Remote decision-making interactive system for power operation and maintenance emergency service cabin

Abstract

The present invention discloses a remote decision-making interactive system for an electric power operation and maintenance emergency service cabin, which relates to the field of electrical control systems. The system includes: a task analysis module for receiving emergency operation tasks, obtaining cloud data information, and evaluating regional information trust; an initial control module for synchronous position tracking control of lighting lamps and PTZ cameras; a linkage acquisition module for activating audio sensors and performing synchronous audio data acquisition; a feedback recognition module for performing image recognition and audio demand recognition; an authentication decision module for performing data authentication on the first feedback control data and the second feedback control data; and an execution module for controlling the response execution of lighting lamps and PTZ cameras. The system solves the technical problems of slow emergency response speed and low operation accuracy in existing electric power operation and maintenance, and achieves the technical effect of improving emergency response speed and operation accuracy.

Description

Technical Field

[0001] The present application relates to the field of electrical control systems, and in particular to a remote decision-making interaction system for an electric power operation and maintenance emergency service cabin. Background Art

[0002] In the field of power operation and maintenance, with the rapid development of smart grids, power facilities are widely distributed and complex, which puts higher requirements on the efficiency and safety of operation and maintenance work. When responding to emergencies or emergency operations, traditional operation and maintenance methods often rely on direct operation and judgment of on-site personnel, which is not only inefficient but also poses great safety risks. In order to improve the speed of emergency response, reduce personnel risks and enhance the accuracy of operations, remote monitoring and decision support systems are particularly important in power operation and maintenance. At present, the remote monitoring technologies used in power operation and maintenance, such as installing cameras for video monitoring and using sensors to collect environmental data, mostly stay at the data collection level, that is, they can only provide basic video and audio data, and cannot automatically adjust the monitoring range or perform in-depth data analysis according to task requirements, resulting in the inability to quickly and accurately identify problems and take effective measures in emergency situations.

[0003] Among the current relevant technologies, power operation and maintenance have technical problems such as slow emergency response and low operation accuracy. Summary of the invention

[0004] The present application provides a remote decision-making interaction system for the power operation and maintenance emergency service cabin, receives emergency operation tasks, conducts trust evaluation on the target operation area based on cloud data, establishes a location trust mark, performs synchronous position tracking control on the lighting and pan-tilt camera through the initial control module, and synchronously activates the audio sensor in combination with the linkage acquisition module to perform real-time acquisition of video and audio data, performs image recognition on the real-time feedback image based on the location trust mark, identifies the demand for the audio data, generates feedback control data, authenticates the feedback control data, controls the response execution of the lighting and pan-tilt camera according to the data authentication result and the authentication response window, and other technical means, thereby achieving a fast and accurate emergency response and achieving the technical effect of improving the emergency response speed and operation accuracy.

[0005] This application provides a remote decision-making interactive system for the power operation and maintenance emergency service cabin, including:

[0006] A task analysis module is used to receive emergency operation tasks, obtain cloud data information of the target operation area, and evaluate the regional information trust of the target operation area based on the emergency operation tasks and cloud data information, and establish a location trust identifier for the target operation area; an initial control module is used to perform synchronous position tracking control of the lighting and pan-tilt camera in the target operation area according to the emergency operation tasks, and record the real-time feedback image of the pan-tilt camera; a linkage acquisition module is used to synchronously activate the audio sensor, execute synchronous audio data acquisition of the target operation area, and establish real-time audio data; a feedback identification module is used to perform image recognition on the real-time feedback image based on the location trust identifier to establish first feedback control data, perform audio demand recognition on the real-time audio data, and establish second feedback control data; an authentication decision module is used to perform data authentication on the first feedback control data and the second feedback control data, and establish an authentication response window according to the data authentication result; an execution module is used to control the response execution of the lighting and the pan-tilt camera based on the data authentication result and the authentication response window.

[0007] In a possible implementation, the execution module is further used for:

[0008] The pan-tilt camera is called through the authentication response window to perform execution monitoring and establish an execution feedback image; the data authentication result is parsed to establish an independent decision result of the second feedback control data; the decision effect of the independent decision result is authenticated on the execution feedback image to generate a decision effect authentication result; if the decision effect authentication result cannot meet the expected threshold, a backtracking instruction is generated; the data authentication result is reconstructed according to the backtracking instruction, and the response execution of the lighting and the pan-tilt camera is controlled according to the reconstructed data authentication result.

[0009] In a possible implementation, the system further includes:

[0010] The penalty module is used to record the decision-making effect authentication result and construct a penalty factor, use the penalty factor to punish the user audio response of this emergency operation task, and perform subsequent data authentication management based on the user audio response penalty.

[0011] In a possible implementation, the real-time feedback image is image recognized based on the location trust identifier, first feedback control data is established, and the following processing is performed:

[0012] Determine whether there is a position below a preset trust threshold in the position trust mark. If so, mark the corresponding position as a dangerous area and generate a reconstruction acquisition instruction; use the reconstruction acquisition instruction to control the joint sensor group to collect additional data in the dangerous area and establish an additional data set; perform position image feature recognition on the real-time feedback image, update the cloud data information through the additional data set, and then identify and authenticate the position image feature recognition result based on the updated cloud data information, and establish the first feedback control data based on the identification and authentication result.

[0013] In a possible implementation, the system further includes:

[0014] Reconstruct the location trust identifier through the updated cloud data information, and establish the location importance weight with the reconstructed location trust identifier, and the sum of all location importance weights is 1; establish evaluation features, which include illumination features, uniformity features, glare features, color temperature features, and light effect features; based on the evaluation features and the location importance weights, perform recognition effect authentication on the updated cloud data information and location image feature recognition results to generate a recognition authentication result.

[0015] In a possible implementation, the synchronously activated audio sensor performs synchronous audio data collection of the target operation area, establishes real-time audio data, and performs the following processing:

[0016] Acquire the array position of the audio sensor; perform homophone authentication based on the array position and audio timing data, and establish a homophone authentication result; locate the sound source based on the homophone authentication result, and perform joint speech content recognition of the homophone authentication result; establish real-time audio data based on the sound source location result, the joint speech content recognition result, and the homophone authentication result.

[0017] In a possible implementation, the system further includes:

[0018] The auxiliary warning unit is used to receive the real-time feedback image and send the real-time feedback image to the dynamic and static feature warning network, perform warning identification of safety risks, establish safety risk warning identification results, and report the safety risk warning identification results through a loudspeaker.

[0019] In a possible implementation, the real-time feedback image is sent to a dynamic and static feature warning network, a warning identification of security risks is performed, a security risk warning identification result is established, and the following processing is performed:

[0020] The feature segmentation of the real-time feedback image is performed through the preprocessing sub-channel to establish static feature identification and dynamic feature identification; the real-time feedback image corresponding to the static feature identification is called, and the calling result is sent to the static time series recognition sub-network to perform security risk warning identification of static features and establish static recognition results; the real-time feedback image corresponding to the dynamic feature identification is called, and the calling result is sent to the dynamic time series recognition sub-network to perform security risk warning identification of dynamic features and establish dynamic recognition results; the security risk warning identification result is established based on the static recognition result and dynamic recognition result.

[0021] It is planned to use the remote decision-making interactive system of the power operation and maintenance emergency service cabin proposed in this application, receive emergency operation tasks through the task analysis module, obtain cloud data information of the target operation area, and evaluate the regional information trust of the target operation area based on the emergency operation tasks and cloud data information, establish a location trust mark of the target operation area, and perform synchronous position tracking control of the lighting and pan-tilt camera in the target operation area according to the emergency operation tasks through the initial control module, and record the real-time feedback image of the pan-tilt camera, synchronously activate the audio sensor through the linkage acquisition module, perform synchronous audio data acquisition of the target operation area, establish real-time audio data, perform image recognition on the real-time feedback image based on the location trust mark through the feedback identification module, establish first feedback control data, perform audio demand recognition on the real-time audio data, establish second feedback control data, perform data authentication on the first feedback control data and the second feedback control data through the authentication decision module, and establish an authentication response window according to the data authentication result, and control the response execution of the lighting and pan-tilt camera based on the data authentication result and the authentication response window through the execution module, thereby achieving the technical effect of improving emergency response speed and operation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic diagram of the structure of the remote decision-making interaction system for the electric power operation and maintenance emergency service cabin provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of a process for establishing safety risk warning identification results in a remote decision-making interactive system for an electric power operation and maintenance emergency service cabin provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: task analysis module 10 , initial control module 20 , linkage collection module 30 , feedback identification module 40 , authentication decision module 50 , execution module 60 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides a remote decision-making interactive system for an electric power operation and maintenance emergency service cabin, such as Figure 1 As shown, the system comprises:

[0030] The task parsing module 10 is used to receive emergency operation tasks, obtain cloud data information of the target operation area, and evaluate the regional information trust of the target operation area based on the emergency operation tasks and cloud data information, and establish a location trust mark of the target operation area. Specifically, the system receives emergency operation tasks from the dispatch center or on-site personnel through a preset interface (such as API, Web interface, message queue, etc.), and the emergency operation tasks refer to operation tasks that need to be executed immediately due to emergencies or emergency situations, such as troubleshooting, equipment repair, etc. The emergency operation tasks contain information such as the geographical location, operation type, and urgency of the target operation area (the specific work location or area specified by the emergency operation task). According to the target operation area specified in the emergency operation task, a connection is established with the cloud server through a 4G communication module or a higher speed communication method (such as 5G, satellite communication, etc.), and pre-stored data (cloud data information) related to the target operation area is obtained from the cloud server, including topographic maps, historical fault records, environmental parameters (such as temperature, humidity, wind speed, etc.), safety risk assessment reports, etc. The acquired cloud data is pre-processed, including data cleaning, format conversion, etc., to ensure the availability and consistency of the data. Using preset algorithms or models, evaluate the integrity, timeliness, and accuracy of the data, and check whether the data is missing, within the validity period, and whether it is consistent with historical data or data from other sources. Based on the evaluation results (including the reliability of the data source, the frequency of data updates, the level of detail of the data, etc.), calculate the trust score of the target operation area. Based on the trust score, generate a unique location trust identifier for the target operation area. This identifier can be a string, a numeric code, or a more complex data structure, which is used to identify the trust of the area in subsequent modules. The location trust identifier is stored in the system's database and associated with other relevant information of the target operation area for quick retrieval and use when needed.

[0031] The initial control module 20 is used to perform synchronous position tracking control of the lighting and the pan-tilt camera in the target operation area according to the emergency operation task, and record the real-time feedback image of the pan-tilt camera. Specifically, the initial control module 20 receives the emergency operation task and its detailed information from the task analysis module 10, including the specific location of the target operation area, operation requirements, etc. The relative position relationship between the current position of the service module and the target operation area is determined by using GPS or other positioning technologies, and the initial position and angle that the pan-tilt camera and the lighting need to reach are set according to the task requirements. Among them, the pan-tilt camera is a camera with directional indication and remote control functions, which can be raised and lowered and rotated 360° to capture images of the target operation area; the lighting is a lamp used to illuminate the target operation area, has directional indication, and its brightness, direction and color can be remotely controlled.

[0032] Send control instructions to the PTZ camera and lighting to start moving or rotating synchronously toward the target work area. During the movement or rotation process, monitor and adjust the position and angle of the PTZ camera and lighting in real time to ensure that they can accurately align with the target work area. Use a closed-loop control system (such as a PID controller) to optimize the adjustment process and reduce errors and jitter. After the PTZ camera is aligned with the target work area, it starts to capture and transmit real-time video images to the system. The initial control module 20 receives these real-time feedback images and stores them on a local or remote server. At the same time, the images can also be displayed in real time on the monitoring screen of the rear technicians for them to monitor the on-site situation. The lighting automatically adjusts the brightness and color temperature according to the ambient light conditions of the target work area to ensure that the target work area has sufficient lighting so that the PTZ camera can capture images clearly and help the rear technicians better judge the on-site situation.

[0033] The linkage acquisition module 30 is used to synchronously activate the audio sensor, perform synchronous audio data acquisition of the target operation area, and establish real-time audio data. Specifically, the linkage acquisition module 30 receives a task trigger signal from the task analysis module 10, which indicates that it is necessary to start audio data acquisition for the target operation area. According to the task trigger signal, the linkage acquisition module 30 sends an activation instruction to the audio sensor (a device that can capture sound and convert it into an electrical signal or a digital signal) to put it into a working state. These audio sensors are distributed outside the service cabin or around the target operation area to capture the sound information on the scene. The audio sensor collects real-time audio data of the target operation area, including the sound, conversation, alarm sound, etc. on the scene. The collected audio data is transmitted to the back-end server or the terminal of the remote technician in real time through the communication module (such as the 4G communication module) inside the service cabin, forming a real-time audio data stream, so that the rear technician can monitor the sound situation on the scene in real time. The linkage acquisition module 30 also ensures that the audio data is synchronized with the real-time feedback image from the pan-tilt camera through mechanisms such as timestamps or serial numbers, so that the audio and image information can be accurately associated in the subsequent analysis and decision-making process.

[0034] In one possible implementation, the synchronously activated audio sensor performs synchronous audio data collection of the target work area and establishes real-time audio data, and also includes: obtaining the array position of the audio sensor; performing homophone source authentication based on the array position and audio timing data, and establishing a homophone source authentication result; locating the sound source with the homophone source authentication result, and performing joint voice content recognition of the homophone source authentication result; and establishing real-time audio data based on the sound source positioning result, the joint voice content recognition result, and the homophone source authentication result.

[0035] Specifically, the system first queries or receives the location information of each audio sensor, which has been preset at the time of deployment and stored in the system's database or can be obtained through the sensor itself. The location information is integrated into a layout diagram of an audio sensor array, including the precise coordinates and relative position relationship of each audio sensor. Among them, the audio sensor array is used to capture sound signals from different directions. When the audio sensor starts to collect sound, it records the timestamp of each sensor receiving the sound at the same time. According to the array position of the audio sensor and the time difference of the sound reaching each sensor (i.e., audio timing data), the sound source localization algorithm (such as TDOA, FDOA, etc.) is used to estimate the location of the sound source, and the sound features (such as spectrum, waveform, etc.) from different sensors are compared to determine whether they come from the same sound source, and complete the same sound source authentication. Based on the results of the same sound source authentication, the sensor data from the same sound source is selected and confirmed, and the location of these sensors and the time difference of the sound arrival are used to determine the specific location of the sound source through a more accurate sound source localization algorithm (such as geometric positioning method, beamforming method, etc.). Integrate the sensor data from the same sound source to form a more complete voice signal, use speech recognition technology (such as ASR, speech recognition algorithm, etc.) to perform content recognition on the integrated voice signal, and extract the text information in the voice. Integrate the sound source positioning results, joint speech content recognition results, and homophone authentication results to form real-time audio data containing rich information, which is transmitted to the back-end server or the terminal of the remote technician in real time for analysis and decision-making. This implementation method obtains the array position of the audio sensor and performs homophone authentication and sound source positioning based on the sound source positioning algorithm to ensure that the captured sound signal comes from the correct sound source and determine the specific location of the sound source. Joint speech content recognition further improves the accuracy and reliability of speech recognition. These steps work together to improve the accuracy and practicality of audio data collection, thereby providing more comprehensive and accurate information support for emergency decision-making.

[0036] The feedback recognition module 40 is used to perform image recognition on the real-time feedback image based on the location trust identifier to establish the first feedback control data, perform audio demand recognition on the real-time audio data, and establish the second feedback control data. Specifically, the feedback recognition module 40 receives the real-time feedback image of the pan-tilt camera from the initial control module 20, and the real-time audio data from the linkage acquisition module 30, and at the same time, obtains the location trust identifier of the target operation area established by the task analysis module 10. Based on the location trust identifier, the real-time feedback image is pre-processed, such as denoising, contrast enhancement, etc., to improve the image quality, and the image recognition algorithm (such as deep learning model, edge detection, feature extraction, etc.) is applied to analyze the pre-processed image to identify key elements in the image, such as equipment status, personnel location, environmental anomalies, etc., and establish the first feedback control data according to the recognition results, including but not limited to abnormality report, equipment status feedback, personnel location tracking, etc. At the same time, pre-process the real-time audio data, such as noise reduction and speech enhancement, to improve the accuracy of speech recognition, apply speech recognition technology (ASR, speech recognition algorithm) to convert audio data into text information, or use audio analysis technology (such as emotion recognition, keyword detection) to analyze the audio content, and identify the needs or instructions in the audio according to the audio content, such as the help signal of the front personnel, the operation guidance request, the abnormal environmental noise, etc., and establish the second feedback control data. Integrate the first feedback control data and the second feedback control data to form a complete feedback report or control instruction.

[0037] In one possible implementation, the image recognition of the real-time feedback image based on the position trust identifier to establish the first feedback control data also includes: determining whether the position trust identifier has a position below a preset trust threshold, and if so, identifying the corresponding position as a dangerous area, and generating a reconstruction acquisition instruction; controlling the joint sensor group to perform additional data acquisition in the dangerous area with the reconstruction acquisition instruction to establish an additional data set; performing position image feature recognition on the real-time feedback image, and after updating the cloud data information with the additional data set, identifying and authenticating the position image feature recognition result based on the updated cloud data information, and establishing the first feedback control data with the identification and authentication result.

[0038] Specifically, read the location trust identification data, which is generated by the task analysis module 10 based on the emergency operation task and cloud data information, traverse each location in the location trust identification, compare its trust with the preset trust threshold, and if it is found that the trust of a location is lower than the preset threshold, then identify the location as a dangerous area and record its location information. Based on the location information of the dangerous area, generate a reconstruction acquisition instruction, which includes the specific location of the dangerous area, the required acquisition data type (such as higher resolution images, infrared images, gas concentrations, etc.) and the acquisition priority. Send the reconstruction acquisition instruction to the joint sensor group (including high-definition cameras, infrared cameras, gas sensors and other sensors), and the joint sensor group performs additional data acquisition on the dangerous area according to the instruction, collects data from each sensor, and integrates it into an additional data set. Use the image recognition algorithm to process the real-time feedback image, extract the position features in the image (such as object position, shape, color, etc.), and generate preliminary image recognition results based on the extracted position image features. The additional data set is integrated with the cloud data information, and the cloud data information is updated, including correcting erroneous data, adding new data types or improving the accuracy of data, etc. The updated cloud data information is used as a reference to identify and authenticate the generated location image feature recognition results, including verifying the accuracy, consistency and credibility of the recognition results. According to the recognition and authentication results, the specific conditions, possible risks and countermeasures to be taken in the dangerous area are determined, and this information is integrated into the first feedback control data to guide emergency response actions. This implementation method conducts more in-depth data collection on potential dangerous areas in a targeted manner by judging the location trust identifier and generating reconstruction collection instructions. The cloud data information is updated through additional data sets, which improves the accuracy and completeness of the data, provides a more reliable basis for subsequent recognition and authentication, and establishes the first feedback control data based on the updated data and recognition and authentication results, achieving the technical effect of more accurately guiding emergency response actions and improving the accuracy of emergency response.

[0039] In one possible implementation, the system also includes: reconstructing the location trust identifier through the updated cloud data information, and establishing the location importance weight with the reconstructed location trust identifier, and the sum of all location importance weights is 1; establishing evaluation features, the evaluation features including illumination features, uniformity features, glare features, color temperature features, and light effect features; based on the evaluation features and the location importance weights, performing recognition effect authentication on the updated cloud data information and location image feature recognition results to generate a recognition authentication result.

[0040] Specifically, compare old and new data, identify changes in location information (such as newly added, deleted, or modified device locations), assign a trust value to each location based on the stability of location information and the quality of historical data, and reallocate location trust identifiers based on the trust value to ensure that each location has an accurate trust identifier. Perform an importance assessment based on the role of the location in the overall environment (such as key lighting areas, crowded areas, etc.), and assign an importance weight to each location based on the assessment results, ensuring that the sum of all location weights is 1 to reflect its importance in the overall environment. Establish evaluation features. The illumination feature reflects the brightness level of the lighting, the uniformity feature reflects the uniformity of the light distribution, the glare feature refers to the glare phenomenon generated by the light source, the color temperature feature refers to the color temperature of the light source, and the light efficiency feature refers to the energy efficiency of the light source, including energy consumption and light output ratio. The updated cloud data information and the location image feature recognition results are integrated to form a comprehensive evaluation data set. The above evaluation features are extracted from the comprehensive data set. According to the location importance weight, the evaluation features of each location are weighted to reflect its importance in the overall evaluation. Based on the weighted evaluation results, the final recognition and certification results are generated, including the lighting quality score of each location, existing problems and improvement suggestions. This implementation method achieves the technical effect of improving the system's responsiveness to environmental changes in the target operation area and the accuracy of decision-making by introducing the steps of reconstruction of location trust identification, allocation of location importance weights, establishment of evaluation features, and recognition effect certification.

[0041] The authentication decision module 50 is used to perform data authentication on the first feedback control data and the second feedback control data, and establish an authentication response window according to the data authentication result. Specifically, the authentication decision module 50 receives the first feedback control data and the second feedback control data from the feedback identification module 40, and pre-processes the received data, including data cleaning (removing noise, outliers, etc.), data formatting, etc., to ensure the consistency and accuracy of subsequent processing. The first feedback control data (image recognition result) is authenticated to check the accuracy and completeness of the recognition result and whether it is consistent with the location trust mark of the target operation area; the second feedback control data (audio demand recognition result) is authenticated to verify the authenticity, validity and urgency of the audio content, and whether it is related to the emergency operation task. In this process, preset authentication rules, thresholds or machine learning models can be used to assist in judgment. According to the results of data authentication, the urgency and importance of the current emergency operation are evaluated to determine whether an immediate response or further action is required, and an authentication response window is established. The authentication response window is a temporary state or interface for transmitting the results and decisions of data authentication and guiding subsequent execution operations. The window defines the time range, priority, required resources and possible response measures of the response.

[0042] The execution module 60 is used to control the response execution of the lighting and the PTZ camera based on the data authentication result and the authentication response window. Specifically, the execution module 60 receives the authentication response window instructions from the authentication decision module 50, which include the data authentication result based on the first feedback control data and the second feedback control data, and an indication of whether to allow subsequent operations. The data authentication result in the authentication response window is parsed to understand which operations are allowed or need to be adjusted. According to the data authentication result, specific control instructions are generated and sent to the lighting and the PTZ camera through the control interface in the service cabin to perform specific operations, such as adjusting the brightness, direction or color of the lighting, and controlling the rotation, zoom and focus of the PTZ camera. The embodiment of the present application adopts technical means such as receiving emergency operation tasks, evaluating the trust of the target operation area based on cloud data, establishing a location trust mark, synchronously tracking and controlling the position of the lighting and the pan-tilt camera through the initial control module, and synchronously activating the audio sensor in combination with the linkage acquisition module to perform real-time acquisition of video and audio data, performing image recognition on the real-time feedback image based on the location trust mark, identifying the demand for the audio data, generating feedback control data, authenticating the feedback control data, and controlling the response execution of the lighting and the pan-tilt camera according to the data authentication result and the authentication response window, thereby realizing a fast and accurate emergency response and achieving the technical effect of improving the emergency response speed and operation accuracy.

[0043] In a possible implementation, the execution module 60 is also used to: call the pan-tilt camera to perform execution monitoring through the authentication response window and establish an execution feedback image; parse the data authentication result and establish an independent decision result of the second feedback control data; perform decision effect authentication on the independent decision result of the execution feedback image and generate a decision effect authentication result; if the decision effect authentication result cannot meet the expected threshold, generate a backtracking instruction; reconstruct the data authentication result according to the backtracking instruction, and control the response execution of the lighting and the pan-tilt camera according to the reconstructed data authentication result.

[0044] Specifically, after the authentication response window is triggered, the execution module 60 sends an instruction to the pan-tilt camera, requesting it to adjust to a specific position or viewing angle. The pan-tilt camera adjusts its position according to the instruction and captures the image of the target operation area in real time. The captured image is transmitted to the system and stored and further analyzed as an execution feedback image. The authenticated first feedback control data and the second feedback control data are parsed. When there is inconsistency between the first and second feedback control data, the second feedback control data shall prevail for the inconsistent part, and the part shall be used as an independent decision result, that is, the independent decision result is the part in the second feedback control data that is inconsistent with the first feedback control data and is determined to have a higher priority according to the preset rules. The execution feedback image is compared and analyzed with the independent decision result to evaluate the effect of the decision in actual execution. Through technical means such as image recognition and feature matching, the execution of the decision is quantitatively evaluated, such as whether the brightness of the lighting is adjusted in place, whether the viewing angle of the pan-tilt camera is accurate, etc. According to the evaluation result, a decision effect authentication result is generated, which reflects the effectiveness and accuracy of the decision in actual application. The decision effect authentication result is compared with the preset expected threshold. If the decision effect authentication result is lower than the expected threshold, it means that the decision effect is not ideal or there is a deviation. At this time, a backtracking instruction is automatically generated to guide the system to return to the data authentication state. According to the backtracking instruction, the data authentication result is re-evaluated and analyzed, such as re-collecting, processing or authenticating some data to eliminate the previous deviation or error. Based on the reconstructed data authentication result, the control instructions for the lighting and PTZ camera are regenerated to ensure that the subsequent execution is more accurate and effective. This implementation method enhances the flexibility and robustness of the system by introducing execution monitoring, independent decision results, decision effect authentication and backtracking mechanism. It allows the system to continuously verify and adjust the decision results during the actual execution process to ensure that the final execution effect meets expectations. At the same time, through the backtracking mechanism, errors can be corrected in time when decision deviations are found to avoid further expansion of the problem. This closed-loop control and feedback mechanism has achieved the technical effect of improving the overall performance and reliability of the system and ensuring the smooth progress of power operation and maintenance emergency services.

[0045] In a possible implementation, the system also includes: a penalty module, which is used to record the decision effect authentication result and construct a penalty factor, use the penalty factor to punish the user audio response of this emergency task, and perform subsequent data authentication management based on the user audio response penalty.

[0046] Specifically, the decision effect authentication result after each task execution is recorded. This result is obtained based on the comparison between the execution feedback image and the independent decision result, and is used to evaluate whether the current decision has achieved the expected effect. According to the quality of the decision effect authentication result, a penalty factor is constructed. This penalty factor is a quantitative indicator used to measure the quality of the current decision effect. If the decision effect is not ideal, the value of the penalty factor will be correspondingly high; conversely, if the decision effect is good, the value of the penalty factor will be low or zero. The penalty factor is used to punish the user's audio response. The user can be a remote operator or a system administrator. Based on the result of the user's audio response punishment, the subsequent data authentication management process is adjusted. For example, if a user frequently makes decision errors, the audit intensity of the user's audio data is automatically increased, or the algorithm parameters of the data authentication are adjusted to improve the accuracy and efficiency of data authentication. This implementation method introduces a penalty module to feedback and adjust the effect of independent decision-making, ensure the accuracy and efficiency of decision-making for similar tasks in the future, and achieve the technical effect of improving the overall effectiveness of emergency services.

[0047] In one possible implementation, the system also includes: an auxiliary warning unit, which is used to receive the real-time feedback image and send the real-time feedback image to the dynamic and static feature warning network, perform warning identification of safety risks, establish safety risk warning identification results, and report the safety risk warning identification results through a loudspeaker.

[0048] Specifically, the auxiliary warning unit receives real-time feedback images from the feedback identification module 40, which are captured by the pan-tilt camera and reflect the current state of the target operation area. The received real-time feedback images are sent to the dynamic and static feature warning network, which is an intelligent analysis system that can identify dynamic changes (such as personnel activities, object movement) and static features (such as equipment status, environmental layout) in the image to assess safety risks. The dynamic and static feature warning network analyzes the real-time feedback images to identify possible safety risks, such as equipment abnormalities, personnel illegal operations, etc. A safety risk warning identification result is established based on the analysis results, and the identified risk type, location and possible impact are clearly indicated. The safety risk warning identification result is reported through a loudspeaker or other audio device, so that the on-site personnel can immediately hear and take corresponding countermeasures. This implementation method introduces an auxiliary warning unit to analyze the dynamic and static features in the feedback image in real time, discover and predict potential safety risks in advance, avoid or reduce the occurrence of safety accidents, and at the same time, the warning results are broadcast to the on-site personnel in a timely manner, prompting them to take quick action to eliminate safety hazards and ensure operation safety, thereby achieving the technical effect of reducing the probability of accidents and the degree of losses.

[0049] like Figure 2As shown, in a possible implementation method, the real-time feedback image is sent to the static and dynamic feature warning network, the warning identification of safety risks is performed, and the safety risk warning identification result is established, which also includes: performing feature segmentation of the real-time feedback image through a preprocessing sub-channel to establish a static feature identifier and a dynamic feature identifier; calling the real-time feedback image corresponding to the static feature identifier, sending the calling result to the static timing recognition sub-network, performing safety risk warning identification of static features, and establishing a static recognition result; calling the real-time feedback image corresponding to the dynamic feature identifier, sending the calling result to the dynamic timing recognition sub-network, performing safety risk warning identification of dynamic features, and establishing a dynamic recognition result; establishing a safety risk warning identification result based on the static recognition result and the dynamic recognition result.

[0050] Specifically, the preprocessing subchannel preprocesses the received real-time feedback image, including denoising, contrast enhancement, etc., to improve the image quality, and uses image processing technology (such as edge detection, region segmentation, etc.) to segment the features in the image, and respectively establish static feature identification (such as equipment status, environmental layout, etc., features that do not change over time) and dynamic feature identification (such as personnel activities, object movement, etc., features that change over time). According to the static feature identification, the corresponding static feature images are extracted from the real-time feedback image, and these images are sent to the static time series recognition subnetwork, which is specifically used to analyze static features and identify possible security risks. In the static time series recognition subnetwork, the input static feature images are deeply analyzed to identify possible security hazards or risks, such as equipment damage, environmental abnormalities, etc., and generate static recognition results. Similarly, according to the dynamic feature identification, the corresponding dynamic feature images are extracted from the real-time feedback image, and these images are sent to the dynamic time series recognition subnetwork, which is specifically used to analyze dynamic features and identify possible security risks. In the dynamic time series recognition subnetwork, the input dynamic feature image is deeply analyzed to identify possible safety hazards or risks, such as illegal operation by personnel, collision of objects, etc., and generate dynamic recognition results. The static recognition results and dynamic recognition results are comprehensively analyzed to evaluate the overall safety risk situation and generate the final safety risk warning recognition results. This result combines both static and dynamic factors to provide a more comprehensive and accurate safety risk assessment. This implementation method more accurately identifies the safety risks in the image by segmenting the real-time feedback image into static features and dynamic features, and sending them to the corresponding time series recognition subnetwork for analysis. Static feature recognition is used to discover problems in the environment or the equipment itself, and dynamic feature recognition is used to capture the risks that may arise from personnel activities or object movement. The combination of the two provides a more comprehensive and accurate safety risk assessment result, thereby achieving the technical effect of providing more effective guidance and support for emergency operations.

[0051] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0052] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The remote decision-making interactive system of the power operation and maintenance emergency service cabin is characterized by: The system comprises: A task analysis module is used to receive emergency operation tasks, obtain cloud data information of the target operation area, and evaluate the regional information trust of the target operation area based on the emergency operation tasks and cloud data information, and establish a location trust mark of the target operation area. The cloud data information includes topographic maps, historical fault records, environmental parameters, and safety risk assessment reports; Among them, based on the emergency operation task and cloud data information, the regional information trust evaluation of the target operation area is carried out, and the location trust mark of the target operation area is established. Specifically, the completeness, timeliness and accuracy of the data are evaluated by using a preset algorithm or model to check whether the data is missing, whether it is within the validity period, and whether it is consistent with historical data; according to the evaluation results including the reliability of the data source, the frequency of data update, and the detail of the data, the trust score of the target operation area is calculated, and based on the trust score, a unique location trust mark is generated for the target operation area; The initial control module is used to perform synchronous position tracking control of the lighting and the PTZ camera in the target operation area according to the emergency operation task, and record the real-time feedback image of the PTZ camera; A linkage acquisition module is used to synchronously activate the audio sensor, perform synchronous audio data acquisition in the target operation area, and establish real-time audio data; A feedback recognition module, configured to perform image recognition on the real-time feedback image based on the location trust identifier to establish first feedback control data, perform audio demand recognition on the real-time audio data to establish second feedback control data; an authentication decision module, configured to perform data authentication on the first feedback control data and the second feedback control data, and establish an authentication response window according to the data authentication result; The data authentication of the first feedback control data and the second feedback control data is performed, and an authentication response window is established according to the data authentication result. Specifically, the first feedback control data is authenticated to check the accuracy and completeness of the recognition result and whether it is consistent with the location trust mark of the target operation area; the second feedback control data is authenticated to verify the authenticity, validity and urgency of the audio content, and whether it is related to the emergency operation task. In this process, preset authentication rules, thresholds or machine learning models are used to assist in judgment. According to the results of data authentication, the urgency and importance of the current emergency operation are evaluated to determine whether an immediate response or further action is required, and an authentication response window is established; An execution module, used for controlling the response execution of the lighting and the PTZ camera based on the data authentication result and the authentication response window; The synchronous activation of the audio sensor to perform synchronous audio data collection in the target operation area and establish real-time audio data also includes: Get the array position of the audio sensor; Performing homophone source authentication based on the array position and the audio timing data, and establishing a homophone source authentication result; Using the homophone authentication result to locate the sound source, and performing joint speech content recognition of the homophone authentication result; Establish real-time audio data based on the sound source positioning results, joint voice content recognition results, and same-sound source authentication results.

2. The electric power operation and maintenance emergency service cabin remote decision-making interactive system according to claim 1, characterized in that: The execution module is also used for: Calling the PTZ camera to perform execution monitoring through the authentication response window and establishing an execution feedback image; Parsing the data authentication result to establish an independent decision result of the second feedback control data, specifically, parsing the authenticated first feedback control data and the second feedback control data, and when there is inconsistency between the first feedback control data and the second feedback control data, the second feedback control data shall prevail for the inconsistent part, and the part shall be used as the independent decision result; Perform decision effect authentication of the independent decision result on the execution feedback image to generate a decision effect authentication result, specifically, compare and analyze the execution feedback image with the independent decision result, evaluate the effect of the decision in actual execution, and quantitatively evaluate the execution of the decision through image recognition and feature matching technical means; If the decision effect authentication result cannot meet the expected threshold, a backtracking instruction is generated; The data authentication result is reconstructed according to the backtracking instruction, and the response execution of the lighting and the pan-tilt camera is controlled according to the reconstructed data authentication result.

3. The electric power operation and maintenance emergency service cabin remote decision-making interactive system according to claim 2, characterized in that: The system further comprises: The penalty module is used to record the decision-making effect authentication result and construct a penalty factor, use the penalty factor to punish the user audio response of this emergency operation task, and perform subsequent data authentication management based on the user audio response penalty.

4. The electric power operation and maintenance emergency service cabin remote decision-making interactive system according to claim 1, characterized in that: The performing image recognition on the real-time feedback image based on the location trust identifier to establish first feedback control data also includes: Determine whether there is a location whose location trust mark is lower than a preset trust threshold. If so, mark the corresponding location as a dangerous area and generate a reconstruction acquisition instruction; Controlling the joint sensor group to collect additional data in the dangerous area with the reconstruction acquisition instruction to establish an additional data set; Performing position image feature recognition on the real-time feedback image, updating the cloud data information by adding a data set, and then performing recognition and authentication on the position image feature recognition result based on the updated cloud data information to establish first feedback control data based on the recognition and authentication result; Performing position image feature recognition on the real-time feedback image, and after updating the cloud data information through the additional data set, the position image feature recognition result based on the updated cloud data information is recognized and authenticated. Specifically, the real-time feedback image is processed by an image recognition algorithm to extract position features in the image, and a preliminary image recognition result is generated based on the extracted position image features; the additional data set is merged with the cloud data information to update the cloud data information, including correcting erroneous data, adding new data types or improving data accuracy, and the updated cloud data information is used as a reference to recognize and authenticate the generated position image feature recognition result, including verifying the accuracy, consistency and credibility of the recognition result.

5. The electric power operation and maintenance emergency service cabin remote decision-making interactive system according to claim 4, characterized in that: The system further comprises: Reconstruct the location trust identifier through the updated cloud data information, and establish the location importance weight with the reconstructed location trust identifier, and the sum of all location importance weights is 1; Reconstruct the location trust mark through the updated cloud data information, specifically, compare the new and old data, identify the changes in location information, assign a trust value to each location based on the stability of the location information and the quality of historical data, and reallocate the location trust mark based on the trust value to ensure that each location has an accurate trust mark; Establishing evaluation features, the evaluation features include illumination feature, uniformity feature, glare feature, color temperature feature, and light efficiency feature, wherein the illumination feature reflects the brightness level of the lighting, the uniformity feature reflects the uniformity of the light distribution, the glare feature refers to the glare phenomenon generated by the light source, the color temperature feature refers to the color temperature of the light source, and the light efficiency feature refers to the energy efficiency of the light source, including energy consumption and light output ratio; Based on the evaluation features and the location importance weights, the updated cloud data information and location image feature recognition results are authenticated to generate a recognition authentication result.

6. The electric power operation and maintenance emergency service cabin remote decision-making interactive system according to claim 1, characterized in that: The system further comprises: The auxiliary warning unit is used to receive the real-time feedback image and send the real-time feedback image to the dynamic and static feature warning network, perform warning identification of safety risks, establish safety risk warning identification results, and report the safety risk warning identification results through a loudspeaker.

7. The electric power operation and maintenance emergency service cabin remote decision-making interactive system according to claim 6, characterized in that: The sending of the real-time feedback image to the static and dynamic feature warning network, performing warning identification of security risks, and establishing a security risk warning identification result also includes: Perform feature segmentation of real-time feedback images through preprocessing sub-channels to establish static feature identification and dynamic feature identification; Calling the real-time feedback image corresponding to the static feature identification, sending the calling result to the static time series recognition sub-network, performing security risk warning recognition of the static feature, and establishing a static recognition result; Calling the real-time feedback image corresponding to the dynamic feature identification, sending the calling result to the dynamic time series recognition sub-network, performing security risk early warning recognition of the dynamic feature, and establishing a dynamic recognition result; A security risk warning identification result is established based on the static identification result and the dynamic identification result.

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