Temperature sensing and radio frequency identification circuit integrated temperature measurement method and system

Abstract

The invention discloses a temperature sensing and radio frequency identification circuit integrated temperature measurement method and system, which are applied to the technical field of temperature sensor data processing, and the method comprises the steps: obtaining the response time when each temperature sensing node collects the temperature according to a corresponding first preset sampling frequency, and the signal transmission delay time of a corresponding radio frequency identification chip; correcting response time by using a compensation matrix constructed according to the signal transmission delay time sequence to obtain an adjusted response time sequence, and respectively determining a sampling configuration scheme and reference task bearing capacity corresponding to the temperature sensing node according to the adjusted response time sequence and equipment parameters; and carrying out temperature acquisition, and carrying out interpolation reconstruction and optimization on the acquired temperature value to obtain optimized temperature data. According to the method, the response speed of the distributed temperature sensing system is adjusted, and the sampling strategy is adjusted in combination with the dynamic change of the distribution characteristics of the temperature field, so that the temperature monitoring precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and in particular to a temperature measurement method and system integrating temperature sensing and radio frequency identification circuits. Background Art

[0002] The fusion technology of temperature sensing and radio frequency identification chip circuits plays a vital role in industrial monitoring, intelligent manufacturing, and equipment health management. It can realize real-time monitoring and data transmission of multiple temperature points in complex environments, and is a core technical means to ensure the safe operation of the system and improve equipment performance.

[0003] Current temperature monitoring solutions primarily rely on single-point sensors or simple multi-point acquisition systems. These methods exhibit significant shortcomings when faced with large-scale distributed temperature monitoring needs. Temperature sensing nodes in different locations experience significant inconsistencies in data acquisition and transmission speeds due to differences in hardware characteristics, environmental conditions, and signal transmission paths. This inconsistency directly impacts the system's data synchronization quality. Inconsistent response speeds further lead to complex impacts on the sampling strategy due to temperature gradient distribution. When equipment operating conditions change, the temperature change rate and gradient distribution in different areas exhibit dynamic characteristics. Traditional fixed sampling strategies are unable to adapt to these changes, resulting in omissions or redundant acquisition of critical temperature information. Therefore, how to ensure temperature monitoring accuracy in distributed temperature sensing systems when response speeds are inconsistent and temperature field distribution characteristics are dynamically changing is an urgent issue in this field. Summary of the Invention

[0004] In order to solve the above technical problems, the embodiments of the present invention provide a temperature measurement method and system integrating temperature sensing and radio frequency identification circuits to solve the technical problem of improving the temperature monitoring accuracy of a distributed temperature sensing system when the response speed is inconsistent and the temperature field distribution characteristics are dynamically changing.

[0005] A first aspect of an embodiment of the present invention provides a temperature measurement method integrating a temperature sensor and a radio frequency identification circuit, the method comprising: Obtaining the response time of each temperature sensor node when collecting temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, to obtain a response time series and a signal transmission delay time series; According to the signal transmission delay time series, a compensation matrix is ​​constructed. If any response time in the response time series is not within the preset response threshold, the compensation matrix is ​​used to correct the response time to obtain an adjusted response time series; Adjusting the first preset sampling frequency according to the adjusted response time sequence to obtain a corresponding second preset sampling frequency, obtaining a spatial sampling density value according to the second preset sampling frequency, and obtaining a sampling configuration scheme according to the spatial sampling density value; Based on the equipment parameters of each temperature sensing node, the corresponding benchmark task carrying capacity is obtained. Temperature is collected according to the benchmark task carrying capacity and the sampling configuration scheme to obtain an initial set of temperature data points. The initial set of temperature data points is interpolated and reconstructed to obtain a temperature field distribution matrix. The temperature field distribution matrix is ​​compared with the actual measurement values ​​to obtain the optimized temperature data of each temperature sensing node.

[0006] In a possible implementation of the first aspect, constructing a compensation matrix according to a signal transmission delay time series includes: Calculate the local clock and reference clock of each temperature sensor node to obtain the corresponding clock difference; According to the clock difference of each temperature sensing node and the corresponding signal transmission delay time, the total delay time of each temperature sensing node is obtained, and a compensation matrix is ​​constructed according to the total delay time.

[0007] In a possible implementation of the first aspect, adjusting the first preset sampling frequency according to the adjusted response time sequence to obtain the second preset sampling frequency includes: According to the adjusted response time series, the temperature of each temperature sensing node is extracted to obtain a temperature data set; The change rate is calculated based on each temperature value to obtain a comprehensive temperature change rate. If the comprehensive temperature change rate is greater than a preset temperature change rate threshold, the first preset sampling frequency is adjusted to obtain a second preset sampling frequency.

[0008] In a possible implementation of the first aspect, calculating according to each temperature value to obtain the comprehensive temperature change rate includes: Calculate according to each temperature value to obtain the spatial temperature gradient distribution matrix; Extract the spatial temperature gradient distribution matrix to obtain the gradient amplitude and gradient direction angle at each position; According to the gradient amplitude at each position and the corresponding preset time interval, the amplitude change rate at each position is obtained, and according to the gradient amplitude at each position and the preset time interval, the direction angle change rate at each position is obtained; The comprehensive temperature change rate is obtained based on the amplitude change rate and direction angle change rate at each position.

[0009] In a possible implementation of the first aspect, calculation is performed according to each temperature value to obtain a spatial temperature gradient distribution matrix, including: Get the location of each temperature sensor node; According to the position and temperature value, calculate the temperature difference between each temperature sensing node and the corresponding adjacent node; Based on the temperature difference of each temperature sensing node, the corresponding temperature gradient value is obtained, and all temperature gradient values ​​are arranged according to the corresponding positions to obtain the spatial temperature gradient distribution matrix.

[0010] In a possible implementation of the first aspect, obtaining a spatial sampling density value according to the second preset sampling frequency, and obtaining a sampling configuration scheme according to the spatial sampling density value include: Based on the second preset sampling frequency, recording the number of each temperature sensing node within the preset range whose second preset sampling frequency is greater than the reference frequency, to obtain a spatial sampling density value; According to a second preset sampling frequency within a preset range, an average temperature gradient amplitude is obtained, and the average temperature gradient amplitude and the spatial sampling density value are combined to obtain a comprehensive density index; Adjusting the second preset sampling frequency according to the comprehensive density index to obtain a third sampling frequency; The temperature sensing nodes are sorted according to the magnitude of the gradient amplitude to obtain a sorting result, and a sampling configuration scheme is obtained based on the sorting result and the third sampling frequency.

[0011] In a possible implementation of the first aspect, adjusting the second preset sampling frequency according to the comprehensive density indicator to obtain the third sampling frequency includes: If the comprehensive density index is greater than the preset density threshold, the ratio of the comprehensive density index to the preset density threshold is calculated to obtain the regional density factor; Based on the regional encryption factor, the second preset sampling frequency within the preset range corresponding to the comprehensive density index is adjusted to obtain a third sampling frequency.

[0012] In a possible implementation of the first aspect, obtaining a corresponding benchmark task carrying capacity based on device parameters of each temperature sensing node includes: After weighted summation of the device parameters of each temperature sensor node, the corresponding node load value is obtained. The device parameters include the current processor occupancy rate, memory usage rate and the length of the queue for data to be processed. The temperature gradient weight coefficient is obtained by using the gradient amplitude and maximum gradient amplitude corresponding to each temperature sensing node. The corresponding task carrying capacity index is obtained according to the node load value and the temperature gradient weight coefficient. The task carrying capacity index of each temperature sensing node is added together to obtain the total task carrying capacity, and the task carrying capacity index of each temperature sensing node is divided by the total task carrying capacity to obtain the corresponding benchmark task carrying capacity.

[0013] In order to solve the same technical problem, a second aspect of an embodiment of the present invention provides a temperature measurement system integrating a temperature sensor and a radio frequency identification circuit, comprising: an acquisition module, configured to acquire the response time of each temperature sensor node when collecting temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, to obtain a response time sequence and a signal transmission delay time sequence; A first adjustment module is configured to construct a compensation matrix based on the signal transmission delay time sequence, and to correct the response time using the compensation matrix if any response time in the response time sequence is not within a preset response threshold, thereby obtaining an adjusted response time sequence; a second adjustment module, configured to adjust the first preset sampling frequency according to the adjusted response time sequence to obtain a corresponding second preset sampling frequency, obtain a spatial sampling density value according to the second preset sampling frequency, and obtain a sampling configuration scheme according to the spatial sampling density value; The optimization module is used to obtain the corresponding benchmark task carrying capacity based on the equipment parameters of each temperature sensing node, collect temperature according to the benchmark task carrying capacity and the sampling configuration scheme, obtain an initial temperature data point set, interpolate and reconstruct the initial temperature data point set to obtain a temperature field distribution matrix, compare the temperature field distribution matrix with the actual measurement value, and obtain the optimized temperature data of each temperature sensing node.

[0014] In a possible implementation manner of the second aspect, the first adjustment module includes a clock difference calculation unit and a compensation matrix construction unit, wherein: A clock difference calculation unit is used to calculate the local clock and reference clock of each temperature sensor node to obtain the corresponding clock difference; The compensation matrix construction unit is used to obtain the total delay time of each temperature sensing node according to the clock difference of each temperature sensing node and the corresponding signal transmission delay time, and construct a compensation matrix according to the total delay time.

[0015] The technical solution of the present invention has the following advantages: The temperature sensing and radio frequency identification circuit integrated temperature measurement method provided in the embodiment of the present application obtains the response time when each temperature sensing node collects temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, then corrects the response time by using the compensation matrix constructed according to the signal transmission delay time sequence, obtains the adjusted response time sequence, adjusts the first preset sampling frequency according to the adjusted response time sequence, obtains the corresponding second preset sampling frequency, obtains the spatial sampling density value according to the second preset sampling frequency, and obtains the sampling configuration scheme according to the spatial sampling density value; the corresponding reference task carrying capacity is obtained based on the device parameters of each temperature sensing node, the temperature collection is performed according to the reference task carrying capacity and the sampling configuration scheme, the initial temperature data point set is obtained, and the optimized temperature data of each temperature sensing node is obtained after the initial temperature data point set is interpolated and reconstructed and optimized. The above method adjusts the response speed of the distributed temperature sensing system, and then adjusts the sampling strategy in combination with the dynamic change of the temperature field distribution characteristics, thereby improving the temperature monitoring precision. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 The flowchart of the temperature sensing and radio frequency identification circuit integrated temperature measurement method in the embodiment of the present application is shown in the figure. Figure 2 The structural block diagram of the temperature sensing and radio frequency identification circuit integrated temperature measurement system in the embodiment of the present application is shown in the figure. The figure marks are: 200, the temperature sensing and radio frequency identification circuit integrated temperature measurement system; 201, the acquisition module; 202, the first adjustment module; 203, the second adjustment module; 204, the optimization module. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0019] The temperature sensing and radio frequency identification circuit integrated temperature measurement method provided in the embodiment of the present application, like Figure 1As shown, Figure 1 This is a flow chart of a temperature measurement method integrating a temperature sensor and a radio frequency identification circuit, including steps S101 to S104. The specific steps are as follows: S101 : Acquire the response time of each temperature sensor node when collecting temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, to obtain a response time sequence and a signal transmission delay time sequence.

[0020] In this embodiment, the response times of individual temperature sensor nodes in a distributed temperature sensing system often vary significantly due to geographical dispersion, hardware differences, and environmental factors. For example, in a temperature monitoring network deployed in a large warehouse environment, temperature sensor nodes located at the warehouse entrance may experience significant wireless signal interference due to the frequent movement of goods in and out, while temperature sensor nodes located deeper in the warehouse may be relatively stable. This difference directly affects the data synchronization accuracy of the entire system.

[0021] Response time refers to the complete interval between each temperature sensor node receiving a command and returning data. The response time of each temperature sensor node is obtained when collecting temperature at its corresponding first preset sampling frequency. Simultaneously, the signal transmission delay of the RFID chip under different ambient temperatures is collected, and a mapping relationship is established between the temperature value and the corresponding signal transmission delay time. For example, the distributed temperature sensing system continuously collects response time for 30 days, with each temperature sensor node recording the response time once an hour.

[0022] The signal transmission delay time acquisition process for RFID chips requires consideration of temperature fluctuations. When the ambient temperature rises from 20°C to 40°C, the propagation speed of the RF signal in air changes slightly, and the frequency of the chip's internal crystal oscillator also drifts. By conducting extensive tests under varying temperature conditions and recording the time interval between signal transmission and reception confirmation, a mapping table can be established between temperature and signal transmission delay time. This mapping exhibits a near-linear characteristic, facilitating subsequent compensation matrix calculations.

[0023] It should be noted that a temperature sensing node refers to a single temperature sensor deployed at a specific monitoring location, responsible for collecting and transmitting temperature data in that local area. Signal transmission delay time can be understood as signal transmission delay time.

[0024] S102: construct a compensation matrix based on the signal transmission delay time sequence. If any response time in the response time sequence is not within a preset response threshold, use the compensation matrix to correct the response time to obtain an adjusted response time sequence.

[0025] In this embodiment, the obtained signal transmission delay time sequence is used to construct a compensation matrix. Specifically, the compensation matrix is an N x N square matrix, where N is the total number of temperature sensing nodes, and each element in the matrix represents the time compensation value from node to node . This compensation value consists of two parts: the fixed hardware signal transmission delay time and the dynamic environmental factors. By combining the time difference value obtained by clock synchronization with the temperature-related signal transmission delay time, the accurate compensation value between each pair of nodes can be calculated. For example, assume that a cold chain logistics warehouse deploys 100 temperature sensing nodes distributed in different shelf levels and areas. Due to the reflection and attenuation of wireless signals between metal shelves, there are millisecond-level differences in response times of temperature sensing nodes at different positions. By recording the fixed time delay difference between each pair of temperature sensing nodes, a compensation matrix is obtained for subsequent timestamp correction.

[0026] When the response time of a certain temperature sensing node is not within the preset response threshold range, it means that the temperature sensing node may have abnormal conditions. The response time of the temperature sensing node with abnormal conditions is corrected by the compensation matrix to obtain an adjusted response time sequence. Specifically, the local timestamps of each temperature sensing node are modified using the compensation coefficients in the compensation matrix, and the local timestamps of each temperature sensing node are added to the corresponding compensation coefficients to obtain corrected timestamps under a unified time reference. This dynamic adjustment mechanism ensures the stability and accuracy of the system during long-term operation. Through continuous monitoring and adaptive adjustment, the distributed temperature sensing system can maintain high-precision time synchronization in complex and variable environments, thereby providing a reliable foundation for subsequent data fusion and analysis.

[0027] It should be noted that the preset response threshold range is obtained by statistically analyzing the response time distribution of each temperature sensing node at different time periods from historical response time data, calculating the average value and standard deviation of the response time of each temperature sensing node, and taking the average value plus or minus three times the standard deviation as the upper and lower limits of the speed difference threshold range to form the threshold range of each temperature sensing node.

[0028] In an embodiment, a compensation matrix is constructed according to the signal transmission delay time sequence, comprising: calculating the local clock and reference clock of each temperature sensing node to obtain the corresponding clock difference value; obtaining the total delay time of each temperature sensing node according to the clock difference value of each temperature sensing node and the corresponding signal transmission delay time, and constructing a compensation matrix according to the total delay time of each temperature sensing node.

[0029] In this embodiment, clock synchronization is performed on all temperature sensor nodes using a timestamp synchronization protocol. First, the difference between each temperature sensor node's local clock and a reference clock is obtained. The time difference between the temperature sensor nodes is added to the signal transmission delay to obtain the total delay between each pair of temperature sensor nodes. A compensation matrix is ​​then constructed based on the total delay of each temperature sensor node. For example, the timestamp correction process involves precise adjustment of the original acquisition time. When temperature sensor node A acquires temperature data of 25.6 degrees Celsius at 10:30:15.235 local time, the corresponding compensation coefficient of 0.012 seconds in the compensation matrix changes the timestamp to 10:30:15.247. This millisecond-level correction may seem minor, but it is crucial in scenarios where precise tracking of temperature trends is required. By uniformly correcting the timestamps of all temperature sensor nodes, the previously chaotic data points are rearranged into an orderly time series. A mechanism for determining when delay exceeds a threshold provides a trigger for the system's adaptive adjustments.

[0030] It should be noted that the original acquisition time can also be understood as the local clock.

[0031] S103. Adjust the first preset sampling frequency according to the adjusted response time sequence to obtain a corresponding second preset sampling frequency, obtain a spatial sampling density value according to the second preset sampling frequency, and obtain a sampling configuration scheme according to the spatial sampling density value.

[0032] In this embodiment, the temperature data collected by each temperature sensor node is corrected based on the adjusted response time sequence. The corrected temperature is then used to adjust the first preset sampling frequency to obtain an adjusted sampling frequency, i.e., a second preset sampling frequency. A spatial sampling density value is calculated using the second preset sampling frequency, and a sampling configuration scheme is derived based on the spatial sampling density value.

[0033] In one embodiment, adjusting the first preset sampling frequency according to the adjusted response time sequence to obtain the second preset sampling frequency includes: According to the adjusted response time series, the temperature of each temperature sensing node is corrected to obtain the corresponding temperature value; The change rate is calculated based on each temperature value to obtain a comprehensive temperature change rate. If the comprehensive temperature change rate is greater than a preset temperature change rate threshold, the first preset sampling frequency is adjusted to obtain a second preset sampling frequency.

[0034] In this embodiment, the temperatures are reordered according to the corrected timestamps to obtain corresponding temperature values ​​to form a temperature set. Then, the spatial temperature gradient distribution matrix is ​​calculated based on the time-corrected temperature data set, and the gradient amplitude and direction change rate are identified to obtain the comprehensive temperature change rate. If the comprehensive temperature change rate exceeds the preset temperature change rate threshold, the adaptive adjustment of the sampling strategy is triggered to obtain the second preset sampling frequency.

[0035] When adjusting the first preset sampling frequency, the sampling frequency adjustment factor of each temperature sensing node is calculated according to the ratio of the comprehensive temperature change rate to the preset temperature change rate threshold, and the first preset sampling frequency of the temperature sensing node is multiplied by the adjustment factor to obtain the second preset sampling frequency.

[0036] The specific process of reordering the temperature data according to the corrected timestamp to form a temperature set is as follows: reordering the temperature data according to the corrected timestamp to form a preliminary corrected temperature sequence, and then judging the degree of deviation between the response delay of each temperature sensor node in the preliminary corrected temperature sequence and the preset delay threshold range. If the response delay of a temperature sensor node is not within the preset delay threshold range, the number of the temperature sensor node, the delay excess amplitude and the delay direction are recorded, and a delay parameter table of the temperature sensor node that needs to be adjusted for the synchronization period is generated.

[0037] It should be noted that the preset delay threshold range is typically determined based on the statistical characteristics of historical data. If the response delay of a temperature sensor node increases from the normal 50 milliseconds to 80 milliseconds due to battery depletion or antenna aging, exceeding the preset upper limit of 60 milliseconds, the system records this anomaly. The temperature sensor node delay parameter table not only contains the temperature sensor node number and the magnitude of the excess, but also records the direction of the delay, that is, whether the delay increased or decreased.

[0038] Based on the delay excess of each temperature sensor node in the temperature sensor node delay parameter table, a time window sliding mechanism dynamically adjusts the data collection time. The excess is divided by the original synchronization period to obtain the adjustment ratio. When the delay excess exceeds the upper limit, the time window is moved forward; when the delay excess falls below the lower limit, the time window is moved backward. The sliding distance is equal to the product of the adjustment ratio and the original period. Temperature data from each temperature sensor node is recollected according to the sliding time window. Data points collected at different times are mapped onto a unified time axis using linear interpolation. The aligned data points are used to construct a time-corrected temperature data set. For example, if a temperature sensor node with an original synchronization period of 1 second is detected to have a delay exceeding the upper limit by 20 milliseconds, an adjustment ratio of 0.02 is calculated. Sliding the time window forward by 20 milliseconds means that the next data collection time for this temperature sensor node is moved forward from the original 10:30:16.000 to 10:30:15.980. This sliding adjustment allows temperature sensor nodes with larger delays to collect data earlier, thereby maintaining synchronization with other temperature sensor nodes during data aggregation. Linear interpolation methods play an important role in the data alignment process.

[0039] When the collection times of different temperature sensing nodes deviate, these discrete data points need to be mapped onto a unified timeline. For example, if temperature sensor node B collects a temperature of 26.2 degrees Celsius at 10:30:15.980, while the standard timeline requires data from 10:30:16.000, linear interpolation can be used to estimate the temperature at that time to be approximately 26.3 degrees Celsius. This interpolation process ensures consistency in the time dimension of data from all temperature sensing nodes. The resulting temperature data set provides a reliable foundation for subsequent temperature trend analysis and anomaly detection.

[0040] In one embodiment, the temperature comprehensive change rate is obtained by performing calculations based on the various temperature values, including: Calculate according to each temperature value to obtain the spatial temperature gradient distribution matrix; Extract the spatial temperature gradient distribution matrix to obtain the gradient amplitude and gradient direction angle at each position; According to the gradient amplitude at each position and the corresponding preset time interval, the amplitude change rate at each position is obtained, and according to the gradient amplitude at each position and the preset time interval, the direction angle change rate at each position is obtained; The comprehensive temperature change rate is obtained based on the amplitude change rate and direction angle change rate at each position.

[0041] In the embodiment, the spatial temperature gradient distribution matrix is calculated according to each temperature value in the time-corrected temperature data set. Exemplarily, the calculation process of the spatial temperature gradient distribution matrix involves the temperature change rate in multiple directions. Assuming that the coordinates of a temperature sensing node A is (5, 3, 2), the temperature of the temperature sensing node A is 28.5 degrees Celsius, the coordinates of a neighboring temperature sensing node B is (6, 3, 2), and the temperature of the temperature sensing node B is 29.2 degrees Celsius, the Euclidean distance between the two temperature sensing nodes is 1 meter, and the temperature gradient from A to B is 0.7 degrees Celsius per meter. By performing similar calculation on all neighboring temperature sensing nodes, the temperature gradient values in different directions of the point can be obtained. These gradient values are arranged according to the spatial positions of the temperature sensing nodes to form a three-dimensional matrix reflecting the temperature change characteristics of the entire monitoring area.

[0042] Then, the gradient amplitude and the gradient direction angle relative to the north direction of each position are extracted from the spatial temperature gradient distribution matrix, the amplitude difference between the current time and the previous time is calculated, and the time difference between the current time and the previous time is calculated. The amplitude change rate is obtained by using the amplitude difference and the time difference. The direction angle difference between the current time and the previous time is calculated, and the direction change rate is obtained by combining the direction angle difference and the time difference. The square root of the sum of the square of the amplitude change rate and the square of the direction change rate is used as the temperature comprehensive change rate.

[0043] The amplitude change rate reflects the time evolution of the temperature change intensity, and the direction change rate reflects the dynamic adjustment of the temperature field structure. The comprehensive change rate obtained by calculating the square root of the sum of the squares of the two rates can comprehensively reflect the dynamic characteristics of the temperature field.

[0044] In an embodiment, the spatial temperature gradient distribution matrix is obtained by calculating each temperature value, including: obtaining the positions of the temperature sensing nodes; calculating the temperature difference between each temperature sensing node and the corresponding neighboring node according to the positions and the temperature values; obtaining the corresponding temperature gradient values based on the temperature difference of each temperature sensing node, and arranging all the temperature gradient values according to the corresponding positions to obtain the spatial temperature gradient distribution matrix.

[0045] In this embodiment, based on the time-corrected temperature data set, the three-dimensional spatial coordinates and corresponding temperature values ​​of each sensing temperature sensor node are extracted. For each temperature sensor node, its neighboring temperature sensor nodes within a preset distance range are determined, and the temperature difference between the temperature sensor node and each neighboring temperature sensor node is calculated and divided by the Euclidean distance to obtain the temperature gradient value in each direction. The gradient values ​​of all temperature sensor nodes are arranged according to the spatial coordinate position to form a spatial temperature gradient distribution matrix. For example, the temperature gradient calculation process involves the temperature change rate in multiple directions. Assume that the coordinates of temperature sensor node A are (5, 3, 2) and the temperature is 28.5 degrees Celsius, and the coordinates of its neighboring temperature sensor node B are (6, 3, 2) and the temperature is 29.2 degrees Celsius. The Euclidean distance between the two temperature sensor nodes is 1 meter, then the temperature gradient from A to B is 0.7 degrees Celsius per meter. By performing similar calculations on all neighboring temperature sensor nodes, the temperature gradient values ​​of the point in different directions can be obtained. These gradient values ​​are arranged according to the spatial location of the temperature sensing nodes, forming a three-dimensional matrix that reflects the temperature variation characteristics of the entire monitoring area. The extraction of gradient amplitude and direction provides basic data for subsequent rate of change analysis.

[0046] It's important to note that the gradient amplitude indicates the severity of the temperature change, while the gradient direction angle indicates the direction of the fastest temperature increase. The angle defined relative to true north provides a unified reference for directional data at different locations. When the cold airflow in the cold aisle is blocked, the direction of the temperature gradient deviates from its original direction toward the hot aisle. This change in direction often indicates a cooling system anomaly. The calculation of the comprehensive rate of change of the temperature gradient utilizes the concept of vector synthesis. For example, when the equipment load in a certain area suddenly increases, not only will the amplitude of the temperature gradient rise rapidly, but the gradient direction may also rotate due to the change in the location of the heat source. The dynamic threshold is set based on the statistical characteristics of historical data, and this method can adapt to the normal fluctuation range of different time periods.

[0047] In one embodiment, obtaining a spatial sampling density value according to the second preset sampling frequency, and obtaining a sampling configuration scheme according to the spatial sampling density value include: Based on the second preset sampling frequency, recording the number of each temperature sensing node within the preset range whose second preset sampling frequency is greater than the reference frequency, to obtain a spatial sampling density value; According to a second preset sampling frequency within a preset range, an average temperature gradient amplitude is obtained, and the average temperature gradient amplitude and the spatial sampling density value are combined to obtain a comprehensive density index; Adjusting the second preset sampling frequency according to the comprehensive density index to obtain a third sampling frequency; The temperature sensing nodes are sorted according to the magnitude of the gradient amplitude to obtain a sorting result, and a sampling configuration scheme is obtained based on the sorting result and the third sampling frequency.

[0048] In the embodiment, the data transmission priority values of the temperature sensing nodes are assigned according to the order of the gradient amplitudes of the respective temperature sensing nodes from large to small. According to the adjusted result of the sampling frequency of each temperature sensing node, i.e., the second preset sampling frequency, the number of temperature sensing nodes with a sampling frequency greater than the reference frequency in each cubic meter space is calculated as a space sampling density value. Meanwhile, the product of the space sampling density value and the average temperature gradient amplitude in the space is taken as a comprehensive density index. When the comprehensive density index exceeds a preset density threshold, the space is marked as a high gradient area. For the marked high gradient area, the sampling frequency is continuously adjusted to obtain a third sampling frequency. The third sampling frequencies, the transmission priority values, and the marks of all temperature sensing nodes are summarized to generate a sampling configuration scheme.

[0049] The calculation of the space sampling density involves the gridding of the three-dimensional space. The entire monitoring area is divided into a plurality of 1 cubic meter space units. The number of temperature sensing nodes with a sampling frequency higher than the reference frequency in each unit is counted. The reference frequency is usually set to 1.2 times the default sampling frequency of the system. The selection of the reference frequency is based on a large amount of practical experience. When the second preset sampling frequency of 5 temperature sensing nodes in a certain space unit exceeds the reference frequency, and the average temperature gradient amplitude of the unit is 0.8 degrees Celsius per meter, the comprehensive density index is 4.0. The introduction of the comprehensive density index enables the system to consider both the sampling density and the degree of temperature change. Specifically, only a high sampling density but a small temperature gradient may not result in a high comprehensive density index. Conversely, a large temperature gradient but a sparse temperature sensing node may also result in a limited comprehensive density index. Only when both factors are high, the area can be identified as a high gradient area that needs to be focused on. This double judgment mechanism avoids blind concentration of resources and improves monitoring efficiency.

[0050] In an embodiment, the second preset sampling frequency is adjusted according to the comprehensive density index to obtain a third sampling frequency, including: If the comprehensive density index is greater than the preset density threshold, a region encryption factor is obtained by calculating the ratio of the comprehensive density index to the preset density threshold. Based on the region encryption factor, the second preset sampling frequency in the preset range corresponding to the comprehensive density index is adjusted to obtain a third sampling frequency.

[0051] In this embodiment, for the marked high-gradient area, the adjusted sampling frequency of all temperature sensor nodes in the area is extracted, that is, the second preset sampling frequency, and the ratio of the comprehensive density index of the area to the preset density threshold is calculated as the area encryption factor. The adjusted sampling frequency of each temperature sensor node in the area is multiplied by the area encryption factor to obtain the third sampling frequency.

[0052] The identification of high-gradient areas triggers further sampling intensification. The regional intensification factor is calculated based on the ratio of the area's comprehensive density index to a preset density threshold. For example, if a region's comprehensive density index is 6.0 and the preset density threshold is 4.0, the regional intensification factor is 1.5. This means that the sampling frequency of all temperature sensor nodes in the region must be increased by 50% on top of the already adjusted frequency. This hierarchical frequency adjustment mechanism enables differentiated monitoring of areas of varying importance. The distributed sampling configuration includes complete parameter information for each temperature sensor node. Each temperature sensor node has a clear final sampling frequency value, data transmission priority value, and region identifier. The advantage of this configuration is that it not only considers the degree of anomaly of individual temperature sensor nodes, but also their spatial distribution characteristics. Temperature sensor nodes located at the edge of a high-gradient area, even if their own temperature changes are minimal, will have their sampling frequency appropriately increased based on regional characteristics, thereby better capturing the spatial evolution of the temperature field. Through this multi-level adaptive adjustment mechanism, the system can dynamically optimize resource allocation based on the real-time monitored temperature field characteristics, avoiding the waste of computing and transmission resources while ensuring monitoring accuracy, and achieving a balance between monitoring efficiency and resource consumption.

[0053] S104. Based on the equipment parameters of each temperature sensing node, the corresponding benchmark task carrying capacity is obtained, and temperature is collected according to the benchmark task carrying capacity and the sampling configuration scheme to obtain an initial temperature data point set. The initial temperature data point set is interpolated and reconstructed to obtain a temperature field distribution matrix. The temperature field distribution matrix is ​​compared with the actual measurement value to obtain the optimized temperature data of each temperature sensing node.

[0054] In this embodiment, device parameters are obtained for each temperature sensor node, including the current processor occupancy rate, memory usage, and queue length for pending data. The benchmark task carrying capacity of each temperature sensor node is then calculated based on the device parameters and the gradient amplitude at each temperature sensor node's location. After determining the benchmark task carrying capacity, the total task load for each temperature sensor node is allocated based on the benchmark task carrying capacity, and the expected load value for each temperature sensor node is calculated. For example, assuming the entire system needs to process 1,000 pieces of temperature data per second and the benchmark task allocation weight for a temperature sensor node is 0.15, the temperature sensor node is expected to process 150 pieces of data per second. Based on the temperature sensor node's processing capacity parameters, the resource usage required to process these data can be estimated, thereby obtaining the expected load value. When the expected load value reaches 85, and the balancing threshold is set to 80, it indicates that the temperature sensor node is overloaded.

[0055] If the expected load value of a temperature sensor node exceeds the preset balancing threshold, the system obtains all directly connected temperature sensor nodes in the network topology. The difference between the balancing threshold and the current load value of each connected temperature sensor node is used as the acceptable task capacity. The excess is redistributed according to the proportion of the acceptable task capacity of each connected temperature sensor node, and the task allocation weights of the connected temperature sensor nodes are updated. For example, if an overloaded temperature sensor node is directly connected to four connected temperature sensor nodes, the current load values ​​of these connected temperature sensor nodes are 50, 55, 45, and 60, respectively, and the balancing threshold is 80. The acceptable task capacity of each temperature sensor node is 30, 25, 35, and 20, respectively. The excess is 5 units of task capacity, which is distributed according to the ratio of 30:25:35:20, transferring 1.35, 1.14, 1.59, and 0.91 units of task capacity to each connected temperature sensor node, respectively. Through this dynamic load balancing mechanism, the system can respond to load changes of temperature sensing nodes in real time, avoiding the timeliness of data processing affected by overload of individual temperature sensing nodes, while ensuring that data in important monitoring areas are processed first, thereby optimizing overall performance.

[0056] Then, according to the task allocation weight and sampling configuration scheme, each temperature sensor node is used to collect temperature, and the actual measurement values ​​and spatial coordinates of the temperature sensor nodes are obtained. After the actual measurement values ​​are fused, a preliminary set of temperature data points is obtained. The specific fusion processing method is: the actual measurement value of each temperature sensor node is multiplied by the corresponding weight coefficient, the weighted temperature values ​​of all temperature sensor nodes at the same time are summed, and then divided by the sum of the weight coefficients to obtain a preliminary set of temperature data points.

[0057] The bilinear interpolation calculation is performed on the initial temperature data point set. Specifically, according to the temperature values and spatial distances of the four nearest data points around each data point to be interpolated, the temperature estimation value of the data point is calculated by inverse distance weighting, and the discrete temperature measurement points are expanded into a temperature field distribution matrix covering the entire monitoring area. For example, for the positions in the monitoring area that are not covered by sensors, the system needs to estimate the temperature value of the position according to the surrounding known temperature points. Assuming that the data point to be interpolated P is surrounded by four known temperature points A, B, C, and D, which form a rectangular area. The interpolation calculation is first performed in the horizontal direction, and the temperature value on the horizontal line of P is calculated according to the distance ratio of P to A and B; then the process is repeated in the vertical direction. The closer the points, the greater the contribution to the interpolation result, and the inverse distance weighting ensures the smooth transition of the temperature field.

[0058] It should be noted that the construction of the temperature field distribution matrix realizes the conversion from discrete measurement to continuous distribution. For example, a warehouse space originally having 30 discrete measurement points generates a 100x100 temperature matrix through interpolation calculation, and each matrix element represents the temperature estimation value of a 0.5m x 0.5m area. This high-resolution temperature field representation enables the system to identify possible temperature abnormal areas between sensors and avoid missing local hotspots due to sparse sensor arrangement.

[0059] The reconstructed temperature field distribution matrix is compared with the actual measurement value, the temperature deviation square sum of each measurement point position is calculated, and the mean square error is obtained by dividing the total number of measurement points, which is used as the reconstruction accuracy evaluation index. At the same time, the total time from data acquisition to temperature field reconstruction completion is recorded as the system response time monitoring data. According to the reconstruction accuracy evaluation index and the system response time monitoring data, the accuracy index is normalized to the interval of zero to one, the response time is converted into a response speed index, the accuracy weight is calculated as the accuracy index divided by the sum of the accuracy index and the speed index, and the speed weight is calculated as the speed index divided by the sum of the accuracy index and the speed index, to obtain a pair of dynamically adjusted weight coefficients. The temperature field distribution matrix is adjusted by the pair of weight coefficients. When the accuracy weight is greater than the speed weight, the number of sampling points for spatial interpolation is increased and the calculation accuracy level is improved; when the speed weight is greater than the accuracy weight, the number of sampling points is reduced and the calculation accuracy level is reduced. According to the adjusted parameters, the temperature field reconstruction is re-executed to obtain the optimized temperature field reconstruction result, i.e. the optimized temperature data. The optimized temperature field reconstruction result realizes the best matching of application requirements and system performance. Through this adaptive parameter adjustment mechanism, the distributed temperature sensing system can improve the response speed as much as possible under the premise of ensuring the basic accuracy requirement, or pursue higher reconstruction accuracy when time permits, truly realizing intelligent temperature monitoring.

[0060] In one embodiment, based on the device parameters of each temperature sensing node, the corresponding benchmark task carrying capacity is obtained, including: After weighted summation of the device parameters of each temperature sensor node, the corresponding node load value is obtained. The device parameters include the current processor occupancy rate, memory usage rate and the length of the queue for data to be processed. The temperature gradient weight coefficient is obtained by using the gradient amplitude and maximum gradient amplitude corresponding to each temperature sensing node. The corresponding task carrying capacity index is obtained according to the node load value and the temperature gradient weight coefficient. The task carrying capacity indexes of each temperature sensing node are added together to obtain the total task carrying capacity, and the task carrying capacity indexes of each temperature sensing node are divided by the total task carrying capacity to obtain the corresponding benchmark task carrying capacity.

[0061] In this embodiment, the temperature sensor node load value is calculated by taking a weighted sum of the current processor utilization, memory usage, and the length of the pending data queue. For example, if a temperature sensor node has a processor utilization of 75%, a memory utilization of 60%, and 20 data records in the pending data queue, the queue length is normalized to a range of 0 to 100. Assume that the normalized length is 40. Using the weights of 0.4, 0.3, and 0.3, the node load value for this temperature sensor node is 75 × 0.4 + 60 × 0.3 + 40 × 0.3 = 60.

[0062] At the same time, the gradient amplitude at each temperature sensor node is extracted. The maximum gradient amplitude among all temperature sensor nodes is found, and the ratio of each temperature sensor node's gradient amplitude to the maximum value is used as the temperature gradient weight coefficient. For example, in a monitoring network consisting of 50 temperature sensor nodes, if the maximum temperature gradient amplitude is 2.5 degrees Celsius per meter and the gradient amplitude at a particular temperature sensor node is 2.0 degrees Celsius per meter, the temperature gradient weight coefficient for that temperature sensor node is 0.8. A higher weight coefficient means that the temperature sensor node receives more processing resources in task allocation. The calculation of the task carrying capacity index reflects the balance between load and importance.

[0063] It should be noted that the introduction of the temperature gradient weight coefficient enables the system to prioritize data processing capabilities in key monitoring areas. When a region experiences drastic temperature changes, the temperature gradient amplitude of the temperature sensing nodes in that region will increase significantly.

[0064] The task-carrying capacity index of each temperature sensor node is calculated by multiplying the inverse of the load value by the temperature gradient weight coefficient. Specifically, the lower the load value of a temperature sensor node, the larger the inverse value, indicating more spare processing capacity. Conversely, the higher the temperature gradient weight coefficient, the more important the region in which the temperature sensor node is located. The product of these two factors forms a comprehensive task-carrying capacity evaluation. For example, a temperature sensor node with a load value of 40 and a weight coefficient of 0.8 has a task-carrying capacity index of 1 / 40 × 0.8 = 0.02.

[0065] Then, the task carrying capacity indexes of all temperature sensing nodes are summed up to obtain the total carrying capacity value, and then the task carrying capacity index of each temperature sensing node is divided by the total carrying capacity value to obtain the benchmark task carrying capacity of each temperature sensing node.

[0066] The temperature sensing and radio frequency identification circuit integrated temperature measurement system provided by the embodiment of the present invention is as follows: Figure 2 As shown, Figure 2 The system block diagram of the temperature sensing and radio frequency identification circuit integrated temperature measurement system 200 includes an acquisition module 201, a first adjustment module 202, a second adjustment module 203 and an optimization module 204, wherein: The acquisition module 201 is used to acquire the response time of each temperature sensor node when collecting temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, to obtain a response time series and a signal transmission delay time series; The first adjustment module 202 is configured to construct a compensation matrix based on the signal transmission delay time sequence. If any response time in the response time sequence is not within a preset response threshold, the compensation matrix is ​​used to correct the response time to obtain an adjusted response time sequence. The second adjustment module 203 is configured to adjust the first preset sampling frequency according to the adjusted response time sequence to obtain a corresponding second preset sampling frequency, obtain a spatial sampling density value according to the second preset sampling frequency, and obtain a sampling configuration scheme according to the spatial sampling density value; The optimization module 204 is used to obtain the corresponding benchmark task carrying capacity based on the equipment parameters of each temperature sensing node, collect temperature according to the benchmark task carrying capacity and the sampling configuration scheme, obtain an initial temperature data point set, interpolate and reconstruct the initial temperature data point set to obtain a temperature field distribution matrix, compare the temperature field distribution matrix with the actual measurement value, and obtain the optimized temperature data of each temperature sensing node.

[0067] In one embodiment, the first adjustment module 202 includes a clock difference calculation unit and a compensation matrix construction unit, wherein: A clock difference calculation unit is used to calculate the local clock and reference clock of each temperature sensor node to obtain the corresponding clock difference; The compensation matrix construction unit is used to obtain the total delay time of each temperature sensing node according to the clock difference of each temperature sensing node and the corresponding signal transmission delay time, and construct a compensation matrix according to the total delay time.

[0068] The specific implementation of the temperature sensing and radio frequency identification circuit integrated temperature measurement system is basically the same as the specific embodiment of the temperature sensing and radio frequency identification circuit integrated temperature measurement method described above, and will not be repeated here.

[0069] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] 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 temperature measurement method integrating temperature sensing and radio frequency identification circuit, characterized in that: include: Obtaining the response time of each temperature sensor node when collecting temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, to obtain a response time series and a signal transmission delay time series; constructing a compensation matrix based on the signal transmission delay time sequence, and if any response time in the response time sequence is not within a preset response threshold range, correcting the response time using the compensation matrix to obtain an adjusted response time sequence; Adjusting the first preset sampling frequency according to the adjusted response time sequence to obtain a corresponding second preset sampling frequency, obtaining a spatial sampling density value according to the second preset sampling frequency, and obtaining a sampling configuration scheme according to the spatial sampling density value; Based on the equipment parameters of each temperature sensing node, the corresponding benchmark task carrying capacity is obtained, temperature is collected according to the benchmark task carrying capacity and the sampling configuration scheme, and an initial temperature data point set is obtained. The initial temperature data point set is interpolated and reconstructed to obtain a temperature field distribution matrix, and the temperature field distribution matrix is ​​compared with the actual measurement value to obtain optimized temperature data of each temperature sensing node.

2. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 1, characterized in that: The constructing of a compensation matrix according to the signal transmission delay time sequence includes: Calculating the local clock and the reference clock of each temperature sensing node to obtain a corresponding clock difference; The total delay time of each temperature sensing node is obtained according to the clock difference of each temperature sensing node and the corresponding signal transmission delay time, and a compensation matrix is ​​constructed according to the total delay time of each temperature sensing node.

3. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 1, characterized in that: The adjusting the first preset sampling frequency according to the adjusted response time sequence to obtain a second preset sampling frequency includes: Correcting the temperature of each of the temperature sensing nodes according to the adjusted response time sequence to obtain a corresponding temperature value; The change rate is calculated according to each of the temperature values ​​to obtain a comprehensive temperature change rate. If the comprehensive temperature change rate is greater than a preset temperature change rate threshold, the first preset sampling frequency is adjusted to obtain a second preset sampling frequency.

4. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 3, characterized in that: The calculation is performed based on each of the temperature values ​​to obtain the comprehensive temperature change rate, including: Calculating according to each of the temperature values ​​to obtain a spatial temperature gradient distribution matrix; Extracting the spatial temperature gradient distribution matrix to obtain the gradient amplitude and gradient direction angle at each position; Obtaining an amplitude change rate at each position based on the gradient amplitude at each position and the corresponding preset time interval, and obtaining an angular change rate at each position based on the gradient amplitude at each position and the preset time interval; The comprehensive temperature change rate is obtained according to the amplitude change rate and the direction angle change rate at each position.

5. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 4, characterized in that: The calculation is performed based on each of the temperature values ​​to obtain a spatial temperature gradient distribution matrix, including: Obtaining the position of each temperature sensing node; Calculating a temperature difference between each of the temperature sensing nodes and a corresponding adjacent node according to the position and the temperature value; Based on the temperature difference of each temperature sensing node, a corresponding temperature gradient value is obtained, and all the temperature gradient values ​​are arranged according to the corresponding positions to obtain a spatial temperature gradient distribution matrix.

6. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 1, characterized in that: Obtaining a spatial sampling density value according to the second preset sampling frequency, and obtaining a sampling configuration scheme according to the spatial sampling density value, includes: Based on the second preset sampling frequency, recording the number of each of the temperature sensing nodes within a preset range whose second preset sampling frequency is greater than the reference frequency, to obtain a spatial sampling density value; Obtaining an average temperature gradient amplitude according to the second preset sampling frequency within the preset range, and obtaining a comprehensive density index by combining the average temperature gradient amplitude and the spatial sampling density value; Adjusting the second preset sampling frequency according to the comprehensive density index to obtain a third sampling frequency; The temperature sensing nodes are sorted according to the magnitude of the gradient amplitude to obtain a sorting result, and a sampling configuration scheme is obtained based on the sorting result and the third sampling frequency.

7. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 6, characterized in that: The adjusting the second preset sampling frequency according to the comprehensive density index to obtain a third sampling frequency includes: If the comprehensive density index is greater than the preset density threshold, the ratio of the comprehensive density index to the preset density threshold is calculated to obtain the regional density factor; Based on the regional encryption factor, the second preset sampling frequency within the preset range corresponding to the comprehensive density index is adjusted to obtain a third sampling frequency.

8. The temperature measurement method integrating temperature sensing and radio frequency identification circuit according to claim 1 or 4, characterized in that: The obtaining of the corresponding benchmark task carrying capacity based on the device parameters of each temperature sensing node includes: After weighted summing of the device parameters of each of the temperature sensor nodes, a corresponding node load value is obtained, wherein the device parameters include current processor occupancy, memory usage, and length of a queue of data to be processed; Using the gradient amplitude and maximum gradient amplitude corresponding to each temperature sensing node, a temperature gradient weight coefficient is obtained, and according to the node load value and the temperature gradient weight coefficient, a corresponding task carrying capacity index is obtained; The task carrying capacity indexes of the temperature sensing nodes are added together to obtain a total task carrying capacity, and the task carrying capacity indexes of the temperature sensing nodes are divided by the total task carrying capacity to obtain a corresponding benchmark task carrying capacity.

9. A temperature measurement system integrating temperature sensing and radio frequency identification circuit, characterized in that: include: an acquisition module, configured to acquire the response time of each temperature sensor node when collecting temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, to obtain a response time sequence and a signal transmission delay time sequence; A first adjustment module is configured to construct a compensation matrix based on the signal transmission delay time sequence, and if any response time in the response time sequence is not within a preset response threshold, correct the response time using the compensation matrix to obtain an adjusted response time sequence; a second adjustment module, configured to adjust the first preset sampling frequency according to the adjusted response time sequence to obtain a corresponding second preset sampling frequency, obtain a spatial sampling density value according to the second preset sampling frequency, and obtain a sampling configuration scheme according to the spatial sampling density value; The optimization module is used to obtain the corresponding benchmark task carrying capacity based on the equipment parameters of each temperature sensing node, collect temperature according to the benchmark task carrying capacity and the sampling configuration scheme, obtain an initial temperature data point set, interpolate and reconstruct the initial temperature data point set to obtain a temperature field distribution matrix, compare the temperature field distribution matrix with the actual measurement value, and obtain optimized temperature data of each temperature sensing node.

10. The temperature sensing and radio frequency identification circuit integrated temperature measurement system according to claim 9, characterized in that: The first adjustment module includes a clock difference calculation unit and a compensation matrix construction unit, wherein: The clock difference calculation unit is used to calculate the local clock and the reference clock of each temperature sensor node to obtain a corresponding clock difference; The compensation matrix construction unit is configured to obtain a total delay time of each temperature sensing node according to a clock difference of each temperature sensing node and a corresponding signal transmission delay time, and to construct a compensation matrix according to the total delay time.

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