Remote temperature monitoring alarm system based on Internet of Things technology

Abstract

The invention belongs to the field of remote temperature monitoring and alarming, relates to the technology of the Internet of Things, and aims to solve the problems that an existing temperature monitoring and alarming system cannot perform analysis of different precisions according to temperature data to reduce false alarm or missing alarm and cannot optimize monitoring setting, in particular to a remote temperature monitoring and alarming system based on the technology of the Internet of Things. Comprising a monitoring network building module, a remote temperature monitoring module, an area state evaluation module, an area temperature alarm module and a monitoring area optimization module. The monitoring network building module, the remote temperature monitoring module, the area state evaluation module, the area temperature alarm module and the monitoring area optimization module are in communication connection in sequence; the area state evaluation module is used for evaluating the state of a monitoring area, the area temperature alarm module is used for analyzing and alarming an abnormal area, and the monitoring area optimization module is used for optimizing monitoring setting; according to the invention, missing report and false report of an abnormal state can be reduced, and the effectiveness of alarm is improved.

Description

Technical Field

[0001] The invention belongs to the field of remote temperature monitoring and alarm, relates to Internet of Things technology, and specifically is a remote temperature monitoring and alarm system based on Internet of Things technology. Background Art

[0002] In many fields of industrial production, warehouse management, agricultural breeding, etc., accurate and timely grasp of environmental temperature conditions is of vital importance to ensuring production safety, improving product quality, and optimizing resource allocation.

[0003] Traditional temperature monitoring methods often rely on manual measurement and recording using thermometers on a regular basis. This method is not only inefficient and labor-intensive, but also difficult to achieve real-time, continuous and comprehensive temperature monitoring. With the advancement of science and technology and the development of Internet of Things technology, remote temperature monitoring systems have gradually replaced traditional manual monitoring methods. These systems can achieve real-time data collection, remote transmission, intelligent analysis and timely alarm.

[0004] However, the existing temperature monitoring and alarm system still has some shortcomings. The division of monitoring areas and network construction are not reasonable enough, which may lead to blind spots in monitoring or unstable data transmission in some areas. In the processing and analysis of temperature data, there is a lack of effective evaluation of temperature variation, making it difficult to accurately reflect the temperature status of the area. In terms of the alarm mechanism, it is impossible to perform graded alarms according to the severity of temperature anomalies, which can easily lead to false alarms or missed alarms. In terms of optimization of monitoring areas, there is a lack of reasonable allocation of monitoring resources, which may lead to waste of resources or insufficient monitoring accuracy.

[0005] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0006] The purpose of the present invention is to provide a remote temperature monitoring and alarm system based on the Internet of Things technology to solve the problem that the existing remote temperature monitoring and alarm system cannot perform analysis of different precisions according to the temperature data of the monitoring area to reduce false alarms or missed alarms, and cannot reasonably allocate monitoring resources to optimize the monitoring settings;

[0007] The technical problem to be solved by the present invention is: how to provide a method for performing analysis of temperature data in the monitoring area with different accuracies to reduce false positives or negative positives, and also to reasonably allocate monitoring resources to optimize the monitoring settings.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A remote temperature monitoring and alarm system based on Internet of Things technology, comprising a monitoring network construction module, a remote temperature monitoring module, a regional state evaluation module, a regional temperature alarm module and a monitoring area optimization module; the monitoring network construction module, the remote temperature monitoring module, the regional state evaluation module, the regional temperature alarm module and the monitoring area optimization module are sequentially connected in communication;

[0010] The monitoring network building module is used to divide the monitoring area and build a communication network system: the area for remote temperature monitoring and alarm is divided into a number of monitoring areas of the same size, and the temperature data of the monitoring area is collected through temperature sensors, and each temperature sensor is connected to each other in the same communication environment;

[0011] The remote temperature monitoring module is used to monitor temperature data in real time: generate a monitoring cycle of fixed duration, obtain a series of temperature data collected by each temperature sensor in the monitoring area at a fixed frequency, and mark them as temperature monitoring values ​​WDn, calculate the temperature variation ratio BBn according to the temperature monitoring value WDn, and perform weighted summation of the temperature monitoring value WDn and the temperature variation ratio BBn to obtain the temperature performance value WB of the monitoring area;

[0012] The regional status evaluation module is used to evaluate the temperature status of the monitoring area according to the monitoring data: obtain the standard temperature range [MIN, MAX] of the monitoring area, compare the temperature performance value WB with the standard temperature range [MIN, MAX], and determine whether the status of the monitoring area is normal or abnormal;

[0013] The regional temperature alarm module is used to analyze and alarm the temperature data of the abnormal area: obtain the high-risk density GWn monitored by the temperature sensor according to the temperature monitoring value WDn, and issue a high-risk alarm or a low-risk alarm to the abnormal area by judging the high-risk density GWn;

[0014] The monitoring area optimization module is used to optimize the monitoring settings of the monitoring area: a temperature monitoring matrix is ​​formed by n groups of temperature monitoring values ​​WDn, and the discrete performance values ​​LSm of the n elements in each column of the temperature monitoring matrix are obtained. The monitoring area is marked as a key area or a general area by comparing the discrete performance values ​​LSm, and the settings of the temperature sensors in the monitoring area are adjusted.

[0015] Furthermore, the monitoring area is divided as follows: an effective monitoring radius R of the temperature sensor is obtained, and the shape of the monitoring area is set as a square area inscribed with a circle with a radius of R; n temperature sensors are evenly set with the center of the inscribed circle in the monitoring area as the center, n = 1, 2...k, k is a positive integer; temperature data of the monitoring area is collected by the temperature sensor, and each temperature sensor is connected to each other in the same communication environment through Wi-Fi to form a wireless sensor network.

[0016] Furthermore, the calculation process of the temperature variation ratio BBn is as follows: summing and averaging the temperature monitoring values ​​WDn to obtain the temperature mean WJn, calculating the standard deviation of the temperature monitoring values ​​to obtain the temperature discrete value WLn, calculating the ratio of the temperature discrete value WLn to the temperature mean WJn to obtain the temperature variation coefficient CVn of different temperature sensors in the monitoring area, and numerically calculating all the temperature variation coefficients CVn in the monitoring area to obtain the temperature variation ratio BBn of each temperature sensor.

[0017] Furthermore, the calculation process of the temperature performance value WB is as follows: the temperature variation ratio BBn is used as the weight for weighted summation of the temperature data of n temperature sensors in the monitoring area, and the temperature mean WJn of the temperature sensor and the corresponding temperature variation ratio BBn are weighted summed to obtain the temperature performance value WB of the monitoring area.

[0018] Furthermore, the evaluation process of the monitoring area status includes: comparing the temperature performance value WB within the monitoring period with the standard temperature range [MIN, MAX]: if the temperature performance value WB≥MIN and the temperature performance value WB≤MAX, it is judged that the temperature of the monitoring area meets the requirements, and the monitoring area is marked as a normal area; if the temperature performance value WB≥MAX or the temperature performance value WB≤MIN, it is judged that the temperature of the monitoring area does not meet the requirements, the monitoring area is marked as an abnormal area, and the temperature monitoring value WDn collected in the abnormal area during the monitoring period is sent to the regional temperature alarm module.

[0019] Furthermore, the process of obtaining the high-risk density GWn includes: establishing a rectangular coordinate system with the temperature monitoring value WDn as the Y-axis of the coordinate system and the monitoring time point as the X-axis of the coordinate system, drawing a scatter plot of the temperature monitoring value WDn-time in the rectangular coordinate system, and each data point on the scatter plot is recorded as a temperature point; setting two straight lines y=a and y=b parallel to the X-axis in the scatter plot, where a is the high temperature threshold and b is the low temperature threshold; comparing the position of the temperature point in the scatter plot with the straight line y=a and the straight line y=b: if the temperature point is above the straight line y=a or below the straight line y=b, the temperature point is marked as a high-risk point, and the ratio of the number of high-risk points to the number of temperature points is recorded as the high-risk density GWn monitored by the temperature sensor corresponding to the scatter plot.

[0020] Furthermore, the process of alarm analysis for abnormal areas includes: if the high-risk density GWn of any temperature sensor in the abnormal area is greater than or equal to the high-risk density threshold GWmax, a high-risk alarm will be issued for the abnormal area, and a high-risk alarm signal will be generated and sent to the mobile phone terminal of the manager; if the high-risk density GWn of all temperature sensors in the abnormal area is less than the high-risk density threshold GWmax, a low-risk alarm will be issued for the abnormal area, and a low-risk alarm signal will be generated and sent to the mobile phone terminal of the manager.

[0021] Furthermore, the process of obtaining the discrete performance value LSm includes: forming an n-row and m-column temperature monitoring matrix from n groups of temperature monitoring values ​​WDn, where n is the number of temperature sensors, and m is the number of temperatures collected by each temperature sensor within the monitoring period; performing variance calculation on the n elements in each column of the temperature monitoring matrix to obtain the discrete performance value LSm corresponding to the column element.

[0022] Furthermore, the discrete performance value LSm is compared with the preset discrete performance threshold LSmax: if any discrete performance value LSm is greater than or equal to the discrete performance threshold LSmax, it is judged that the temperature monitoring values ​​WDn of the n temperature sensors in the monitoring area are different, and the monitoring area is marked as a critical area; if all discrete performance values ​​LSm are less than the discrete performance threshold LSmax, it is judged that the temperature monitoring values ​​WDn of the n temperature sensors in the monitoring area are not different, and the monitoring area is marked as a general area; the general area and the key area are sent to the management personnel's mobile phone terminal, and the settings of the temperature sensors in the monitoring area are adjusted.

[0023] The present invention has the following beneficial effects:

[0024] 1. The monitoring area is reasonably divided through the monitoring network construction module, and multiple temperature sensors are set up to collect temperature data of the monitoring area in parallel, which not only ensures the richness of temperature data but also sets a certain amount of redundancy for the temperature sensors to ensure the continuity of temperature data collection;

[0025] 2. The remote temperature monitoring module can monitor temperature data in real time. By setting multiple temperature sensors and performing data processing and weighted calculation, it can more accurately reflect the actual temperature conditions in the monitoring area and provide data support for status judgment in the monitoring area.

[0026] 3. The regional status assessment module and the regional temperature alarm module can perform intelligent assessment based on the temperature status, distinguish between normal areas and abnormal areas, and make graded alarms based on the degree of abnormality, thus reducing missed reports and false alarms of abnormal conditions and improving the pertinence and effectiveness of alarms;

[0027] 4. The monitoring area optimization module is used to classify and optimize the monitoring area. The difference between the data collected by the temperature sensor is judged according to the discrete degree of the temperature data. The number of temperature sensors is reasonably adjusted to improve the resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A system block diagram of the present invention as a whole;

[0030] Figure 2 Schematic diagram of a scatter plot in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] like Figure 1-2 As shown, a remote temperature monitoring and alarm system based on the Internet of Things technology includes a monitoring network construction module, a remote temperature monitoring module, a regional state evaluation module, a regional temperature alarm module and a monitoring area optimization module; the monitoring network construction module, the remote temperature monitoring module, the regional state evaluation module, the regional temperature alarm module and the monitoring area optimization module are sequentially connected in communication;

[0033] The monitoring network building module is used to divide the monitoring area and build a communication network system: divide the area for remote temperature monitoring and alarm into several monitoring areas of the same size, obtain the effective monitoring radius R of the temperature sensor, and set the shape of the monitoring area as a square area inscribed with a circle with a radius of R; take the center of the inscribed circle in the monitoring area as the center, evenly set n temperature sensors, n = 1, 2...k, k is a positive integer; collect temperature data of the monitoring area through temperature sensors, and each temperature sensor is connected to each other in the same communication environment through Wi-Fi to form a wireless sensor network to realize remote data transmission; reasonably divide the monitoring area through the monitoring network building module, set multiple temperature sensors to collect temperature data of the monitoring area in parallel, which not only ensures the richness of temperature data but also sets a certain amount of redundancy for the temperature sensor to ensure the continuity of temperature data collection.

[0034] The remote temperature monitoring module is used to monitor temperature data in real time: generate a monitoring cycle of fixed duration, obtain a series of temperature data collected by each temperature sensor in the monitoring area at a fixed frequency, and mark them as temperature monitoring values ​​WDn; sum and average the temperature monitoring values ​​WDn to obtain the temperature mean value WJn, calculate the standard deviation of the temperature monitoring values ​​to obtain the temperature discrete value WLn, and calculate the ratio of the temperature discrete value WLn to the temperature mean value WJn to obtain the temperature variation coefficient CVn of different temperature sensors in the monitoring area; the temperature variation coefficient CV is used to measure the degree of dispersion of temperature data. The larger the value of the temperature variation coefficient CVn, the greater the fluctuation of the data collected by the temperature sensor, and the more sensitive the monitoring of temperature changes is; through the formula The temperature variation coefficient CVn of all the temperature in the monitoring area is numerically calculated to obtain the temperature variation ratio BBn of each temperature sensor; the temperature variation ratio BBn is used as the weight of the weighted sum of the temperature data of n temperature sensors in the monitoring area, and the formula is used. The temperature mean WJn of the temperature sensor and the corresponding temperature variation ratio BBn are weighted and calculated to obtain the temperature performance value WB of the monitoring area; the remote temperature monitoring module can monitor the temperature data in real time, set up multiple temperature sensors and perform data processing and weighted calculation, which can more accurately reflect the actual temperature conditions of the monitoring area and provide data support for the status judgment of the monitoring area.

[0035] The regional status evaluation module is used to evaluate the temperature status of the monitoring area according to the monitoring data: obtain the standard temperature range [MIN, MAX] of the monitoring area, where MIN is the minimum value of the standard temperature and MAX is the maximum value of the standard temperature; compare the temperature performance value WB within the monitoring period with the standard temperature range [MIN, MAX]: if the temperature performance value WB≥MIN and the temperature performance value WB≤MAX, then it is judged that the temperature of the monitoring area meets the requirements, and the monitoring area is marked as a normal area; if the temperature performance value WB≥MAX or the temperature performance value WB≤MIN, then it is judged that the temperature of the monitoring area does not meet the requirements, the monitoring area is marked as an abnormal area, and the temperature monitoring value WDn collected in the abnormal area during the monitoring period is sent to the regional temperature alarm module.

[0036] The regional temperature alarm module is used to analyze and alarm the temperature data of the abnormal area: obtain a series of temperature monitoring values ​​WDn collected by the temperature sensor in the abnormal area during the monitoring period, and establish a rectangular coordinate system with the temperature monitoring value WDn as the Y-axis of the coordinate system and the monitoring time point as the X-axis of the coordinate system. Draw a scatter plot of the temperature monitoring value WDn-time in the rectangular coordinate system, and each data point on the scatter plot is recorded as a temperature point; set two straight lines y=a and y=b parallel to the X-axis in the scatter plot, where a is the high temperature threshold and b is the low temperature threshold; compare the position of the temperature point in the scatter plot with the straight line y=a and the straight line y=b: if the temperature point is above the straight line y=a or below the straight line y=b, mark the temperature point as a high-risk point, and record the ratio of the number of high-risk points to the number of temperature points as the temperature corresponding to the scatter plot The high-risk density GWn monitored by the sensor is compared with the preset high-risk density threshold GWmax: if the high-risk density GWn of any temperature sensor in the abnormal area is greater than or equal to the high-risk density threshold GWmax, a high-risk alarm will be issued for the abnormal area, and a high-risk alarm signal will be generated and sent to the mobile phone terminal of the manager; if the high-risk density GWn of all temperature sensors in the abnormal area is less than the high-risk density threshold GWmax, a low-risk alarm will be issued for the abnormal area, and a low-risk alarm signal will be generated and sent to the mobile phone terminal of the manager; through the regional status assessment module and the regional temperature alarm module, intelligent assessment can be performed according to the temperature status, normal areas and abnormal areas can be distinguished, and graded alarms can be performed according to the degree of abnormality, reducing the underreporting and false alarms of abnormal conditions and improving the pertinence and effectiveness of the alarm.

[0037] The monitoring area optimization module is used to optimize the monitoring settings of the monitoring area: a series of temperature monitoring values ​​WDn of different temperature sensors in the monitoring area are obtained through the remote temperature monitoring module, and a temperature monitoring matrix with n rows and m columns is formed by n groups of temperature monitoring values ​​WDn, where n is the number of temperature sensors, and m is the number of temperatures collected by each temperature sensor during the monitoring period; the n elements in each column of the temperature monitoring matrix are subjected to variance calculation to obtain the discrete performance value LSm corresponding to the column element, and the discrete performance value LSm is compared with the preset discrete performance threshold LSmax: if any discrete performance value LSm is greater than or equal to the discrete performance threshold LSmax, it is judged that the temperature monitoring values ​​WDn of the n temperature sensors in the monitoring area are different, and the monitoring area is marked as a critical area; if all discrete performance values ​​LSm are less than The discrete performance threshold LSmax determines that the temperature monitoring values ​​WDn of the n temperature sensors in the monitoring area are not different, and the monitoring area is marked as a general area; the general area and the key area are sent to the management personnel's mobile phone terminal, and the settings of the temperature sensors in the monitoring area are adjusted: the key area still maintains n temperature sensors to ensure the accuracy of temperature monitoring, and the general area is adjusted to set a temperature sensor, which is placed at the center of the tangent circle in the monitoring area, and the average value of its temperature monitoring value WD during the monitoring period is used as the value of the temperature performance value WB, which is used for temperature monitoring and alarm in the monitoring area; the monitoring area is classified and optimized through the monitoring area optimization module, and the difference between the data collected by the temperature sensor is judged according to the discrete degree of the temperature data, and the number of temperature sensors is reasonably adjusted to improve the resource utilization efficiency.

[0038] A remote temperature monitoring and alarm system based on Internet of Things technology, when working, divides the monitoring area and reasonably arranges it according to the effective monitoring radius of the temperature sensor to build a communication network; the remote temperature monitoring module can obtain data according to a fixed period and frequency, and calculate the temperature performance value that can reflect the temperature condition of the area; the regional status evaluation module compares the temperature performance value with the preset standard temperature range to determine whether the monitoring area is a normal area or an abnormal area; the regional temperature alarm module accurately analyzes the degree of temperature abnormality in the abnormal area by drawing a scatter plot and setting a threshold, thereby realizing high-risk alarm and low-risk alarm, and sending corresponding signals to management personnel in time; the monitoring area optimization module distinguishes between key areas and general areas by analyzing the temperature monitoring data, and reasonably adjusts the settings of temperature sensors in the monitoring area, thereby optimizing resource allocation while ensuring the monitoring effect.

[0039] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0040] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0041] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A remote temperature monitoring and alarm system based on Internet of Things technology, characterized in that: It includes a monitoring network construction module, a remote temperature monitoring module, a regional state evaluation module, a regional temperature alarm module and a monitoring area optimization module; the monitoring network construction module, the remote temperature monitoring module, the regional state evaluation module, the regional temperature alarm module and the monitoring area optimization module are sequentially connected in communication; The monitoring network building module is used to divide the monitoring area and build a communication network system: the area for remote temperature monitoring and alarm is divided into a number of monitoring areas of the same size, and the temperature data of the monitoring area is collected through temperature sensors, and each temperature sensor is connected to each other in the same communication environment; The remote temperature monitoring module is used to monitor temperature data in real time: generate a monitoring cycle of fixed duration, obtain a series of temperature data collected by each temperature sensor in the monitoring area at a fixed frequency, and mark them as temperature monitoring values ​​WDn, calculate the temperature variation ratio BBn according to the temperature monitoring value WDn, and perform weighted summation of the temperature monitoring value WDn and the temperature variation ratio BBn to obtain the temperature performance value WB of the monitoring area; The regional status evaluation module is used to evaluate the temperature status of the monitoring area according to the monitoring data: obtain the standard temperature range [MIN, MAX] of the monitoring area, compare the temperature performance value WB with the standard temperature range [MIN, MAX], and determine whether the status of the monitoring area is normal or abnormal; The regional temperature alarm module is used to analyze and alarm the temperature data of the abnormal area: obtain the high-risk density GWn monitored by the temperature sensor according to the temperature monitoring value WDn, and issue a high-risk alarm or a low-risk alarm to the abnormal area by judging the high-risk density GWn; The monitoring area optimization module is used to optimize the monitoring settings of the monitoring area: a temperature monitoring matrix is ​​formed by n groups of temperature monitoring values ​​WDn, and the discrete performance values ​​LSm of the n elements in each column of the temperature monitoring matrix are obtained. The monitoring area is marked as a key area or a general area by comparing the discrete performance values ​​LSm, and the settings of the temperature sensors in the monitoring area are adjusted.

2. According to claim 1, a remote temperature monitoring and alarm system based on Internet of Things technology is characterized in that: The method for dividing the monitoring area is as follows: obtain the effective monitoring radius R of the temperature sensor, then set the shape of the monitoring area to a square area inscribed with a circle with a radius of R; take the center of the inscribed circle in the monitoring area as the center, evenly set n temperature sensors, n = 1, 2...k, k is a positive integer; collect temperature data of the monitoring area through temperature sensors, and each temperature sensor is connected to each other in the same communication environment through Wi-Fi to form a wireless sensor network.

3. According to claim 2, a remote temperature monitoring and alarm system based on Internet of Things technology is characterized in that: The calculation process of the temperature variation ratio BBn is as follows: summing and averaging the temperature monitoring values ​​WDn to obtain the temperature mean WJn, calculating the standard deviation of the temperature monitoring values ​​to obtain the temperature discrete value WLn, calculating the ratio of the temperature discrete value WLn to the temperature mean WJn to obtain the temperature variation coefficient CVn of different temperature sensors in the monitoring area, and numerically calculating all the temperature variation coefficients CVn in the monitoring area to obtain the temperature variation ratio BBn of each temperature sensor.

4. According to claim 3, a remote temperature monitoring and alarm system based on Internet of Things technology is characterized in that: The calculation process of the temperature performance value WB is as follows: the temperature variation ratio BBn is used as the weight for weighted summation of the temperature data of n temperature sensors in the monitoring area, and the temperature mean WJn of the temperature sensor and the corresponding temperature variation ratio BBn are weighted summed to obtain the temperature performance value WB of the monitoring area.

5. The remote temperature monitoring and alarm system based on Internet of Things technology according to claim 4 is characterized in that: The evaluation process of the monitoring area status includes: comparing the temperature performance value WB within the monitoring period with the standard temperature range [MIN, MAX]: if the temperature performance value WB≥MIN and the temperature performance value WB≤MAX, it is judged that the temperature of the monitoring area meets the requirements, and the monitoring area is marked as a normal area; if the temperature performance value WB≥MAX or the temperature performance value WB≤MIN, it is judged that the temperature of the monitoring area does not meet the requirements, the monitoring area is marked as an abnormal area, and the temperature monitoring value WDn collected in the abnormal area during the monitoring period is sent to the regional temperature alarm module.

6. The remote temperature monitoring and alarm system based on Internet of Things technology according to claim 5 is characterized in that: The acquisition process of the high-risk density GWn includes: establishing a rectangular coordinate system with the temperature monitoring value WDn as the Y-axis of the coordinate system and the monitoring time point as the X-axis of the coordinate system, drawing a scatter plot of the temperature monitoring value WDn-time in the rectangular coordinate system, and each data point on the scatter plot is recorded as a temperature point; setting two straight lines y=a and y=b parallel to the X-axis in the scatter plot, where a is the high temperature threshold and b is the low temperature threshold; comparing the position of the temperature point in the scatter plot with the straight line y=a and the straight line y=b: if the temperature point is above the straight line y=a or below the straight line y=b, the temperature point is marked as a high-risk point, and the ratio of the number of high-risk points to the number of temperature points is recorded as the high-risk density GWn monitored by the temperature sensor corresponding to the scatter plot.

7. The remote temperature monitoring and alarm system based on Internet of Things technology according to claim 6 is characterized in that: The process of alarm analysis for abnormal areas includes: if the high-risk density GWn of any temperature sensor in the abnormal area is greater than or equal to the high-risk density threshold GWmax, a high-risk alarm will be issued for the abnormal area, and a high-risk alarm signal will be generated and sent to the mobile phone terminal of the manager; if the high-risk density GWn of all temperature sensors in the abnormal area is less than the high-risk density threshold GWmax, a low-risk alarm will be issued for the abnormal area, and a low-risk alarm signal will be generated and sent to the mobile phone terminal of the manager.

8. The remote temperature monitoring and alarm system based on Internet of Things technology according to claim 7 is characterized in that: The process of obtaining the discrete performance value LSm includes: forming an n-row and m-column temperature monitoring matrix from n groups of temperature monitoring values ​​WDn, where n is the number of temperature sensors and m is the number of temperatures collected by each temperature sensor within the monitoring period; performing variance calculation on the n elements in each column of the temperature monitoring matrix to obtain the discrete performance value LSm corresponding to the column element.

9. The remote temperature monitoring and alarm system based on Internet of Things technology according to claim 8 is characterized in that: The discrete performance value LSm is compared with the preset discrete performance threshold LSmax: if any discrete performance value LSm is greater than or equal to the discrete performance threshold LSmax, it is judged that the temperature monitoring values ​​WDn of the n temperature sensors in the monitoring area are different, and the monitoring area is marked as a critical area; if all discrete performance values ​​LSm are less than the discrete performance threshold LSmax, it is judged that the temperature monitoring values ​​WDn of the n temperature sensors in the monitoring area are not different, and the monitoring area is marked as a general area; the general area and the key area are sent to the administrator's mobile phone terminal, and the settings of the temperature sensors in the monitoring area are adjusted.

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