LED advertisement screen system based on cloud service and intelligent monitoring method thereof

Abstract

The invention relates to the technical field of intelligent monitoring, and particularly discloses an LED advertising screen system based on cloud service and an intelligent monitoring method thereof, and the method comprises the steps: 1, presetting a monitoring period, carrying out the state detection of each lamp bead of an LED advertising screen, and generating a lamp bead fault signal according to a state detection result; 2, analyzing the fault conditions of the lamp beads in the past time period based on the lamp bead fault signals, and carrying out fault grade division on the lamp beads based on an analysis result; 3, dividing the LED advertisement screen to obtain a plurality of monitoring plates, evaluating the whole monitoring plates on the basis of the distribution condition of lamp beads with different fault levels in each monitoring plate, and generating a suggested replacement signal and a continuous maintenance signal on the basis of an evaluation result; by means of real-time monitoring, fault grade division, plate evaluation and the like, accurate monitoring and effective maintenance of the states of the lamp beads of the LED advertisement screen are achieved, the service life of the advertisement screen is prolonged, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to a cloud service-based LED advertising screen system and an intelligent monitoring method thereof. Background Art

[0002] As an important medium for modern urban publicity, the stability and reliability of LED advertising screens are of vital importance. However, due to the large number of lamp beads and their susceptibility to environmental and usage conditions, LED advertising screens will inevitably have lamp bead failures during use. Although traditional monitoring methods also include some intelligent detection technologies, such as fault detection based on image recognition and monitoring based on sensor networks, these methods are often limited by computing resources, data transmission and processing capabilities, resulting in low monitoring efficiency and difficulty in timely and accurate detection and processing of faults. Especially when the scale of the advertising screen is large, these problems are particularly prominent. It is impossible to classify the fault levels of the lamp beads according to the historical fault data of each lamp bead and the distribution of lamp bead faults, distinguish the severity of the faults, and thus conduct sector evaluation and achieve precise maintenance. Therefore, it is of great significance to develop an intelligent monitoring method that can monitor the status of LED advertising screen lamp beads in real time and give maintenance suggestions based on the fault situation. Summary of the invention

[0003] The purpose of the present invention is to provide a cloud service-based LED advertising screen system and an intelligent monitoring method thereof to solve the technical problems in the above background.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] In a first aspect, the present invention provides a cloud service-based LED advertising screen intelligent monitoring method, comprising:

[0006] Step 1: Preset the monitoring cycle, perform lamp bead status detection on each lamp bead of the LED advertising screen, and generate a lamp bead fault signal based on the status detection result;

[0007] Step 2: Based on the lamp bead fault signal, analyze the fault conditions of the lamp bead in the past period, and classify the fault levels of the lamp bead based on the analysis results;

[0008] Step 3: Divide the LED advertising screen into several monitoring panels, and evaluate the monitoring panels as a whole based on the distribution of lamp beads with different fault levels in each monitoring panel, and generate replacement recommendation signals and continuous maintenance signals based on the evaluation results. The specific process is as follows:

[0009] Based on any monitoring plate, the monitoring plate is divided into multiple pixel areas with a fixed number of lamp beads, and the regional fault level coefficient of each pixel area is calculated;

[0010] Based on the regional fault level coefficient, the fault conditions of the lamp beads in the pixel area are analyzed, and based on the analysis results, the pixel area is marked as a prone to fault area;

[0011] Extract all fault-prone areas, merge the interconnected fault-prone areas, obtain several unconnected fault-prone ranges, analyze the number of fault-prone areas and fault-prone ranges, and calculate the plate maintenance coefficient of the monitored plate;

[0012] Based on the plate maintenance coefficient, the monitoring plate is evaluated as a whole, and a recommended replacement signal and a continuous maintenance signal are generated based on the evaluation results.

[0013] As a further solution of the present invention: the process of performing lamp bead status detection on each lamp bead of the LED advertising screen and generating a lamp bead fault signal according to the status detection result is as follows:

[0014] Based on the arrangement position of each lamp bead in the LED advertising screen, the LED advertising screen is divided into multiple rows and columns;

[0015] Based on any row line, mark it as a row line to be detected, provide an open circuit detection voltage to the row line to be detected, and pull down the potential of other row lines except the row line to be detected to a first preset value;

[0016] Then detect whether there is a column line in each column line whose potential is lower than the second preset value. If so, determine that the lamp bead at the intersection of the column lines whose row line potential is lower than the second preset value is a lamp bead in an open circuit state, mark it as a faulty lamp bead, and generate a lamp bead fault signal.

[0017] As a further solution of the present invention: based on the lamp bead fault signal, the fault conditions of the lamp bead in the past period are analyzed, and based on the analysis results, the process of classifying the fault levels of the lamp bead is as follows:

[0018] Calculate the failure coefficient of the lamp beads based on the failure conditions of the lamp beads in the past period;

[0019] Preset a first fault coefficient threshold and a second fault coefficient threshold, and compare and analyze the fault coefficient with the first fault coefficient threshold and the second fault coefficient threshold respectively;

[0020] If the fault coefficient is less than or equal to the first threshold value of the fault coefficient, the lamp bead is classified as a third-level fault lamp bead;

[0021] If the fault coefficient is greater than the first threshold value of the fault coefficient and the fault coefficient is less than or equal to the second threshold value of the fault coefficient, the lamp bead is classified as a secondary fault lamp bead;

[0022] If the fault coefficient is greater than the second threshold value of the fault coefficient, the lamp bead is classified as a first-level faulty lamp bead.

[0023] As a further solution of the present invention: the process of obtaining the failure coefficient of the lamp bead is:

[0024] Extract the start time and end time of each consecutive lamp bead fault signal generated in the past period, and at the same time, extract the number of lamp bead maintenance times in the past period;

[0025] By formula: , calculate the failure coefficient G of the lamp bead, where W is the number of maintenance times, It is the interval time from the start time of the i-th continuous lamp bead fault signal generation to the most recent maintenance before it in the past period, is the start time of the i-th consecutive lamp bead fault signal generation in the past period, is the end time of the i-th continuous lamp bead fault signal generation in the past period, i is 1, 2, 3, ..., n, n is the total number of continuous lamp bead fault signals generated in the past period, and are preset proportional coefficients, and are all greater than 0. It is represented as the preset first hyperparameter, exp() is an exponential function with a natural constant as the base, and e is a natural constant.

[0026] As a further solution of the present invention: the process of obtaining the regional fault level coefficient is:

[0027] The number of lamp beads of each lamp bead fault level in the pixel area is extracted, and a weighted sum is performed based on the lamp bead fault level to obtain the regional fault level coefficient.

[0028] As a further solution of the present invention: based on the regional fault level coefficient, the fault condition of the lamp beads in the pixel area is analyzed, and based on the analysis result, the process of marking the pixel area as a prone-to-failure area is as follows:

[0029] Preset a regional fault level coefficient threshold, and compare and analyze the regional fault level coefficient of each pixel area with the regional fault level coefficient threshold;

[0030] If the regional fault level coefficient is greater than the regional fault level coefficient threshold, the pixel area is marked as a prone-to-failure area.

[0031] As a further solution of the present invention: the process of obtaining the plate maintenance coefficient is:

[0032] The number of fault-prone areas and fault-prone ranges are extracted respectively, and the ratio of the total number of fault-prone areas to the total number of fault-prone ranges is calculated to obtain a fault discrete value;

[0033] By formula: , calculate the plate maintenance coefficient WH, where SG is the sum of the fault level coefficients of all fault-prone intervals, SY is the total number of fault-prone areas, and GL is the discrete value of the fault, where , , are preset scaling factors, and are all greater than 0.

[0034] As a further solution of the present invention: based on the plate maintenance coefficient, the monitoring plate is evaluated as a whole, and the process of generating a replacement recommendation signal and a continuous maintenance signal based on the evaluation result is as follows:

[0035] Preset the plate maintenance coefficient threshold, and compare and analyze the plate maintenance coefficient with the plate maintenance coefficient threshold;

[0036] If the plate maintenance coefficient is less than or equal to the plate maintenance coefficient threshold, a continuous maintenance signal is generated;

[0037] If the plate maintenance coefficient is greater than the plate maintenance coefficient threshold, a replacement recommendation signal is generated.

[0038] In a second aspect, the present invention provides a cloud service-based LED advertising screen system, the system comprising:

[0039] Lamp bead detection module: preset monitoring cycle, perform lamp bead status detection on each lamp bead of the LED advertising screen, and generate lamp bead fault signal according to the status detection result;

[0040] Fault classification module: Based on the fault signal of the lamp bead, the fault condition of the lamp bead in the past period is analyzed, and the fault level of the lamp bead is classified based on the analysis result;

[0041] Fault assessment module: Divide the LED advertising screen into several monitoring panels, and evaluate the monitoring panels as a whole based on the distribution of lamp beads with different fault levels in each monitoring panel. Based on the evaluation results, a replacement recommendation signal and a continuous maintenance signal are generated.

[0042] Beneficial effects of the present invention:

[0043] The present invention performs status detection on each lamp bead of the LED advertising screen to determine whether the lamp bead is in a normal working state, and generates a fault signal when a lamp bead fault is detected; the lamp bead is classified into fault levels according to fault data of past time periods, including extracting key information such as the generation and end time of the fault signal, the number of maintenance times, and calculating the fault coefficient of the lamp bead through a formula, and the lamp bead is classified into primary, secondary and tertiary faults according to the size of the fault coefficient to distinguish the severity of the fault; in order to more comprehensively evaluate the status of the advertising screen, the present invention also divides the advertising screen into multiple monitoring panels, and in each panel, calculates the regional fault level coefficient according to the fault level of the lamp bead, and identifies the prone to fault areas, and obtains a number of unconnected prone to fault ranges by merging the interconnected prone to fault areas, and calculates the fault discrete value to reflect the distribution of the fault in the panel; based on According to the comparison result of the plate maintenance coefficient (calculated by comprehensively considering factors such as the total fault level coefficient of the prone-to-fault area, the number of prone-to-fault areas and the discrete value of faults) and the preset threshold, a replacement recommendation signal or a continuous maintenance signal is generated. If the plate maintenance coefficient is high, it indicates that the lamp beads in the plate are in serious condition, and it is recommended to replace some or all of the lamp beads; if it is low, just continue to execute according to the current maintenance plan; the present invention realizes real-time monitoring and accurate fault location of each lamp bead of the LED advertising screen. At the same time, the present invention also analyzes the fault conditions of the lamp beads in the past time period, divides the lamp beads into fault levels, and evaluates the monitoring plate as a whole according to the distribution of different fault levels, and generates a replacement recommendation signal and a continuous maintenance signal, which not only improves the monitoring efficiency, but also reduces the maintenance cost, helps to extend the service life of the advertising screen and improve the maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] Figure 1 is a flowchart of Embodiment 1 of the present invention;

[0046] Figure 2 It is a system block diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] Embodiment 1:

[0049] See also Figure 1 , Figure 2As shown, the cloud service-based LED advertising screen intelligent monitoring method described in the embodiment of the present invention includes:

[0050] Step 1: Preset the monitoring cycle, perform lamp bead status detection on each lamp bead of the LED advertising screen, and generate a lamp bead fault signal based on the status detection result;

[0051] Among them, the monitoring period includes but is not limited to: 1 hour, 1 day, 1 week;

[0052] It should be explained that the open and closed circuit detection is to determine whether the circuit is in an open or closed state by detecting the current or voltage state in the circuit. In the LED advertising screen, each lamp bead is connected to a specific circuit, so the current or voltage state of these circuits can be detected to determine whether the lamp bead is working normally;

[0053] In some implementation schemes, the process of performing a lamp bead status detection on each lamp bead of the LED advertising screen and generating a lamp bead fault signal according to the status detection result is as follows:

[0054] Based on the arrangement position of each lamp bead in the LED advertising screen, the LED advertising screen is divided into multiple rows and columns;

[0055] Based on any row line, mark it as a row line to be detected, provide an open circuit detection voltage to the row line to be detected, and pull down the potential of other row lines except the row line to be detected to a first preset value;

[0056] It needs to be explained that the open circuit detection voltage is greater than the conduction voltage of the lamp bead;

[0057] It should be explained that the first preset value is less than the conduction voltage of each lamp bead and greater than 0;

[0058] Then, it is detected whether there is a column line whose potential is lower than the second preset value among the column lines. If there is, it is determined that the lamp bead at the intersection of the column lines whose row line potential to be detected is lower than the second preset value is a lamp bead in an open circuit state, marked as a faulty lamp bead, and a lamp bead fault signal is generated;

[0059] Step 2: Based on the lamp bead fault signal, analyze the fault conditions of the lamp bead in the past period, and classify the fault levels of the lamp bead based on the analysis results, and classify the lamp bead into primary fault lamp bead, secondary fault lamp bead and tertiary fault lamp bead;

[0060] Among them, past periods include but are not limited to: 1 week, 1 month, 6 months;

[0061] In some embodiments, the start time and the end time of each consecutive lamp bead fault signal generated by the lamp bead in the past period are extracted, and at the same time, the number of maintenance times of the lamp bead in the past period is extracted;

[0062] It should be explained that if the lamp bead continuously generates a lamp bead fault signal within a continuous monitoring cycle, it is regarded as a continuous lamp bead fault signal generation, and the start time of the lamp bead fault signal generation is the start time of the continuous monitoring cycle of the continuous generation of the lamp bead fault signal, and the end time of the lamp bead fault signal generation is the end time of the continuous monitoring cycle of the continuous generation of the lamp bead fault signal;

[0063] It should be explained that the number of maintenance times refers to the total number of maintenance times of the location of the lamp bead in the past period, and the maintenance includes but is not limited to: replacing lamp beads and repairing circuits;

[0064] By formula: , calculate the failure coefficient G of the lamp bead, where W is the number of maintenance times, It is the interval time from the start time of the i-th continuous lamp bead fault signal generation to the most recent maintenance before it in the past period, is the start time of the i-th consecutive lamp bead fault signal generation in the past period, is the end time of the i-th continuous lamp bead fault signal generation in the past period, i is 1, 2, 3, ..., n, n is the total number of continuous lamp bead fault signals generated in the past period, and are preset proportional coefficients, and are all greater than 0. It is represented as the preset first hyperparameter, exp() is an exponential function with a natural constant as the base, and e is a natural constant;

[0065] The exemplary preset first hyperparameter is 0.01. In specific applications, the implementer can set it according to the specific situation, but it is necessary to ensure that the preset first hyperparameter is greater than 0;

[0066] Preset a first fault coefficient threshold and a second fault coefficient threshold, and compare and analyze the fault coefficient with the first fault coefficient threshold and the second fault coefficient threshold respectively;

[0067] wherein the first threshold value of the fault coefficient is less than the second threshold value of the fault coefficient;

[0068] If the fault coefficient is less than or equal to the first threshold value of the fault coefficient, it means that the fault of the lamp bead is relatively minor, and the lamp bead is classified as a third-level fault lamp bead;

[0069] If the fault coefficient is greater than the first threshold value of the fault coefficient and the fault coefficient is less than or equal to the second threshold value of the fault coefficient, it means that the fault condition of the lamp bead is relatively moderate, and the lamp bead is classified as a secondary fault lamp bead;

[0070] If the fault coefficient is greater than the second threshold value of the fault coefficient, it means that the fault of the lamp bead is relatively serious, and the lamp bead is classified as a first-level fault lamp bead;

[0071] Step 3: Divide the LED advertising screen into several monitoring panels, and evaluate the monitoring panels as a whole based on the distribution of lamp beads with different fault levels in each monitoring panel, and generate a replacement recommendation signal and a continuous maintenance signal based on the evaluation results:

[0072] It should be explained that the basis for the division of LED advertising screens is: according to the physical structure and size of the LED advertising screen, it is divided into multiple rectangular or irregular shaped panels, each panel contains a certain number of lamp beads and related circuits, so as to facilitate maintenance;

[0073] In some implementation schemes, based on any monitoring plate, the monitoring plate is divided into a plurality of pixel areas with a fixed number of lamp beads, and the regional fault level coefficient of each pixel area is calculated;

[0074] Among them, the process of obtaining the regional fault level coefficient is:

[0075] The number of lamp beads of each lamp bead fault level in the pixel area is extracted, and weighted summation is performed based on the lamp bead fault level to obtain the regional fault level coefficient;

[0076] It should be explained that the regional fault level coefficient is obtained by weighted summation based on the fault level of the lamp beads, wherein the weight of the total number of lamp beads of the first-level fault lamp beads is greater than the weight of the total number of lamp beads of the second-level fault lamp beads, which is greater than the weight of the total number of lamp beads of the third-level fault lamp beads;

[0077] Preset a regional fault level coefficient threshold, and compare and analyze the regional fault level coefficient of each pixel area with the regional fault level coefficient threshold;

[0078] If the regional fault level coefficient is less than or equal to the regional fault level coefficient threshold, it means that the overall fault situation of the lamp beads in the pixel area corresponding to the regional fault level coefficient is relatively minor;

[0079] If the regional fault level coefficient is greater than the regional fault level coefficient threshold, it means that the overall fault situation of the lamp beads in the pixel area corresponding to the regional fault level coefficient is relatively serious, that is, the pixel area is marked as a prone to fault area;

[0080] Extract all fault-prone areas, merge the interconnected fault-prone areas respectively, and obtain several unconnected fault-prone areas;

[0081] The number of fault-prone areas and fault-prone ranges are extracted respectively, and the ratio of the total number of fault-prone areas to the total number of fault-prone ranges is calculated to obtain a fault discrete value;

[0082] It should be explained that the fault discrete value represents the degree of discreteness of the fault distribution within the monitoring plate. The higher the value of the fault discrete value, the more dispersed the fault points are in the monitoring plate.

[0083] By formula: , calculate the plate maintenance coefficient WH, where SG is the sum of the fault level coefficients of all fault-prone intervals, SY is the total number of fault-prone areas, and GL is the discrete value of the fault, where , , are preset scaling factors, and are all greater than 0;

[0084] Preset the plate maintenance coefficient threshold, and compare and analyze the plate maintenance coefficient with the plate maintenance coefficient threshold;

[0085] If the panel maintenance coefficient is less than or equal to the panel maintenance coefficient threshold, it means that the fault condition of the lamp beads in the monitoring panel is relatively good, that is, a continuous maintenance signal is generated. This signal means that the current maintenance plan should be continued, and the faulty lamp beads should be regularly inspected and repaired to maintain the normal operation of the LED advertising screen.

[0086] If the panel maintenance coefficient is greater than the panel maintenance coefficient threshold, it means that the lamp bead failure in the monitoring panel is serious, that is, a replacement recommendation signal is generated, which means that some or all of the lamp beads in the monitoring panel should be replaced to avoid further spread of the fault and affect the overall performance and life of the LED advertising screen;

[0087] The technical solution of the embodiment of the present invention is mainly as follows: by performing status detection on each lamp bead of the LED advertising screen, it is determined whether the lamp bead is in a normal working state, and when a lamp bead fault is detected, a fault signal is generated; the lamp beads are classified into fault levels according to the fault data of past time periods, including extracting key information such as the generation and end time of the fault signal, the number of maintenance times, etc., and the fault coefficient of the lamp bead is calculated by a formula, and the lamp bead is classified into primary, secondary and tertiary faults according to the size of the fault coefficient to distinguish the severity of the fault; in order to more comprehensively evaluate the status of the advertising screen, the present invention also divides the advertising screen into multiple monitoring blocks, and in each block, the regional fault level coefficient is calculated according to the fault level of the lamp bead, and the prone to fault areas are identified, and a number of unconnected prone to fault ranges are obtained by merging the interconnected prone to fault areas, and the fault discrete value is calculated to reflect the fault in the block. Distribution; based on the comparison result of the plate maintenance coefficient (calculated by comprehensively considering factors such as the total fault level coefficient of the prone-to-fault area, the number of prone-to-fault areas and the discrete value of faults) and the preset threshold, a replacement recommendation signal or a continuous maintenance signal is generated. If the plate maintenance coefficient is high, it indicates that the lamp beads in the plate are in serious condition, and it is recommended to replace some or all of the lamp beads; if it is low, just continue to execute according to the current maintenance plan; the present invention realizes real-time monitoring and accurate fault location of each lamp bead of the LED advertising screen. At the same time, the present invention also analyzes the fault conditions of the lamp beads in the past time period, divides the lamp beads into fault levels, and evaluates the monitoring plate as a whole according to the distribution of different fault levels, and generates a replacement recommendation signal and a continuous maintenance signal, which not only improves the monitoring efficiency, but also reduces the maintenance cost, helps to extend the service life of the advertising screen and improve the maintenance efficiency.

[0088] Embodiment 2:

[0089] Based on Example 1, please refer to Figure 2 As shown, a cloud service-based LED advertising screen system according to an embodiment of the present invention includes:

[0090] Lamp bead detection module: preset monitoring cycle, perform lamp bead status detection on each lamp bead of the LED advertising screen, and generate lamp bead fault signal according to the status detection result;

[0091] Fault classification module: Based on the fault signal of the lamp bead, the fault condition of the lamp bead in the past period is analyzed, and the fault level of the lamp bead is classified based on the analysis result;

[0092] Fault assessment module: Divide the LED advertising screen into several monitoring panels, and evaluate the monitoring panels as a whole based on the distribution of lamp beads with different fault levels in each monitoring panel. Based on the evaluation results, a replacement recommendation signal and a continuous maintenance signal are generated.

[0093] 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 cloud service-based LED advertising screen intelligent monitoring method, characterized in that: include: Step 1: Preset the monitoring cycle, perform lamp bead status detection on each lamp bead of the LED advertising screen, and generate a lamp bead fault signal based on the status detection result; Step 2: Based on the lamp bead fault signal, analyze the fault conditions of the lamp bead in the past period, and classify the fault levels of the lamp bead based on the analysis results; Step 3: Divide the LED advertising screen into several monitoring panels, and evaluate the monitoring panels as a whole based on the distribution of lamp beads with different fault levels in each monitoring panel, and generate replacement recommendation signals and continuous maintenance signals based on the evaluation results. The specific process is as follows: Based on any monitoring plate, the monitoring plate is divided into multiple pixel areas with a fixed number of lamp beads, and the regional fault level coefficient of each pixel area is calculated; Based on the regional fault level coefficient, the fault conditions of the lamp beads in the pixel area are analyzed, and based on the analysis results, the pixel area is marked as a prone to fault area; Extract all fault-prone areas, merge the interconnected fault-prone areas, obtain several unconnected fault-prone ranges, analyze the number of fault-prone areas and fault-prone ranges, and calculate the plate maintenance coefficient of the monitored plate; Based on the plate maintenance coefficient, the monitoring plate is evaluated as a whole, and a recommended replacement signal and a continuous maintenance signal are generated based on the evaluation results.

2. According to the cloud service-based LED advertising screen intelligent monitoring method of claim 1, it is characterized in that: The process of detecting the status of each lamp bead of the LED advertising screen and generating a lamp bead fault signal based on the status detection result is as follows: Based on the arrangement position of each lamp bead in the LED advertising screen, the LED advertising screen is divided into multiple rows and columns; Based on any row line, mark it as a row line to be detected, provide an open circuit detection voltage to the row line to be detected, and pull down the potential of other row lines except the row line to be detected to a first preset value; Then detect whether there is a column line in each column line whose potential is lower than the second preset value. If so, determine that the lamp bead at the intersection of the column lines whose row line potential is lower than the second preset value is a lamp bead in an open circuit state, mark it as a faulty lamp bead, and generate a lamp bead fault signal.

3. According to the cloud service-based LED advertising screen intelligent monitoring method of claim 1, it is characterized in that: Based on the lamp bead fault signal, the fault conditions of the lamp bead in the past period are analyzed, and based on the analysis results, the process of classifying the fault levels of the lamp bead is as follows: Calculate the failure coefficient of the lamp beads based on the failure conditions of the lamp beads in the past period; Preset a first fault coefficient threshold and a second fault coefficient threshold, and compare and analyze the fault coefficient with the first fault coefficient threshold and the second fault coefficient threshold respectively; If the fault coefficient is less than or equal to the first threshold value of the fault coefficient, the lamp bead is classified as a third-level fault lamp bead; If the fault coefficient is greater than the first threshold value of the fault coefficient and the fault coefficient is less than or equal to the second threshold value of the fault coefficient, the lamp bead is classified as a secondary fault lamp bead; If the fault coefficient is greater than the second threshold value of the fault coefficient, the lamp bead is classified as a first-level faulty lamp bead.

4. According to the cloud service-based LED advertising screen intelligent monitoring method of claim 3, it is characterized in that: The process of obtaining the failure coefficient of the lamp bead is: Extract the start time and end time of each consecutive lamp bead fault signal generated in the past period, and at the same time, extract the number of lamp bead maintenance times in the past period; By formula: , calculate the failure coefficient G of the lamp bead, where W is the number of maintenance times, It is the interval time from the start time of the i-th continuous lamp bead fault signal generation to the most recent maintenance before it in the past period, is the start time of the i-th consecutive lamp bead fault signal generation in the past period, is the end time of the i-th continuous lamp bead fault signal generation in the past period, i is 1, 2, 3, ..., n, n is the total number of continuous lamp bead fault signals generated in the past period, and are preset proportional coefficients, and are all greater than 0. It is represented as the preset first hyperparameter, exp() is an exponential function with a natural constant as the base, and e is a natural constant.

5. According to the cloud service-based LED advertising screen intelligent monitoring method of claim 1, it is characterized in that: The process of obtaining the regional fault level coefficient is as follows: The number of lamp beads of each lamp bead fault level in the pixel area is extracted, and a weighted sum is performed based on the lamp bead fault level to obtain the regional fault level coefficient.

6. The cloud service-based LED advertising screen intelligent monitoring method according to claim 5 is characterized in that: Based on the regional fault level coefficient, the fault conditions of the lamp beads in the pixel area are analyzed, and based on the analysis results, the process of marking the pixel area as a prone to fault area is as follows: Preset a regional fault level coefficient threshold, and compare and analyze the regional fault level coefficient of each pixel area with the regional fault level coefficient threshold; If the regional fault level coefficient is greater than the regional fault level coefficient threshold, the pixel area is marked as a prone-to-failure area.

7. The cloud service-based LED advertising screen intelligent monitoring method according to claim 6 is characterized in that: The process of obtaining the plate maintenance coefficient is: The number of fault-prone areas and fault-prone ranges are extracted respectively, and the ratio of the total number of fault-prone areas to the total number of fault-prone ranges is calculated to obtain a fault discrete value; By formula: , calculate the plate maintenance coefficient WH, where SG is the sum of the fault level coefficients of all fault-prone intervals, SY is the total number of fault-prone areas, and GL is the discrete value of the fault, where , , are preset scaling factors, and are all greater than 0.

8. The cloud service-based LED advertising screen intelligent monitoring method according to claim 7 is characterized in that: Based on the plate maintenance coefficient, the monitoring plate is evaluated as a whole, and the process of generating a recommended replacement signal and a continuous maintenance signal based on the evaluation results is as follows: Preset the plate maintenance coefficient threshold, and compare and analyze the plate maintenance coefficient with the plate maintenance coefficient threshold; If the plate maintenance coefficient is less than or equal to the plate maintenance coefficient threshold, a continuous maintenance signal is generated; If the plate maintenance coefficient is greater than the plate maintenance coefficient threshold, a replacement recommendation signal is generated.

9. A cloud service-based LED advertising screen system, characterized in that: A cloud service-based LED advertising screen intelligent monitoring method applied to any one of claims 1-8, the system comprising: Lamp bead detection module: preset monitoring cycle, perform lamp bead status detection on each lamp bead of the LED advertising screen, and generate lamp bead fault signal according to the status detection result; Fault classification module: Based on the fault signal of the lamp bead, the fault condition of the lamp bead in the past period is analyzed, and the fault level of the lamp bead is classified based on the analysis result; Fault assessment module: Divide the LED advertising screen into several monitoring panels, and evaluate the monitoring panels as a whole based on the distribution of lamp beads with different fault levels in each monitoring panel. Based on the evaluation results, a replacement recommendation signal and a continuous maintenance signal are generated.

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