A dynamic data acquisition and processing method and system for wearable exoskeleton equipment

Abstract

The present invention relates to the field of skeletal equipment technology, and discloses a dynamic data acquisition and processing method and system for wearable exoskeleton equipment, comprising the following steps: collecting environmental data through a sensor array integrated in the exoskeleton equipment, calculating the average terrain slope and average altitude based on accelerometer and barometric sensor data, and obtaining terrain assessment coefficient data for power inspection areas from historical data. The present invention can reasonably adjust the acquisition frequency based on environmental complexity and task requirements. In complex terrain or critical inspection tasks, the acquisition frequency can be increased to obtain detailed data, while the frequency can be reduced when the environment is stable. Reducing the data acquisition frequency can effectively reduce the operating hours of components such as device sensors and reduce overall energy consumption. This helps extend the battery life of the equipment, reduces the frequency of charging, and eliminates the need for inspectors to frequently interrupt inspections to recharge, thereby improving the continuity and efficiency of inspection work.

Description

Technical Field

[0001] The present invention relates to the technical field of skeletal equipment, and in particular to a dynamic data acquisition and processing method and system for wearable exoskeleton equipment. Background Art

[0002] Wearable exoskeleton robots are playing a key role in power transmission line inspections. They offer a weight-reducing function, transferring some of the weight of equipment carried by workers to the ground, significantly reducing physical exertion. This provides significant advantages in complex terrain, such as mountainous areas. Their motion-assistance features provide real-time power based on human movements, making walking and climbing more stable and flexible. Equipped with a wide range of sensors, they can not only collect motion and environmental data, but also monitor workers' physiological indicators to ensure their safety. High-definition cameras capture images and video, providing a basis for line condition analysis, while a built-in navigation and positioning system ensures accurate inspection routes. This suite of features significantly improves inspection efficiency, enhances safety, improves data quality, reduces labor intensity, and effectively ensures the stable operation of power transmission lines.

[0003] However, in the practical application of wearable exoskeleton equipment in power inspections, technicians found that in complex terrain areas such as mountainous areas, due to the large changes in terrain slope and obvious differences in altitude, exoskeleton equipment faces different assistance needs, and the high-altitude environment affects human functions and electronic equipment performance. However, the existing data collection frequency is fixed, resulting in the collected data being unable to fully reflect the equipment operating status and environmental conditions, making it difficult to effectively ensure the quality of inspections. For example, minor faults in some key equipment were not discovered in time due to missing data, affecting the stable operation of the power system. In relatively flat and environmentally stable areas, exoskeleton equipment continues to collect data at a higher frequency, generating a large amount of duplicate and low-value data, causing the equipment to consume power quickly and require frequent charging, seriously affecting the progress of inspections. Summary of the Invention

[0004] The purpose of the present invention is to provide a dynamic data acquisition and processing method and system for wearable exoskeleton equipment to solve the above-mentioned problem that in complex terrain areas such as mountainous areas, due to large changes in terrain slope and obvious differences in altitude, exoskeleton equipment faces different assistance requirements, and the high-altitude environment affects human functions and electronic equipment performance. However, the existing data acquisition frequency is fixed, resulting in the collected data being unable to fully reflect the equipment operating status and environmental conditions, making it difficult to effectively ensure the quality of inspections.

[0005] In a first aspect, the present invention provides a method for collecting and processing dynamic data for a wearable exoskeleton device, comprising the following steps:

[0006] The exoskeleton device collects environmental data through the sensor array integrated in it, calculates the average terrain slope and average altitude based on the accelerometer and air pressure sensor data, and obtains the terrain assessment coefficient data of the power inspection area from historical data;

[0007] Obtain the power area to be inspected and calculate the task level judgment coefficient based on historical fault data and equipment information;

[0008] The data collection frequency is dynamically adjusted based on the task level judgment coefficient data and terrain assessment coefficient data of the power area to be inspected.

[0009] In a second aspect, the present invention provides a dynamic data acquisition and processing system for a wearable exoskeleton device, the system comprising:

[0010] Terrain Assessment Module: This module collects environmental data through the sensor array integrated into the exoskeleton device, calculates the average terrain slope and average altitude based on accelerometer and air pressure sensor data, and obtains the terrain assessment coefficient of the power inspection area from historical data;

[0011] Task evaluation module: obtains the power area to be inspected and calculates the task level judgment coefficient based on historical fault data and equipment information;

[0012] Frequency adjustment module: Based on the task level judgment coefficient data and terrain assessment coefficient data of the power area to be inspected, the data collection frequency is dynamically adjusted.

[0013] Beneficial effects of the present invention:

[0014] The present invention can reasonably adjust the collection frequency according to the complexity of the environment and the task requirements. When performing complex terrain or critical inspection tasks, the collection frequency can be increased to obtain detailed data, and the frequency can be reduced when the environment is stable. Reducing the data collection frequency can effectively reduce the working hours of equipment sensors and other components and reduce overall energy consumption. This helps to extend the battery life of the equipment and reduce the charging frequency, so that inspection personnel do not need to frequently interrupt inspections to charge, thereby improving the consistency and efficiency of inspection work.

[0015] By dynamically adjusting the acquisition frequency, the present invention can concentrate more resources in key areas and time periods, increase the acquisition frequency in areas where key equipment is concentrated and the risk of failure is high, better utilize resources to obtain key information, and timely discover potential problems. It can reduce the frequency in low-risk areas, avoid resource waste, and achieve reasonable allocation and efficient utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of a dynamic data acquisition and processing method for a wearable exoskeleton device of the present invention;

[0018] Figure 2 It is a structural schematic diagram of a dynamic data acquisition and processing system for a wearable exoskeleton device according to the present invention;

[0019] Figure 3 It is a structural schematic diagram of a dynamic data acquisition and processing device for a wearable exoskeleton device according to the present invention.

[0020] In the figure: 3, computer device; 301, processor; 302, memory; 303, computer program; DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention. Example 1

[0022] Figure 1 This is a flowchart of a dynamic data acquisition and processing method for a wearable exoskeleton device, provided in accordance with a first embodiment of the present invention. This embodiment of the present invention is applicable to power inspections in complex terrain. This dynamic data acquisition and processing method for a wearable exoskeleton device can be executed by a dynamic data acquisition and processing system for a wearable exoskeleton device. This dynamic data acquisition and processing system for a wearable exoskeleton device can be implemented by software and / or hardware. This dynamic data acquisition and processing system for a wearable exoskeleton device can be configured in a dynamic data acquisition and processing device for a wearable exoskeleton device. Optionally, the dynamic data acquisition and processing device for a wearable exoskeleton device can be an electronic device, such as a laptop, desktop computer, or smart tablet, although this embodiment of the present invention does not limit this.

[0023] Dynamically adjust the data collection frequency based on environmental complexity and task requirements. In complex terrain or critical inspection tasks, increase the collection frequency to obtain more detailed data. When the environment is relatively stable, appropriately reduce the frequency to reduce data volume and energy consumption.

[0024] An embodiment of the present invention provides a method for collecting and processing dynamic data for a wearable exoskeleton device, which specifically includes the following steps:

[0025] Step 1: Collect environmental data through sensors on the exoskeleton device and calculate terrain assessment coefficient data;

[0026] Among them, environmental data include terrain slope and altitude;

[0027] The reason for determining terrain complexity based on terrain slope and altitude is that wearable exoskeleton devices need to provide varying degrees of assistance to assist workers in walking and operating on terrains of varying slopes. As altitude increases, atmospheric pressure and oxygen levels decrease, leading to altitude sickness in the human body. This can reduce workers' physical function and work efficiency, making inspection tasks more difficult. High altitude environments also have a certain impact on the performance of electronic equipment, potentially affecting sensor accuracy and, consequently, data collection accuracy.

[0028] In some embodiments, the terrain slope and altitude of several power inspection areas are obtained from a historical database, wherein the terrain slope is calculated by acceleration and the altitude is calculated by air pressure;

[0029] The acceleration values ​​collected by the exoskeleton device on the three axes (x, y, z) through the accelerometer are: 、 、 ;

[0030] Calculate the tilt angle in the xy plane , the calculation formula is ;

[0031] Combined with the exoskeleton's own posture compensation (i.e., the angular velocity data collected by the gyroscope), the complementary filtering algorithm eliminates the interference caused by the accelerometer's movement to obtain the final terrain slope. The specific process is as follows:

[0032] Set the initial terrain slope angle estimate, for example, set the initial estimated angle to 0, and the angular velocity measured by the gyroscope and time interval , according to the formula Calculate the angle predicted by the gyroscope ,in, is the angle predicted by the gyroscope at the previous moment;

[0033] The average of the tilt angular velocity and the predicted angular velocity is taken as the final terrain slope;

[0034] Set the terrain slope analysis period. The terrain slope analysis period can be set to 10 seconds. The specific setting is determined by those skilled in the art based on analysis requirements and experience. Obtain all final terrain slopes in the terrain slope analysis period and perform average processing to obtain the average terrain slope.

[0035] The air pressure value P of the exoskeleton device is obtained through the air pressure sensor, and the calculation formula of air pressure value and altitude is: ,in, For reference altitude, sea level is usually taken as 0. is the reference temperature, generally 288.15K, L is the vertical temperature gradient, about 0.0065K / m, The reference air pressure is 101325Pa. The collected air pressure value P can be substituted into the formula to calculate the altitude. ;

[0036] Based on calculated altitude Combined with GPS altitude measurement , use the Kalman filter algorithm to perform data fusion and calculate the final altitude h;

[0037] Set the altitude analysis period. The altitude analysis period can be set to 1 minute. The specific setting is determined by those skilled in the art based on analysis requirements and experience. Obtain all final altitudes during the altitude analysis period and perform average processing to obtain the average altitude.

[0038] Based on any power inspection area, the power inspection area is divided into several evaluation sections, and the terrain evaluation value of each evaluation section is calculated;

[0039] The average terrain slope and the average altitude are normalized, wherein the normalization is maximum-minimum normalization; the sum of the normalized average terrain slope and the average sea wave height is used as the terrain assessment value;

[0040] The terrain assessment value is a comprehensive quantitative indicator of the complexity of power inspection tasks due to terrain slope and altitude. The larger the value, the greater the physical challenge of the terrain (the dual pressure of steep slopes and high altitudes); the higher the requirements for workers and equipment (stronger physical support and more robust equipment algorithms are required);

[0041] The more difficult the inspection task is to execute (e.g., it takes longer, requires more resources, or has the risk of task failure).

[0042] The terrain assessment value of each assessment section is averaged to obtain the terrain assessment coefficient of the power inspection area;

[0043] Step 2: Obtain the current power inspection task and calculate the task level judgment coefficient based on historical data;

[0044] In some embodiments, a current power inspection task is obtained, marked as a power area to be inspected, based on any power area to be inspected;

[0045] Extracting historical fault data of the power area to be inspected, wherein the historical fault data includes: the number of historical faults and the duration of historical faults;

[0046] Traverse all power equipment in the power area to be inspected, based on any power equipment;

[0047] Count the total area of ​​power outage affected by power equipment failure, calculate the ratio of the total power outage area to the total area of ​​the power area to be inspected, and obtain the impact coefficient;

[0048] Set the fault duration limit, and perform a ratio process on the historical fault duration and the fault duration limit to obtain the fault duration ratio;

[0049] The influence coefficient is multiplied by the fault duration ratio to obtain the fault characterization value. The fault characterization values ​​corresponding to all historical fault times are averaged to obtain the fault characterization mean z.

[0050] Set a fault characterization threshold, mark the power equipment with a fault characterization threshold greater than the fault characterization threshold as critical equipment, and mark the power equipment with a fault characterization threshold less than or equal to the fault characterization threshold as non-critical equipment;

[0051] Count the number of critical equipment n and non-critical equipment m in the power area to be inspected;

[0052] Obtain the geographic coordinates of all key equipment in the area to be inspected, where the geographic coordinates of key equipment are obtained through the Global Positioning System (GPS) or Geographic Information System (GIS);

[0053] Set the spatial weight matrix based on distance. If the distance between two key devices is is less than the distance threshold d, then the weight , otherwise the weight is zero;

[0054] Among them, the distance between the two key devices The calculation formula is: , where i≠j, i, j=1, 2, ..., n;

[0055] It should be noted that when i=j, the weight is zero;

[0056] Calculate the Moran coefficient I, the calculation formula is: ,in, , represents the mean value of the i-th and j-th fault characterizations, represents the mean of the fault characterization means of n key devices;

[0057] The Moran coefficient I ranges from -1 to 1. If I is greater than zero, it indicates that key equipment is spatially clustered, i.e., key equipment tends to be concentrated together in space. If I is zero, it indicates that the distribution of key equipment is random, with no obvious clustering or dispersion trend. If I is less than zero, it indicates that key equipment is spatially discrete, i.e., key equipment tends to be distributed far away from each other. The closer the value is to -1, the higher the degree of dispersion.

[0058] The large number of critical equipment and their dense distribution mean that there are a large number of potential risk points in the power area to be inspected that may affect the stable operation of the power system. For example, in a substation area where many critical equipment are concentrated, these critical equipment are closely distributed. Once a device fails, it is very likely to trigger a chain reaction, affecting other critical equipment in the surrounding area, thereby expanding the scope of the power outage and posing a serious threat to the stability and reliability of the power system. Therefore, in order to ensure the normal operation of the power system, these equipment need to be inspected more frequently and in a more detailed manner, which requires more manpower, material resources and time costs. Therefore, the corresponding task level of the power to be inspected is higher.

[0059] The ratio of the number of key equipment to the number of non-key equipment in the power area to be inspected is calculated, and then multiplied by the Moran coefficient to obtain the task level judgment coefficient;

[0060] The calculation of the task level judgment coefficient has the following functions: First, it quantifies and integrates many complex factors, which is more scientific and reasonable than simply classifying task levels based on equipment type or geographical location. It allows inspection personnel to quickly understand the importance of each area and provide an accurate basis for subsequent work;

[0061] Secondly, based on the calculated task level judgment coefficient, inspection resources such as manpower and material resources can be allocated in a targeted manner. For high-level task areas with large task level judgment coefficients, more professional technicians and advanced testing equipment can be deployed to increase the frequency and depth of inspections. For low-level task areas with small coefficients, resource investment can be appropriately reduced.

[0062] Third, the task level judgment coefficient is related to the data collection frequency, which helps to improve the pertinence of inspection work. In high-level task areas, since it is crucial to the stable operation of the power system, the data collection frequency is increased based on the calculation results to fully obtain information such as equipment operating status and environmental parameters, and to promptly discover potential problems. In low-level task areas, the collection frequency is appropriately reduced to balance data volume and energy consumption.

[0063] Fourth, reasonable task level division and resource allocation effectively ensure the stable operation of the power system. Through key inspections of high-level task areas, hidden equipment failures can be discovered and handled in a timely manner to prevent the expansion of failures and power outages. Routine inspections of low-level task areas can also maintain the normal operation of equipment.

[0064] Step 3: Process the task level judgment coefficient data and terrain assessment coefficient data of the power area to be inspected, and then dynamically adjust the data collection frequency;

[0065] In some implementations, all power areas to be inspected are obtained based on any one power area to be inspected;

[0066] Obtain the total period of the extracted historical data, divide it into several historical periods, and obtain the terrain assessment coefficient and task level judgment coefficient of each historical period;

[0067] Obtain the current power area to be inspected, and use the moving average method to predict the current terrain assessment coefficient and task level judgment coefficient based on the terrain assessment coefficient and task level judgment coefficient of the historical period. The specific process is as follows:

[0068] Set the moving average period p. The moving average period p can be set according to the characteristics of data changes. If the data changes frequently, you can set p=3. If the data changes relatively steadily, you can set p=5.

[0069] Calculate the predicted value of the current terrain assessment coefficient based on the moving average period and terrain assessment coefficient data sequence ;

[0070] Based on the moving average period and task level judgment coefficient data series, calculate the predicted value of the current task level judgment coefficient ;

[0071] Predicted value based on current terrain assessment coefficient and the predicted value of the pre-task level judgment coefficient Determine the terrain assessment coefficient corresponding to the historical period with high similarity and task level judgment coefficient The specific process is:

[0072] The Euclidean distance formula is used to calculate the distance value, and the calculated distance value is marked as the similarity value XS to measure the similarity. The calculation formula is: ;

[0073] Extract the historical period with the largest similarity value, mark it as a similar period, and obtain the terrain evaluation coefficient and task level judgment coefficient corresponding to the similar period;

[0074] The sum of the terrain assessment coefficient and the task level judgment coefficient is used as the inspection representation value;

[0075] Regarding inspection characterization values: the terrain assessment coefficient quantifies the environmental challenges posed by terrain complexity (slope, altitude) to inspection tasks. For example, steep slopes require greater assistance from exoskeletons, and high altitudes affect personnel function and equipment accuracy. The mission level judgment coefficient quantifies the failure risk of power equipment and the importance of the task. For example, areas with a high concentration of critical equipment are prone to cascading failures and require frequent inspections. Adding the terrain assessment coefficient and the mission level judgment coefficient reflects the combined pressure of environmental complexity and mission criticality.

[0076] Get the completeness coefficient of the collected data corresponding to the similar period. The acquisition process is as follows:

[0077] Organize the data records collected by various sensors in similar time periods and identify different types of data;

[0078] Different types of data include, but are not limited to, the collection time and recording of equipment operating parameters (voltage, current, temperature, etc.), environmental data (terrain slope, altitude, temperature and humidity, etc.), and task-related data (number of faults, power outage area, etc.);

[0079] For example, during a similar period, voltage data is collected every 5 minutes starting from 08:00, with a total of 12 records collected, and terrain slope data is collected every 10 minutes, with 6 records collected;

[0080] Compare the actual collected data records with the pre-set collection frequency and time range, and count the number of missing data of each type;

[0081] For example, if 15 voltage data items were planned to be collected, but only 12 were actually collected, then 3 voltage data items are missing. If 8 terrain slope data items were planned to be collected, but only 6 were actually collected, meaning 2 are missing. For each type of data, the number of missing items is recorded to provide data support for subsequent calculations.

[0082] The completeness coefficient of collected data is calculated by the formula: completeness coefficient = (actual amount of data collected ÷ amount of data that should be collected) × 100%;

[0083] Taking voltage data as an example, its completeness coefficient = (12 ÷ 15) × 100% = 80%; the completeness coefficient of terrain slope data = (6 ÷ 8) × 100% = 75%. For multiple different types of data, weights are set according to their importance and a weighted average is calculated to obtain the comprehensive collected data completeness coefficient. Assuming that the weight of voltage data is 0.6 and the weight of terrain slope data is 0.4, the comprehensive collected data completeness coefficient for this similar period = 80% × 0.6 + 75% × 0.4 = 78%;

[0084] The sum of the integrity coefficient corresponding to the complete period and the inspection characterization value is used as the frequency selection coefficient;

[0085] Regarding the frequency selection coefficient: the completeness coefficient quantifies the quality of sensor data collected during similar historical periods; the inspection representation value quantifies the overall challenge of the current task and reflects the intensity of the demand for data collection frequency in the current scenario. The sum of the two reflects a two-way indicator of data quality and task requirements.

[0086] Set a frequency selection coefficient threshold, and mark similar time periods that are greater than the frequency selection coefficient threshold as selected time periods, otherwise, mark them as non-selected time periods;

[0087] Extract the selected period with the largest frequency selection coefficient, obtain the data collection frequency of the selected period, and mark the collection frequency of the selected period as the collection frequency of the current power inspection task;

[0088] If all are non-selective periods, extract the non-selective period with the largest frequency selection coefficient, calculate the difference between the frequency selection coefficient corresponding to the selection period and the frequency selection coefficient threshold, take the absolute value of the difference, and calculate the ratio with the frequency selection coefficient threshold to obtain the frequency adjustment coefficient;

[0089] The data collection frequency of the non-selected period with the largest frequency selection coefficient is multiplied by the frequency adjustment coefficient to obtain the collection frequency adjustment amount, and the difference between the data collection frequency of the non-selected period with the largest frequency selection coefficient and the collection frequency adjustment amount is used as the collection frequency of the current power inspection task;

[0090] The technical solution of this embodiment is as follows: first, sensors on the exoskeleton device are used to collect environmental data such as terrain slope and altitude. The average terrain slope and average altitude are calculated by combining accelerometers, air pressure sensors, and corresponding algorithms, such as complementary filtering algorithm and Kalman filtering algorithm, to obtain terrain assessment coefficient data. Then, the current power inspection task is obtained. Combined with historical data such as the number of historical faults and fault duration, the power equipment is traversed to count the number of key and non-key equipment, and the Moran coefficient is calculated to obtain the task level judgment coefficient. Finally, based on the task level judgment coefficient and the terrain assessment coefficient, the historical data is processed. The similar time period is found by using the moving average method for prediction and the Euclidean distance to calculate the similarity. The data integrity coefficient is calculated, and finally the data collection frequency is dynamically adjusted.

[0091] From the perspective of data collection, the collection frequency can be reasonably adjusted according to the complexity of the environment and task requirements. In complex terrain or critical inspection tasks, the collection frequency can be increased to obtain detailed data, and the frequency can be reduced when the environment is stable to reduce data volume and energy consumption.

[0092] From the perspective of inspection resource allocation, the calculation of task level judgment coefficient provides a basis for the scientific allocation of inspection resources, and can reasonably arrange human and material resources for different task level areas;

[0093] In terms of ensuring the stable operation of the power system, through differentiated inspection strategies for different levels of task areas, we can promptly discover and deal with potential faults in high-level areas, maintain normal operation of equipment in low-level areas, and effectively ensure the stable and reliable operation of the power system. Example 2

[0094] Based on the above embodiments, Figure 2 As shown, an embodiment of the present invention provides a dynamic data acquisition and processing system for a wearable exoskeleton device, specifically comprising:

[0095] Terrain assessment module: collects environmental data through sensors on the exoskeleton device and calculates terrain assessment coefficient data;

[0096] Among them, environmental data include terrain slope and altitude;

[0097] Task evaluation module: obtains the current power inspection task and calculates the task level judgment coefficient based on historical data;

[0098] Frequency adjustment module: Based on the task level judgment coefficient data and terrain assessment coefficient data of the power area to be inspected, the data collection frequency is dynamically adjusted. Example 3

[0099] like Figure 3As shown, an embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, a dynamic data acquisition and processing method for a wearable exoskeleton device as described in any one of the above methods is implemented.

[0100] The computer device 3 may be a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302 .

[0101] Figure 3 This is merely an example of the computer device 3 and does not constitute a limitation on the computer device 3 . The computer device 3 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 3 may also include input and output devices, network access devices, etc.

[0102] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0103] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard drive or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the computer device 3. Furthermore, the memory 302 may include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is about to be output. Example 4

[0104] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements a dynamic data acquisition and processing method for a wearable exoskeleton device as described in any one of the above methods.

[0105] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the camera / terminal device, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0106] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0107] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] In the embodiments disclosed in this application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features.

[0109] On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0112] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A dynamic data acquisition and processing method for a wearable exoskeleton device, characterized in that: The following steps are involved: The exoskeleton device collects environmental data through the sensor array integrated in it, calculates the average terrain slope and average altitude based on the accelerometer and air pressure sensor data, and obtains the terrain assessment coefficient data of the power inspection area from historical data; Each power inspection area is divided into several assessment sections. The terrain assessment value of each assessment section is calculated based on the average terrain slope and average altitude. The terrain assessment coefficient of the power inspection area is obtained by performing average processing. Based on the task level judgment coefficient data and terrain assessment coefficient data of the power area to be inspected, the data collection frequency is dynamically adjusted; Obtain the power area to be inspected and calculate the task level judgment coefficient based on historical fault data and equipment information; Obtain the power area to be inspected, traverse the power equipment in the area, and calculate the number of key equipment and non-key equipment in the power area to be inspected, as well as the Moran coefficient; The ratio of the number of key equipment to the number of non-key equipment in the power area to be inspected is calculated, and then multiplied by the Moran coefficient to obtain the task level judgment coefficient; The process of dynamically adjusting the data acquisition frequency is as follows: Calculate the integrity coefficient and inspection characterization value of each power area to be inspected, and use the sum of the integrity coefficient and inspection characterization value corresponding to the complete period as the frequency selection coefficient; The similar time periods with frequency selection coefficients greater than the threshold are marked as selected time periods, otherwise, they are marked as non-selected time periods; Extract the selection period with the largest frequency selection coefficient, obtain the data collection frequency of the selection period, and use it as the collection frequency of the current power inspection task; If all are non-selective periods, extract the non-selective period with the largest frequency selection coefficient, calculate the difference between the frequency selection coefficient corresponding to the selection period and the frequency selection coefficient threshold, take the absolute value of the difference, and calculate the ratio with the frequency selection coefficient threshold to obtain the frequency adjustment coefficient; The process of obtaining the integrity coefficient and inspection characterization value is as follows: Obtain terrain assessment coefficients and task level judgment coefficients corresponding to similar time periods; The sum of the terrain assessment coefficient and the task level judgment coefficient is used as the inspection representation value; Organize the data records collected by various sensors in similar time periods, identify different types of data, and calculate the integrity coefficient of collected data; The data collection frequency of the non-selected period with the largest frequency selection coefficient is multiplied by the frequency adjustment coefficient to obtain the collection frequency adjustment amount, and the difference between the data collection frequency of the non-selected period with the largest frequency selection coefficient and the collection frequency adjustment amount is used as the collection frequency of the current power inspection task.

2. A dynamic data acquisition and processing method for a wearable exoskeleton device according to claim 1, characterized in that: The process of obtaining the number of key equipment and non-key equipment is as follows: Based on the number of historical faults and the duration of historical faults, calculate the impact coefficient and the ratio of fault duration; The influence coefficient is multiplied by the fault duration ratio to obtain the fault characterization value, and the average value is processed to obtain the fault characterization mean value; Mark the power equipment with a value greater than the fault characterization threshold as critical equipment, and mark the power equipment with a value less than or equal to the fault characterization threshold as non-critical equipment; Count the number of critical equipment and non-critical equipment in the power area to be inspected.

3. A dynamic data acquisition and processing method for a wearable exoskeleton device according to claim 2, characterized in that: The process of obtaining the ratio of influence coefficient to fault duration is: Extract the historical fault count and duration of the power area to be inspected; Count the total area of ​​power outage affected by power equipment failure and calculate the ratio with the total area of ​​power area to be inspected to obtain the impact coefficient; The fault duration ratio is obtained by ratioing the historical fault duration to the fault duration limit.

4. The method for dynamic data acquisition and processing for a wearable exoskeleton device according to claim 1, characterized in that: The process of obtaining the Moran coefficient is as follows: Obtain the geographic coordinates of all key equipment in the area to be inspected, set a spatial weight matrix based on distance, and calculate the Moran coefficient using a formula based on the fault characterization value and the spatial weight matrix.

5. The method for dynamic data acquisition and processing for a wearable exoskeleton device according to claim 1, characterized in that: The process of obtaining the similar time period is as follows: Divide the total period of the extracted historical data into several historical periods, and obtain the terrain assessment coefficient and task level judgment coefficient of each historical period; The predicted value of the current terrain assessment coefficient and the predicted value of the current mission level judgment coefficient using the moving average method; Based on the predicted value of the current terrain assessment coefficient, the predicted value of the current task level judgment coefficient, and the terrain assessment coefficient and task level judgment coefficient corresponding to the historical period, the Euclidean distance formula is used to calculate the distance value, which is marked as the similarity value; Extract the historical periods with the largest similarity value and mark them as similar periods.

6. A dynamic data acquisition and processing system for a wearable exoskeleton device, characterized in that: The system is used to execute the method according to any one of claims 1 to 5, and the system comprises: Terrain Assessment Module: This module collects environmental data through the sensor array integrated into the exoskeleton device, calculates the average terrain slope and average altitude based on accelerometer and air pressure sensor data, and obtains the terrain assessment coefficient of the power inspection area from historical data; Task evaluation module: obtains the power area to be inspected and calculates the task level judgment coefficient based on historical fault data and equipment information; Frequency adjustment module: Based on the task level judgment coefficient data and terrain assessment coefficient data of the power area to be inspected, the data collection frequency is dynamically adjusted.

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