A remote monitoring system and method for street lamp status based on big data

Abstract

The present invention discloses a remote monitoring system and method for street lamp status based on big data, which relates to the technical field of street lamp monitoring. It includes a local module, an edge node module and a platform module. A plurality of local modules are provided, and each local module is respectively matched with each street lamp. The local module includes: a light sensing unit for monitoring the brightness and heat of the street lamp to generate corresponding brightness data and heat data; a carrier wave unit for collecting the power data of the street lamp and transmitting the power data, brightness data and heat data of the street lamp to the corresponding edge node module. Through the light sensing unit and the carrier wave unit, the present invention comprehensively collects the brightness, heat and power data of the street lamp, forms multi-dimensional data analysis, improves the accuracy of fault identification, and reduces the interference of environmental changes and street lamp type differences on fault judgment through primary screening and secondary data comparison, effectively reducing the false alarm rate.

Description

Technical Field

[0001] The present invention relates to the technical field of street lamp monitoring, and specifically provides a remote monitoring system and method for street lamp status based on big data. Background Art

[0002] With the rapid advancement of the construction of smart cities, the intelligent level of urban lighting systems has been gradually improved. As an important part of urban infrastructure, the stable operation of street lamps is of great significance for ensuring the lives of citizens and enhancing the urban image.

[0003] However, traditional street lamp status monitoring methods have many limitations. They mainly rely on manual inspections and simple fault alarm systems, suffering from problems such as low efficiency, long maintenance cycles, and untimely problem responses.

[0004] After retrieval, Chinese Patent (Publication No.: CN117057784A) discloses a method and system for monitoring the operating status of street lamps. This patent obtains real-time street lamp lighting image data and street lamp light induction intensity data through a sensor device, and transmits the data to a server through a ZigBee wireless communication module. The to-be-trained real-time street lamp lighting image data is input into a target ResNet convolutional neural network model for training to obtain real-time street lamp status data. A street lamp maintenance measure database is established, and the status of the street lamp is judged based on the real-time street lamp status data and the street lamp light induction intensity data. If the street lamp is in a to-be-maintained state, a street lamp maintenance measure is generated for the to-be-maintained state street lamp according to the street lamp maintenance measure database, and the street lamp maintenance measure is sent to the server for warning. If the street lamp is in a to-be-inspected state, the to-be-inspected state street lamp is monitored through the sensor device.

[0005] In the prior art, traditional monitoring systems mainly rely on single sensors or simple circuit detection means, usually only able to obtain partial data of street lamps, and lacking an effective mechanism for multi-dimensional trend analysis of data and utilization of historical fault data, resulting in relatively low accuracy of fault identification. Therefore, the present invention proposes a remote monitoring system and method for street lamp status based on big data. Summary of the Invention

[0006] The purpose of the present invention is to provide a remote monitoring system and method for street lamp status based on big data to solve the problems mentioned in the above background art.

[0007] The present invention can be achieved through the following technical solutions: A remote monitoring system for street lamp status based on big data includes a local module, an edge node module, and a platform module;

[0008] A plurality of said local modules are provided, and each local module corresponds to each street lamp respectively. When each local module is working, it obtains the working data of the corresponding street lamp, and the local module includes:

[0009] A light sensing unit that monitors the brightness and heat of the street lamp and generates corresponding brightness data and heat data;

[0010] A carrier unit that collects the power data of the street lamp and transmits the power data, brightness data, and heat data of the street lamp to the corresponding edge node module;

[0011] The edge node module is used to receive the power data, brightness data, and heat data of each street lamp in the corresponding area, compare them with the built-in screening model, obtain the status fluctuation information of each street lamp that matches the screening model, and the edge node module transmits the status fluctuation information to the platform module. The edge node includes:

[0012] A comparison unit that regularly updates the screening model and compares it with the power data, brightness data, and heat data of each street lamp. The comparison unit marks the power data, brightness data, and heat data that do not match the screening model as abnormal and adds a timestamp;

[0013] The marked and timestamped parts in the power data, brightness data, and heat data of each street lamp are the status fluctuation information of the corresponding street lamp;

[0014] A transmission unit that transmits the marked and timestamped power data, brightness data, and heat data and the remaining normal power data, brightness data, and heat data to the platform module;

[0015] After receiving the power data, brightness data, and heat data of each edge node, the platform module stores the normal power data, brightness data, and heat data, and matches the marked and timestamped parts in the power data, brightness data, and heat data, that is, the status fluctuation information of each street lamp, with big data to predict the failure of the street lamp and issue a warning signal.

[0016] A further technical improvement of the present invention lies in: the comparison method of the screening model includes the following steps:

[0017] S1. Preset the normal ranges of the power data, brightness data, and heat data of each street lamp in the corresponding area;

[0018] The normal ranges of the power data, brightness data, and heat data are dynamically adjusted based on factors such as season, climate, and service life;

[0019] S2. After the power data, brightness data, and heat data fluctuate beyond their corresponding normal ranges, mark them and add a timestamp to the marked power data, brightness data, or heat data to generate status fluctuation information;

[0020] And during the comparison, analyze the power data, brightness data, and heat data independently;

[0021] S3. After the edge node module generates data fluctuation information, based on the actual geographical locations of each street lamp, add location information to the corresponding data fluctuation information;

[0022] S4. The edge node module collects the power data, brightness data, or heat data within the past time period of the status fluctuation information and generates an abnormal data set;

[0023] Finally, the edge node module transmits the abnormal data set, together with the power data, brightness data, and heat data, to the platform module.

[0024] A further technical improvement of the present invention is that: the edge node module generates secondary comparison data based on the average values of the brightness data and heat data of each street lamp in the corresponding area, comprehensively analyzes the brightness data and heat data of each street lamp in the corresponding area, reduces factors such as environmental changes and differences in street lamp types, and avoids marking a certain street lamp as faulty in isolation;

[0025] And when the edge node module generates secondary comparison data, smooth the brightness data and heat data of each street lamp in the corresponding area to reduce the impact of high-discrepancy brightness data or heat data on the whole.

[0026] A further technical improvement of the present invention is that: the edge node module is provided with a buffer;

[0027] When the brightness data or heat data does not match the screening model but does not exceed the secondary comparison data, the edge node module marks the brightness data or heat data as "to be confirmed abnormal" and transports it to the buffer. Within the subsequent N time windows, if the brightness data or heat data of this street lamp exceeds the secondary comparison data, or the number of "to be confirmed abnormal" marked reaches the preset judgment threshold, then convert the "to be confirmed abnormal" mark of the corresponding brightness data or heat data into an abnormal mark.

[0028] A further technical improvement of the present invention is that: before the edge node generates secondary comparison data, calculate whether the change trends of the brightness data and heat data in the corresponding area within the past time window are consistent. The calculation method of the change trend includes:

[0029] a1. Calculate the first-order differences of the brightness data and heat data of each street lamp;

[0030] a2. Based on the first-order differences of the brightness data and heat data of each street lamp, calculate the mean change in brightness data and the mean change in heat data for each street lamp in the area, and calculate the standard deviation of the change in brightness data and the standard deviation of the change in heat data;

[0031] a3. Calculate whether the change trends of the brightness data and heat data of each street lamp in the corresponding area over the past W moments are consistent through the Pearson correlation coefficient;

[0032] The formula is:

[0033]

[0034] where ρL,T(t) is the Pearson correlation coefficient;

[0035] ΔL i (t) is the change in brightness data of the i-th street lamp at time t;

[0036] ΔT i (t) is the change in heat data of the i-th street lamp at time t;

[0037] μΔL(t) is the mean of the changes in brightness data of all street lamps in the area at time t;

[0038] μΔT(t) is the mean of the changes in heat data of all street lamps in the area at time t;

[0039] a4. Average all the results of the Pearson correlation coefficient in a3 over W moments. The formula used is:

[0040] where ρL,T(t) is the Pearson correlation coefficient;

[0041] μ ρL,T (W) is the average Pearson correlation coefficient over W moments;

[0042] W end and W start are the end and start times of the W-th moment;

[0043] a5. Set the error range e and generate a judgment condition: |μ ρL,T (W) - 1 < e;

[0044] If the difference between the Pearson correlation coefficient μ ρL,T (W) and 1 is less than the preset error range e, it is considered that the change trends of the brightness data and heat data within the W-th moment are consistent and are used for secondary comparison data;

[0045] If the change trends of the brightness data and the heat data are inconsistent, the generation of the secondary comparison data is cancelled.

[0046] A further technical improvement of the present invention lies in that: based on the timestamps in the status fluctuation information, the platform module respectively generates abnormal detection axes of time series for the brightness data, heat data, and power data of the corresponding street lamps;

[0047] By calculating the time differences between timestamps of each abnormal detection axis, the abnormal frequency of the corresponding street lamp is judged.

[0048] A further technical improvement of the present invention lies in that: the platform module sets multiple frequency threshold ranges, and based on each frequency threshold range, the platform module sets corresponding computing resource allocation levels;

[0049] When the frequency threshold range is low, it indicates that the frequency of the status fluctuation information of the corresponding street lamp is high. The platform module will give priority to mobilizing more computing resources to increase the monitoring intensity of the status fluctuation information of this street lamp and timely match it with possible failure modes in the big data.

[0050] A further technical improvement of the present invention lies in that: the platform module constructs a multi-dimensional failure mode library based on the historical failure data of the street lamps, and associates the brightness data, heat data, and power data of the street lamps with the failures;

[0051] When the platform module makes the association, it compares the brightness data, heat data, and power data with the failure types, and extracts data features related to the failure types, including brightness data fluctuations, heat data fluctuations, and power data fluctuations.

[0052] The present invention also discloses a method for remotely monitoring the status of street lamps based on big data. The monitoring method includes the following steps:

[0053] Step 1: Monitor each street lamp through multiple local modules respectively to obtain the brightness data, heat data, and power data of the corresponding street lamp;

[0054] Step 2: Receive the brightness data, heat data, and power data of each local module in the corresponding area through the edge node module, and compare them with the brightness data, heat data, and power data of each street lamp through the screening model built in the edge node module;

[0055] Step 3: The edge node module marks the power data, brightness data, and heat data that do not match the screening model as abnormal, and adds timestamps to generate status fluctuation information;

[0056] Step 4: The platform module receives the power data, brightness data, and heat data of each edge node, stores the normal power data, brightness data, and heat data, and matches the marked and timestamped parts of the power data, brightness data, and heat data, that is, the status fluctuation information of each street lamp, with big data to predict street lamp failures and issue warning signals.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] Through the light sensing unit and the carrier unit, the present invention comprehensively collects the brightness, heat, and power data of street lamps, forms multi-dimensional data analysis, improves the accuracy of fault identification, and reduces the interference of environmental changes and street lamp type differences on fault judgment through primary screening and secondary data comparison, effectively reducing the false alarm rate;

[0059] At the same time, through the first-order difference and the Pearson correlation coefficient, the present invention analyzes the change trend and consistency of data, realizes the dynamic verification of abnormal data, further improves the accuracy of fault judgment, and through the frequency threshold range, the platform module can dynamically allocate computing resources to preferentially process the street lamp data with high-frequency status fluctuations, improving the real-time response ability of the system;

[0060] Furthermore, the present invention constructs a fault mode library by combining historical fault data, matches the real-time monitoring data with the fault mode, predicts street lamp failures in advance and issues early warnings, improving the intelligent operation and maintenance ability of the system BRIEF DESCRIPTION OF THE DRAWINGS

[0061] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0062] Figure 1 It is the system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, with reference to the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and effects according to the present invention.

[0064] Embodiment 1

[0065] Please refer to Figure 1 As shown, the present invention provides a remote monitoring system for street lamp status based on big data, including a local module, an edge node module, and a platform module;

[0066] There are multiple local modules, and each local module is respectively matched with each street lamp. When each local module is working, it obtains the working data of the corresponding street lamp, and the local module includes:

[0067] A light sensing unit monitors the brightness and heat of a street lamp to generate corresponding brightness data and heat data;

[0068] A carrier unit includes a power sensor incorporated into the street lamp circuit, collects the power data of the street lamp, and transmits the power data, brightness data, and heat data of the street lamp to the corresponding edge node module through power line carrier communication technology;

[0069] The edge node module is used to receive the power data, brightness data, and heat data of each street lamp in the corresponding area, compare them with the built-in screening model, obtain the status fluctuation information of each street lamp that matches the screening model, and the edge node module transmits the status fluctuation information to the platform module. The edge node includes:

[0070] A comparison unit regularly updates the screening model, compares the screening model with the power data, brightness data, and heat data of each street lamp, and the comparison unit marks the power data, brightness data, and heat data that do not match the screening model and adds a timestamp;

[0071] The marked and timestamp-added parts of the power data, brightness data, and heat data of each street lamp are the status fluctuation information of the corresponding street lamp;

[0072] A transmission unit transmits the marked and timestamp-added power data, brightness data, and heat data and the remaining normal power data, brightness data, and heat data to the platform module;

[0073] The comparison method of the screening model includes the following steps:

[0074] S1. Preset the normal ranges of the power data, brightness data, and heat data of each street lamp in the corresponding area;

[0075] The normal ranges of the power data, brightness data, and heat data are dynamically adjusted based on factors such as season, climate, and service life;

[0076] S2. After the power data, brightness data, and heat data fluctuate beyond the corresponding normal ranges, mark them and add a timestamp to the marked power data, brightness data, or heat data to generate status fluctuation information;

[0077] And during the comparison, the power data, brightness data, and heat data are analyzed independently;

[0078] S3. After the edge node module generates the data fluctuation information, based on the actual geographical locations of each street lamp, add location information to the corresponding data fluctuation information;

[0079] S4. The edge node module collects the power data, brightness data, or heat data of the status fluctuation information in the past time period and generates an abnormal data set;

[0080] Finally, the edge node module transmits the abnormal data set, together with the power data, brightness data, and heat data, to the platform module;

[0081] The platform module receives the power data, brightness data, and heat data of each edge node, stores the normal power data, brightness data, and heat data, and matches the marked and timestamped parts of the power data, brightness data, and heat data, that is, the status fluctuation information of each street lamp, with the big data to predict the faults of the street lamps and issue warning signals;

[0082] Based on the timestamps in the status fluctuation information, the platform module generates time series anomaly detection axes for the brightness data, heat data, and power data of the corresponding street lamps respectively;

[0083] By calculating the time differences between timestamps of each anomaly detection axis, the abnormal frequency of the corresponding street lamp is judged;

[0084] The platform module sets multiple frequency threshold ranges, and based on each frequency threshold range, sets the corresponding computing resource allocation levels;

[0085] When the frequency threshold range is low, it indicates that the corresponding street lamp has a high frequency of status fluctuation information. The platform module will preferentially mobilize more computing resources to increase the monitoring intensity of the status fluctuation information of this street lamp and promptly match it with the possible fault patterns in the big data;

[0086] And the platform module constructs a multi-dimensional fault pattern library based on the historical fault data of the street lamps, and associates the brightness data, heat data, and power data of the street lamps with the faults;

[0087] When the platform module makes the association, it compares the brightness data, heat data, and power data with the fault types, and extracts the data features related to the fault types, including brightness data fluctuations, heat data fluctuations, and power data fluctuations.

[0088] Embodiment 2

[0089] A remote monitoring system for street lamp status based on big data includes a local module, an edge node module, and a platform module;

[0090] There are multiple local modules, and each local module is respectively matched with each street lamp. When each local module works, it obtains the working data of the corresponding street lamp, and the local module includes:

[0091] The light sensing unit monitors the brightness and heat of the street lamp, generating corresponding brightness data and heat data;

[0092] The carrier unit collects the power data of the street lamp and transmits the power data, brightness data and heat data of the street lamp to the corresponding edge node module;

[0093] The edge node module is used to receive the power data, brightness data and heat data of each street lamp in the corresponding area, compare them with the built-in screening model, obtain the status fluctuation information of each street lamp matching the screening model, and the edge node module transmits the status fluctuation information to the platform module. The edge node includes:

[0094] The comparison unit regularly updates the screening model, compares the screening model with the power data, brightness data and heat data of each street lamp, and the comparison unit marks the power data, brightness data and heat data that do not match the screening model and adds a timestamp;

[0095] The marked and timestamp-added parts in the power data, brightness data and heat data of each street lamp are the status fluctuation information of the corresponding street lamp;

[0096] The transmission unit transmits the marked and timestamp-added power data, brightness data and heat data and the remaining normal power data, brightness data and heat data to the platform module;

[0097] The comparison method of the screening model includes the following steps:

[0098] S1. Preset the normal ranges of the power data, brightness data and heat data of each street lamp in the corresponding area;

[0099] The normal ranges of the power data, brightness data and heat data are dynamically adjusted based on factors such as season, climate and service life;

[0100] S2. After the power data, brightness data and heat data fluctuate beyond the corresponding normal ranges, mark them and add a timestamp to the marked power data, brightness data or heat data to generate status fluctuation information;

[0101] And during the comparison, the power data, brightness data and heat data are analyzed independently;

[0102] S3. After the edge node module generates the data fluctuation information, based on the actual geographical locations of each street lamp, add location information to the corresponding data fluctuation information;

[0103] S4. The edge node module collects the power data, brightness data or heat data within the past time period of the status fluctuation information and generates an abnormal data set;

[0104] The last edge node module transmits the abnormal data set, together with power data, brightness data, and heat data, to the platform module;

[0105] Compared with Embodiment 1, in Embodiment 2, the edge node module generates secondary comparison data based on the average values of the brightness data and heat data of each street lamp in the corresponding area, comprehensively analyzes the brightness data and heat data of each street lamp in the corresponding area, reduces factors such as environmental changes and street lamp type differences, and avoids marking a certain street lamp as faulty in isolation;

[0106] Moreover, when the edge node module generates secondary comparison data, it smooths the brightness data and heat data of each street lamp in the corresponding area to reduce the impact of high-discrepancy brightness data or heat data on the whole;

[0107] Before generating the secondary comparison data, the edge node calculates whether the change trends of the brightness data and heat data in the corresponding area within the past time window are consistent. The calculation method of the change trend includes:

[0108] a1. Calculate the first-order differences of the brightness data and heat data of each street lamp;

[0109] The formula for the first-order difference of the brightness data is: ΔL i (t) = L i (t) - L i (t - 1);

[0110] Among them, ΔL i (t) is the change amount of the brightness data of the i-th street lamp at time t;

[0111] L i (t) is the brightness data of the i-th street lamp at time t;

[0112] L i (t - 1) is the brightness data of the i-th street lamp at time t - 1;

[0113] The formula for the first-order difference of the heat data is: ΔT i (t) = T i (t) - T i (t - 1);

[0114] ΔT i (t) is the change amount of the heat data of the i-th street lamp at time t;

[0115] T i (t) is the heat data of the i-th street lamp at time t;

[0116] T i(t - 1) is the heat data of the i-th street lamp at time t - 1;

[0117] a2. Calculate the mean values of the change amounts of the brightness data and the change amounts of the heat data of each street lamp in the statistical area, and calculate the standard deviation of the change amount of the brightness data and the standard deviation of the change amount of the heat data;

[0118] The formula for the mean value of the change amount of the brightness data is:

[0119] Among them, μΔL(t) is the mean value of the change amounts of the brightness data of all street lamps in the area at time t;

[0120] N is the number of street lamps in this area;

[0121] ΔL i (t) is the change amount of the brightness data of the i-th street lamp at time t;

[0122] The standard deviation of the change amount of the brightness data is:

[0123]

[0124] Among them, σΔL(t) is the standard deviation of the change amounts of the brightness data of all street lamps in the corresponding area at time t, which reflects the fluctuation range of the brightness data. The larger its value, the greater the change range;

[0125] The formula for the mean value of the change amount of the heat data is:

[0126] Among them, μΔT(t) is the mean value of the change amounts of the heat data of all street lamps in the area at time t;

[0127] N is the number of street lamps in this area;

[0128] ΔT i (t) is the change amount of the brightness data of the i-th street lamp at time t;

[0129] The standard deviation of the change amount of the heat data is:

[0130]

[0131] Among them, σΔT(t) is the standard deviation of the change amounts of the heat data of all street lamps in the corresponding area at time t, which reflects the fluctuation range of the heat data. The larger its value, the greater the change range;

[0132] a3. Calculate whether the change trends of the brightness data and the heat data of each street lamp in the corresponding area in the past W moments are consistent;

[0133] In this embodiment, the Pearson correlation coefficient is used to measure whether the change trends between brightness and heat are consistent. The formula is as follows:

[0134]

[0135] where ΔL i (t) is the change amount of the brightness data of the i-th street lamp at time t;

[0136] ΔT i (t) is the change amount of the heat data of the i-th street lamp at time t;

[0137] μΔL(t) is the mean value of the change amounts of the brightness data of all the street lamps in the area at time t;

[0138] μΔT(t) is the mean value of the change amounts of the heat data of all the street lamps in the area at time t;

[0139] a4. Average all the results of the Pearson correlation coefficient in step a3 within W moments. The formula used is as follows:

[0140] where μ ρL,T (W) is the average Pearson correlation coefficient within W moments;

[0141] W end and W start are the end and start moments of the W moments;

[0142] μ ρL,T (W) If it is close to 1, it indicates that within the W moments, the change trends of the brightness data and the heat data are highly consistent;

[0143] If it is close to 0, it indicates that the relationship between the change trends of the brightness data and the heat data is weak, and there may be anomalies;

[0144] a5. Set an error range e as the condition for judging the change trends of the brightness data and the heat data of each street lamp. The judgment condition is: |μ ρL,T (W) - 1 < e;

[0145] If the difference between the Pearson correlation coefficient μ ρL,T (W) and 1 is less than the preset error range e, it is considered that the change trends of the brightness data and the heat data within the W moments are consistent and can be used for secondary comparison data;

[0146] If the change trends of the brightness data and the heat data are inconsistent, cancel the step of generating secondary comparison data;

[0147] And the edge node module is provided with a buffer;

[0148] When the brightness data or heat data does not match the screening model but does not exceed the secondary comparison data, the edge node module marks the brightness data or heat data as "to be confirmed abnormal" and sends it to the buffer. Within the subsequent N time windows, if the brightness data or heat data of the street lamp exceeds the secondary comparison data, or the number marked as "to be confirmed abnormal" reaches the preset judgment threshold, the "to be confirmed abnormal" mark of the corresponding brightness data or heat data is converted into an abnormal mark;

[0149] The platform module receives the power data, brightness data, and heat data of each edge node, stores the normal power data, brightness data, and heat data, and matches the marked and timestamp-added parts of the power data, brightness data, and heat data, that is, the status fluctuation information of each street lamp, with big data to predict the faults of the street lamps and issue warning signals;

[0150] The platform module generates abnormal detection axes in time series for the brightness data, heat data, and power data of the corresponding street lamps respectively based on the timestamps in the status fluctuation information;

[0151] By calculating the time differences between timestamps on the abnormal detection axes, the abnormal frequency of the corresponding street lamp is judged;

[0152] The platform module sets multiple frequency threshold ranges, and based on each frequency threshold range, sets the corresponding calculation resource allocation levels;

[0153] When the frequency threshold range is low, it indicates that the frequency of the status fluctuation information of the corresponding street lamp is high. The platform module will give priority to mobilizing more computing resources to increase the monitoring intensity of the status fluctuation information of the street lamp and promptly match it with the possible fault modes in the big data;

[0154] And the platform module constructs a multi-dimensional fault mode library based on the historical fault data of the street lamps, and correlates the brightness data, heat data, and power data of the street lamps with the faults;

[0155] When the platform module makes the correlation, it compares the brightness data, heat data, and power data with the fault types, and extracts the data features related to the fault types, including brightness data fluctuations, heat data fluctuations, and power data fluctuations.

[0156] The above are only the preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above in its preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications using the above-disclosed technical content to obtain equivalent embodiments with equivalent changes. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A remote monitoring system for street lamp status based on big data, comprising a local module, an edge node module and a platform module, characterized in that: A plurality of local modules are provided, and each local module is respectively matched with each street lamp, and the local module includes: A light sensing unit that monitors the brightness and heat of the street lamp and generates corresponding brightness data and heat data; A carrier unit that collects the power data of the street lamp and transmits the power data, brightness data and heat data of the street lamp to the corresponding edge node module; The edge node module includes: A comparison unit that periodically updates the screening model and compares the screening model with the power data, brightness data and heat data of each street lamp. The comparison unit marks the power data, brightness data and heat data that do not match the screening model as abnormal and adds a timestamp; A transmission unit that transmits the marked and timestamped power data, brightness data and heat data and the normal power data, brightness data and heat data to the platform module; After receiving the power data, brightness data and heat data of each edge node, the platform module matches the marked and timestamped parts of the power data, brightness data and heat data with big data, predicts the failure of the street lamp, and issues a warning signal; The comparison method of the screening model includes the following steps: S1. Preset the normal ranges of the power data, brightness data and heat data of each street lamp in the corresponding area; S2. After the power data, brightness data and heat data fluctuate beyond the corresponding normal ranges, mark them and add a timestamp to the marked power data, brightness data or heat data to generate status fluctuation information; S3. After generating the data fluctuation information, the edge node module adds location information to the corresponding data fluctuation information based on the actual geographical locations of each street lamp; S4. The edge node module collects the power data, brightness data or heat data within the past time period of the status fluctuation information and generates an abnormal data set; Finally, the edge node module transmits the abnormal data set and the power data, brightness data and heat data to the platform module together; The edge node module generates secondary comparison data based on the average values of the brightness data and heat data of each street lamp in the corresponding area; When generating the secondary comparison data, the edge node module smooths the brightness data and heat data of each street lamp in the corresponding area; The edge node module is provided with a buffer; When the brightness data or heat data does not match the screening model but does not exceed the secondary comparison data, the edge node module marks the brightness data or heat data as "to be confirmed as abnormal" and conveys it to the buffer; Within the subsequent N time windows, if the brightness data or heat data of the street lamp exceeds the secondary comparison data, or the number of "to be confirmed as abnormal" marks reaches the preset judgment threshold, the "to be confirmed as abnormal" mark of the corresponding brightness data or heat data is converted into an abnormal mark; Before generating the secondary comparison data, the edge node calculates whether the change trends of the brightness data and the heat data in the corresponding area are consistent within the past time window. The calculation method of the change trend includes: a1. Calculate the first-order differences of the brightness data and the heat data of each street lamp; a2. Based on the first-order differences of the brightness data and the heat data of each street lamp, statistically calculate the average change amounts of the brightness data and the heat data of each street lamp in the area, and calculate the standard deviation of the change amount of the brightness data and the standard deviation of the change amount of the heat data; a3. Calculate whether the change trends of the brightness data and the heat data of each street lamp in the past W moments in the corresponding area are consistent through the Pearson correlation coefficient; The formula is: ； Among them, is the Pearson correlation coefficient; is the change amount of the brightness data of the i-th street lamp at time t; is the change amount of the heat data of the i-th street lamp at time t; is the mean value of the change amount of the brightness data of all street lamps in the area within the time t; is the mean value of the change in the heat data of all street lights in the area within the time period t; a4. Average all the results of the Pearson correlation coefficient in step a3 within W moments. The formula used is: ； Among them, is the Pearson correlation coefficient; is the average Pearson correlation coefficient within W moments; and are the end and start times at time W; a5. Set the error range e and generate a judgment condition: ； If the Pearson correlation coefficient is less than the preset error range e from 1, it is considered that the change trends of the brightness data and the heat data within the W moment are consistent and can be used for secondary comparison data; If the change trends of the brightness data and the heat data are inconsistent, cancel the step of generating the secondary comparison data; The platform module generates an anomaly detection axis of time series for the brightness data, heat data, and power data of the corresponding street lamp respectively based on the timestamps in the state fluctuation information; Judge the anomaly frequency of the corresponding street lamp by calculating the time differences between the timestamps of the anomaly detection axes.

2. The remote monitoring system for street lamp status based on big data according to claim 1, characterized in that The platform module sets multiple frequency threshold ranges, and based on each frequency threshold range, sets the corresponding computing resource allocation level; When the frequency threshold range is low, it indicates that the frequency of the state fluctuation information of the corresponding street lamp is high. The platform module will give priority to mobilizing more computing resources to increase the monitoring intensity of the state fluctuation information of this street lamp and promptly match it with the possible failure modes in the big data.

3. A remote monitoring system for street lamp status based on big data according to claim 1, characterized in that, The platform module constructs a multi-dimensional failure mode library based on the historical failure data of the street lamp, and associates the brightness data, heat data, and power data of the street lamp with the failure; When the platform module makes the association, it compares the brightness data, heat data, and power data with the failure types, and extracts the data features related to the failure types, including the brightness data fluctuation, heat data fluctuation, and power data fluctuation.

4. The monitoring method of a remote monitoring system for street lamp status based on big data according to any one of claims 1-3, characterized in that, This monitoring method includes the following steps: Step 1. Monitor each street lamp through multiple local modules respectively to obtain the brightness data, heat data, and power data of the corresponding street lamp; Step 2. Receive the brightness data, heat data, and power data of each local module in the corresponding area through the edge node module, and compare them with the brightness data, heat data, and power data of each street lamp through the screening model built in the edge node module; Step 3. The edge node module marks the power data, brightness data, and heat data that do not match the screening model as anomalies, and adds timestamps to generate state fluctuation information; Step 4. The platform module stores the normal power data, brightness data, and heat data after receiving the power data, brightness data, and heat data of each edge node, and matches the marked and timestamp-added parts of the power data, brightness data, and heat data, that is, the state fluctuation information of each street lamp, with the big data to predict the failure of the street lamp and send out a warning signal.

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