Building lighting monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of building illumination control, and discloses a building illumination monitoring method and system based on the Internet of Things, and the method comprises the following steps: 1, carrying out the partitioning analysis of the total region of a building, obtaining the illumination data of each illumination region, and calculating the illumination state representation value of each illumination region; wherein the illumination data comprises illumination brightness and illumination duration. The illumination data of the monitoring period, namely the illumination brightness and the illumination duration, are obtained, the illumination state characterization value is calculated based on the illumination brightness and the illumination duration, whether the illumination state is abnormal or not is judged according to the illumination state characterization value obtained through calculation, and the monitoring period of the abnormal illumination state is obtained. Therefore, the lighting state of the building can be monitored, abnormal lighting can be found in time, then effective adjustment is carried out, and influences caused by the poor lighting state or energy loss and waste caused by the high lighting state are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of building lighting control, and in particular to a monitoring method and system for building lighting based on the Internet of Things. Background Art

[0002] In the existing building lighting system, in order to ensure the lighting of users in the corridor, voice-controlled lights are often installed in the entire corridor. It can not only meet the lighting needs of users in the corridor, but also improve the comfort and safety of the living environment; However, at present, there is no system for monitoring the lighting status in the voice-controlled lighting devices of some buildings. If the lighting status is poor, it cannot provide sufficient lighting for the users passing through. If the lighting status exceeds the normal state, it will increase the energy consumption of electricity, resulting in energy waste. In addition, the lighting status cannot be judged in time, causing certain inconveniences. In view of this, we propose a building lighting monitoring method and system based on the Internet of Things. Summary of the invention

[0003] The object of the present invention is to provide a method and system for monitoring building lighting based on the Internet of Things to solve the above-mentioned problems.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A method and system for monitoring building lighting based on the Internet of Things, comprising the following steps: Step 1: Performing a zoning analysis on the total area of ​​a building, obtaining lighting data of each lighting area, and calculating a lighting state characterization value of each lighting area; wherein the lighting data includes lighting brightness and lighting duration; Step 2: Obtaining a lighting state characterization value of each monitoring period, evaluating the lighting state of the lighting area, and obtaining a lighting abnormality monitoring period; Step 3: Obtaining personnel activity information data of each lighting area, and calculating a personnel activity evaluation value; wherein the personnel activity information data includes the number of people passing through and the length of stay; Step 4: Obtaining a lighting state characterization value and a personnel activity evaluation value within a monitoring period, calculating a lighting state characterization deviation value of an abnormal monitoring period, and determining the cause of the abnormality.

[0005] As a further solution of the present invention: the process of obtaining the lighting state characterization value is: The corridor in the building is set as the analysis target, and the analysis target is divided into several lighting areas; A monitoring period is preset, the monitoring period is divided into a number of monitoring time points, and the time period between two adjacent monitoring time points is marked as a monitoring period; During the monitoring period, obtain the lighting brightness and lighting duration of each lighting area; The process of obtaining the lighting brightness of the lighting area is as follows: each lighting area is divided into a number of lighting sub-areas, the lighting brightness of each lighting sub-area is obtained, and the lighting brightness of all lighting sub-areas is summed and averaged to obtain the lighting brightness of the lighting area; The lighting brightness of each lighting area is processed by comparing it with the standard value of lighting brightness to obtain the lighting brightness ratio; The lighting duration is processed by ratio with the monitoring period to obtain the lighting time ratio; The lighting brightness ratio is multiplied by the lighting time ratio to obtain a lighting state representation value.

[0006] As a further solution of the present invention: obtaining the lighting anomaly monitoring period: During the monitoring cycle, a lighting state characterization value is obtained for each monitoring period, and the lighting state characterization value is compared with a lighting state characterization threshold range; If the lighting state characterization value is not within the lighting state characterization threshold range, generating a lighting state abnormality signal; The monitoring period corresponding to the generation of the abnormal lighting status signal is obtained, which is the abnormal monitoring period.

[0007] As a further solution of the present invention: the process of obtaining the personnel activity evaluation value is: During the monitoring period, obtain the number of people passing through each lighting area; In each lighting area, the number of people passing through the lighting area is processed by ratio with the monitoring period to obtain the turnover rate of people passing through during the monitoring period; The staff turnover rate during the monitoring period is compared with the staff turnover benchmark value during the monitoring period to obtain the staff turnover rate ratio; Obtain the personnel stay time ratio TL, effective time ratio YX and activity peak time ratio GF; Substitute the personnel stay time ratio TL, effective time ratio YX and activity peak time ratio GF into the formula , the time characterization value SJ is calculated, where a1, a2, and a3 are weight coefficients, a1+a2+a3=1; The personnel turnover rate ratio is multiplied by the time characterization value to obtain the personnel activity evaluation value.

[0008] As a further solution of the present invention: the process of obtaining the benchmark value of the staff turnover rate is as follows: Extract multiple monitoring cycles from the historical data, obtain the corresponding personnel turnover ratios in multiple monitoring time periods in each monitoring cycle, extract the maximum personnel turnover rate ratio in each monitoring cycle, sum and average the maximum personnel turnover rate ratios corresponding to multiple monitoring cycles, which is the benchmark value of the personnel turnover rate.

[0009] As a further solution of the present invention: the process of obtaining the personnel stay time ratio TL and the effective time ratio YX is: In each lighting area, obtain the time when each person starts to pass through the lighting area and the time when they leave the lighting area, and calculate the stay time of each person; The residence time of all personnel passing through the lighting area is summed and averaged to obtain the mean residence time of personnel; The mean of the personnel stay time is processed by ratio with the monitoring period to obtain the personnel stay time ratio TL; Get the effective time period of each person during their stay, integrate all the effective time periods, and get the total effective time period; The effective total period is processed with the monitoring period to obtain the effective time ratio YX; The process of integrating the valid time periods is as follows: marking the minimum value among the start values ​​of all valid time periods as the start value of the total valid time period, and marking the maximum value among the end values ​​of all valid time periods as the end value of the total valid time period.

[0010] As a further solution of the present invention: the process of obtaining the activity peak time ratio GF is as follows: The total effective time period is divided into several sub-time periods. In each sub-time period, the overlap period between the effective time period of the person during the stay time and the sub-time period is obtained, and all the overlap period are compared with the overlap period threshold; If the overlapping time period is greater than or equal to the overlapping time period threshold, it means that the person's effective activity time in this sub-time period is longer, and the person is marked as an active person in this sub-time period; Obtain the number of active persons marked in each sub-time period, extract the maximum value among the number of active persons marked in each sub-time period, and mark the sub-time period corresponding to the maximum value as the activity peak sub-time period; The activity peak sub-time period is compared with the monitoring period to obtain the activity peak time ratio GF.

[0011] As a further solution of the present invention: the process of obtaining the lighting state characterization deviation value and performing the judgment is as follows: Obtain the lighting status characterization value and personnel activity evaluation value corresponding to each monitoring time point during the monitoring period; The lighting status representation value and the personnel evaluation value at the normal monitoring time point are used as training data, and a model that can predict the lighting status representation value and the personnel evaluation value is trained through machine learning or statistical methods, which is the lighting model; Obtain the personnel activity assessment value during the abnormal monitoring period, bring it into the lighting model, and calculate the corresponding theoretical lighting state representation value; The difference between the theoretical lighting state characterization value and the lighting state characterization value corresponding to the abnormal monitoring period is calculated, and the absolute value of the difference is the lighting state characterization deviation value; comparing the lighting state characterization deviation value with a lighting state characterization deviation threshold; If the lighting state characterization deviation value is less than the lighting state characterization deviation threshold, an abnormal irrelevant signal is generated; If the lighting state characterization deviation value is greater than or equal to the lighting state characterization deviation threshold, an abnormality-related signal is generated.

[0012] As a further solution of the present invention: the process of determining the cause of the abnormality is as follows: Based on the generation of the abnormality-irrelevant signal, obtaining the illumination state characterization value corresponding to the generation of the abnormality-irrelevant signal and the corresponding monitoring period, and marking the corresponding monitoring period as the analysis period; If the lighting status characterization value is less than or equal to the minimum value of the lighting status characterization threshold range, the lighting device is inspected and checked; If the lighting state characterization value is greater than the maximum value of the lighting state characterization threshold range, then obtaining the ambient sound data of the lighting area during the analysis period, wherein the ambient sound data includes sound frequency and sound intensity; With the time of the analysis period as the X-axis and the sound frequency as the Y-axis, a two-dimensional model is established. The sound frequency is brought into the two-model model to draw a sound frequency curve. With the sound frequency threshold range of human activities as the baseline, two standard lines parallel to the X-axis are drawn, which are the first standard line and the second standard line. During the analysis period, the time value of the sound frequency being above the first standard line and below the second standard line is obtained, and the sum is performed to obtain the total non-personnel frequency time, and the total non-personnel sound frequency time is processed by ratio with the analysis period to obtain the non-personnel sound frequency time proportion PL; During the analysis period, the acquired sound intensities are summed and averaged to obtain a sound intensity mean, and the acquired sound intensity mean is ratioed with the sound intensity threshold to obtain a sound intensity ratio QJ; During the analysis period, the maximum value of the sound intensity is extracted, the maximum value of the sound intensity is subtracted from the sound intensity threshold, the absolute value of the difference is processed with the sound intensity threshold, and the sound intensity deviation ratio QD is obtained; Substitute the non-personnel sound frequency time ratio PL, sound intensity ratio QJ and sound intensity deviation ratio QD into the formula , calculate and obtain the abnormal sound characterization value SY, where s1, s2, and s3 are preset proportional coefficients; comparing the abnormal sound characterization value with an abnormal sound characterization threshold; If the abnormal sound characterization value is less than or equal to the abnormal sound characterization threshold, a sound signal without non-personnel influence is generated; Based on the generated sound signal without human influence, the lighting device is inspected and checked; If the abnormal sound characterization value is less than or equal to the abnormal sound characterization threshold, a non-personnel-affected sound signal is generated; Based on the generated sound signal with no human influence, a device with sound recognition sensitivity and sound screening is added to the lighting device.

[0013] As a further solution of the present invention: comprising: Lighting data acquisition and calculation module: performs partition analysis on the total area of ​​the building, obtains lighting data of each lighting area, and calculates the lighting status representation value of each lighting area; Lighting state judgment module: obtains the lighting state characterization value of each monitoring period, evaluates the lighting state of the lighting area, and obtains the lighting abnormality monitoring period; Personnel data acquisition and calculation module: obtains personnel activity information data in each lighting area and calculates personnel activity evaluation values; Abnormal analysis and judgment module: obtain the lighting status characterization value and personnel activity evaluation value within the monitoring period, calculate the lighting status characterization deviation value during the abnormal monitoring period, and determine the cause of the abnormality.

[0014] Beneficial effects of the present invention: (1) The present invention obtains lighting data of a monitoring period, namely, lighting brightness and lighting duration, and calculates a lighting status characterization value based on the lighting brightness and lighting duration. It determines whether the lighting status is abnormal by using the calculated lighting status characterization value, and obtains the monitoring period of abnormal lighting status. Thus, the lighting status of a building can be monitored, lighting abnormalities can be discovered in a timely manner, and effective adjustments can be made to reduce the impact caused by poor lighting status or energy loss and waste caused by high lighting status. (2) The present invention obtains information data on human activity during a monitoring period, i.e., the number of people passing through and the length of stay, calculates a human activity evaluation value, and trains a model of lighting state characterization values ​​and human activity evaluation values, i.e., a lighting model. The lighting state characterization deviation value of the abnormal monitoring period is calculated based on the lighting model to determine whether the abnormal lighting state is related to changes in human activity. If an abnormality-irrelevant signal is generated, the lighting state characterization value is judged. If the lighting state characterization value is less than or equal to the minimum value of the lighting state characterization threshold range, the lighting device is inspected and checked. If the lighting state characterization value is greater than the maximum value of the lighting state characterization threshold range, the sound characterization value of the analysis period is calculated to determine whether there is an impact caused by non-human sounds. Inspection or adjustment is performed based on the judgment result. In this way, when an abnormal lighting state occurs, the cause of the abnormality can be promptly determined, thereby reducing the energy consumption of the lighting device caused by the abnormality and the impact caused by insufficient lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 It is a flow chart of a monitoring method for building lighting based on the Internet of Things of the present invention; Figure 2 It is a schematic diagram of a lighting state characterization value acquisition process in a building lighting monitoring method based on the Internet of Things of the present invention; Figure 3 It is a schematic diagram of a process for obtaining a personnel activity evaluation value in a method for monitoring building lighting based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1 See also Figure 1 - Figure 3 As shown, the present invention is a monitoring method for building lighting based on the Internet of Things, comprising the following steps: Step 1: Perform a zoning analysis on the total area of ​​the building, obtain lighting data for each lighting area, and calculate the lighting status representation value of each lighting area; Among them, the lighting data includes lighting brightness and lighting duration; In some embodiments, a corridor in a building is set as an analysis target, and the analysis target is divided into a number of lighting areas; A monitoring period is preset, the monitoring period is divided into a number of monitoring time points, and the time period between two adjacent monitoring time points is marked as a monitoring period; During the monitoring period, obtain the lighting brightness and lighting duration of each lighting area; Exemplarily, the process of obtaining the lighting brightness of the lighting area is: dividing each lighting area into a plurality of lighting sub-areas, obtaining the lighting brightness of each lighting sub-area, and summing and averaging the lighting brightness of all lighting sub-areas to obtain the lighting brightness of the lighting area; The lighting brightness of each lighting area is processed by ratio with a standard lighting brightness value (the standard lighting brightness value is set by a person skilled in the art based on experience) to obtain a lighting brightness ratio; The lighting duration is processed by ratio with the monitoring period to obtain the lighting time ratio; The lighting brightness ratio is multiplied by the lighting time ratio to obtain a lighting state representation value; Step 2: Obtain the lighting status characterization value of each monitoring period, evaluate the lighting status of the lighting area, and obtain the lighting abnormality monitoring period; In some embodiments, within the monitoring cycle, the lighting state characterization value of each monitoring period is obtained, and the lighting state characterization value is compared with the lighting state characterization threshold range (the lighting state characterization threshold range is set by those skilled in the art based on historical experimental data summary); If the lighting state characterization value is within the lighting state characterization threshold range, it means that the lighting brightness and lighting duration of the monitoring period are outside the specified range, the lighting environment is in a normal state, and a lighting state normal signal is generated; If the lighting state characterization value is not within the lighting state characterization threshold range, it means that the lighting brightness or lighting duration during the monitoring period is outside the specified range, the lighting environment is in an abnormal state, and a lighting state abnormality signal is generated; The monitoring period corresponding to the generation of the normal lighting status signal is obtained, which is the normal monitoring period; the monitoring period corresponding to the generation of the abnormal lighting status signal is obtained, which is the abnormal monitoring period; The core technology of this embodiment is: by acquiring the lighting data of the monitoring period, that is, the lighting brightness and the lighting duration, and calculating the lighting state characterization value based on the lighting brightness and the lighting duration, judging whether the lighting state is abnormal by the calculated lighting state characterization value, and obtaining the monitoring period of the abnormal lighting state, the lighting state of the building can be monitored, the lighting abnormality can be discovered in time, and then effective adjustments can be made to reduce the impact caused by poor lighting conditions or energy loss and waste caused by high lighting conditions.

[0019] Example 2 Based on the above embodiments, the present invention is a monitoring method for building lighting based on the Internet of Things, which also includes the following steps: Step 3: Obtaining personnel activity information data in each lighting area and calculating the personnel activity evaluation value; Among them, the personnel activity information data includes the number of people passing through and the length of stay; In some embodiments, during the monitoring period, the number of people passing through each lighting area is obtained; In each lighting area, the number of people passing through the lighting area is processed by ratio with the monitoring period to obtain the turnover rate of people passing through during the monitoring period; The staff turnover rate during the monitoring period is compared with the staff turnover benchmark value during the monitoring period to obtain the staff turnover rate ratio; Exemplarily, the process of obtaining the benchmark value of the staff turnover rate during the monitoring period is as follows: extract multiple monitoring periods from the historical data, obtain the staff turnover ratios corresponding to multiple monitoring periods in each monitoring period, extract the maximum staff turnover ratio in each monitoring period, and sum and average the maximum staff turnover ratios corresponding to multiple monitoring periods, which is the benchmark value of the staff turnover rate; In each lighting area, obtain the time when each person starts to pass through the lighting area and the time when they leave the lighting area, and calculate the stay time of each person; The residence time of all personnel passing through the lighting area is summed and averaged to obtain the mean residence time of personnel; The mean of the personnel stay time is processed by ratio with the monitoring period to obtain the personnel stay time ratio TL; Get the effective time period of each person during their stay, integrate all the effective time periods, and get the total effective time period; It is necessary to explain that the effective time period is: the effective time period is the time when the person passing through the lighting area takes action during the time he stays in the lighting area, which is the effective time period; The effective total period is processed with the monitoring period to obtain the effective time ratio YX; Exemplarily, the process of integrating the valid time periods is as follows: marking the minimum value among the starting values ​​of all valid time periods as the starting value of the total valid time period, and marking the maximum value among the ending values ​​of all valid time periods as the ending value of the total valid time period; The total effective time period is divided into several sub-time periods. In each sub-time period, the overlap period between the effective time period and the sub-time period during the personnel's stay time is obtained (the overlap period means that the effective time period and the sub-time period have an intersection), and all the overlap periods are compared with the overlap period threshold (the overlap period threshold is half of the sub-time period); If the overlapping time period is less than the overlapping time period threshold, it means that the effective activity time of the person in the sub-time period is short, and the person is marked as an inactive person in the sub-time period; If the overlapping time period is greater than or equal to the overlapping time period threshold, it means that the person's effective activity time in this sub-time period is longer, and the person is marked as an active person in this sub-time period; Obtain the number of active persons marked in each sub-time period, extract the maximum value among the number of active persons marked in each sub-time period, and mark the sub-time period corresponding to the maximum value as the activity peak sub-time period; The activity peak sub-period is processed by ratio with the monitoring period to obtain the activity peak time ratio GF; Substitute the personnel stay time ratio TL, effective time ratio YX and activity peak time ratio GF into the formula , the time characterization value SJ is calculated, where a1, a2, and a3 are weight coefficients, a1+a2+a3=1; It needs to be explained that the meaning of the time characterization value is as follows: the time characterization value is calculated by the personnel residence time ratio, effective time ratio and activity peak time ratio; Among them, the personnel stay time ratio is calculated by the mean personnel stay time, which reflects the average stay time of personnel in the lighting area. That is, the larger the mean personnel stay time, the longer the required lighting time. The effective time ratio is calculated from the effective time period, which reflects the activity duration of personnel during their stay, that is, the longer the effective time period of personnel, the longer the required lighting time; The activity peak time ratio is calculated from the activity peak time period, which reflects the activity density of people in this time period. That is, the longer the activity peak time period is, the longer the required lighting time is, and the greater the required brightness is. The staff turnover ratio is multiplied by the time representation value to obtain the staff activity evaluation value; It needs to be explained that the meaning of the personnel activity evaluation value is: the personnel activity evaluation value is calculated by the time representation value and the personnel turnover rate ratio, that is, the larger the time representation value is, the longer the required lighting time is and the greater the lighting brightness is; the personnel turnover rate ratio is calculated by the personnel turnover rate, that is, the larger the personnel turnover rate is, the more personnel flow is in the lighting area, and the greater the required lighting brightness is; Step 4: Obtain the lighting status characterization value and personnel activity assessment value within the monitoring period, calculate the lighting status characterization deviation value during the abnormal monitoring period, and determine the cause of the abnormality; In some embodiments, the lighting state representation value and the personnel activity evaluation value corresponding to each monitoring time point in the monitoring period are obtained; The lighting status representation value and the personnel evaluation value at the normal monitoring time point are used as training data, and a model that can predict the lighting status representation value and the personnel evaluation value is trained through machine learning or statistical methods, which is the lighting model; Obtain the personnel activity assessment value during the abnormal monitoring period, bring it into the lighting model, and calculate the corresponding theoretical lighting state representation value; The difference between the theoretical lighting state characterization value and the lighting state characterization value corresponding to the abnormal monitoring period is calculated, and the absolute value of the difference is the lighting state characterization deviation value; Compare the lighting state characterization deviation value with the lighting state characterization deviation threshold (the lighting state characterization deviation threshold is set by a person skilled in the art based on historical experimental data); If the lighting state representation deviation value is less than the lighting state representation deviation threshold, it means that the lighting state abnormality is irrelevant to the change of personnel activities, and an abnormality-irrelevant signal is generated; If the lighting state representation deviation value is greater than or equal to the lighting state representation deviation threshold, it means that the lighting state abnormality is related to the change of personnel activities, and an abnormality-related signal is generated; Based on the generation of the abnormality-irrelevant signal, obtaining the illumination state characterization value corresponding to the generation of the abnormality-irrelevant signal and the corresponding monitoring period, and marking the corresponding monitoring period as the analysis period; If the lighting status characterization value is less than or equal to the minimum value of the lighting status characterization threshold range, it means that the lighting device may be damaged during the monitoring period, and the lighting device should be repaired and checked; If the lighting state representation value is greater than the maximum value of the lighting state representation threshold range, it means that other sounds may have affected the lighting device during the monitoring period, causing the lighting brightness to be higher or the lighting duration to be longer; Acquiring environmental sound data of the lighting area during the analysis period, wherein the environmental sound data includes sound frequency and sound intensity; With the time of the analysis period as the X-axis and the sound frequency as the Y-axis, a two-dimensional model is established. The sound frequency is brought into the two-model model to draw a sound frequency curve. With the sound frequency threshold range of human activities as the baseline, two standard lines parallel to the X-axis are drawn, which are the first standard line and the second standard line. The sound frequency threshold range of human activities is set by technicians in this field through experimental tests on people of different ages and heights; During the analysis period, the time value of the sound frequency being above the first standard line and below the second standard line is obtained, and the sum is performed to obtain the total non-personnel frequency time, and the total non-personnel sound frequency time is processed by ratio with the analysis period to obtain the non-personnel sound frequency time proportion PL; During the analysis period, the acquired sound intensities are summed and averaged to obtain the sound intensity mean, and the acquired sound intensity mean is ratioed with the sound intensity threshold (the sound intensity threshold is set by a person skilled in the art based on historical experimental test summary) to obtain the sound intensity ratio QJ; During the analysis period, the maximum value of the sound intensity is extracted, the maximum value of the sound intensity is subtracted from the sound intensity threshold, the absolute value of the difference is processed with the sound intensity threshold, and the sound intensity deviation ratio QD is obtained; Substitute the non-personnel sound frequency time ratio PL, sound intensity ratio QJ and sound intensity deviation ratio QD into the formula , calculate and obtain the abnormal sound characterization value SY, where s1, s2, and s3 are preset proportional coefficients; Compare the abnormal sound characterization value with the abnormal sound characterization threshold (the abnormal sound characterization threshold is set by a person skilled in the art based on historical experimental data); If the abnormal sound characterization value is less than or equal to the abnormal sound characterization threshold, it means that the impact of non-personnel sounds during the analysis period is small, and a sound signal without non-personnel impact is generated; Based on the generated sound signal without human influence, the lighting device is inspected and checked; If the abnormal sound characterization value is less than or equal to the abnormal sound characterization threshold, it means that the impact of non-personnel sound during the analysis period is relatively large, and a non-personnel impact sound signal is generated; Based on the generated sound signal with no human influence, a device for sound recognition sensitivity and sound screening is added to the lighting device; The core technology of this embodiment is: by obtaining personnel activity information data within the monitoring period, that is, the number of people passing through and the length of stay, calculating the personnel activity evaluation value, and training a model of lighting state characterization values ​​and personnel activity evaluation values, that is, a lighting model, and calculating the lighting state characterization deviation value of the abnormal monitoring period based on the lighting model, judging whether the lighting state abnormality is related to changes in personnel activities, if an abnormality-irrelevant signal is generated, judging the lighting state characterization value, if the lighting state characterization value is less than or equal to the minimum value of the lighting state characterization threshold range, inspecting and troubleshooting the lighting device, if the lighting state characterization value is greater than the maximum value of the lighting state characterization threshold range, calculating the sound characterization value of the analysis period, judging whether there is an impact caused by non-personnel sounds, and performing inspections or adjustments based on the judgment results, so that when an abnormal lighting state occurs, the cause of the abnormality can be promptly judged, thereby reducing the energy consumption of the lighting device caused by the abnormality and the impact caused by insufficient lighting.

[0020] Example 3 Based on the above embodiments, the present invention is a monitoring system for building lighting based on the Internet of Things, comprising: Lighting data acquisition and calculation module: performs partition analysis on the total area of ​​the building, obtains lighting data of each lighting area, and calculates the lighting status representation value of each lighting area; Lighting state judgment module: obtains the lighting state characterization value of each monitoring period, evaluates the lighting state of the lighting area, and obtains the lighting abnormality monitoring period; Personnel data acquisition and calculation module: obtains personnel activity information data in each lighting area and calculates personnel activity evaluation values; Abnormal analysis and judgment module: obtain the lighting status characterization value and personnel activity evaluation value within the monitoring period, calculate the lighting status characterization deviation value during the abnormal monitoring period, and determine the cause of the abnormality.

[0021] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A monitoring method for building lighting based on the Internet of Things, characterized in that: The following steps are involved: Step 1: Perform a zoning analysis on the total area of ​​the building, obtain lighting data for each lighting area, and calculate the lighting status representation value of each lighting area; Among them, the lighting data includes lighting brightness and lighting duration; Step 2: Obtain the lighting status characterization value of each monitoring period, evaluate the lighting status of the lighting area, and obtain the lighting abnormality monitoring period; Step 3: Obtaining personnel activity information data in each lighting area and calculating the personnel activity evaluation value; Among them, the personnel activity information data includes the number of people passing through and the length of stay; Step 4: Obtain the lighting status characterization value and personnel activity assessment value during the monitoring period, calculate the lighting status characterization deviation value during the abnormal monitoring period, and determine the cause of the abnormality.

2. The method for monitoring building lighting based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the lighting status characterization value is as follows: The corridor in the building is set as the analysis target, and the analysis target is divided into several lighting areas; A monitoring period is preset, the monitoring period is divided into a number of monitoring time points, and the time period between two adjacent monitoring time points is marked as a monitoring period; During the monitoring period, obtain the lighting brightness and lighting duration of each lighting area; The process of obtaining the lighting brightness of the lighting area is as follows: each lighting area is divided into a number of lighting sub-areas, the lighting brightness of each lighting sub-area is obtained, and the lighting brightness of all lighting sub-areas is summed and averaged to obtain the lighting brightness of the lighting area; The lighting brightness of each lighting area is processed by comparing it with the standard value of lighting brightness to obtain the lighting brightness ratio; The lighting duration is processed by ratio with the monitoring period to obtain the lighting time ratio; The lighting brightness ratio is multiplied by the lighting time ratio to obtain a lighting state representation value.

3. The method for monitoring building lighting based on the Internet of Things according to claim 2, characterized in that: Get the abnormal lighting monitoring period: During the monitoring cycle, a lighting state characterization value is obtained for each monitoring period, and the lighting state characterization value is compared with a lighting state characterization threshold range; If the lighting state characterization value is not within the lighting state characterization threshold range, generating a lighting state abnormality signal; The monitoring period corresponding to the generation of the abnormal lighting status signal is obtained, which is the abnormal monitoring period.

4. The method for monitoring building lighting based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the personnel activity evaluation value is as follows: During the monitoring period, obtain the number of people passing through each lighting area; In each lighting area, the number of people passing through the lighting area is processed by ratio with the monitoring period to obtain the turnover rate of people passing through during the monitoring period; The staff turnover rate during the monitoring period is compared with the staff turnover benchmark value during the monitoring period to obtain the staff turnover rate ratio; Obtain the personnel stay time ratio TL, effective time ratio YX and activity peak time ratio GF; Substitute the personnel stay time ratio TL, effective time ratio YX and activity peak time ratio GF into the formula , the time characterization value SJ is calculated, where a1, a2, and a3 are weight coefficients, a1+a2+a3=1; The personnel turnover rate ratio is multiplied by the time characterization value to obtain the personnel activity evaluation value.

5. The method for monitoring building lighting based on the Internet of Things according to claim 4, characterized in that: The process of obtaining the benchmark value of staff turnover rate is as follows: Extract multiple monitoring cycles from the historical data, obtain the corresponding personnel turnover ratios in multiple monitoring time periods in each monitoring cycle, extract the maximum personnel turnover rate ratio in each monitoring cycle, sum and average the maximum personnel turnover rate ratios corresponding to multiple monitoring cycles, which is the benchmark value of the personnel turnover rate.

6. The method for monitoring building lighting based on the Internet of Things according to claim 4, characterized in that: The process of obtaining the personnel stay time ratio TL and the effective time ratio YX is as follows: In each lighting area, obtain the time when each person starts to pass through the lighting area and the time when they leave the lighting area, and calculate the stay time of each person; The residence time of all personnel passing through the lighting area is summed and averaged to obtain the mean residence time of personnel; The mean of the personnel stay time is processed by ratio with the monitoring period to obtain the personnel stay time ratio TL; Get the effective time period of each person during their stay, integrate all the effective time periods, and get the total effective time period; The effective total period is processed with the monitoring period to obtain the effective time ratio YX; The process of integrating the valid time periods is as follows: marking the minimum value among the start values ​​of all valid time periods as the start value of the total valid time period, and marking the maximum value among the end values ​​of all valid time periods as the end value of the total valid time period.

7. The method for monitoring building lighting based on the Internet of Things according to claim 6, characterized in that: The process of obtaining the activity peak time ratio GF is as follows: The total effective time period is divided into several sub-time periods. In each sub-time period, the overlap period between the effective time period of the person during the stay time and the sub-time period is obtained, and all the overlap period are compared with the overlap period threshold; If the overlapping time period is greater than or equal to the overlapping time period threshold, it means that the person's effective activity time in this sub-time period is longer, and the person is marked as an active person in this sub-time period; Obtain the number of active persons marked in each sub-time period, extract the maximum value among the number of active persons marked in each sub-time period, and mark the sub-time period corresponding to the maximum value as the activity peak sub-time period; The activity peak sub-time period is compared with the monitoring period to obtain the activity peak time ratio GF.

8. The method for monitoring building lighting based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the lighting status characterization deviation value and making a judgment is as follows: Obtain the lighting status characterization value and personnel activity evaluation value corresponding to each monitoring time point during the monitoring period; The lighting status representation value and the personnel evaluation value at the normal monitoring time point are used as training data, and a model that can predict the lighting status representation value and the personnel evaluation value is trained through machine learning or statistical methods, which is the lighting model; Obtain the personnel activity assessment value during the abnormal monitoring period, bring it into the lighting model, and calculate the corresponding theoretical lighting state representation value; The difference between the theoretical lighting state characterization value and the lighting state characterization value corresponding to the abnormal monitoring period is calculated, and the absolute value of the difference is the lighting state characterization deviation value; comparing the lighting state characterization deviation value with a lighting state characterization deviation threshold; If the lighting state characterization deviation value is less than the lighting state characterization deviation threshold, an abnormal irrelevant signal is generated; If the lighting state characterization deviation value is greater than or equal to the lighting state characterization deviation threshold, an abnormality-related signal is generated.

9. The method for monitoring building lighting based on the Internet of Things according to claim 8, characterized in that: The process of determining the cause of the abnormality is as follows: Based on the generation of the abnormality-irrelevant signal, obtaining the illumination state characterization value corresponding to the generation of the abnormality-irrelevant signal and the corresponding monitoring period, and marking the corresponding monitoring period as the analysis period; If the lighting status characterization value is less than or equal to the minimum value of the lighting status characterization threshold range, the lighting device is inspected and checked; If the lighting state characterization value is greater than the maximum value of the lighting state characterization threshold range, then obtaining the ambient sound data of the lighting area during the analysis period, wherein the ambient sound data includes sound frequency and sound intensity; With the time of the analysis period as the X-axis and the sound frequency as the Y-axis, a two-dimensional model is established. The sound frequency is brought into the two-model model to draw a sound frequency curve. With the sound frequency threshold range of human activities as the baseline, two standard lines parallel to the X-axis are drawn, which are the first standard line and the second standard line. During the analysis period, the time value of the sound frequency being above the first standard line and below the second standard line is obtained, and the sum is performed to obtain the total non-personnel frequency time, and the total non-personnel sound frequency time is processed by ratio with the analysis period to obtain the non-personnel sound frequency time proportion PL; During the analysis period, the acquired sound intensities are summed and averaged to obtain a sound intensity mean, and the acquired sound intensity mean is ratioed with the sound intensity threshold to obtain a sound intensity ratio QJ; During the analysis period, the maximum value of the sound intensity is extracted, the maximum value of the sound intensity is subtracted from the sound intensity threshold, the absolute value of the difference is processed with the sound intensity threshold, and the sound intensity deviation ratio QD is obtained; Substitute the non-personnel sound frequency time ratio PL, sound intensity ratio QJ and sound intensity deviation ratio QD into the formula , calculate and obtain the abnormal sound characterization value SY, where s1, s2, and s3 are preset proportional coefficients; comparing the abnormal sound characterization value with an abnormal sound characterization threshold; If the abnormal sound characterization value is less than or equal to the abnormal sound characterization threshold, a sound signal without non-personnel influence is generated; Based on the generated sound signal without human influence, the lighting device is inspected and checked; If the abnormal sound characterization value is less than or equal to the abnormal sound characterization threshold, a non-personnel-affected sound signal is generated; Based on the generated sound signal with no human influence, a device with sound recognition sensitivity and sound screening is added to the lighting device.

10. A building lighting monitoring system based on the Internet of Things, the system implementing the method according to any one of claims 1 to 9, characterized in that: include: Lighting data acquisition and calculation module: performs partition analysis on the total area of ​​the building, obtains lighting data of each lighting area, and calculates the lighting status representation value of each lighting area; Lighting state judgment module: obtains the lighting state characterization value of each monitoring period, evaluates the lighting state of the lighting area, and obtains the lighting abnormality monitoring period; Personnel data acquisition and calculation module: obtains personnel activity information data in each lighting area and calculates personnel activity evaluation values; Abnormal analysis and judgment module: obtain the lighting status characterization value and personnel activity evaluation value within the monitoring period, calculate the lighting status characterization deviation value during the abnormal monitoring period, and determine the cause of the abnormality.

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