Centralized storage and unified management method, system, equipment and medium for remote video monitoring system of power generation enterprise

Abstract

The invention discloses a centralized storage and unified management method, system, equipment and medium for a remote video monitoring system of a power generation enterprise, and belongs to the technical field of video monitoring. Identifying a monitoring blind area; replacing equipment according to an evaluation and classification result, and adding a high-definition network camera in a monitoring blind area; constructing a three-layer network architecture comprising an access layer, a convergence layer and a core layer, wherein the core layer adopts redundant connection; optimizing a hard disk video recorder deployment scheme based on the network topological relation; establishing a double-layer storage architecture, and adjusting a data uploading strategy according to a network state and a video recording type; unified access of devices of different manufacturers is realized through protocol conversion; and generating a monitoring interface according to the user role. Scientific classified updating of equipment is realized through a multi-dimensional equipment evaluation model, a transmission path is shortened through network hop count minimization and optimization deployment, bandwidth peak shifting utilization is realized through double-layer storage and intelligent scheduling, and unified and efficient management is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of video monitoring, and particularly relates to a centralized storage and unified management method, system and device of a remote video monitoring system of a power generation enterprise and a medium. BACKGROUND

[0002] The video monitoring system is an important part of the safety production management of the power generation enterprise, and is used for monitoring the equipment running state and personnel operation situation of the production area in real time. With the increase of the running years of the power generation enterprise, the video monitoring system gradually exposes problems such as equipment aging, system fragmentation and decentralized management. In the prior art, the video monitoring system of the power generation enterprise usually has the following technical problems: First, the equipment update lacks unified planning. The video monitoring equipment of the power generation enterprise is usually purchased and installed in batches in different periods, and the performance parameters of the equipment are greatly different. When the equipment is updated, there is no systematic evaluation method, and it is impossible to scientifically judge which equipment needs to be replaced first. The extreme way of replacing all or only repairing without replacing is often used, resulting in high modification cost or poor monitoring effect.

[0003] Second, the storage architecture is scattered and inefficient. The traditional video monitoring system adopts a scattered storage mode, and the video data is scattered and stored on multiple hard disk video recorders in the whole plant. The deployment position of the hard disk video recorder lacks optimization, resulting in a long data transmission path. At the same time, all video data is uploaded to the storage device in real time, which easily causes bandwidth congestion under the condition of limited network bandwidth, and affects the normal communication of other production businesses.

[0004] Third, the platform management is fragmented and chaotic. The power generation enterprise has constructed multiple independent monitoring platforms in different periods. These platforms are provided by different manufacturers, use different communication protocols, and cannot be interconnected. The administrator needs to log in to multiple platforms to view the monitoring screen of the whole plant. At the same time, the monitoring interface seen by all users is the same, and it is impossible to provide targeted monitoring screens according to the responsibility range of different post personnel. SUMMARY

[0005] In view of the above problems, the present application provides a centralized storage and unified management method, system, device and medium of a remote video monitoring system of a power generation enterprise.

[0006] Therefore, the present application solves the problems of high modification cost or poor monitoring effect caused by the lack of scientific evaluation of equipment update in the existing video monitoring system of the power generation enterprise, the long data transmission path and network bandwidth congestion caused by the lack of optimization of the deployment position of the hard disk video recorder and the real-time uploading of the video data, and the low management efficiency caused by the inability of multiple independent platforms to interconnect and the inability of the user interface to be customized.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a centralized storage and unified management method for a remote video monitoring system for power generation enterprises, comprising, Collect equipment information of existing video surveillance equipment in power generation enterprises, including equipment configuration parameters and operating status parameters; evaluate and classify the equipment based on the equipment information; collect work area distribution information to identify monitoring blind spots; Based on the assessment and classification results, replace the equipment and add high-definition network cameras in the monitoring blind spots; construct a three-layer network architecture, which includes an access layer, an aggregation layer and a core layer. The core layer adopts redundant connections, and the aggregation layer is connected to different core layer nodes. Calculate the transmission path from the DVR deployment location to the centralized storage device based on network topology, select an optimized DVR deployment scheme; deploy the centralized storage device; establish a two-tier storage architecture, with the edge storage layer storing short-term recordings and the centralized storage layer storing long-term recordings, and adjust the data upload strategy according to network status and recording type; Identify the communication protocol types of devices from different manufacturers, and convert different protocols into a unified protocol through protocol conversion; establish a unified monitoring platform; establish a mapping relationship between user roles and device groups, and generate corresponding monitoring interfaces based on user roles.

[0008] As a preferred embodiment of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises according to the present invention, the step of evaluating and classifying the equipment based on the equipment information includes extracting evaluation parameters from equipment configuration parameters and operating status parameters; A multi-dimensional equipment evaluation model is established, wherein the multi-dimensional equipment evaluation model sets judgment conditions for different evaluation dimensions; Based on the comparison results between the equipment's evaluation parameters and judgment criteria, the equipment is divided into different modification categories.

[0009] As a preferred embodiment of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises according to the present invention, the method for identifying monitoring blind spots includes: acquiring the distribution information of the operating areas of the power generation enterprise, and determining the target areas that need to be monitored based on the level of operational risk; Obtain the installation location coordinates and monitoring coverage radius of existing video surveillance equipment, and calculate the monitoring coverage area of ​​each device; Spatial matching analysis is performed between the target area and the monitoring coverage area. When there is a space in the target area that does not overlap with any monitoring coverage area, the space is marked as a monitoring blind spot.

[0010] As a preferred embodiment of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises described in this invention, the construction of the three-layer network architecture includes deploying core layer switches in the core layer, with the core layer switches connected to each other via redundant links; At the aggregation layer, aggregation layer switches are deployed according to the factory area, and each aggregation layer switch is connected to a different core layer switch; Access layer switches are deployed according to the distribution of devices at the access layer. The access layer switches are connected to the aggregation layer switches, and the high-definition network cameras are connected to the network architecture through the access layer switches.

[0011] As a preferred embodiment of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises described in this invention, the step of calculating the transmission path from the deployment location of the hard disk recorder to the centralized storage device based on network topology and selecting an optimized hard disk recorder deployment scheme includes: establishing a plant-wide network topology model, taking the core layer switch connected to the centralized storage device as the root node, and calculating the number of switches traversed from each candidate hard disk recorder deployment location to the root node as the network hop count; Under the constraint that the number of cameras connected to each hard disk recorder does not exceed a preset access limit, calculate the average network hop count for all cameras in the plant for different deployment schemes; The deployment scheme that minimizes the average network hop count for all cameras in the plant was chosen as the hard disk recorder deployment scheme.

[0012] The beneficial effects of this preferred technical solution are as follows: By establishing a network topology model with the core layer switch connected to the centralized storage device as the root node, the number of switches traversed from the deployment location of each candidate hard disk recorder to the root node is calculated as the network hop count. Under the constraint that the number of cameras connected to each hard disk recorder does not exceed a preset access limit, the deployment scheme that minimizes the average network hop count for all cameras in the plant is selected, fundamentally reducing the number of network nodes traversed during video data transmission. Since each additional network hop requires data to undergo a reception, processing, and forwarding process through a switch, increasing transmission latency and consuming the switch's port bandwidth resources, minimizing the network hop count directly shortens the data transmission path, reduces end-to-end transmission latency, and alleviates the bandwidth pressure on switches at all levels, especially the core layer switch. Simultaneously, a shorter transmission path means fewer devices involved in data transmission, reducing the risk of data transmission interruption due to intermediate device failures and improving the reliability and data upload stability of the entire video surveillance system.

[0013] As a preferred embodiment of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises according to the present invention, the step of adjusting the data upload strategy according to network status and recording type includes setting a network bandwidth occupancy threshold and obtaining the current bandwidth occupancy value of the hard disk recorder when uploading recording data to the centralized storage device in real time through a monitoring module; When the current bandwidth usage is lower than the network bandwidth usage threshold, the hard disk recorder uploads the recording data at the first bitrate. When the current bandwidth usage reaches the network bandwidth usage threshold, the recorded data is identified by type. Recordings within the time period before and after the alarm are marked as alarm recordings, and recordings from other time periods are marked as regular recordings. Alarm recordings are uploaded at the first bitrate, while regular recordings are uploaded at a second bitrate lower than the first bitrate. Within a preset time period, the network bandwidth usage threshold is adjusted to a value higher than that of the normal time period, and the regular video recordings previously uploaded at the second bitrate are re-uploaded at the first bitrate.

[0014] The beneficial effects of this preferred technical solution are as follows: By setting a network bandwidth occupancy threshold and monitoring the current bandwidth occupancy value in real time, different upload bitrates are applied to different types of video data based on bandwidth occupancy. Furthermore, the bandwidth occupancy threshold is adjusted within a preset time period for batch uploads, establishing an intelligent data upload scheduling mechanism that distinguishes between video types and time period characteristics. The core of this mechanism is: when network bandwidth is sufficient, all video data is uploaded normally at the standard bitrate, ensuring data transmission efficiency; when network bandwidth is tight, by identifying the type of video data, alarm videos from the time period before and after the alarm trigger are distinguished from regular videos from other time periods, prioritizing the high bitrate upload of alarm videos while reducing the bitrate of regular videos. This ensures timely transmission and storage of critical event videos under limited network bandwidth conditions, avoiding the loss or delay of important video data due to insufficient bandwidth; during periods of low network load, the previously reduced bitrate regular videos are re-uploaded at a high bitrate by increasing the bandwidth occupancy threshold, achieving staggered utilization of network bandwidth resources. This ensures both real-time response needs in emergencies and that all video data is ultimately stored in complete quality on centralized storage devices.

[0015] As a preferred embodiment of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises described in this invention, the step of establishing a mapping relationship between user roles and device groups, and generating corresponding monitoring interfaces based on user roles, includes: pre-defining multiple user roles, configuring authorized device groups and operation permissions for each user role, wherein the operation permissions include preview permissions, playback permissions, PTZ control permissions, and video recording download permissions; Multiple monitoring scene templates are predefined, and each monitoring scene template contains a set of identification information for associated devices; When a user logs into the unified monitoring platform, the system identifies the user's role and retrieves the corresponding authorized device group based on that role. The authorized device groups are matched with the monitoring scene templates, and the monitoring scene templates that intersect with the authorized device groups are filtered out. The equipment in the authorized equipment group is prioritized based on the risk level of the area where the equipment is located and the importance of the equipment. Based on the screen size of the user terminal and the device priority sorting results, a multi-screen split monitoring interface layout is generated and pushed to the user terminal.

[0016] This invention provides a centralized storage and unified management system for a remote video monitoring system for power generation enterprises.

[0017] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a centralized storage and unified management system for a remote video monitoring system of a power generation enterprise, comprising: a status assessment module, used to collect equipment information of existing video monitoring equipment of the power generation enterprise, the equipment information including equipment configuration parameters and operating status parameters, and to assess and classify the equipment based on the equipment information; and to collect work area distribution information and identify monitoring blind spots; The network construction module is used to replace equipment and add high-definition network cameras in monitoring blind spots based on the evaluation and classification results; it constructs a three-layer network architecture, which includes an access layer, an aggregation layer and a core layer. The core layer adopts redundant connections, and the aggregation layer is connected to different core layer nodes. The storage optimization module is used to calculate the transmission path from the deployment location of the hard disk recorder to the centralized storage device based on the network topology, select the optimal hard disk recorder deployment scheme, deploy the centralized storage device, establish a two-layer storage architecture, with the edge storage layer storing short-term recordings and the centralized storage layer storing long-term recordings, and adjust the data upload strategy according to the network status and recording type. The platform's unified module is used to identify the communication protocol types of devices from different manufacturers, convert different protocols into a unified protocol through protocol conversion, establish a unified monitoring platform, establish a mapping relationship between user roles and device groups, and generate corresponding monitoring interfaces based on user roles.

[0018] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the centralized storage and unified management method of a remote video monitoring system for power generation enterprises.

[0019] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the centralized storage and unified management method for a remote video monitoring system for power generation enterprises.

[0020] The beneficial effects of this invention are as follows: By establishing a multi-dimensional equipment evaluation model to scientifically classify existing equipment, extreme approaches such as complete replacement or only repair without replacement are avoided, achieving a balance between transformation costs and monitoring effectiveness. Through a disk video recorder deployment optimization method based on minimizing network hops, the number of network nodes traversed during video data transmission is reduced, shortening the data transmission path, lowering end-to-end transmission latency, alleviating bandwidth pressure on the core switch, and reducing the risk of data transmission interruption due to intermediate equipment failures. By establishing a dual-layer storage architecture and dynamically adjusting data upload strategies based on network status and recording type, high-bitrate uploads of alarm recordings are prioritized when network bandwidth is tight, while regular recordings are uploaded in batches during idle periods, achieving peak-shifting utilization of network bandwidth resources. This ensures both real-time response in emergencies and the complete quality storage of all recorded data. Protocol conversion enables unified access and management of equipment from different manufacturers. The automatic generation of customized monitoring interfaces based on user roles allows personnel in different positions to directly view monitoring screens relevant to their responsibilities, improving monitoring accuracy and management efficiency. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an overall flowchart of a centralized storage and unified management method for a remote video monitoring system for power generation enterprises, provided as an embodiment of the present invention. Detailed Implementation

[0023] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1This is one embodiment of the present invention, which provides a centralized storage and unified management method for a remote video monitoring system for power generation enterprises, including: Step 1: Collect equipment information of existing video surveillance equipment in power generation companies. The equipment information includes equipment configuration parameters and operating status parameters. Based on the equipment information, evaluate and classify the equipment; collect information on the distribution of work areas and identify monitoring blind spots. Step 2: Replace equipment and add high-definition network cameras in the blind spots of monitoring based on the evaluation and classification results; construct a three-layer network architecture, which includes an access layer, an aggregation layer and a core layer. The core layer adopts redundant connections, and the aggregation layer is connected to different core layer nodes respectively. Step 3: Calculate the transmission path from the DVR deployment location to the centralized storage device based on the network topology, and select an optimized DVR deployment scheme; deploy the centralized storage device; establish a two-tier storage architecture, with the edge storage layer storing short-term recordings and the centralized storage layer storing long-term recordings, and adjust the data upload strategy according to the network status and recording type; Step 4: Identify the communication protocol types of devices from different manufacturers, convert different protocols into a unified protocol through protocol conversion; establish a unified monitoring platform; establish a mapping relationship between user roles and device groups, and generate corresponding monitoring interfaces based on user roles.

[0025] This embodiment is applied to the upgrade of video surveillance systems in power generation enterprises. During long-term operation, power generation enterprises' video surveillance systems require systematic evaluation of existing equipment to determine upgrade plans, identification and filling of monitoring blind spots to achieve full coverage, optimization of hard disk recorder deployment and storage architecture to improve data transmission and storage efficiency, and integration of equipment and platforms from different manufacturers for unified management. This embodiment scientifically classifies existing equipment by establishing an equipment evaluation model, identifies monitoring blind spots through spatial analysis, determines the optimal deployment location of hard disk recorders through network topology optimization, improves storage system efficiency by establishing a two-layer storage architecture and intelligent data upload strategy, achieves unified access for different devices through protocol conversion, and provides customized monitoring screens for different users through a user role-based interface automatic generation mechanism.

[0026] This embodiment achieves centralized storage and unified management of a remote video monitoring system for power generation enterprises through four steps. Step 1: Collect configuration parameters and operating status parameters of existing equipment, establish a multi-dimensional evaluation model to classify the equipment, and simultaneously collect information on the distribution of work areas. Spatial matching analysis is used to identify monitoring blind spots, providing a scientific basis for equipment updates and replenishment. Step 2: Implement equipment replacement based on the equipment evaluation and classification results. Add high-definition network cameras to the monitoring blind spots, constructing a three-layer network architecture including an access layer, aggregation layer, and core layer. The core layer uses redundant connections, and the aggregation layer connects to different core layer nodes, establishing a stable and reliable network transmission foundation. Step 3: Calculate the network hop count from the hard disk recorder deployment location to the centralized storage device, select the deployment scheme that minimizes the average hop count, deploy the centralized storage device, and establish a two-layer storage architecture combining edge storage and centralized storage layers. Configure a data upload strategy that dynamically adjusts based on network bandwidth usage and recording type to improve network bandwidth utilization efficiency and data storage reliability. Step 4 identifies the communication protocol types of devices from different manufacturers, unifies different protocols into a standard protocol through protocol conversion, establishes a unified monitoring platform to achieve centralized management of all devices, establishes a mapping relationship between user roles and device groups, and automatically generates a customized monitoring interface containing authorized devices based on the user's role when the user logs in, thereby improving the targeting and efficiency of monitoring operations.

[0027] Example 2, an embodiment of the present invention, provides a centralized storage and unified management method for a remote video monitoring system for power generation enterprises, based on the previous embodiment, comprising: Step 1: Collect equipment information of existing video surveillance equipment in the power generation company. The equipment information includes equipment configuration parameters and operating status parameters. Based on the equipment information, evaluate and classify the equipment. Collect work area distribution information and identify monitoring blind spots, including the following steps A1-A6: A1: Extract evaluation parameters from equipment configuration parameters and operating status parameters; A2: Establish a multi-dimensional equipment evaluation model, wherein the multi-dimensional equipment evaluation model sets judgment conditions for different evaluation dimensions; A3: Based on the comparison results between the equipment's evaluation parameters and judgment conditions, the equipment is divided into different modification categories; A4: Obtain information on the distribution of power generation companies' operating areas and determine the target areas that need to be monitored based on the level of operational risk. A5: Obtain the installation location coordinates and monitoring coverage radius of existing video surveillance equipment, and calculate the monitoring coverage area of ​​each device; A6: Perform spatial matching analysis between the target area and the monitoring coverage area. When there is a space in the target area that does not overlap with any monitoring coverage area, mark that space as a monitoring blind spot.

[0028] In this embodiment of the application, step A2, the multi-dimensional device evaluation model is implemented by establishing a threshold comparison mechanism, specifically including the following steps: The first step is to extract resolution parameters from the device configuration parameters, establish a resolution dimension, and set a resolution judgment threshold for the resolution dimension. This threshold is used to distinguish between high-definition and non-high-definition devices. The actual resolution parameters of each device are compared with the resolution judgment threshold. When the device resolution is lower than the resolution judgment threshold, it is marked as "below standard" in the resolution dimension record of that device, indicating that the device does not meet the current monitoring requirements in terms of image acquisition capabilities. The second step is to extract the service life parameter from the operating status parameters, establish the service life dimension, and set a service life judgment threshold for the service life dimension. This threshold is determined based on the design service life of the equipment and the actual aging pattern. The actual service life of each piece of equipment is compared with the service life judgment threshold. When the service life of the equipment exceeds the service life judgment threshold, it is marked as "overdue operation" in the service life dimension record of that equipment, indicating that the equipment has entered the aging stage and its reliability may decline. The third step is to extract fault frequency parameters from the operating status parameters, establish a fault frequency dimension, and set a fault frequency judgment threshold for the fault frequency dimension. This threshold is determined based on equipment maintenance records and industry experience. The actual number of faults of each piece of equipment within the statistical period is compared with the fault frequency judgment threshold. When the equipment fault frequency exceeds the fault frequency judgment threshold, it is marked as "high frequency fault" in the fault frequency dimension record of that equipment, indicating that the equipment has poor stability. The fourth step is to summarize the marking status of each device in three dimensions and establish a device evaluation result table. This table records the marking status of each device in the dimensions of resolution, years of operation, and failure frequency. The fifth step involves classifying and judging the equipment based on the equipment evaluation results table. When a piece of equipment is marked in both the resolution and failure frequency dimensions, it is determined that the equipment has both performance and stability issues and is classified as "immediate replacement," requiring priority for replacement. When a piece of equipment is marked in only one dimension—resolution, service life, or failure frequency—it is determined that the equipment has only one aspect of the problem and is classified as "replaceable at an opportune time," allowing for replacement based on the maintenance plan and budget. When a piece of equipment is not marked in any dimension, it is determined that the equipment's performance and condition are good and is classified as "continue to be used," requiring no replacement.

[0029] In an optional implementation, in step A2, the multi-dimensional equipment evaluation model can be implemented by establishing a weighted scoring mechanism, specifically including the following steps: The first step is to assign a first weighting coefficient to the resolution dimension, a second weighting coefficient to the service life dimension, and a third weighting coefficient to the failure frequency dimension, based on the power generation company's emphasis on different performance indicators of the video monitoring system. The sum of the three weighting coefficients is equal to 1. The larger the weighting coefficient, the greater the impact of that dimension on the equipment evaluation results. The second step is to normalize the resolution parameters. A preset standard resolution is selected as a reference value, and the ratio of the device's actual resolution to the standard resolution is calculated to obtain the resolution normalization value. This normalization value reflects the degree to which the device's resolution is higher or lower than the standard. The resolution normalization value is multiplied by the first weighting coefficient to obtain the device's score in the resolution dimension. The third step is to normalize the service life parameter. A preset design service life is selected as a reference value, and the ratio of the actual service life of the equipment to the design service life is calculated to obtain the service life normalization value. This normalization value reflects the degree of equipment aging. The service life normalization value is multiplied by the second weighting coefficient to obtain the score of the equipment in the service life dimension. The fourth step is to normalize the fault frequency parameter, select a preset acceptable fault frequency as a reference value, calculate the ratio of the actual fault frequency of the equipment to the acceptable fault frequency, and obtain the normalized fault frequency value. This normalized value reflects the degree to which the equipment's stability deviates from the normal level. Multiply the normalized fault frequency value by the third weighting coefficient to obtain the equipment's score in the fault frequency dimension. The fifth step is to sum the scores for the equipment in the resolution dimension, the service life dimension, and the failure frequency dimension to obtain the comprehensive score of the equipment. This comprehensive score reflects the overall status of the equipment in multiple dimensions. Step 6: Set a first comprehensive scoring threshold and a second comprehensive scoring threshold, where the first comprehensive scoring threshold is higher than the second comprehensive scoring threshold. The first comprehensive scoring threshold is used to distinguish equipment with serious problems, and the second comprehensive scoring threshold is used to distinguish equipment with general problems. When the comprehensive score of an equipment is higher than the first comprehensive scoring threshold, the equipment is determined to have a serious problem and is classified as requiring immediate replacement. When the comprehensive score of an equipment is between the first and second comprehensive scoring thresholds, the equipment is determined to have a general problem and is classified as requiring replacement at an opportune time. When the comprehensive score of an equipment is lower than the second comprehensive scoring threshold, the equipment is determined to be in good condition and is classified as requiring continued use.

[0030] In another optional implementation, in step A2, the multi-dimensional equipment evaluation model can also be implemented by establishing a decision tree judgment mechanism, specifically including the following steps: The first step is to establish a decision tree model, using the resolution parameter as the first-level decision node, setting a resolution decision threshold as the branching condition for this node, comparing the actual resolution parameter of the device with the resolution decision threshold, and then branching the device to different decision branches based on the comparison result. The second step involves the device entering the first judgment branch when its resolution is below the resolution judgment threshold. A second-level judgment node is then established under this branch, using the fault frequency parameter as the node's condition and setting the fault frequency judgment threshold as the branching condition. The actual fault frequency of the device is compared with the fault frequency judgment threshold. If the device's fault frequency exceeds the threshold, it is determined that the device simultaneously suffers from insufficient image quality and poor stability, and the classification result is directly output as "immediate replacement." If the device's fault frequency does not exceed the threshold, a third-level judgment node is established under the second-level judgment node. The third step, in the third-level judgment node, uses the service life parameter as the judgment basis and sets a service life judgment threshold as the branch condition. The actual service life of the equipment is compared with the service life judgment threshold. If the service life of the equipment exceeds the service life judgment threshold, it is determined that although the failure frequency is acceptable, the equipment has entered the aging period, and the output classification result is "replace when appropriate"; if the service life of the equipment does not exceed the service life judgment threshold, it is determined that the equipment only has image quality problems but is still acceptable overall, and the output classification result is "continue to use". Fourth, when the device resolution reaches or exceeds the resolution judgment threshold, the device enters the second judgment branch. Under the second judgment branch, a second-level judgment node is established, using the operating years parameter as the second-level judgment node, and setting the operating years judgment threshold as the branch condition for this node. The actual operating years of the device are compared with the operating years judgment threshold. If the operating years of the device do not exceed the operating years judgment threshold, the image quality of the device is determined to be up to standard and has not aged, and the classification result is directly output as "continue to be used". If the operating years of the device exceed the operating years judgment threshold, a third-level judgment node is established under the second-level judgment node. Fifth, in the third-level judgment node, the fault frequency parameter is used as the judgment basis, and the fault frequency judgment threshold is set as the branch condition. The actual fault frequency of the device is compared with the fault frequency judgment threshold. If the fault frequency of the device exceeds the fault frequency judgment threshold, it is determined that although the image quality meets the standard, the device is old and has frequent faults. The output classification result is "replace when appropriate". If the fault frequency of the device does not exceed the fault frequency judgment threshold, it is determined that although the device is old, it is stable in operation. The output classification result is "continue to use".

[0031] In this embodiment of the application, step A6, spatial matching analysis is achieved by establishing a coordinate-system-based spatial geometric calculation method, which specifically includes the following steps: The first step is to mark the spatial extent of the target region in a unified planar coordinate system, representing the target region as a polygonal region defined by multiple vertex coordinates. Each vertex coordinate contains x-coordinate and y-coordinate information. By connecting these vertices, a closed boundary of the target region is formed. The second step is to mark the installation location coordinates of each existing video surveillance device in the same coordinate system. These coordinates represent the position of the device in the plane coordinate system. The monitoring coverage radius of each device is then obtained. This monitoring coverage radius is determined by the device's focal length, installation height, and monitoring angle. The third step is to draw a circular area in the coordinate system with the installation location coordinates of each device as the center and the monitoring coverage radius of the device as the radius. This circular area is the monitoring coverage area of ​​the device. Mark the monitoring coverage areas of all existing video surveillance devices in the plant in the coordinate system to form a monitoring coverage area distribution map. The fourth step is to determine the spatial relationship between the boundary polygon of the target area and the circles of all monitored coverage areas. The specific determination method is as follows: for each spatial point within the polygon of the target area, calculate the distance from that point to the center of all monitored coverage areas. If the distance from that point to the center of a certain monitored coverage area is less than or equal to the radius of that monitored coverage area, then the point is within the monitoring coverage area. If the distance from that point to the center of all monitored coverage areas is greater than their respective radii, then the point is not within any monitoring coverage area. The fifth step is to traverse all spatial points within the target area, count the spatial points that are not within any monitoring coverage area, aggregate these spatial points to form a continuous uncovered space, and when there is a continuous uncovered space in the target area, mark the uncovered space as a monitoring blind spot and record the location coordinates and spatial range of the monitoring blind spot.

[0032] In an optional implementation, in step A6, spatial matching analysis can be achieved by establishing a raster-based spatial coverage calculation method, specifically including the following steps: The first step is to divide the entire power plant area into multiple grid units, each representing a square area of ​​fixed size. The size of the grid unit is determined according to the monitoring accuracy requirements. A unique grid number is assigned to each grid unit, and a correspondence between the grid unit and its actual spatial location is established. The second step is to determine all the raster cells covered by the target area based on the spatial extent of the target area, and mark these raster cells as the raster cells that need to be monitored, thus forming a raster set for the target area; The third step is to calculate the number of grid cells that each existing video surveillance device can cover, based on its installation location and coverage radius. The specific calculation method is as follows: determine whether the distance from the center point of the grid cell to the device's installation location is less than or equal to the device's coverage radius. If so, the grid cell is covered by the device. All grid cells that can be covered by existing devices are then aggregated to form a monitoring coverage grid set. The fourth step is to perform set operations on the target area grid set and the monitoring coverage grid set to calculate the grid cells in the target area grid set that do not belong to the monitoring coverage grid set. These grid cells are the grid cells that are not monitored. The fifth step is to perform connectivity analysis on the uncovered grids, and aggregate spatially adjacent uncovered grids into connected regions. When the number of grids contained in a connected region exceeds the preset blind spot determination threshold, the connected region is marked as a monitoring blind spot, and the grid number and corresponding actual spatial range contained in the monitoring blind spot are recorded.

[0033] In another alternative implementation, in step A6, spatial matching analysis can also be achieved by establishing a spatial coverage evaluation method based on field-of-view overlap, specifically including the following steps: The first step is to divide the target area into multiple detection points. The distribution density of the detection points is determined according to the monitoring accuracy requirements. Spatial coordinates are assigned to each detection point, and the coordinates of the detection points cover the entire spatial range of the target area. The second step is to perform a visibility analysis on each detection point. The specific analysis method is as follows: calculate the spatial relationship between each detection point and each existing video surveillance device, and determine whether the detection point is within the monitoring field of view of the device. The judgment conditions include: whether the distance from the detection point to the device is within the monitoring coverage radius, whether the azimuth angle of the detection point relative to the device is within the horizontal field of view of the device, and whether there are any obstructions between the detection point and the device that block the line of sight. The third step is to create a list of visible devices for each detection point, record the device numbers of all devices that can monitor that detection point, and calculate the number of visible devices for each detection point. A detection point with zero visible devices indicates that the point is not within the monitoring field of any device. The fourth step is to count the detection points in the target area where there are zero visible devices, perform cluster analysis on these detection points according to their spatial location, identify the spatially continuous groups of uncovered detection points, and calculate the spatial area covered by each group of uncovered detection points. The fifth step is to mark the spatial area covered by the uncovered detection point group as a monitoring blind zone when the area exceeds the preset blind zone area threshold. Record the boundary range and size of the monitoring blind zone, and analyze the reasons for the formation of the monitoring blind zone, including excessive equipment deployment spacing, the equipment monitoring direction not covering the area, or the presence of obstructions blocking the monitoring line of sight.

[0034] Step 2: Replace equipment and add high-definition network cameras in blind spots based on the evaluation and classification results; construct a three-layer network architecture, which includes an access layer, an aggregation layer, and a core layer. The core layer uses redundant connections, and the aggregation layer is connected to different core layer nodes, including the following steps B1-B3: B1: Deploy core layer switches in the core layer, and connect core layer switches through redundant links; B2: Deploy aggregation layer switches according to the factory area in the aggregation layer, with each aggregation layer switch connected to a different core layer switch; B3: Deploy access layer switches according to the device distribution at the access layer. The access layer switches are connected to the aggregation layer switches. High-definition network cameras are connected to the network architecture through the access layer switches.

[0035] In this embodiment of the application, step 2, the three-layer network architecture is implemented by establishing a hierarchical deployment structure of the core layer, aggregation layer, and access layer, specifically including the following steps: The first step is to select the electronic equipment room of the power generation company's production office building as the deployment location for the core switches. Two core layer switches are deployed at this location and connected by two independent fiber optic links to form a redundant link. When one fiber optic link fails, the data traffic is automatically switched to the other fiber optic link to continue transmission. The two core layer switches together constitute the redundant connection structure of the core layer. The second step involves determining the deployment locations of the aggregation layer switches based on the regional division of the power plant area. Specifically, the first aggregation layer switch is deployed in the electronic equipment room of the boiler area, the second in the electronic equipment room of the steam turbine area, the third in the electronic equipment room of the coal conveying area, and the fourth in the electronic equipment room of the desulfurization area. Each aggregation layer switch is connected to the first core layer switch via one fiber optic link and simultaneously connected to the second core layer switch via another independent fiber optic link, forming a network topology where each aggregation layer switch is connected to both core layer switches. The third step is to deploy access layer switches at the access layer based on the actual distribution locations of the high-definition network cameras. The specific deployment method is as follows: select a suitable installation location near each monitoring point to deploy the access layer switch. Each access layer switch is connected to the corresponding aggregation layer switch in its area via a network cable. Each access layer switch provides multiple network ports for connecting high-definition network cameras. The fourth step is to connect the high-definition network camera to the nearest access layer switch via a Category 6 unshielded network cable. The video data collected by the camera is uploaded from the access layer switch to the aggregation layer switch, then from the aggregation layer switch to the core layer switch, and finally from the core layer switch to the centralized storage device, forming a complete data transmission path from the camera to the storage device.

[0036] In an optional implementation, in step 2, the three-layer network architecture can be implemented by establishing a ring-redundant topology, specifically including the following steps: The first step is to deploy two core layer switches in the core layer, labeling them Core Switch A and Core Switch B respectively. Two redundant links are established between Core Switch A and Core Switch B. The first redundant link is a direct connection using 10 Gigabit fiber, and the second redundant link is a connection using 10 Gigabit fiber via a backup path. The two redundant links form a ring connection structure. The second step involves deploying multiple aggregation layer switches at the aggregation layer based on the factory area division. These switches are labeled as Aggregation Switch 1 to Aggregation Switch N according to their spatial location. Each aggregation layer switch establishes an uplink with each of the two core layer switches. Simultaneously, each aggregation layer switch also establishes a lateral link with its next adjacent aggregation layer switch. The last aggregation layer switch establishes a lateral link with the first aggregation layer switch, forming a ring topology within the aggregation layer. The third step involves deploying access layer switches at the access layer based on the density of devices. Each access layer switch connects to the aggregation layer switch in its area. In some critical areas, access layer switches connect to two different aggregation layer switches simultaneously, forming dual uplinks to improve network reliability in critical areas. The fourth step is that when a link in the network fails, the network automatically activates the loop protection protocol to block the faulty link and re-establish the data transmission channel through other paths in the ring topology, thereby realizing automatic fault recovery of the network. When the faulty link is repaired, the network automatically returns to the normal topology state.

[0037] In another alternative implementation, step 2, the three-layer network architecture can also be implemented by establishing a partitioned network structure, specifically including the following steps: The first step is to deploy two core layer switches at the core layer. The two core layer switches establish a logical aggregated link between multiple physical links through link aggregation technology. Link aggregation virtualizes multiple physical links into one logical link, thereby improving the bandwidth capacity and link redundancy capability of the core layer. The second step involves deploying aggregation layer switches at the aggregation layer according to the production management zones of the power generation enterprise. The entire plant is divided into Production Zone 1, Production Zone 2, Production Zone 3, and Auxiliary Production Zone. One or more aggregation layer switches are deployed in each zone. The aggregation layer switch in Production Zone 1 is responsible for aggregating the data traffic of all access layer devices in that zone, the aggregation layer switch in Production Zone 2 is responsible for aggregating the data traffic of Production Zone 2, and so on. Each zone's aggregation layer switch is connected to two core layer switches through an independent uplink. The third step is to establish an independent access layer network within each partition. Based on the deployment location and number of high-definition network cameras in that partition, deploy a corresponding number of access layer switches. All access layer switches within the same partition are connected to the aggregation layer switch of that partition, and access layer switches in different partitions achieve data interaction through their respective aggregation layer switches. The fourth step is to configure an independent virtual LAN identifier for each partition. Logical isolation between different partitions is achieved through virtual LAN technology. Video data traffic in production zone 1 is labeled as virtual LAN 1, and video data traffic in production zone 2 is labeled as virtual LAN 2. Data traffic in each partition shares the transmission channel on the physical link, but logical isolation is achieved through virtual LAN identifiers to avoid mutual interference between data traffic in different partitions. When abnormal traffic occurs in the network of a certain partition, the abnormal traffic is restricted to the virtual LAN of that partition and will not affect the normal communication of other partitions.

[0038] Step 3: Calculate the transmission path from the DVR deployment location to the centralized storage device based on the network topology, select the optimal DVR deployment scheme; deploy the centralized storage device; establish a two-tier storage architecture, with the edge storage layer storing short-term recordings and the centralized storage layer storing long-term recordings, and adjust the data upload strategy according to network status and recording type, including the following steps C1-C7: C1: Establish a factory-wide network topology model, taking the core layer switch connected to the centralized storage device as the root node, and calculate the number of switches from each candidate hard disk recorder deployment location to the root node as the network hop count; C2: Under the constraint that the number of cameras connected to each hard disk recorder does not exceed the preset access limit, calculate the average network hop count for all cameras in the plant for different deployment schemes; C3: Select the deployment scheme that minimizes the average network hop count of all cameras in the plant as the hard disk recorder deployment scheme; C4: Sets a network bandwidth usage threshold and uses a monitoring module to obtain the current bandwidth usage value of the hard disk recorder when uploading video data to the centralized storage device in real time; C5: When the current bandwidth usage is lower than the network bandwidth usage threshold, the hard disk recorder uploads the recording data at the first bitrate; C6: When the current bandwidth usage reaches the network bandwidth usage threshold, the recorded data is identified by type. Recordings within the time period before and after the alarm is triggered are marked as alarm recordings, and recordings in other time periods are marked as regular recordings. Alarm recordings are uploaded at the first bitrate, and regular recordings are uploaded at a second bitrate lower than the first bitrate. C7: Within a preset time period, adjust the network bandwidth usage threshold to a value higher than that of the normal time period, and re-upload the regular recordings that were previously uploaded at the second bitrate at the first bitrate.

[0039] In this embodiment of the application, step 3, calculating the transmission path from the deployment location of the hard disk recorder to the centralized storage device based on the network topology, and selecting an optimized hard disk recorder deployment scheme is achieved by establishing a network hop count minimization calculation method, specifically including the following steps: The first step is to establish a factory-wide network topology model, which includes all switch nodes and the connections between them. The core layer switch connected to the centralized storage device is marked as the root node, and the root node is assigned a node number of 0. The second step is to determine the candidate deployment locations for the hard disk recorders. These candidate locations are the electronic equipment rooms in each area, including the electronic equipment rooms in the boiler area, steam turbine area, coal conveying area, and desulfurization area. A unique location number is assigned to each candidate deployment location. The third step involves calculating the transmission path from each candidate deployment location to the root node using a breadth-first search algorithm. Specifically, starting from the candidate deployment location, the path first reaches the aggregation layer switch where that location is located, recording the first switch node traversed. Then, it reaches the core layer switch from the aggregation layer switch, recording the second switch node traversed. Finally, it reaches the root node. The number of switches traversed along the path from the candidate deployment location to the root node is counted; this number represents the network hop count. The network hop count for each candidate deployment location is recorded in the candidate location table. The fourth step is to count the number of high-definition network cameras in each area, and determine the number of hard disk recorders that need to be deployed in each area based on the preset access limit of the hard disk recorders. The preset access limit is the maximum number of cameras that a single hard disk recorder can connect to. The number of hard disk recorders that need to be deployed in the area is calculated by dividing the number of cameras in the area by the preset access limit and rounding up. Fifth, for areas where multiple DVRs need to be deployed, select the location with the lowest network hop count from among the multiple candidate deployment locations in that area. If there is only one candidate deployment location in an area, deploy the DVR directly at that location. If there are multiple candidate deployment locations in an area with the same network hop count, select the location closest to the center of the camera distribution in that area. Step 6: Calculate the average network hop count corresponding to the entire factory's hard disk recorder deployment plan. The specific calculation method is as follows: multiply the number of cameras in each area by the network hop count of the hard disk recorder deployment location in that area to obtain the total hop count for that area. Add up the total hop counts of all areas and then divide by the total number of cameras in the entire factory to obtain the average network hop count for all cameras in the factory. This average network hop count reflects the data transmission efficiency of the entire deployment plan.

[0040] In an optional implementation, in step 3, the transmission path from the deployment location of the hard disk recorder to the centralized storage device is calculated based on the network topology. Selecting an optimized hard disk recorder deployment scheme can be achieved by establishing a bandwidth load balancing optimization method, specifically including the following steps: The first step is to establish a network topology model, identify the bandwidth capacity of each network link, designate the links between the core layer and the aggregation layer as 10 Gigabit links, and the links between the aggregation layer and the access layer as gigabit links, and record the bandwidth capacity parameters of each link in the topology model; The second step is to determine the candidate deployment locations for the hard disk recorders and calculate the transmission path from each candidate deployment location to the centralized storage device. For each transmission path, identify all network links along the path and record the bandwidth capacity of these links. The third step is to count the number of high-definition network cameras in each area and the video bitrate of each camera, and calculate the total video data traffic for that area. The total video data traffic is equal to the number of cameras multiplied by the video bitrate of each camera. This traffic will be uploaded to a centralized storage device via a hard disk recorder. The fourth step is to evaluate the network link bandwidth usage of different deployment schemes. The specific evaluation method is as follows: Assuming that a hard disk recorder is deployed at a certain candidate deployment location, calculate the data traffic that each link on the transmission path from that location to the centralized storage device needs to carry. Add the total video data traffic of the area to the occupied bandwidth of each link on the path, and calculate the bandwidth utilization rate of each link. The bandwidth utilization rate is equal to the occupied bandwidth of the link divided by the link bandwidth capacity. The fifth step is to select a deployment scheme that maximizes the bandwidth utilization of each network link. The specific selection method is as follows: compare the bandwidth utilization of each link under different deployment schemes, calculate the maximum link bandwidth utilization of each deployment scheme, and select the deployment scheme with the minimum maximum link bandwidth utilization. This scheme can avoid the situation where the bandwidth of one link is excessively occupied while the bandwidth of other links is idle, thus achieving balanced utilization of network bandwidth. Step 6: Verify whether the selected deployment plan meets the bandwidth requirements. Check whether the bandwidth utilization rate of all network links is lower than the preset bandwidth utilization limit. If the bandwidth utilization rate of all links is lower than the limit, the deployment plan is feasible. If the bandwidth utilization rate of any link exceeds the limit, adjust the deployment plan and add disk recorder deployment points in locations with sufficient network bandwidth to distribute data traffic until the bandwidth utilization rate of all links meets the requirements.

[0041] In another optional implementation, in step 3, the transmission path from the deployment location of the hard disk recorder to the centralized storage device is calculated based on the network topology. Selecting an optimized hard disk recorder deployment scheme can also be achieved by establishing a fault redundancy assessment and optimization method, specifically including the following steps: The first step is to establish a network topology model, identify the key nodes and key links in the network. Key nodes include core layer switches and aggregation layer switches, and key links include the connection links between the core layer and the aggregation layer. Analyze the scope of the failure impact of each key node and key link. The second step is to calculate the primary transmission path and backup transmission path from each candidate deployment location of the hard disk recorder to the centralized storage device. The primary transmission path is the path with the fewest network hops, and the backup transmission path is the alternative path that can be used when a node or link on the primary transmission path fails. Record the nodes and links traversed by the primary and backup transmission paths. The third step is to evaluate the path redundancy of each candidate deployment location. Path redundancy is measured by the degree of overlap between the primary transmission path and the backup transmission path. The number of common nodes and common links between the primary and backup transmission paths is calculated. The fewer common nodes and common links, the higher the independence of the two paths and the better the path redundancy. When the primary transmission path fails, the backup transmission path can independently provide data transmission services. The fourth step is to calculate a redundancy score for each candidate deployment location. The redundancy score comprehensively considers the network hop count and path redundancy of the location. The specific calculation method is as follows: the network hop count is normalized to obtain a hop count score, the smaller the hop count, the higher the score; the path redundancy is normalized to obtain a redundancy score, the better the redundancy, the higher the score; the hop count score and the redundancy score are weighted and summed according to preset weight coefficients to obtain the comprehensive redundancy score of the candidate deployment location. Fifth, under the constraint that the number of cameras connected to each hard disk recorder does not exceed the preset access limit, the candidate deployment location with the highest comprehensive redundancy score is selected as the deployment location of the hard disk recorder. This deployment scheme can ensure a shorter data transmission path and provide a reliable backup transmission path in case of network failure, thereby improving the reliability of the system. The sixth step is to conduct fault simulation tests on the selected deployment scheme, simulating scenarios such as core layer switch failure, aggregation layer switch failure, and link failure, to verify whether data transmission from the hard disk recorder to the centralized storage device can continue through the backup path under various fault scenarios, evaluate the fault switching time and network performance after fault recovery, and ensure that the deployment scheme has sufficient fault response capabilities in practical applications.

[0042] Step 4: Identify the communication protocol types of devices from different manufacturers, convert different protocols to a unified protocol through protocol conversion; establish a unified monitoring platform; establish a mapping relationship between user roles and device groups, and generate corresponding monitoring interfaces based on user roles, including the following steps D1-D6: D1: Predefine multiple user roles, and configure authorized device groups and operation permissions for each user role. The operation permissions include preview permissions, playback permissions, PTZ control permissions, and video recording download permissions. D2: Multiple monitoring scene templates are predefined, and each monitoring scene template contains a set of identification information for associated devices; D3: When a user logs into the unified monitoring platform, the system identifies the user's role and retrieves the corresponding authorized device group based on that user role. D4: Match the authorized device groups with the monitoring scene templates, and filter out the monitoring scene templates that intersect with the authorized device groups; D5: Prioritize the devices in the authorized equipment group based on the risk level of the area where the devices are located and the importance of the devices themselves; D6: Based on the screen size of the user terminal and the device priority sorting results, generate a multi-screen split monitoring interface layout and push it to the user terminal.

[0043] Example 2 is an embodiment of the present invention, which provides a centralized storage and unified management system for a remote video monitoring system for power generation enterprises, including: The status assessment module is used to collect equipment information of existing video surveillance equipment in power generation companies. The equipment information includes equipment configuration parameters and operating status parameters. Based on the equipment information, the module assesses and classifies the equipment; it also collects work area distribution information to identify monitoring blind spots. The network construction module is used to replace equipment and add high-definition network cameras in monitoring blind spots based on the evaluation and classification results; it constructs a three-layer network architecture, which includes an access layer, an aggregation layer and a core layer. The core layer adopts redundant connections, and the aggregation layer is connected to different core layer nodes. The storage optimization module is used to calculate the transmission path from the deployment location of the hard disk recorder to the centralized storage device based on the network topology, select the optimal hard disk recorder deployment scheme, deploy the centralized storage device, establish a two-layer storage architecture, with the edge storage layer storing short-term recordings and the centralized storage layer storing long-term recordings, and adjust the data upload strategy according to the network status and recording type. The platform's unified module is used to identify the communication protocol types of devices from different manufacturers, convert different protocols into a unified protocol through protocol conversion, establish a unified monitoring platform, establish a mapping relationship between user roles and device groups, and generate corresponding monitoring interfaces based on user roles.

[0044] This embodiment also provides an electronic device applicable to a centralized storage and unified management method for a remote video monitoring system for a power generation enterprise, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the centralized storage and unified management method for a remote video monitoring system for a power generation enterprise as proposed in the above embodiment.

[0045] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a centralized storage and unified management method for a remote video monitoring system for power generation enterprises as proposed in the above embodiments.

[0046] The storage medium proposed in this embodiment and the centralized storage and unified management method for a remote video monitoring system for power generation enterprises proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0047] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0048] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A centralized storage and unified management method of a remote video monitoring system of a power generation enterprise, characterized in that: The application relates to a power generation enterprise video monitoring system and a construction method thereof. Device information of existing video monitoring equipment of a power generation enterprise is collected, the device information comprises device configuration parameters and running state parameters, and the devices are evaluated and classified based on the device information; distribution information of a work area is collected, and a monitoring blind area is identified; According to the evaluation and classification results, the devices are replaced, and a high-definition network camera is added in the monitoring blind area; a three-layer network architecture is constructed, the three-layer network architecture comprises an access layer, a convergence layer and a core layer, the core layer adopts redundant connection, and the convergence layer is connected to different core layer nodes respectively; A transmission path from a hard disk video recorder deployment position to a centralized storage device is calculated based on a network topology relationship, and an optimized hard disk video recorder deployment scheme is selected; the centralized storage device is deployed; A double-layer storage architecture is established, a short-term video is stored in an edge storage layer, a long-term video is stored in a centralized storage layer, and a data uploading strategy is adjusted according to a network state and a video type; Different communication protocol types of devices of different manufacturers are identified, different protocols are converted into a unified protocol through protocol conversion, a unified monitoring platform is established, a mapping relationship between a user role and a device group is established, and a corresponding monitoring interface is generated according to the user role.

2. The centralized storage and unified management method of a power plant remote video monitoring system according to claim 1, characterized in that: The evaluation and classification of the devices based on the device information comprises extracting evaluation parameters from the device configuration parameters and the running state parameters; A multi-dimensional device evaluation model is established, the multi-dimensional device evaluation model sets a judgment condition for different evaluation dimensions; According to a comparison result of the evaluation parameters of the devices and the judgment condition, the devices are divided into different transformation categories.

3. The centralized storage and unified management method of a power plant remote video monitoring system according to claim 2, characterized in that: The monitoring blind area is identified by obtaining distribution information of a work area of the power generation enterprise, determining a target area needing to be monitored according to a work risk level, obtaining installation position coordinates and a monitoring coverage radius of existing video monitoring equipment, calculating a monitoring coverage area of each device, and performing spatial matching analysis on the target area and the monitoring coverage area. The three-layer network architecture is constructed by deploying core layer switches in the core layer, connecting the core layer switches through redundant links, deploying convergence layer switches in the convergence layer according to a plant area, connecting each convergence layer switch to different core layer switches, deploying access layer switches in the access layer according to device distribution, connecting the access layer switches to the convergence layer switches, and connecting the high-definition network cameras to the network architecture through the access layer switches. The transmission path from the hard disk video recorder deployment position to the centralized storage device is calculated based on the network topology relationship, and the optimized hard disk video recorder deployment scheme is selected by establishing a full-plant network topology model, taking a core layer switch connected to the centralized storage device as a root node, calculating a switch quantity passed by each candidate hard disk video recorder deployment position to the root node as network hops, calculating average network hops of all plant cameras corresponding to different deployment schemes under the constraint condition that the number of connected cameras of each hard disk video recorder does not exceed a preset upper limit, and selecting a deployment scheme with minimum average network hops of all plant cameras as the hard disk video recorder deployment scheme.

4. The centralized storage and unified management method of a power plant remote video monitoring system according to claim 3, characterized in that: ​ ​ ​ 5. The centralized storage and unified management method of a power plant remote video monitoring system according to claim 4, characterized in that: ​ ​ ​ 6. The centralized storage and unified management method of a power plant remote video monitoring system according to claim 4, characterized in that: The adjusting data uploading strategy according to the network state and the video type comprises setting a network bandwidth occupation threshold, and acquiring a current bandwidth occupation value in real time when the hard disk video recorder uploads video data to the centralized storage device through a monitoring module; When the current bandwidth occupation value is lower than the network bandwidth occupation threshold, the hard disk video recorder uploads the video data at a first code rate; When the current bandwidth occupation value reaches the network bandwidth occupation threshold, the video data is identified by type, the video in a time period before and after an alarm trigger is marked as alarm video, and the video in other time periods is marked as regular video, the alarm video is continuously uploaded at the first code rate, and the regular video is uploaded at a second code rate lower than the first code rate; The network bandwidth occupation threshold is adjusted to a value higher than that in a regular period within a preset time period, and the regular video previously uploaded at the second code rate is re-uploaded at the first code rate.

7. The centralized storage and unified management method of a power plant remote video monitoring system according to claim 4, characterized in that: The mapping relationship between the user roles and the device groups is established, and the corresponding monitoring interface is generated according to the user roles, which comprises predefining a plurality of user roles, configuring an authorized device group and an operation permission for each user role, and the operation permission comprises a pre-view permission, a playback permission, a pan-tilt control permission and a video download permission; A plurality of monitoring scene templates are predefined, and each monitoring scene template comprises identification information of a group of associated devices; When a user logs in to the unified monitoring platform, the user role to which the user belongs is identified, and the corresponding authorized device group is extracted according to the user role; The authorized device group and the monitoring scene template are matched, and the monitoring scene template having an intersection with the authorized device group is screened out; The devices in the authorized device group are prioritized according to the risk level of the area where the devices are located and the importance of the devices; According to the screen size of the user terminal and the device priority sorting result, a multi-picture segmented monitoring interface layout is generated and pushed to the user terminal.

8. A centralized storage and unified management system of a remote video monitoring system of a power generation enterprise, which applies the centralized storage and unified management method of the remote video monitoring system of the power generation enterprise according to any one of claims 1-7, characterized in that, It comprises: A state evaluation module is used to collect device information of existing video monitoring devices of a power generation enterprise, the device information comprises device configuration parameters and running state parameters, and the devices are evaluated and classified based on the device information; the distribution information of the working area is collected, and the monitoring blind area is identified; A network construction module is used to replace the devices according to the evaluation classification result and add high-definition network cameras in the monitoring blind area; a three-layer network architecture is constructed, the three-layer network architecture comprises an access layer, a convergence layer and a core layer, the core layer adopts redundant connection, and the convergence layer is connected to different core layer nodes respectively; A storage optimization module is used to calculate a transmission path from the deployment position of the hard disk video recorder to the centralized storage device based on the network topology relationship, and select an optimized hard disk video recorder deployment scheme; The centralized storage device is deployed; A double-layer storage architecture is established, the edge storage layer stores short-term video, and the centralized storage layer stores long-term video; a data uploading strategy is adjusted according to the network state and the video type; A platform unification module is used to identify the communication protocol types of different manufacturers' devices, convert different protocols into a unified protocol through protocol conversion, and establish a unified monitoring platform; A mapping relationship between user roles and device groups is established, and a corresponding monitoring interface is generated according to the user roles. 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. The computer program is executed by the processor to implement the steps of the centralized storage and unified management method of the power generation enterprise remote video monitoring system in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the centralized storage and unified management method of the power generation enterprise remote video monitoring system in any one of claims 1 to 7.

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