Indoor distribution weak current well environment intelligent monitoring system and method

Abstract

The invention discloses an intelligent monitoring system and method for the environment of a room-division weak-current well, relates to the technical field of intelligent monitoring of the environment of a vertical shaft, and solves the problem that the sampling frequency of environment monitoring is difficult to adjust adaptively according to the environment in the environment monitoring of the room-division weak-current well. And monitoring data is difficult to self-correct according to the change of the environment temperature. According to the method, the characteristic output power of each time period is obtained, the dynamic sampling frequency of each time period is determined based on the characteristic output power, and the environment temperature in the weak current well of the to-be-monitored room is collected by adopting the corresponding dynamic sampling frequency; performing cross analysis on the environment temperature collected in each time period, and determining an intelligent regulation and control factor of the to-be-monitored room divided weak current well; performing intelligent calibration on the environment temperature of the to-be-monitored indoor weak current well based on the intelligent regulation and control factor; the flexibility and accuracy of intelligent monitoring of the shaft environment can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of shaft monitoring, relates to intelligent monitoring technology for 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 an 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 sensor accuracy 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 by obtaining the characteristic output power of each time period, and collects the environmental temperature in the to-be-monitored indoor distribution weak current shaft by using the corresponding dynamic sampling frequency; performs cross-analysis on 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 performs intelligent calibration on the environmental temperature of the to-be-monitored indoor distribution weak current shaft based on the intelligent regulation factor to solve the above problems.

[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; The dynamic setting module: is used to determine multiple time periods of the indoor distribution weak current shaft based on the noise value in the indoor distribution weak current shaft, the output power magnitude of the indoor distribution power supply, and the peak time period 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; The data sampling module: is used to collect the ambient temperature in the indoor distribution weak current well to be monitored at corresponding dynamic sampling frequencies in each time period; The intelligent calibration module: is used to perform cross-analysis on the ambient temperature collected in each time period to determine the intelligent regulation factor of the indoor distribution weak current well to be monitored; and perform intelligent calibration on the ambient temperature of the indoor distribution weak current well to be monitored based on the intelligent regulation factor; The database: is used to store data.

[0006] Preferably, before determining 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 periods of the regional type, it further includes: Obtain the peak time periods when the indoor distribution power supply equipment corresponding to the regional type where the current indoor distribution weak current well is located is used; wherein, the regional type includes commercial areas, residential areas, industrial areas, office areas, and transportation hubs; Extract the noise values at multiple time points in the indoor distribution weak current well in the historical time period, and obtain 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; Extract the output power of the indoor distribution power supply at multiple time points in the previous n days in the current indoor distribution weak current well, and obtain the average value of the output power of the indoor distribution power supply at multiple time points in the previous n days in the peak time period and the average value of the output power of the indoor distribution power supply at multiple time points in the previous n days in the non-peak time period; wherein, n is obtained by manual setting.

[0007] Preferably, determining 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 periods of the regional type includes: Mark the peak time period or non-peak time period 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 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; Extract the average value YZ of the noise value and the average value GZ of the output power 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)); 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; If the target time period is a non-peak time period, the target time period is divided 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 proportion adjustment coefficients set manually, and β1 + β2 = 1, β1 < β2.

[0008] Preferably, the obtaining of the characteristic output power of each of the time periods includes: 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 non-removed historical output power; where the defined threshold is obtained through experience; 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 LDZ of the historical output power in the power group corresponding to the current time period as the average value LDZ, mark the average value LQZ 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.

[0009] It should be noted that the fixed time point of the day is generally 0:00 of the day.

[0010] Preferably, the determining of the dynamic sampling frequency of each of the time periods based on the characteristic output power includes: 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 yes, set the dynamic sampling frequency of the current time period to the sampling frequency one F1; if not, jump to A2; where the power standard range is set manually; both the sampling frequency one F1 and the sampling frequency two F2 are set manually, and the sampling frequency one F1 is greater than the 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 of the current time period to the sampling frequency two 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 an artificially set distribution coefficient greater than 0.

[0011] 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.

[0012] Preferably, collecting the environmental temperature in the to-be-monitored indoor distribution weak current well using the corresponding dynamic sampling frequency includes: Extracting the dynamic sampling frequency of the current time period, and setting the reference time point for collecting the environmental temperature within the current time period based on the sampling frequency At the reference time point collecting the environmental temperature through a plurality of temperature sensors placed in the indoor distribution weak current well; Based on the formula obtaining 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 an artificially set 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 collects the temperature value.

[0013] It should be noted that through the time deviation value a time window is defined to accommodate the small deviation between the actual collection time of the temperature sensor and the reference time point .

[0014] It should be noted that in , represents the j-th temperature sensor in the set .

[0015] It should be noted that if a certain sensor has no data within the window, it is marked as missing or interpolated using neighboring sensors.

[0016] Preferably, determining the intelligent regulation factor of the to-be-monitored indoor distribution weak current well includes: B1: Extracting the environmental temperature data sets corresponding to the current reference time point and each historical reference time point recorded in the previous m times ​, the reference time points are obtained based on formula (1) The corresponding reference temperature ZT: (1); Where 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; 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): (2); Where are all manually set proportional adjustment coefficients greater than 0, and , ; represents taking the mode, is the percentile of several differences , and the percentile x is manually set; B3: Obtain the difference BCZ between the environmental temperature collected by the target sensor at the current reference time point and the reference temperature ZT, and judge whether the absolute value of the difference BCZ minus the standard deviation value BC is less than the difference threshold; if yes, obtain the proportion of the absolute value of the difference BCZ minus the standard deviation value BC to the absolute value of the difference BCZ, and mark the proportion as the intelligent regulation factor; if not, 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 environmental temperature of the target sensor in the current environmental temperature data set , re-obtain the reference temperature ZT at the current reference time point and re-obtain the intelligent regulation factors of each temperature sensor; where the difference threshold is manually set.

[0017] 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 time point multiplied by the corresponding weight and then summed.

[0018] Preferably, the intelligent calibration of the environmental temperature of the to-be-monitored indoor 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 not, set H to +1; 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 this reference time point to obtain the ambient temperature of the current temperature sensor at this reference time point Set warning information according to the ambient temperature of the current temperature sensor at this reference time point where φ is a proportional adjustment coefficient, and the value range of φ is (0, 2].

[0019] Preferably, the setting of the warning information includes: Extract the ambient temperature of each temperature sensor at this reference time point in sequence Judge whether the ambient temperature exceeds the temperature warning threshold; if yes, send a signal that the temperature of the current temperature sensor exceeds the standard; if not, do nothing; where the temperature warning threshold is obtained through experience.

[0020] The second aspect of the present invention provides an intelligent monitoring method for the environment of a distributed antenna system (DAS) weak current well, including the following steps: Based on the noise value in the DAS weak current well, the output power of the DAS power supply, and the peak time period of the regional type, determine multiple time periods of the DAS 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; In each time period, collect the ambient temperature in the DAS weak current well to be monitored at the corresponding dynamic sampling frequency; Perform cross-analysis on the ambient temperature collected in each time period to determine the intelligent regulation factor of the DAS weak current well to be monitored; based on the intelligent regulation factor, perform intelligent calibration on the ambient temperature of the DAS weak current well to be monitored.

[0021] Compared with the prior art, the beneficial effects of the present invention are: 1. The present invention obtains the characteristic output power of each time period, determines the dynamic sampling frequency of each of the time periods based on the characteristic output power, and uses the corresponding dynamic sampling frequency to collect the ambient temperature in the to-be-monitored indoor distribution weak current shaft; performs cross-analysis on the ambient temperatures collected within 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 ambient temperature of the to-be-monitored indoor distribution weak current shaft based on the intelligent regulation factor, solving the technical problems that in the environmental monitoring of the indoor distribution weak current shaft, 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; the present invention can improve the flexibility and accuracy of intelligent monitoring of the shaft environment.

[0022] 2. 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 use time, which may lead to loopholes in the system's safety warning and have certain potential safety hazards; when the present invention performs intelligent monitoring on the environment of the indoor distribution weak current shaft, 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, and doing so can identify the faults or deviations of individual sensors, thereby improving the reliability of the overall system.

[0023] 3. The present invention obtains a dynamic sampling frequency through predictive analysis of the characteristic output power of the time period, and subsequent temperature sensors 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. Doing so can collect more data during critical periods, improve the accuracy of monitoring, and reduce the amount of data during stable periods to avoid redundancy. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0025] Figure 1 It is a schematic diagram of the operation steps of the present invention; Figure 2 It is a schematic diagram of the system module of the present invention; Figure 3 It is a schematic diagram of the operation steps for obtaining the time period of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0027] 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; 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; Data sampling module: used to collect the environmental temperature in the indoor distribution weak current well to be monitored at the corresponding dynamic sampling frequency during each time period; 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 indoor distribution weak current well to be monitored; perform intelligent calibration on the environmental temperature of the indoor distribution weak current well to be monitored based on the intelligent regulation factor; Database: used to store data.

[0028] Please refer to Figure 3 , before determining the 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, the present application further includes: Obtain the peak time period of the indoor distribution power supply equipment used corresponding to the area type of the location where the current indoor distribution weak current well is located; where the area type includes commercial areas, residential areas, industrial areas, office areas, and transportation hubs; Extract the noise values at multiple time points in the indoor distribution weak current well in the historical time period, and obtain 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; Extract the output power of the indoor distribution power supply at multiple time points in the first n days in the current indoor distribution weak current well, and obtain the average value of the output power of the indoor distribution power supply at multiple time points in the first n days in the peak time period and the average value of the output power of the indoor distribution power supply at multiple time points in the non-peak time period in the first n days; where n is obtained by manual setting.

[0029] In the present application, determining the 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 includes: Mark the peak period or off-peak period where both the average noise value and the average output power are less than the corresponding determination thresholds as non-target periods, and divide the non-target periods into several time periods according to the corresponding fixed duration GCi; among them, 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 the fixed duration one GC1 and the fixed duration two GC2; Extract the average noise value YZ and the average output power GZ of the target 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)); if the target period is a peak period, divide the target period into several time periods according to the dynamic duration DC1; if the target period is an off-peak period, divide the target period into several time periods according to the dynamic duration DC2; among them, 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.

[0030] It should be noted that the present invention performs personalized time period settings for the current in-building distribution weak current well, which can enable the environmental data monitoring of the in-building distribution weak current well to be adjusted according to the working state and environmental state of the in-building distribution equipment. Through "on-demand monitoring" and "dynamic adaptation", the personalized time period realizes a comprehensive improvement in resource efficiency, equipment reliability, and operation and maintenance intelligence level.

[0031] It should be noted that the analysis of the noise value is added when designing the time period because: the noise may be caused by abnormal equipment operation or external construction activities. Incorporating the analysis of the noise value can enable the in-building distribution weak current well environmental intelligent monitoring system to promote the transformation of the monitoring mode from "passive response" to "active prevention", and prevent potential safety hazards caused by abnormal equipment operation or external construction.

[0032] It should be noted that the peak period is obtained by manual setting. For example, the peak periods in residential areas are: 7:00 - 8:00, 18:00 - 22:00; the peak periods in office areas are: 9:00 - 11:00, 14:00 - 17:30.

[0033] It should be noted that to obtain the average noise value at multiple time points in the peak period and the average noise value at multiple time points in the off-peak period, it can be illustrated by examples: 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 value of the noise values at each time point from 00:00 to 10:00 on February 25, 2025, and obtain the average value of the noise values at each time point from 10:00 to 13:00 on February 25, 2025.

[0034] It should be noted that obtaining the average value of the output power of the in-building distribution power supply at multiple time points in the n days before the peak time period and the average value of the output power of the in-building distribution power supply at multiple time points in the n days before the non-peak time period can be illustrated as follows: If n is 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 value of the output power of the in-building distribution power supply at each time point in the time period from 00:00 to 10:00 for each day from February 21 to February 25, 2025, and obtain the average value of the output power of the in-building distribution power supply at each time point in the time period from 10:00 to 13:00 for each day from February 21 to February 25, 2025.

[0035] 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.

[0036] It should be noted that marking the peak time period or non-peak time period where 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 durations can be elaborated as follows: Divide the peak time period where the average noise value is less than the noise threshold and the average output power is less than the output power threshold 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; Divide the non-peak time period where the average noise value is less than the noise threshold and the average output power is less than the output power threshold into several time periods according to the fixed duration two.

[0037] 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.

[0038] It should be noted that the peak time period or non-peak time period where the average noise value and the average output power are not less than the corresponding determination thresholds is the target time period.

[0039] It should be noted that the average value YZ of the noise values and the average value GZ of the output power in the target period are extracted in sequence, and the corresponding dynamic duration DCi is obtained based on the formula DCi = GCi × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)), which can be elaborated as follows: If the target period is a peak period, the average value YZ of the noise values and the average value GZ of the output power in the target period are extracted, and the corresponding dynamic duration DC1 is obtained based on the formula DC1 = GC1 × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)); If the target period is a non-peak period, the average value YZ of the noise values and the average value GZ of the output power in the target period are extracted, and the corresponding dynamic duration DC2 is obtained based on the formula DC2 = GC2 × exp(-α × (β1 × (YZ - YDZ) / YDZ + β2 × (GZ - GDZ) / GDZ)).

[0040] It should be noted that when i = 1, it represents the relevant data in the peak period, such as: the fixed duration one GC1 and the dynamic duration DC1; when i = 2, it represents the relevant data in the non-peak period, such as: the fixed duration two GC2 and the dynamic duration DC2.

[0041] It should be noted that the division method of dividing the non-target period into several time periods according to the corresponding fixed duration GCi, dividing the target period into several time periods according to the dynamic duration DC1, and dividing the target period into several time periods according to the dynamic duration DC2 can be as follows: The target period or non-target 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; For example: If the target 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.

[0042] It should be noted that α is the amplitude adjustment coefficient of the exp() function set manually, and α is used to represent the influence degree of the average value YZ of the noise values and the average value GZ of the output power 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.

[0043] It should be noted that both β1 and β2 are artificially set proportional adjustment coefficients. β1 < β2 because: the data related to the average noise value YZ is multiplied by β1, and the data related to the average output power GZ is multiplied by β2. When dividing the time period for 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 proportional adjustment coefficient β2 is designed to be greater than the proportional adjustment coefficient β1.

[0044] In this application, obtaining the characteristic output power of each of the said time periods includes: Obtaining the historical output power within each time period at a fixed time point of the day, dividing the historical output power into several power groups according to the time period; 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 - judging the variance until the variance of this power group is less than the corresponding defined threshold, then obtaining the average value of the non - removed historical output power; where the defined threshold is obtained through experience; Obtaining the average value QZ of the output power in the previous time period at the starting 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 multiplying the average value QZ by the ratio one to obtain the characteristic output power TL of the current time period.

[0045] It is worth noting that by comparing the average value of the historical output power within the time period with the average value of the output power of the 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 forming a medium - and long - term scheduling baseline for historical laws and triggering real - time correction amounts for dynamic changes, improving the accuracy of the characteristic output power.

[0046] It should be noted that the fixed time point of the day is generally 0:00 of the day.

[0047] It should be noted that the starting time point of each time period can be exemplified as follows: if the time period is 14:00 - 14:30, then the starting time point of this time period is 14:00.

[0048] 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.

[0049] In this application, determining the dynamic sampling frequency for each of the said time periods based on the characteristic output power includes: 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; wherein, 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; 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; wherein, is the ceiling symbol, BG is the middle value of the power standard range; is an amplitude adjustment coefficient set manually, and the value range of is (0, 2]; is a distribution coefficient greater than 0 set manually.

[0050] 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. 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.

[0051] 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.

[0052] It should be noted that is a distribution coefficient greater than 0 set manually. This distribution coefficient can be understood as a manually set divisor. Specifically, it is obtained manually based on the difference between the sampling frequency F2 and the sampling frequency F1. The greater the difference between the sampling frequency F2 and the sampling frequency F1, the greater the value of the distribution coefficient the greater the difference between the sampling frequency F2 and the sampling frequency F1, the smaller the value of the distribution coefficient ; for example, if you want to obtain the average value of F1 + F2, then the value of is 2.

[0053] In this application, the corresponding dynamic sampling frequency is adopted to collect the ambient temperature in the weak current well of the indoor distribution system to be monitored, including: 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 weak current well of the indoor distribution system at the reference time point ; 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 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 value collected by the temperature sensor .

[0054] It should be noted that the present invention allows for a small time deviation in 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.

[0055] 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 use 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 micro clock deviation or network delay in actual collection.

[0056] It should be noted that the reference time point for collecting the ambient temperature within the current time period is set based on the sampling frequency DF , which can be illustrated by the following example: 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.

[0057] It should be noted that a time window is defined through the time deviation value to accommodate the small deviation between the actual collection time of the temperature sensor and the reference time point .

[0058] It should be noted that in , represents the j-th temperature sensor in the set . For example: ; represents all temperature sensors must satisfy the existence condition, that is, each temperature sensor should be in the set .

[0059] It should be noted that if a certain sensor has no data within the window, it is marked as missing or interpolated using neighboring sensors.

[0060] It should be noted that represents the temperature value actually collected by the temperature sensor at the actual data collection time point when it is in .

[0061] It should be noted that when the temperature sensors collect data, the data they collect is mapped to a unified time point , and the data is recorded into to form an aligned data set .

[0062] In this application, determining the intelligent regulation factor of the to-be-monitored in-building weak current shaft includes: B1: Extract the environmental temperature data sets corresponding to the current reference time point and the respective 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): ; (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; B2: Sequentially extract each temperature sensor in the environmental temperature data set , 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); wherein, They are all manually set proportional adjustment coefficients greater than 0, and , ; denotes taking the mode, m is obtained by manual setting, is a number of differences of the percentile, and the percentile x is obtained by manual setting; 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 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 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 data set and re-obtain the reference temperature ZT at the current reference time point and re-obtain the intelligent regulation factors of each temperature sensor; among them, the difference threshold is obtained by manual setting.

[0063] 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 use time, which may lead to loopholes in the system's safety warning and have certain potential safety hazards; when the present invention performs intelligent monitoring on 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 and thus improve the reliability of the overall system.

[0064] It should be noted that when the absolute value of the difference BCZ of a certain target sensor minus the standard difference BC is less than the difference threshold in the present invention, it turns to B1 to remove the ambient temperature of the target sensor in the current ambient temperature data set and re-obtain the reference temperature ZT at the current reference time point and re-obtain the intelligent regulation factors of each temperature sensor, which is used to remove the interference of abnormal temperature sensors on the intelligent regulation factors and improve the accuracy of data analysis.

[0065] 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.

[0066] It should be noted that in formula (1), ​Can be understood as a sensor At the moment The temperature value of And the environmental temperature dataset The average value of The absolute deviation of. When the input is a scalar, such as the temperature value, the norm symbol ||∙|| can be degraded into the absolute value symbol |∙| for understanding.

[0067] In another embodiment, there are temperature sensor 1, temperature sensor 2, and temperature sensor 3; and in the current environmental temperature dataset Among them, the temperature recorded by temperature sensor 1 at the current reference time point Is 25°C, the temperature recorded by temperature sensor 2 at the current reference time point Is 26°C, and the temperature recorded by temperature sensor 3 at the current reference time point Is 30°C; Then the average value of the environmental temperature dataset The value of Is (25 + 26 + 30) / 3 = 27°C; Then the standard deviation of the environmental temperature dataset The value of Is: ; Therefore, the weight of temperature sensor 1: ; The weight of temperature sensor 2: ; The weight of temperature sensor 3: ; Therefore, in the current environmental temperature dataset The reference temperature ZT = (25×0.396 + 26×0.629 + 30×0.249) / (0.396 + 0.629 + 0.249) = 26.46°C.

[0068] It should be noted that when obtaining the difference between the environmental temperature collected by the target sensor at each reference time point And the corresponding reference temperature ZT Among them, at each reference time point Are the first m historical reference time points of each record ; The difference between the environmental temperature collected and the corresponding reference temperature ZT Is the environmental temperature collected at the reference time point Minus the value of the reference temperature ZT corresponding to the reference time point ; The corresponding reference temperature ZT is the environmental temperature dataset at the reference time point ​The corresponding reference temperature ZT.

[0069] It should be noted that are all artificially set proportional adjustment coefficients greater than 0, and because: 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 artificially selected percentile can more accurately reflect the human tendency towards the result. Therefore, the proportional adjustment coefficient in the present invention is set to be the largest; and the mode can better express the central tendency of this set of data compared to the average value. Therefore, the value of the proportional adjustment coefficient set in the present invention is greater than the proportional adjustment coefficient .

[0070] It should be noted that is the percentile of the difference , the percentile x is artificially 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 artificially set percentile is 60%, then the data at the 60% position in the sorted order is the percentile of a number of differences . If there is no data at the 60% position, then the data closest to the 60% position is used as the percentile of a number of differences .

[0071] It should be noted that the difference BCZ between the environmental temperature collected at the current reference time point and the reference temperature ZT is: the environmental temperature collected at the current reference time point minus the reference temperature ZT corresponding to the current reference time point .

[0072] In the present application, intelligent calibration of the environmental temperature of the to-be-monitored indoor distribution weak current well based on the intelligent regulation factor includes: Sequentially extract the intelligent regulation factor ZR of each temperature sensor at the current reference time point , and 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; Based on the formula JZ = φ×(BCZ + H×BCZ×ZR), obtain the calibration difference JZ, and at the current reference time point The reference temperature ZT plus the calibration difference JZ gives the ambient temperature of the current temperature sensor at this reference time point and set the warning information according to the ambient temperature of the current temperature sensor at this reference time point ; where φ is the proportional adjustment coefficient, and the value range of φ is (0, 2].

[0073] Setting the warning information in this application 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 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.

[0074] 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: 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, 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; In each time period, collect the ambient temperature in the indoor distribution weak current well to be monitored at the corresponding dynamic sampling frequency; Perform cross-analysis on the ambient temperature collected in each time period to determine the intelligent regulation factor of the indoor distribution weak current well to be monitored; perform intelligent calibration on the ambient temperature of the indoor distribution weak current well to be monitored based on the intelligent regulation factor.

[0075] Some data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the real situation; 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.

[0076] The working principle of the present invention: The present invention first determines multiple time periods of in-building distribution weak current wells. Through "on-demand monitoring" and "dynamic adaptation" of personalized time periods, it realizes an overall improvement in resource efficiency, equipment reliability, and the level of intelligent operation and maintenance. It obtains the characteristic output power of each time period. By forming historical rules to form a medium- and long-term scheduling baseline and a dual benchmark for triggering real-time correction amounts with dynamic changes, it improves the accuracy of the characteristic output power. Based on the characteristic output power, it determines the dynamic sampling frequency of each time period and uses the corresponding dynamic sampling frequency to collect the ambient temperature in the to-be-monitored in-building distribution weak current well. It 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 distribution weak current well. This step finds the intelligent regulation factor that can perform self-correction of temperature sensors when the ambient temperature changes, improving the reliability of the overall system. Based on the intelligent regulation factor, it conducts intelligent calibration on the ambient temperature of the to-be-monitored in-building distribution weak current well.

[0077] 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 indoor weak current well environment, characterized in that: include: A data sampling module, and a dynamic setting module and an intelligent calibration module connected thereto; The dynamic setting module is used to determine multiple time periods of the indoor weak current well based on the noise value in the indoor weak current well, the output power of the indoor power supply, and the peak time period of the regional 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; The data sampling module is used to collect the ambient temperature in the weak current well of the monitored room using the corresponding dynamic sampling frequency in each time period; The intelligent calibration module is used to perform cross-analysis on the ambient temperature collected in each time period to determine the intelligent control factor of the weak current well of the room to be monitored; Based on the intelligent control factor, the ambient temperature of the indoor weak current well to be monitored is intelligently calibrated.

2. According to claim 1, a room-divided weak current well environment intelligent monitoring system is characterized in that: Before determining the multiple time periods of the indoor weak current well based on the noise value in the indoor weak current well, the output power of the indoor power supply, and the peak time period of the area type, the method further includes: Obtain the peak time period of indoor power supply equipment usage corresponding to the area type where the current indoor weak current well is located; the area types include commercial areas, residential areas, industrial areas, office areas and transportation hubs; Extract the noise values ​​at multiple time points in the indoor weak current well 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; Extract the output power of the indoor power supply in the current indoor weak current well at multiple time points in the previous n days, obtain the average output power of the indoor power supply at multiple time points in the previous n days during the peak time period and the average output power of the indoor power supply at multiple time points in the previous n days during the non-peak time period.

3. According to claim 2, a room-divided weak current well environment intelligent monitoring system is characterized in that: Determining multiple time periods of the indoor weak current well based on the noise value in the indoor weak current well, the output power of the indoor power supply, and the peak time period of the area type includes: The peak time period or non-peak time period in which the average noise value and the average output power value are both less than the corresponding judgment threshold is marked as a non-target time period, and the non-target time period is divided into several time periods according to the corresponding fixed time length GCi; wherein the judgment threshold includes the noise threshold YDZ and the output power threshold GDZ; the value of i is 1 or 2, and the fixed time length includes fixed time length one GC1 and fixed time length two GC2; The average noise value YZ and the average output power GZ of the target period are extracted in sequence, and the corresponding dynamic duration DCi is obtained 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, the target time period is divided into several time periods according to the dynamic duration DC1; if the target time period is a non-peak time period, the target time period is divided 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]; β1 and β2 are both proportional adjustment coefficients, and β1+β2=1, β1<β2.

4. According to claim 1, the intelligent monitoring system for indoor weak current well environment is characterized in that: The obtaining of the characteristic output power of each time period includes: At a fixed time point on the day, the historical output power in each time period is obtained, and the historical output power is divided into several power groups according to the time period; the variance of the historical output power in each power group is obtained, and it is determined whether the variance is less than the corresponding definition threshold; if yes, the average value of the historical output power in the power group is obtained; if no, the historical output power with the largest difference from the mode in the power group is removed, and the variance judgment is performed again until the variance of the power group is less than the corresponding definition threshold, and the average value of the historical output power that has not been removed is obtained; At the starting time point of each time period, the average value QZ of the output power in the previous time period is obtained, the average value of the historical output power in the power group corresponding to the current time period is marked as the average value LDZ, the average value of the historical output power in the power group corresponding to the previous time period is marked as the average value LQZ, the ratio of the average value LDZ to the average value LQZ is marked as ratio one, and the average value QZ is multiplied by the ratio one to obtain the characteristic output power TL of the current time period.

5. The intelligent monitoring system for indoor weak current well environment according to claim 1 is characterized in that: The determining of the dynamic sampling frequency of each time period based on the characteristic output power includes: 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 yes, set the dynamic sampling frequency of the current time period to sampling frequency one F1; if no, jump to A2; wherein 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 of the current time period to sampling frequency F2; if no, based on the formula Get the dynamic sampling frequency DF of the current time period; where, is the round-up symbol, BG is the middle value of the power standard range; is the amplitude adjustment coefficient, and The value range is (0,2]; is a distribution coefficient greater than 0.

6. The intelligent monitoring system for indoor weak current well environment according to claim 1 is characterized in that: The method of using the corresponding dynamic frequency to collect the ambient temperature in the weak current well of the room to be monitored includes: Extract the dynamic sampling frequency of the current time period, and set the reference time point for collecting ambient temperature in the current time period based on the sampling frequency , at the baseline time point The ambient temperature is collected by several temperature sensors placed in the indoor weak current well; Based on the formula Get the base time point Collected ambient temperature data set ; Wherein, j is the temperature sensor number, and k is the number of the reference time point for collecting the ambient temperature; is the time deviation value; is the time point when the temperature sensor actually collects data, is the collection of all temperature sensors; Indicates temperature sensor The collected temperature value.

7. The intelligent monitoring system for indoor weak current well environment according to claim 6 is characterized in that: The step of determining the intelligent control factor of the weak current well in the room to be monitored includes: 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: (1)； 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; B2: Extract the ambient temperature data sets 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 , based on formula (2), the standard deviation value BC of the target sensor is obtained: (2)； in, are all proportional adjustment coefficients greater than 0, and , ; It means taking the majority, For some difference percentile of B3: Get the target sensor at this benchmark time point The difference BCZ between the collected ambient temperature and the reference temperature ZT is determined to 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 the difference BCZ, and mark the ratio as the intelligent control factor; if no, send out an abnormal signal recorded by the current target sensor, do not perform intelligent calibration on the current target sensor, and jump to B1 to remove the target sensor in this ambient temperature data set. The ambient temperature in the current benchmark time point is retrieved The reference temperature ZT and the intelligent control factors of each temperature sensor are retrieved.

8. The intelligent monitoring system for indoor weak current well environment according to claim 7 is characterized in that: The intelligent calibration of the ambient temperature of the indoor weak current well to be monitored based on the intelligent control factor includes: Extract the temperature of each temperature sensor at this benchmark time point in turn The intelligent control factor ZR is determined, and whether the difference BCZ corresponding to the intelligent control factor ZR is greater than the standard deviation BC; if yes, H is set to -1; if no, H is set to +1; Based on the formula JZ=φ×(BCZ+H×BCZ×ZR), the calibration difference JZ is obtained, and the reference time point The reference temperature ZT plus the calibration difference JZ is used to obtain the current temperature sensor at this reference time point The ambient temperature of the current temperature sensor at this reference time point The ambient temperature is used to set the warning information; where φ is the proportional adjustment coefficient, and the value range of φ is (0,2].

9. The intelligent monitoring system for indoor weak current well environment according to claim 8 is characterized in that: The setting of early warning information includes: Extract the temperature of each temperature sensor at this benchmark time point in turn The ambient temperature is determined to determine whether the ambient temperature exceeds the temperature warning threshold; if yes, a temperature exceeding limit signal of the current temperature sensor is issued; if no, no operation is performed.

10. A method for intelligent monitoring of indoor weak current well environment, based on the operation of an intelligent monitoring system for indoor weak current well environment according to any one of claims 1 to 9, characterized in that: The following steps are involved: Based on the noise value in the indoor weak current well, the output power of the indoor power supply, and the peak time period of the regional type, multiple time periods of the indoor weak current well are determined; the characteristic output power of each of the time periods is obtained, and the dynamic sampling frequency of each of the time periods is determined based on the characteristic output power; In each time period, the corresponding dynamic frequency is used to collect the ambient temperature in the weak current well of the room to be monitored; Cross-analyze the ambient temperatures collected in each time period to determine the intelligent control factor of the weak current well in the room to be monitored; Based on the intelligent control factor, the ambient temperature of the indoor weak current well to be monitored is intelligently calibrated.

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