Polling scheduling method and device of video monitoring equipment and computer equipment

Abstract

This application relates to a polling scheduling method, apparatus, and computer device for video surveillance equipment. The method includes: establishing a communication connection with the video surveillance equipment and collecting multi-dimensional operational indicators based on a preset window length; determining the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment based on the multi-dimensional operational indicators and historical polling scheduling information of the video surveillance equipment; determining a comprehensive health score of the video surveillance equipment based on the connection success rate, response time score, equipment stability score, and historical reliability score; determining the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score; and performing polling scheduling of the video surveillance equipment through a task executor based on the polling scheduling frequency. This scheme can dynamically adjust the monitoring frequency and strategy according to the actual operating conditions of the equipment, ensuring system stability.

Description

Technical Field

[0001] This application relates to the field of video surveillance equipment management technology, and in particular to a polling scheduling method, apparatus and computer equipment for video surveillance equipment. Background Technology

[0002] Against the backdrop of the explosive growth in the scale of smart city video surveillance systems, the existing video surveillance equipment status monitoring mainly adopts the following technical solutions: fixed-period polling technology, which uses a uniform fixed interval to check the status of all devices and record the results through a timed task; manual polling technology, in which maintenance personnel manually configure different polling frequencies according to the importance of the devices based on their subjective experience; and simple threshold triggering technology, which shortens the polling interval when an anomaly occurs based on a single monitoring indicator such as the number of times the device goes offline, and then gradually restores the device after it recovers.

[0003] Existing video surveillance equipment status monitoring technologies suffer from the following problems: First, they lack multi-dimensional equipment status perception and health assessment technologies, relying solely on single or a few indicators to evaluate equipment, leading to a high misjudgment rate of video surveillance equipment health and an inability to identify progressive equipment failures and potential risks. Second, they lack adaptive dynamic polling scheduling algorithms based on equipment status, resulting in a significant waste of computational and network resources, insufficient monitoring of high-risk faulty equipment, and an inability to dynamically adjust polling strategies based on equipment business importance and real-time status. Third, they lack intelligent resource allocation and load balancing mechanisms for large-scale equipment, resulting in uneven system resource load, the absence of a quantitative evaluation mechanism for equipment priority, and a significant performance drop in scheduling algorithms at the scale of tens of thousands of devices, making it difficult to guarantee the overall performance and stability of the system.

[0004] Therefore, the problems that need to be solved are how to accurately judge the operating status of the equipment, dynamically adjust the monitoring frequency and strategy according to the actual operating conditions of the equipment, and reasonably allocate the system resources of the status monitoring and scheduling system of large-scale distributed video surveillance equipment and balance the workload of each node to ensure the stability of the system. Summary of the Invention

[0005] Based on this, it is necessary to provide a polling scheduling method, device, and computer equipment for video surveillance equipment that can dynamically adjust the monitoring frequency and strategy according to the actual operating conditions of the equipment, reasonably allocate system resources of the status monitoring and scheduling system of large-scale distributed video surveillance equipment, balance the workload of each node, and ensure the stability of the system, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a polling scheduling method for video surveillance equipment, the method being executed through a distributed video surveillance system, the method comprising:

[0007] Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length; the multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection;

[0008] Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined.

[0009] The overall health score of the video surveillance device is determined based on the connection success rate, the response time score, the device stability score, and the historical reliability score.

[0010] Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through the task executor.

[0011] In one embodiment, based on the multi-dimensional operational metrics and the historical polling scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined, including:

[0012] Based on the connection status of the communication connection, determine the number of successful connections and the number of failed connections of the video surveillance equipment, and determine the connection success rate of the video surveillance equipment based on the number of successful connections and the total number of connections.

[0013] Determine the response time score based on the polling response time;

[0014] Based on the historical polling scheduling information of the video surveillance equipment, determine the number of polling failures and the total number of polling attempts. Based on the number of polling failures, the total number of polling attempts, and the coefficient of variation of the equipment's polling status, determine the equipment stability score.

[0015] The time weight of the historical polling scheduling information is determined based on the time dimension of the historical polling scheduling information.

[0016] Based on the time weight, the time-weighted polling failure rate is determined, and based on the time-weighted polling failure rate, the basic reliability score is determined.

[0017] The historical reliability score is determined based on the basic reliability score and the device availability of the video surveillance equipment.

[0018] In one embodiment, the polling scheduling method for the video surveillance equipment further includes:

[0019] Based on the polling response time of video surveillance equipment in historical polling scheduling tasks, determine the mean response time and the standard deviation of the response time.

[0020] The ratio of the standard deviation of the response time to the mean of the response time is used as the coefficient of variation of the device polling status.

[0021] In one embodiment, the polling scheduling frequency of the video surveillance equipment is determined based on the overall health score, including:

[0022] Based on the health score, determine the health adjustment factor for the video surveillance equipment;

[0023] Determine the equipment operating status among multi-dimensional operating indicators, and determine the frequency factor of the status change of the video surveillance equipment;

[0024] Determine network quality factors based on network quality data;

[0025] Based on the service importance, health score, time interval since the last polling and scheduling, and network quality data of the video surveillance equipment, the service priority is determined, and the service importance factor is determined based on the service priority of the video surveillance equipment.

[0026] The polling scheduling frequency of the video surveillance equipment is determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor.

[0027] In one embodiment, determining the device operating status among multi-dimensional operating indicators and determining the state change frequency factor of the video surveillance device includes:

[0028] The number of times the operating status of the video surveillance equipment changes is determined based on the equipment operating status in the multi-dimensional operating indicators;

[0029] The frequency of the operating status changes of the video surveillance equipment is determined based on the number of operating status changes and the preset maximum expected number of changes.

[0030] The frequency factor of the state change of the video surveillance equipment is determined based on the frequency of the changes in the operating state.

[0031] In one embodiment, determining the polling scheduling frequency of the video surveillance equipment based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor includes:

[0032] The multi-factor weights are determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor.

[0033] Boundary constraints are applied to the multi-factor weights to determine the polling scheduling frequency of the video surveillance equipment.

[0034] In one embodiment, based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled by a task executor, including:

[0035] A distributed lock is created for the video surveillance device based on its device identifier.

[0036] The distributed lock is acquired through the task executor;

[0037] After successful acquisition, based on the polling scheduling frequency, the task executor performs polling scheduling on the video surveillance devices corresponding to the distributed lock.

[0038] Secondly, this application also provides a polling scheduling device for video surveillance equipment, which is applied to a distributed video surveillance system. The polling scheduling device for video surveillance equipment includes:

[0039] The operation indicator acquisition module is used to establish a communication connection with the video surveillance equipment and collect multi-dimensional operation indicators based on a preset window length. The multi-dimensional operation indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and communication connection status.

[0040] The device health rating determination module is used to determine the connection success rate, response time rating, device stability rating, and historical reliability rating of the video surveillance equipment based on the multi-dimensional operating indicators and the historical polling scheduling information of the video surveillance equipment.

[0041] The status awareness service module is used to determine the comprehensive health score of the video surveillance device based on the connection success rate, the response time score, the device stability score, and the historical reliability score.

[0042] The polling scheduling module is used to determine the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score, and to perform polling scheduling of the video surveillance equipment through the task executor based on the polling scheduling frequency.

[0043] Thirdly, this application also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0044] Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length; the multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection;

[0045] Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined.

[0046] The overall health score of the video surveillance device is determined based on the connection success rate, the response time score, the device stability score, and the historical reliability score.

[0047] Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through the task executor.

[0048] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0049] Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length; the multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection;

[0050] Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined.

[0051] The overall health score of the video surveillance device is determined based on the connection success rate, the response time score, the device stability score, and the historical reliability score.

[0052] Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through the task executor.

[0053] The aforementioned polling scheduling method, apparatus, and computer equipment for video surveillance equipment establish a communication connection with the video surveillance equipment and collect multi-dimensional operational indicators based on a preset window length. These multi-dimensional operational indicators include: polling response time of the video surveillance equipment, network quality data, equipment operating status, and communication connection status. Based on the multi-dimensional operational indicators and historical polling scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined. Based on the connection success rate, response time score, equipment stability score, and the historical reliability score, a comprehensive health score of the video surveillance equipment is determined. Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through a task executor. This solves the problems of insufficient assessment of the fault rate and potential risks of video surveillance equipment, high resource utilization when polling and scheduling video health equipment, low efficiency, and poor system stability when polling and scheduling video surveillance equipment. The above solution establishes a communication connection with video surveillance equipment and collects multi-dimensional operational indicators such as polling response time, network quality, operating status, and connection status according to a preset window. It then combines historical polling scheduling information to comprehensively calculate the equipment's connection success rate, response time score, stability score, and historical reliability score, thereby obtaining a comprehensive equipment health score. Based on this score, the polling scheduling frequency is adaptively determined, and the task executor completes the polling scheduling. This enables a comprehensive and accurate assessment of the equipment's status, improving the rationality, relevance, and efficiency of polling scheduling, and ensuring stable and reliable equipment operation. It can dynamically adjust the monitoring frequency and strategy according to the actual operating conditions of the equipment, ensuring system stability. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a polling scheduling method for video surveillance equipment in one embodiment;

[0055] Figure 2 This is a flowchart illustrating a method for determining device health scores in one embodiment;

[0056] Figure 3 This is a flowchart illustrating a method for determining the polling scheduling frequency of a video surveillance device in one embodiment.

[0057] Figure 4 This is a flowchart illustrating a method for determining the polling scheduling frequency of a video surveillance device in another embodiment;

[0058] Figure 5 This is a structural block diagram of the polling scheduling device of a video surveillance equipment in one embodiment;

[0059] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] In one embodiment, such as Figure 1 As shown, a polling scheduling method for video surveillance equipment is provided, which is executed through a distributed video surveillance system. In this embodiment, the method includes the following steps:

[0062] S110. Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on the preset window length.

[0063] Multi-dimensional operational metrics include: polling response time of video surveillance equipment, network quality data, equipment operating status, and communication connection status.

[0064] It's important to note that video surveillance equipment refers to monitoring devices that need to be polled and scheduled by a distributed video surveillance system through task executors. Polling and scheduling video surveillance equipment means that the distributed video surveillance system periodically accesses the status, video stream, and alarm information of the video surveillance equipment in a fixed order to ensure that the equipment is online, the footage is normal, and the data is viewable. A distributed video surveillance system includes: a monitoring center, distributed scheduling nodes, task executors, a Redis cluster, a Kafka message queue, and a time-series database. The monitoring center is responsible for device management and status monitoring of the video surveillance equipment. Distributed scheduling nodes are used to execute the polling and scheduling tasks for the video surveillance equipment. Task executors are the servers in the distributed system that actually access, poll, and collect camera status data. Distributed scheduling nodes are responsible for task allocation, weight calculation, dynamic scheduling, and load balancing decisions; task executors are responsible for receiving scheduling tasks, competing for device-specific distributed locks, and completing device polling and status collection at a specified frequency. The two have a collaborative relationship of centralized scheduling and distributed execution. The Redis cluster provides distributed locks and status caching. The Kafka message queue is used to implement inter-node communication and event distribution for the distributed scheduling nodes. Time-series databases are used to store historical status data of devices.

[0065] Response time refers to the time from when the system sends a status query command to the video device to when it receives the device's feedback result; it is an indicator of the device's service response performance. Connection status refers to the connectivity and validity of the link when a large-scale distributed video surveillance system establishes a communication connection with the video device through standard protocols; it is an indicator of whether the device can be accessed normally by the system. Network quality data refers to the transmission quality of the communication link between the system and the video device, including characteristics such as bandwidth, packet loss rate, and latency fluctuations; it is an indicator reflecting the stability of network transmission. Device operating status refers to the real-time working status of the video device's hardware and software, including whether the device is powered on normally and whether its functions are enabled; it is an indicator reflecting the device's own operating condition. The preset window length can be 24 hours. For example, if the network quality is poor, the network quality data can be 0.0; if the network quality is average, the network quality data can be 0.5; if the network quality is excellent, the network quality data can be 1.0.

[0066] Specifically, the distributed video surveillance system establishes communication connections with various video surveillance devices within the system through standard protocols such as ONVIF, GB / T28181, or RTSP, thereby collecting device status data. Through these established communication connections, the system collects polling response times, network quality data, device operating status, and communication connection status from the video surveillance devices. Based on a preset window length, the system stores the collected response times, network quality data, device operating status, and communication connection status in a sliding window to determine multi-dimensional operational indicators.

[0067] For example, based on a preset window length, the collected connection status, response time, network quality data, and device operation status of the video surveillance equipment can be stored in a sliding window. This can be achieved by continuously storing and maintaining the polling history data of the video surveillance equipment in the most recent 24 hours in a scrolling manner, automatically removing expired data over time, and always retaining the latest 24-hour response time, network quality data, device operation status, and communication connection status, providing real-time basis for stability scoring, status change frequency, network quality, and adaptive adjustment of polling strategies.

[0068] S120. Based on multi-dimensional operational indicators and historical polling scheduling information of video surveillance equipment, determine the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment.

[0069] Specifically, based on multi-dimensional operational metrics and historical polling and scheduling information of video surveillance equipment, the connection success rate (ConnectionRate), response time score (ResponseScore), device stability score (StabilityScore), and historical reliability score (ReliabilityScore) of the video surveillance equipment are determined. It should be noted that the status awareness service module needs to recalculate the connection success rate, response time score, device stability score, and historical reliability score every time the video surveillance equipment is polled and scheduled.

[0070] For example, such as Figure 2 As shown, based on the multi-dimensional operational indicators and the historical polling scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined, including:

[0071] S1201. Based on the connection status of the communication connection, determine the number of successful connections and the number of failed connections of the video surveillance equipment, and determine the connection success rate of the video surveillance equipment based on the number of successful connections and the total number of connections.

[0072] For example, the connection success rate of video surveillance equipment is calculated as follows:

[0073] ConnectionRate = SuccessfulConnections / TotalConnectionAttempts.

[0074] Among them, SuccessfulConnections represents the number of successful connections, TotalConnectionAttempts represents the total number of connections, and ConnectionRate represents the connection success rate of the video surveillance device.

[0075] S1202. Determine the response time score based on the polling response time.

[0076] For example, the response time score is calculated as follows:

[0077] ResponseScore = {

[0078] 1.0, if ResponseTime ≤ 100ms

[0079] 1.5 - ResponseTime / 200, if 100ms < ResponseTime ≤ 300ms

[0080] 0.5 - ResponseTime / 1000, if 300ms < ResponseTime ≤ 500ms

[0081] 0.0, if ResponseTime > 500ms

[0082] }

[0083] If the polling response time of the video surveillance device is 50ms, the response time score is 1.0, indicating excellent response speed; if the polling response time is 200ms, the response time score is 0.5, indicating average response speed; if the polling response time is 400ms, the response time score is 0.1, indicating poor response speed; and if the polling response time is 600ms, the response time score is 0.0, indicating a timeout.

[0084] S1203. Based on the historical polling scheduling information of the video surveillance equipment, determine the number of polling failures and the total number of polling attempts. Based on the number of polling failures, the total number of polling attempts, and the coefficient of variation of the equipment's polling status, determine the equipment stability score.

[0085] For example, the device stability score is determined as follows:

[0086] StabilityScore =1- (FailureCount / TotalPolls)×(1 +VarianceCoefficient / 100).

[0087] Where StabilityScore is the device stability score, FailureCount is the number of polling failures, TotalPolls is the total number of polling attempts, and VarianceCoefficient is the coefficient of variation of the device polling status.

[0088] For example, the coefficient of variation of the device polling status can be determined as follows:

[0089] Based on the polling response time of the video surveillance equipment in historical polling scheduling tasks, the mean response time and the standard deviation of the response time are determined; the ratio of the standard deviation of the response time to the mean response time is used as the coefficient of variation of the equipment's polling status.

[0090] For example, the coefficient of variation of the device polling status is determined as follows:

[0091] VarianceCoefficient = (σ / μ)×100%.

[0092] Where σ is the standard deviation of the response time and μ is the mean of the response time.

[0093] For example, if the response times of the video surveillance device for the most recent 10 polling iterations are: 120 milliseconds, 125 milliseconds, 118 milliseconds, 130 milliseconds, 122 milliseconds, 135 milliseconds, 119 milliseconds, 128 milliseconds, 123 milliseconds, and 140 milliseconds, then the average response time of the video surveillance device is:

[0094] μ= (120+125+118+130+122+135+119+128+123+140) / 10 = 126ms.

[0095] The standard deviation of the response time of this video surveillance equipment is:

[0096] σ=√[((120-126)²+(125-126)²+...+(140-126)²) / 10] = 7.3ms.

[0097] The coefficient of variation for this video surveillance equipment is:

[0098] VarianceCoefficient=(7.3 / 126)×100%=5.8%.

[0099] The above scheme, by calculating the mean and standard deviation of historical polling response times and using the ratio of the two as the coefficient of variation, can objectively quantify the degree of fluctuation in equipment response, providing accurate and reliable data for assessing equipment operational stability.

[0100] S1204. Determine the time weight of historical polling scheduling information based on the time dimension of historical polling scheduling information.

[0101] For example, the time weight of historical polling scheduling information is determined as follows:

[0102] TimeWeight(i) = 1 / (1 + 0.1×DaysAgo(i)).

[0103] Where TimeWeight(i) is the time weight of the historical polling scheduling information, and DaysAgo(i) is the time dimension of the historical polling scheduling information, that is, the number of days from the current time for the i-th historical polling data.

[0104] S1205. Determine the time-weighted polling failure rate based on the time weight, and determine the basic reliability score based on the time-weighted polling failure rate.

[0105] For example, the failure rate of time-weighted polling is determined as follows:

[0106] WeightedFailureRate =Σ(TimeWeight(i)) / 30.

[0107] WeightedFailureRate is the time-weighted polling failure rate.

[0108] The basic reliability score is determined as follows:

[0109] BaseScore = exp(-WeightedFailureRate).

[0110] BaseScore is the basic reliability score.

[0111] S1206. Determine the historical reliability score based on the basic reliability score and the equipment availability of the video surveillance equipment.

[0112] For example, the device stability score is determined as follows:

[0113] ReliabilityScore = BaseScore×AvailabilityRat.

[0114] The ReliabilityScore represents the device stability score, and AvailabilityRat represents the device availability rate of the video surveillance equipment. The device availability rate ranges from 0 to 1, where 0 = completely unavailable and 1 = 100% available. The device availability rate is calculated as: the actual online time of the device and the time it can be normally polled ÷ the total monitoring time of the system for that device.

[0115] The connection success rate, response time score, equipment stability score, and historical reliability score of video surveillance equipment can be used as equipment health scores.

[0116] For example, the statistical data for a 30-day operation of a video surveillance device may include: a total statistical time of 30 days × 24 hours, or 720 hours; actual operating time of 708 hours, with 12 hours of downtime; and 3 fault records, including one fault occurring on day 2, one on day 10, and one on day 25. The time weights corresponding to each fault can be: fault weight on day 2 = 1 / (1+0.1×2) = 0.833; fault weight on day 10 = 1 / (1+0.1×10) = 0.500; and fault weight on day 25 = 1 / (1+0.1×25) = 0.286. Furthermore, the time-weighted polling failure rate is (0.833+0.500+0.286) / 30 = 0.054 times / day; the base reliability score is exp(-0.054) = 0.947; the availability rate of the video surveillance equipment is 708 / 720 = 0.983 (98.3%); and the reliability score is 0.947×0.983 = 0.931.

[0117] The above scheme, through multi-dimensional operational indicators and historical polling scheduling information, comprehensively calculates device connection success rate, response time score, stability score and historical reliability score, which can comprehensively and accurately reflect the network connectivity quality, response performance, operational stability and long-term reliability of video surveillance equipment, making the equipment status assessment more objective and comprehensive, and providing accurate data support for subsequent polling scheduling strategies.

[0118] S130. Determine the comprehensive health score of the video surveillance equipment based on the connection success rate, response time score, equipment stability score, and historical reliability score.

[0119] Specifically, the system's status awareness service module determines the overall health score of the video surveillance equipment based on connection success rate, response time score, device stability score, and historical reliability score.

[0120] For example, the comprehensive health score of video surveillance equipment is determined as follows:

[0121] HealthScore=w1×ConnectionRate+w2×ResponseScore+w3×StabilityScore+w4×ReliabilityScore.

[0122] Among them, HealthScore is the comprehensive health score of video surveillance equipment, w1=0.4, w2=0.3, w3=0.2, w4=0.1.

[0123] S140. Based on the comprehensive health score, determine the polling scheduling frequency of the video surveillance equipment, and based on the polling scheduling frequency, perform polling scheduling on the video surveillance equipment through the task executor.

[0124] Specifically, based on the comprehensive health score of the video surveillance equipment, a reasonable polling frequency for the video surveillance equipment is set, and then the task executor conducts regular and orderly status inspections of the video surveillance equipment according to the polling frequency.

[0125] For example, the correspondence between the overall health score and the polling scheduling frequency is shown in Table 1.

[0126] Table 1

[0127]

[0128] For example, before polling and scheduling the video surveillance equipment via the task executor, the process also includes:

[0129] The weighted round-robin algorithm determines the weight of each task executor based on its maximum processing capacity and the load it has already carried; and based on the executor weight, round-robin scheduling tasks are assigned to each task executor.

[0130] The method for determining the actuator weights is as follows:

[0131] ExecutorWeight = MaxCapacity - CurrentLoad.

[0132] Wherein, ExecutorWeight is the executor weight, MaxCapacity is the maximum processing capacity of the task executor, and CurrentLoad is the load already carried by the task executor.

[0133] For example, executor A has a MaxCapacity of 100 and CurrentLoad of 30, so ExecutorWeight is 70; executor B has a MaxCapacity of 100 and CurrentLoad of 80, so ExecutorWeight is 20; and executor C has a MaxCapacity of 100 and CurrentLoad of 100, so ExecutorWeight is 0. In this case, the task allocation ratio for executors A, B, and C is 70:20:0 = 78%:22%:0%.

[0134] The above scheme uses a weighted round-robin algorithm to dynamically calculate the weight of the executor by combining the maximum processing capacity of the task executor with the load it already carries, and then allocates the round-robin scheduling tasks according to the weight, which can achieve balanced task load distribution and improve the overall scheduling stability and execution efficiency.

[0135] For example, such as Figure 3 As shown, the polling scheduling frequency of video surveillance equipment is determined based on the comprehensive health score, including:

[0136] S1401. Determine the health adjustment factor of the video surveillance equipment based on the health score.

[0137] For example, the HealthFactor is calculated as follows:

[0138] HealthFactor = {

[0139] 0.5, if HealthScore < 0.3

[0140] 2.25 - 2.5×HealthScore,if 0.3 ≤ HealthScore < 0.7

[0141] -1.25 + 2.5×HealthScore, if 0.7 ≤ HealthScore < 0.9

[0142] 2.0, if HealthScore ≥ 0.9

[0143] }

[0144] S1402. Determine the equipment operating status in the multi-dimensional operating indicators and determine the frequency factor of the status change of the video surveillance equipment.

[0145] Among them, the state change frequency factor can quantify the impact of changes in the operating state of video surveillance equipment on the polling strategy.

[0146] For example, the methods for determining the frequency factor of state changes of video surveillance equipment include:

[0147] Based on the equipment operating status in the multi-dimensional operating indicators, determine the number of times the operating status of the video surveillance equipment changes; based on the number of times the operating status changes and the preset maximum expected number of changes, determine the frequency of the operating status changes of the video surveillance equipment; based on the frequency of the operating status changes, determine the frequency factor of the status changes of the video surveillance equipment.

[0148] For example, the method for determining the frequency of changes in the operating status of video surveillance equipment is as follows:

[0149] StateChangeFrequency=min(1.0, ActualChanges / MaxExpectedChanges).

[0150] Among them, StateChangeFrequency is the frequency of state changes of the video surveillance equipment, ActualChanges is the number of times the operating state changes, and MaxExpectedChanges is the maximum expected number of changes.

[0151] The method for determining the frequency factor of status changes of video surveillance equipment is as follows:

[0152] ChangeFactor=1.0-0.5×StateChangeFrequency.

[0153] ChangeFactor is the frequency factor of state changes of video surveillance equipment.

[0154] The above scheme determines the number and frequency of state changes by the equipment's operating status, and then obtains the state change frequency factor, which can objectively quantify the degree of equipment state fluctuation and provide accurate state disturbance basis for polling scheduling.

[0155] S1403. Determine the network quality factor based on the network quality data.

[0156] For example, the network quality factor is determined as follows:

[0157] NetworkFactor = 0.8 + 0.4 × NetworkQualityScore.

[0158] Among them, NetworkQualityScore is network quality data, and NetworkFactor is network quality factor.

[0159] S1404. Based on the service importance, health score, time interval from the last polling and scheduling, and network quality data of the video surveillance equipment, determine the service priority, and determine the service importance factor based on the service priority of the video surveillance equipment.

[0160] For example, business priorities are determined as follows:

[0161] Priority=α×BusinessImportance+β×(1-HealthScore)+γ×TimeSinceLastPoll +δ×NetworkQuality.

[0162] Wherein, BusinessImportance represents business importance, HealthScore represents the overall health score, TimeSinceLastPoll represents the time interval since the last polling and scheduling, NetworkQualityScore represents network quality data, α=0.4, β=0.3, γ=0.2, δ=0.1.

[0163] Furthermore, the method for determining the business importance factor is as follows:

[0164] BusinessFactor = {

[0165] 0.5, if Priority = High (critical business equipment)

[0166] 1.0, if Priority = Medium (Regular Business Equipment)

[0167] 1.5, if Priority = Low (non-critical equipment)

[0168] 2.0, if Priority = Maintenance (Maintenance Mode Device)

[0169] }

[0170] Among them, BusinessFactor is the business importance factor, and Priority is the business priority.

[0171] S1405. Determine the polling scheduling frequency of the video surveillance equipment based on the health adjustment factor, state change frequency factor, network quality factor, and the service importance factor.

[0172] For example, if HealthScore=0.2, then HealthFactor=0.5, and the polling interval of the video surveillance device is shortened by 50%; if HealthScore=0.5, then HealthFactor=1.0, and the polling interval of the video surveillance device remains unchanged; if HealthScore=0.8, then HealthFactor=0.75, and the polling interval of the video surveillance device is extended by 33%; if HealthScore=0.95, then HealthFactor=2.0, and the polling interval of the video surveillance device is extended by 100%.

[0173] Furthermore, if the state of the video surveillance device changes ≤ 2 times, then StateChangeFrequency = 0.1, ChangeFactor = 0.95, and the polling interval of the video surveillance device is shortened by 5%; if the state of the video surveillance device changes 3-10 times: StateChangeFrequency = 0.5, ChangeFactor = 0.75, and the polling interval of the video surveillance device is increased by 33%; if the state of the video surveillance device changes 11-20 times: StateChangeFrequency = 0.8, ChangeFactor = 0.6, and the polling interval of the video surveillance device is increased by 67%; if the state of the video surveillance device changes > 20 times: StateChangeFrequency = 1.0, ChangeFactor = 0.5, and the polling interval of the video surveillance device is increased by 100%.

[0174] Furthermore, if the network quality is extremely poor, then NetworkFactor=0.8, and the polling scheduling frequency of the video surveillance equipment increases by 25%; if the network quality is average, then NetworkFactor=1.0, and the polling scheduling frequency of the video surveillance equipment remains at the standard frequency; if the network quality is excellent, then NetworkFactor=1.2, and the polling scheduling frequency of the video surveillance equipment decreases by 17%.

[0175] Based on the pre-set correspondence between the business importance factor and the polling frequency of the video surveillance equipment, the polling frequency of the video surveillance equipment is further adjusted.

[0176] The above scheme determines the health adjustment factor through a comprehensive health score, obtains the status change frequency factor by combining the equipment operating status, determines the network quality factor based on network quality data, and determines the service priority and service importance factors based on the equipment service importance, health, polling interval, and network quality. Finally, it combines the four factors to determine the polling scheduling frequency, which can make the polling scheduling more in line with the actual operating status of the equipment, network conditions, and service value, and achieve adaptive, refined, and rational polling scheduling for video surveillance equipment.

[0177] For example, such as Figure 4 As shown, the polling scheduling frequency of video surveillance equipment is determined based on the health adjustment factor, state change frequency factor, network quality factor, and the aforementioned service importance factor, including:

[0178] S210. Determine the multi-factor weights based on the health adjustment factor, the state change frequency factor, the network quality factor, and the business importance factor.

[0179] Specifically, the health adjustment factor, state change frequency factor, network quality factor, and business importance factor are multiplied together to obtain the multi-factor weights.

[0180] S220. Apply boundary constraints to the multi-factor weights to determine the polling scheduling frequency of the video surveillance equipment.

[0181] For example, the method for determining the polling scheduling frequency of video surveillance equipment is as follows:

[0182] FinalInterval=max(30, min(3600, Interval)).

[0183] Where FinalInterval is the polling frequency and Interval is the multi-factor weight.

[0184] For example, if the current operating status of the video surveillance device is: BaseInterval = 300 seconds, HealthScore = 0.6 and HealthFactor = 0.75, status changes 5 times and ChangeFactor = 0.75, network quality 0.8 and NetworkFactor = 1.12, business priority is high priority and BusinessFactor = 0.5, then the system's adaptive scheduler module can calculate: Interval = 300 × 0.75 × 0.75 × 1.12 × 0.5 = 94.5 seconds, FinalInterval = max(30, min(3600, 94.5)) = 94.5 seconds.

[0185] The above scheme, by multiplying four factors to obtain multi-factor weights and applying reasonable boundary constraints, can scientifically calculate a stable and compliant polling scheduling frequency, achieving adaptive and precise adjustment of the polling strategy while ensuring that scheduling parameters remain within a reasonable range. It also enables differentiated and personalized scheduling based on the real-time status of each device, eliminating the need for uniform fixed-cycle inspections, and significantly improving scheduling accuracy and resource utilization efficiency.

[0186] For example, based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled by the task executor, including:

[0187] A distributed lock is created for the video surveillance equipment based on its device identifier; the distributed lock is acquired through a task executor; after successful acquisition, the video surveillance equipment corresponding to the distributed lock is scheduled through a polling scheduler based on the polling scheduler frequency.

[0188] Among them, the distributed lock is a mechanism used in the distributed polling scheduling system to achieve mutual exclusion access of video surveillance equipment. Each device corresponds to a dedicated distributed lock. The task executor must acquire the lock before it can perform the polling operation, which avoids multiple executors accessing the same device at the same time and ensures the accuracy of polling data and the utilization rate of system resources.

[0189] For example:

[0190] / / Distributed Lock Mechanism

[0191] RLock lock=redissonClient.getLock("polling:device:" + deviceId);

[0192] if (lock.tryLock(5, 30, TimeUnit.SECONDS)) {

[0193] / / Execute polling task

[0194] performDevicePolling(deviceId);

[0195] }

[0196] Here, RLock is the distributed lock, and deviceId is the device identifier of the video surveillance device. The Redisson client, based on Redis, creates a unique distributed lock for each video surveillance device with a specified ID. The unique identifier of the distributed lock is "polling:device:" + deviceId, ensuring that each video surveillance device corresponds to an independent distributed lock. tryLock indicates that the task executor attempts to acquire the distributed lock; parameter 5 indicates that the task executor will wait a maximum of 5 seconds to acquire the distributed lock; parameter 30 indicates that if the task executor acquires the distributed lock, the lock will be held for 30 seconds; TimeUnit.SECONDS indicates that the parameter unit is seconds.

[0197] The above scheme, during the polling scheduling of video surveillance equipment according to a predetermined polling frequency, first creates a unique distributed lock for each device, using its unique identifier as the core. Then, the task executor attempts to acquire this distributed lock. Only after successfully acquiring the distributed lock does the video surveillance equipment corresponding to that distributed lock undergo polling scheduling according to the preset polling frequency. This mechanism achieves mutual exclusion access to the polling operation of a single device through distributed locks, effectively preventing multiple task executors from polling the same device simultaneously, preventing duplicate data collection, and avoiding waste of system and equipment resources. At the same time, combined with the preset polling scheduling frequency, it ensures the accuracy and uniqueness of the polling data, strictly follows the scheduling rhythm adapted to the device status, and improves the overall orderliness and resource utilization of the polling scheduling.

[0198] In the aforementioned polling scheduling method for video surveillance equipment, a communication connection is established with the video surveillance equipment, and multi-dimensional operational indicators are collected based on a preset window length. These multi-dimensional operational indicators include: polling response time of the video surveillance equipment, network quality data, equipment operating status, and communication connection status. Based on the multi-dimensional operational indicators and the historical polling scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined. Based on the connection success rate, response time score, equipment stability score, and the historical reliability score, a comprehensive health score of the video surveillance equipment is determined. Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through a task executor. This method solves the problems of insufficient assessment of the fault rate and potential risks of video surveillance equipment, high resource utilization when polling and scheduling video health equipment, low efficiency, and poor system stability when polling and scheduling video surveillance equipment. The above solution establishes a communication connection with video surveillance equipment and collects multi-dimensional operational indicators such as polling response time, network quality, operating status, and connection status according to a preset window. It then combines historical polling scheduling information to comprehensively calculate the equipment's connection success rate, response time score, stability score, and historical reliability score, thereby obtaining a comprehensive equipment health score. Based on this score, the polling scheduling frequency is adaptively determined, and the task executor completes the polling scheduling. This enables a comprehensive and accurate assessment of the equipment's status, improving the rationality, relevance, and efficiency of polling scheduling, and ensuring stable and reliable equipment operation. It can dynamically adjust the monitoring frequency and strategy according to the actual operating conditions of the equipment, ensuring system stability.

[0199] For example, based on the above embodiments, the polling scheduling method for video surveillance equipment includes:

[0200] Distributed video surveillance systems establish communication connections with various video surveillance devices within the system via standard protocols such as ONVIF, GB / T28181, or RTSP to collect device status data. Through these established communication connections, the system collects polling response times, network quality data, device operating status, and communication connection status from the video surveillance devices. Based on a preset window length, the system uses a sliding window to store the collected response times, network quality data, device operating status, and communication connection status to determine multi-dimensional operational indicators. The distributed video surveillance system includes: a monitoring center, distributed scheduling nodes, task executors, a Redis cluster, a Kafka message queue, and a time-series database. Specifically, the sliding window storage of the collected connection status, response times, network quality data, and device operating status based on the preset window length can be achieved by continuously storing and maintaining the polling history data of the video surveillance devices for the most recent 24 hours in a rolling manner, automatically removing expired data over time, and always retaining the latest 24-hour response times, network quality data, device operating status, and communication connection status. This provides real-time data for stability scoring, status change frequency, network quality, and adaptive adjustments to the polling strategy.

[0201] Based on the connection status of the communication connection, determine the number of successful connections and the number of failed connections of the video surveillance equipment, and determine the connection success rate of the video surveillance equipment based on the number of successful connections and the total number of connections.

[0202] A response time score is determined based on the polling response time. If the polling response time of the video surveillance device is 50ms, the response time score is 1.0, indicating excellent response speed; if the polling response time is 200ms, the response time score is 0.5, indicating average response speed; if the polling response time is 400ms, the response time score is 0.1, indicating poor response speed; and if the polling response time is 600ms, the response time score is 0.0, indicating a timeout.

[0203] Based on the polling response time of the video surveillance equipment in historical polling scheduling tasks, the mean response time and the standard deviation of the response time are determined; the ratio of the standard deviation of the response time to the mean response time is used as the coefficient of variation of the equipment's polling status.

[0204] Based on the historical polling scheduling information of the video surveillance equipment, determine the number of polling failures and the total number of polling attempts. Then, based on the number of polling failures, the total number of polling attempts, and the coefficient of variation of the equipment's polling status, determine the equipment stability score.

[0205] Based on the time dimension of historical polling scheduling information, determine the time weight of the historical polling scheduling information. Based on the time weight, determine the time-weighted polling failure rate, and based on the time-weighted polling failure rate, determine the basic reliability score.

[0206] Historical reliability scores are determined based on the baseline reliability score and the availability rate of the video surveillance equipment. The availability rate ranges from 0 to 1, where 0 = completely unavailable and 1 = 100% available. The availability rate is calculated as: the actual online time of the device and the time it can be normally polled ÷ the total monitoring time of the system for that device.

[0207] The system's status awareness service module determines the overall health score of the video surveillance equipment based on connection success rate, response time score, device stability score, and historical reliability score.

[0208] Based on the comprehensive health score of the video surveillance equipment, a reasonable polling frequency for the equipment is set. Then, the task executor conducts regular and orderly status checks on the video surveillance equipment according to this polling frequency. Before the task executor performs polling scheduling on the video surveillance equipment, the process includes: determining the executor weight of each task executor based on its maximum processing capacity and current load using a weighted polling algorithm; and assigning polling scheduling tasks to each task executor based on its weight.

[0209] Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, including: determining the health adjustment factor of the video surveillance equipment based on the health score.

[0210] Based on the device operating status in multi-dimensional operating indicators, determine the number of times the video surveillance equipment's operating status changes; based on the number of operating status changes and the preset maximum expected number of changes, determine the frequency of operating status changes for the video surveillance equipment; based on the frequency of operating status changes, determine the frequency factor of the video surveillance equipment's status changes. Based on network quality data, determine the network quality factor. Based on the service importance corresponding to the video surveillance equipment, the comprehensive health score, the time interval since the last polling scheduling, and network quality data, determine the service priority, and based on the service priority of the video surveillance equipment, determine the service importance factor.

[0211] The polling frequency of video surveillance equipment is determined based on the health adjustment factor, state change frequency factor, network quality factor, and the aforementioned service importance factor. If HealthScore = 0.2, then HealthFactor = 0.5, and the polling interval of the video surveillance equipment is shortened by 50%; if HealthScore = 0.5, then HealthFactor = 1.0, and the polling interval remains unchanged; if HealthScore = 0.8, then HealthFactor = 0.75, and the polling interval is extended by 33%; if HealthScore = 0.95, then HealthFactor = 2.0, and the polling interval is extended by 100%. Furthermore, if the state of the video surveillance device changes ≤ 2 times, then StateChangeFrequency = 0.1, ChangeFactor = 0.95, and the polling interval of the video surveillance device is shortened by 5%; if the state of the video surveillance device changes 3-10 times: StateChangeFrequency = 0.5, ChangeFactor = 0.75, and the polling interval of the video surveillance device is increased by 33%; if the state of the video surveillance device changes 11-20 times: StateChangeFrequency = 0.8, ChangeFactor = 0.6, and the polling interval of the video surveillance device is increased by 67%; if the state of the video surveillance device changes > 20 times: StateChangeFrequency = 1.0, ChangeFactor = 0.5, and the polling interval of the video surveillance device is increased by 100%. Furthermore, if the network quality is extremely poor, then NetworkFactor=0.8, and the polling scheduling frequency of the video surveillance equipment increases by 25%; if the network quality is average, then NetworkFactor=1.0, and the polling scheduling frequency of the video surveillance equipment remains at the standard frequency; if the network quality is excellent, then NetworkFactor=1.2, and the polling scheduling frequency of the video surveillance equipment decreases by 17%.

[0212] Based on the pre-set correspondence between the business importance factor and the polling frequency of the video surveillance equipment, the polling frequency of the video surveillance equipment is further adjusted.

[0213] Furthermore, a multi-factor weight can be obtained by multiplying the health adjustment factor, the state change frequency factor, the network quality factor, and the business importance factor. Boundary constraints are then applied to the multi-factor weights to determine the polling scheduling frequency of the video surveillance equipment.

[0214] A distributed lock is created for each video surveillance device based on its device identifier. The task executor acquires this distributed lock. Upon successful acquisition, the task executor performs a round-robin scheduling of the video surveillance devices corresponding to the distributed lock, based on the polling frequency. The distributed lock is a mechanism in the distributed polling scheduling system used to ensure mutual exclusion access to video surveillance devices. Each device corresponds to a unique distributed lock, and the task executor must acquire the lock before performing the polling operation. This prevents multiple executors from accessing the same device simultaneously, ensuring the accuracy of polling data and the utilization rate of system resources.

[0215] In the aforementioned polling scheduling method for video surveillance equipment, a communication connection is established with the video surveillance equipment, and multi-dimensional operational indicators are collected based on a preset window length. These multi-dimensional operational indicators include: polling response time of the video surveillance equipment, network quality data, equipment operating status, and communication connection status. Based on the multi-dimensional operational indicators and the historical polling scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined. Based on the connection success rate, response time score, equipment stability score, and the historical reliability score, a comprehensive health score of the video surveillance equipment is determined. Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through a task executor. This method solves the problems of insufficient assessment of the fault rate and potential risks of video surveillance equipment, high resource utilization when polling and scheduling video health equipment, low efficiency, and poor system stability when polling and scheduling video surveillance equipment. The above solution establishes a communication connection with video surveillance equipment and collects multi-dimensional operational indicators such as polling response time, network quality, operating status, and connection status according to a preset window. It then combines historical polling scheduling information to comprehensively calculate the equipment's connection success rate, response time score, stability score, and historical reliability score, thereby obtaining a comprehensive equipment health score. Based on this score, the polling scheduling frequency is adaptively determined, and the task executor completes the polling scheduling. This enables a comprehensive and accurate assessment of the equipment's status, improving the rationality, relevance, and efficiency of polling scheduling, and ensuring stable and reliable equipment operation. It can dynamically adjust the monitoring frequency and strategy according to the actual operating conditions of the equipment, ensuring system stability.

[0216] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0217] Based on the same inventive concept, this application also provides a polling scheduling device for a video surveillance device to implement the polling scheduling method for the video surveillance device described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the polling scheduling device for video surveillance devices provided below can be found in the limitations of the polling scheduling method for video surveillance devices described above, and will not be repeated here.

[0218] In one embodiment, such as Figure 5 As shown, a polling scheduling device for video surveillance equipment is provided, comprising: an operation index acquisition module 501, an equipment health score determination module 502, a status awareness service module 503, and a polling scheduling module 504, wherein:

[0219] The operation indicator acquisition module 501 is used to establish a communication connection with the video surveillance equipment and collect multi-dimensional operation indicators based on a preset window length. The multi-dimensional operation indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection.

[0220] The device health rating determination module 502 is used to determine the connection success rate, response time rating, device stability rating and historical reliability rating of the video surveillance equipment based on the multi-dimensional operating indicators and the historical polling scheduling information of the video surveillance equipment.

[0221] The status awareness service module 503 is used to determine the comprehensive health score of the video surveillance device based on the connection success rate, the response time score, the device stability score, and the historical reliability score.

[0222] The polling scheduling module 504 is used to determine the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score, and to perform polling scheduling on the video surveillance equipment through the task executor based on the polling scheduling frequency.

[0223] For example, the device health rating determination module 502 is specifically used for:

[0224] Based on the connection status of the communication connection, determine the number of successful connections and the number of failed connections of the video surveillance equipment, and determine the connection success rate of the video surveillance equipment based on the number of successful connections and the total number of connections.

[0225] Determine the response time score based on the polling response time;

[0226] Based on the historical polling scheduling information of the video surveillance equipment, determine the number of polling failures and the total number of polling attempts. Based on the number of polling failures, the total number of polling attempts, and the coefficient of variation of the equipment's polling status, determine the equipment stability score.

[0227] The time weight of the historical polling scheduling information is determined based on the time dimension of the historical polling scheduling information.

[0228] Based on the time weight, the time-weighted polling failure rate is determined, and based on the time-weighted polling failure rate, the basic reliability score is determined.

[0229] The historical reliability score is determined based on the basic reliability score and the device availability of the video surveillance equipment.

[0230] Furthermore, the equipment health rating determination module 502 is also specifically used for:

[0231] Based on the polling response time of video surveillance equipment in historical polling scheduling tasks, determine the mean response time and the standard deviation of the response time.

[0232] The ratio of the standard deviation of the response time to the mean of the response time is used as the coefficient of variation of the device polling status.

[0233] For example, the polling scheduling module 504 is specifically used for:

[0234] Based on the health score, determine the health adjustment factor for the video surveillance equipment;

[0235] Determine the equipment operating status among multi-dimensional operating indicators, and determine the frequency factor of the status change of the video surveillance equipment;

[0236] Determine network quality factors based on network quality data;

[0237] Based on the service importance, health score, time interval since the last polling and scheduling, and network quality data of the video surveillance equipment, the service priority is determined, and the service importance factor is determined based on the service priority of the video surveillance equipment.

[0238] The polling scheduling frequency of the video surveillance equipment is determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor.

[0239] For example, the polling scheduling module 504 is also specifically used for:

[0240] The number of times the operating status of the video surveillance equipment changes is determined based on the equipment operating status in the multi-dimensional operating indicators;

[0241] The frequency of the operating status changes of the video surveillance equipment is determined based on the number of operating status changes and the preset maximum expected number of changes.

[0242] The frequency factor of the state change of the video surveillance equipment is determined based on the frequency of the changes in the operating state.

[0243] For example, the polling scheduling module 504 is also specifically used for:

[0244] The multi-factor weights are determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor.

[0245] Boundary constraints are applied to the multi-factor weights to determine the polling scheduling frequency of the video surveillance equipment.

[0246] For example, the polling scheduling module 504 is also specifically used for:

[0247] A distributed lock is created for the video surveillance device based on its device identifier.

[0248] The distributed lock is acquired through the task executor;

[0249] After successful acquisition, based on the polling scheduling frequency, the task executor performs polling scheduling on the video surveillance devices corresponding to the distributed lock.

[0250] Each module in the polling and scheduling device of the aforementioned video surveillance equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0251] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a polling scheduling method for video surveillance equipment. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0252] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0253] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0254] Step 1: Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length. The multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection.

[0255] Step 2: Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, determine the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment.

[0256] Step 3: Determine the comprehensive health score of the video surveillance device based on the connection success rate, response time score, device stability score, and historical reliability score;

[0257] Step 4: Determine the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score, and perform polling scheduling of the video surveillance equipment through the task executor based on the polling scheduling frequency.

[0258] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0259] Step 1: Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length. The multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection.

[0260] Step 2: Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, determine the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment.

[0261] Step 3: Determine the comprehensive health score of the video surveillance device based on the connection success rate, response time score, device stability score, and historical reliability score;

[0262] Step 4: Determine the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score, and perform polling scheduling of the video surveillance equipment through the task executor based on the polling scheduling frequency.

[0263] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0264] Step 1: Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length. The multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection.

[0265] Step 2: Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, determine the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment.

[0266] Step 3: Determine the comprehensive health score of the video surveillance device based on the connection success rate, response time score, device stability score, and historical reliability score;

[0267] Step 4: Determine the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score, and perform polling scheduling of the video surveillance equipment through the task executor based on the polling scheduling frequency.

[0268] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0269] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0270] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0271] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A polling scheduling method of a video monitoring device, characterized by, The method is executed through a distributed video surveillance system, and the method includes: Establish a communication connection with the video surveillance equipment and collect multi-dimensional operating indicators based on a preset window length; the multi-dimensional operating indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and the connection status of the communication connection; Based on the multi-dimensional operational indicators and the historical polling and scheduling information of the video surveillance equipment, the connection success rate, response time score, equipment stability score, and historical reliability score of the video surveillance equipment are determined. The overall health score of the video surveillance device is determined based on the connection success rate, the response time score, the device stability score, and the historical reliability score. Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, and based on the polling scheduling frequency, the video surveillance equipment is polled and scheduled through the task executor.

2. The method of claim 1, wherein, The process of determining the connection success rate, response time score, device stability score, and historical reliability score of the video surveillance equipment based on the multi-dimensional operational indicators and historical polling scheduling information of the video surveillance equipment includes: Based on the connection status of the communication connection, determine the number of successful connections and the number of failed connections of the video surveillance equipment, and determine the connection success rate of the video surveillance equipment based on the number of successful connections and the total number of connections. Determine the response time score based on the polling response time; Based on the historical polling scheduling information of the video surveillance equipment, determine the number of polling failures and the total number of polling attempts. Based on the number of polling failures, the total number of polling attempts, and the coefficient of variation of the equipment's polling status, determine the equipment stability score. The time weight of the historical polling scheduling information is determined based on the time dimension of the historical polling scheduling information. Based on the time weight, the time-weighted polling failure rate is determined, and based on the time-weighted polling failure rate, the basic reliability score is determined. The historical reliability score is determined based on the basic reliability score and the device availability of the video surveillance equipment.

3. The method of claim 2, wherein, Also includes: Based on the polling response time of video surveillance equipment in historical polling scheduling tasks, determine the mean response time and the standard deviation of the response time. The ratio of the standard deviation of the response time to the mean of the response time is used as the coefficient of variation of the device polling status.

4. The method of claim 1, wherein, Based on the comprehensive health score, the polling scheduling frequency of the video surveillance equipment is determined, including: Based on the health score, determine the health adjustment factor for the video surveillance equipment; Determine the equipment operating status among multi-dimensional operating indicators, and determine the frequency factor of the status change of the video surveillance equipment; Determine network quality factors based on network quality data; Based on the service importance of the video surveillance equipment, its comprehensive health score, the time interval since the last polling and scheduling, and network quality data, the service priority is determined, and the service importance factor is determined based on the service priority of the video surveillance equipment. The polling scheduling frequency of the video surveillance equipment is determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor.

5. The method according to claim 4, characterized in that, Determine the equipment operating status among multi-dimensional operating indicators, and determine the frequency factor of the status change of the video surveillance equipment, including: The number of times the operating status of the video surveillance equipment changes is determined based on the equipment operating status in the multi-dimensional operating indicators; The frequency of the operating status changes of the video surveillance equipment is determined based on the number of operating status changes and the preset maximum expected number of changes. The frequency factor of the state change of the video surveillance equipment is determined based on the frequency of the changes in the operating state.

6. The method of claim 4, wherein, The polling scheduling frequency of video surveillance equipment is determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor, including: The multi-factor weights are determined based on the health adjustment factor, the state change frequency factor, the network quality factor, and the service importance factor. Boundary constraints are applied to the multi-factor weights to determine the polling scheduling frequency of the video surveillance equipment.

7. The method of claim 1, wherein, The step of polling and scheduling the video surveillance equipment based on the polling frequency via a task executor includes: A distributed lock is created for the video surveillance device based on its device identifier. The distributed lock is acquired through the task executor; After successful acquisition, based on the polling frequency, the task executor performs polling scheduling on the video surveillance devices corresponding to the distributed lock.

8. A polling scheduling apparatus of a video monitoring device, characterized by comprising: The polling scheduling device for the video surveillance equipment is applied to a distributed video surveillance system, and the polling scheduling device for the video surveillance equipment includes: The operation indicator acquisition module is used to establish a communication connection with the video surveillance equipment and collect multi-dimensional operation indicators based on a preset window length. The multi-dimensional operation indicators include: the polling response time of the video surveillance equipment, network quality data, equipment operating status, and communication connection status. The device health rating determination module is used to determine the connection success rate, response time rating, device stability rating, and historical reliability rating of the video surveillance equipment based on the multi-dimensional operating indicators and the historical polling scheduling information of the video surveillance equipment. The status awareness service module is used to determine the comprehensive health score of the video surveillance device based on the connection success rate, the response time score, the device stability score, and the historical reliability score. The polling scheduling module is used to determine the polling scheduling frequency of the video surveillance equipment based on the comprehensive health score, and to perform polling scheduling of the video surveillance equipment through the task executor based on the polling scheduling frequency. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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