Maintenance monitoring method, device, equipment and storage medium based on dynamic bit rate

Abstract

The present invention provides a maintenance monitoring method, device, equipment and storage medium based on dynamic bit rate, the method comprising: receiving a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; determining the predicted construction duration of the current maintenance task based on the maintenance monitoring request; determining the target bit rate parameters of the video to be monitored based on the remaining flow information, the predicted construction duration and the current fault level; and sending the target bit rate parameters to the monitoring device so that the monitoring device monitors the current maintenance task based on the target bit rate parameters. The present invention controls the bit rate of the on-site monitoring device by predicting the required monitoring time based on different maintenance tasks and determining the appropriate bit rate parameters in combination with the remaining flow, thereby avoiding monitoring interruptions caused by traffic exhaustion during maintenance, and meeting the real-time monitoring requirements of the power equipment maintenance process.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to a maintenance monitoring method, device, equipment and storage medium based on dynamic bit rate. Background Art

[0002] Power facilities are often deployed outdoors and are susceptible to damage. When a power facility fails, maintenance personnel are dispatched to the site to repair it. Without dedicated monitoring equipment, temporary monitoring equipment is often set up on-site to monitor the maintenance personnel's progress. This equipment uses SIM card data traffic to transmit surveillance video for real-time monitoring. Because SIM card data traffic is typically limited, if the data traffic runs out during maintenance, monitoring will be interrupted. Therefore, existing technologies cannot meet the demand for real-time monitoring of power equipment maintenance. Summary of the Invention

[0003] The present invention aims to provide a maintenance monitoring method, device, equipment and storage medium based on dynamic bit rate to solve the above technical problems, thereby meeting the real-time monitoring requirements of the maintenance process of power equipment.

[0004] In order to solve the above technical problems, the present invention provides a maintenance monitoring method based on dynamic bit rate, comprising:

[0005] Receive a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request includes maintenance personnel information, power equipment type information, fault type information, and remaining flow information;

[0006] Determining a predicted construction duration of the current maintenance task based on the maintenance monitoring request;

[0007] Determining a target bitrate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task;

[0008] The target bit rate parameter is sent to the monitoring device, so that the monitoring device performs a monitoring operation on the current maintenance task based on the target bit rate parameter.

[0009] Furthermore, the determining of a target bit rate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level includes:

[0010] Determining a first bit rate parameter and a second bit rate parameter of the video to be monitored based on the current fault level; wherein the bit rate value corresponding to the first bit rate parameter is greater than the bit rate value corresponding to the second bit rate parameter;

[0011] Calculate a first predicted flow corresponding to the first bit rate parameter and a second predicted flow corresponding to the second bit rate parameter according to the predicted construction duration;

[0012] If it is determined that the remaining traffic information meets the requirement of the first predicted traffic, the first bit rate parameter is used as the target bit rate parameter;

[0013] If it is determined that the remaining flow information does not meet the requirement of the first predicted flow, then determining whether the remaining flow information meets the requirement of the second predicted flow;

[0014] If yes, use the second bit rate parameter as the target bit rate parameter;

[0015] If not, a flow shortage reminder message is sent to the monitoring device.

[0016] Furthermore, determining the first bit rate parameter and the second bit rate parameter of the video to be monitored based on the current fault level specifically includes:

[0017] Dividing the available bit rate parameters of the monitoring device into a high bit rate interval, a medium bit rate interval, and a low bit rate interval; wherein any bit rate value in the medium bit rate interval is greater than any bit rate value in the low bit rate interval, and any bit rate value in the medium bit rate interval is less than any bit rate value in the high bit rate interval;

[0018] When it is determined that the current fault level is greater than a preset fault level, determining the first bit rate parameter based on the high bit rate interval, and determining the second bit rate parameter based on the low bit rate interval;

[0019] When it is determined that the current fault level is not greater than the preset fault level, the first bit rate parameter is determined based on the medium bit rate interval, and the second bit rate parameter is determined based on the low bit rate interval.

[0020] Furthermore, determining the predicted construction duration of the current maintenance task based on the maintenance monitoring request includes:

[0021] Calculating based on the maintenance personnel information, the power equipment type information and the fault type information and arbitrarily combining them into a plurality of prediction information sets;

[0022] Determining the construction duration stage corresponding to each prediction information set based on the correspondence between the pre-stored prediction information set and the construction duration;

[0023] The predicted construction duration of the current maintenance task is determined based on the construction duration stage corresponding to each of the prediction information sets.

[0024] Furthermore, the calculation based on the maintenance personnel information, the power equipment type information and the fault type information and arbitrary combination into several prediction information sets include:

[0025] Obtaining personnel classification information of each maintenance personnel corresponding to the current maintenance task according to the maintenance personnel information;

[0026] Acquire technical scoring information of each maintenance worker based on the maintenance worker classification information, and determine the project technical scoring information of the current maintenance task based on the technical scoring information of each maintenance worker;

[0027] Based on the personnel classification information, the project technical score information, the power equipment type information and the fault type information, they are arbitrarily combined into several prediction information sets.

[0028] Furthermore, the determining of the predicted construction duration of the current maintenance task based on the construction duration stage corresponding to each of the prediction information sets includes:

[0029] Obtaining the correlation between each of the prediction information sets and the construction duration stage based on a preset mining algorithm, and classifying each prediction information set as a strong frequent set or a weak frequent set according to a preset correlation threshold;

[0030] Taking the construction duration stage corresponding to each of the prediction information sets as normal distribution duration information, and respectively obtaining the expected duration value of each of the normal distribution duration information;

[0031] The predicted construction duration of the current maintenance task is determined by taking a weighted average of all expected duration values; wherein the weight of the prediction information set classified as a strong frequent set is greater than the weight of the prediction information set classified as a weak frequent set.

[0032] Furthermore, the maintenance personnel information includes one or more of name, work number and face verification data.

[0033] The present invention also provides a maintenance monitoring device based on dynamic bit rate, comprising:

[0034] A request receiving module is used to receive a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request includes maintenance personnel information, power equipment type information, fault type information and remaining flow information;

[0035] A duration prediction module, configured to determine a predicted construction duration of the current maintenance task based on the maintenance monitoring request;

[0036] a bitrate determination module, configured to determine a target bitrate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task;

[0037] The parameter sending module is used to send the target bit rate parameter to the monitoring device, so that the monitoring device monitors the current maintenance task based on the target bit rate parameter.

[0038] The present invention also provides a terminal device, comprising a processor and a memory storing a computer program, wherein the processor implements any one of the maintenance monitoring methods based on dynamic bit rate when executing the computer program.

[0039] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the maintenance monitoring methods based on dynamic bit rate.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention provides a maintenance monitoring method, device, equipment and storage medium based on dynamic bit rate, the method comprising: receiving a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request comprises maintenance personnel information, power equipment type information, fault type information and remaining flow information; determining a predicted construction duration of the current maintenance task based on the maintenance monitoring request; determining a target bit rate parameter of a video to be monitored based on the remaining flow information, the predicted construction duration and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task; sending the target bit rate parameter to the monitoring device, so that the monitoring device performs monitoring operations on the current maintenance task based on the target bit rate parameter.

[0042] The present invention predicts the monitoring duration of different maintenance tasks according to different maintenance tasks, and determines appropriate bit rate parameters based on the remaining traffic to control the bit rate of on-site monitoring equipment, thereby avoiding monitoring interruptions caused by traffic exhaustion during maintenance, and can meet the real-time monitoring needs of the power equipment maintenance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 1 is a flow chart of a maintenance monitoring method based on dynamic bit rate provided by the present invention;

[0044] Figure 2It is a structural diagram of the maintenance monitoring device based on dynamic bit rate provided by the present invention. DETAILED DESCRIPTION

[0045] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] See Figure 1 The embodiment of the present invention provides a maintenance monitoring method based on dynamic bit rate, which may include the following steps:

[0047] S1. Receive a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request includes maintenance personnel information, power equipment type information, fault type information, and remaining flow information; further, the maintenance personnel information includes one or more of name, work number, and face verification data.

[0048] S2. Determine the predicted construction duration of the current maintenance task based on the maintenance monitoring request;

[0049] S3. Determine a target bitrate parameter for the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task;

[0050] S4. Send the target bit rate parameter to the monitoring device, so that the monitoring device monitors the current maintenance task based on the target bit rate parameter.

[0051] It should be noted that when maintenance personnel arrive on-site to repair a faulty power facility, they can enter the type of power equipment, the type of fault, and basic information about the maintenance personnel. Simultaneously, the monitoring device automatically queries the remaining data traffic on the SIM card and packages this information into a maintenance monitoring request, which is then sent to the streaming media platform via the monitoring device. Based on the information in the maintenance monitoring request and pre-configured algorithm logic, the predicted construction duration of the current maintenance task can be determined. Then, based on the remaining traffic, an appropriate monitoring video bit rate is selected to control the monitoring device's operations. This ensures that monitoring tasks during the current maintenance operation will not be interrupted due to insufficient traffic, thus meeting the need for real-time monitoring of the power equipment maintenance process.

[0052] Furthermore, step S3 may include:

[0053] S31. Determine a first bit rate parameter and a second bit rate parameter of the video to be monitored based on the current fault level; wherein the bit rate value corresponding to the first bit rate parameter is greater than the bit rate value corresponding to the second bit rate parameter;

[0054] S32. Calculate a first predicted flow rate corresponding to the first bit rate parameter and a second predicted flow rate corresponding to the second bit rate parameter according to the predicted construction duration;

[0055] S33: If it is determined that the remaining traffic information meets the first predicted traffic requirement, use the first bit rate parameter as the target bit rate parameter;

[0056] S34. If it is determined that the remaining flow information does not meet the requirement of the first predicted flow, determine whether the remaining flow information meets the requirement of the second predicted flow;

[0057] S35: If yes, use the second bit rate parameter as the target bit rate parameter;

[0058] S36: If not, the insufficient flow reminder information is sent to the monitoring device.

[0059] It should be noted that when determining the target bit rate parameters, a higher bit rate and a lower bit rate can be selected according to the current fault level, and the required traffic corresponding to the two can be calculated according to the predicted construction time. Then, a judgment is made based on the remaining traffic. If the remaining traffic can support the required traffic corresponding to the higher bit rate, the higher bit rate is directly used as the target bit rate parameter. If the remaining traffic can only support the required traffic corresponding to the lower bit rate, the lower bit rate is used as the target bit rate parameter. If the remaining traffic cannot support the required traffic corresponding to the lower bit rate, the maintenance monitoring will not be started for the time being, and an insufficient traffic reminder message will be sent to the monitoring equipment to remind the on-site maintenance personnel to replace the SIM card and start the monitoring operation after ensuring sufficient traffic.

[0060] It is understandable that when determining whether the remaining traffic meets the monitoring traffic requirements of the current maintenance task, a certain amount of redundant traffic needs to be reserved. In other words, when calculating the required traffic, in addition to the required traffic calculated based on the predicted construction duration and the preset bit rate, a preset offset traffic is added to the calculated traffic as the predicted traffic required for monitoring this maintenance task. This is done to prevent the actual monitoring traffic from exceeding the predicted traffic, resulting in monitoring interruption.

[0061] Furthermore, step S31 may include:

[0062] S311. Divide the available bit rate parameters of the monitoring device into a high bit rate interval, a medium bit rate interval, and a low bit rate interval; wherein any bit rate value in the medium bit rate interval is greater than any bit rate value in the low bit rate interval, and any bit rate value in the medium bit rate interval is less than any bit rate value in the high bit rate interval;

[0063] S312: When it is determined that the current fault level is greater than a preset fault level, determining the first bit rate parameter based on the high bit rate interval, and determining the second bit rate parameter based on the low bit rate interval;

[0064] S313: When it is determined that the current fault level is not greater than the preset fault level, determine the first bit rate parameter based on the medium bit rate interval, and determine the second bit rate parameter based on the low bit rate interval.

[0065] It should be noted that, in an embodiment of the present invention, the available bit rates of the monitoring equipment can be divided into three bit rate intervals: high, medium and low. If the current fault level is important (the importance level is higher), the bit rate value in the high bit rate interval is selected first, and the bit rate value in the low bit rate interval is selected second; if the current fault level is general (the importance level is lower), the bit rate value in the medium bit rate interval is selected first, and the bit rate value in the low bit rate interval is selected second.

[0066] Understandably, the purpose of using a low-bitrate range is to minimize the bitrate, thereby ensuring sufficient traffic flow and uninterrupted real-time monitoring. Monitoring at this point generally meets requirements, and on-site personnel can provide oversight in the event of misidentification. However, the bitrate in the medium-bitrate range is generally insufficient for monitoring and identifying critical faults. Since this doesn't meet the accuracy requirements for critical repairs, it's best to avoid wasting traffic and simply use a low-bitrate range for monitoring.

[0067] Furthermore, step S2 may include:

[0068] S21, performing calculations based on the maintenance personnel information, the power equipment type information, and the fault type information and arbitrarily combining them into several prediction information sets;

[0069] S22. Determine the construction duration stage corresponding to each prediction information set based on the pre-stored correspondence between the prediction information set and the construction duration;

[0070] S23. Determine the predicted construction duration of the current maintenance task based on the construction duration stage corresponding to each of the prediction information sets.

[0071] It should be noted that multiple sets of prediction information sets can be determined based on the maintenance personnel information, the power equipment type information and the fault type information, and then the corresponding construction duration stages can be determined based on the correlation between these prediction information sets and the construction duration. Finally, based on these construction duration stages, the total construction prediction duration of the current maintenance task is solved according to the preset algorithm.

[0072] Furthermore, step S21 may include:

[0073] S211. Obtaining personnel classification information of each maintenance personnel corresponding to the current maintenance task according to the maintenance personnel information;

[0074] S212: Acquire technical scoring information of each maintenance worker based on the maintenance worker classification information, and determine the project technical scoring information of the current maintenance task based on the technical scoring information of each maintenance worker;

[0075] S213 , arbitrarily combining the personnel classification information, the project technical score information, the power equipment type information, and the fault type information into several prediction information sets.

[0076] It should be noted that by querying the professional titles and length of service of the maintenance personnel, dividing the professional titles and length of service into multiple stages, counting the number of professional titles and length of service in each stage, the personnel grading information can be obtained, and then the total project technical score of the current maintenance task can be calculated based on the multiple maintenance personnel corresponding to the current maintenance task; then the personnel grading information, project technical score information, power equipment type information and fault type information can be used as elements to arbitrarily combine into multiple prediction information sets, which are used to predict the construction forecast duration of the current maintenance task.

[0077] Furthermore, step S23 may include:

[0078] S231, obtaining the correlation between each of the prediction information sets and the construction duration stage based on a preset mining algorithm, and classifying each prediction information set as a strong frequent set or a weak frequent set according to a preset correlation threshold;

[0079] S232: taking the construction duration phase corresponding to each of the prediction information sets as normal distribution duration information, and respectively obtaining the expected duration value of each of the normal distribution duration information;

[0080] S233. Perform weighted averaging on all expected duration values ​​to determine the predicted construction duration of the current maintenance task; wherein the weight of the prediction information set classified as a strong frequent set is greater than the weight of the prediction information set classified as a weak frequent set.

[0081] It should be noted that based on the historical records of power equipment maintenance, a preset mining algorithm, such as the Apriori algorithm, can be used to mine frequent sets between various prediction information combinations and the stages of construction duration. Based on the correlation, these sets are divided into strong frequent sets and weak frequent sets. Strong frequent sets refer to sets that are strongly correlated with the stages of construction duration, while weak frequent sets refer to sets that are weakly correlated with the stages of construction duration. Other sets that are unrelated to the stages of construction duration are discarded.

[0082] Following the above method, we calculate the personnel classification information and the technical score of the maintenance project based on the basic information of the maintenance personnel. We then divide and classify each prediction information set into strong frequent sets and weak frequent sets. We then find the stages of construction duration corresponding to these strong frequent sets and weak frequent sets. We then treat these stages as normally distributed, take the expected value of the probability, and take a weighted average of the expected values ​​to obtain the total predicted construction duration. The weight of the strong frequent set is greater than that of the weak frequent set.

[0083] Based on the above solution, in order to facilitate a better understanding of the maintenance monitoring method based on dynamic bit rate provided by the embodiment of the present invention, the working principle and implementation process of the embodiment of the present invention are described in detail below:

[0084] 1. Trigger maintenance tasks through manual alarms, drone inspections, and power data self-monitoring, and dispatch maintenance personnel to the site to repair faulty power facilities.

[0085] After initially determining the fault, the maintenance personnel set up monitoring equipment facing the power facilities, input the type of power equipment, the type of fault, and the basic information of the maintenance personnel, and encapsulate them into a monitoring request. The monitoring equipment automatically queries the remaining traffic on the SIM card and sends the monitoring request to the streaming platform.

[0086] Basic information includes name, work number, and facial data. Facial data is used for on-site interaction with the registration center to verify whether maintenance personnel are actually present to repair power equipment.

[0087] 2. When the streaming media platform is offline, it will pre-query the historical records of power equipment maintenance. According to the maintenance personnel's name and work number, it will go to the registration center to query the maintenance personnel's title and length of service. The title and length of service will be divided into multiple stages. The number of titles and length of service in each stage will be counted to obtain personnel classification information and calculate the technical score for each maintenance project.

[0088] Specifically, first, the weighted sum of professional title and length of service is calculated. The higher the professional title and the longer the length of service, the greater the weight. In this way, a technical score is assigned to each maintenance personnel. Then, the technical scores of each maintenance personnel are added together to obtain the technical score of the maintenance project.

[0089] In addition, the duration of construction is divided into multiple phases.

[0090] The type of power equipment, the type of fault, the personnel classification information, and the technical score of the maintenance project are arbitrarily combined, and Apriori is used to mine the frequent sets between each combination and the stage of construction duration.

[0091] According to the correlation, it is divided into strong frequent sets and weak frequent sets. Strong frequent sets refer to sets that are strongly correlated with the stage of construction duration, and weak frequent sets refer to sets that are weakly correlated with the stage of construction duration. Other sets that are not correlated with the stage of construction duration are discarded.

[0092] 3. The streaming media platform receives the monitoring request and parses the monitoring request to obtain the remaining traffic of the SIM card, the type of power equipment, the type of fault, and the basic information of the maintenance personnel.

[0093] According to the above method, the personnel classification information and the technical score of the maintenance project are calculated based on the basic information of the maintenance personnel, and divided into multiple combinations according to the strong frequent sets and weak frequent sets. The stages of construction duration corresponding to the strong frequent sets and weak frequent sets are found. These stages are regarded as normal distributions respectively, and the expected values ​​in probability are taken. The expected values ​​are weighted averaged to obtain the predicted duration. The weight of the strong frequent set is greater than that of the weak frequent set.

[0094] 4. The streaming media platform pre-trains various monitoring models. Each monitoring model maps a specified type of power equipment and a specified type of fault. That is, under the given type of power equipment and the type of fault, the monitoring model is used to monitor the maintenance process of the maintenance personnel.

[0095] Depending on the performance, some monitoring models can monitor any type of faults of a specified type of power equipment, while some monitoring models can only monitor a specified type of faults of a specified type of power equipment.

[0096] It should be noted that monitoring models are a relatively mature technology for AI monitoring in factory assembly lines. They primarily combine target detection algorithms with some logic. Target detection algorithms such as YOLOv3, R-CNN, and SSD are all acceptable, and can be selected based on the business. Training methods can also be based on existing technologies. The logic is as follows: Repairing power equipment follows certain operating procedures. For example, first switching off the switch, then removing the transformer casing, then removing the damaged coil, and then installing the coil. The target detection algorithm will detect the transformer casing, the coil inside the transformer (this is the damaged coil, limited by the transformer's range), and the coil outside the transformer (this is the new coil). The monitoring model uses the judgment sequence formed by these targets to determine whether the maintenance personnel followed the operating procedures.

[0097] 5. Considering that the less information the image data contains, the lower the accuracy, the streaming media platform divides the image data bit rate into three levels when testing the monitoring capabilities of the monitoring model.

[0098] The first level is high bitrate, which ensures a high amount of information in the video stream, thereby maximizing monitoring accuracy. At this stage, the bitrate may overflow, meaning that increasing the bitrate does not significantly improve monitoring accuracy. This level is set when the current fault has a significant impact on business operations.

[0099] The second level is medium bitrate, which ensures monitoring accuracy while ensuring a certain amount of information in the video stream. At this stage, increasing the bitrate contributes significantly to improving monitoring accuracy. In terms of business, this level is set if there is sufficient remaining traffic and the current fault has a moderate impact.

[0100] The third level is low bit rate. The amount of information in the video stream is lost to a certain extent, which affects the accuracy of monitoring, but it is basically usable. At this stage, if the remaining traffic is insufficient, low bit rate is used for monitoring first, and high bit rate or medium bit rate video streams are cached locally on the monitoring device depending on the size of the storage space. When the remaining traffic is sufficient or the local area network (such as WiFi) is connected, the high bit rate or medium bit rate video stream is retransmitted to the streaming platform.

[0101] It should be noted that the higher the bit rate of the surveillance video, the greater the traffic consumption. However, when the bit rate reaches a certain level, there is no obvious gain for subsequent analysis and maintenance. Therefore, if the bit rate is not controlled under the condition of a given traffic flow, it is easy to cause waste of traffic.

[0102] 6. The streaming platform calculates the estimated traffic based on the duration and the bitrate at each level, and adds a biased traffic to the final estimated traffic. The biased traffic is to prevent monitoring interruptions caused by insufficient estimated duration.

[0103] If the estimated flow rate is greater than the remaining flow rate, the flow rate is insufficient; if the estimated flow rate is less than or equal to the remaining flow rate, the flow rate is sufficient.

[0104] The streaming platform queries the power grid monitoring center for the fault severity level. This level is manually calibrated. If the fault is rated as "normal," the second level is selected if traffic is sufficient, and the third level is used if traffic is insufficient. If the fault is rated as "critical," the first level is selected if traffic is sufficient, and the third level is used if traffic is insufficient.

[0105] It should be noted that for the third level, a maximum bitrate value is selected in the third level to calculate the final estimated traffic, so that the estimated traffic is less than or equal to the remaining traffic. However, if the lower limit value of the third level cannot satisfy the estimated traffic being less than or equal to the remaining traffic, the monitoring equipment will be notified to prompt the maintenance personnel to replace the SIM card.

[0106] Finally, the streaming platform notifies the monitoring device of the final bitrate decision.

[0107] 7. It should be noted that, after receiving the video, the embodiment of the present invention inputs it into the monitoring model in real time to perform real-time monitoring recognition.

[0108] The required traffic for monitoring can be understood as the product of duration and bit rate. The duration is predicted through frequent item mining. At a given duration, the remaining traffic of the SIM card meets the transmission requirements. At this time, under this constraint, the bit rate can be selected according to the maintenance needs.

[0109] It is understandable that the video shot at the maintenance site is not sent to the back-end storage, but is for real-time monitoring and identification. If real-time monitoring is not required, it can be recorded on site and then imported into the back-end system through a PC. Because real-time monitoring requires traffic, it is necessary to dynamically adjust the bit rate.

[0110] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: while ensuring that monitoring requirements are met, costs are reduced, monitoring interruptions are prevented, and the demand for real-time monitoring of the power equipment maintenance process is met.

[0111] It should be noted that for the above method or process embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0112] See Figure 2 The embodiment of the present invention further provides a maintenance monitoring device based on a dynamic bit rate, comprising:

[0113] Request receiving module 1, used to receive a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request includes maintenance personnel information, power equipment type information, fault type information and remaining flow information;

[0114] Duration prediction module 2, used to determine the construction prediction duration of the current maintenance task based on the maintenance monitoring request;

[0115] A bit rate determination module 3 is configured to determine a target bit rate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task;

[0116] The parameter sending module 4 is configured to send the target bit rate parameter to the monitoring device, so that the monitoring device monitors the current maintenance task based on the target bit rate parameter.

[0117] Furthermore, the bit rate determination module 3 is specifically configured to:

[0118] Determining a first bit rate parameter and a second bit rate parameter of the video to be monitored based on the current fault level; wherein the bit rate value corresponding to the first bit rate parameter is greater than the bit rate value corresponding to the second bit rate parameter;

[0119] Calculate a first predicted flow corresponding to the first bit rate parameter and a second predicted flow corresponding to the second bit rate parameter according to the predicted construction duration;

[0120] If it is determined that the remaining traffic information meets the requirement of the first predicted traffic, the first bit rate parameter is used as the target bit rate parameter;

[0121] If it is determined that the remaining flow information does not meet the requirement of the first predicted flow, then determining whether the remaining flow information meets the requirement of the second predicted flow;

[0122] If yes, use the second bit rate parameter as the target bit rate parameter;

[0123] If not, a flow shortage reminder message is sent to the monitoring device.

[0124] Furthermore, the bit rate determination module 3 is specifically configured to:

[0125] Dividing the available bit rate parameters of the monitoring device into a high bit rate interval, a medium bit rate interval, and a low bit rate interval; wherein any bit rate value in the medium bit rate interval is greater than any bit rate value in the low bit rate interval, and any bit rate value in the medium bit rate interval is less than any bit rate value in the high bit rate interval;

[0126] When it is determined that the current fault level is greater than a preset fault level, determining the first bit rate parameter based on the high bit rate interval, and determining the second bit rate parameter based on the low bit rate interval;

[0127] When it is determined that the current fault level is not greater than the preset fault level, the first bit rate parameter is determined based on the medium bit rate interval, and the second bit rate parameter is determined based on the low bit rate interval.

[0128] Furthermore, the duration prediction module 2 is specifically configured to:

[0129] Calculating based on the maintenance personnel information, the power equipment type information and the fault type information and arbitrarily combining them into a plurality of prediction information sets;

[0130] Determining the construction duration stage corresponding to each prediction information set based on the correspondence between the pre-stored prediction information set and the construction duration;

[0131] The predicted construction duration of the current maintenance task is determined based on the construction duration stage corresponding to each of the prediction information sets.

[0132] Furthermore, the duration prediction module 2 is specifically configured to:

[0133] Obtaining personnel classification information of each maintenance personnel corresponding to the current maintenance task according to the maintenance personnel information;

[0134] Acquire technical scoring information of each maintenance worker based on the maintenance worker classification information, and determine the project technical scoring information of the current maintenance task based on the technical scoring information of each maintenance worker;

[0135] Based on the personnel classification information, the project technical score information, the power equipment type information and the fault type information, they are arbitrarily combined into several prediction information sets.

[0136] Furthermore, the duration prediction module 2 is specifically configured to:

[0137] Obtaining the correlation between each of the prediction information sets and the construction duration stage based on a preset mining algorithm, and classifying each prediction information set as a strong frequent set or a weak frequent set according to a preset correlation threshold;

[0138] Taking the construction duration stage corresponding to each of the prediction information sets as normal distribution duration information, and respectively obtaining the expected duration value of each of the normal distribution duration information;

[0139] The predicted construction duration of the current maintenance task is determined by taking a weighted average of all expected duration values; wherein the weight of the prediction information set classified as a strong frequent set is greater than the weight of the prediction information set classified as a weak frequent set.

[0140] Furthermore, the maintenance personnel information includes one or more of name, work number and face verification data.

[0141] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention. The maintenance monitoring device based on dynamic bit rate provided by the embodiment of the present invention can implement the maintenance monitoring method based on dynamic bit rate provided by any method embodiment of the present invention.

[0142] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the maintenance monitoring methods based on dynamic bit rate.

[0143] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0144] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0145] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0146] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0147] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0148] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0149] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A maintenance monitoring method based on dynamic bit rate, characterized in that: include: Receive a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request includes maintenance personnel information, power equipment type information, fault type information, and remaining flow information; Determining a predicted construction duration of the current maintenance task based on the maintenance monitoring request; Determining a target bitrate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task; The target bit rate parameter is sent to the monitoring device, so that the monitoring device performs a monitoring operation on the current maintenance task based on the target bit rate parameter.

2. The maintenance monitoring method based on dynamic bit rate according to claim 1, characterized in that: The determining of a target bit rate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level includes: Determining a first bit rate parameter and a second bit rate parameter of the video to be monitored based on the current fault level; wherein the bit rate value corresponding to the first bit rate parameter is greater than the bit rate value corresponding to the second bit rate parameter; Calculate a first predicted flow corresponding to the first bit rate parameter and a second predicted flow corresponding to the second bit rate parameter according to the predicted construction duration; If it is determined that the remaining traffic information meets the requirement of the first predicted traffic, the first bit rate parameter is used as the target bit rate parameter; If it is determined that the remaining flow information does not meet the requirement of the first predicted flow, then determining whether the remaining flow information meets the requirement of the second predicted flow; If yes, use the second bit rate parameter as the target bit rate parameter; If not, a flow shortage reminder message is sent to the monitoring device.

3. The maintenance monitoring method based on dynamic bit rate according to claim 2 is characterized in that: The determining of the first bit rate parameter and the second bit rate parameter of the video to be monitored based on the current fault level specifically includes: Dividing the available bit rate parameters of the monitoring device into a high bit rate interval, a medium bit rate interval, and a low bit rate interval; wherein any bit rate value in the medium bit rate interval is greater than any bit rate value in the low bit rate interval, and any bit rate value in the medium bit rate interval is less than any bit rate value in the high bit rate interval; When it is determined that the current fault level is greater than a preset fault level, determining the first bit rate parameter based on the high bit rate interval, and determining the second bit rate parameter based on the low bit rate interval; When it is determined that the current fault level is not greater than the preset fault level, the first bit rate parameter is determined based on the medium bit rate interval, and the second bit rate parameter is determined based on the low bit rate interval.

4. The maintenance monitoring method based on dynamic bit rate according to claim 1, characterized in that: The determining of the predicted construction duration of the current maintenance task based on the maintenance monitoring request includes: Calculating based on the maintenance personnel information, the power equipment type information and the fault type information and arbitrarily combining them into a plurality of prediction information sets; Determining the construction duration stage corresponding to each prediction information set based on the correspondence between the pre-stored prediction information set and the construction duration; The predicted construction duration of the current maintenance task is determined based on the construction duration stage corresponding to each of the prediction information sets.

5. The maintenance monitoring method based on dynamic bit rate according to claim 4 is characterized in that: The calculation based on the maintenance personnel information, the power equipment type information and the fault type information and arbitrary combination into several prediction information sets include: Obtaining personnel classification information of each maintenance personnel corresponding to the current maintenance task according to the maintenance personnel information; Acquire technical scoring information of each maintenance worker based on the maintenance worker classification information, and determine the project technical scoring information of the current maintenance task based on the technical scoring information of each maintenance worker; Based on the personnel classification information, the project technical score information, the power equipment type information and the fault type information, they are arbitrarily combined into several prediction information sets.

6. The maintenance monitoring method based on dynamic bit rate according to claim 4, characterized in that: The determining the predicted construction duration of the current maintenance task based on the construction duration stage corresponding to each of the prediction information sets includes: Obtaining the correlation between each of the prediction information sets and the construction duration stage based on a preset mining algorithm, and classifying each prediction information set as a strong frequent set or a weak frequent set according to a preset correlation threshold; Taking the construction duration stage corresponding to each of the prediction information sets as normal distribution duration information, and respectively obtaining the expected duration value of each of the normal distribution duration information; The predicted construction duration of the current maintenance task is determined by taking a weighted average of all expected duration values; wherein the weight of the prediction information set classified as a strong frequent set is greater than the weight of the prediction information set classified as a weak frequent set.

7. The maintenance monitoring method based on dynamic bit rate according to claim 1, characterized in that: The maintenance personnel information includes one or more of name, work number and face verification data.

8. A maintenance monitoring device based on dynamic bit rate, characterized in that: include: A request receiving module is used to receive a maintenance monitoring request uploaded by a monitoring device based on a current maintenance task; wherein the maintenance monitoring request includes maintenance personnel information, power equipment type information, fault type information and remaining flow information; A duration prediction module, configured to determine a predicted construction duration of the current maintenance task based on the maintenance monitoring request; a bitrate determination module, configured to determine a target bitrate parameter of the video to be monitored based on the remaining traffic information, the predicted construction duration, and the current fault level; wherein the current fault level is obtained based on a preset monitoring center query, and the video to be monitored is a monitoring video for the current maintenance task; The parameter sending module is used to send the target bit rate parameter to the monitoring device, so that the monitoring device monitors the current maintenance task based on the target bit rate parameter.

9. A terminal device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the maintenance monitoring method based on dynamic bit rate described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the maintenance monitoring method based on dynamic bit rate as described in any one of claims 1 to 7 is implemented.

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