different heat exchange characteristics and stable operating time under different air flow characteristics. The stable operating time is the duration during which the combined operation characteristics of the equipment are maintained within a preset range.

[0128] In this embodiment, to extract the stable operating time under different heat exchange characteristics, the data can first be divided into different heat exchange characteristic categories based on the heat exchange characteristic parameters in the collaborative operation data.

[0129] Then, under each heat exchange characteristic category, monitor whether the combined operating characteristics of the monitoring equipment remain within a preset range. When the combined operating characteristics of the equipment exceed the preset range, record the time point at this point. The difference between this time point and the time point at which the equipment begins to operate stably under that heat exchange characteristic category is the stable operating time under that heat exchange characteristic.

[0130] The process for extracting stable operating time under different airflow characteristics is similar to that described above. The index extraction module classifies the data into different airflow characteristic categories based on the airflow characteristic parameters in the collaborative operation data.

[0131] Under each airflow characteristic category, monitor the changes in the combined operating characteristics of the equipment. When the combined operating characteristics of the equipment exceed the preset range, calculate the time difference from the start of stable operation to this point, which is the stable operating time under that airflow characteristic.

[0132] Step S136: Integrate the collaborative operation smoothness index and the environmental adaptability index to generate the mode test results. The mode test results include test data for the startup phase, test data for the stable operation phase, and test data for the shutdown phase. Each set of test data includes the corresponding collaborative operation smoothness index and environmental adaptability index.

[0133] In this embodiment, after extracting the collaborative operation smoothness index and the environmental adaptability index, these indicators can be classified and integrated according to the stage of equipment operation.

[0134] For the test data during the startup phase, it can include the startup synchronization rate as an indicator of smooth collaborative operation, as well as environmental adaptability indicators under the corresponding heat exchange characteristics and airflow characteristics during the startup process, such as the duration for which the equipment maintains a stable startup state under specific heat exchange characteristics and the duration for which it maintains a stable startup state under specific airflow characteristics.

[0135] The test data during the stable operation phase will include indicators of smooth operation, such as the load distribution deviation rate, as well as environmental adaptability indicators such as the stable operation time under different heat exchange characteristics and airflow characteristics during the stable operation process.

[0136] The inspection data during the shutdown phase will include the shutdown synchronization rate, a smoothness indicator of coordinated operation, as well as environmental adaptability indicators under the corresponding heat exchange characteristics and airflow characteristics during the shutdown process, such as the duration of the equipment maintaining a stable shutdown state under specific heat exchange characteristics and the duration of the equipment maintaining a stable shutdown state under specific airflow characteristics.

[0137] After integration, the above inspection data can be used to generate pattern inspection results according to the preset format and stored in the inspection result library of the running and adapted system. At the same time, the notification module of the Internet of Things platform will send a message to the relevant management personnel that the inspection is completed.

[0138] Step S140: Adjust the equipment operation coordination mode according to the mode test results to obtain the target coordination mode and send it to the equipment linkage control system.

[0139] Step S141: Analyze the smoothness index of coordinated operation and the environmental adaptability index in the mode test results, and determine the adjustment direction of the equipment combination operation characteristics at each stage. The adjustment direction is to increase the start-up synchronization when the start-up synchronization rate is lower than the preset value, increase the shutdown synchronization when the shutdown synchronization rate is lower than the preset value, decrease the deviation rate when the load distribution deviation rate is higher than the preset value, and adjust the equipment combination operation characteristics of the corresponding stage to adapt to the environmental parameter when the stable running time under any environmental parameter is lower than the preset value.

[0140] In this embodiment, when parsing the pattern verification results, the pattern analysis module can first separate the collaborative operation smoothness index and the environmental adaptability index into the startup phase, stable operation phase, and shutdown phase.

[0141] If the startup synchronization rate is lower than the preset value during the startup phase, it indicates that the synchronization of the equipment during startup is poor. The adjustment should be to improve the consistency of the trend of state changes during the startup process of each device, such as adjusting the startup delay time of each device to make their startup rhythm more consistent.

[0142] During the stable operation phase, if the load distribution deviation rate is higher than the preset value, it indicates that the actual load distribution deviates significantly from the expected load distribution ratio. The adjustment should focus on reducing this deviation to bring the actual load distribution ratio of each device closer to the ratio specified in the equipment combination operation characteristics. Simultaneously, if the stable operating time under certain environmental parameters is lower than the preset value, it indicates that the equipment combination operation characteristics are not well-suited to these environmental parameters. Adjustments to the equipment combination operation characteristics during the stable operation phase are needed, such as adjusting the operating parameters of each device, to improve stable operation under these environmental parameters.

[0143] During the shutdown phase, if the shutdown synchronization rate is lower than the preset value, it indicates that the synchronization of the equipment during shutdown is poor. The adjustment should be to improve the consistency of the state decay trend of each piece of equipment during shutdown, such as adjusting the shutdown delay time of each piece of equipment to make their shutdown rhythm more consistent.

[0144] Step S142: For the startup phase, adjust the startup synchronization parameter in the equipment combination operation characteristics according to the startup synchronization rate. The startup synchronization parameter includes the allowable range of startup delay time difference of each device.

[0145] In this embodiment, regarding adjustments during the startup phase, if the startup synchronization rate is lower than a preset value, the allowable range for the startup delay time difference between devices needs to be narrowed. For example, if the startup delay time difference between devices was originally allowed to be within a certain range, this range can now be narrowed to make the startup times of each device more similar.

[0146] During the adjustment process, it is necessary to refer to the detailed data of the startup phase in the model test results and analyze the deviation of the startup time of each device. For device combinations with large startup time deviations, their startup delay time settings should be adjusted as a priority.

[0147] The adjusted startup synchronization parameters require redefining the time interval for issuing startup commands to each device to ensure that the state change trends of each device can better match during startup, thereby improving the startup synchronization compliance rate.

[0148] Step S143: For the stable operation phase, adjust the load distribution ratio in the equipment combination operation characteristics according to the load distribution deviation rate and the stable operation time under different environmental parameters, so that the load distribution ratio is more compatible with the heat exchange characteristics and air flow characteristics.

[0149] In this embodiment, when adjusting the load allocation ratio during the stable operation phase, if the load allocation deviation rate is higher than the preset value, it is necessary to adjust the ratio according to the direction of the deviation between the actual load allocation and the expected ratio. For example, if the actual load ratio of one chiller unit is higher than the expected ratio, while that of another is lower than the expected ratio, then the load allocation ratio of the former needs to be reduced and the load allocation ratio of the latter increased.

[0150] Simultaneously, considering the stable operating time under different environmental parameters, for cases where the stable operating time is short under specific heat exchange characteristics, the impact of these heat exchange characteristics on equipment load is analyzed, and the load allocation ratio is adjusted to adapt to these heat exchange characteristics. For example, in environments where heat exchange areas are concentrated, the load allocation ratio of equipment closer to these areas can be appropriately increased.

[0151] For cases where the stable operating time under specific airflow characteristics is relatively short, the impact of these airflow characteristics on equipment heat dissipation and other aspects is also analyzed, and the load distribution ratio is adjusted so that the combined operating characteristics of the equipment can better adapt to the aforementioned airflow characteristics.

[0152] Step S144: For the shutdown phase, adjust the shutdown synchronization parameter in the equipment combination operation characteristics according to the shutdown synchronization rate. The shutdown synchronization parameter includes the allowable range of the shutdown delay time difference of each piece of equipment.

[0153] In this embodiment, for adjustments during the shutdown phase, if the shutdown synchronization rate is lower than a preset value, the allowable range of shutdown delay time differences between various devices needs to be narrowed. Previously, the allowable time difference range might have been large, leading to dispersed device shutdown times. Narrowing this range now makes the shutdown times of each device more similar.

[0154] During adjustment, refer to the detailed data of the shutdown phase in the test results of the reference mode, identify the equipment combinations with large deviations in shutdown time, and focus on adjusting their shutdown delay time settings.

[0155] The adjusted shutdown synchronization parameters redefine the time interval for issuing shutdown commands for each device, ensuring that the state decay trends of each device can better match during shutdown, thereby improving the shutdown synchronization compliance rate.

[0156] Step S145: Based on the adjusted operating characteristics of the equipment combination at each stage, adjust the threshold in the state transition trigger condition accordingly so that the state transition trigger condition matches the adjusted operating characteristics of the equipment combination.

[0157] In this embodiment, the thresholds in the state transition trigger conditions need to be adapted to the adjusted combined operating characteristics of the equipment. For example, during the startup phase, if the adjusted startup synchronization parameters require higher synchronization, the thresholds for changes in heat exchange characteristics and airflow characteristics during the transition from the startup phase to the stable operation phase in the state transition trigger conditions may need to be appropriately increased to ensure that the equipment enters the stable operation phase after achieving a higher startup synchronization.

[0158] During the stable operation phase, if the adjusted load distribution ratio changes, the device's own runtime threshold for transitioning from the stable operation phase to the shutdown phase in the state transition trigger condition may need to be adjusted according to the new load conditions, so that the device can enter the shutdown phase after running for an appropriate duration.

[0159] The threshold values ​​for changes in heat exchange characteristics and airflow characteristics also need to be adjusted accordingly based on the adjusted stable operation phase equipment combination operation characteristics to ensure that when environmental characteristics change, state transitions can be triggered in a timely manner, so that the equipment combination operation characteristics can adapt to the new environment.

[0160] Step S146: Integrate the adjusted equipment combination operation characteristics and state transition trigger conditions to generate a preliminary collaborative mode.

[0161] In this embodiment, when integrating and adjusting the combined operating characteristics and state transition triggering conditions of the equipment, it is necessary to organize them according to the phase sequence of equipment operation. First, determine the combined operating characteristics of the equipment in the startup phase and the corresponding state transition triggering conditions, that is, the conditions for transitioning from the startup phase to the stable operation phase.

[0162] Next are the equipment combination operation characteristics and corresponding state transition triggering conditions during the stable operation phase, i.e., the conditions for transitioning from the stable operation phase to the shutdown phase. Finally, there are the equipment combination operation characteristics during the shutdown phase. Since equipment operation ends after the shutdown phase, there is no need for corresponding transition conditions to the next phase, but it is necessary to clearly define the markers for shutdown completion.

[0163] During the integration process, it is necessary to ensure that the logical relationship between the operational characteristics of the equipment combination and the state transition triggering conditions at each stage is clear and consistent, avoiding conflicts or improper connections. The preliminary collaborative model generated after integration will be stored in a model library, awaiting further testing.

[0164] Step S147: Input the preliminary collaborative mode into the running adaptation system for retesting. If the collaborative operation smoothness index and environmental adaptability index of the retest both meet the preset standards, then it is determined as the target collaborative mode.

[0165] In this embodiment, the process of inputting the preliminary collaborative mode into the running adaptation system is the same as the previous process of importing the device running collaborative mode. The running adaptation system can control the operation of virtual devices in the virtual environment according to the preliminary collaborative mode, and re-extract the collaborative operation smoothness index and the environmental adaptability index.

[0166] During the retest, the preset standards remain the same as before. If all indicators meet or exceed the standards, it indicates that the preliminary collaborative mode can meet the collaborative operation requirements and environmental adaptability requirements of the computer room equipment. At this point, the preliminary collaborative mode is determined as the target collaborative mode. If any indicator fails to meet the preset standards during the retest, it is necessary to return to steps S143 to S146 to readjust the equipment combination operation characteristics and state transition triggering conditions at each stage until the retest passes.

[0167] Step S148: Convert the target collaborative mode into an instruction format recognizable by the equipment linkage control system. The instruction format includes start instructions, operating parameter instructions, shutdown instructions and corresponding execution time points for each device.

[0168] In this embodiment, the target coordination mode exists in the form of a data model, containing information such as the operational characteristics of the device combination at each stage and the triggering conditions for state transitions. The instruction conversion module of the IoT platform can parse the target coordination mode, converting the coordination characteristics of the startup stage into startup instructions for each device, specifying the startup sequence and startup time of each device; converting the coordination characteristics of the stable operation stage into operating parameter instructions for each device, such as the specific numerical range of parameters like the cooling capacity of the chiller unit, the flow rate of the cooling water pump, and the pressure of the chilled water pump; and converting the coordination characteristics of the shutdown stage into shutdown instructions for each device, specifying the shutdown sequence and shutdown time of each device.

[0169] Meanwhile, the instruction conversion module can determine the execution time of each instruction based on the state transition trigger conditions. For example, when the heat exchange characteristics reach a certain change threshold, the corresponding operating parameter adjustment instruction should be executed at that time; when the equipment's own running time reaches a threshold, the corresponding shutdown instruction should be triggered at that time. The above instructions will be encapsulated according to the communication protocol and data format specified by the equipment linkage control system to ensure that the equipment linkage control system can accurately identify and parse them.

[0170] Step S149: The target collaborative mode is sent to the device linkage control system through the communication interface of the Internet of Things platform. The device linkage control system receives, stores and prepares to execute the mode.

[0171] In this embodiment, the communication interface of the IoT platform adopts an Ethernet interface, which supports the TCP / IP protocol and enables bidirectional data transmission with the device linkage control system. Before sending the target coordination mode, the communication interface can verify the instruction format to ensure the integrity and correctness of the data, and avoid the device linkage control system from failing to execute instructions normally due to data transmission errors.

[0172] After successful verification, the communication interface sends the target coordination mode to the equipment linkage control system in the form of data packets. Upon receiving the data packets, the receiving module of the equipment linkage control system unpacks and verifies them, confirming that the data packets have not been tampered with and conform to the instruction format requirements. After successful verification, the equipment linkage control system stores the target coordination mode in its local instruction database and assigns a unique identifier to each instruction for tracking and management during execution. Simultaneously, the equipment linkage control system enters a ready-to-execute state, monitoring the heat exchange characteristics, airflow characteristics, and equipment operating status in the machine room in real time, awaiting the triggering of the corresponding instruction execution conditions.

[0173] Step S150: Track the state feedback sequence of the device running in the target collaborative mode, update the device environment association sequence based on the state feedback sequence, generate the updated device environment association sequence and store it in the association sequence library of the IoT platform.

[0174] For example, step S151: Real-time collection of status data of devices running in the target collaborative mode through the sensor network of the Internet of Things platform. The status data includes the status changes of each device during startup, stable operating parameters, and status decay during shutdown.

[0175] In this embodiment, the sensor network consists of various sensors installed on equipment such as chillers, cooling water pumps, and chilled water pumps, including temperature sensors, pressure sensors, flow sensors, and speed sensors. During equipment startup, the speed sensor collects real-time data on the speed changes of the chiller compressor and the impeller speed changes of the cooling water pump and chilled water pump, forming startup process state change data. During stable equipment operation, the temperature sensor collects the operating temperature of the equipment, the pressure sensor collects the pressure of the water pipeline, and the flow sensor collects the water flow rate; these data together constitute stable operating parameters. During equipment shutdown, each sensor continues to collect data on parameter decay, such as a gradual decrease in speed and pressure, forming shutdown process state decay data.

[0176] This status data is transmitted to the IoT platform in real time via wired or wireless transmission through the sensor network. During transmission, encryption algorithms are used to encrypt the data, ensuring its security and integrity and preventing tampering or leakage. The IoT platform's receiving module decrypts and verifies the received data. Once verified, the data is stored in a real-time database for subsequent processing.

[0177] Step S152: Simultaneously collect heat exchange characteristic data and air flow characteristic data in the computer room. The heat exchange characteristic data includes real-time heat transfer direction and heat exchange area distribution. The air flow characteristic data includes real-time airflow circulation path and flow velocity characteristics.

[0178] In this embodiment, heat exchange characteristic data within the computer room is collected by temperature sensors distributed at different locations. By analyzing the temperature changes of each sensor at different times, the real-time heat transfer direction is determined. For example, in the initial stage of equipment operation, the heat transfer direction is mainly from the chiller unit to the surrounding environment. As the operating time increases, the heat transfer direction may change due to the effect of the computer room ventilation system. The distribution of heat exchange areas is determined by the temperature gradient distribution of multiple temperature sensors. Areas with larger temperature gradients are the primary heat exchange areas, and areas with smaller temperature gradients are the secondary heat exchange areas.

[0179] Airflow characteristic data is collected by airflow sensors installed at the top, bottom, and around the equipment in the computer room. These sensors record the direction and speed of airflow in real time, thus determining the real-time airflow circulation path, such as the path of air entering from the air inlet of the computer room, passing through the equipment's heat dissipation area, and then exiting from the air outlet. Flow velocity characteristics refer to the magnitude and variation of airflow velocity at different locations; for example, the airflow velocity is faster near the equipment and slower in areas farther away.

[0180] Heat exchange characteristic data and airflow characteristic data are collected at the same frequency and timestamp as equipment status data to ensure accurate correspondence in subsequent processing. At the same time, the collected data is also encrypted before being transmitted to the IoT platform and stored in a real-time database along with the status data.

[0181] Step S153: Associate the status data with the heat exchange characteristic data and the air flow characteristic data by timestamp to generate a status feedback sequence. Each timestamp of the status feedback sequence contains the corresponding equipment status data, heat exchange characteristic data and air flow characteristic data.

[0182] In this embodiment, the sequence generation module of the IoT platform can extract status data, heat exchange characteristic data, and airflow characteristic data from the real-time database. All of these data have a timestamp at the time of collection. The sequence generation module associates the device status data, heat exchange characteristic data, and airflow characteristic data at the same timestamp according to the order of the timestamps, forming a data unit.

[0183] For example, at a certain timestamp, the equipment status data includes the chiller compressor speed, cooling water pump flow rate, and chilled water pump pressure; the heat exchange characteristic data includes the heat transfer direction and heat exchange zone boundary at that moment; and the airflow characteristic data includes the airflow circulation path nodes and the flow velocity at each node at that moment. Combining these data together constitutes the content corresponding to that timestamp in the status feedback sequence.

[0184] All data are processed sequentially according to the timestamps to form a continuous state feedback sequence. Each data unit in the sequence fully reflects the relationship between the device's operating status and environmental characteristics at the corresponding moment.

[0185] Step S154: Align the state feedback sequence with the original equipment environment association sequence by timestamp, compare the differences in equipment state, heat exchange characteristics, and air flow characteristics at the same timestamp, and replace the corresponding data in the original equipment environment association sequence with the data in the state feedback sequence for timestamps with differences.

[0186] In this embodiment, the original device environment association sequence is a data sequence previously generated and stored in the association sequence library, containing the correlation between historical device operating states and environmental characteristics. The sequence comparison module of the IoT platform can align the timeline of the state feedback sequence with that of the original device environment association sequence, ensuring that the timestamp start points and intervals of the two sequences are consistent.

[0187] For each identical timestamp, the sequence alignment module can compare the device status data, heat exchange characteristic data, and airflow characteristic data in the status feedback sequence and the original device environment association sequence one by one. The comparison method is to check whether the characteristic parameters of each data are consistent, such as whether the operating parameter range of the device is the same, whether the heat transfer direction is consistent, and whether the airflow circulation path is consistent.

[0188] When a discrepancy is found in the data at a certain timestamp, such as different operating parameter ranges of the equipment or changes in the distribution of the heat exchange area, the sequence comparison module can replace the data at the corresponding timestamp in the original equipment environment association sequence with the data at that timestamp in the status feedback sequence to ensure the timeliness and accuracy of the data.

[0189] Step S155: For the newly added timestamp in the status feedback sequence, add the corresponding equipment status data, heat exchange characteristic data, and air flow characteristic data to the original equipment environment association sequence.

[0190] In this embodiment, the status feedback sequence may contain timestamps not present in the original device environment association sequence. These newly added timestamps typically correspond to the device operating status under new operating conditions or new environmental conditions. The sequence comparison module can identify these newly added timestamps and add the device status data, heat exchange characteristic data, and airflow characteristic data corresponding to each newly added timestamp to the end of the original device environment association sequence or the corresponding time position according to the timestamp order, thereby expanding the time range of the original device environment association sequence.

[0191] For example, the original device environment association sequence has a time range from 8:00 AM to 5:00 PM on a certain day, while the status feedback sequence contains timestamps and corresponding data from 5:00 PM to 10:00 PM on the same day. The sequence comparison module can add this new data to the original sequence, so that the device environment association sequence can cover a more complete device operation time period.

[0192] Step S156: Sort the updated device environment association sequence by timestamp, check the integrity of the updated device environment association sequence, and store the updated device environment association sequence that passes the check into the association sequence library of the IoT platform, overwriting the original device environment association sequence.

[0193] In this embodiment, the updated device environment association sequence may have a disordered timestamp order due to data replacement and addition. It can be reordered and arranged in the order of timestamps from earliest to latest to ensure the temporal continuity of the sequence.

[0194] When checking the integrity of the updated equipment environment association sequence, the sequence processing module can check for missing timestamps and whether each timestamp contains complete equipment status data, heat exchange characteristic data, and airflow characteristic data. If missing timestamps or incomplete data are found, the sequence processing module can issue a prompt message to notify relevant personnel to complete the data or re-collect it; if the check passes, it indicates that the updated equipment environment association sequence is complete and valid.

[0195] Finally, the sequence processing module stores the updated device environment association sequences that have passed the checks into the association sequence library of the IoT platform, overwriting the original device environment association sequences. The association sequence library adopts a distributed storage architecture, which has data backup and disaster recovery capabilities to ensure data security and reliability. At the same time, it supports fast query and retrieval so that this data can be efficiently accessed when building new device operation collaboration modes in the future.

[0196] Figure 2 The illustration shows exemplary hardware and software components of an IoT-based building refrigeration room operation and maintenance management system 100 that can implement the ideas of this application, according to some embodiments of this application. For example, processor 120 can be used in the IoT-based building refrigeration room operation and maintenance management system 100 and to perform the functions in this application.

[0197] For example, a building refrigeration room operation and maintenance management system 100 based on an IoT platform may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the building refrigeration room operation and maintenance management system 100 based on an IoT platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The building refrigeration room operation and maintenance management system 100 based on an IoT platform also includes an I / O interface 150 between the computer and other input / output devices.

[0198] Furthermore, this embodiment of the invention also provides a readable storage medium, which has computer-executable instructions pre-set in it. When the processor executes the computer-executable instructions, the above-mentioned building refrigeration room operation and maintenance management method based on the Internet of Things platform is implemented.

[0199] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A building refrigeration machine room operation and maintenance management method based on an Internet of Things platform, characterized in that, The method includes: The operating status sequence and environmental impact sequence of the building's refrigeration room equipment are correlated to generate an equipment-environment correlation sequence. The operating status sequence includes the equipment's start-up and shutdown transition characteristics, and the environmental impact sequence includes the heat exchange characteristics and airflow characteristics within the room. Based on the device environment association sequence, a device operation coordination mode is constructed, which includes device combination operation characteristics and state transition triggering conditions. The device operation collaboration mode is input into the operation adaptation system of the Internet of Things platform for compatibility testing to obtain the mode test results, which include the smoothness of collaborative operation and environmental adaptability. Based on the pattern verification results, the equipment operation coordination mode is adjusted to obtain the target coordination mode and sent to the equipment linkage control system; The system tracks the state feedback sequence of the device when it is running in the target collaborative mode, updates the device environment association sequence based on the state feedback sequence, generates the updated device environment association sequence, and stores it in the association sequence library of the IoT platform.

2. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 1, characterized in that, The associated operating status sequence and environmental impact sequence of the building's refrigeration room equipment generate an equipment-environment association sequence, including: The startup operation features and shutdown transition features of the operating state sequence of the building refrigeration equipment are extracted. The startup operation features include the state change trend during startup and the transition time before stable operation. The shutdown transition features include the state decay trend during shutdown and the preparation time before complete shutdown. The heat exchange characteristics and airflow characteristics within the computer room are obtained from the environmental impact sequence. The heat exchange characteristics include the direction of heat transfer and the distribution of heat exchange areas. The airflow characteristics include the airflow circulation path and flow velocity characteristics. The startup and operation features and heat exchange features are correlated and mapped. The state change trend during startup is aligned with the heat transfer direction along the time axis. The change in heat transfer direction corresponding to each state change node is determined. At the same time, the transition time before stable operation is correlated with the expansion time of the heat exchange area distribution, and the change nodes of the heat exchange area within the transition time are marked. The shutdown transition characteristics and airflow characteristics are correlated and mapped. The state decay trend during the shutdown process is aligned with the contraction trend of the airflow circulation path along the time axis to determine the airflow path change corresponding to each decay node. At the same time, the preparation time before complete shutdown is correlated with the decay time of the flow velocity characteristics, and the nodes of flow velocity change within the preparation time are marked. The association mapping results of the startup and operation features and heat exchange features are arranged in chronological order to generate a first association chain. The association mapping results of the shutdown transition features and airflow features are arranged in chronological order to generate a second association chain. The first association chain and the second association chain are merged to generate an equipment environment association sequence. Each time node of the equipment environment association sequence contains the specific state of the corresponding startup and operation feature or shutdown transition feature, heat exchange feature or airflow feature.

3. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 2, characterized in that, The process involves associating and mapping the startup and operation characteristics with the heat exchange characteristics, aligning the state change trends during startup with the heat transfer direction along the time axis, determining the heat transfer direction change corresponding to each state change node, and simultaneously associating the transition time before stable operation with the expansion time of the heat exchange area distribution, marking the change nodes of the heat exchange area within the transition time, including: The startup process state change trend in the startup running characteristics is decomposed into multiple continuous state change nodes over time, and each state change node corresponds to a running parameter value. The heat transfer direction in the heat exchange characteristics is decomposed into multiple continuous direction change nodes over time, and each direction change node corresponds to a heat transfer direction. Align the state change nodes and direction change nodes along the time axis, calculate the time difference between each state change node and the nearest direction change node, and if the time difference is within a preset range, determine that the state change node and the direction change node are associated, and record the association relationship. Extract the transition duration before stable operation from the startup and operation characteristics, and determine the start and end times of the transition duration; Extract the expansion duration of the heat exchange region distribution from the heat exchange characteristics, and determine the start and end times of the expansion duration, wherein the expansion duration is the time for the heat exchange region to expand from the initial range to the stable range; Align the transition duration and extension duration along the timeline. If the difference between their start and end times is within a preset range, the transition duration and extension duration are considered to be related. During the transition period, the state of the heat exchange area distribution is extracted at fixed time intervals, and the change nodes of the heat exchange area are marked. The change nodes are the time points when the range of the heat exchange area changes significantly. The orientation change nodes associated with each state change node and the heat exchange area change nodes within the transition time are integrated to generate an association mapping result between startup operation characteristics and heat exchange characteristics. The association mapping result includes a timestamp, the corresponding state change node, orientation change node, and heat exchange area change node.

4. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 2, characterized in that, The process involves mapping the shutdown transition characteristics and airflow characteristics together, aligning the state decay trend during shutdown with the contraction trend of the airflow circulation path along the time axis, determining the airflow path change corresponding to each decay node, and simultaneously associating the preparation time before complete shutdown with the decay time of the flow velocity characteristics, marking the nodes of flow velocity change within the preparation time, including: The shutdown process state decay trend in the shutdown transition feature is decomposed into multiple consecutive decay nodes over time, and each decay node corresponds to a decay value of an operating parameter. The contraction trend of the airflow circulation path in the airflow characteristics is decomposed into multiple continuous path change nodes over time, and each path change node corresponds to an airflow circulation path range. Align the decay nodes and path change nodes along the time axis, calculate the time difference between each decay node and the nearest path change node, and if the time difference is within a preset range, determine that the decay node is associated with the path change node and record the association relationship. Extract the preparation time before complete shutdown from the shutdown transition features, and determine the start and end times of the preparation time. Extract the decay time of the flow velocity feature from the airflow characteristics, and determine the start and end times of the decay time, wherein the decay time is the time it takes for the flow velocity to decay from a stable value to zero; Align the preparation time and decay time along the time axis. If the difference between their start and end times is within a preset range, the preparation time and decay time are considered to be related. During the preparation period, the state of the flow velocity characteristics is extracted at fixed time intervals, and the nodes of change in flow velocity are marked. The nodes of change are the time points when the flow velocity changes significantly. By integrating the path change nodes associated with each decay node and the flow velocity change nodes within the preparation time, a correlation mapping result between the shutdown transition characteristics and the airflow characteristics is generated. The correlation mapping result includes a timestamp, the corresponding decay node, the path change node, and the flow velocity change node.

5. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 1, characterized in that, The construction of the device operation collaboration mode based on the device environment association sequence includes: The device environment association sequence is parsed, and the combination of operating states of different devices at the same time node is extracted and marked as a collaborative state node. The collaborative state node includes the combination of device start-up and operation characteristics or the combination of shutdown and transition characteristics. The device combination operation characteristics are extracted from the collaborative state nodes. The device combination operation characteristics include the start-up synchronization degree, shutdown synchronization degree, and load distribution ratio of each device in the combination. The start-up synchronization degree is the degree of consistency of the state change trend of each device during the start-up process, and the shutdown synchronization degree is the degree of consistency of the state decay trend of each device during the shutdown process. The conditions for the transition of collaborative state nodes in the equipment environment association sequence are analyzed to determine the state transition trigger conditions. The state transition trigger conditions include the change threshold of heat exchange characteristics, the change threshold of air flow characteristics, and the equipment's own running time threshold. The combined operation characteristics of the equipment are classified into three stages: startup, stable operation, and shutdown. Each stage corresponds to a set of combined operation characteristics of the equipment. The equipment combination operation characteristics of each stage are associated with the corresponding state transition trigger conditions to generate the stage transition logic of the equipment operation coordination mode. The stage transition logic stipulates that when any state transition trigger condition is met, the equipment combination operation characteristics will transition from the current stage to the next stage. By integrating the phase transition logic and the equipment combination operation characteristics of each phase, a complete equipment operation coordination mode is generated. The equipment operation coordination mode includes the coordination characteristics of the startup phase, the coordination characteristics of the stable operation phase, the coordination characteristics of the shutdown phase, and the corresponding state transition triggering conditions.

6. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 5, characterized in that, The process of extracting equipment combination operation characteristics from the collaborative state nodes includes the startup synchronization degree, shutdown synchronization degree, and load distribution ratio of each device within the combination. The startup synchronization degree is the degree of consistency in the state change trends during the startup process of each device, and the shutdown synchronization degree is the degree of consistency in the state decay trends during the shutdown process of each device. This includes: Nodes in the startup phase are selected from the collaborative status nodes, and the status change trend data of each device during the startup process are extracted. The similarity of state change trend data between any two devices is calculated by the degree of fit of the trend curves. Based on the similarity among all devices, the startup synchronization degree is calculated, which is the average of the similarity among all devices; Nodes in the shutdown phase are selected from the collaborative status nodes, and the status decay trend data of each device during the shutdown process is extracted. Calculate the similarity of state decay trend data between any two devices. Based on the similarity among all devices, calculate the shutdown synchronization degree, which is the average of the similarity among all devices. Nodes in a stable operating phase are selected from the collaborative status nodes, and load data of each device is extracted. Calculate the proportion of each device's load data to the total combined load data to obtain the load distribution ratio; The startup synchronization, shutdown synchronization, and load distribution ratio are classified according to equipment combination categories, and each equipment combination category corresponds to a set of equipment combination operation characteristics; Statistical analysis is performed on the operating characteristics of multiple equipment combinations of the same equipment combination category, and the average value is calculated as the typical operating characteristics of the equipment combination category. The typical operating characteristics include average start-up synchronization, average shutdown synchronization, and average load distribution ratio.

7. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 1, characterized in that, The process of inputting the device operation collaboration mode into the IoT platform's operation adaptation system for compatibility testing, and obtaining the mode test results, includes: The device operation collaboration mode is imported into the operation adaptation system of the Internet of Things platform. The operation adaptation system includes a virtual device model that is consistent with the actual data center equipment model and quantity, as well as a virtual environment model that can simulate different heat exchange characteristics and air flow characteristics. Multiple sets of environmental parameters are set in the virtual environment model. Each set of environmental parameters includes heat transfer direction, heat exchange area distribution, airflow circulation path, and flow velocity characteristics. Under each set of environmental parameters, the virtual device model is controlled to run according to the device operation coordination mode, and the coordination operation data of the virtual device model in the startup phase, stable operation phase, and shutdown phase is recorded. The smoothness index of collaborative operation is extracted from the collaborative operation data. The smoothness index of collaborative operation includes the start-up synchronization rate, the shutdown synchronization rate, and the load distribution deviation rate. The start-up synchronization rate is the percentage of time during the start-up process when the synchronization meets the preset standard. The shutdown synchronization rate is the percentage of time during the shutdown process when the synchronization meets the preset standard. The load distribution deviation rate is the degree of deviation between the actual load distribution ratio and the load distribution ratio in the equipment combination operation characteristics. Environmental adaptability indicators are extracted from collaborative operation data. These indicators include stable operating time under different heat exchange characteristics and stable operating time under different air flow characteristics. The stable operating time is the duration during which the combined operation characteristics of the equipment are maintained within a preset range. The smoothness of collaborative operation and environmental adaptability indicators are integrated to generate model verification results. The model verification results include verification data for the startup phase, verification data for the stable operation phase, and verification data for the shutdown phase. Each set of verification data includes the corresponding smoothness of collaborative operation and environmental adaptability indicators.

8. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 7, characterized in that, The extraction of collaborative operation smoothness indicators from collaborative operation data includes: Extract the startup phase operation records from the collaborative operation data. The startup phase operation records include the startup process status change trend data of each device and the corresponding timestamp. The real-time startup synchronization degree during the startup process is calculated based on the state change trend data. The real-time startup synchronization degree is compared with the preset startup synchronization degree standard, and the duration for which the real-time startup synchronization degree meets the standard is counted. Calculate the startup synchronization compliance rate, which is the ratio of the duration of compliance to the total duration of the startup phase; The operation records during the shutdown phase are extracted from the collaborative operation data. These records include data on the decline trend of the shutdown process status of each device and the corresponding timestamp. The real-time shutdown synchronization degree during the shutdown process is calculated based on the state decay trend data. The real-time shutdown synchronization degree is compared with the preset shutdown synchronization degree standard, and the duration for which the real-time shutdown synchronization degree meets the standard is counted. Calculate the shutdown synchronization compliance rate, which is the ratio of the duration of compliance with the standard to the total duration of the shutdown phase; Extract operation records for the stable operation phase from the collaborative operation data. The operation records for the stable operation phase include real-time load data of each device and corresponding timestamps. The actual load allocation ratio is calculated based on real-time load data. The actual load allocation ratio is compared with the load allocation ratio in the equipment combination operation characteristics, and the deviation value at each time point is calculated. Calculate the load distribution deviation rate, which is the average of the deviation values ​​at all time points; The startup synchronization rate, shutdown synchronization rate, and load distribution deviation rate are integrated to generate a collaborative operation smoothness index.

9. The method for operation and maintenance management of building refrigeration room based on an Internet of Things platform according to claim 1, characterized in that, The step of adjusting the equipment operation coordination mode based on the pattern verification result, obtaining the target coordination mode, and sending it to the equipment linkage control system includes: The smoothness of collaborative operation and environmental adaptability in the mode test results are analyzed to determine the adjustment direction of the equipment combination operation characteristics at each stage. The adjustment direction is to increase the startup synchronization when the startup synchronization rate is lower than the preset value, increase the shutdown synchronization when the shutdown synchronization rate is lower than the preset value, decrease the deviation rate when the load distribution deviation rate is higher than the preset value, and adjust the equipment combination operation characteristics of the corresponding stage to adapt to the environmental parameter when the stable running time under any environmental parameter is lower than the preset value. For the startup phase, the startup synchronization parameter in the equipment combination operation characteristics is adjusted according to the startup synchronization rate. The startup synchronization parameter includes the allowable range of startup delay time difference between each device. For the stable operation phase, the load distribution ratio in the equipment combination operation characteristics is adjusted according to the load distribution deviation rate and the stable operation time under different environmental parameters, so that the load distribution ratio is more compatible with the heat exchange characteristics and air flow characteristics. For the shutdown phase, the shutdown synchronization parameter in the equipment combination operation characteristics is adjusted according to the shutdown synchronization rate. The shutdown synchronization parameter includes the allowable range of the shutdown delay time difference of each piece of equipment. Based on the adjusted equipment combination operation characteristics of each stage, the thresholds in the state transition triggering conditions are adjusted accordingly to match the adjusted equipment combination operation characteristics. The adjusted equipment combination operation characteristics and state transition trigger conditions are integrated to generate a preliminary collaborative mode; The preliminary collaborative mode is input into the running adaptation system for retesting. If the collaborative operation smoothness index and environmental adaptability index of the retest both meet the preset standards, it is determined as the target collaborative mode. The target coordination mode is converted into an instruction format that can be recognized by the equipment linkage control system. The instruction format includes the start instruction, operating parameter instruction, shutdown instruction and corresponding execution time point of each device. The target collaborative mode is sent to the device linkage control system through the communication interface of the Internet of Things platform. The device linkage control system receives, stores and prepares to execute the mode.

10. A building refrigeration room operation and maintenance management system based on an Internet of Things (IoT) platform, characterized in that, The system includes a processor and a memory, the memory being connected to the processor. The memory is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the memory to implement the building refrigeration room operation and maintenance management method based on an Internet of Things platform as described in any one of claims 1-9.

Images