High-voltage passive induction temperature monitoring system

Abstract

The invention relates to the field of temperature monitoring, and discloses a high-voltage passive induction temperature monitoring system, which comprises a plurality of temperature sensors, the plurality of temperature sensors are arranged in different cabinets and channels in a machine room; a control unit; the control unit is used for sending a detection instruction to each temperature sensor and receiving a feedback temperature signal; the control unit generates a temperature change curve of each monitoring point according to the received temperature signals; the control unit constructs a three-dimensional temperature distribution model of the whole machine room based on the temperature change curve of each monitoring point, identifies a potential hot spot area based on the three-dimensional temperature distribution model and predicts the temperature change trend of the potential hot spot area; a refrigeration unit; the refrigeration unit is used for adjusting the temperature of the located area; the refrigeration unit is communicated with the control unit; wherein the control unit adjusts the power of each refrigeration unit based on the identified potential hot spot areas.

Description

Technical Field

[0001] The present invention relates to the field of temperature monitoring, and in particular to a high-voltage passive induction temperature monitoring system. Background Art

[0002] The safety and reliability of power equipment is a crucial component of ultra-large-scale power transmission and distribution, and the security of power grids. Therefore, real-time monitoring of the safe operation of power equipment is essential. Long-term grid operation data shows that most electrical equipment failures are caused by high currents, equipment aging, and insulation degradation, leading to high temperatures and potentially serious consequences such as combustion and explosion.

[0003] In power systems, temperature fluctuations in high-voltage electrical equipment are a crucial indicator for safe and stable operation. During operation, the contact resistance of high-voltage electrical equipment increases due to factors such as manufacturing, oxidation, and arc shock, causing the temperature to rise. When the temperature rises to a certain level, the mechanical and electrical strength of the equipment decreases. In severe cases, this can lead to short circuits or even damage, posing a serious threat to the safe operation of the power grid. Real-time temperature monitoring of electrical equipment can help on-duty personnel identify problems early, eliminate potential hazards, and ensure the safe operation of the power system. Traditional temperature monitoring and control systems often use wired temperature sensors for data collection, which presents challenges such as complex wiring and poor scalability. Furthermore, existing systems often rely on fixed thresholds to detect temperature anomalies, lacking the ability to predict temperature trends and effectively intervene in potential hotspots in advance.

[0004] How to solve the above technical problems is a technical difficulty that needs to be overcome by those skilled in the art. Summary of the Invention

[0005] The present invention provides a high-voltage passive induction temperature monitoring system to at least partially solve the above technical problems.

[0006] In order to solve the above technical problems, the present invention provides a high voltage passive induction temperature monitoring system, comprising: Several temperature sensors; several of the temperature sensors are deployed in different cabinets and channels in the computer room; Control unit; the control unit is used to send detection instructions to each temperature sensor and receive feedback temperature signals; the control unit generates a temperature change curve for each monitoring point based on the received temperature signals; the control unit constructs a three-dimensional temperature distribution model of the entire computer room based on the temperature change curve of each monitoring point, identifies potential hot spots based on the three-dimensional temperature distribution model, and predicts the temperature change trend of the potential hot spots; Refrigeration unit; the refrigeration unit is used to adjust the temperature of the area; the refrigeration unit communicates with the control unit; The control unit adjusts the power of each cooling unit based on the identified potential hotspot areas.

[0007] In an optional embodiment, the control unit generates a temperature change curve for each monitoring point according to the received temperature signal, including: Perform time stamp synchronization processing on the temperature signals sent by each temperature sensor to establish a unified time series to obtain temperature data; The temperature data is smoothly fitted using interpolation method to generate the temperature change curve; The control unit constructs a three-dimensional temperature distribution model of the entire equipment room based on the temperature change curve of each monitoring point, including: Extracting temperature change rate based on temperature change curve; Establish a three-dimensional space grid based on the cabinet coordinates; The temperature change rate extracted from each monitoring point is mapped to the corresponding grid nodes of the three-dimensional space grid to obtain a discrete feature field; Perform spatial interpolation on the discrete characteristic field to generate a three-dimensional temperature distribution model; Identify potential hotspots based on a 3D temperature distribution model, including: Extracting a region where the temperature change rate exceeds a first temperature change rate based on the three-dimensional temperature distribution model, and recording it as a first region; The first area is screened again based on the real-time temperature value, and areas where the real-time temperature value exceeds the set threshold are retained as potential hot spots.

[0008] In an optional embodiment, predicting the temperature change trend of the potential hotspot area includes: A first-order autoregressive model is constructed based on the extracted temperature change rate features; The current temperature value and temperature change rate are input into the first-order autoregressive model, and the temperature prediction value for the first time period in the future is calculated to obtain the temperature change trend curve; among them, the prediction range of the temperature change rate is limited to the preset change rate, and the temperature prediction value exceeding the preset change rate is truncated.

[0009] In an optional embodiment, the control unit adjusts the power of each refrigeration unit based on the identified potential hotspot area, including: Determine the difference between the predicted temperature of the potential hotspot area and the target temperature; the target temperature is the most suitable temperature for the server equipment to operate; The heat required to be removed is calculated using a heat calculation formula based on the spatial volume of the potential hotspot area, the specific heat capacity of air, and the time required for the predicted temperature to reach the target temperature; distributing the increased cooling capacity to the surrounding cooling units in inverse proportion to the spatial distance; The power of each refrigeration unit is adjusted by PID controller; The increased cooling capacity is distributed to the surrounding cooling units in inverse proportion to the spatial distance, including: Determine the location of each cooling unit around the potential hotspot area, and calculate the straight-line distance between each cooling unit and the center of the potential hotspot area; Calculating the allocation weight of each refrigeration unit according to the straight-line distance; wherein the closer the distance, the higher the allocation weight; The power of each refrigeration unit is adjusted by a PID controller, including: The power adjustment amount is calculated using a PID control algorithm based on the deviation between the current actual power and the target power, as well as the deviation between the real-time temperature data and the target temperature; wherein the target power is determined by the allocated cooling capacity.

[0010] In an optional embodiment, establishing a three-dimensional space grid based on the cabinet coordinates further includes: Increase mesh density in areas with large temperature gradients to improve model resolution; Reduce the mesh density in areas where the temperature changes gently to reduce the amount of calculation; Adjusting the grid distribution according to the change of real-time temperature data; wherein, adjusting the grid distribution according to the change of real-time temperature data includes: Using the geometric center coordinates of the cabinet as the reference point, divide the three-dimensional space of the computer room into initial grids and set the side lengths of the initial grids. Assign a unique identification number to each grid node and establish a correspondence table between grid nodes and cabinet and channel locations; For the temperature data collected by each temperature sensor, calculate its temperature gradient vector in three-dimensional space; For adjacent grid nodes, the temperature gradient value between nodes is obtained by interpolating the temperature difference between the nodes; Configure a temperature gradient strength index for each grid node; the temperature gradient strength index is the weighted average of the temperature gradients of the adjacent grids around the node; When the temperature gradient intensity index of a grid node is greater than a first threshold, the grid and the surrounding adjacent grids are subdivided into quadtrees to reduce the grid side length to half of the original length; When the temperature gradient intensity index of a grid node is less than a second threshold, the grid is merged with surrounding grids with similar temperature gradient intensities, so that the grid side length is doubled; After each adjustment, the identification number and corresponding relationship table of the grid nodes are updated.

[0011] In an optional embodiment, when adjusting the power of the refrigeration unit, the control unit further includes: According to the layout of the computer room, the cooling units whose straight-line distance is less than the first preset distance are marked as adjacent, and an undirected graph G = (V, E) is constructed, where the node V represents the cooling unit and the edge E represents the adjacent relationship; Assign a weight value W to each edge; the weight value is calculated based on the temperature adjustment correlation between the two refrigeration units in the historical operation data, and the higher the correlation, the greater the weight; After the initial allocation of cooling capacity, the power difference between adjacent cooling units is calculated. If the power difference exceeds the preset value, the particle swarm optimization algorithm is used to obtain the target power value of each cooling unit with the multi-objective function of minimizing the mean square error of temperature regulation, minimizing the power difference, and minimizing the total energy consumption. The particle swarm optimization algorithm is used, including: Each particle is defined as a set of cooling unit power allocation schemes, with the particle dimension equal to the number of cooling units. The initial position and velocity of the particles are randomly generated, with the position range being from the minimum power to the maximum power of each cooling unit, and the velocity range being set to the maximum adjustment step.

[0012] For each particle, the mean square error of temperature regulation, the sum of power differences between adjacent units, and the total energy consumption of the system are calculated, and then substituted into the multi-objective function to calculate the fitness value; Update the particle velocity and position based on the particle's own optimal position and the global optimal position; after each iteration, check whether the particle position meets the power constraint. If not, perform boundary processing; When the maximum number of iterations is reached or the convergence condition is met, the power allocation scheme corresponding to the global optimal particle is output as the target power value of each refrigeration unit.

[0013] In an optional embodiment, the system further includes an alarm module, and the control unit determines the temperature value of the received temperature signal. When the temperature value is greater than the set temperature, the control unit sends an alarm instruction to the alarm module, and the alarm module sends a sound prompt to the outside world.

[0014] In an optional embodiment, the temperature sensor is a wireless passive temperature sensor.

[0015] Compared to existing technologies, this invention offers at least the following advantages: By deploying passive temperature sensors within the computer room, real-time temperature data from various cabinets and aisles can be collected. A control unit processes the collected temperature signals, constructs a three-dimensional temperature distribution model, identifies potential hotspots, and predicts their temperature trends. Based on these predictions, the control unit adjusts the cooling unit power to regulate the computer room temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system block diagram of a high-voltage passive induction temperature monitoring system provided by the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Reference Figure 1 The first embodiment of the present invention provides a high voltage passive induction temperature monitoring system, comprising: A high voltage passive induction temperature monitoring system comprising: Several temperature sensors are deployed in various cabinets and aisles within the equipment room. High-voltage passive induction temperature sensors: These sensors require no external power supply and instead utilize electromagnetic induction, thermoelectric power generation, and other methods to generate energy from the surrounding environment to achieve temperature sensing. In high-voltage environments, these sensors can avoid potential safety hazards introduced by power lines while reducing wiring costs and maintenance.

[0019] A control unit is configured to send detection commands to each temperature sensor and receive feedback temperature signals. Based on the received temperature signals, the control unit generates a temperature change curve for each monitoring point. Based on the temperature change curves at each monitoring point, the control unit constructs a three-dimensional temperature distribution model for the entire computer room. Based on the temperature change curves at each monitoring point, the control unit identifies potential hotspots and predicts temperature change trends in these hotspots. The control unit exchanges data and sends commands with the temperature sensors and cooling units. By processing the feedback signals from the temperature sensors, comprehensive monitoring and analysis of the computer room's temperature status is achieved. A temperature change curve is a chronological arrangement of temperature data collected by the temperature sensors, smoothed using mathematical methods such as interpolation. It visually reflects how the temperature at each monitoring point changes over time. The three-dimensional temperature distribution model is generated by dividing the computer room into a grid based on cabinet coordinates. The temperature change rate of each monitoring point is mapped to the grid nodes, and spatial interpolation is used to generate the model. This model provides a three-dimensional and comprehensive view of the temperature distribution within the computer room. Potential hotspots are identified in the three-dimensional temperature distribution model by setting temperature change rates and real-time temperature thresholds. These areas are areas where the temperature may rise rapidly or is already at a high level, posing a risk of equipment overheating.

[0020] Refrigeration unit; the refrigeration unit is used to adjust the temperature of the area; the refrigeration unit communicates with the control unit; The control unit adjusts the power of each cooling unit based on the identified potential hotspots. Cooling units, such as fan units, regulate the temperature in specific areas of the computer room. They communicate with the control unit, receiving power adjustment commands to achieve cooling.

[0021] This application realizes the real-time collection of the temperature of different cabinets and channels by deploying passive induction temperature sensors in the computer room. The control unit performs in-depth processing on the collected temperature signals, constructs a three-dimensional temperature distribution model, identifies potential hot spots, and predicts their temperature change trends. Based on the prediction results, the control unit adjusts the power of the refrigeration unit to achieve temperature regulation of the computer room. Due to the use of passive induction temperature sensors, the safety risks and wiring difficulties of external power supplies in high-voltage environments are avoided; the three-dimensional temperature distribution model constructed by the control unit based on the temperature change curve, combined with the identification of potential hot spots and temperature trend prediction, can detect temperature abnormalities in advance and predict their development trends, which is more forward-looking than traditional fixed threshold judgments. The control unit adjusts the power of the refrigeration unit according to the prediction results, taking into account the collaborative work between refrigeration units, avoiding power conflicts and over-compensation, effectively reducing the equipment failure rate, and extending the service life of the equipment.

[0022] In one embodiment, the control unit generates a temperature change curve for each monitoring point according to the received temperature signal, including: The temperature signals sent by each temperature sensor are timestamped and synchronized to establish a unified time series to obtain temperature data. Specifically, precise time stamps are added to the signals collected by different temperature sensors, and the clock deviation of each sensor is calibrated through an algorithm to ensure that all temperature data are aligned on the same time scale to avoid data distortion due to sampling time differences.

[0023] Interpolation methods are used to smoothly fit the temperature data and generate a temperature curve. Specifically, between known discrete temperature data points, the temperature values ​​at intermediate moments are estimated through methods such as Lagrange interpolation and spline interpolation, smoothing data fluctuations to generate a continuous temperature curve.

[0024] The control unit constructs a three-dimensional temperature distribution model of the entire equipment room based on the temperature change curve of each monitoring point, including: Extracting temperature change rate based on temperature change curve; A three-dimensional spatial grid based on the cabinet coordinates is established. Specifically, the three-dimensional space of the computer room is divided into regular or irregular cubic grid units based on the actual cabinet coordinates. Each grid node corresponds to a location in the space and is used to carry data such as temperature change rate.

[0025] The temperature change rate extracted from each monitoring point is mapped to the corresponding grid nodes of the three-dimensional spatial grid to obtain a discrete feature field. Specifically, the temperature change rate of each monitoring point is mapped to the three-dimensional spatial grid nodes to form a discrete distribution data set that describes the spatial characteristics of the temperature change in the computer room.

[0026] The discrete feature field is spatially interpolated to generate a three-dimensional temperature distribution model. Specifically, based on the known data points in the discrete feature field, the temperature change rate of unmonitored locations in the grid is estimated using algorithms such as Kriging interpolation to generate a continuous three-dimensional temperature distribution model.

[0027] Identify potential hotspots based on a 3D temperature distribution model, including: Extracting a region where the temperature change rate exceeds a first temperature change rate based on the three-dimensional temperature distribution model, and recording it as a first region; The first area is screened again based on the real-time temperature value, and areas where the real-time temperature value exceeds the set threshold are retained as potential hot spots.

[0028] Specifically, the original temperature signal is timestamped and interpolated to eliminate time errors and data noise. The temperature change rate is extracted based on the curve, and a three-dimensional spatial grid is constructed based on the cabinet coordinates. The temperature change rate is mapped to the grid nodes to form a discrete feature field. The discrete data is converted into a continuous three-dimensional temperature distribution model through a spatial interpolation algorithm. The temperature change rate threshold and the real-time temperature threshold are combined to double-screen potential hotspot areas.

[0029] In one embodiment, predicting the temperature change trend of the potential hotspot area includes: A first-order autoregressive model is constructed based on the extracted temperature change rate features. Specifically, a first-order autoregressive model is a time series forecasting model that predicts future values ​​based on the variable's lagged values ​​(i.e., past values). This approach captures the dynamic trends of temperature changes by establishing a linear relationship between the temperature change rate and its previous value.

[0030] The current temperature value and temperature change rate are input into the first-order autoregressive model, and the temperature prediction value for the first time period in the future is calculated to obtain the temperature change trend curve; among them, the prediction range of the temperature change rate is limited to the preset change rate, and the temperature prediction value exceeding the preset change rate is truncated.

[0031] Specifically, by using a first-order autoregressive model and leveraging the time series characteristics of the temperature change rate to establish a forecasting model, the system can effectively capture short-term trends in temperature changes. This allows for earlier identification of abnormal temperature changes in potential hotspots, compared to simple empirical judgments or fixed threshold warnings. By taking the current temperature value and the temperature change rate as input and dynamically calculating the forecast value based on historical data patterns, the system avoids the lag caused by static forecasts, effectively reducing the risk of equipment overheating and energy waste.

[0032] In one embodiment, the control unit adjusts the power of each cooling unit based on the identified potential hotspot area, including: Determine the difference between the predicted temperature of the potential hotspot area and the target temperature; the target temperature is the most suitable temperature for the server equipment to operate; The heat required to be removed is calculated using a heat calculation formula based on the spatial volume of the potential hotspot area, the specific heat capacity of the air, and the time required for the predicted temperature to reach the target temperature; distributing the increased cooling capacity to the surrounding cooling units in inverse proportion to the spatial distance; The power of each refrigeration unit is adjusted by PID controller; The increased cooling capacity is distributed to the surrounding cooling units in inverse proportion to the spatial distance, including: Determine the location of each cooling unit around the potential hotspot area, and calculate the straight-line distance between each cooling unit and the center of the potential hotspot area; Calculating the allocation weight of each refrigeration unit according to the straight-line distance; wherein the closer the distance, the higher the allocation weight; The power of each refrigeration unit is adjusted by a PID controller, including: The power adjustment amount is calculated using a PID control algorithm based on the deviation between the current actual power and the target power, as well as the deviation between the real-time temperature data and the target temperature; wherein the target power is determined by the allocated cooling capacity.

[0033] Specifically, by determining the difference between the predicted temperature and the target temperature in a potential hotspot area, the target deviation for temperature regulation can be clearly defined, avoiding blind adjustments. The amount of heat to be removed is calculated based on the spatial volume of the potential hotspot area, the specific heat capacity of the air, and the time required for the predicted temperature to reach the target temperature. This integration of actual physical parameters and time factors makes the calculated cooling capacity more accurate. The increased cooling capacity is distributed to surrounding cooling units in inverse proportion to their spatial distance. The locations of surrounding cooling units are determined with the potential hotspot as the center, and the straight-line distance is calculated to determine the allocation weight. The closer the distance, the higher the weight. This takes into account spatial location relationships, prioritizing cooling units closer to the potential hotspot to take on more cooling tasks. This allows for more efficient cooling of the hotspot area, reduces energy waste during the cooling process, and improves cooling efficiency. A PID controller calculates the power adjustment amount based on the deviation between the current actual power and the target power, as well as the deviation between the real-time temperature data and the target temperature. The PID control algorithm enables dynamic adjustment of cooling unit power. The PID controller can adjust the power regulation amount in real time according to factors such as the size of the deviation and the rate of change, so that the power of the refrigeration unit can reach the target power quickly and stably, thereby more effectively adjusting the real-time temperature to the target temperature, ensuring that the server equipment operates at the most suitable temperature, reducing the probability of equipment failure due to excessive temperature, and extending the service life of the equipment.

[0034] In one embodiment, establishing a three-dimensional space grid based on the cabinet coordinates further includes: Increase mesh density in areas with large temperature gradients to improve model resolution; Reduce the mesh density in areas where the temperature changes gently to reduce the amount of calculation; Adjusting the grid distribution according to the change of real-time temperature data; wherein, adjusting the grid distribution according to the change of real-time temperature data includes: Using the geometric center coordinates of the cabinet as the reference point, divide the three-dimensional space of the computer room into initial grids and set the side lengths of the initial grids. Assign a unique identification number to each grid node and establish a correspondence table between grid nodes and cabinet and channel locations; For the temperature data collected by each temperature sensor, calculate its temperature gradient vector in three-dimensional space; For adjacent grid nodes, the temperature gradient value between nodes is obtained by interpolating the temperature difference between the nodes; Configure a temperature gradient strength index for each grid node; the temperature gradient strength index is the weighted average of the temperature gradients of the adjacent grids around the node; When the temperature gradient intensity index of a grid node is greater than a first threshold, the grid and the surrounding adjacent grids are subdivided into quadtrees to reduce the grid side length to half of the original length; When the temperature gradient intensity index of a grid node is less than a second threshold, the grid is merged with surrounding grids with similar temperature gradient intensities, so that the grid side length is doubled; After each adjustment, the identification number and corresponding relationship table of the grid nodes are updated.

[0035] Specifically, the initial grid is divided based on the geometric center coordinates of the cabinet, and unique numbers are assigned to the nodes and a position correspondence table is established; the temperature gradient vector is calculated for the temperature sensor data, and the temperature gradient value obtained by interpolation between nodes is combined to configure a temperature gradient intensity index based on the weighted average of adjacent grids for each grid node, which can quantify the severity of temperature changes in each area; when the temperature gradient intensity index of the grid node is greater than the first threshold, the quadtree subdivision strategy is used to reduce the grid side length, which can increase the grid density in areas with large temperature gradients and complex changes, improve the resolution of the three-dimensional temperature distribution model in key areas, and avoid missing hot spots; when the index is less than the second threshold, the grids are merged to expand the side length, which can reduce the grid density in areas with gentle temperature changes, effectively reducing unnecessary calculations; updating the number and correspondence table after each grid adjustment enables the system to dynamically optimize the grid distribution according to real-time temperature data.

[0036] In one embodiment, when adjusting the power of the refrigeration unit, the control unit further includes: Based on the equipment room layout, cooling units with a straight-line distance less than a first preset distance are marked as neighbors. An undirected graph G = (V, E) is constructed, where nodes V represent cooling units and edges E represent neighbor relationships. Specifically, an undirected graph G = (V, E) is a mathematical model consisting of a set of nodes V and a set of edges E, where edges have no direction. In this scheme, each cooling unit is abstracted as a node V. If the straight-line distance between two cooling units is less than the first preset distance, an undirected edge E exists between them, indicating a neighbor relationship.

[0037] Assign a weight value W to each edge; the weight value is calculated based on the temperature adjustment correlation between the two refrigeration units in the historical operation data, and the higher the correlation, the greater the weight; After the initial allocation of cooling capacity, the power difference between adjacent cooling units is calculated. If the power difference exceeds the preset value, the particle swarm optimization algorithm is used to obtain the target power value of each cooling unit with the multi-objective function of minimizing the mean square error of temperature regulation, minimizing the power difference, and minimizing the total energy consumption. The particle swarm optimization algorithm is used, including: Each particle is defined as a set of cooling unit power allocation schemes, with the particle dimension equal to the number of cooling units. The initial position and velocity of the particles are randomly generated, with the position range being from the minimum power to the maximum power of each cooling unit, and the velocity range being set to the maximum adjustment step.

[0038] For each particle, the mean square error of temperature regulation, the sum of power differences between adjacent units, and the total energy consumption of the system are calculated, and then substituted into the multi-objective function to calculate the fitness value; Particle velocities and positions are updated based on their own optimal positions and the global optimal position. After each iteration, the particle position is checked to see if it meets the power constraints. If not, boundary processing is performed. Specifically, boundary processing ensures that the particle position (and thus the power allocation scheme) remains within the physically feasible range. If the updated particle position exceeds the minimum or maximum power limit of the cooling unit, it is forced to stay within the boundary value.

[0039] When the maximum number of iterations is reached or the convergence condition is met, the power allocation scheme corresponding to the global optimal particle is output as the target power value of each refrigeration unit.

[0040] Specifically, by constructing an undirected graph with cooling units as nodes and adjacent relationships as edges, and assigning edge weights based on historical operating data, the degree of temperature regulation correlation between cooling units can be quantified. After initially allocating cooling capacity, the power difference is calculated and compared with a preset difference, enabling timely identification of potential power conflicts or imbalances between adjacent cooling units. A particle swarm optimization algorithm is introduced, abstracting the power allocation plan for each group of cooling units into particles. By randomly initializing the particle positions and velocities, the algorithm searches within a feasible power range. During the iteration process, a multi-objective fitness function is constructed based on the mean square error of temperature regulation, the sum of the power differences, and the total energy consumption, driving the algorithm toward high temperature uniformity, balanced power, and low energy consumption. The speed and position of the particles are updated based on their own optimal positions and the global optimal position, and boundary handling is performed for power constraints exceeding the constraints, ensuring that the generated power allocation plan not only meets physical constraints but also achieves an optimal balance between multiple objectives. The resulting power allocation plan, corresponding to the globally optimal particle, effectively avoids power conflicts and overcompensation between cooling units, achieving precise temperature regulation in the computer room, and significantly reducing system energy consumption while ensuring stable equipment operation.

[0041] In one embodiment, the system further includes an alarm module, and the control unit determines the temperature value of the received temperature signal. When the temperature value is greater than the set temperature, the control unit sends an alarm instruction to the alarm module, and the alarm module sends a sound prompt to the outside world.

[0042] In one embodiment, the temperature sensor is a wireless passive temperature sensor.

[0043] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A high voltage passive induction temperature monitoring system, characterized in that: include: Several temperature sensors; several of the temperature sensors are deployed in different cabinets and channels in the computer room; control unit; The control unit is used to send detection instructions to each temperature sensor and receive feedback temperature signals; the control unit generates a temperature change curve for each monitoring point based on the received temperature signals; the control unit constructs a three-dimensional temperature distribution model of the entire computer room based on the temperature change curve of each monitoring point, identifies potential hot spots based on the three-dimensional temperature distribution model, and predicts the temperature change trend of the potential hot spots; Refrigeration unit; the refrigeration unit is used to adjust the temperature of the area; the refrigeration unit communicates with the control unit; The control unit adjusts the power of each cooling unit based on the identified potential hotspot areas.

2. A high voltage passive induction temperature monitoring system according to claim 1, characterized in that: The control unit generates a temperature change curve for each monitoring point according to the received temperature signal, including: Perform time stamp synchronization processing on the temperature signals sent by each temperature sensor to establish a unified time series to obtain temperature data; The temperature data is smoothly fitted using interpolation method to generate the temperature change curve; The control unit constructs a three-dimensional temperature distribution model of the entire equipment room based on the temperature change curve of each monitoring point, including: Extracting temperature change rate based on temperature change curve; Establish a three-dimensional space grid based on the cabinet coordinates; The temperature change rate extracted from each monitoring point is mapped to the corresponding grid nodes of the three-dimensional space grid to obtain a discrete feature field; Perform spatial interpolation on the discrete characteristic field to generate a three-dimensional temperature distribution model; Identify potential hotspots based on a 3D temperature distribution model, including: Extracting a region where the temperature change rate exceeds a first temperature change rate based on the three-dimensional temperature distribution model, and recording it as a first region; The first area is screened again based on the real-time temperature value, and areas where the real-time temperature value exceeds the set threshold are retained as potential hot spots.

3. A high voltage passive induction temperature monitoring system according to claim 2, characterized in that: Predict the temperature change trend of the potential hotspot area, including: A first-order autoregressive model is constructed based on the extracted temperature change rate features; The current temperature value and temperature change rate are input into the first-order autoregressive model, and the temperature prediction value for the first time period in the future is calculated to obtain the temperature change trend curve; among them, the prediction range of the temperature change rate is limited to the preset change rate, and the temperature prediction value exceeding the preset change rate is truncated.

4. A high voltage passive induction temperature monitoring system according to claim 3, characterized in that: The control unit adjusts the power of each cooling unit based on identified potential hotspot areas, including: Determine the difference between the predicted temperature of the potential hotspot area and the target temperature; the target temperature is the most suitable temperature for the server equipment to operate; The heat required to be removed is calculated using a heat calculation formula based on the spatial volume of the potential hotspot area, the specific heat capacity of air, and the time required for the predicted temperature to reach the target temperature; distributing the increased cooling capacity to the surrounding cooling units in inverse proportion to the spatial distance; The power of each refrigeration unit is adjusted by PID controller; The increased cooling capacity is distributed to the surrounding cooling units in inverse proportion to the spatial distance, including: Determine the location of each cooling unit around the potential hotspot area, and calculate the straight-line distance between each cooling unit and the center of the potential hotspot area; Calculating the allocation weight of each refrigeration unit according to the straight-line distance; wherein the closer the distance, the higher the allocation weight; The power of each refrigeration unit is adjusted by a PID controller, including: The power adjustment amount is calculated using a PID control algorithm based on the deviation between the current actual power and the target power, as well as the deviation between the real-time temperature data and the target temperature; wherein the target power is determined by the allocated cooling capacity.

5. A high voltage passive induction temperature monitoring system according to claim 4, characterized in that: Establishing a three-dimensional space grid based on the cabinet coordinates also includes: Increase mesh density in areas with large temperature gradients to improve model resolution; Reduce the mesh density in areas where the temperature changes gently to reduce the amount of calculation; Adjusting the grid distribution according to the change of real-time temperature data; wherein, adjusting the grid distribution according to the change of real-time temperature data includes: Using the geometric center coordinates of the cabinet as the reference point, divide the three-dimensional space of the computer room into initial grids and set the side lengths of the initial grids. Assign a unique identification number to each grid node and establish a correspondence table between grid nodes and cabinet and channel locations; For the temperature data collected by each temperature sensor, calculate its temperature gradient vector in three-dimensional space; For adjacent grid nodes, the temperature gradient value between nodes is obtained by interpolating the temperature difference between the nodes; Configure a temperature gradient strength index for each grid node; the temperature gradient strength index is the weighted average of the temperature gradients of the adjacent grids around the node; When the temperature gradient intensity index of a grid node is greater than a first threshold, the grid and the surrounding adjacent grids are subdivided into quadtrees to reduce the grid side length to half of the original length; When the temperature gradient intensity index of a grid node is less than a second threshold, the grid is merged with surrounding grids with similar temperature gradient intensities, so that the grid side length is doubled; After each adjustment, the identification number and corresponding relationship table of the grid nodes are updated.

6. A high voltage passive induction temperature monitoring system according to claim 5, characterized in that: When adjusting the power of the refrigeration unit, the control unit further includes: According to the layout of the computer room, the cooling units whose straight-line distance is less than the first preset distance are marked as adjacent, and an undirected graph G = (V, E) is constructed, where the node V represents the cooling unit and the edge E represents the adjacent relationship; Assign a weight value W to each edge; the weight value is calculated based on the temperature adjustment correlation between the two refrigeration units in the historical operation data, and the higher the correlation, the greater the weight; After the initial allocation of cooling capacity, the power difference between adjacent cooling units is calculated. If the power difference exceeds the preset value, the particle swarm optimization algorithm is used to obtain the target power value of each cooling unit with the multi-objective function of minimizing the mean square error of temperature regulation, minimizing the power difference, and minimizing the total energy consumption. The particle swarm optimization algorithm is used, including: Each particle is defined as a set of cooling unit power allocation schemes, with the particle dimension equal to the number of cooling units. The initial position and velocity of the particle are randomly generated, with the position range being from the minimum power to the maximum power of each cooling unit, and the velocity range being set to the maximum adjustment step size. For each particle, the mean square error of temperature regulation, the sum of power differences between adjacent units, and the total energy consumption of the system are calculated, and then substituted into the multi-objective function to calculate the fitness value; Update the particle velocity and position based on the particle's own optimal position and the global optimal position; after each iteration, check whether the particle position meets the power constraint. If not, perform boundary processing; When the maximum number of iterations is reached or the convergence condition is met, the power allocation scheme corresponding to the global optimal particle is output as the target power value of each refrigeration unit.

7. A high voltage passive induction temperature monitoring system according to claim 6, characterized in that: The system further includes an alarm module. The control unit determines the temperature value of the received temperature signal. When the temperature value is greater than a set temperature, the control unit sends an alarm instruction to the alarm module, and the alarm module sends a sound prompt to the outside world.

8. A high voltage passive induction temperature monitoring system according to claim 7, characterized in that: The temperature sensor is a wireless passive temperature sensor.

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