An intelligent monitoring system and method for the indoor distribution weak current shaft environment

Abstract

The present invention discloses an intelligent monitoring system and method for the indoor distribution weak current shaft environment, which relates to the technical field of intelligent monitoring of shaft environments, and solves the technical problems that in the environmental monitoring of indoor distribution weak current shafts, it is difficult to adaptively adjust the sampling frequency of environmental monitoring according to the environment, and it is difficult to self-correct the monitoring data according to the change of environmental temperature; the present invention determines the dynamic sampling frequency of each of the time periods based on the characteristic output power obtained for each time period, and uses the corresponding dynamic sampling frequency to collect the environmental temperature in the to-be-monitored indoor distribution weak current shaft; performs cross-analysis on the environmental temperatures collected in each of the time periods to determine the intelligent regulation factor of the to-be-monitored indoor distribution weak current shaft; and performs intelligent calibration on the environmental temperature of the to-be-monitored indoor distribution weak current shaft based on the intelligent regulation factor; the present invention can improve the flexibility and accuracy of intelligent monitoring of shaft environments.

Description

Technical Field

[0001] The present invention belongs to the field of shaft monitoring, relates to the technology of intelligent monitoring of shaft environment, and specifically is an intelligent monitoring system and method for the environment of indoor distribution weak current shaft. Background Art

[0002] The weak current shaft belongs to the electrical shaft and is the electrical shaft for laying weak current lines. Among them, the indoor distribution weak current shaft is a dedicated shaft or passage in a building for centrally arranging weak current equipment and cables related to the indoor distribution system. At present, most intelligent monitoring systems for the environment of indoor distribution weak current shafts are difficult to adaptively adjust the sampling frequency of environmental monitoring according to the environment during the environmental monitoring of indoor distribution weak current shafts, and cannot predict the output power fluctuation period, automatically reduce the sampling frequency during low power periods, and automatically increase the sampling frequency during high power periods, which increases the energy consumption and data redundancy of the system; at the same time, most intelligent monitoring systems for the environment of indoor distribution weak current shafts are difficult to self-correct the monitoring data according to the change of environmental temperature, ignoring the problem that the accuracy of the sensor is affected by the environment or inaccurate due to long-term operation, which increases the error and potential safety hazard of system analysis.

[0003] Therefore, the present invention discloses an intelligent monitoring system and method for the environment of indoor distribution weak current shafts to solve the above technical problems. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention provides an intelligent monitoring system and method for the environment of indoor distribution weak current shafts to solve the technical problems that it is difficult to adaptively adjust the sampling frequency of environmental monitoring according to the environment and it is difficult to self-correct the monitoring data according to the change of environmental temperature during the environmental monitoring of indoor distribution weak current shafts. The present invention determines the dynamic sampling frequency of each of the time periods based on the characteristic output power by obtaining the characteristic output power of each time period, and uses the corresponding dynamic sampling frequency to collect the environmental temperature in the to-be-monitored indoor distribution weak current shaft; cross-analyzes the environmental temperature collected in each of the time periods to determine the intelligent regulation factor of the to-be-monitored indoor distribution weak current shaft; and solves the above problems by intelligently calibrating the environmental temperature of the to-be-monitored indoor distribution weak current shaft based on the intelligent regulation factor.

[0005] To achieve the above object, the first aspect of the present invention provides an intelligent monitoring system for the environment of indoor distribution weak current shafts, including: a data sampling module, and a dynamic setting module, an intelligent calibration module and a database connected thereto;

[0006] The dynamic setting module: is used to determine multiple time periods of the in-building weak current shaft based on the noise value in the in-building weak current shaft, the output power of the in-building power supply, and the peak time periods of the area type; obtain the characteristic output power of each of the time periods, and determine the dynamic sampling frequency of each of the time periods based on the characteristic output power;

[0007] The data sampling module: is used to collect the ambient temperature in the to-be-monitored in-building weak current shaft at the corresponding dynamic sampling frequency during each time period;

[0008] The intelligent calibration module: is used to perform cross-analysis on the ambient temperature collected during each of the time periods to determine the intelligent regulation factor of the to-be-monitored in-building weak current shaft; perform intelligent calibration on the ambient temperature of the to-be-monitored in-building weak current shaft based on the intelligent regulation factor;

[0009] The database: is used to store data.

[0010] Preferably, before determining the multiple time periods of the in-building weak current shaft based on the noise value in the in-building weak current shaft, the output power of the in-building power supply, and the peak time periods of the area type, it further includes:

[0011] Obtain the peak time periods of the in-building power supply equipment used corresponding to the area type where the current in-building weak current shaft is located; wherein, the area type includes commercial areas, residential areas, industrial areas, office areas, and transportation hubs;

[0012] Extract the noise values at multiple time points in the in-building weak current shaft in the historical time periods, and obtain the average value of the noise values at multiple time points in the peak time periods and the average value of the noise values at multiple time points in the non-peak time periods;

[0013] Extract the output power of the in-building power supply at multiple time points in the current in-building weak current shaft in the previous n days, and obtain the average value of the output power of the in-building power supply at multiple time points in the previous n days in the peak time periods and the average value of the output power of the in-building power supply at multiple time points in the previous n days in the non-peak time periods; wherein, n is obtained through manual setting.

[0014] Preferably, determining the multiple time periods of the in-building weak current shaft based on the noise value in the in-building weak current shaft, the output power of the in-building power supply, and the peak time periods of the area type includes:

[0015] Mark the peak time periods or non-peak time periods where both the average noise value and the average output power are less than the corresponding determination thresholds as non-target time periods, and divide the non-target time periods into several time periods according to the corresponding fixed duration GCi; wherein, the determination thresholds include the manually set noise threshold YDZ and output power threshold GDZ; the value of i is 1 or 2, and the fixed durations include fixed duration one GC1 and fixed duration two GC2;

[0016] Extract the average noise value YZ and the average output power GZ of the target time period in sequence, and obtain the corresponding dynamic duration DCi based on the formula DCi = GCi × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ));

[0017] If the target time period is a peak time period, divide the target time period into several time periods according to the dynamic duration DC1;

[0018] If the target time period is a non-peak time period, divide the target time period into several time periods according to the dynamic duration DC2; where α is the amplitude adjustment coefficient of the exp() function set manually, and the value range of α is (0, 2]; both β1 and β2 are the ratio adjustment coefficients set manually, and β1 + β2 = 1, β1 < β2.

[0019] Preferably, the obtaining of the characteristic output power of each of the time periods includes:

[0020] Obtain the historical output power within each time period at a fixed time point of the day, divide the historical output power into several power groups according to the time periods; obtain the variance of the historical output power in each power group, and determine whether the variance is less than the corresponding defined threshold; if yes, obtain the average value of the historical output power in this power group; if not, remove the historical output power with the largest difference from the mode in this power group, and re-judge the variance until the variance of this power group is less than the corresponding defined threshold, and then obtain the average value of the historical output power that has not been removed; where the defined threshold is obtained through experience;

[0021] Obtain the average value QZ of the output power in the previous time period at the start time point of each time period, mark the average value of the historical output power in the power group corresponding to the current time period as the average value LDZ, mark the average value of the historical output power in the power group corresponding to the previous time period as the average value LQZ, mark the ratio of the average value LDZ to the average value LQZ as ratio one, and multiply the average value QZ by the ratio one to obtain the characteristic output power TL of the current time period.

[0022] It should be noted that the fixed time point of the day is generally 0:00 of the day.

[0023] Preferably, the determining of the dynamic sampling frequency of each of the time periods based on the characteristic output power includes:

[0024] A1: At the starting time point of each time period, extract the characteristic output power TL of the current time period, and determine whether the characteristic output power TL exceeds the maximum value of the power standard range; if so, set the dynamic sampling frequency of the current time period to the sampling frequency F1; if not, jump to A2; where the power standard range is obtained through manual setting; both the sampling frequency F1 and the sampling frequency F2 are obtained through manual setting, and the sampling frequency F1 is greater than the sampling frequency F2;

[0025] A2: Determine whether the characteristic output power TL is less than the minimum value of the power standard range; if so, set the dynamic sampling frequency of the current time period to the sampling frequency F2; if not, based on the formula: Obtain the dynamic sampling frequency DF of the current time period; where, Is the ceiling symbol, BG is the middle value of the power standard range; Is the amplitude adjustment coefficient set manually, and The value range of is (0, 2]; Is the distribution coefficient greater than 0 set manually.

[0026] It should be noted that when the value of the dynamic sampling frequency DF is greater than the sampling frequency F1, the value of the sampling frequency F1 is used to update the dynamic sampling frequency DF; when the value of the dynamic sampling frequency DF is less than the sampling frequency F2, the value of the sampling frequency F2 is used to update the dynamic sampling frequency DF.

[0027] Preferably, collecting the environmental temperature in the indoor distribution weak current well to be monitored using the corresponding dynamic sampling frequency includes:

[0028] Extract the dynamic sampling frequency of the current time period, and set the reference time point for collecting the environmental temperature within the current time period based on the sampling frequency , at the reference time point Collect the environmental temperature through several temperature sensors placed in the indoor distribution weak current well;

[0029] Based on the formula Obtain the environmental temperature data set collected at the reference time point ; where, j is the temperature sensor number, and k is the number of the reference time point for collecting the environmental temperature; Is the time deviation value set manually; Is the time point when the temperature sensor actually collects data, Is the set of all temperature sensors; Represents the temperature sensor Collect the temperature value.

[0030] It should be noted that through the time deviation value ​Define a time window to accommodate the small deviation between the actual acquisition time of the temperature sensor and the reference time point .

[0031] It should be noted that in , represents the j-th temperature sensor in the set .

[0032] It should be noted that if a certain sensor has no data within the window, it is marked as missing or interpolated using adjacent sensors.

[0033] Preferably, the determination of the intelligent regulation factor for the weak current shaft to be monitored includes:[[]]

[0034] B1: Extract the environmental temperature data sets corresponding to the current reference time point and the historical reference time points recorded in the previous m times , and obtain the reference temperature ZT corresponding to each reference time point based on formula (1):

[0035]

[0036] (1); wherein, is the average value of the corresponding environmental temperature data set , is the standard deviation of the corresponding environmental temperature data set ; r represents the number of temperature sensors;

[0037] B2: Extract each temperature sensor in the environmental temperature data set in turn, mark the extracted temperature sensor as the target sensor, and obtain the difference between the environmental temperature collected by the target sensor at each reference time point and the corresponding reference temperature ZT, and obtain the standard deviation value BC of the target sensor based on formula (2):

[0038] (2);

[0039] wherein, are all artificially set proportional adjustment coefficients greater than 0, and , ; represents taking the mode, is the percentile of a number of differences , and the percentile x is obtained by artificial setting;

[0040] B3: Obtain the target sensor at the current reference time point The difference BCZ between the collected ambient temperature and the reference temperature ZT is obtained, and it is judged whether the absolute value of the difference BCZ minus the standard difference BC is less than the difference threshold; if so, the ratio of the absolute value of the difference BCZ minus the standard difference BC to the absolute value of the difference BCZ is obtained, and the ratio is marked as the intelligent regulation factor; if not, an abnormal signal of the current target sensor is sent, the current target sensor is not intelligently calibrated, and the process jumps to B1 to remove the ambient temperature of the target sensor in the current ambient temperature dataset in the ambient temperature, and the reference temperature ZT at the current reference time point is re-obtained and the intelligent regulation factors of each temperature sensor are re-obtained; wherein, the difference threshold is obtained by manual setting.

[0041] It should be noted that in formula (1), the denominator is the sum of weights, and the numerator is the temperature value of each sensor at the time point multiplied by the corresponding weight and then summed.

[0042] Preferably, the intelligent calibration of the ambient temperature of the to-be-monitored indoor distribution weak current well based on the intelligent regulation factor includes:

[0043] The intelligent regulation factors ZR of each temperature sensor at the current reference time point are sequentially extracted, and it is judged whether the difference BCZ corresponding to the intelligent regulation factor ZR is greater than the standard difference BC; if so, H is set to -1; if not, H is set to +1; Based on the formula JZ = φ×(BCZ + H×BCZ×ZR), the calibration difference JZ is obtained, and the reference temperature ZT at the current reference time point is added with the calibration difference JZ to obtain the ambient temperature of the current temperature sensor at the current reference time point

[0044] and the warning information is set according to the ambient temperature of the current temperature sensor at the current reference time point; wherein, φ is a proportional adjustment coefficient, and the value range of φ is (0, 2].

[0045]

[0045] Preferably, the setting of the warning information includes:

[0046] The ambient temperatures of each temperature sensor at the current reference time point are sequentially extracted, and it is judged whether the ambient temperature exceeds the temperature warning threshold; if so, a temperature exceeding standard signal of the current temperature sensor is sent; if not, no operation is performed; wherein, the temperature warning threshold is obtained by experience.

[0047] The second aspect of the present invention provides an intelligent monitoring method for the environment of an indoor distribution weak current well, including the following steps:

[0048] ​Determine multiple time periods of the in-building distribution weak current shaft based on the noise value in the in-building distribution weak current shaft, the output power of the in-building distribution power supply, and the peak time periods of the area type; obtain the characteristic output power of each time period, and determine the dynamic sampling frequency of each time period based on the characteristic output power;

[0049] At each time period, collect the ambient temperature in the to-be-monitored in-building distribution weak current shaft at the corresponding dynamic sampling frequency;

[0050] Perform cross-analysis on the ambient temperatures collected in each time period to determine the intelligent regulation factor of the to-be-monitored in-building distribution weak current shaft; perform intelligent calibration on the ambient temperature of the to-be-monitored in-building distribution weak current shaft based on the intelligent regulation factor.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] 1. The present invention obtains the characteristic output power of each time period, determines the dynamic sampling frequency of each time period based on the characteristic output power, and collects the ambient temperature in the to-be-monitored in-building distribution weak current shaft at the corresponding dynamic sampling frequency; performs cross-analysis on the ambient temperatures collected in each time period to determine the intelligent regulation factor of the to-be-monitored in-building distribution weak current shaft; performs intelligent calibration on the ambient temperature of the to-be-monitored in-building distribution weak current shaft based on the intelligent regulation factor, solving the technical problems that it is difficult to adaptively adjust the sampling frequency of environmental monitoring according to the environment and it is difficult to self-correct the monitoring data according to the change of the ambient temperature in the in-building distribution weak current shaft environment monitoring; the present invention can improve the flexibility and accuracy of intelligent monitoring of the shaft environment.

[0053] 2. In the use of temperature sensors, there may be a certain deviation between the actually collected temperature and the true temperature due to quality problems or long use time of some temperature sensors, which may lead to loopholes in the system's safety warning and have certain potential safety hazards; when the present invention performs intelligent monitoring of the in-building distribution weak current shaft environment, by performing self and cross-analysis among multiple sensors on each temperature sensor, an intelligent regulation factor capable of self-correcting the temperature sensor when the ambient temperature changes is found, which can identify the faults or deviations of individual sensors and thus improve the reliability of the overall system.

[0054] 3. The present invention obtains a dynamic sampling frequency through predictive analysis of the characteristic output power of time periods. Subsequently, the temperature sensor can collect corresponding data according to this dynamic sampling frequency, enabling the system to automatically reduce the sampling frequency during low-power periods and automatically increase the sampling frequency during high-power periods by predicting the output power fluctuation period. This can collect more data during critical periods, improve the accuracy of monitoring, and reduce the amount of data during stable periods to avoid redundancy. Description of the Drawings

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a schematic diagram of the operation steps of the present invention;

[0057] Figure 2 It is a schematic diagram of the system modules of the present invention;

[0058] Figure 3 It is a schematic diagram of the operation steps for obtaining the time period of the present invention. Detailed implementation manners

[0059] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0060] Please refer to Figure 1 , an embodiment of the first aspect of the present invention provides an intelligent monitoring system for the indoor distribution weak current well environment, including: a data sampling module, and a dynamic setting module, an intelligent calibration module and a database connected thereto;

[0061] Dynamic setting module: used to determine multiple time periods of the indoor distribution weak current well based on the noise value in the indoor distribution weak current well, the output power of the indoor distribution power supply, and the peak time period of the area type; obtain the characteristic output power of each time period, and determine the dynamic sampling frequency of each time period based on the characteristic output power;

[0062] Data sampling module: used to collect the environmental temperature in the to-be-monitored indoor distribution weak current well at the corresponding dynamic sampling frequency during each time period;

[0063] Intelligent calibration module: used to perform cross-analysis on the environmental temperature collected during each time period to determine the intelligent regulation factor of the to-be-monitored indoor distribution weak current well; perform intelligent calibration on the environmental temperature of the to-be-monitored indoor distribution weak current well based on the intelligent regulation factor;

[0064] Database: used to store data.

[0065] Please refer to Figure 3, before determining multiple time periods of the in-building weak current shaft based on the noise value in the in-building weak current shaft, the output power of the in-building power supply, and the peak time period of the area type in this application, it further includes:

[0066] Obtain the peak time period when the in-building power supply equipment corresponding to the area type where the current in-building weak current shaft is located is in use; where the area type includes commercial areas, residential areas, industrial areas, office areas, and transportation hubs;

[0067] Extract the noise values at multiple time points in the in-building weak current shaft during the historical time period, and obtain the average value of the noise values at multiple time points during the peak time period and the average value of the noise values at multiple time points during the non-peak time period;

[0068] Extract the output power of the in-building power supply at multiple time points in the current in-building weak current shaft in the previous n days, and obtain the average value of the output power of the in-building power supply at multiple time points in the previous n days during the peak time period and the average value of the output power of the in-building power supply at multiple time points in the previous n days during the non-peak time period; where n is obtained through manual setting.

[0069] In this application, when determining multiple time periods of the in-building weak current shaft based on the noise value in the in-building weak current shaft, the output power of the in-building power supply, and the peak time period of the area type, it includes:

[0070] Mark the peak time period or non-peak time period in which both the average noise value and the average output power are less than the corresponding determination thresholds as non-target time periods, and divide the non-target time periods into several time periods according to the corresponding fixed duration GCi; where the determination thresholds include the noise threshold YDZ and the output power threshold GDZ; the value of i is 1 or 2, and the fixed durations include fixed duration one GC1 and fixed duration two GC2;

[0071] Successively extract the average value YZ of the noise value and the average value GZ of the output power of the target time period, and obtain the corresponding dynamic duration DCi based on the formula DCi = GCi × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)); if the target time period is the peak time period, divide the target time period into several time periods according to the dynamic duration DC1; if the target time period is the non-peak time period, divide the target time period into several time periods according to the dynamic duration DC2; where α is the amplitude adjustment coefficient of the exp() function set manually, and the value range of α is (0, 2]; both β1 and β2 are the ratio adjustment coefficients set manually, and β1 + β2 = 1, β1 < β2.

[0072] It should be noted that the present invention sets personalized time periods for the current in-building weak current shaft, which enables the environmental data monitoring of the in-building weak current shaft to be adjusted according to the working state and environmental state of the in-building equipment. Through "demand-based monitoring" and "dynamic adaptation", the personalized time periods have achieved an overall improvement in resource efficiency, equipment reliability, and the level of intelligent operation and maintenance.

[0073] It should be noted that the analysis of noise values is included when designing the time periods because: Noise may be caused by abnormal equipment operation or external construction activities. Incorporating the analysis of noise values can enable the intelligent monitoring system of the in-building weak current shaft environment to promote the transformation of the monitoring mode from "passive response" to "active prevention", preventing potential safety hazards caused by abnormal equipment operation or external construction.

[0074] It should be noted that the peak time periods are set manually. For example, the peak time periods in a residential area are: 7:00 - 8:00, 18:00 - 22:00; the peak time periods in an office area are: 9:00 - 11:00, 14:00 - 17:30.

[0075] It should be noted that to obtain the average of the noise values at multiple time points during the peak time period and the average of the noise values at multiple time points during the non-peak time period, it can be illustrated as follows:

[0076] If the current time is February 26, 2025, the first non-peak time period of the day is 00:00 - 10:00, and the first peak time period is 10:00 - 13:00, then obtain the average of the noise values at each time point from 00:00 to 10:00 on February 25, 2025, and obtain the average of the noise values at each time point from 10:00 to 13:00 on February 25, 2025.

[0077] It should be noted that to obtain the average of the output power of the in-building power supply at multiple time points in the n days before the peak time period and the average of the output power of the in-building power supply at multiple time points in the n days before the non-peak time period, it can be illustrated as follows:

[0078] If n is set to 5, the current time is February 26, 2025, the first non-peak time period of the day is 00:00 - 10:00, and the first peak time period is 10:00 - 13:00, then obtain the average of the output power of the in-building power supply at each time point from 00:00 to 10:00 in each day from February 21 to February 25, 2025, and obtain the average of the output power of the in-building power supply at each time point from 10:00 to 13:00 in each day from February 21 to February 25, 2025.

[0079] It should be noted that the fixed duration corresponding to the peak time period is the fixed duration one GC1, and the fixed duration corresponding to the non-peak time period is the fixed duration two GC2.

[0080] It should be noted that a peak time period or a non-peak time period with both the average noise value and the average output power less than the corresponding determination thresholds is marked as a non-target time period. The non-target time period is divided into several time periods according to the corresponding fixed duration, which can be elaborated as follows:

[0081] The peak time period with the average noise value less than the noise threshold and the average output power less than the output power threshold is divided into several time periods according to the fixed duration one; among them, the determination threshold corresponding to the average noise value is the noise threshold, and the determination threshold corresponding to the average output power is the output power threshold;

[0082] The non-peak time period with the average noise value less than the noise threshold and the average output power less than the output power threshold is divided into several time periods according to the fixed duration two.

[0083] It should be noted that the noise threshold, the output power threshold, the fixed duration one, and the fixed duration two are all obtained through manual setting.

[0084] It should be noted that a peak time period or a non-peak time period in which the average noise value and the average output power are not less than the corresponding determination thresholds is a target time period.

[0085] It should be noted that the average value YZ of the noise value and the average value GZ of the output power of the target time period are extracted in sequence. Based on the formula DCi = GCi × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)), the corresponding dynamic duration DCi can be obtained, which can be elaborated as follows:

[0086] If the target time period is a peak time period, the average value YZ of the noise value and the average value GZ of the output power of the target time period are extracted. Based on the formula DC1 = GC1 × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)), the corresponding dynamic duration DC1 is obtained;

[0087] If the target time period is a non-peak time period, the average value YZ of the noise value and the average value GZ of the output power of the target time period are extracted. Based on the formula DC2 = GC2 × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)), the corresponding dynamic duration DC2 is obtained.

[0088] It should be noted that when i = 1, it represents the relevant data in the peak time period, such as: fixed duration one GC1 and dynamic duration DC1; when i = 2, it represents the relevant data in the non-peak time period, such as: fixed duration two GC2 and dynamic duration DC2.

[0089] It should be noted that the division method of dividing the non-target time period into several time periods according to the corresponding fixed duration GCi, dividing the target time period into several time periods according to the dynamic duration DC1, and dividing the target time period into several time periods according to the dynamic duration DC2 can be:

[0090] The target time period or non-target time period is divided into several time periods with a complete duration according to the corresponding duration. If there is a remaining incomplete duration, the remaining incomplete duration is used as a time period; among them, the duration of the complete duration is equal to the corresponding duration, and the duration of the incomplete duration is less than the corresponding duration.

[0091] For example: if the target time period is 13:00 - 15:00 and the dynamic duration DC1 = 25 min, the time periods divided from 13:00 - 15:00 are: 13:00 - 13:25, 13:25 - 13:50, 13:50 - 14:15, 14:15 - 14:40, 14:40 - 15:00.

[0092] It should be noted that α is the amplitude adjustment coefficient of the exp() function set manually. α is used to measure the influence degree of the average value of the noise value YZ and the average value of the output power GZ on the dynamic duration DCi; when other conditions remain unchanged, the larger α is, the larger the value of the dynamic duration DCi is, and the smaller α is, the smaller the value of the dynamic duration DCi is.

[0093] It should be noted that both β1 and β2 are proportion adjustment coefficients set manually. β1 < β2 because: the relevant calculation data multiplied by β1 is the average value of the noise value YZ, and the relevant calculation data multiplied by β2 is the average value of the output power GZ. When dividing the time periods for the intelligent monitoring of the indoor distribution weak current well environment, the importance of the output power is greater than that of the noise. Therefore, the value of the proportion adjustment coefficient β2 is designed to be greater than the proportion adjustment coefficient β1.

[0094] In this application, obtaining the characteristic output power of each of the above-mentioned time periods includes:

[0095] Obtain the historical output power within each time period at a fixed time point on the same day, divide the historical output power into several power groups according to the time periods; obtain the variance of the historical output power in each power group, and determine whether the variance is less than the corresponding defined threshold; if yes, obtain the average value of the historical output power in this power group; if no, remove the historical output power with the largest difference from the mode in this power group, and re-judge the variance until the variance of this power group is less than the corresponding defined threshold, then obtain the average value of the non-removed historical output power; wherein, the defined threshold is obtained through experience.

[0096] Obtain the average value QZ of the output power in the previous time period at the start time point of each time period, mark the average value of the historical output power in the power group corresponding to the current time period as the average value LDZ, mark the average value of the historical output power in the power group corresponding to the previous time period as the average value LQZ, mark the ratio of the average value LDZ to the average value LQZ as ratio one, and multiply the average value QZ by the ratio one to obtain the characteristic output power TL of the current time period.

[0097] It should be noted that by comparing the average value of the historical output power within the time period with the average value of the output power on the same day, the characteristic output power of the current time period obtained by the present invention is a characteristic output power that not only reflects the historical operation law but also has the characteristics of real-time dynamic change, constituting a dual benchmark of the medium- and long-term scheduling baseline formed by the historical law and the real-time correction amount triggered by the dynamic change, improving the accuracy of the characteristic output power.

[0098] It should be noted that the fixed time point on the same day is generally 0:00 on the same day.

[0099] It should be noted that the start time point of each time period can be illustrated by an example: if the time period is 14:00 - 14:30, then the start time point of this time period is 14:00.

[0100] It should be noted that marking the ratio of the average value LDZ to the average value LQZ as ratio one and multiplying the average value QZ by the ratio one to obtain the characteristic output power TL of the current time period can be expressed by the formula: TL = QZ × LDZ / LQZ.

[0101] In this application, determining the dynamic sampling frequency of each of the time periods based on the characteristic output power includes:

[0102] A1: At the start time point of each time period, extract the characteristic output power TL of the current time period, and determine whether the characteristic output power TL exceeds the maximum value of the power standard range; if so, set the dynamic sampling frequency of the current time period to sampling frequency one F1; if not, jump to A2; where the power standard range is obtained through manual setting; both sampling frequency one F1 and sampling frequency two F2 are obtained through manual setting, and sampling frequency one F1 is greater than sampling frequency two F2;

[0103] A2: Determine whether the characteristic output power TL is less than the minimum value of the power standard range; if so, set the dynamic sampling frequency of the current time period to sampling frequency two F2; if not, based on the formula:

[0104] obtain the dynamic sampling frequency DF of the current time period; where is the ceiling symbol, BG is the middle value of the power standard range; is the amplitude adjustment coefficient set manually, and the value range of is (0, 2]; is the distribution coefficient greater than 0 set manually.

[0105] It should be noted that in the present invention, a dynamic sampling frequency is obtained through predictive analysis of the characteristic output power of the time period, and subsequently the temperature sensor can collect corresponding data according to this dynamic sampling frequency, enabling the system to automatically reduce the sampling frequency during low power periods and automatically increase the sampling frequency during high power periods by predicting the output power fluctuation period. This can collect more data during critical periods, improve the accuracy of monitoring, and reduce the data volume during stable periods to avoid redundancy.

[0106] It should be noted that when the value of the dynamic sampling frequency DF is greater than the value of sampling frequency one F1, the value of sampling frequency one F1 is used to update the dynamic sampling frequency DF; when the value of the dynamic sampling frequency DF is less than the value of sampling frequency two F2, the value of sampling frequency two F2 is used to update the dynamic sampling frequency DF.

[0107] It should be noted that is the distribution coefficient greater than 0 set manually. This distribution coefficient can be understood as a divisor set manually. Specifically, it is obtained manually according to the difference between sampling frequency two F2 and sampling frequency one F1. The greater the difference between sampling frequency two F2 and sampling frequency one F1, the greater the value of the distribution coefficient and the smaller the value of the distribution coefficient when the difference between sampling frequency two F2 and sampling frequency one F1 is greater; for example, if you want to obtain the average value of F1 + F2, then the value of is 2.

[0108] In this application, the ambient temperature in the monitored in-building weak current shaft is collected by adopting the corresponding dynamic sampling frequency, including:

[0109] Extract the dynamic sampling frequency of the current time period, and set the reference time point for collecting the ambient temperature within the current time period based on the sampling frequency , and collect the ambient temperature through a number of temperature sensors placed in the in-building weak current shaft at the reference time point ;

[0110] Based on the formula obtain the ambient temperature data set collected at the reference time point ; where j is the temperature sensor number, and k is the number of the reference time point for collecting the ambient temperature; is the manually set time deviation value; is the time point when the temperature sensor actually collects data, is the set time for the temperature sensor to collect data, is the set of all temperature sensors; represents the temperature sensor collects the temperature value.

[0111] It should be noted that the present invention allows for a small time deviation of the temperature sensor, avoiding an increase in system complexity caused by strict synchronization requirements, ensuring that subsequent analysis is based on the same time reference, and improving the reliability of the algorithm.

[0112] It should be noted that in the use of temperature sensors, some temperature sensors may have a certain deviation between the actual collection time and the set collection time due to quality problems or long usage time. The present invention aligns the data collected by multiple temperature sensors at different actual collection time points but close to to a unified reference time point to solve the problem of minute clock deviation or network delay in actual collection.

[0113] It should be noted that setting the reference time point for collecting the ambient temperature within the current time period based on the sampling frequency DF , can be illustrated by an example:

[0114] The sampling frequency DF is to collect once every minute, and the time range of the current time period is 14:00 - 15:00; then = 14:01, = 14:02,... = 15:00.

[0115] It should be noted that a time window is defined through the time deviation value to accommodate the actual collection time of the temperature sensor and the reference time point Slight deviation between .

[0116] It should be noted that in middle, Representing a collection The jth temperature sensor in , for example: ; Indicates all temperature sensors All must satisfy the existence condition, that is, each temperature sensor should be in the set middle.

[0117] It should be noted that if a sensor has no data in the window, it is marked as missing or interpolated using adjacent sensors.

[0118] It should be noted that Indicates temperature sensor At the actual time of data collection In The actual temperature value collected at that time.

[0119] It should be noted that after the temperature sensors collect data, the data they collect is mapped to a unified time point. and record the data in In the above example, we can form an aligned dataset .

[0120] In this application, the intelligent control factors of the indoor weak current well to be monitored are determined, including:

[0121] B1: Extract the current benchmark time point And the benchmark time points of each history recorded in the previous m times Corresponding ambient temperature data set , based on formula (1), we can get the benchmark time points The corresponding reference temperature ZT:

[0122] (1);

[0123] in, is the corresponding ambient temperature data set The average value of is the corresponding ambient temperature data set The standard deviation of; r represents the number of temperature sensors;

[0124] B2: Extract the ambient temperature data set in sequence Each temperature sensor in the image is marked as the target sensor, and the target sensor is obtained at each benchmark time point. The difference between the collected ambient temperature and the corresponding reference temperature ZT , the standard deviation BC of the target sensor is obtained based on formula (2):

[0125] (2);

[0126] wherein, are all proportion adjustment coefficients greater than 0 set manually, and , ; denotes taking the mode, m is obtained by manual setting, is the percentile of a number of differences , and the percentile x is obtained by manual setting;

[0127] B3: Obtain the difference BCZ between the ambient temperature collected by the target sensor at the current reference time point and the reference temperature ZT, and determine whether the absolute value of the difference BCZ minus the standard deviation BC is less than the difference threshold; if yes, obtain the ratio of the absolute value of the difference BCZ minus the standard deviation BC to the absolute value of BCZ, and mark the ratio as the intelligent regulation factor; if no, send an abnormal signal for the current target sensor, do not perform intelligent calibration on the current target sensor, and jump to B1 to remove the ambient temperature of the target sensor in the current ambient temperature dataset , re-obtain the reference temperature ZT at the current reference time point and re-obtain the intelligent regulation factors of each temperature sensor; wherein, the difference threshold is obtained by manual setting.

[0128] It should be noted that in the use of temperature sensors, some temperature sensors may have a certain deviation between the actually collected temperature and the true temperature due to quality problems or long usage time, which may lead to loopholes in the system's safety warning and have certain potential safety hazards; when the present invention conducts intelligent monitoring of the indoor distribution weak current well environment, by performing self and cross-analysis among multiple sensors on each temperature sensor, an intelligent regulation factor capable of self-correcting the temperature sensor when the ambient temperature changes is found, which can identify the faults or deviations of individual sensors, thereby improving the reliability of the overall system.

[0129] It should be noted that when the absolute value of the difference BCZ of a certain target sensor minus the standard deviation BC is less than the difference threshold in the present invention, it jumps to B1 to remove the ambient temperature of the target sensor in the current ambient temperature dataset , re-obtain the reference temperature ZT at the current reference time point and re-obtain the intelligent regulation factors of each temperature sensor. This is used to remove the interference of abnormal temperature sensors on the intelligent regulation factors and improve the accuracy of data analysis.

[0130] It should be noted that in formula (1), the denominator is the sum of weights, and the numerator is the temperature value of each sensor at the time point multiplied by the corresponding weight and then summed up.

[0131] It should be noted that in formula (1), it can be understood as the absolute deviation between the temperature value of the sensor at the moment and the average value of the ambient temperature dataset . When the input is a scalar, such as the temperature value, the norm symbol ||∙|| can be degraded to the absolute value symbol |∙| for understanding.

[0132] In another embodiment, there are temperature sensor 1, temperature sensor 2, and temperature sensor 3; and in this ambient temperature dataset the temperature recorded by temperature sensor 1 at this reference time point is 25 °C, the temperature recorded by temperature sensor 2 at this reference time point is 26 °C, and the temperature recorded by temperature sensor 3 at this reference time point is 30 °C;

[0133] Then the average value of the ambient temperature dataset is (25 + 26 + 30) / 3 = 27 °C;

[0134] Then the standard deviation of the ambient temperature dataset is:

[0135] ;

[0136] Therefore, the weight of temperature sensor 1: ;

[0137] The weight of temperature sensor 2: ;

[0138] The weight of temperature sensor 3: ;

[0139] Therefore, in this ambient temperature dataset the reference temperature ZT = (25×0.396 + 26×0.629 + 30×0.249) / (0.396 + 0.629 + 0.249) = 26.46 °C.

[0140] It should be noted that obtaining the target sensor at each reference time point ​​The difference between the collected ambient temperature and the corresponding reference temperature ZT Among them, at each reference time point Are the respective historical reference time points of the first m records ; The difference between the collected ambient temperature and the corresponding reference temperature ZT Is at the reference time point The collected ambient temperature minus the reference temperature ZT corresponding to the reference time point ; The corresponding reference temperature ZT is the ambient temperature dataset at the reference time point Corresponding reference temperature ZT

[0141] It should be noted that Are all manually set proportional adjustment coefficients greater than 0, and The reason is that: the proportional adjustment coefficient Is multiplied by the average value of the difference , The proportional adjustment coefficient Is multiplied by the mode of the difference , The proportional adjustment coefficient Is multiplied by the percentile of the difference ; For a set of data, the manually selected percentile can more accurately reflect the manual preference for the result. Therefore, the proportional adjustment coefficient In the present invention is set to be the largest; and the mode can express the central tendency of this set of data more than the average. Therefore, the proportional adjustment coefficient Set in the present invention is greater than the proportional adjustment coefficient .

[0142] It should be noted that Is the percentile of the difference , The percentile x is manually set, and the percentile is the value at a specific percentile in the data; for example: sort the difference From largest to smallest. If the manually set percentile is 60%, then the data at the 60% position in the order is the percentile of several differences , If there is no data at the 60% position, then the data closest to the 60% position is used as the percentile of several differences .

[0143] It should be noted that the difference BCZ between the ambient temperature collected at the current reference time point And the reference temperature ZT is: the ambient temperature collected at the current reference time point Minus the reference temperature ZT corresponding to the current reference time point .

[0144] ​In this application, the intelligent calibration of the environmental temperature of the to-be-monitored indoor distribution weak current well based on the intelligent regulation factor includes:

[0145] Successively extract the intelligent regulation factor ZR of each temperature sensor at the current reference time point Judge whether the difference BCZ corresponding to the intelligent regulation factor ZR is greater than the standard difference BC; if yes, set H to -1; if no, set H to +1;

[0146] Based on the formula JZ = φ×(BCZ + H×BCZ×ZR), obtain the calibration difference JZ, and add the calibration difference JZ to the reference temperature ZT at the current reference time point to obtain the environmental temperature of the current temperature sensor at the current reference time point Set the warning information according to the environmental temperature of the current temperature sensor at the current reference time point ; where φ is the proportional adjustment coefficient, and the value range of φ is (0, 2].

[0147] In this application, setting the warning information includes:

[0148] Successively extract the environmental temperature of each temperature sensor at the current reference time point Judge whether the environmental temperature exceeds the temperature warning threshold; if yes, send a signal that the temperature of the current temperature sensor exceeds the standard; if no, do nothing; where the temperature warning threshold is obtained through experience.

[0149] Please refer to Figure 2 , the second aspect embodiment of the present invention provides an intelligent monitoring method for the indoor distribution weak current well environment, including the following steps:

[0150] Based on the noise value in the indoor distribution weak current well, the output power of the indoor distribution power supply, and the peak time period of the regional type, determine multiple time periods of the indoor distribution weak current well; obtain the characteristic output power of each time period, and determine the dynamic sampling frequency of each time period based on the characteristic output power;

[0151] In each time period, collect the environmental temperature in the to-be-monitored indoor distribution weak current well at the corresponding dynamic sampling frequency;

[0152] Perform cross-analysis on the environmental temperatures collected in each time period to determine the intelligent regulation factor of the to-be-monitored indoor distribution weak current well; perform intelligent calibration on the environmental temperature of the to-be-monitored indoor distribution weak current well based on the intelligent regulation factor.

[0153] Some of the data in the above formula are taken as numerical values after removing the dimension. The formula is the one closest to the actual situation obtained through software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0154] The working principle of the present invention:

[0155] The present invention first determines multiple time periods of the in-building weak current shaft. This step realizes the comprehensive improvement of resource efficiency, equipment reliability, and operation and maintenance intelligence level through "on-demand monitoring" and "dynamic adaptation" of personalized time periods; obtains the characteristic output power of each time period. This step forms a medium- and long-term scheduling baseline by constituting historical rules and a dual baseline for dynamically changing and triggering real-time correction amounts, improving the accuracy of the characteristic output power; determines the dynamic sampling frequency of each time period based on the characteristic output power, and uses the corresponding dynamic sampling frequency to collect the ambient temperature in the to-be-monitored in-building weak current shaft; conducts cross-analysis on the ambient temperature collected in each time period to determine the intelligent regulation factor of the to-be-monitored in-building weak current shaft. This step finds the intelligent regulation factor that can self-correct the temperature sensor when the ambient temperature changes, improving the reliability of the overall system; performs intelligent calibration on the ambient temperature of the to-be-monitored in-building weak current shaft based on the intelligent regulation factor.

[0156] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent monitoring system for the indoor distribution weak current well environment, characterized in that, Including: a data sampling module, a dynamic setting module and an intelligent calibration module connected thereto; the dynamic setting module: for determining a plurality of time periods of the in-building weak current well based on the noise value in the in-building weak current well, the output power of the in-building power supply, and the peak time period of the area type; obtaining the characteristic output power of each of the time periods, and determining the dynamic sampling frequency of each of the time periods based on the characteristic output power; the data sampling module: for collecting the ambient temperature in the to-be-monitored in-building weak current well at a corresponding dynamic sampling frequency in each time period; the intelligent calibration module: for cross-analyzing the ambient temperature collected in each time period to determine the intelligent regulation factor of the to-be-monitored in-building weak current well; intelligently calibrating the ambient temperature of the to-be-monitored in-building weak current well based on the intelligent regulation factor.

2. The intelligent monitoring system for the indoor distribution weak current well environment according to claim 1, wherein Before determining the plurality of time periods of the in-building weak current well based on the noise value in the in-building weak current well, the output power of the in-building power supply, and the peak time period of the area type, it further includes: obtaining the peak time period when the in-building power supply equipment corresponding to the area type where the current in-building weak current well is located is used; wherein the area type includes commercial area, residential area, industrial area, office area and transportation hub; extracting the noise values at multiple time points in the in-building weak current well in the historical time period, and obtaining the average value of the noise values at multiple time points in the peak time period and the average value of the noise values at multiple time points in the non-peak time period; extracting the output powers of the in-building power supply at multiple time points in the previous n days in the current in-building weak current well, and obtaining the average value of the output powers of the in-building power supply at multiple time points in the previous n days in the peak time period and the average value of the output powers of the in-building power supply at multiple time points in the previous n days in the non-peak time period.

3. The intelligent monitoring system for the indoor distribution weak current shaft environment according to claim 2, characterized in that, Determining the plurality of time periods of the in-building weak current well based on the noise value in the in-building weak current well, the output power of the in-building power supply, and the peak time period of the area type includes: marking the peak time period or non-peak time period in which both the average noise value and the average output power are less than the corresponding determination thresholds as non-target time periods, and dividing the non-target time periods into several time periods according to the corresponding fixed duration GCi; wherein the determination thresholds include a noise threshold YDZ and an output power threshold GDZ; the value of i is 1 or 2, and the fixed durations include a fixed duration one GC1 and a fixed duration two GC2; successively extracting the average value YZ of the noise value and the average value GZ of the output power of the target time period, and obtaining the corresponding dynamic duration DCi based on the formula DCi = GCi × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)); if the target time period is a peak time period, dividing the target time period into several time periods according to the dynamic duration DC1; if the target time period is a non-peak time period, dividing the target time period into several time periods according to the dynamic duration DC2; wherein α is the amplitude adjustment coefficient of the exp() function, and the value range of α is (0, 2]; both β1 and β2 are proportional adjustment coefficients, and β1 + β2 = 1, β1 < β2.

4. An intelligent monitoring system for the indoor distribution weak current well environment according to claim 1, characterized in that The obtaining of the characteristic output power for each of the time periods includes: Obtaining the historical output power within each time period at a fixed time point on the same day, dividing the historical output power into several power groups according to the time periods; obtaining the variance of the historical output power in each power group, and determining whether the variance is less than the corresponding defined threshold; if so, obtaining the average value of the historical output power in this power group; if not, removing the historical output power with the largest difference from the mode in this power group, and re-performing the variance determination until the variance of this power group is less than the corresponding defined threshold, then obtaining the average value of the historical output power that has not been removed; Obtaining the average value QZ of the output power in the previous time period at the start time point of each time period, marking the average value of the historical output power in the power group corresponding to the current time period as the average value LDZ, marking the average value of the historical output power in the power group corresponding to the previous time period as the average value LQZ, marking the ratio of the average value LDZ to the average value LQZ as ratio one, and obtaining the characteristic output power TL of the current time period by multiplying the average value QZ by the ratio one.

5. The intelligent monitoring system for the indoor distribution weak current shaft environment according to claim 1, wherein, The determining of the dynamic sampling frequency for each of the time periods based on the characteristic output power includes: A1: At the start time point of each time period, extracting the characteristic output power TL of the current time period, and determining whether the characteristic output power TL exceeds the maximum value of the power standard range; if so, setting the dynamic sampling frequency of the current time period to sampling frequency one F1; if not, jumping to A2; where sampling frequency one F1 is greater than sampling frequency two F2; A2: Determine whether the characteristic output power TL is less than the minimum value of the power standard range; if yes, set the dynamic sampling frequency for the current time period to the sampling frequency F2; if no, obtain the dynamic sampling frequency DF for the current time period based on the formula ; where is the ceiling symbol, BG is the middle value of the power standard range; is the amplitude adjustment coefficient, and has a value range of (0, 2]; is a distribution coefficient greater than 0.

6. The intelligent monitoring system for the indoor distribution weak current shaft environment according to claim 1, wherein, Collecting the ambient temperature in the to-be-monitored in-building distribution weak current well at the corresponding dynamic sampling frequency; Extract the dynamic sampling frequency of the current time period, and set the reference time point for collecting the ambient temperature within the current time period based on the sampling frequency , at the reference time point collect the ambient temperature through a number of temperature sensors placed in the indoor distribution weak current well; Based on the formula the reference time point is obtained the collected environmental temperature data set ; where j is the temperature sensor number, and k is the number of the reference time point for collecting the environmental temperature; is the time deviation value; is the time point when the temperature sensor actually collects data, is the set of all temperature sensors; represents the temperature sensor the collected temperature value.

7. An intelligent monitoring system for the indoor distribution weak current shaft environment according to claim 6, characterized in that The determining of the intelligent regulation factor for the to-be-monitored in-building distribution weak current well includes: B1: Extract the current reference time point and the reference time points of each of the previous m historical records for the corresponding ambient temperature data sets , and obtain the reference temperature ZT corresponding to each reference time point based on formula (1) : (1)； wherein, is the average value of the corresponding ambient temperature data set , is the standard deviation of the corresponding ambient temperature data set ; r represents the number of temperature sensors; B2: Extract each temperature sensor in the environmental temperature dataset in sequence, mark the extracted temperature sensor as the target sensor, and obtain the difference between the environmental temperature collected by the target sensor at each reference time point and the corresponding reference temperature ZT , and obtain the standard deviation value BC of the target sensor based on formula (2): ​ (2)； Among them, are all proportional adjustment coefficients greater than 0, and , ; represents taking the mode, is the percentile of a number of differences ; B3: Obtain the target sensor at the current reference time point Calculate the difference BCZ between the ambient temperature collected by the target sensor at the current reference time point and the reference temperature ZT, and determine whether the absolute value of the difference BCZ minus the standard difference BC is less than the difference threshold. If yes, obtain the ratio of the absolute value of the difference BCZ minus the standard difference BC to the absolute value of the difference BCZ, and mark this ratio as the intelligent regulation factor. If no, send a signal indicating that the current target sensor record is abnormal, do not perform intelligent calibration on the current target sensor, and jump to B1 to remove the ambient temperature of the target sensor from the current ambient temperature data set and re-obtain the ambient temperature at the current reference time point as well as the intelligent regulation factors of each temperature sensor again 8. The intelligent monitoring system for the indoor distribution weak current shaft environment according to claim 7, characterized in that The intelligent calibration of the ambient temperature of the to-be-monitored in-building distribution weak current well based on the intelligent regulation factor includes: Extract the intelligent regulation factor ZR of each temperature sensor at this reference time point in sequence Judge whether the difference BCZ corresponding to the intelligent regulation factor ZR is greater than the standard difference BC; if yes, set H to -1; if no, set H to +1; The calibration difference JZ is obtained based on the formula JZ = φ × (BCZ + H × BCZ × ZR), and the reference temperature ZT at the current reference time point is added with the calibration difference JZ to obtain the ambient temperature of the current temperature sensor at the current reference time point . The warning information is set according to the ambient temperature of the current temperature sensor at the current reference time point . Here, φ is the proportional adjustment coefficient, and the value range of φ is (0, 2].

9. The intelligent monitoring system for the indoor distribution weak current shaft environment according to claim 8, characterized in that, The setting of the warning information includes: Extract the ambient temperature of each temperature sensor at this reference time point in sequence and determine whether the ambient temperature exceeds the temperature warning threshold. If it does, send a signal indicating that the temperature of the current temperature sensor exceeds the standard; if not, do nothing.

10. An intelligent monitoring method for the indoor distribution weak current well environment, which operates based on the intelligent monitoring system for the indoor distribution weak current well environment described in any one of claims 1 to 9, characterized in that, Including the following steps: Based on the noise value in the in-building distribution weak current well, the output power magnitude of the in-building distribution power supply, and the peak time periods of the area type, determining multiple time periods of the in-building distribution weak current well; obtaining the characteristic output power for each of the time periods, and determining the dynamic sampling frequency for each of the time periods based on the characteristic output power; At each time period, collecting the ambient temperature in the to-be-monitored in-building distribution weak current well at the corresponding dynamic sampling frequency; Performing cross-analysis on the ambient temperature collected in each of the time periods to determine the intelligent regulation factor for the to-be-monitored in-building distribution weak current well; Intelligently calibrating the ambient temperature of the to-be-monitored in-building distribution weak current well based on the intelligent regulation factor.

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