Multimedia equipment resource scheduling system and method based on big data

Abstract

The invention discloses a multimedia equipment resource scheduling system and method based on big data, and relates to the technical field of data analysis, and the method comprises the following steps: monitoring the operation state of multimedia equipment and the use behavior of a user, collecting data, and storing the data in an equipment analysis database; adjusting a multimedia equipment content resource scheduling strategy, and when a user puts forward an equipment use request, allocating and scheduling equipment resources according to the requirements of the user and the multimedia equipment content resource scheduling strategy; real-time monitoring is carried out according to the operation state data and the equipment fault data of the equipment, and when the equipment fault occurs, fault early warning is given out in time, and maintenance measures are taken; and recording distribution and scheduling data of equipment resources and equipment fault data. According to the method, the scheduling strategy of the content resources is dynamically adjusted based on user behavior data analysis, personalized resource allocation is carried out according to user preferences and equipment use conditions, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a multimedia device resource scheduling system and method based on big data. Background Art

[0002] Data analysis refers to the use of appropriate analytical methods to analyze large amounts of collected data, summarize, understand and digest them in order to maximize the development of data functions and play the role of data.

[0003] With the continuous expansion of multimedia device applications, especially in large venues, education and entertainment scenarios, device management and resource scheduling have become important technical challenges. Current technologies are mostly focused on basic device monitoring or resource scheduling, lacking a unified comprehensive solution, especially in real-time data analysis, fault prediction and content recommendation. There are the following deficiencies: lack of real-time monitoring and data collection. Existing solutions often only monitor key parameters, but the real-time data collection of device operating status and user behavior is not comprehensive enough, resulting in the inability to effectively identify and warn of device failures; the allocation and scheduling of multimedia content resources mostly fail to fully consider user preferences, making it difficult to provide users with personalized recommendations; the fault warning mechanism is imperfect, and common fault monitoring methods are usually based on static device thresholds, failing to detect device anomalies based on dynamic changes, and may result in missed reports or false reports. Summary of the invention

[0004] The purpose of the present invention is to provide a multimedia device resource scheduling system and method based on big data to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a multimedia device resource scheduling method based on big data, the method comprising the following steps: Step S1, continuously monitor the operating status of multimedia devices and the user's usage behavior in real time, continuously collect new data, and store the collected data in a device analysis database; the device analysis database includes the operating status data of multimedia devices, the user's usage behavior data and device failure data; Step S2: Analyze the user's usage behavior data stored in the device analysis database, and adjust the multimedia device content resource scheduling strategy according to the analysis results; when the user makes a device usage request, allocate and schedule device resources according to the user's needs and the multimedia device content resource scheduling strategy; Step S3: Real-time monitoring is performed based on the equipment operation status data and equipment failure data in the equipment analysis database. When equipment failure occurs, a fault warning is issued in time and maintenance measures are taken; Step S4: after each use of multimedia device resources, record the allocation and scheduling data of the device resources, as well as the device failure data, and input the recorded data into the device analysis database as the device failure data.

[0006] In step S1, the operating status of the multimedia device and the user's usage behavior are continuously monitored in real time, new data is continuously collected, and the collected data is stored in the device analysis database, specifically: Built-in sensors in multimedia devices, including temperature sensors, light sensors and infrared sensors, monitor the operating status of multimedia devices in real time, measure the temperature of multimedia devices through temperature sensors, detect the brightness of multimedia devices through light sensors, and collect environmental humidity data and environmental temperature through infrared sensors; The data collection function is embedded in the operating system of multimedia devices, and the user's operation behavior is recorded through software, including the playing time, the number of channel visits and the dwell time, the frequency of use of different multimedia devices, the user interaction and the user rating; The collected data is stored in the device analysis database for subsequent analysis.

[0007] In step S2, the user's usage behavior data stored in the device analysis database is analyzed, and the multimedia device content resource scheduling strategy is adjusted according to the analysis results; when the user makes a device usage request, the specific steps of allocating and scheduling device resources according to the user's needs and the multimedia device content resource scheduling strategy are as follows: Step S2-1, traverse the device analysis database, record the user's playback time, channel dwell time, frequency of use of different multimedia devices, user interaction and user rating data through software, and calculate the user's popularity of different channels in different multimedia devices. The macro expression is: ; Wherein, P represents the popularity of different channels in different multimedia devices; Q represents the frequency of use of different multimedia devices; F represents the number of visits to the channel; T represents the playback time of the multimedia device; t represents the length of stay on the channel; I represents the user interaction of different channels; obtained by dividing the number of comments on the channel by the total number of comments on all channels; R represents the average value of the user's rating of the channel, 0≤R≤10; obtained through the data collection box that pops up after each channel viewing. When the rating is not filled in, the user's rating is recorded as 10*k, and k represents the ratio of the user's viewing time to the average viewing time; α, β and γ represent the weights of the number of channel visits, the channel's stay time ratio and the user's subjective feelings, respectively, α+β+γ=1; Step S2-2, respectively calculate the popularity of different channels in different multimedia devices for different users, and establish a user viewing list, in which the users and the popularity of different channels in different multimedia devices are recorded. When the user makes a request to use the device, the multimedia device recommends channels in order of popularity. When the user requests to use a specified multimedia device, the multimedia device recommends channels in order of popularity in the specified multimedia device.

[0008] In step S3, real-time monitoring is performed based on the equipment operation status data and equipment failure data in the equipment analysis database. When equipment failure occurs, a fault warning is issued in time, and maintenance measures are taken as follows: Step S3-1, traverse the device analysis database, extract the running status data of the device in the device analysis database, obtain the temperature data, brightness data, ambient humidity data and ambient temperature data of the multimedia device in real time through the built-in sensor in the multimedia device, record the playback time and voltage data of the multimedia device; calculate the playback difference data of the multimedia device, the specific calculation formula is: ; Among them, s(x) represents the difference value of different multimedia devices at time x; u(x) represents the voltage value of the multimedia device at time x; L(x) represents the brightness value of the multimedia device at time x, and λ represents the brightness decay rate constant; T represents the playback time of the multimedia device; H(x) represents the environmental humidity data of the multimedia device at time x; R0 represents the initial internal resistance of the battery of the multimedia device; p represents the rated power of the multimedia device; μ represents the constant of the influence of humidity on the internal resistance of the battery; d(x) represents the surface temperature of the multimedia device at time x; D(x) represents the historical average surface temperature of the multimedia device; through the playback difference calculation formula of the multimedia device, the playback difference data s(x) of the multimedia device is calculated in real time; Step S3-2, traverse the device analysis database, extract device fault data, obtain temperature data, brightness data, ambient humidity data and ambient temperature data when the multimedia device fails, collect the playback time and voltage data of the multimedia device; substitute the formula described in step S3-1 to calculate the fault difference data s'(x) of the multimedia device; Step S3-3: Place s'(x) and s(x) on the same time scale for comparative analysis. Specifically, when the condition is met: When, s'(x) max and s'(x) min They represent the maximum and minimum difference values ​​of the fault difference data of the multimedia device, Indicates the average value of the fault difference data of the multimedia device. It is judged that the multimedia device is normal and no warning is issued. When a multimedia device fails, it is judged that a fault has occurred, a fault warning is issued in time, the maintenance personnel information pops up on the multimedia device, and information and text messages are pushed to the maintenance personnel to understand the cause of the fault and power outage, and to perform maintenance in time.

[0009] In step S4, after each use of multimedia device resources, the specific steps of recording the allocation and scheduling data of device resources and device failure data, and inputting the recorded data as device failure data into the device analysis database are: Step S4-1, after each use of multimedia device resources, record the allocation and scheduling data of device resources, and record the device failure data collected during the real-time monitoring process; Step S4-2: input the recorded data into the equipment analysis database as equipment failure data.

[0010] A multimedia device resource scheduling system based on big data, the multimedia device resource scheduling system comprising a data acquisition module, a data analysis module, a content resource scheduling module, a real-time monitoring and early warning module, a data storage and management module and a user interface module; The data collection module is used to collect the running status data of multimedia devices and the usage behavior data of users in real time, and store them in the device analysis database; The data analysis module is used to analyze the user's usage behavior data and adjust the multimedia content resource scheduling strategy according to the analysis results; The content resource scheduling module is used to recommend, allocate and schedule device content resources based on scheduling strategies and user requests; The real-time monitoring and early warning module is used to monitor the operating status of the equipment, detect faults and issue early warnings; The data storage and management module is used to store and manage all collected and generated data, including operation status data, user behavior data and fault data, to provide data support for data analysis, resource scheduling and fault detection; and to archive the resource allocation and fault records of used equipment for subsequent analysis and optimization; The user interface module is used to interact with the user, display recommended content and receive device requests; and display recommended channel content to the user.

[0011] The data acquisition module includes a sensor unit, an operation behavior acquisition unit and a data transmission unit; The sensor unit includes a temperature sensor, a light sensor and an infrared sensor, which are used to collect the temperature, brightness and ambient humidity and temperature data of the device; The operation behavior collection unit is embedded in the operating system and records the user's operation behavior through software, including playback time, channel visit times, stay time, usage frequency, interactivity and user rating; The data transmission unit is used to transmit the collected data to the equipment analysis database.

[0012] The data analysis module includes a user behavior analysis unit and a scheduling strategy generation unit; The user behavior analysis unit is used to traverse the device analysis database and calculate the user's popularity of different channels based on the playback time, channel visit times and stay time; The scheduling strategy generating unit is used to generate and adjust the scheduling strategy of the device content resources according to the analysis result.

[0013] The content resource scheduling module includes a resource allocation unit and a recommendation engine unit; The resource allocation unit is used to give priority to recommending channels with high user popularity according to the popularity and needs of users; The recommendation engine unit is used to generate a channel recommendation list personalized for the user to ensure the efficiency of using multimedia content resources.

[0014] The real-time monitoring and early warning module includes a status monitoring unit, a fault detection unit and an early warning push unit; The state monitoring unit is used to read the temperature, brightness, ambient humidity and voltage data of the device in real time, and calculate the playback difference data; The fault detection unit is used to extract fault data, calculate fault difference data, and place the playback difference data and the fault difference data on the same time scale for comparative analysis to determine whether the multimedia device has a fault; The early warning push unit is used to trigger an alarm and push maintenance information to maintenance personnel when a device fails.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses built-in sensors and user behavior data records of the operating system to collect multiple device operating status and user operation data such as temperature, brightness, humidity, etc. in real time to ensure the comprehensiveness and real-time nature of device status data; 2. The present invention dynamically adjusts the scheduling strategy of content resources based on user behavior data analysis, performs personalized resource allocation according to user preferences and device usage, and improves user experience; 3. The present invention calculates the changes in the equipment operating status in real time through the difference calculation formula, and compares the fault status differences, thereby realizing a dynamic and intelligent fault detection and early warning mechanism, and promptly reminding and dispatching maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the steps of a multimedia device resource scheduling system and method based on big data according to the present invention; Figure 2 The present invention is a structural schematic diagram of a multimedia device resource scheduling system and method based on big data. 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: Figure 1-Figure 2 As shown, the present invention provides a technical solution, such as Figure 1 As shown, a multimedia device resource scheduling method based on big data comprises the following steps: Step S1, continuously monitor the operating status of multimedia devices and the user's usage behavior in real time, continuously collect new data, and store the collected data in a device analysis database; the device analysis database includes the operating status data of multimedia devices, the user's usage behavior data and device failure data; Step S2: Analyze the user's usage behavior data stored in the device analysis database, and adjust the multimedia device content resource scheduling strategy according to the analysis results; when the user makes a device usage request, allocate and schedule device resources according to the user's needs and the multimedia device content resource scheduling strategy; Step S3: Real-time monitoring is performed based on the equipment operation status data and equipment failure data in the equipment analysis database. When equipment failure occurs, a fault warning is issued in time and maintenance measures are taken; Step S4: after each use of multimedia device resources, record the allocation and scheduling data of the device resources, as well as the device failure data, and input the recorded data into the device analysis database as the device failure data.

[0019] In step S1, the operating status of the multimedia device and the user's usage behavior are continuously monitored in real time, new data is continuously collected, and the collected data is stored in the device analysis database, specifically: Built-in sensors in multimedia devices, including temperature sensors, light sensors and infrared sensors, monitor the operating status of multimedia devices in real time, measure the temperature of multimedia devices through temperature sensors, detect the brightness of multimedia devices through light sensors, and collect environmental humidity data and environmental temperature through infrared sensors; The data collection function is embedded in the operating system of multimedia devices, and the user's operation behavior is recorded through software, including the playing time, the number of channel visits and the dwell time, the frequency of use of different multimedia devices, the user interaction and the user rating; The collected data is stored in the device analysis database for subsequent analysis.

[0020] In step S2, the user's usage behavior data stored in the device analysis database is analyzed, and the multimedia device content resource scheduling strategy is adjusted according to the analysis results; when the user makes a device usage request, the specific steps of allocating and scheduling device resources according to the user's needs and the multimedia device content resource scheduling strategy are as follows: Step S2-1, traverse the device analysis database, record the user's playback time, channel dwell time, frequency of use of different multimedia devices, user interaction and user rating data through software, and calculate the user's popularity of different channels in different multimedia devices. The macro expression is: ; Wherein, P represents the popularity of different channels in different multimedia devices; Q represents the frequency of use of different multimedia devices; F represents the number of visits to the channel; T represents the playback time of the multimedia device; t represents the length of stay on the channel; I represents the user interaction of different channels; obtained by dividing the number of comments on the channel by the total number of comments on all channels; R represents the average value of the user's rating of the channel, 0≤R≤10; obtained through the data collection box that pops up after each channel viewing. When the rating is not filled in, the user's rating is recorded as 10*k, and k represents the ratio of the user's viewing time to the average viewing time; α, β and γ represent the weights of the number of channel visits, the channel's stay time ratio and the user's subjective feelings, respectively, α+β+γ=1; Step S2-2, respectively calculate the popularity of different channels in different multimedia devices for different users, and establish a user viewing list, in which the users and the popularity of different channels in different multimedia devices are recorded. When the user makes a request to use the device, the multimedia device recommends channels in order of popularity. When the user requests to use a specified multimedia device, the multimedia device recommends channels in order of popularity in the specified multimedia device.

[0021] In step S3, real-time monitoring is performed based on the equipment operation status data and equipment failure data in the equipment analysis database. When equipment failure occurs, a fault warning is issued in time, and maintenance measures are taken as follows: Step S3-1, traverse the device analysis database, extract the running status data of the device in the device analysis database, obtain the temperature data, brightness data, ambient humidity data and ambient temperature data of the multimedia device in real time through the built-in sensor in the multimedia device, record the playback time and voltage data of the multimedia device; calculate the playback difference data of the multimedia device, the specific calculation formula is: ; Among them, s(x) represents the difference value of different multimedia devices at time x; u(x) represents the voltage value of the multimedia device at time x; L(x) represents the brightness value of the multimedia device at time x, and λ represents the brightness decay rate constant; T represents the playback time of the multimedia device; H(x) represents the environmental humidity data of the multimedia device at time x; R0 represents the initial internal resistance of the battery of the multimedia device; p represents the rated power of the multimedia device; μ represents the constant of the influence of humidity on the internal resistance of the battery; d(x) represents the surface temperature of the multimedia device at time x; D(x) represents the historical average surface temperature of the multimedia device; through the playback difference calculation formula of the multimedia device, the playback difference data s(x) of the multimedia device is calculated in real time; Step S3-2, traverse the device analysis database, extract device fault data, obtain temperature data, brightness data, ambient humidity data and ambient temperature data when the multimedia device fails, collect the playback time and voltage data of the multimedia device; substitute the formula described in step S3-1 to calculate the fault difference data s'(x) of the multimedia device; Step S3-3: Place s'(x) and s(x) on the same time scale for comparative analysis. Specifically, when the condition is met: When, s'(x) max and s'(x) min They represent the maximum and minimum difference values ​​of the fault difference data of the multimedia device, Indicates the average value of the fault difference data of the multimedia device. It is judged that the multimedia device is normal and no warning is issued. When a multimedia device fails, it is judged that a fault has occurred, a fault warning is issued in time, the maintenance personnel information pops up on the multimedia device, and information and text messages are pushed to the maintenance personnel to understand the cause of the fault and power outage, and to perform maintenance in time.

[0022] In step S4, after each use of multimedia device resources, the specific steps of recording the allocation and scheduling data of device resources and device failure data, and inputting the recorded data as device failure data into the device analysis database are: Step S4-1, after each use of multimedia device resources, record the allocation and scheduling data of device resources, and record the device failure data collected during the real-time monitoring process; Step S4-2: input the recorded data into the equipment analysis database as equipment failure data.

[0023] We monitor the multimedia display screen in a conference room. The system collects real-time data and predicts faults on these devices. The data collection and analysis process for these devices is as follows: Data collection uses built-in sensors and operating system data collection functions to monitor the status of the device and the user's usage behavior, and stores them in the device analysis database; the specific monitoring data is as follows: Temperature sensor: records the device temperature every hour, ranging from 30°C to 70°C.

[0024] Light Sensor: Records projection brightness in lumens, ranging from 1000 to 3000 lumens.

[0025] Infrared sensor: collects ambient humidity and temperature (ambient temperature 20°C to 30°C, humidity 40% to 60%).

[0026] User behavior data: including the user's channel playback time, number of visits, dwell time, device usage frequency, interaction and rating.

[0027] The temperature during the projector's operation fluctuated between 35°C and 65°C, and the light intensity was 2500 lumens; users played a channel for 2 hours in one day, with an average stay time of 30 minutes, 5 visits, and a user rating of 8 points.

[0028] Data analysis: The system analyzes user behavior data, adjusts content resource scheduling strategies based on user preferences and device usage frequency, and prioritizes channels that users are interested in. User popularity calculation: Equipment usage frequency Q=10; Channel visit times F = 5; Total playback time T = 120min; Channel stay time t=30min; User interaction I = 0.5 (indicates the proportion of comments on this channel to the total number of comments); The average user rating is R=8; Substituting into the formula: ; Wherein, P represents the popularity of different channels in different multimedia devices; Q represents the frequency of use of different multimedia devices; F represents the number of visits to the channel; T represents the playback time of the multimedia device; t represents the length of stay on the channel; I represents the user interaction of different channels; obtained by dividing the number of comments on the channel by the total number of comments on all channels; R represents the average value of the user's rating of the channel, 0≤R≤10; obtained through the data collection box that pops up after each channel viewing. When the rating is not filled in, the user's rating is recorded as 10*k, and k represents the ratio of the user's viewing time to the average viewing time; α, β and γ represent the weights of the number of channel visits, the channel's stay time ratio and the user's subjective feelings, respectively, α+β+γ=1; The weights α=0.3, β=0.4, γ=0.3 are used to calculate the user's popularity P for the channel; Recommendation strategy: Based on the calculated popularity, when a user makes a request to use the channel again, channels with higher popularity will be recommended first.

[0029] Fault detection and early warning, the system uses the difference data calculation formula to monitor the operating status of the equipment and analyze in real time whether a fault occurs.

[0030] Difference data calculation: Voltage u(x)=220V; Brightness L(x) = 2500 lumens; Ambient humidity H(x)=0.5; Surface temperature d(x) = 55∘C; The historical average surface temperature D(x) = 60∘C; Brightness decay rate constant λ = 0.02; Playing time T = 120 minutes; Equipment rated power p=300W; In an environment with a relative humidity of less than 50%, for a well-sealed battery, the effect of humidity on the internal resistance is small, but when the relative humidity exceeds 70% - 80% and lasts for a long time, the internal resistance of the battery may increase significantly. For a lead-acid battery placed in an environment with a humidity of 90% for a week, the internal resistance may increase by 10% - 30%. The humidity influence constant generally varies according to the specific environment; The initial internal resistance of lithium-ion batteries is about 10-50 milliohms, and the initial internal resistance of batteries for larger tablet computers is about 20-80 milliohms. In an environment with a relative humidity of 40%-60%, the effect of humidity on the internal resistance of lithium-ion batteries is small, and the influence coefficient can be ignored. When the relative humidity exceeds 70% and lasts for a long time, the internal resistance of lithium-ion batteries will gradually increase, and the humidity influence constant μ=0.1; in an environment with a high humidity of 80%-90% for about 1 week, the internal resistance may increase by about 10%-20%, and the humidity influence constant μ=0.15; The initial internal resistance of NiMH batteries is about 5-30 milliohms. In the normal use environment humidity range (40%-70%), the effect of humidity on the internal resistance of NiMH batteries is relatively small, and the humidity influence constant μ=0.05. However, when the humidity exceeds 80%, the negative electrode of the NiMH battery may be corroded by the water in the electrolyte for a long time, resulting in an increase in internal resistance. After 1-2 weeks, the internal resistance may increase by about 15%-25%, and the humidity influence constant μ=0.2. The initial internal resistance of alkaline dry batteries is about 70 milliohms to 200 milliohms. In a low humidity environment (30% - 50%), alkaline dry batteries are less affected by humidity, and the humidity influence coefficient can be approximated to 0. However, in a high humidity environment (70% - 90%), the metal shell and electrodes outside the battery may be corroded over time, and the internal electrolyte may also be diluted, thereby increasing the internal resistance. After exposure for 2 - 3 weeks, the internal resistance may increase by 20% - 30%, and the humidity influence constant μ=0.25; The difference value calculation formula is: ; Among them, s(x) represents the difference value of different multimedia devices at time x; u(x) represents the voltage value of the multimedia device at time x; L(x) represents the brightness value of the multimedia device at time x, and λ represents the brightness decay rate constant; T represents the playback time of the multimedia device; H(x) represents the environmental humidity data of the multimedia device at time x; R0 represents the initial internal resistance of the battery of the multimedia device; p represents the rated power of the multimedia device; μ represents the constant of the influence of humidity on the internal resistance of the battery; d(x) represents the surface temperature of the multimedia device at time x; D(x) represents the historical average surface temperature of the multimedia device; the playback difference data s(x) of the multimedia device is calculated in real time through the playback difference calculation formula of the multimedia device; s'(x) and s(x) are calculated respectively; s'(x) and s(x) are placed on the same time scale for comparative analysis. When the conditions are met When, s'(x) max and s'(x) minThey represent the maximum and minimum difference values ​​of the fault difference data of the multimedia device, Indicates the average value of the fault difference data of the multimedia device. It is judged that the multimedia device is normal and no warning is issued. When a multimedia device fails, it is judged that a fault has occurred, a fault warning is issued in time, the maintenance personnel information pops up on the multimedia device, and information and text messages are pushed to the maintenance personnel to understand the cause of the fault and power outage, and to perform maintenance in time.

[0031] Fault record, after each use of the equipment, the system records the allocated equipment resources and fault data. If the projector has an over-temperature warning on that day, these data will be entered into the equipment analysis database to provide a basis for future fault analysis and resource scheduling.

[0032] like Figure 2 As shown, a multimedia device resource scheduling system based on big data, the multimedia device resource scheduling system includes a data acquisition module, a data analysis module, a content resource scheduling module, a real-time monitoring and early warning module, a data storage and management module and a user interface module; The data acquisition module is used to collect the running status data of multimedia devices and the user's usage behavior data in real time, and store them in the device analysis database; the data analysis module is used to analyze the user's usage behavior data and adjust the multimedia content resource scheduling strategy according to the analysis results; the content resource scheduling module is used to recommend, allocate and schedule device content resources based on the scheduling strategy and user requests; The real-time monitoring and early warning module is used to monitor the operating status of the equipment, detect faults and issue early warnings; the data storage and management module is used to store and manage all collected and generated data; including operating status data, user behavior data and fault data, providing data support for data analysis, resource scheduling and fault detection; and archiving the used equipment resource allocation and fault records to facilitate subsequent analysis and optimization; the user interface module is used to interact with users, display recommended content and receive device requests; and display recommended channel content to users.

[0033] The data acquisition module includes a sensor unit, an operation behavior acquisition unit and a data transmission unit; The sensor unit includes a temperature sensor, a light sensor and an infrared sensor, which are used to collect the temperature, brightness and ambient humidity and temperature data of the device; The operation behavior collection unit is embedded in the operating system and records the user's operation behavior through software, including playback time, channel visit times, stay time, usage frequency, interactivity and user rating; The data transmission unit is used to transmit the collected data to the equipment analysis database.

[0034] The data analysis module includes a user behavior analysis unit and a scheduling strategy generation unit; The user behavior analysis unit is used to traverse the device analysis database and calculate the user's popularity of different channels based on the playback time, channel visit times and stay time; The scheduling strategy generating unit is used to generate and adjust the scheduling strategy of the device content resources according to the analysis result.

[0035] The content resource scheduling module includes a resource allocation unit and a recommendation engine unit; The resource allocation unit is used to give priority to recommending channels with high user popularity according to the popularity and needs of users; The recommendation engine unit is used to generate a channel recommendation list personalized for the user to ensure the efficiency of using multimedia content resources.

[0036] The real-time monitoring and early warning module includes a status monitoring unit, a fault detection unit and an early warning push unit; The state monitoring unit is used to read the temperature, brightness, ambient humidity and voltage data of the device in real time, and calculate the playback difference data; The fault detection unit is used to extract fault data, calculate fault difference data, and place the playback difference data and the fault difference data on the same time scale for comparative analysis to determine whether the multimedia device has a fault; The early warning push unit is used to trigger an alarm and push maintenance information to maintenance personnel when a device fails.

[0037] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A multimedia device resource scheduling method based on big data, characterized in that: The method comprises the following steps: Step S1, monitoring the operating status of multimedia devices and the user's usage behavior, collecting data, and storing the collected data in a device analysis database; The device analysis database includes the running status data of multimedia devices, the user's usage behavior data and the device failure data; Step S2: Analyze the user's usage behavior data and adjust the multimedia device content resource scheduling strategy; When a user makes a request to use a device, the device resources are allocated and scheduled according to the user's needs and the multimedia device content resource scheduling strategy; Step S3: Real-time monitoring is performed based on the equipment's operating status data and equipment failure data. When equipment failure occurs, a fault warning is issued in a timely manner, and maintenance measures are taken; Step S4: after each use of multimedia device resources, record the allocation and scheduling data of the device resources, as well as the device failure data, and input the recorded data into the device analysis database as the device failure data.

2. The method for scheduling multimedia device resources based on big data according to claim 1, characterized in that: In step S1, the operating status of the multimedia device and the user's usage behavior are continuously monitored in real time, new data is continuously collected, and the collected data is stored in the device analysis database, specifically: Built-in sensors in multimedia devices, including temperature sensors, light sensors and infrared sensors, monitor the operating status of multimedia devices in real time, measure the temperature of multimedia devices through temperature sensors, detect the brightness of multimedia devices through light sensors, and collect environmental humidity data and environmental temperature through infrared sensors; The data collection function is embedded in the operating system of multimedia devices, and the user's operation behavior is recorded through software, including the playing time, the number of channel visits and the dwell time, the frequency of use of different multimedia devices, the user interaction and the user rating; The collected data is stored in the device analysis database for subsequent analysis.

3. The method for scheduling multimedia device resources based on big data according to claim 2, characterized in that: In step S2, the user's usage behavior data stored in the device analysis database is analyzed, and the multimedia device content resource scheduling strategy is adjusted according to the analysis results; when the user makes a device usage request, the specific steps of allocating and scheduling device resources according to the user's needs and the multimedia device content resource scheduling strategy are as follows: Step S2-1, traverse the device analysis database, record the user's playback time, channel dwell time, frequency of use of different multimedia devices, user interaction and user rating data through software, and calculate the user's popularity of different channels in different multimedia devices. The specific expression is: ; Where P represents the popularity of different channels in different multimedia devices; Q represents the frequency of use of different multimedia devices; F represents the number of visits to the channel; T represents the playback time of the multimedia device; t represents the duration of stay on the channel; I represents the user interaction of different channels; R represents the average value of the user's rating of the channel, 0≤R≤10; α, β and γ represent the weights of the number of visits to the channel, the duration of stay on the channel and the subjective feelings of the user, respectively, α+β+γ=1; Step S2-2, respectively calculate the popularity of different channels in different multimedia devices for different users, and establish a user viewing list, in which the users and the popularity of different channels in different multimedia devices are recorded. When the user makes a request to use the device, the multimedia device recommends channels in order of popularity. When the user requests to use a specified multimedia device, the multimedia device recommends channels in order of popularity in the specified multimedia device.

4. The method for scheduling multimedia device resources based on big data according to claim 3, characterized in that: In step S3, real-time monitoring is performed based on the equipment operation status data and equipment failure data in the equipment analysis database. When equipment failure occurs, a fault warning is issued in time, and maintenance measures are taken as follows: Step S3-1, traverse the device analysis database, extract the running status data of the device in the device analysis database, obtain the temperature data, brightness data, ambient humidity data and ambient temperature data of the multimedia device in real time through the built-in sensor in the multimedia device, record the playback time and voltage data of the multimedia device; calculate the playback difference data of the multimedia device, the specific calculation formula is: ； Among them, s(x) represents the difference value of different multimedia devices at time x; u(x) represents the voltage value of the multimedia device at time x; L(x) represents the brightness value of the multimedia device at time x, and λ represents the brightness decay rate constant; T represents the playback time of the multimedia device; H(x) represents the environmental humidity data of the multimedia device at time x; R0 represents the initial internal resistance of the battery of the multimedia device; p represents the rated power of the multimedia device; μ represents the constant of the influence of humidity on the internal resistance of the battery; d(x) represents the surface temperature of the multimedia device at time x; D(x) represents the historical average surface temperature of the multimedia device; through the playback difference calculation formula of the multimedia device, the playback difference data s(x) of the multimedia device is calculated in real time; Step S3-2, traverse the device analysis database, extract device fault data, obtain temperature data, brightness data, ambient humidity data and ambient temperature data when the multimedia device fails, collect the playback time and voltage data of the multimedia device; substitute the formula described in step S3-1 to calculate the fault difference data s'(x) of the multimedia device; Step S3-3: Place s'(x) and s(x) on the same time scale for comparative analysis. Specifically, when the condition is met: When, s'(x) max and s'(x) min They represent the maximum and minimum difference values ​​of the fault difference data of the multimedia device, Indicates the average value of the fault difference data of the multimedia device. It is judged that the multimedia device is normal and no warning is issued. When a multimedia device fails, it is judged that a fault has occurred, a fault warning is issued in time, the maintenance personnel information pops up on the multimedia device, and information and text messages are pushed to the maintenance personnel to understand the cause of the fault and power outage, and to perform maintenance in time.

5. The method for scheduling multimedia device resources based on big data according to claim 1, characterized in that: In step S4, after each use of multimedia device resources, the specific steps of recording the allocation and scheduling data of device resources and device failure data, and inputting the recorded data as device failure data into the device analysis database are: Step S4-1, after each use of multimedia device resources, record the allocation and scheduling data of device resources, and record the device failure data collected during the real-time monitoring process; Step S4-2: input the recorded data into the equipment analysis database as equipment failure data.

6. A multimedia device resource scheduling system based on big data, characterized in that: The multimedia device resource scheduling system includes a data acquisition module, a data analysis module, a content resource scheduling module, a real-time monitoring and early warning module, a data storage and management module and a user interface module; The data collection module is used to collect the running status data of multimedia devices and the usage behavior data of users in real time, and store them in the device analysis database; The data analysis module is used to analyze the user's usage behavior data and adjust the multimedia content resource scheduling strategy according to the analysis results; The content resource scheduling module is used to recommend, allocate and schedule device content resources based on scheduling strategies and user requests; The real-time monitoring and early warning module is used to monitor the operating status of the equipment, detect faults and issue early warnings; The data storage and management module is used to store and manage all collected and generated data; Including operation status data, user behavior data and fault data, providing data support for data analysis, resource scheduling and fault detection; The resource allocation and fault records of used equipment will be archived to facilitate subsequent analysis and optimization; The user interface module is used to interact with the user, display recommended content and receive device requests; and display recommended channel content to the user.

7. The multimedia device resource scheduling system based on big data according to claim 6 is characterized in that: The data acquisition module includes a sensor unit, an operation behavior acquisition unit and a data transmission unit; The sensor unit includes a temperature sensor, a light sensor and an infrared sensor, which are used to collect the temperature, brightness and ambient humidity and temperature data of the device; The operation behavior collection unit is embedded in the operating system and records the user's operation behavior through software, including playback time, channel visit times, stay time, usage frequency, interactivity and user rating; The data transmission unit is used to transmit the collected data to the equipment analysis database.

8. The multimedia device resource scheduling system based on big data according to claim 7, characterized in that: The data analysis module includes a user behavior analysis unit and a scheduling strategy generation unit; The user behavior analysis unit is used to traverse the device analysis database and calculate the user's popularity of different channels based on the playback time, channel visit times and stay time; The scheduling strategy generating unit is used to generate and adjust the scheduling strategy of the device content resources according to the analysis result.

9. The multimedia device resource scheduling system based on big data according to claim 8, characterized in that: The content resource scheduling module includes a resource allocation unit and a recommendation engine unit; The resource allocation unit is used to give priority to recommending channels with high user popularity according to the popularity and needs of users; The recommendation engine unit is used to generate a channel recommendation list personalized for the user to ensure the efficiency of using multimedia content resources.

10. The multimedia device resource scheduling system based on big data according to claim 9, characterized in that: The real-time monitoring and early warning module includes a status monitoring unit, a fault detection unit and an early warning push unit; The state monitoring unit is used to read the temperature, brightness, ambient humidity and voltage data of the device in real time, and calculate the playback difference data; The fault detection unit is used to extract fault data, calculate fault difference data, and place the playback difference data and the fault difference data on the same time scale for comparative analysis to determine whether the multimedia device has a fault; The early warning push unit is used to trigger an alarm and push maintenance information to maintenance personnel when a device fails.

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