Intelligent digital management method for electrical product

Abstract

The invention belongs to the field of data processing, and particularly relates to an intelligent digital management method of an electrical product. The method comprises the steps of setting a matched measurement device for a target object; an electro-optical crystal fiber sensor connected with the rotating electrode is carried in the measuring device; the sensor is dynamically controlled to rotate through the rotating electrode so as to dynamically adjust the relative position between the sensor and the electric field where the target object is located; acquiring a motion track of the sensor in the measurement process, and acquiring electric field intensity information obtained by measuring the target object at different relative positions through the sensor; and inputting the motion trail and the electric field intensity information into the measurement optimization model, carrying out optimization adjustment on the electric field intensity information, and executing a corresponding automatic management process on the target object based on the target electric field intensity information. According to the method, the measurement accuracy can be improved, real-time monitoring and automatic decision making are realized, data-driven equipment maintenance and optimization can be carried out, and powerful support is provided for electrical equipment management.

Description

Technical Field

[0001] The present application belongs to the field of data processing, and in particular, relates to an intelligent digital management method for electrical products. Background Art

[0002] High voltage DC fields will generate strong electromagnetic radiation, and this electromagnetic interference may affect the normal operation of electronic components inside the measuring instrument.

[0003] In related technologies, interference signals may be superimposed on the output signals of sensors, causing deviations in measurement data. For electronic circuit-based measuring devices, such as oscilloscopes and voltmeters, these interferences may cause jitter or distortion in the displayed waveform or value.

[0004] In addition, in the related technology, the phenomenon of static electricity is also more obvious in the high-voltage DC environment. Static electricity accumulation may cause damage to measuring equipment and sensors. For example, electrostatic discharge may damage sensitive components in the sensor or change its electrical properties, thereby affecting the measurement accuracy. Moreover, the static electricity field may also interact with the measured DC electric field to produce a complex electric field distribution, which brings difficulties to the measurement.

[0005] Therefore, in order to solve at least one of the above technical problems, it is urgent to propose a new intelligent digital management solution for electrical products. Summary of the invention

[0006] The present application provides an intelligent digital management method for electrical products, which is used to propose a new intelligent digital management solution for electrical products.

[0007] In a first aspect, the present application provides an intelligent digital management method for electrical products, which is applied to a high-voltage DC field strength measurement scenario of an electrical device to be tested, and the method comprises: A matching measuring device is provided for the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optical crystal fiber sensor connected to a rotating electrode; Dynamically controlling the electro-optic crystal fiber sensor to rotate by means of the rotating electrode to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located; Acquire the motion trajectory of the electro-optic crystal fiber sensor during the measurement process, and acquire the electric field strength information obtained by measuring the target object at different relative positions through the electro-optic crystal fiber sensor; the crystal refractive index in the electro-optic crystal fiber sensor is proportional to the electric field strength information; The motion trajectory and the electric field strength information are input into a measurement optimization model, and the electric field strength information is optimized and adjusted to obtain the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change; The target electric field strength information is stored in a management database, and a corresponding automatic management process is executed on the target object based on the target electric field strength information.

[0008] In a second aspect, an embodiment of the present application provides an intelligent digital management device for electrical products, which is applied to a high-voltage DC field strength measurement scenario of an electrical device to be tested, and the device includes: A setting unit is configured to set a measuring device matching the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optical crystal fiber sensor connected to a rotating electrode; a control unit configured to dynamically control the electro-optic crystal fiber sensor to perform rotational motion through the rotating electrode, so as to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located; an acquisition unit configured to acquire a motion trajectory of the electro-optical crystal fiber sensor during a measurement process, and acquire electric field strength information obtained by measuring a target object at different relative positions through the electro-optical crystal fiber sensor; a crystal refractive index in the electro-optical crystal fiber sensor is proportional to the electric field strength information; An adjustment unit is configured to input the motion trajectory and the electric field strength information into a measurement optimization model, optimize and adjust the electric field strength information, and obtain the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change; The management unit is configured to store the target electric field strength information in a management database, and execute a corresponding automatic management process on the target object based on the target electric field strength information.

[0009] In a third aspect, an embodiment of the present application provides a computing device, the computing device comprising: at least one processor, memory, and input-output unit; The memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the intelligent digital management method for electrical products of the first aspect.

[0010] In a fourth aspect, a computer-readable storage medium is provided, which includes instructions. When the instructions are executed on a computer, the computer executes the intelligent digital management method for electrical products of the first aspect.

[0011] In the technical solution provided by the embodiment of the present application, first, a matching measuring device is set for the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optic crystal fiber sensor connected to a rotating electrode. Then, the electro-optic crystal fiber sensor is dynamically controlled to rotate by the rotating electrode to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located. Then, the motion trajectory of the electro-optic crystal fiber sensor during the measurement process is obtained, and the electric field strength information obtained by measuring the target object at different relative positions is obtained by the electro-optic crystal fiber sensor; the crystal refractive index in the electro-optic crystal fiber sensor is proportional to the electric field strength information. Then, the motion trajectory and the electric field strength information are input into the measurement optimization model, the electric field strength information is optimized and adjusted, and the target electric field strength information is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change. Finally, the target electric field strength information is stored in a management database, and a corresponding automatic management process is executed on the target object based on the target electric field strength information.

[0012] In the technical solution of this application, the measurement optimization model can improve the measurement accuracy, and the combination of multi-position measurement and dynamic optimization can further reduce the accumulation of errors. In addition, by rotating the electrode to collect optical signal data at multiple angles, the data integrity is further increased, which helps to finally obtain three-dimensional electric field information and associate it for storage. The automated management is efficient, and real-time monitoring and automatic decision-making can be achieved. Data-driven equipment maintenance and optimization can also be performed, providing strong support for electrical equipment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a flow chart of an intelligent digital management method for electrical products according to an embodiment of the present application; Figure 2 It is a schematic diagram of the principle of an intelligent digital management method for electrical products according to an embodiment of the present application; Figure 3 It is a structural schematic diagram of an intelligent digital management device for electrical products according to an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0016] The embodiment of the present application provides an intelligent digital management method for electrical products.

[0017] Specifically, first, the rotation of the electro-optical crystal fiber sensor is controlled by rotating the electrode to obtain the electric field strength information at different positions. This method can fully cover the electric field around the target electrical equipment and avoid the limitations of single-point measurement. For example, for electrical equipment with complex shapes or uneven electric field distribution, single-position measurement may produce large errors due to local electric field anomalies. Multi-position measurement combined with the measurement optimization model can comprehensively analyze and optimize the data at each position, so that the target electric field strength information output in the end is closer to the true value.

[0018] Furthermore, the measurement optimization model simulates the change of electric field strength according to the motion trajectory of the sensor and dynamically optimizes the measurement data. During the measurement process, errors that may be caused by environmental factors (such as temperature, humidity, electromagnetic interference, etc.) can be discovered and corrected in a timely manner. For example, if the electric field strength measurement value at a certain location fluctuates abnormally due to interference, the model can refer to the data at other locations and the normal electric field distribution law to adjust the abnormal value, thereby reducing the accumulation of errors and effectively improving the measurement accuracy.

[0019] Furthermore, the rotational movement of the sensor enables the acquisition of electric field strength information in three-dimensional space. Not only can the electric field data at different plane positions be obtained, but also the electric field changes in the height direction can be taken into account. This is very important for accurately describing the electric field distribution around electrical equipment, especially for electrical equipment with a certain height or multi-layer structure. For example, in the measurement of high-voltage equipment in a substation, this method can fully record the changes in the electric field strength of the equipment in the vertical and horizontal directions, providing comprehensive data support for subsequent electric field analysis and equipment evaluation.

[0020] Next, the motion trajectory and electric field strength information are input into the database together, so that the data has temporal and spatial correlation. This associative storage method facilitates the subsequent dynamic analysis of the electric field distribution. For example, the change process of the electric field strength under a specific motion path can be traced, and the gradient change of the electric field, the peak position, and the relationship between each position can be understood. This is of great significance for studying the spatial distribution characteristics and time evolution laws of the electric field.

[0021] Finally, based on the target electric field strength information stored in the management database, the system can realize real-time monitoring of electrical equipment. Once the electric field strength changes abnormally, such as exceeding the preset safety threshold or not conforming to the normal operation law, the system can automatically trigger the corresponding management process. For example, in the power system, if the electric field strength around the high-voltage DC equipment suddenly increases, it may indicate that the equipment has failed or the external environment has changed. The system can automatically issue an alarm to notify the maintenance personnel to check, or take some emergency control measures according to the preset rules, such as adjusting the equipment operation parameters, so as to improve the safety and reliability of the equipment operation. In addition, the accumulated large amount of electric field strength data and motion trajectory data can provide data support for the maintenance and optimization of electrical equipment. By analyzing historical data, the changing trend of the electric field strength of the equipment can be predicted and a maintenance plan can be formulated in advance. For example, based on the long-term changes in the electric field strength, it can be determined whether the insulation performance of the equipment has decreased, and then whether the equipment needs to be insulated or upgraded. At the same time, these data can also be used to optimize the deployment and measurement strategy of the measuring device, further improving the measurement efficiency and accuracy.

[0022] Compared with the problem of strong electric field interference in the related art, in the embodiment of the present application, in the first aspect, the electro-optical crystal fiber sensor uses the characteristic that the refractive index of the crystal is proportional to the electric field strength for measurement. As a signal transmission medium, the optical fiber itself has good electrical insulation, which enables the sensor to effectively avoid interference caused by the direct action of the electric field on the signal transmission part in a strong electric field environment. Compared with traditional electrical sensors, optical fiber sensors are less likely to be affected by electromagnetic interference, because optical fibers transmit optical signals rather than electrical signals, and it is difficult for strong electric fields to directly interfere with optical signals. Furthermore, the sensor is dynamically controlled to rotate by rotating electrodes, so that the sensor obtains electric field strength information at different positions. This method of dynamically adjusting the relative position can disperse the strong electric field interference in space. For example, if a certain position is subjected to local strong electromagnetic interference, resulting in a deviation in the measured data, then as the sensor rotates, the data obtained at other positions can compensate for this deviation to a certain extent. Moreover, by obtaining data at multiple positions, the interference situation can be comprehensively evaluated and abnormal data points that may be interfered with can be identified.

[0023] Secondly, the measurement optimization model can simulate the change of the electric field strength corresponding to the target object under the motion trajectory. When there is strong electric field interference, the interference signal usually causes the measured data to fluctuate in a way that does not conform to the normal electric field distribution law. The model can identify these abnormally fluctuating data points by learning the distribution characteristics and change laws of the normal electric field, and filter or correct them during the optimization and adjustment process. For example, if the electric field strength measurement value at a certain position differs too much from the normal electric field strength change trend at that position simulated by the model, the model can reduce the weight of the data point or correct it through other data points. Furthermore, based on the dynamic optimization of the electric field strength information of the model, the measurement error caused by strong electric field interference can be corrected in time. Since the model is comprehensively optimized based on the motion trajectory and the measurement data of multiple positions, the cumulative error caused by interference can be reduced to a certain extent. For example, in the process of the sensor rotating one circle, even if the data at some positions are interfered with, the model can also use the data at other undisturbed or less interfered positions to optimize the overall measurement results, so that the target electric field strength information output in the end is more accurate.

[0024] Thirdly, the target electric field strength information is stored in the management database, so that a large amount of measurement data can be accumulated. By analyzing these historical data, the changing rules of the measurement data under different strong electric field interference conditions can be understood. For example, when it is found that the fluctuation amplitude of the measurement data in a certain period of time is significantly increased and different from the distribution characteristics of the normal measurement data in the past, it can be inferred that there may be strong electric field interference. At the same time, by comparing with historical data, the current measurement data can be more accurately evaluated and corrected. The automatic management process is executed based on the target electric field strength information, and the strong electric field interference situation can be automatically processed according to the preset rules. For example, if the detected electric field strength information exceeds the normal range and it is judged that it is an abnormal situation caused by strong electric field interference, the automatic management process can start the corresponding alarm mechanism to remind the operator to pay attention; or the working parameters of the measuring device can be automatically adjusted, such as adjusting the rotation speed of the sensor, changing the measurement frequency, etc., to obtain more accurate data, thereby reducing the impact of strong electric field interference on the measurement results.

[0025] The technical solution of this application adopts a measurement optimization model to improve measurement accuracy, and combines multi-position measurement with dynamic optimization to further reduce error accumulation. In addition, by rotating the electrode to collect optical signal data at multiple angles, data integrity is further increased, which helps to finally obtain three-dimensional electric field information and associate it for storage. Automated management is efficient, and real-time monitoring and automatic decision-making can be achieved. Data-driven equipment maintenance and optimization can also be performed, providing strong support for electrical equipment management.

[0026] The intelligent digital management solution for electrical products provided in the embodiment of the present application can be executed by an electronic device, which can be a server, a server cluster, or a cloud server. The electronic device can also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a dedicated device (such as a dedicated terminal device with an intelligent digital management system for electrical products). In an optional embodiment, a service program for executing the intelligent digital management solution for electrical products can be installed on the electronic device.

[0027] Figure 1 A schematic diagram of an intelligent digital management method for electrical products provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: 101, setting a matching measuring device for a target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optical crystal fiber sensor connected to a rotating electrode; 102, dynamically controlling the electro-optic crystal fiber sensor to rotate by the rotating electrode to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located; 103, obtaining a motion trajectory of the electro-optic crystal fiber sensor during the measurement process, and obtaining electric field strength information obtained by measuring the target object at different relative positions through the electro-optic crystal fiber sensor; the crystal refractive index in the electro-optic crystal fiber sensor is proportional to the electric field strength information; 104, inputting the motion trajectory and the electric field strength information into a measurement optimization model, optimizing and adjusting the electric field strength information, and obtaining the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change; 105. Store the target electric field strength information in a management database, and execute a corresponding automatic management process on the target object based on the target electric field strength information.

[0028] In the embodiment of the present application, the electro-optic crystal is one of the core components of this sensor. The electro-optic effect refers to the change in the refractive index of certain crystals under the action of an external electric field. This change has a certain functional relationship with the electric field strength. For example, for the linear electro-optic effect (Pockels effect), the change in the refractive index of the crystal is proportional to the electric field strength. By detecting the change in the refractive index of the electro-optic crystal, the electric field strength can be indirectly measured. The components of the sensor are described in detail below: Fiber optic transmission: Fiber optics play the role of transmitting optical signals in this sensor. The optical signal propagates in the optical fiber and enters the electro-optic crystal. When the electric field acts on the electro-optic crystal, the refractive index of the crystal changes, causing the polarization state, phase and other characteristics of the light to change. These changed optical signals are then transmitted through the optical fiber for subsequent detection.

[0029] Rotating electrode: The design of rotating electrode has its unique advantages. On the one hand, it can change the relative position between the electrode and the electric field, and the electric field information in different directions can be obtained by rotation. Compared with the traditional fixed electrode, the rotating electrode can more comprehensively reflect the spatial distribution of the electric field. For example, in a high-voltage DC field, the direction of the electric field is fixed, but the electric field strength at different positions may be different. The rotating electrode can sample the electric field at different angles.

[0030] Electrode part: Rotating electrodes need to have good conductivity and mechanical stability. Electrodes can usually be made of metal materials such as copper and aluminum. The shape and size of the electrode should be designed to take into account the effective coupling with the electric field. For example, the surface area and shape of the electrode (such as spherical, disc-shaped, etc.) will affect its interaction with the electric field. In addition, the rotating axis of the electrode must ensure high-precision rotation to reduce mechanical friction and swing to ensure measurement accuracy.

[0031] Electro-optical crystal: The selection of electro-optical crystals should be determined based on the range of electric field strength and wavelength to be measured. Common electro-optical crystals include lithium niobate (LiNbO3) and lithium tantalate (LiTaO3). The packaging of the crystal is also very important. It is necessary to ensure that the crystal can be stably placed in the sensor and has good optical and electrical connections with the electrodes and optical fibers. For example, the coupling between the crystal and the optical fiber can be lens coupling or direct coupling to ensure that the optical signal can enter and exit the crystal efficiently.

[0032] Optical fiber: Choose the right type of optical fiber, such as single-mode fiber or multi-mode fiber. Single-mode fiber has better transmission characteristics and is suitable for high-precision measurement. The length of the optical fiber should be determined according to the actual measurement environment and signal transmission requirements. At the same time, the connection points between the optical fiber and the electro-optical crystal and other optical components should be well sealed and fixed to prevent external environmental factors (such as dust, water vapor, etc.) from interfering with the optical signal transmission.

[0033] The following is a detailed description of the implementation and effect of each step: In 101, the electro-optical crystal fiber sensor and the matching rotating electrode and measuring device with appropriate parameters and specifications are selected according to the type, voltage level, size and environment of the electrical equipment to be tested. For example, for high-voltage and large-size equipment, it is necessary to select a sensor with high sensitivity, large measurement range and adaptability to strong electric field environment.

[0034] In this way, it is ensured that the measuring device is highly compatible with the target electrical equipment, laying the foundation for the subsequent accurate measurement of the electric field strength, improving the pertinence and reliability of the measurement, and avoiding measurement errors or even equipment damage due to device mismatch.

[0035] In 102, a motor drive control system is used to accurately control the rotation speed, angle and direction of the rotating electrode, so that the electro-optic crystal fiber sensor performs stable and uniform rotational motion according to a preset trajectory or program, thereby achieving dynamic adjustment of its relative position with the target electric field.

[0036] Therefore, by changing the position of the sensor, multi-directional electric field information can be obtained, which can more comprehensively reflect the distribution of the electric field around the electrical equipment, effectively reduce the one-sidedness of the measurement data caused by a single position, and improve the integrity and representativeness of the data.

[0037] 103, during the rotation of the sensor, its motion trajectory is recorded in real time with the help of high-precision position monitoring equipment (such as gyroscopes, encoders, etc.), and at the same time, the optical signal changes are converted into electrical signals through the optical detection system of the electro-optical crystal fiber sensor, thereby obtaining the electric field strength information at different positions.

[0038] This achieves the synchronous collection of motion trajectory and electric field strength information, which not only obtains the numerical value of the electric field strength, but also clarifies its corresponding spatial position, providing a rich and accurate data source for subsequent data analysis and model processing, and helping to gain a deeper understanding of the spatial characteristics of the electric field.

[0039] In 104, the collected motion trajectory and electric field strength data are input into a pre-built and trained measurement optimization model. The model analyzes and processes the input data using algorithms (such as neural network algorithms, regression algorithms, etc.) by learning a large amount of historical data and known electric field distribution laws, identifying and correcting possible measurement errors and outliers, thereby optimizing the electric field strength information.

[0040] In this way, the accuracy and stability of electric field strength measurement are significantly improved, and the influence of environmental interference, sensor error and other factors on the measurement results is effectively reduced, making the final output target electric field strength information closer to the true value, providing a more reliable basis for the evaluation and management of electrical equipment.

[0041] In 105, the optimized target electric field strength information is stored in the management database in the form of structured data, and a corresponding index and query system is established. At the same time, according to the preset electric field strength threshold, change trend and other judgment conditions, an automatic management program is written, and when specific conditions are met, corresponding operation instructions are automatically triggered, such as alarm notification, equipment adjustment, etc.

[0042] This facilitates long-term storage, query and analysis of electric field strength data, providing historical data support for equipment operation status monitoring, fault diagnosis and performance evaluation. The automatic management process based on these data can timely detect abnormal electric field conditions, take quick measures, ensure the safe and stable operation of electrical equipment, and improve the intelligence level and efficiency of equipment management.

[0043] Combining the above steps, dynamic measurement and data optimization are used to accurately obtain the electric field strength information around electrical equipment and use it for automatic management. The electric field strength at different positions is obtained by using the rotating electro-optical crystal fiber sensor, while recording the motion trajectory. With the help of the measurement optimization model, the electric field changes are simulated in combination with the trajectory to optimize the strength information. Finally, the stored information is used for automatic management to ensure the safe and efficient operation of the equipment.

[0044] As an optional embodiment, before setting a matching measuring device for the target object in 101, the appearance image information of the target object can also be scanned; the appearance image information at least includes: depth image information of the target object; the appearance image information comes from image data obtained by shooting under multiple viewing angles. Then, based on the appearance image information, the target deployment position of the electro-optic crystal fiber sensor is predicted by optimizing the deployment model.

[0045] Specifically, by acquiring appearance image data containing depth image information, the external shape, structural characteristics and spatial positional relationship of different components of the target electrical equipment can be clearly presented. Based on these detailed information, the optimized deployment model can accurately identify critical and complex areas where the electric field strength changes, such as near the electrodes of the equipment and parts with irregular shapes that easily cause the electric field to be distorted. Deploying sensors in these key areas can more specifically obtain information reflecting the overall electric field characteristics of the equipment, avoid missing important measurement points, and make the measurement data more comprehensive. Cover the electric field conditions around the equipment.

[0046] The appearance image information obtained from multi-angle shooting helps to understand the spatial form of the device from multiple dimensions. Based on this, the optimized deployment model can comprehensively consider the device characteristics under different angles, reasonably plan the deployment positions of multiple sensors, form an effective layout in three-dimensional space, and realize three-dimensional measurement of the electric field around the device, further improving the comprehensiveness of the measurement, rather than being limited to local or plane measurement.

[0047] If the target deployment location of the sensor is not predicted in advance based on the appearance image, it may be necessary to try to place sensors in different locations around the equipment to obtain appropriate data, which will consume a lot of time and manpower. After the target location is determined by optimizing the deployment model, the sensor can be directly deployed at the location where valid data is most likely to be obtained, reducing the number of blind attempts and thus improving the overall efficiency of the measurement work. This advantage is more obvious when facing complex and large electrical equipment.

[0048] Based on the electric field distribution characteristics of the equipment analyzed from the appearance image, the optimization deployment model can determine the number of sensors required and the optimal deployment position of each sensor, avoiding the waste of resources caused by excessive deployment of sensors or incomplete measurement data caused by insufficient number of sensors. The limited sensor resources can be reasonably allocated to maximize the measurement efficiency.

[0049] Depth images and other external image information can reflect the actual environment of the target object, such as whether there are any obstructions or other interference sources around the device. The optimized deployment model can take these environmental factors into consideration when predicting the sensor location, and select a location with less interference and more accurate reflection of the electric field strength for deployment, thereby reducing the interference of external factors on the measurement results and improving the accuracy of the measurement and the reliability of the data.

[0050] Since the appearance image information directly shows the real appearance of the device, the optimized deployment model can simulate the electric field distribution that is more in line with the actual situation based on it, and then accurately arrange the sensor position according to the electric field distribution law. In this way, the data obtained by the sensor can more truly reflect the electric field state of the device, reduce the measurement error caused by unreasonable position, and improve the accuracy of the measurement results.

[0051] After the sensor deployment position is determined by the appearance image information, the electric field strength information obtained by subsequent measurements has a clear correspondence with the spatial position of the equipment. When performing data analysis, the electric field strength data can be more intuitively interpreted in combination with the appearance structure of the equipment, and the distribution characteristics and change trends of the electric field in different parts can be analyzed, which helps to better understand the electrical performance and operating status of the equipment and provide strong support for subsequent equipment management decisions.

[0052] Based on the sensor deployment determined by the appearance image, the data of each measurement has a standardized association method in the spatial position, which is convenient for unified comparison, analysis and integration of measurement data at different times and under different working conditions, and helps to establish a long-term equipment electric field monitoring database, providing a coherent and reliable data basis for the full life cycle management of the equipment.

[0053] As an optional embodiment, in the above steps, predicting the target deployment position of the electro-optical crystal fiber sensor by optimizing the deployment model based on the appearance image information includes: By optimizing the extraction layer in the deployment model, the outline of the target object is extracted from the appearance image information; by optimizing the construction layer in the deployment model, a virtual three-dimensional model of the target object is established based on the extracted outline; by optimizing the prediction layer in the deployment model, the deployment situation is predicted based on the virtual three-dimensional model and sensor attribute information to obtain a sensor deployment score corresponding to each candidate deployment position; the sensor attribute information includes at least: the electro-optical coefficient, transparency, and stability of the electro-optical crystal in the electro-optical crystal fiber sensor; by optimizing the optimization layer in the deployment model, the deployment position is screened based on the sensor deployment score to obtain the target deployment position of the target object.

[0054] In principle, first, the extraction layer of the optimized deployment model is used to extract the outline of the target object from the appearance image information. This step is to identify the boundary and shape features of the target electrical equipment in the image through image recognition and processing technology, and provide basic contour information for the subsequent establishment of a three-dimensional model. Next, the construction layer constructs a virtual three-dimensional model based on the extracted outline. It uses computer graphics and three-dimensional modeling algorithms to convert two-dimensional contour information into a model in three-dimensional space, which can more realistically simulate the spatial shape and structure of the target object. This virtual three-dimensional model can be regarded as a digital copy of the target electrical equipment in the computer, which contains spatial information such as the geometric shape and size of the equipment. Then, the prediction layer predicts the deployment situation based on the virtual three-dimensional model and sensor attribute information. Since the three-dimensional model reflects the spatial structure of the equipment, combined with the properties of the sensor (such as electro-optical coefficient, transparency, stability, etc.), the situation after deploying sensors at different locations can be estimated by means of physical field simulation, mathematical calculation or machine learning algorithm. For example, according to the electric field distribution theory, the electric field strength, electric field gradient and other parameters at different locations are calculated in combination with the shape of the equipment, and the influence of the sensor's own properties on the measurement is considered, so as to generate a sensor deployment score for each candidate deployment location. Finally, the optimization layer selects the target deployment location based on these sensor deployment scores. By comparing the scores of each candidate location, the location with the highest score is selected as the target deployment location. This process can use methods such as sorting and threshold judgment to ensure that the selected location can meet the measurement requirements to the greatest extent, taking into account the electric field distribution characteristics of the equipment and the performance advantages of the sensor.

[0055] Based on the principle, this method can bring many effects. From the perspective of measurement accuracy, by establishing a virtual three-dimensional model and combining it with sensor properties for prediction, sensors can be accurately deployed in key areas of electric field strength changes or locations that can fully reflect the electric field distribution, thereby obtaining more accurate electric field strength information. For example, near the edge of a device or electrode where the electric field distortion is large, sensors can be reasonably arranged to make the measurement data closer to the true value.

[0056] From the perspective of efficiency, it avoids the repeated position adjustment that may be caused by random deployment of sensors. Through model prediction and screening, the optimal deployment position is determined at one time, reducing the time cost of testing and adjustment, which can significantly improve work efficiency, especially in the measurement scenarios of complex equipment or large-scale equipment groups.

[0057] In terms of resource utilization, the properties of sensors are fully considered so that each sensor can play its maximum performance advantage. For example, according to the characteristics of electro-optical crystals, sensors are deployed in locations where they can fully utilize their electro-optical coefficient, transparency and other advantages, avoiding the waste of sensor resources and extending the service life of sensors, because placing them in a more suitable environment can reduce damage caused by harsh environments or improper use.

[0058] In addition, this method also facilitates the subsequent management and analysis of measurement data. Since the deployment location of the sensor is determined based on the shape and model of the target object, the measurement data has a clear correspondence with the spatial position of the device, which is convenient for in-depth interpretation combined with the device structure in subsequent analysis, helping to better understand the electrical characteristics and operating status of the device.

[0059] As an optional embodiment, in the above steps, the method for obtaining the sensor deployment score corresponding to each candidate deployment position is expressed as the following formula: ;in, represents the sensor deployment score corresponding to the i-th candidate deployment location, represents the weight corresponding to the jth influencing factor, represents the value of the jth influencing factor under the i-th candidate deployment location, represents the multi-factor interaction weight value, It indicates the influence degree of the kth influencing factor on another influencing factor l. It represents the influence of the lth influencing factor on another influencing factor k, where n and m are constants.

[0060] In this way, by introducing the factor interaction weight value and the split factor sum, the interaction between different factors can be quantified in a refined manner. This quantification method can more accurately describe the impact of various factors on the sensor deployment effect at different candidate deployment locations. Compared with the simple weighted summation formula, it can more deeply explore the internal connection between factors, thereby improving the accuracy and scientificity of the scoring.

[0061] In complex electrical equipment high-voltage DC field strength measurement scenarios, there are multiple factors that are intertwined and influence each other. This formula can adapt well to this complexity. Whether considering the relationship between the multiple attributes of the sensor itself, or the interaction between the sensor attributes and the electric field distribution characteristics of the equipment, it can accurately score by reasonably setting parameters, providing a more reliable basis for selecting the optimal sensor deployment location.

[0062] The sensor deployment score calculated by this formula can be used to effectively sort and compare candidate deployment locations. Candidate locations with high scores are more likely to be optimal sensor deployment locations after comprehensive consideration of multiple factors and their interactions. This helps the optimization layer to quickly and accurately screen out target deployment locations, avoids the subjectivity and blindness that may be caused by deployment decisions based solely on experience or simple rules, and improves the rationality and effectiveness of sensor deployment.

[0063] In practical applications, if it is found that the impact of certain factors on the measurement results has changed, or there are new factors that need to be considered, the calculation method of the sensor deployment score can be flexibly updated by adjusting the weights, interaction weights, split factors and other parameters in the formula to adapt to new deployment requirements and scenario changes, providing strong support for dynamically optimizing sensor deployment strategies.

[0064] As an optional embodiment, in 103, obtaining the motion trajectory of the electro-optical crystal fiber sensor during the measurement process includes: A measurement center point is selected from the target object as the origin of the measurement space coordinate system; a measurement space coordinate system is established based on the origin, and the position coordinates of each measurement point of the electro-optical crystal fiber sensor during the measurement process are obtained to construct corresponding displacement information; the real-time posture and real-time acceleration of the electro-optical crystal fiber sensor during the measurement process are obtained; based on the real-time posture and the real-time acceleration, the corresponding motion state change information of the electro-optical crystal fiber sensor in the motion trajectory is constructed; and the displacement information and the motion state change information are used as the motion trajectory.

[0065] Specifically, the measurement center point is selected from the target object as the origin of the measurement space coordinate system. This approach is intended to provide a unified and fixed reference for the subsequent accurate description of the sensor position. For example, for electrical equipment with a relatively regular shape, its geometric center can be selected as the origin; for irregular equipment, a key position with relatively symmetrical electric field distribution can be selected as the origin, making the measurement around the equipment more logical and convenient in spatial positioning.

[0066] After establishing a measurement space coordinate system (such as the common Cartesian coordinate system) based on the determined origin, the position coordinates of each measurement point of the electro-optical crystal fiber sensor during the measurement process can be obtained. By recording the coordinate values ​​corresponding to the position of the sensor at different times, displacement information is constructed. This is actually using coordinates to quantify the position change of the sensor relative to the origin, just like marking the coordinates of different locations on a map to determine the movement path, which clearly shows the position movement of the sensor in the space around the device.

[0067] During the movement of the sensor, the real-time attitude reflects its angle, orientation and other states in space, which can be obtained with the help of high-precision attitude sensors (such as gyroscopes, accelerometers and other integrated inertial measurement units). The real-time acceleration reflects the change in the sensor's movement speed, which is also obtained through the corresponding acceleration measurement device. These data are critical to accurately grasp the dynamic characteristics of the sensor, because knowing only the change in position coordinates is not enough to fully describe its movement. The attitude and acceleration information can further refine the movement details.

[0068] Based on the acquired real-time posture and acceleration, the motion state change information is constructed. For example, the change in posture means that the direction of the sensor has changed, and the change in acceleration can reflect whether its movement is accelerating, decelerating, or at a constant speed. Combining the displacement information with the motion state change information fully outlines the motion trajectory of the sensor during the entire measurement process, and can clearly present the various state changes from the starting position, the path to the final position, and the motion process.

[0069] In this way, by clearly defining the measurement space coordinate system and recording the position coordinates of each measurement point, the position of the sensor relative to the target object in three-dimensional space can be accurately determined. Not only can the final position be known, but also each point it passes through can be clearly understood. Combined with the information on the change in motion state, the motion trajectory of the sensor can be accurately reproduced in environments such as computer simulation, which is very helpful for analyzing the measurement process, troubleshooting possible problems, and reviewing data collection.

[0070] Obtaining the motion state change information constructed by real-time posture and real-time acceleration allows users to fully understand the motion characteristics of the sensor. When analyzing the measured electric field strength information, these motion characteristics can be associated for comprehensive consideration. For example, when the electric field strength data at a certain location is found to be abnormal, the motion state of the sensor at that location can be checked (whether it is accelerating or staying steadily, etc.) to determine whether the motion state affects the measurement results, so as to process and interpret the data more scientifically.

[0071] Since the motion trajectory is fully recorded, including detailed displacement and motion state changes, the entire measurement process is more transparent and traceable. This helps to improve the reliability of the measurement results, because the rationality of the data can be verified from the perspective of the motion trajectory. At the same time, when showing the measurement to other people or in subsequent reports, the rich motion trajectory information can make the measurement results more interpretable, making it easier for others to understand the scientific nature and accuracy of the measurement.

[0072] Such detailed motion trajectory information can serve as an important basis for subsequent optimization of measurement plans. For example, if it is found that the trajectory coverage of a certain area in a certain measurement is not comprehensive or the motion state is not ideal, resulting in poor data quality, the sensor's motion mode can be adjusted in a targeted manner during the next measurement. In addition, for situations where repeated measurements are required for comparative verification, the previous motion trajectory can be used as a reference template to ensure the consistency and comparability of each measurement.

[0073] As an optional embodiment, in 103, obtaining electric field strength information obtained by measuring the target object at different relative positions through the electro-optical crystal fiber sensor includes: Acquire correction and compensation information of the electro-optic crystal fiber sensor; collect corresponding optical signal change information at each measuring point through the electro-optic crystal fiber sensor; the optical signal change information at least includes: phase change information of the electro-optic crystal fiber sensor; perform phase correction on the optical signal change information using the correction and compensation information; based on a preset mapping relationship between the optical signal phase and the electric field strength, convert the corrected optical signal change information into corresponding electric field strength information.

[0074] It is understandable that, first of all, the correction compensation information of the electro-optic crystal fiber sensor is obtained because the sensor itself may have some system errors or be affected by environmental factors and produce deviations. These correction compensation information can be obtained through pre-calibration experiments or theoretical calculations, and are used to correct the errors of the sensor during the measurement process.

[0075] When collecting the corresponding optical signal change information at each measurement point through the electro-optic crystal fiber sensor, the phase change information is particularly critical. Due to the characteristics of the electro-optic crystal, the change in electric field strength will cause the change in the crystal refractive index, which in turn causes the phase of light to change when it propagates in the crystal. There is an inherent physical connection between the phase change of this optical signal and the electric field strength.

[0076] The purpose of using correction compensation information to perform phase correction on the optical signal change information is to eliminate the interference of the sensor itself and external factors (such as temperature, mechanical stress, etc.) on the phase of the optical signal, so that the obtained phase change information can more accurately reflect the real change of the electric field strength.

[0077] Finally, based on the preset mapping relationship between the optical signal phase and the electric field strength, the corrected optical signal change information is converted into the corresponding electric field strength information. This mapping relationship is usually derived through experimental calibration or theoretical formula based on the electro-optical effect. It establishes a quantitative conversion relationship between the optical signal phase change and the electric field strength, thereby realizing the conversion from optical signal measurement to electric field strength measurement.

[0078] From the perspective of accuracy, by correcting the optical signal change information with the correction compensation information, the influence of sensor errors and environmental factors on the measurement results can be effectively reduced, making the converted electric field strength information more accurate. For example, the optical signal phase drift caused by temperature changes can be corrected, thereby more accurately reflecting the true value of the electric field strength.

[0079] In terms of reliability, conversion based on the preset mapping relationship provides a reliable physical and mathematical basis for obtaining electric field strength information. This standardized conversion process improves the reliability of the measurement results, makes data under different measurement conditions comparable, and facilitates long-term monitoring and analysis of the electric field strength of electrical equipment.

[0080] From the perspective of measurement integrity, since the electric field strength information at different relative positions can be obtained, combined with the motion trajectory of the sensor, the spatial distribution of the electric field strength around the electrical equipment can be fully depicted. This is of great value for in-depth understanding of the electric field characteristics of electrical equipment, evaluation of its operating status, and subsequent fault diagnosis.

[0081] Further optionally, before actual measurement, the electro-optic crystal fiber sensor needs to be calibrated. This can be achieved by conducting experiments in a standard electric field environment with known electric field strength. For example, a standard electric field generator is used to generate a stable electric field of known strength, the sensor is placed in it, and the phase change of the optical signal is recorded. By changing the electric field strength and repeatedly measuring the phase change of the optical signal, a standard curve of electric field strength and optical signal phase change is established. In this process, the error information caused by the sensor's own characteristics (such as the inhomogeneity of the electro-optic crystal, the microbend loss of the optical fiber, etc.) and environmental factors (such as temperature, humidity, etc.) is also recorded. This error information is an important part of the correction and compensation information.

[0082] In addition to experimental calibration, some possible error factors can also be calculated in combination with theoretical knowledge of the electro-optic effect. For example, for electro-optic crystals, based on the relationship between its electro-optic coefficient and the electric field intensity and the light refractive index (such as the Pockels effect formula), the influence of the non-ideal internal structure of the crystal on light propagation is considered to calculate the theoretically possible phase deviation. At the same time, the optical properties of the optical fiber, such as the potential influence of the refractive index distribution and mode coupling of the optical fiber on the phase of the optical signal, are considered. The compensation amount corresponding to these factors is estimated through theoretical models, and combined with the information obtained from experimental calibration to form complete correction compensation information.

[0083] Electro-optic crystal fiber sensors work based on the electro-optic effect. When the sensor is in an electric field environment, the electric field will cause the refractive index of the electro-optic crystal to change. During the measurement process, the optical signal is transmitted to the detector through the optical fiber, and the detector can detect the phase change information of the optical signal. For example, using an optical detection device such as an interferometer, the optical signal containing the phase change is interfered with the reference optical signal, and the phase change of the optical signal is obtained by analyzing the movement of the interference fringes or the change in light intensity. When collecting the phase change information of the optical signal, it is necessary to pay attention to the fact that various factors may affect it. In addition to the phase change caused by the electric field, the movement of the sensor (such as vibration, rotation, etc.) may cause a slight deformation of the optical fiber, thereby changing the propagation path and phase of the light in the optical fiber. In addition, changes in ambient temperature will also change the refractive index of the electro-optic crystal, thereby affecting the phase of the optical signal. Therefore, during the acquisition process, it is necessary to simultaneously record information such as the motion state of the sensor and the ambient temperature for subsequent comprehensive analysis and correction.

[0084] As an optional embodiment, in 103, the process of converting the corrected optical signal change information into corresponding electric field strength information based on the mapping relationship is expressed as the following formula:

[0085] in, is the optical signal phase difference measured by the electro-optic crystal fiber sensor at the i-th position, is the electric field intensity corresponding to the ith position, is the optical signal propagation distance, is the wavelength of the optical signal, is the refractive index of the electro-optic crystal, is the photoelectric coefficient, and is the temperature correction coefficient, Indicates the propagation distance under the first temperature condition The light intensity after Indicates the propagation distance under the second temperature condition After the light intensity.

[0086] During the phase correction process, the collected optical signal phase change is directly corrected based on the correction compensation information obtained. For example, the correction compensation information shows that under the current ambient temperature, the temperature will cause the optical signal phase drift, and the drift amount is a specific value. Then the total phase change measured is subtracted from the phase drift caused by the temperature to eliminate the influence of temperature. Similarly, if it is determined through calibration experiments that the sensor itself has a fixed phase deviation, the fixed phase deviation is also subtracted from the total phase change measured. For the phase change caused by the movement of the sensor, the specific value of the phase deviation caused by the movement should be calculated according to the recorded motion state (such as acceleration, angular velocity, etc.) and the pre-established motion and phase change model, and then the corresponding correction operation should be performed.

[0087] Since environmental factors and sensor states may change dynamically, phase correction needs to be performed in real time. During the measurement process, the correction compensation information is continuously updated to adapt to these changes. For example, by real-time monitoring of the ambient temperature, the dynamic model of temperature-phase change (which may be an empirical formula obtained through experimental fitting or a theoretical formula derived based on physical principles) is used to calculate the phase deviation caused by temperature in real time and correct the phase of the optical signal in a timely manner. Similarly, for the phase change caused by the movement of the sensor, dynamic correction is performed based on the real-time motion state feedback to ensure that the measured phase change of the optical signal reflects the change caused by the electric field strength as accurately as possible.

[0088] In order to further improve the accuracy of the correction, an iterative optimization method can be used. After a preliminary correction, the possible residual phase error is inferred based on the difference between the converted electric field strength information and the expected value (for example, the estimated value of the electric field strength obtained based on the known characteristics of the electrical equipment or other reference measurement methods). Then, the correction compensation information is adjusted and the correction process is performed again. Through multiple iterations, the corrected optical signal phase change information is made more consistent with the phase change caused by the actual electric field strength, thereby improving the accuracy of the electric field strength measurement.

[0089] In 104, the motion trajectory and the electric field strength information are input into the measurement optimization model, and the electric field strength information is optimized and adjusted to obtain the target electric field strength information that is finally output. In the embodiment of the present application, the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change.

[0090] As an optional embodiment, first, a physical model of the electric field around the target electrical device is constructed based on electromagnetic theory. For simple geometric shapes (such as spheres, cylinders, etc.), analytical solutions (such as Gauss's law, etc.) can be used to describe the electric field distribution. For electrical equipment with complex shapes, numerical calculation methods (such as finite element method and boundary element method) may be required to solve the electric field. These methods divide the space around the device into discrete units, combine the boundary conditions such as the device's geometry and charge distribution, and solve Maxwell's equations to obtain the distribution of the electric field strength in space.

[0091] Incorporate the motion trajectory information of the sensor into the electric field simulation. Considering that the electric field environment in which the sensor is located is constantly changing when it is at different positions (determined by the motion trajectory). According to the position coordinates, posture, and motion speed of the sensor, the electric field position and theoretical value of the electric field strength corresponding to the sensor at each time step are determined. At the same time, the measured electric field strength information is preprocessed, including data cleaning (removing obviously abnormal data points, such as outliers caused by electromagnetic interference), normalization (mapping the electric field strength data to a specific range, such as [0,1], to facilitate subsequent model processing) and other operations. For example, a neural network model (such as a multi-layer perceptron, a convolutional neural network, a recurrent neural network, etc.) or a traditional mathematical model (such as a regression model, an interpolation model, etc.) can be selected to construct a measurement optimization model. The neural network model has a strong nonlinear fitting ability and can learn complex laws of electric field strength changes; the traditional mathematical model can quickly obtain results through simple mathematical formula fitting when the data distribution has a certain law. Taking the neural network as an example, a multi-layer perceptron model is constructed. The input layer includes the sensor position information (such as coordinates, posture, etc.) and the original electric field strength measurement value, and the output layer is the optimized electric field strength value.

[0092] Exemplarily, the simulated electric field strength theoretical value (obtained by combining the motion trajectory and the electric field physical model) and the actual measured electric field strength value after preprocessing are used as training data. The training data is divided into a training set, a validation set, and a test set, generally in a certain proportion (e.g., 70% - 80% for training, 10% - 15% for validation, and 10% - 15% for testing).

[0093] During the training process, a loss function is defined to measure the difference between the model output and the true value (the simulated theoretical electric field strength value). For regression problems, commonly used loss functions include mean square error (MSE), mean absolute error (MAE), etc. The parameters of the model (such as the weights and biases of the neural network) are adjusted through optimization algorithms (such as stochastic gradient descent, Adam, etc.) to continuously reduce the value of the loss function. During the training process, a validation set is used to monitor whether the model is overfitting (that is, the model performs well on the training set, but the performance decreases on the test set). If overfitting occurs, regularization methods (such as L1 and L2 regularization, Dropout, etc.) can be used to alleviate it.

[0094] In the actual measurement process, the real-time sensor motion trajectory information and electric field strength measurement data are input into the trained measurement optimization model. The measurement optimization model optimizes the current measured electric field strength based on the input information and the previously learned law of electric field strength changes.

[0095] For example, the measurement optimization model can make weighted adjustments to the current measured electric field strength value based on the electric field strength change trend between the current position of the sensor and the previous position, as well as the simulated distribution of the electric field strength in the surrounding space. If the current measured value does not match the electric field strength change trend predicted by the model, and the difference exceeds a certain threshold, the model can correct the current measured value through interpolation, fitting and other methods based on the measured data and simulated data of the surrounding positions, thereby achieving dynamic optimization.

[0096] By simulating the changes in electric field strength, the measurement optimization model can identify errors in the measurement data and correct them. For example, when the sensor is subject to external electromagnetic interference or its own performance fluctuations, resulting in deviations in the measured value of electric field strength, the model can determine whether the measured value is reasonable based on the electric field distribution law corresponding to the motion trajectory, and correct it to a result closer to the true value. This dynamic optimization can effectively reduce measurement errors and improve the accuracy of electric field strength measurements.

[0097] The measurement optimization model can fuse the measurement data at different locations and times and calibrate them. Since the sensor obtains the electric field strength information at multiple locations during the movement, the measurement optimization model can comprehensively consider this data and combine it with the electric field simulation results, making the various measurement data more coordinated and consistent, avoiding the problem of inaccurate data caused by single-point measurement errors or local anomalies.

[0098] In the actual measurement environment, there are various noises (such as electromagnetic noise, noise caused by environmental vibration, etc.) that affect the stability of the measurement data. The measurement optimization model can filter out the impact of these noises on the measurement data by learning the normal variation law of the electric field strength. For example, for the small fluctuations in the electric field strength measurement value caused by high-frequency electromagnetic noise, the model can treat these fluctuations as noise and smooth them according to the low-frequency characteristics of the electric field distribution and the motion trajectory information, thereby outputting more stable electric field strength data.

[0099] The measurement optimization model can evaluate the credibility of the measurement data. According to the input motion trajectory and measurement data, combined with the simulated electric field strength changes, a credibility index is assigned to each measurement data point. For data points with lower credibility, lower weights can be given or further verification can be performed in the subsequent analysis and decision-making process, thereby improving the reliability of the entire measurement data.

[0100] Based on the motion trajectory of the sensor and the optimized electric field strength data, the spatial electric field distribution around the target electrical equipment can be reconstructed more accurately. Not only can the numerical value of the electric field strength be obtained, but also the gradient change and anisotropy of the electric field strength in space can be understood. This is of great significance for comprehensively evaluating the electric field characteristics of electrical equipment and analyzing the impact of the electric field on the surrounding environment and equipment.

[0101] Since the measurement optimization model can optimize the electric field strength information in real time, it can effectively monitor the dynamic changes of the electric field around electrical equipment. For example, during equipment startup, fault occurrence, or load change, it can capture the changes in electric field strength in a timely manner, providing more timely and accurate information for equipment status monitoring and fault diagnosis.

[0102] As an optional embodiment, in 104, the specific implementation method of optimizing and adjusting the electric field strength information to obtain the target electric field strength information finally outputted is expressed as the following formula:

[0103] in, Indicates the original measured electric field strength value The corresponding weight coefficient is, Expressed as the weight coefficient corresponding to the electric field intensity distribution function, Represents the electric field strength value collected at the i-th position The electric field intensity distribution function obtained by finite element simulation is: Indicates the value of electric field strength The spatial position vector of Indicates the value of electric field strength The spatial position vector of represents the relative importance coefficient of the jth position in the process of optimizing the electric field strength, represents the optical signal phase corresponding to the jth position, m is a constant. The jth position is the core position of the electric field intensity distribution function corresponding to the ith position.

[0104] In some embodiments, the intelligent digital management device for electrical products can be deployed separately, referring to Figure 2 The intelligent digital management method of electrical products provided in the embodiment of the present application can be based on Figure 2 The intelligent digital management system of an electrical product is shown as being implemented. The intelligent business analysis and optimization system may include a server 01 and a terminal device 02. The terminal device 02 may be a functional module arranged in a measuring device, or may be other equipment connected to the measuring device.

[0105] Specifically, the server 01 sets a matching measuring device for the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optic crystal fiber sensor connected to a rotating electrode. The terminal device 02 dynamically controls the electro-optic crystal fiber sensor to rotate through the rotating electrode to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located. The terminal device 02 obtains the motion trajectory of the electro-optic crystal fiber sensor during the measurement process, and through the electro-optic crystal fiber sensor, the terminal device 02 obtains the electric field strength information obtained by measuring the target object at different relative positions; the crystal refractive index in the electro-optic crystal fiber sensor is proportional to the electric field strength information. The terminal device 02 transmits the collected data to the server 01. The server 01 inputs the motion trajectory and the electric field strength information into the measurement optimization model, optimizes and adjusts the electric field strength information, and obtains the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimizes the measured electric field strength information based on the change. Furthermore, the server 01 stores the target electric field strength information in a management database, and executes a corresponding automatic management process on the target object based on the target electric field strength information.

[0106] It should be noted that the computing device involved in the embodiments of the present application may be a server and / or a terminal device.

[0107] The server involved in the embodiments of the present application can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, as well as big data and artificial intelligence platforms.

[0108] In the embodiment of the present application, the measurement optimization model can improve the measurement accuracy, and the combination of multi-position measurement and dynamic optimization can further reduce the accumulation of errors. In addition, by rotating the electrode to collect optical signal data at multiple angles, the data integrity is further increased, which helps to finally obtain three-dimensional electric field information and store it in association. The automated management is efficient, and real-time monitoring and automatic decision-making can be achieved. Data-driven equipment maintenance and optimization can also be performed, providing strong support for electrical equipment management.

[0109] In another embodiment of the present application, an intelligent digital management device for electrical products is also provided. Figure 3 As shown, the device is applied to the high-voltage DC field strength measurement scenario of the electrical equipment to be tested, and the device includes the following units: A setting unit is configured to set a measuring device matching the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optical crystal fiber sensor connected to a rotating electrode; a control unit configured to dynamically control the electro-optic crystal fiber sensor to perform rotational motion through the rotating electrode, so as to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located; an acquisition unit configured to acquire a motion trajectory of the electro-optical crystal fiber sensor during a measurement process, and acquire electric field strength information obtained by measuring a target object at different relative positions through the electro-optical crystal fiber sensor; a crystal refractive index in the electro-optical crystal fiber sensor is proportional to the electric field strength information; An adjustment unit is configured to input the motion trajectory and the electric field strength information into a measurement optimization model, optimize and adjust the electric field strength information, and obtain the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change; The management unit is configured to store the target electric field strength information in a management database, and execute a corresponding automatic management process on the target object based on the target electric field strength information.

[0110] Further optionally, before the setting unit sets a matching measurement device for the target object, it is further configured to: Scanning the appearance image information of the target object; the appearance image information at least includes: depth image information of the target object; the appearance image information comes from image data captured from multiple viewing angles; The target deployment position of the electro-optical crystal fiber sensor is predicted by optimizing the deployment model based on the appearance image information.

[0111] Further optionally, the setting unit predicts the target deployment position of the electro-optical crystal fiber sensor by optimizing the deployment model based on the appearance image information, and is configured to: Extracting the outline of the target object from the outline image information by optimizing the extraction layer in the deployment model; By optimizing the construction layers in the deployment model, a virtual 3D model of the target object is established based on the extracted shape contours; By optimizing the prediction layer in the deployment model, the deployment situation is predicted based on the virtual three-dimensional model and sensor attribute information to obtain the sensor deployment score corresponding to each candidate deployment position; the sensor attribute information at least includes: the electro-optic coefficient, transparency, and stability of the electro-optic crystal in the electro-optic crystal fiber sensor; By optimizing the optimization layer in the deployment model, the deployment location is screened based on the sensor deployment score to obtain the target deployment location of the target object.

[0112] Further optionally, the method for obtaining the sensor deployment score corresponding to each candidate deployment position is expressed as the following formula: ; in, represents the sensor deployment score corresponding to the i-th candidate deployment location, represents the weight corresponding to the jth influencing factor, represents the value of the jth influencing factor under the i-th candidate deployment location, represents the multi-factor interaction weight value, It indicates the influence degree of the kth influencing factor on another influencing factor l. It represents the influence of the lth influencing factor on another influencing factor k, where n and m are constants.

[0113] Further optionally, the acquiring unit, wherein acquiring the motion trajectory of the electro-optical crystal fiber sensor during the measurement process, is configured as follows: Select the measurement center point from the target object as the origin of the measurement space coordinate system; A measurement space coordinate system is established based on the origin, and the position coordinates of each measurement point of the electro-optical crystal fiber sensor during the measurement process are obtained to construct corresponding displacement information; Acquiring the real-time posture and real-time acceleration of the electro-optical crystal fiber sensor during the measurement process; Based on the real-time posture and the real-time acceleration, constructing the motion state change information corresponding to the electro-optical crystal fiber sensor in the motion trajectory; The displacement information and the motion state change information are used as the motion trajectory.

[0114] Further optionally, the acquiring unit, which acquires the electric field strength information obtained by measuring the target object at different relative positions through the electro-optical crystal fiber sensor, is configured as follows: Acquiring correction compensation information of the electro-optic crystal fiber sensor; The optical signal change information corresponding to each measuring point is collected by the electro-optic crystal fiber sensor; the optical signal change information at least includes: phase change information of the electro-optic crystal fiber sensor; Performing phase correction on the optical signal change information using the correction compensation information; Based on a preset mapping relationship between the optical signal phase and the electric field strength, the corrected optical signal change information is converted into corresponding electric field strength information.

[0115] Further optionally, the process of converting the corrected optical signal change information into corresponding electric field intensity information based on the mapping relationship is expressed as the following formula: ; in, is the optical signal phase difference measured by the electro-optic crystal fiber sensor at the i-th position, is the electric field intensity corresponding to the ith position, is the optical signal propagation distance, is the wavelength of the optical signal, is the refractive index of the electro-optic crystal, is the photoelectric coefficient, and is the temperature correction coefficient, Indicates the propagation distance under the first temperature condition The light intensity after Indicates the propagation distance under the second temperature condition After the light intensity.

[0116] In the embodiment of the present application, the measurement optimization model can improve the measurement accuracy, and the combination of multi-position measurement and dynamic optimization can further reduce the accumulation of errors. In addition, by rotating the electrode to collect optical signal data at multiple angles, the data integrity is further increased, which helps to finally obtain three-dimensional electric field information and store it in association. The automated management is efficient, and real-time monitoring and automatic decision-making can be achieved. Data-driven equipment maintenance and optimization can also be performed, providing strong support for electrical equipment management.

[0117] In another embodiment of the present application, an electronic device is provided, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the intelligent digital management method of electrical products described in the method embodiment when executing the program stored in the memory.

[0118] The communication bus mentioned in the above electronic equipment can be Figure 4 The communication bus 1140 in the embodiment may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, and the like.

[0119] For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0120] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0121] The memory 1130 may include a random access memory (RAM) or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0122] The processor 1110 may be a general purpose processor, including a central processing unit (Central Processor). It can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0123] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement each step that can be executed by an electronic device in the above method embodiment.

Claims

1. An intelligent digital management method for electrical products, characterized in that: Applied to the scenario of high-voltage DC field strength measurement of electrical equipment to be tested, the method comprises: A matching measuring device is provided for the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optical crystal fiber sensor connected to a rotating electrode; Dynamically controlling the electro-optic crystal fiber sensor to rotate by means of the rotating electrode to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located; Acquire the motion trajectory of the electro-optic crystal fiber sensor during the measurement process, and acquire the electric field strength information obtained by measuring the target object at different relative positions through the electro-optic crystal fiber sensor; the crystal refractive index in the electro-optic crystal fiber sensor is proportional to the electric field strength information; The motion trajectory and the electric field strength information are input into a measurement optimization model, and the electric field strength information is optimized and adjusted to obtain the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change; The target electric field strength information is stored in a management database, and a corresponding automatic management process is executed on the target object based on the target electric field strength information.

2. The intelligent digital management method for electrical products according to claim 1, characterized in that: Before setting a matching measuring device for the target object, the method further includes: Scanning the appearance image information of the target object; the appearance image information at least includes: depth image information of the target object; the appearance image information comes from image data captured from multiple viewing angles; The target deployment position of the electro-optical crystal fiber sensor is predicted by optimizing the deployment model based on the appearance image information.

3. The intelligent digital management method for electrical products according to claim 2, characterized in that: The predicting the target deployment position of the electro-optical crystal fiber sensor by optimizing the deployment model based on the appearance image information includes: Extracting the outline of the target object from the outline image information by optimizing the extraction layer in the deployment model; By optimizing the construction layers in the deployment model, a virtual 3D model of the target object is established based on the extracted shape contours; By optimizing the prediction layer in the deployment model, the deployment situation is predicted based on the virtual three-dimensional model and sensor attribute information to obtain the sensor deployment score corresponding to each candidate deployment position; the sensor attribute information at least includes: the electro-optic coefficient, transparency, and stability of the electro-optic crystal in the electro-optic crystal fiber sensor; By optimizing the optimization layer in the deployment model, the deployment location is screened based on the sensor deployment score to obtain the target deployment location of the target object.

4. The intelligent digital management method for electrical products according to claim 3, characterized in that: The sensor deployment score corresponding to each candidate deployment location is obtained as follows: ; in, represents the sensor deployment score corresponding to the i-th candidate deployment location, represents the weight corresponding to the jth influencing factor, represents the value of the jth influencing factor under the i-th candidate deployment location, represents the multi-factor interaction weight value, It indicates the influence degree of the kth influencing factor on another influencing factor l. It represents the influence of the lth influencing factor on another influencing factor k, where n and m are constants.

5. The intelligent digital management method for electrical products according to claim 1, characterized in that: The obtaining of the motion trajectory of the electro-optical crystal fiber sensor during the measurement process includes: Select the measurement center point from the target object as the origin of the measurement space coordinate system; A measurement space coordinate system is established based on the origin, and the position coordinates of each measurement point of the electro-optical crystal fiber sensor during the measurement process are obtained to construct corresponding displacement information; Acquiring the real-time posture and real-time acceleration of the electro-optical crystal fiber sensor during the measurement process; Based on the real-time posture and the real-time acceleration, constructing the motion state change information corresponding to the electro-optical crystal fiber sensor in the motion trajectory; The displacement information and the motion state change information are used as the motion trajectory.

6. The intelligent digital management method for electrical products according to claim 5, characterized in that: The step of obtaining electric field strength information obtained by measuring the target object at different relative positions by means of the electro-optical crystal fiber sensor includes: Acquiring correction compensation information of the electro-optic crystal fiber sensor; The optical signal change information corresponding to each measuring point is collected by the electro-optic crystal fiber sensor; the optical signal change information at least includes: phase change information of the electro-optic crystal fiber sensor; Performing phase correction on the optical signal change information using the correction compensation information; Based on a preset mapping relationship between the optical signal phase and the electric field strength, the corrected optical signal change information is converted into corresponding electric field strength information.

7. The intelligent digital management method for electrical products according to claim 6, characterized in that: The process of converting the corrected optical signal change information into corresponding electric field strength information based on the mapping relationship is expressed as the following formula: ; in, is the optical signal phase difference measured by the electro-optic crystal fiber sensor at the i-th position, is the electric field intensity corresponding to the ith position, is the optical signal propagation distance, is the wavelength of the optical signal, is the refractive index of the electro-optic crystal, is the photoelectric coefficient, and is the temperature correction coefficient, Indicates the propagation distance under the first temperature condition The light intensity after Indicates the propagation distance under the second temperature condition After the light intensity.

8. An intelligent digital management device for electrical products, characterized in that: Applicable to the scenario of high-voltage DC field strength measurement of electrical equipment to be tested, the device comprises: A setting unit is configured to set a measuring device matching the target object; wherein the target object is an electrical device to be measured; the measuring device is equipped with an electro-optical crystal fiber sensor connected to a rotating electrode; a control unit configured to dynamically control the electro-optic crystal fiber sensor to perform rotational motion through the rotating electrode, so as to dynamically adjust the relative position between the electro-optic crystal fiber sensor and the electric field where the target object is located; an acquisition unit configured to acquire a motion trajectory of the electro-optical crystal fiber sensor during a measurement process, and acquire electric field strength information obtained by measuring a target object at different relative positions through the electro-optical crystal fiber sensor; a crystal refractive index in the electro-optical crystal fiber sensor is proportional to the electric field strength information; An adjustment unit is configured to input the motion trajectory and the electric field strength information into a measurement optimization model, optimize and adjust the electric field strength information, and obtain the target electric field strength information that is finally output; the measurement optimization model is used to simulate the change of the electric field strength corresponding to the target object under the motion trajectory, and dynamically optimize the measured electric field strength information based on the change; The management unit is configured to store the target electric field strength information in a management database, and execute a corresponding automatic management process on the target object based on the target electric field strength information.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the intelligent digital management method of electrical products as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the intelligent digital management method for electrical products according to any one of claims 1 to 7 is implemented.

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