Equipment safety intelligent sensing and early warning system and method based on Internet of Things

Abstract

The invention discloses an equipment safety intelligent sensing and early warning system and method based on the Internet of Things, and belongs to the technical field of safety monitoring. A miniature temperature sensor is installed on operation equipment, an environment temperature sensor is installed in a cabinet, and all the sensors transmit collected temperature data through the Internet of Things technology. Time slices are divided, day is taken as a period, a period label is added, an average temperature value fed back by the miniature temperature sensor and an average temperature value fed back by the environment temperature sensor in the period time slices are collected, and a linkage coefficient fluctuation value and a temperature change rate are calculated; analyzing and outputting an early warning value; by calculating the relation between the operating equipment temperature and the environment temperature and the temperature change trend, the abnormal temperature recognition accuracy is effectively improved, it can be ensured that early warning is triggered in time when the temperature of the equipment in the cabinet is abnormal, and the operating safety of the equipment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of security monitoring technology, and in particular to an intelligent perception early warning system and method for device security based on the Internet of Things. Background Art

[0002] With the rapid development of information technology and big data analysis technology, cabinets, as the main carriers of information equipment, are receiving more and more attention for their security monitoring and management. Traditional cabinet management systems mainly rely on manual inspections or simple monitoring equipment, which makes it difficult to capture and process complex environmental changes inside the cabinet in real time, especially the impact of temperature changes on equipment safety. In recent years, with the advancement of data acquisition and processing technology, combined with the Internet of Things and sensor technology, cabinet safety management systems based on temperature monitoring have gradually emerged. However, most of the existing technical solutions are limited to the collection of a single data source and simple threshold judgment, and lack the ability to comprehensively analyze multi-dimensional data, resulting in limited early warning capabilities in complex environments and inability to fully guarantee the safe operation of equipment in the cabinet.

[0003] The deficiencies of existing technologies are mainly reflected in the following aspects. First, most traditional monitoring systems only consider the data of a single temperature sensor, while ignoring the multi-point temperature differences inside the cabinet and its correlation with the external ambient temperature. This simple temperature monitoring method can often only detect extreme temperature anomalies and it is difficult to identify potential temperature trends and changes. Secondly, most existing temperature monitoring systems use fixed thresholds for early warning. When the temperature inside the cabinet fluctuates greatly, false alarms or missed alarms are prone to occur, reducing the accuracy of the early warning. In addition, current technologies lack comprehensive analysis of multi-time dimension data, and cannot effectively analyze the periodic laws of temperature changes and their impact on equipment, thereby failing to provide effective preventive maintenance measures. These technical shortcomings limit the ability of existing cabinet monitoring systems to cope with complex environmental changes, and cannot meet the needs of modern data centers and information equipment for high-precision and high-reliability monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide a device safety intelligent perception warning system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A device security intelligent perception early warning method based on the Internet of Things, the method comprises the following steps:

[0007] All running devices in the cabinet are uniformly numbered, and micro temperature sensors are installed on the running devices, and ambient temperature sensors are installed at different locations in the cabinet; the time range within a day is divided into x time segments according to equal time scale intervals, and periodic time segments are constructed with days as a cycle, and the average temperature values ​​fed back by the micro temperature sensors and the average temperature values ​​fed back by the ambient temperature sensors in the periodic time segments are collected; based on the average temperature values ​​fed back by the micro temperature sensors and the average temperature values ​​fed back by the ambient temperature sensors, the linkage coefficient fluctuation value is calculated; based on the periodic time segments, the temperature change rate is calculated; based on the linkage coefficient fluctuation value and the temperature change rate, an early warning value is designed.

[0008] As a preferred solution of the device security intelligent perception early warning method based on the Internet of Things described in the present invention, all running devices in the cabinet are uniformly numbered, and a running device set KP={KP i |i∈[1,I]}, where, KP i represents the i-th running device in the cabinet, and I represents the total number of running devices in the cabinet.

[0009] A micro temperature sensor is installed on the operating device, wherein one micro temperature sensor is installed corresponding to one operating device, and a micro temperature sensor set MTS={MTS i |i∈[1,I]}, where MTS i represents the micro temperature sensor installed on the i-th running device; installing ambient temperature sensors at different positions in the cabinet, constructing an ambient temperature sensor set ATS = {ATS b |b∈[1,B]}, where ATS b represents the bth ambient temperature sensor in the cabinet, and B represents the total number of ambient temperature sensors in the cabinet.

[0010] As a preferred solution of the device security intelligent perception warning method based on the Internet of Things described in the present invention, the time range within a day is divided into x time segments according to the equal time scale interval ΔT, and the time segment set TI = {TI t |t∈[1,x]}, where TI t represents the tth time segment, x represents the total number of time segments; with a day as the cycle, the time segment TI t Additional periodic label, recorded as periodic time segment TI t (k), where k represents the cycle number.

[0011] In the cycle time segment TI tIn (k), the data of the micro temperature sensor and the data of the ambient temperature sensor are collected to construct a micro temperature sensor data set D = {MTS i [TI t (k)]|i∈[1,I],t∈[1,x]} and the ambient temperature sensor dataset E={ATS b [TI t (k)]|b∈[1,B],t∈[1,x]}, where MTS i [TI t (k)] represents the periodic time segment TI t (k) Micro temperature sensor MTS i Average temperature value of feedback, ATS b [TI t (k)] represents the periodic time segment TI t (k) Ambient temperature sensor ATS b The average temperature value fed back.

[0012] As a preferred solution of the device security intelligent perception warning method based on the Internet of Things described in the present invention, for the periodic time segment TI t (k) Calculate all the micro temperature sensors MTS i and ambient temperature sensor ATS b The composite average temperature value is calculated as follows:

[0013]

[0014] Among them, MCAT[TI t (k)] represents all micro temperature sensors MTS i In the cycle time segment TI t (k) composite average temperature value, ACAT[TI t (k)] indicates all ambient temperature sensors ATS b In the cycle time segment TI t (k) The composite average temperature value within.

[0015] Design temperature linkage coefficient, the calculation formula is:

[0016]

[0017] Among them, TIC[TI t (k)] represents the temperature linkage coefficient, max(MCAT[TI t (k)], ACAT[TI t (k)]) represents the temperature sensor MTS iThe composite mean temperature value MCAT[TI t (k)] and ambient temperature sensor ATS b The composite average temperature value ACAT[TI t (k)] and take the maximum value.

[0018] In the present invention, by calculating the difference between two composite average temperature values, the difference represents the direct difference between the local temperature of the running equipment and the ambient temperature. The larger the difference, the greater the degree to which the local temperature may be affected by external or internal factors, reflecting potential abnormalities. The temperature linkage coefficient reflects the relative temperature difference between the running equipment and the ambient temperature. The larger the value, the greater the temperature difference between the running equipment and the environment, which means that the possibility of abnormalities is greater.

[0019] Based on the temperature linkage coefficient, calculate the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) Fluctuation value of linkage coefficient during the process Among them, TIC[TI t+1 (k)] represents the periodic time segment TI t+1 (k) Temperature linkage coefficient.

[0020] When the linkage coefficient fluctuation value is positive, it means that the relative difference between the average temperature of the micro temperature sensor and the ambient temperature is increasing; when the linkage coefficient fluctuation value is negative, it means that the relative difference between the average temperature of the micro temperature sensor of the running equipment and the ambient temperature is decreasing.

[0021] Calculation of Micro Temperature Sensors MTS i In the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) The temperature change rate during the process is calculated as:

[0022]

[0023] Among them, MCAT[TI t+1 (k)] represents all micro temperature sensors MTS i In the cycle time segment TI t+1 (k) The composite average temperature value at time.

[0024] In the present invention, the temperature change rate is intended to quantify the temperature change between two adjacent cycle time segments TI. t (k) and TI t+1 (k), the composite average temperature value MCAT[TI t(k)] When the temperature change rate is positive, it means that the average temperature of the micro temperature sensor is increasing; when the temperature change rate is negative, it means that the average temperature of the micro temperature sensor is decreasing.

[0025] As a preferred solution of the device security intelligent perception early warning method based on the Internet of Things described in the present invention, based on the linkage coefficient fluctuation value ATIC[TI t (k)→TI t+1 (k)] and the temperature change rate TCR[TI t (k)→TI t+1 (k)], analyze and output the warning value, the calculation formula is:

[0026]

[0027] Among them, FTV represents the warning value, ε is the preset stability factor, max(|ΔTIC[TI t (k)]|,|TCR[TI t (k)→TI t+1 (k)]|) means taking the maximum value from the fluctuation value of linkage coefficient and temperature change rate;

[0028] In the present invention, the effect of the ambient temperature on the self-heating temperature is analyzed by monitoring the ambient temperature fed back by the ambient temperature sensor and the self-heating temperature on the running equipment fed back by the micro temperature sensor. In practice, the running equipment will generate self-heating temperature during operation, and this self-heating temperature is uncontrollable, but the self-heating temperature can be controlled by the ambient temperature to cool down or heat up. The present invention multiplies the linkage coefficient fluctuation value and the temperature change rate. If the product of the linkage coefficient fluctuation value and the temperature change rate is positive, it means that the linkage coefficient fluctuation value and the temperature change rate have the same change trend; if the product is negative, it means that the linkage coefficient fluctuation value is negative. If the trend of the dynamic value and the temperature change rate are inconsistent, the temperature change trends offset each other; the denominator ensures that the larger temperature change dominates; when the FTV value is positive and large, it indicates that the trends between the linkage coefficient fluctuation value and the temperature change rate are consistent, and the ambient temperature has a more obvious cooling or heating control effect on the self-heating of the running equipment. This situation indicates that the system control is safe and there is no need to take timely countermeasures; when the FTV value is negative and large, it indicates that there is a reverse trend in the temperature change, and the ambient temperature has a poor cooling or heating control effect on the self-heating of the running equipment, which may cause system instability or equipment damage.

[0029] Preset warning threshold θ;

[0030] If FTV ≥ θ, no warning is triggered;

[0031] If FTV<θ, an early warning is triggered.

[0032] An intelligent perception and early warning system for equipment safety based on the Internet of Things, the system includes: an operating equipment and sensor layout module, a time segment data acquisition module, a temperature linkage and change analysis module, and an early warning mechanism and temperature evaluation module.

[0033] The operating device and sensor arrangement module is used to uniformly number all the operating devices in the cabinet, install micro temperature sensors on the operating devices, and install environmental temperature sensors at different positions in the cabinet.

[0034] The time segment data acquisition module divides the time range within a day into x time segments according to equal time scale intervals, and constructs periodic time segments with days as a cycle, and collects the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor within the periodic time segments.

[0035] The temperature linkage and change analysis module calculates the linkage coefficient fluctuation value based on the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor; and calculates the temperature change rate based on the periodic time segment.

[0036] The early warning mechanism and temperature assessment module designs an early warning value based on the linkage coefficient fluctuation value and the temperature change rate.

[0037] Furthermore, the operating device and sensor arrangement module also includes an operating device numbering unit and a sensor arrangement unit.

[0038] The running device numbering unit uniformly numbers all running devices in the cabinet and constructs a running device set KP={KP i |i∈[1,I]}, where, KP i represents the i-th running device in the cabinet, and I represents the total number of running devices in the cabinet.

[0039] The sensor arrangement unit installs a micro temperature sensor on the operating device, wherein one micro temperature sensor is installed corresponding to one operating device, and a micro temperature sensor set MTS={MTS i |i∈[1,I]} where MTS i represents the micro temperature sensor installed on the i-th running device; installing ambient temperature sensors at different positions in the cabinet, constructing an ambient temperature sensor set ATS = {ATS b |b∈[1,B]}, where ATS b represents the bth ambient temperature sensor in the cabinet, and B represents the total number of ambient temperature sensors in the cabinet.

[0040] Furthermore, the time segment data collection module also includes a time segment division and period labeling unit and a temperature data collection and construction unit.

[0041] The time segment division and period labeling unit divides the time range within a day into x time segments according to the equal time scale interval ΔT, and constructs a time segment set TI={TI t |t∈[1,x]}, where TI t represents the tth time segment, x represents the total number of time segments; with a day as the cycle, the time segment TI t Additional periodic label, recorded as periodic time segment TI t (k), where k represents the cycle number.

[0042] The temperature data acquisition and construction unit, in the cycle time segment TI t In (k), the data of the micro temperature sensor and the data of the ambient temperature sensor are collected to construct a micro temperature sensor data set D = {MTS i [TI t (k)]||i∈[1,I],t∈[1,x]} and the ambient temperature sensor dataset E={ATS b [TI t (k)]|b∈[1,B],t∈[1,x]}, where MTS i [TI t (k)] represents the periodic time segment TI t (k) Micro temperature sensor MTS i Average temperature value of feedback, ATS b [TI t (k)] represents the periodic time segment TI t (k) Ambient temperature sensor ATS b The average temperature value fed back.

[0043] Furthermore, the temperature linkage and change analysis module also includes a composite average temperature calculation unit and a linkage coefficient fluctuation value analysis unit.

[0044] The composite average temperature calculation unit, for the period time segment TI t (k) Calculate all the micro temperature sensors MTS i and ambient temperature sensor ATS b The composite average temperature value.

[0045] The linkage coefficient fluctuation value analysis unit designs the temperature linkage coefficient and calculates the temperature linkage coefficient in the cycle time segment TI based on the temperature linkage coefficient. t(k) to the periodic time segment TI t+1 (k) The fluctuation value of the linkage coefficient during the process, calculate the micro temperature sensor MTS i In the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) Temperature change rate during the process.

[0046] Furthermore, the early warning mechanism and temperature assessment module also includes a final early warning temperature calculation unit and an early warning threshold setting and triggering unit.

[0047] The warning calculation unit analyzes and outputs a warning value based on the linkage coefficient fluctuation value and the temperature change rate.

[0048] The warning threshold setting and triggering unit presets a warning threshold; if the warning value is greater than or equal to the warning threshold, no warning is triggered; if the warning value is less than the warning threshold, a warning is triggered.

[0049] Compared with the prior art, the beneficial effects achieved by the present invention are: by arranging and collecting data on the operating equipment and ambient temperature sensors, accurate temperature monitoring and temperature trend analysis within a time period are achieved, thereby providing a solid data foundation for the safe management of equipment in the cabinet. The calculation of the linkage coefficient fluctuation value and the temperature change rate reveals the relationship between the operating equipment temperature and the ambient temperature and the temperature change trend, effectively improving the accuracy of abnormal temperature identification. Finally, by designing the warning value and setting the warning threshold, it is ensured that the equipment in the cabinet can trigger a warning in time when the temperature is abnormal, thereby ensuring the safety of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0051] in:

[0052] Figure 1 The present invention is a flow chart of a device security intelligent perception early warning method based on the Internet of Things.

[0053] Figure 2 It is a structural schematic diagram of an equipment safety intelligent perception early warning system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0055] See also Figure 1 In the first embodiment of the present invention, a device security intelligent perception warning method based on the Internet of Things is provided, and the method comprises the following steps:

[0056] Step S1: uniformly number all running devices in the cabinet, install micro temperature sensors on the running devices, and install ambient temperature sensors at different locations in the cabinet.

[0057] Specifically, all running devices in the cabinet are uniformly numbered, and a running device set KP={KP i |i∈[1,I]}, where, KP i represents the i-th running device in the cabinet, and I represents the total number of running devices in the cabinet.

[0058] A micro temperature sensor is installed on the operating device, wherein one micro temperature sensor is installed corresponding to one operating device, and a micro temperature sensor set MTS={MTS i |i∈[1,I]}, where MTS i represents the micro temperature sensor installed on the i-th running device; installing ambient temperature sensors at different positions in the cabinet, constructing an ambient temperature sensor set ATS = {ATS b |b∈[1,B]}, where ATS b represents the bth ambient temperature sensor in the cabinet, and B represents the total number of ambient temperature sensors in the cabinet.

[0059] Step S2: Divide the time range within a day into x time segments according to equal time scale intervals, and use days as a cycle, add periodic labels to the time segments, record them as periodic time segments, and collect the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor within the periodic time segments.

[0060] Specifically, the time range of a day is divided into x time segments according to the equal time scale interval ΔT, and a time segment set TI = {TI t |t∈[1,x]}, where TI t represents the tth time segment, x represents the total number of time segments; with a day as the cycle, the time segment TIt Additional periodic label, recorded as periodic time segment TI t (k), where k represents the cycle number.

[0061] Furthermore, in the periodic time segment TI t In (k), the data of the micro temperature sensor and the data of the ambient temperature sensor are collected to construct a micro temperature sensor data set D = {MTS i [TI t (k)]|i∈[1,I],t∈[1,x]} and the ambient temperature sensor dataset E={ATS b [TI t (k)]|b∈[1,B],t∈[1,x]}, where MTS i [TI t (k)] represents the periodic time segment TI t (k) Micro temperature sensor MTS i Average temperature value of feedback, ATS b [TI t (k)] represents the periodic time segment TI t (k) Ambient temperature sensor ATS b The average temperature value fed back.

[0062] Step S3: Calculate the linkage coefficient fluctuation value based on the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor; and calculate the temperature change rate based on the periodic time segment.

[0063] Specifically, for the periodic time segment TI t (k) Calculate all the micro temperature sensors MTS i and ambient temperature sensor ATS b The composite average temperature value is calculated as follows:

[0064]

[0065] Among them, MCAT[TI t (k)] represents all micro temperature sensors MTS i In the cycle time segment TI t (k) composite average temperature value, ACAT[TI t (k)] indicates all ambient temperature sensors ATS b In the cycle time segment TI t (k) The composite average temperature value within.

[0066] For example, assuming that the cycle number k is 1, the uniform time scale interval ΔT is 10, the total number of running devices in the cabinet I is 3, the total number of ambient temperature sensors in the cabinet B is 2, the time segment is 1, MTS1[TI1(1)] is 25 degrees, MTS2[TI1(1)] is 27 degrees, MTS3[TI1(1)] is 26 degrees, ATS1[TI1(1)] is 24 degrees, and ATS2[TI1(1)] is 25 degrees, substitute into the above formula, and we get:

[0067] Design temperature linkage coefficient, the calculation formula is:

[0068]

[0069] Among them, TIC[TI t (k)] represents the temperature linkage coefficient, max(MCAT[TI t (k)],ACAT[TI t (k)]) represents the temperature sensor MTS i The composite mean temperature value MCAT[TI t (k)] and ambient temperature sensor ATS b The composite average temperature value ACAR[TI t (k)] and take the maximum value.

[0070] For example, based on the above calculation, the micro temperature sensor MTS i Composite average temperature value and ambient temperature sensor ATS b Substituting the composite average temperature value into the formula, we can get:

[0071] Preferably, based on the temperature linkage coefficient, the periodic time segment TI is calculated. t (k) to the periodic time segment TI t+1 (k) Fluctuation value of linkage coefficient during the process Among them, TIC[TI t+1 (k)] represents the periodic time segment TI t+1 (k) Temperature linkage coefficient.

[0072] Example 1: Assume that at the periodic time segment TI2(1), MTS1[TI2(1)] is 26 degrees, MTS2[TI2(1)] is 28 degrees, MTS3[TI2(1)] is 27 degrees, ATS1[TI2(1)] is 25 degrees, and ATS2[TI2(1)] is 26 degrees.

[0073] Example 2: Assume that at the periodic time segment TI2(1), MTS1[TI2(1)] is 26 degrees, MTS2[TI2(1)] is 28 degrees, MTS3[TI2(1)] is 27 degrees, ATS1[TI2(1)] is 24 degrees, and ATS2[TI2(1)] is 26 degrees.

[0074] Calculation of Micro Temperature Sensors MTS i In the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) The temperature change rate during the process is calculated as:

[0075]

[0076] Among them, MCAT[TI t+1 (k)] represents all micro temperature sensors MTS i In the cycle time segment TI t+1 (k) The composite average temperature value at time.

[0077] For example, based on the above calculated results, substituting into the formula, we get

[0078] Step S4: Analyze and output a warning value based on the linkage coefficient fluctuation value and the temperature change rate.

[0079] Specifically, based on the linkage coefficient fluctuation value ΔTIC[TI t (k)→TI t+1 (k)] and the temperature change rate TCR[TI t (k)→TI t+1 (k)], analyze and output the warning value, the calculation formula is:

[0080]

[0081] Among them, FTV represents the warning value, ε is the preset stability factor, max(|ΔTIC[TI t (k)]|,|TCR[TI t (k)→TI t+1 (k)]|) means taking the maximum value from the linkage coefficient fluctuation value and the temperature change rate.

[0082] Preset warning threshold θ;

[0083] If FTV ≥ θ, no warning is triggered;

[0084] If FTV<θ, an early warning is triggered.

[0085] For example, assuming that the preset stability factor ε is 0.01 and the warning threshold θ is 0, based on the result calculated in Example 1 above, we can substitute it into the formula and get If FTV < warning threshold θ, the warning is triggered. Based on the result of Example 2 above, substitute it into the formula and we get If FTV is greater than the warning threshold θ, no warning is triggered.

[0086] See also Figure 2 In the second embodiment of the present invention, an intelligent perception warning system for equipment safety based on the Internet of Things is provided, which includes: an operating equipment and sensor layout module, a time segment data acquisition module, a temperature linkage and change analysis module, and an early warning mechanism and temperature evaluation module.

[0087] The operating device and sensor arrangement module is used to uniformly number all the operating devices in the cabinet, install micro temperature sensors on the operating devices, and install environmental temperature sensors at different positions in the cabinet.

[0088] The time segment data acquisition module divides the time range within a day into x time segments according to equal time scale intervals, and constructs periodic time segments with days as a cycle, and collects the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor within the periodic time segments.

[0089] The temperature linkage and change analysis module calculates the linkage coefficient fluctuation value based on the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor; and calculates the temperature change rate based on the periodic time segment.

[0090] The early warning mechanism and temperature assessment module designs an early warning value based on the linkage coefficient fluctuation value and the temperature change rate.

[0091] Furthermore, the operating device and sensor arrangement module also includes an operating device numbering unit and a sensor arrangement unit.

[0092] The running device numbering unit uniformly numbers all running devices in the cabinet and constructs a running device set KP={KP i |i∈[1,I]}, where, KP i represents the i-th running device in the cabinet, and I represents the total number of running devices in the cabinet.

[0093] The sensor arrangement unit installs a micro temperature sensor on the operating device, wherein one micro temperature sensor is installed corresponding to one operating device, and a micro temperature sensor set MTS={MTS i |i∈[1,I]}, where MTSi represents the micro temperature sensor installed on the i-th running device; installing ambient temperature sensors at different positions in the cabinet, constructing an ambient temperature sensor set ATS = {ATS b |b∈[1,B]}, where ATS b represents the bth ambient temperature sensor in the cabinet, and B represents the total number of ambient temperature sensors in the cabinet.

[0094] Furthermore, the time segment data collection module also includes a time segment division and period labeling unit and a temperature data collection and construction unit.

[0095] The time segment division and period labeling unit divides the time range within a day into x time segments according to the equal time scale interval ΔT, and constructs a time segment set TI={TI t |t∈[1,x]}, where TI t represents the tth time segment, x represents the total number of time segments; with a day as the cycle, the time segment TI t Additional periodic label, recorded as periodic time segment TI t (k), where k represents the cycle number.

[0096] The temperature data acquisition and construction unit, in the cycle time segment TI t In (k), the data of the micro temperature sensor and the data of the ambient temperature sensor are collected to construct a micro temperature sensor data set D = {MTS i [TI t (k)]|i∈[1,I],t∈[1,x]} and the ambient temperature sensor dataset E={ATS b [TI t (k)]|b∈[1,B],t∈[1,x]}, where MTS i [TI t (k)] represents the periodic time segment TI t (k) Micro temperature sensor MTS i Average temperature value of feedback, ATS b [TI t (k)] represents the periodic time segment TI t (k) Ambient temperature sensor ATS b The average temperature value fed back.

[0097] Furthermore, the temperature linkage and change analysis module also includes a composite average temperature calculation unit and a linkage coefficient fluctuation value analysis unit.

[0098] The composite average temperature calculation unit, for the period time segment TIt (k) Calculate all the micro temperature sensors MTS i and ambient temperature sensor ATS b The composite average temperature value.

[0099] The linkage coefficient fluctuation value analysis unit designs the temperature linkage coefficient and calculates the temperature linkage coefficient in the cycle time segment TI based on the temperature linkage coefficient. t (k) to the periodic time segment TI t+1 (k) The fluctuation value of the linkage coefficient during the process, calculate the micro temperature sensor MTS i In the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) Temperature change rate during the process.

[0100] Furthermore, the early warning mechanism and temperature assessment module also includes an early warning calculation unit and an early warning threshold setting and triggering unit.

[0101] The warning calculation unit analyzes and outputs a warning value based on the linkage coefficient fluctuation value and the temperature change rate.

[0102] The warning threshold setting and triggering unit presets a warning threshold; if the warning value is greater than or equal to the warning threshold, no warning is triggered; if the warning value is less than the warning threshold, a warning is triggered.

[0103] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0104] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A device security intelligent perception early warning method based on the Internet of Things, characterized in that: The method comprises the following steps: Step S1: uniformly number all running devices in the cabinet, install micro temperature sensors on the running devices, install ambient temperature sensors at different locations in the cabinet, and each sensor transmits the collected temperature data through the Internet of Things technology; Step S2: Divide the time range within a day into x time segments according to equal time scale intervals, and take a day as a cycle, add a periodic label to the time segment, record it as a periodic time segment, and collect the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor within the periodic time segment; Step S3: calculating the linkage coefficient fluctuation value based on the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor; and calculating the temperature change rate based on the periodic time segment; Step S4: Analyze and output a warning value based on the linkage coefficient fluctuation value and the temperature change rate.

2. According to the device security intelligent perception early warning method based on the Internet of Things according to claim 1, it is characterized in that: The step S1 comprises the following steps: Uniformly number all running devices in the cabinet and construct a running device set KP = {KP i |i∈[1,I]}, where, KP i represents the i-th running device in the cabinet, and I represents the total number of running devices in the cabinet; A micro temperature sensor is installed on the operating device, wherein one micro temperature sensor is installed corresponding to one operating device, and a micro temperature sensor set MTS={MTS i |i∈[1,I]}, where MTS i represents the micro temperature sensor installed on the i-th running device; installing ambient temperature sensors at different positions in the cabinet, constructing an ambient temperature sensor set ATS = {ATS b |b∈[1,B]}, where ATS b represents the bth ambient temperature sensor in the cabinet, and B represents the total number of ambient temperature sensors in the cabinet.

3. According to the device security intelligent perception early warning method based on the Internet of Things according to claim 1, it is characterized in that: The step S2 comprises the following steps: According to the equal time scale interval ΔT, the time range of a day is divided into x time segments, and the time segment set TI = {TI t |t∈[1,x]}, where TI t represents the tth time segment, x represents the total number of time segments; with a day as the cycle, the time segment TI t Additional periodic label, recorded as periodic time segment TI t (k), where k represents the cycle number; In the cycle time segment TI t In (k), the data of the micro temperature sensor and the data of the ambient temperature sensor are collected to construct a micro temperature sensor data set D = {MTS i [TI t (k)]|i∈[1,I],t∈[1,x]} and the ambient temperature sensor dataset E={ATS b [TI t (k)]|b∈[1,B],t∈[1,x]}, where MTS i [TI t (k)] represents the periodic time segment TI t (k) Micro temperature sensor MTS i Average temperature value of feedback, ATS b [TI t (k)] represents the periodic time segment TI t (k) Ambient temperature sensor ATS b The average temperature value fed back.

4. According to claim 3, a device security intelligent perception early warning method based on the Internet of Things is characterized in that: The step S3 comprises the following steps: For the periodic time segment TI t (k) Calculate all the micro temperature sensors MTS i and ambient temperature sensor ATS b The composite average temperature value is calculated as follows: Among them, MCAT[TI t (k)] represents all micro temperature sensors MTS i In the cycle time segment TI t (k) composite average temperature value, ACAT[TI t (k)] indicates all ambient temperature sensors ATS b In the cycle time segment TI t (k) The composite average temperature value within; Design temperature linkage coefficient, the calculation formula is: Among them, TIC[TI t (k)] represents the temperature linkage coefficient, max(MCAT[TI t (k)], ACAT[TI t (k)]) represents the temperature sensor MTS i The composite mean temperature value MCAT[TI t (k)] and ambient temperature sensor ATS b The composite average temperature value ACAT[TI t (k)] and take the maximum value; Based on the temperature linkage coefficient, calculate the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) Fluctuation value of linkage coefficient during the process Among them, TIC[TI t+1 (k)] represents the periodic time segment TI t+1 (k) Temperature linkage coefficient; Calculation of Micro Temperature Sensors MTS i In the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) The temperature change rate during the process is calculated as: Among them, MCAT[TI t+1 (k)] represents all micro temperature sensors MTS i In the cycle time segment TI t+1 (k) The composite average temperature value at time.

5. According to the device security intelligent perception early warning method based on the Internet of Things according to claim 1, it is characterized in that: Described step S4 comprises the following steps: Based on the linkage coefficient fluctuation value ΔTIC[TI t (k)→TI t+1 (k)] and the temperature change rate TCR[TI t (k)→TI t+1 (k)], analyze and output the warning value, the calculation formula is: Among them, FTV represents the warning value, ε is the preset stability factor, max(|ΔTIC[TI t (k)]|,|TCR[TI t (k)→TI t+1 (k)]|) means taking the maximum value from the fluctuation value of linkage coefficient and temperature change rate; Preset warning threshold θ; If FTV ≥ θ, no warning is triggered; If FTV<θ, an early warning is triggered.

6. An intelligent perception and early warning system for device security based on the Internet of Things, executing an intelligent perception and early warning method for device security based on the Internet of Things as claimed in any one of claims 1 to 5, characterized in that: The system includes: operating equipment and sensor layout module, time segment data acquisition module, temperature linkage and change analysis module and early warning mechanism and temperature assessment module; The operating equipment and sensor arrangement module is used to uniformly number all the operating equipment in the cabinet, install micro temperature sensors on the operating equipment, and install ambient temperature sensors at different locations in the cabinet; The time segment data acquisition module divides the time range within a day into x time segments according to equal time scale intervals, and constructs periodic time segments with a day as a cycle, and collects the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor within the periodic time segment; The temperature linkage and change analysis module calculates the linkage coefficient fluctuation value based on the average temperature value fed back by the micro temperature sensor and the average temperature value fed back by the ambient temperature sensor; and calculates the temperature change rate based on the periodic time segment; The early warning mechanism and temperature assessment module designs an early warning value based on the linkage coefficient fluctuation value and the temperature change rate.

7. According to claim 6, the device safety intelligent perception warning system based on the Internet of Things is characterized by: The operating equipment and sensor placement module also includes an operating equipment numbering unit and a sensor placement unit; The running device numbering unit uniformly numbers all running devices in the cabinet and constructs a running device set KP={KP i |i∈[1,I]}, where, KP i represents the i-th running device in the cabinet, and I represents the total number of running devices in the cabinet; The sensor arrangement unit installs a micro temperature sensor on the operating device, wherein one micro temperature sensor is installed corresponding to one operating device, and a micro temperature sensor set MTS={MTS i |i∈[1,I]}, where MTS i represents the micro temperature sensor installed on the i-th running device; installing ambient temperature sensors at different positions in the cabinet, constructing an ambient temperature sensor set ATS = {ATS b |b∈[1,B]}, where ATS b represents the bth ambient temperature sensor in the cabinet, and B represents the total number of ambient temperature sensors in the cabinet.

8. According to claim 7, the device safety intelligent perception warning system based on the Internet of Things is characterized by: The time segment data acquisition module also includes a time segment division and period labeling unit and a temperature data acquisition and construction unit; The time segment division and period labeling unit divides the time range within a day into x time segments according to the equal time scale interval ΔT, and constructs a time segment set TI={TI t |t∈[1,x]}, where TI t represents the tth time segment, x represents the total number of time segments; with a day as the cycle, the time segment TI t Additional periodic label, recorded as periodic time segment TI t (k), where k represents the cycle number; The temperature data acquisition and construction unit, in the cycle time segment TI t In (k), the data of the micro temperature sensor and the data of the ambient temperature sensor are collected to construct a micro temperature sensor data set D = {MTS i [TI t (k)]|i∈[1,B],t∈[1,x]} and the ambient temperature sensor dataset E={ATS b [TI b (k)]|b∈[1,B],t∈[1,x]}, where MTS i [TI t (k)] represents the periodic time segment TI t (k) Micro temperature sensor MTS i Average temperature value of feedback, ATS b [TI t (k)] represents the periodic time segment TI t (k) Ambient temperature sensor ATS b The average temperature value fed back.

9. The device safety intelligent perception warning system based on the Internet of Things according to claim 8 is characterized by: The temperature linkage and change analysis module also includes a composite average temperature calculation unit and a linkage coefficient fluctuation value analysis unit; The composite average temperature calculation unit, for the period time segment TI t (k) Calculate all the micro temperature sensors MTS i and ambient temperature sensor ATS b The composite average temperature value of The linkage coefficient fluctuation value analysis unit designs the temperature linkage coefficient and calculates the temperature linkage coefficient in the cycle time segment TI based on the temperature linkage coefficient. t (k) to the periodic time segment TI t+1 (k) The fluctuation value of the linkage coefficient during the process, calculate the micro temperature sensor MTS i In the cycle time segment TI t (k) to the periodic time segment TI t+1 (k) Temperature change rate during the process.

10. The device safety intelligent perception warning system based on the Internet of Things according to claim 9 is characterized by: The early warning mechanism and temperature assessment module also includes an early warning calculation unit and an early warning threshold setting and triggering unit; The warning calculation unit analyzes and outputs a warning value based on the linkage coefficient fluctuation value and the temperature change rate; The warning threshold setting and triggering unit presets the warning threshold; If the warning value is greater than or equal to the warning threshold, no warning is triggered; If the warning value is less than the warning threshold, a warning is triggered.

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