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

Abstract

The application discloses a temperature sensing and radio frequency identification circuit integrated temperature measurement method and system, which is applied to the technical field of temperature sensor data processing, and is used for obtaining the response time of each temperature sensing node when collecting temperature according to a corresponding first preset sampling frequency, and the signal transmission delay time of a corresponding radio frequency identification chip, then correcting the response time by using a compensation matrix constructed according to the signal transmission delay time sequence, obtaining an adjusted response time sequence, and then respectively determining a sampling configuration scheme and a reference task bearing capacity corresponding to the temperature sensing node according to the adjusted response time sequence and equipment parameters, collecting temperature, and performing interpolation reconstruction and optimization on the collected temperature value to obtain optimized temperature data. 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, so that the temperature monitoring precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sensors, in particular to a temperature sensing and radio frequency identification circuit integrated temperature measurement method and system. BACKGROUND

[0002] Temperature sensing and radio frequency identification chip circuit fusion technology plays a crucial role in industrial monitoring, intelligent manufacturing and equipment health management, and can realize real-time monitoring and data transmission of multiple temperature points in complex environments, which is a core technology for ensuring system safe operation and improving equipment performance.

[0003] Current temperature monitoring solutions mainly rely on single-point sensors or simple multi-point acquisition systems, which have obvious shortcomings when facing large-scale distributed temperature monitoring needs. Due to differences in hardware characteristics, environmental conditions and signal transmission paths, temperature sensing nodes at different locations have significant inconsistencies in data acquisition and transmission speed, which directly affects the data synchronization quality of the system. Inconsistent response speed further leads to complex effects of temperature gradient distribution on sampling strategies. When the operating conditions of the equipment change, the temperature change rate and gradient distribution in different regions exhibit dynamic characteristics, and traditional fixed sampling strategies cannot adapt to such changes, resulting in missing or redundant collection of critical temperature information. Therefore, how to solve the problem of ensuring temperature monitoring accuracy in the case of inconsistent response speed and dynamic changes in temperature field distribution characteristics in distributed temperature sensing systems is a problem that needs to be solved in the field. SUMMARY

[0004] To solve the above technical problems, the present application embodiment provides a temperature sensing and radio frequency identification circuit integrated temperature measurement method and system to solve the technical problem of improving temperature monitoring accuracy in the case of inconsistent response speed and dynamic changes in temperature field distribution characteristics in distributed temperature sensing systems.

[0005] The first aspect of the present application embodiment provides a temperature sensing and radio frequency identification circuit integrated temperature measurement method, the method comprising:

[0006] Obtaining the response time of each temperature sensing 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 the response time sequence and the signal transmission delay time sequence;

[0007] According to the signal transmission delay time sequence, a compensation matrix is constructed, and if any one of the response time sequence is not within the preset response threshold, the response time is corrected using the compensation matrix to obtain the adjusted response time sequence;

[0008] According to the adjusted response time sequence, the first preset sampling frequency is adjusted to obtain a corresponding second preset sampling frequency, a spatial sampling density value is obtained according to the second preset sampling frequency, and a sampling configuration scheme is obtained according to the spatial sampling density value;

[0009] Based on the device parameters of each temperature sensing node, a corresponding reference task carrying capacity is obtained, temperature collection is performed according to the reference task carrying capacity and the sampling configuration scheme, an initial temperature data point set is obtained, interpolation reconstruction is performed on the initial temperature data point set, a temperature field distribution matrix is obtained, and the temperature field distribution matrix is compared with an actual measurement value to obtain an optimized temperature data of each temperature sensing node.

[0010] In a possible implementation manner of the first aspect, a compensation matrix is constructed according to the signal transmission delay time sequence, including:

[0011] The local clock of each temperature sensing node and the reference clock are calculated to obtain a corresponding clock difference value;

[0012] According to the clock difference value of each temperature sensing node and the corresponding signal transmission delay time, a total delay time of each temperature sensing node is obtained, and a compensation matrix is constructed according to the total delay time.

[0013] In a possible implementation manner of the first aspect, the first preset sampling frequency is adjusted to obtain the second preset sampling frequency according to the adjusted response time sequence, including:

[0014] According to the adjusted response time sequence, the temperature of each temperature sensing node is extracted to obtain a temperature data set;

[0015] According to the change rate calculation of each temperature value, a temperature comprehensive change rate is obtained, and if the temperature comprehensive change rate is greater than a preset temperature change rate threshold, the first preset sampling frequency is adjusted to obtain the second preset sampling frequency.

[0016] In a possible implementation manner of the first aspect, the temperature comprehensive change rate is obtained according to the calculation of each temperature value, including:

[0017] According to the calculation of each temperature value, a spatial temperature gradient distribution matrix is obtained;

[0018] The spatial temperature gradient distribution matrix is extracted to obtain the gradient amplitude and the gradient direction angle of each position;

[0019] According to the gradient amplitude of each position and the corresponding preset time interval, the amplitude change rate of each position is obtained, and according to the gradient amplitude of each position and the preset time interval, the direction angle change rate of each position is obtained;

[0020] According to the amplitude variation rate and the direction angle variation rate of each position, a temperature comprehensive variation rate is obtained.

[0021] In a possible implementation of the first aspect, the spatial temperature gradient distribution matrix is obtained according to the temperature values, and includes:

[0022] The positions of the temperature sensing nodes are obtained.

[0023] According to the positions and the temperature values, temperature difference values of each temperature sensing node and corresponding adjacent nodes are calculated.

[0024] Based on the temperature difference values of each temperature sensing node, corresponding temperature gradient values are obtained, and all the temperature gradient values are arranged according to corresponding positions to obtain the spatial temperature gradient distribution matrix.

[0025] In a possible implementation of the first aspect, the spatial sampling density value is obtained according to the second preset sampling frequency, and the sampling configuration scheme is obtained according to the spatial sampling density value, and includes:

[0026] Based on the second preset sampling frequency, the number of temperature sensing nodes in the preset range whose second preset sampling frequency is greater than the reference frequency is recorded to obtain a spatial sampling density value.

[0027] According to the second preset sampling frequency in the preset range, an average temperature gradient amplitude is obtained, and the average temperature gradient amplitude and the spatial sampling density value are used to obtain a comprehensive density index.

[0028] The second preset sampling frequency is adjusted according to the comprehensive density index to obtain a third sampling frequency.

[0029] According to the size of the gradient amplitude, each temperature sensing node is sorted to obtain a sorting result, and based on the sorting result and the third sampling frequency, a sampling configuration scheme is obtained.

[0030] In a possible implementation of the first aspect, the third sampling frequency is obtained by adjusting the second preset sampling frequency according to the comprehensive density index, and includes:

[0031] If the comprehensive density index is greater than a preset density threshold, a ratio of the comprehensive density index to the preset density threshold is calculated to obtain a regional encryption factor.

[0032] Based on the regional encryption factor, the second preset sampling frequency in the preset range corresponding to the comprehensive density index is adjusted to obtain the third sampling frequency.

[0033] In a possible implementation of the first aspect, the corresponding reference task carrying capacity is obtained based on the device parameters of each temperature sensing node, and includes:

[0034] The device parameters of each temperature sensing node are weighted and summed to obtain a corresponding node load value, wherein the device parameters include a current processor occupancy rate, a memory usage rate and a to-be-processed data queue length;

[0035] The temperature gradient weight coefficient is obtained by using the gradient amplitude corresponding to each temperature sensing node and the maximum gradient amplitude, and the corresponding task carrying capacity index is obtained according to the node load value and the temperature gradient weight coefficient.

[0036] The task carrying capacity indexes of the temperature sensing nodes are added to obtain a total task carrying capacity, and the task carrying capacity indexes of the temperature sensing nodes are respectively divided by the total task carrying capacity to obtain corresponding reference task carrying capacities.

[0037] To solve the same technical problem, a second aspect of an embodiment of the present application provides a temperature sensing and radio frequency identification circuit integrated temperature measurement system, comprising:

[0038] The acquisition module is configured to acquire a response time when each temperature sensing node collects temperature according to a corresponding first preset sampling frequency, and a signal transmission delay time of a corresponding radio frequency identification chip, to obtain a response time sequence and a signal transmission delay time sequence.

[0039] The first adjustment module is configured to construct a compensation matrix according to the signal transmission delay time sequence, and correct the response time by using the compensation matrix if any one of the response times in the response time sequence is not within a preset response threshold, to obtain an adjusted response time sequence.

[0040] The second adjustment module 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.

[0041] The optimization module is configured to obtain a corresponding reference task carrying capacity based on device parameters of each temperature sensing node, collect temperature according to the reference task carrying capacity and the sampling configuration scheme to obtain an initial temperature data point set, perform interpolation reconstruction on the initial temperature data point set to obtain a temperature field distribution matrix, and compare the temperature field distribution matrix with an actual measurement value to obtain an optimized temperature data of each temperature sensing node.

[0042] In a possible implementation manner of the second aspect, the first adjustment module comprises a clock difference value calculation unit and a compensation matrix construction unit, wherein,

[0043] The clock difference value calculation unit is configured to calculate a local clock of each temperature sensing node and a reference clock to obtain a corresponding clock difference value.

[0044] The compensation matrix construction unit is configured to obtain a total delay time of each temperature sensing node according to the clock difference value and the corresponding signal transmission delay time of each temperature sensing node, and construct a compensation matrix according to the total delay time.

[0045] The technical scheme has the following advantages:

[0046] The temperature sensing and radio frequency identification circuit integrated temperature measurement method provided by the embodiment of the 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

[0047] In order to more clearly illustrate the specific embodiments of the present application or the technical scheme in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 The flowchart of the temperature sensing and radio frequency identification circuit integrated temperature measurement method in the embodiment of the application is shown in the figure.

[0049] Figure 2 The structural block diagram of the temperature sensing and radio frequency identification circuit integrated temperature measurement system in the embodiment of the application is shown in the figure.

[0050] The figure mark is: 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

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0052] The temperature sensing and radio frequency identification circuit integrated temperature measurement method provided by the embodiments of the present application is as shown in Figure 1 Figure 1 The temperature sensing and radio frequency identification circuit integrated temperature measurement method flow chart, comprising steps S101 to S104, each step is specifically as follows:

[0053] S101, obtaining the response time of each temperature sensing 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.

[0054] In the present embodiment, in the distributed temperature sensing system, each temperature sensing node has significant differences in response time due to geographical location dispersion, hardware differences and environmental factors. For example, in a large warehouse environment, the temperature monitoring network deployed at the entrance of the warehouse may be disturbed by frequent goods in and out, while the temperature sensing node located in the deep part of the warehouse is relatively stable. Such differences will directly affect the data synchronization accuracy of the whole system.

[0055] The response time refers to the complete time interval from receiving the instruction to returning the data of each temperature sensing node. The response time of each temperature sensing node when collecting temperature according to the corresponding first preset sampling frequency is obtained, and the signal transmission delay time of the radio frequency identification chip under different environmental temperatures is collected, and the temperature value and the corresponding signal transmission delay time form a mapping relationship. Exemplarily, the distributed temperature sensing system continuously collects the response time for 30 days, and each temperature sensing node records once per hour.

[0056] The signal transmission delay time collection process of the radio frequency identification chip needs to consider the influence of temperature change. When the environmental temperature rises from 20℃ to 40℃, the propagation speed of radio frequency signal in the air will change slightly, and the crystal oscillator frequency inside the chip will also drift. By testing a large number of times under different temperature conditions, the time interval from sending a signal to receiving a confirmation is recorded, and a mapping relationship table of temperature and signal transmission delay time can be established. This mapping relationship shows an approximately linear characteristic, which is convenient for subsequent compensation matrix calculation.

[0057] ​It should be noted that the temperature sensing node refers to a single temperature sensor deployed at a specific monitoring location, responsible for temperature data collection and transmission in the local area. The signal transmission delay time can be understood as the signal transmission delay time.

[0058] S102, according to the signal transmission delay time sequence, a compensation matrix is constructed, if any one of the response time sequence is not in the preset response threshold, the response time is corrected by using the compensation matrix, and the adjusted response time sequence is obtained.

[0059] In this embodiment, the compensation matrix is constructed by using the obtained signal transmission delay time sequence. 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 The compensation value is composed of two parts: the fixed hardware signal transmission delay time and the dynamic environmental influence factor. 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, assuming that a cold chain logistics warehouse deploys 100 temperature sensing nodes, which are distributed in different shelf levels and areas. Due to the reflection and attenuation of wireless signals between metal shelves, the response times of temperature sensing nodes in different positions differ by milliseconds. By recording the fixed time delay difference between each pair of temperature sensing nodes, a compensation matrix is obtained for subsequent timestamp correction.

[0060] When the response time of a certain temperature sensing node is not in 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 using the compensation matrix, and the adjusted response time sequence is obtained. Specifically, the local timestamp of each temperature sensing node is modified by using the compensation coefficient in the compensation matrix, and the local timestamp of each temperature sensing node is added to the corresponding compensation coefficient to obtain the corrected timestamp under the 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.

[0061] It should be noted that the preset response threshold range is obtained by statistically analyzing the response time distribution of each temperature sensing node in 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, forming the threshold range of each temperature sensing node.

[0062] In an embodiment, a compensation matrix is constructed according to the signal transmission delay time sequence, including:

[0063] The local clock of each temperature sensing node and the reference clock are calculated to obtain a corresponding clock difference value;

[0064] According to the clock difference value 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 of each temperature sensing node.

[0065] In this embodiment, all temperature sensing nodes are clock-synchronized through a timestamp synchronization protocol. First, the difference between the local clock and the reference clock of each temperature sensing node is obtained, the time difference between the temperature sensing nodes is added to the signal transmission delay time to obtain the total delay time between the temperature sensing nodes, and a compensation matrix is constructed according to the total delay time of each temperature sensing node. The timestamp correction process involves accurate adjustment of the original collection time. When temperature sensing node A collects temperature data 25.6 degrees Celsius at local time 10:30:15.235, according to the corresponding compensation coefficient 0.012 seconds in the compensation matrix, the corrected timestamp becomes 10:30:15.247. This millisecond-level correction may seem small, but it is crucial in scenarios that require accurate tracking of temperature change trends. By uniformly correcting the timestamps of all temperature sensing nodes, originally disordered data points are rearranged into an ordered time sequence. The judgment mechanism for delay exceeding the threshold value provides a trigger condition for adaptive adjustment of the system.

[0066] It should be noted that the original collection time can also be understood as the local clock.

[0067] S103, according to the adjusted response time sequence, adjusting the first preset sampling frequency 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.

[0068] In this embodiment, after the temperature data collected by each temperature sensing node is corrected according to the adjusted response time sequence, the corrected temperature is used to adjust the first preset sampling frequency to obtain an adjusted sampling frequency, i.e., a second preset sampling frequency. The second preset sampling frequency is used for calculation to obtain a spatial sampling density value, and a sampling configuration scheme is obtained according to the spatial sampling density value.

[0069] In an embodiment, the first preset sampling frequency is adjusted according to the adjusted response time sequence to obtain a second preset sampling frequency, including:

[0070] According to the adjusted response time sequence, the temperature of each temperature sensing node is corrected to obtain a corresponding temperature value;

[0071] According to the change rate calculation of each temperature value, a temperature comprehensive change rate is obtained, and if the temperature comprehensive 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.

[0072] In the embodiment, the temperature is reordered according to the corrected timestamp to obtain a corresponding temperature value, forming a temperature set, and then the spatial temperature gradient distribution matrix is calculated according to the temperature data set after time sequence correction, the gradient amplitude and direction change rate are identified, and then the temperature comprehensive change rate is obtained, and if the temperature comprehensive change rate exceeds the preset temperature change rate threshold, the adaptive adjustment of the sampling strategy is triggered to obtain a second preset sampling frequency.

[0073] 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 temperature comprehensive 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.

[0074] The specific process of reordering the temperature data according to the corrected timestamp to form a temperature set is as follows: the temperature data is reordered according to the corrected timestamp to form a preliminary corrected temperature sequence, and then the deviation degree of the response delay of each temperature sensing node in the preliminary corrected temperature sequence from the preset delay threshold range is judged, if the response delay of a certain temperature sensing node is not in the preset delay threshold range, the number, delay exceeding amplitude and delay direction of the temperature sensing node are recorded, and a temperature sensing node delay parameter table that needs to be adjusted in synchronization cycle is generated.

[0075] It should be noted that the preset delay threshold range is usually determined based on the statistical characteristics of historical data. When the response delay of a certain temperature sensing node increases from the normal 50 milliseconds to 80 milliseconds due to the decrease of battery power or the aging of antenna, which exceeds the preset upper limit of 60 milliseconds, the system records the abnormal information. The temperature sensing node delay parameter table not only contains the temperature sensing node number and the exceeding amplitude, but also records the delay direction, i.e. whether the delay is increased or decreased.

[0076] According to the delay exceeding amplitude of each temperature sensing node in the temperature sensing node delay parameter table, the data collection time is dynamically adjusted through a time window sliding mechanism, the exceeding amplitude is divided by the original synchronization period to obtain an adjustment ratio, when the delay exceeding amplitude exceeds the upper limit, the time window is slid forward, when the delay exceeding amplitude is lower than the lower limit, the time window is slid backward, and the sliding distance is equal to the product of the adjustment ratio and the original period. According to the time window after sliding, the temperature data of each temperature sensing node is re-collected, for the data points collected at different times, a linear interpolation method is used to map them to a unified time axis, and a time sequence corrected temperature data set is constructed through the aligned data points. Exemplarily, when the original synchronization period of the temperature sensing node is 1 second, and it is detected that the delay exceeds the upper limit by 20 milliseconds, the adjustment ratio is calculated as 0.02. The time window is slid forward by 20 milliseconds, which means that the next data collection time of the temperature sensing node is advanced from the originally scheduled 10:30:16.000 to 10:30:15.980. This sliding adjustment enables the temperature sensing node with larger delay to collect data in advance, so as to keep synchronization with other temperature sensing nodes when data is summarized. The linear interpolation method plays an important role in data alignment.

[0077] When the collection times of different temperature sensing nodes are deviated, these discrete data points need to be mapped to a unified time axis. For example, the temperature sensing node B collects the temperature of 26.2 degrees Celsius at 10:30:15.980, while the standard time axis requires the data at 10:30:16.000, through linear interpolation, the temperature value at this time is estimated to be about 26.3 degrees Celsius. This interpolation processing ensures the consistency of the data of all temperature sensing nodes in the time dimension, and finally forms a temperature data set, which lays a reliable foundation for subsequent temperature trend analysis and anomaly detection.

[0078] In an embodiment, according to each temperature value, a temperature comprehensive change rate is calculated, including:

[0079] According to each temperature value, a spatial temperature gradient distribution matrix is calculated;

[0080] The spatial temperature gradient distribution matrix is extracted to obtain the gradient amplitude and gradient direction angle of each position;

[0081] According to the gradient amplitude of each position and the corresponding preset time interval, the amplitude change rate of each position is obtained, and according to the gradient amplitude of each position and the preset time interval, the direction angle change rate of each position is obtained;

[0082] According to the amplitude change rate and the direction angle change rate of each position, a temperature comprehensive change rate is obtained.

[0083] 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 temperature variation rates in multiple directions. Assuming that the coordinates of a temperature sensing node A are (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 are (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 calculations on all neighboring temperature sensing nodes, the temperature gradient values of the temperature sensing node in different directions 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 variation characteristics of the entire monitoring area.

[0084] 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 variation 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 variation 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 variation rate and the square of the direction variation rate is used as the temperature comprehensive variation rate.

[0085] The amplitude variation rate reflects the time evolution of the temperature variation intensity, and the direction variation rate reflects the dynamic adjustment of the temperature field structure. The comprehensive variation 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.

[0086] In an embodiment, the spatial temperature gradient distribution matrix is obtained by calculating each temperature value, including:

[0087] The positions of the temperature sensing nodes are obtained.

[0088] The temperature difference between each temperature sensing node and the corresponding neighboring node is calculated according to the positions and the temperature values.

[0089] The temperature gradient values corresponding to each temperature sensing node are obtained based on the temperature difference between the temperature sensing nodes, and all the temperature gradient values are arranged according to the corresponding positions to obtain the spatial temperature gradient distribution matrix.

[0090] In the embodiment, according to the time-corrected temperature data set, the three-dimensional spatial coordinates and the corresponding temperature values of each temperature sensing node are extracted, the neighboring temperature sensing nodes of each temperature sensing node within a preset distance range are determined, the temperature difference between the temperature sensing node and each neighboring temperature sensing node is divided by the Euclidean distance to obtain the temperature gradient value in each direction, and the gradient values of all temperature sensing nodes are arranged according to the spatial coordinate positions to form a spatial temperature gradient distribution matrix. The temperature gradient calculation process involves the temperature change rate in multiple directions. Assuming that the coordinates of a temperature sensing node A are (5, 3, 2), the temperature is 28.5 degrees Celsius, the coordinates of a neighboring temperature sensing node B are (6, 3, 2), and the temperature 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 calculations 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. The extraction of gradient amplitude and direction provides basic data for subsequent change rate analysis.

[0091] It should be noted that the gradient amplitude represents the degree of temperature change, and the gradient direction angle indicates the direction in which the temperature rises fastest. The angle definition with respect to the north direction makes the direction data of different positions have a unified reference benchmark. When the cold airflow of the cold aisle is blocked, the direction of the temperature gradient will deflect from the original direction pointing to the hot aisle, and this direction change often indicates an abnormality of the refrigeration system. The calculation of the comprehensive change rate of the temperature gradient adopts the idea of vector composition. For example, when the load of a device in a certain area suddenly increases, not only will the amplitude of the temperature gradient rapidly increase, but the gradient direction may also rotate due to the change in the position of the heat source. The setting of the dynamic threshold is based on the statistical characteristics of historical data, and this method can adapt to the normal fluctuation range at different times.

[0092] In an embodiment, according to the second preset sampling frequency, a spatial sampling density value is obtained, and according to the spatial sampling density value, a sampling configuration scheme is obtained, including:

[0093] Based on the second preset sampling frequency, the number of temperature sensing nodes in the preset range whose second preset sampling frequency is greater than the reference frequency is recorded to obtain a spatial sampling density value;

[0094] According to the second preset sampling frequency in the preset range, an average temperature gradient amplitude is obtained, and the average temperature gradient amplitude and the spatial sampling density value are used to obtain a comprehensive density index;

[0095] According to the comprehensive density index, the second preset sampling frequency is adjusted to obtain a third sampling frequency;

[0096] The gradient amplitudes are ranked according to the sizes to obtain a ranking result, and a sampling configuration scheme is obtained based on the ranking result and the third sampling frequency.

[0097] In the embodiment, the gradient amplitudes of the temperature sensing nodes are ranked from large to small, and the transmission priority values of the data of the temperature sensing nodes are assigned according to the ranking positions. According to the adjusted result of the sampling frequencies of the temperature sensing nodes, that is, the second preset sampling frequency, the number of the temperature sensing nodes with the sampling frequency greater than the reference frequency in each cubic meter space is calculated as a space sampling density value. Meanwhile, combined with the average temperature gradient amplitude in the space, the product of the space sampling density value and the average temperature gradient amplitude 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 the temperature sensing nodes are summarized to generate a sampling configuration scheme.

[0098] The calculation of the space sampling density involves the grid processing of the three-dimensional space. The entire monitoring area is divided into a plurality of 1 cubic meter space units, and the number of the temperature sensing nodes with the 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, and 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 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 temperature change degree. Specifically, only a region with high sampling density but small temperature gradient may not have a high comprehensive density index, and vice versa. Only when both are high, the region will be identified as a high gradient area that needs to be focused on. This double judgment mechanism avoids blind concentration of resources and improves the monitoring efficiency.

[0099] In an embodiment, the second preset sampling frequency is adjusted according to the comprehensive density index to obtain a third sampling frequency, including:

[0100] 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.

[0101] 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 the third sampling frequency.

[0102] In the embodiment, for the marked high gradient region, the adjusted sampling frequency of all temperature sensing nodes in the region, i.e., the second preset sampling frequency, is extracted, the ratio of the integrated density index of the region to the preset density threshold is calculated as a region encryption factor, and the third sampling frequency is obtained by multiplying the adjusted sampling frequency of each temperature sensing node in the region by the region encryption factor.

[0103] The identification of the high gradient region triggers a further sampling encryption process, and the calculation of the region encryption factor is based on the ratio of the integrated density index of the region to the preset density threshold. For example, when the integrated density index of a region is 6.0 and the preset density threshold is 4.0, the region encryption factor is 1.5. This means that the sampling frequency of all temperature sensing nodes in the region will be increased by 50% on the basis of the adjusted sampling frequency. Through this hierarchical frequency adjustment mechanism, the system realizes differentiated monitoring of regions with different importance levels. The distributed sampling configuration scheme contains complete parameter information of each temperature sensing node. Each temperature sensing node has a clear final sampling frequency value, a data transmission priority value, and a region mark. The advantage of this configuration scheme is that it not only considers the abnormality degree of a single temperature sensing node, but also takes into account the spatial distribution characteristics. The temperature sensing nodes located at the edge of the high gradient region, even if their temperature changes are not large, will appropriately increase the sampling frequency due to the region characteristics, so as to better capture the spatial evolution process of the temperature field. Through this multi-level adaptive adjustment mechanism, the system can dynamically optimize resource allocation according to the real-time monitored temperature field characteristics, ensure monitoring accuracy, avoid waste of computing and transmission resources, and achieve a balance between monitoring efficiency and resource consumption.

[0104] S104, based on the device parameters of each temperature sensing node, obtaining a corresponding reference task carrying capacity, collecting temperature according to the reference task carrying capacity and the sampling configuration scheme to obtain an initial temperature data point set, performing interpolation reconstruction on the initial temperature data point set to obtain a temperature field distribution matrix, and comparing the temperature field distribution matrix with the actual measured value to obtain an optimized temperature data of each temperature sensing node.

[0105] In the embodiment, device parameters in each temperature sensing node are acquired, wherein the device parameters include current processor occupancy, memory usage and data queue length to be processed, and then the reference task carrying capacity of each temperature sensing node is calculated according to the device parameters and the gradient amplitude of the location where the temperature sensing node is located. After obtaining the reference task carrying capacity, the total task quantity is allocated to each temperature sensing node according to the reference task carrying capacity, and then the expected load value of each temperature sensing node is calculated. For example, assuming that the whole system needs to process 1000 temperature data per second, and the reference task allocation weight of a certain temperature sensing node is 0.15, then the temperature sensing node is expected to process 150 data per second. According to the processing capacity parameters of the temperature sensing node, the resource occupation required for processing these data can be estimated, so as to obtain the expected load value. When the expected load value reaches 85 and the balance threshold is set to 80, it is indicated that the temperature sensing node will be in an overload state.

[0106] If the expected load value of a certain temperature sensing node exceeds the preset balance threshold, all directly connected temperature sensing nodes of the temperature sensing node in the network topology are acquired, the difference between the balance threshold and the current load value of each connected temperature sensing node is taken as the receivable task quantity, the excess part is redistributed according to the proportion of the receivable task quantity of each connected temperature sensing node, and the task allocation weight of the temperature sensing node connected thereto is updated. For example, the overload temperature sensing node is directly connected to four connected temperature sensing nodes, the current load values of these connected temperature sensing nodes are 50, 55, 45 and 60 respectively, the balance threshold is 80, and the receivable task quantities of the temperature sensing nodes are 30, 25, 35 and 20 respectively. The excess part is 5 units of task quantity, which is distributed according to the proportion of 30:25:35:20, and 1.35, 1.14, 1.59 and 0.91 units of task quantity are transferred to each connected temperature sensing node respectively. Through this dynamic load balancing mechanism, the system can respond to the load change of the temperature sensing node in real time, avoid that an individual temperature sensing node is overloaded to affect the timeliness of data processing, ensure that the data in the important monitoring area is processed preferentially, and realize the optimization of the overall performance.

[0107] Then, temperature collection is performed by using each temperature sensing node according to the task allocation weight and the sampling configuration scheme, actual measurement values and temperature sensing node spatial coordinates are obtained, after fusion processing of the actual measurement values, a preliminary temperature data point set is obtained, and the specific fusion processing manner is as follows: the actual measurement value of each temperature sensing node is multiplied by the corresponding weight coefficient, the weighted temperature values of all temperature sensing nodes at the same time are summed, and then the sum is divided by the total weight coefficient to obtain the preliminary temperature data point set.

[0108] 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.

[0109] 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 only 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.

[0110] 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 to the 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.

[0111] In an embodiment, the reference task carrying capacity of each temperature sensing node is obtained based on the device parameters of the temperature sensing node, including:

[0112] The device parameters of each temperature sensing node are weighted and summed to obtain a corresponding node load value, wherein the device parameters include the current processor occupancy rate, memory usage rate, and length of the to-be-processed data queue.

[0113] The temperature gradient weight coefficient is obtained using the gradient amplitude corresponding to each temperature sensing node and the maximum gradient amplitude, and the task carrying capacity index of the corresponding temperature sensing node is obtained according to the node load value and the temperature gradient weight coefficient.

[0114] The task carrying capacity indices of all temperature sensing nodes are added to obtain the total task carrying capacity, and the task carrying capacity indices of all temperature sensing nodes are divided by the total task carrying capacity to obtain the reference task carrying capacity of the corresponding temperature sensing node.

[0115] In the embodiment, the current processor occupancy rate, memory usage rate, and length of the to-be-processed data queue are weighted and summed to obtain the temperature sensing node load value. For example, the processor occupancy rate of a certain temperature sensing node is 75%, the memory usage rate is 60%, and there are 20 data records in the to-be-processed data queue. Through normalization processing, the queue length is mapped to the range of 0 to 100, and it is assumed that the normalized value is 40. According to the weight ratio of 0.4, 0.3, and 0.3, the node load value of the temperature sensing node is 75*0.4+60*0.3+40*0.3=60.

[0116] At the same time, the gradient amplitude of the position where each temperature sensing node is located is extracted, the maximum gradient amplitude is found from all temperature sensing nodes, and the ratio of the gradient amplitude of each temperature sensing node to the maximum value is taken as the temperature gradient weight coefficient. For example, in a monitoring network containing 50 temperature sensing nodes, the maximum temperature gradient amplitude is 2.5 degrees Celsius per meter, and the gradient amplitude of the position where a certain temperature sensing node is located is 2.0 degrees Celsius per meter. Therefore, the temperature gradient weight coefficient of the temperature sensing node is 0.8. A higher weight coefficient means that the temperature sensing node will obtain more processing resources in task allocation. The calculation of the task carrying capacity index reflects the balance between load and importance.

[0117] It should be noted that the introduction of the temperature gradient weight coefficient enables the system to preferentially guarantee the data processing capacity of the key monitoring area. When there is a sharp temperature change in a certain area, the temperature gradient amplitude of the temperature sensing node in the area will significantly increase.

[0118] The task carrying capacity index of each temperature sensing node is calculated by the product of the reciprocal of the load value and the temperature gradient weight coefficient. Specifically, the lower the load value of the temperature sensing node, the greater the reciprocal, indicating that there is more residual processing capacity. At the same time, the higher the temperature gradient weight coefficient, the more important the area where the temperature sensing node is located. The product of the two forms a comprehensive task carrying capacity evaluation. A temperature sensing node with a load value of 40 and a weight coefficient of 0.8 has a task carrying capacity index of 1 / 40 x 0.8 = 0.02.

[0119] Then the total carrying capacity value is obtained by summing up the task carrying capacity indexes of all temperature sensing nodes, and the reference task carrying capacity of each temperature sensing node is obtained by dividing the task carrying capacity index of each temperature sensing node by the total carrying capacity value.

[0120] The temperature sensing and radio frequency identification circuit integrated temperature measurement system provided by the embodiment of the present application is as shown in Figure 2 As shown in 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

[0121] The acquisition module 201 is configured to acquire 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, to obtain a response time sequence and a signal transmission delay time sequence.

[0122] The first adjustment module 202 is configured to construct a compensation matrix according to the signal transmission delay time sequence, and correct the response time using the compensation matrix if any response time in the response time sequence is not within a preset response threshold, to obtain an adjusted response time sequence.

[0123] 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.

[0124] The optimization module 204 is configured to obtain the corresponding reference task carrying capacity based on the device parameters of each temperature sensing node, collect temperature according to the reference task carrying capacity and the sampling configuration scheme to obtain an initial temperature data point set, perform interpolation reconstruction on the initial temperature data point set to obtain a temperature field distribution matrix, and compare the temperature field distribution matrix with an actual measurement value to obtain the optimized temperature data of each temperature sensing node.

[0125] In an embodiment, the first adjustment module 202 includes a clock difference value calculation unit and a compensation matrix construction unit, wherein

[0126] a clock difference value calculation unit configured to calculate the local clock of each temperature sensing node and the reference clock to obtain a corresponding clock difference value;

[0127] a compensation matrix construction unit configured to obtain a total delay time of each temperature sensing node according to the clock difference value and the corresponding signal transmission delay time of each temperature sensing node, and to construct a compensation matrix according to the total delay time.

[0128] The specific implementation of the temperature sensing and radio frequency identification circuit integrated temperature measurement system is basically the same as that of the above-mentioned temperature sensing and radio frequency identification circuit integrated temperature measurement method, and will not be described here.

[0129] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as falling within the scope of the present application.

[0130] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A temperature measurement method integrating temperature sensing and radio frequency identification circuitry, characterized in that, include: The response time of each temperature sensing node when collecting temperature at the corresponding first preset sampling frequency and the signal transmission delay time of the corresponding radio frequency identification chip are obtained to obtain the response time sequence and the signal transmission delay time sequence. Based on 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 range, the compensation matrix is ​​used to correct the response time to obtain an adjusted response time series. Based on the adjusted response time sequence, the temperature of each temperature sensing node is corrected to obtain the corresponding temperature value. Based on each temperature value, a spatial temperature gradient distribution matrix is ​​calculated. The spatial temperature gradient distribution matrix is ​​extracted to obtain the gradient magnitude and gradient direction angle at each location. Based on the gradient magnitude at each location and the corresponding preset time interval, the rate of change of the magnitude at each location is obtained. Based on the gradient magnitude at each location and the preset time interval, the rate of change of the orientation angle at each location is obtained. Based on the rate of change of the magnitude and the rate of change of the orientation angle at each location, the overall temperature change rate is obtained. If the overall temperature change rate is greater than the preset temperature change rate threshold, the first preset sampling frequency is adjusted to obtain the second preset sampling frequency. Based on the second preset sampling frequency, the spatial sampling density value is obtained. Based on the spatial sampling density value, the sampling configuration scheme is obtained. Based on the device parameters of each temperature sensing node, the corresponding baseline task carrying capacity is obtained. Temperature is collected according to the baseline 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 measured value to obtain the optimized temperature data of each temperature sensing node.

2. The temperature measurement method integrating temperature sensing and radio frequency identification circuits as described in claim 1, characterized in that, The step of constructing a compensation matrix based on the signal transmission delay time series includes: The local clock and reference clock of each temperature sensing node are calculated to obtain the corresponding clock difference. Based on 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 based on the total delay time of each temperature sensing node.

3. The temperature measurement method integrating temperature sensing and radio frequency identification circuits as described in claim 1, characterized in that, The calculation based on each of the temperature values ​​to obtain the spatial temperature gradient distribution matrix includes: Obtain the position of each of the temperature sensing nodes; Based on the location and the temperature value, calculate the temperature difference between each temperature sensing node and its corresponding neighboring node; Based on the temperature difference value of each of the temperature sensing nodes, the corresponding temperature gradient value is obtained. All the temperature gradient values ​​are arranged according to their corresponding positions to obtain a spatial temperature gradient distribution matrix.

4. The temperature measurement method integrating temperature sensing and radio frequency identification circuits as described in claim 1, characterized in that, The step of obtaining a spatial sampling density value based on the second preset sampling frequency, and obtaining a sampling configuration scheme based on the spatial sampling density value, includes: Based on the second preset sampling frequency, the number of each temperature sensing node whose second preset sampling frequency is greater than the reference frequency within the preset range is recorded to obtain the spatial sampling density value; Based on the second preset sampling frequency within the preset range, the 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. The second preset sampling frequency is adjusted according to the comprehensive density index to obtain the third sampling frequency; The temperature sensing nodes are sorted according to the magnitude of the gradient to obtain a sorting result. Based on the sorting result and the third sampling frequency, a sampling configuration scheme is obtained.

5. The temperature measurement method integrating temperature sensing and radio frequency identification circuits as described in claim 4, characterized in that, The step of adjusting the second preset sampling frequency according to the comprehensive density index 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 encryption 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 the third sampling frequency.

6. The temperature measurement method integrating temperature sensing and radio frequency identification circuits as described in claim 1, characterized in that, The baseline task carrying capacity is obtained based on the device parameters of each of the temperature sensing nodes, including: After weighted summation of the device parameters of each temperature sensing node, the corresponding node load value is obtained. The device parameters include the current processor utilization rate, memory utilization rate, and the length of the data queue to be processed. By utilizing the gradient magnitude and maximum gradient magnitude of each temperature sensing node, a temperature gradient weighting coefficient is obtained. Based on the node load value and the temperature gradient weighting coefficient, the corresponding task carrying capacity index is obtained. The total task capacity is obtained by summing the task capacity indices of each temperature sensing node, and the corresponding baseline task capacity is obtained by dividing the task capacity index of each temperature sensing node by the total task capacity.

7. A temperature measurement system integrating temperature sensing and radio frequency identification circuitry, characterized in that, A method for integrating temperature sensing and radio frequency identification circuits as described in any one of claims 1-6, comprising: The acquisition module is used to acquire the response time of each temperature sensing node when it collects temperature according to the corresponding first preset sampling frequency, and the signal transmission delay time of the corresponding radio frequency identification chip, so as to obtain the response time sequence and the signal transmission delay time sequence. The first adjustment module is used to construct a compensation matrix based on the signal transmission delay time sequence. If any response time in the response time sequence is not at 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 is used 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 baseline task carrying capacity based on the device parameters of each temperature sensing node, collect temperature data according to the baseline task carrying capacity and the sampling configuration scheme to obtain an initial temperature data point set, interpolate and reconstruct the initial temperature data point set to obtain a temperature field distribution matrix, and compare the temperature field distribution matrix with the actual measured value to obtain the optimized temperature data of each temperature sensing node.

8. The temperature sensing and radio frequency identification integrated temperature measurement system as described in claim 7, 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 reference clock of each temperature sensing 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 based on the clock difference of each temperature sensing node and the corresponding signal transmission delay time, and to construct a compensation matrix based on the total delay time.

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