Comprehensive monitoring device, method and equipment for vehicle cable

Abstract

The invention discloses a comprehensive monitoring device, method and equipment for a vehicle cable, and belongs to the technical field of vehicle cables. The device comprises a motion data acquisition module used for acquiring motion data of a vehicle; the multi-source data acquisition module is used for determining a monitoring period of each cable according to the motion data and performing multi-source data acquisition by adopting a preset sensor; wherein the sensor comprises one or more of a micro optical fiber sensor, a wireless current transformer and a high-frequency capacitance sensor; the data fusion module is used for carrying out fusion processing on the multi-source data; the intelligent identification module is used for carrying out intelligent identification on the vehicle cable based on the fusion result; and the recording and early warning module is used for writing the identification result into a log and generating an early warning report under the condition that abnormal data or overload data exists. According to the technical scheme, the vehicle cable can be inspected in all directions and comprehensively analyzed, so that the operation safety of the vehicle cable is improved.

Description

Technical Field

[0001] This application belongs to the technical field of vehicle cables, and particularly relates to a comprehensive monitoring device, method, and equipment for vehicle cables. Background Art

[0002] With the rapid development of technology, vehicles, as one of the important means of transportation, carry more and more functions. With the addition of different functions, the driving and riding experiences of vehicles are gradually improving. For the functional operation of vehicles, the vehicle cables laid inside play a crucial role.

[0003] Currently, for the monitoring of vehicle cables, there is no relatively scientific monitoring strategy. Usually, it is to monitor whether the functions of the vehicle are normal, so as to reflect whether there are operation failures in the vehicle cables from the side. Moreover, the monitoring of vehicle cables still mainly relies on visual inspections and parameter inspections during regular maintenance, which cannot show the data of the vehicle in the moving state. The data acquisition method is single and one-sided, lacking the basic conditions for comprehensive monitoring. Therefore, how to comprehensively monitor vehicle cables has become an important issue concerned in the field of vehicle transportation. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a comprehensive monitoring device, method, and equipment for vehicle cables, aiming to monitor different parameters of different cables when the vehicle is in different motion states, so as to achieve comprehensive monitoring of vehicle cables. While ensuring operation economy, it can conduct a comprehensive inspection of vehicle cables, comprehensively analyze and determine whether there are abnormal data or overloaded data, thereby improving the operation safety of vehicle cables, providing a data basis for subsequent vehicle failures, and also improving the operation safety of vehicles and the driving experience of users.

[0005] In a first aspect, the embodiments of this application provide a comprehensive monitoring device for vehicle cables, and the device includes:

[0006] A motion data acquisition module, configured to acquire motion data of the vehicle;

[0007] A multi-source data acquisition module, configured to determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, perform multi-source data acquisition using pre-set sensors; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor;

[0008] A data fusion module, configured to perform fusion processing on the multi-source data to obtain a fusion result;

[0009] An intelligent recognition module, which is used to intelligently recognize vehicle cables based on the fusion result, and identify whether there is abnormal data or overloaded data in the vehicle cables;

[0010] A recording and warning module, which is used to write the recognition result into a log, and generate a warning report in the case of abnormal data or overloaded data.

[0011] Furthermore, the multi-source data acquisition module includes:

[0012] A monitoring period determination unit, which is used to determine the monitoring period adopted for each cable by the current motion data according to the data interval corresponding to the motion data and the preset association relationship between the data interval and the monitoring period;

[0013] A data acquisition control unit, which is used to perform multi-source data acquisition using a pre-set sensor after the monitoring period arrives.

[0014] Furthermore, the data acquisition control unit is specifically used for:

[0015] After the monitoring period arrives, send a collection instruction to a pre-set sensor and receive the collection data fed back by the sensor;

[0016] Identify whether there is a sensor that has not executed the collection instruction;

[0017] If not, construct the collection data fed back by the sensor into multi-source data;

[0018] If so, resend the collection instruction to the sensor that has not executed the collection instruction, and generate a sensor failure prompt message if the collection data fed back by the sensor is not received within a preset time period.

[0019] Furthermore, the data fusion module includes:

[0020] A vectorization processing unit, which is used to perform vectorization processing on multi-source data according to a preset vectorization standard respectively to obtain a plurality of vectorized data;

[0021] A splicing unit, which is used to perform splicing processing on a plurality of vectorized data to obtain a fusion result.

[0022] Furthermore, the intelligent recognition module is specifically used for:

[0023] Based on the fusion result and the normal mapping interval between pre-set multi-source data, intelligently recognize vehicle cables to identify whether there is abnormal data or overloaded data in the vehicle cables.

[0024] Furthermore, the intelligent recognition module is specifically used for:

[0025] Based on the fusion results obtained from multiple monitoring cycles, perform data comparison between monitoring cycles for vehicle cables to identify whether there are abnormal data or overloaded data in the vehicle cables.

[0026] Further, the abnormal data includes: abnormal cable transmission capacity, abnormal change in the dielectric constant of the insulating layer, and abnormal change amplitude in the resistivity of the cable core.

[0027] In a second aspect, an embodiment of the present application provides a comprehensive monitoring method for vehicle cables, and the method includes:

[0028] Obtain the motion data of the vehicle;

[0029] Determine the monitoring cycle for each cable according to the motion data, and after reaching the monitoring cycle, use pre-set sensors to collect multi-source data; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor;

[0030] Perform fusion processing on the multi-source data to obtain a fusion result;

[0031] Based on the fusion result, perform intelligent identification on the vehicle cables to identify whether there are abnormal data or overloaded data in the vehicle cables;

[0032] Write the identification result into a log, and generate a warning report in the case of abnormal data or overloaded data.

[0033] Further, determining the monitoring cycle for each cable according to the motion data, and after reaching the monitoring cycle, using pre-set sensors to collect multi-source data includes:

[0034] Determine the monitoring cycle adopted for each cable by the current motion data according to the data interval corresponding to the motion data and the preset association relationship between the data interval and the monitoring cycle;

[0035] After reaching the monitoring cycle, use pre-set sensors to collect multi-source data.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0037] Fourthly, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0038] Fifthly, an embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0039] In the embodiment of the present application, a motion data acquisition module is configured to acquire motion data of a vehicle; a multi-source data acquisition module is configured to determine a monitoring period for each cable according to the motion data, and after the monitoring period arrives, perform multi-source data acquisition using a pre-set sensor; wherein, the sensor includes one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor; a data fusion module is configured to perform fusion processing on the multi-source data to obtain a fusion result; an intelligent identification module is configured to perform intelligent identification on the vehicle cable based on the fusion result to identify whether there is abnormal data or overloaded data in the vehicle cable; a recording and warning module is configured to write the identification result into a log, and generate a warning report in the case of abnormal data or overloaded data. The above technical solution can monitor different parameters of different cables under different motion states of the vehicle, can achieve comprehensive monitoring of the vehicle cable, realize all-round inspection of the vehicle cable while ensuring operation economy, and comprehensively analyze and determine whether there is abnormal data or overloaded data, thereby improving the operation safety of the vehicle cable and providing a data basis for subsequent vehicle failures, and also being able to improve the operation safety of the vehicle and the driving experience of users. Description of the Drawings

[0040] Figure 1 is a schematic structural diagram of a comprehensive monitoring device for vehicle cables provided by Embodiment 1 of the present application;

[0041] Figure 2 is a schematic structural diagram of a comprehensive monitoring device for vehicle cables provided by Embodiment 2 of the present application;

[0042] Figure 3 is a schematic structural diagram of a comprehensive monitoring device for vehicle cables provided by Embodiment 3 of the present application;

[0043] Figure 4 is a schematic flowchart of a comprehensive monitoring method for vehicle cables provided by Embodiment 4 of the present application;

[0044] Figure 5 is a schematic structural diagram of an electronic device provided by Embodiment 5 of the present application. Detailed Embodiments

[0045] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further describes specific embodiments of the present application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the convenience of description, only parts related to the present application rather than all content are shown in the drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the process can be terminated, but there can also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0046] The following clearly describes the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application belong to the scope of protection of the present application.

[0047] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0048] The following describes in detail the comprehensive monitoring device, method, and equipment for vehicle cables provided in the embodiments of the present application with reference to the accompanying drawings and through specific embodiments and their application scenarios.

[0049] Embodiment 1

[0050] Figure 1 is a schematic structural diagram of the comprehensive monitoring device for vehicle cables provided in Embodiment 1 of the present application. As Figure 1 shown, the device includes:

[0051] A motion data acquisition module 110, configured to acquire motion data of the vehicle;

[0052] The multi-source data acquisition module 120 is used to determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, multi-source data is acquired using pre-set sensors; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor;

[0053] The data fusion module 130 is used to perform fusion processing on the multi-source data to obtain a fusion result;

[0054] The intelligent identification module 140 is used to perform intelligent identification on the vehicle cable based on the fusion result, and identify whether there is abnormal data or overloaded data in the vehicle cable;

[0055] The recording and warning module 150 is used to write the identification result into a log, and generate a warning report when there is abnormal data or overloaded data.

[0056] Among them, the vehicle can refer to various means of transportation driven by power and traveling on the road, such as fuel vehicles, electric vehicles, and motorcycles. The motion data can be various data generated during the vehicle's movement, such as speed, acceleration, driving mileage, and steering angle. These data can reflect the vehicle's operating state.

[0057] In this solution, relevant motion data can be collected and read from the data source to prepare for subsequent processing. Specifically, low-power data acquisition technology can be used, which can efficiently obtain data without affecting the normal operation of the vehicle.

[0058] The multi-source data acquisition module 120 can determine the cable monitoring period according to the vehicle motion data, and perform multi-source data acquisition at an appropriate time.

[0059] Among them, the monitoring period can be the time interval for data acquisition and monitoring of the cable, which is dynamically adjusted according to the vehicle motion data to ensure that cable data can be obtained in a timely and accurate manner under different vehicle operating states. The vehicle cable monitored here can include wire harnesses for transmitting electrical energy or signals, which play an important role in power transmission and signal transmission in the vehicle.

[0060] The sensor can be a device used to detect physical quantities and convert them into electrical signals or other forms of signals. Here, it includes a micro optical fiber sensor that can detect information such as strain and temperature using the physical properties of the optical fiber, a wireless current transformer that measures the current in the cable non-contact, and a high-frequency capacitance sensor that detects capacitance changes to obtain relevant physical quantities.

[0061] The multi-source data is various data about the cable collected by different types of sensors, such as current, temperature, and strain.

[0062] In this solution, the appropriate monitoring period for each cable can be calculated based on vehicle motion data. Specifically, machine learning algorithms can be used to dynamically optimize the determination of the monitoring period according to historical data and real-time motion data. For example, when the vehicle is turning, the monitoring period for the cables related to the turning action is shortened, for example, from the original 5 seconds to 0.5 seconds. At the moment when the predetermined monitoring period is reached, subsequent data collection operations are triggered.

[0063] The data fusion module 130 can be used to comprehensively process multi-source data.

[0064] The fusion result can be the comprehensive data obtained after being processed by the data fusion module. It integrates multi-source data from different sensors and can more accurately reflect the actual state of the cable.

[0065] In this solution, the fusion processing can be to integrate, analyze, and process multi-source data, eliminate redundancy and conflicts between data, and extract more valuable information. Specifically, a fusion algorithm based on deep learning can be used, which can automatically learn the associations and features between data and improve the fusion effect.

[0066] The intelligent recognition module 140 can be a module used to intelligently analyze and judge cable data.

[0067] Among them, abnormal data can be data that is significantly different from the data characteristics under the normal operating state of the cable, which may indicate that there are faults or potential problems with the cable. Overload data can be data in which parameters such as current and voltage exceed their rated carrying capacity during the operation of the cable. Long-term overload operation may cause cable damage.

[0068] In this solution, artificial intelligence algorithms such as neural networks and decision trees can be used to analyze and judge the fusion result to identify whether there is abnormal data or overload data in the cable.

[0069] The implementation code is as follows:

[0070]

[0071]

[0072]

[0073] Among them, the normal_ranges dictionary is defined to store the normal value ranges of each feature, and the overload_thresholds dictionary is defined to store the overload thresholds of each feature.

[0074] The intelligent recognition function intelligent_recognition takes a fused result dictionary as input, traverses each feature in the dictionary, checks whether its value is within the normal range, and if not, adds abnormal information to the abnormal_info list. At the same time, it checks whether the value of any feature exceeds the overload threshold, and if so, adds overload information to the list.

[0075] A fused result dictionary fusion_result_example is defined, which contains the values of three features: current, temperature, and resistance.

[0076] Call the intelligent_recognition function to perform intelligent recognition on the example fused result, and output the corresponding information according to the returned result.

[0077] The recording and warning module 150 is a module in the system responsible for recording the recognition results and generating warning reports when necessary.

[0078] After the intelligent recognition module identifies the cable data, it can determine whether there is abnormal data or overload data in the cable. It can be recorded separately in a log manner, such as recording the recognition results of each cable during operation, and generating a report when there is abnormal data or overload data in the cable, which contains detailed information about the abnormal situation and is used to notify relevant personnel for timely handling.

[0079] This solution can store the recognition results in a log file, and use a distributed file system to store the log to improve the storage reliability and scalability of the log. When the recognition results show that there is abnormal data or overload data, a warning report is generated according to the preset template and rules.

[0080] For the technical solution provided in this embodiment, the motion data acquisition module is used to acquire the motion data of the vehicle; the multi-source data acquisition module is used to determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, perform multi-source data acquisition using pre-set sensors; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor; the data fusion module is used to perform fusion processing on the multi-source data to obtain a fused result; the intelligent recognition module is used to perform intelligent recognition on the vehicle cable based on the fused result to identify whether there is abnormal data or overload data in the vehicle cable; the recording and warning module is used to write the recognition results into the log and generate a warning report when there is abnormal data or overload data. This technical solution can comprehensively and timely monitor the operating status of the vehicle cable, detect abnormalities and overload situations in advance, provide strong guarantee for the safe operation of the vehicle cable, and at the same time, the recorded log information is also helpful for subsequent fault analysis and system optimization.

[0081] Embodiment 2

[0082] Based on the above embodiment, this embodiment is further optimized. Specifically, the multi-source data acquisition module includes: a monitoring period determination unit, configured to determine the monitoring period adopted for each cable by the current motion data according to the data interval corresponding to the motion data and the preset association relationship between the data interval and the monitoring period; a data acquisition control unit, configured to perform multi-source data acquisition using pre-set sensors after the monitoring period arrives. Figure 2 It is a schematic structural diagram of a comprehensive monitoring device for vehicle cables provided in Embodiment 2 of the present application. As Figure 2 shown, the device includes:

[0083] A motion data acquisition module 210, configured to acquire motion data of the vehicle;

[0084] A multi-source data acquisition module 220, configured to determine the monitoring period for each cable according to the motion data, and perform multi-source data acquisition using pre-set sensors after the monitoring period arrives; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor;

[0085] A data fusion module 230, configured to perform fusion processing on the multi-source data to obtain a fusion result;

[0086] An intelligent identification module 240, configured to perform intelligent identification on the vehicle cables based on the fusion result, and identify whether there is abnormal data or overloaded data in the vehicle cables;

[0087] A recording and warning module 250, configured to write the identification result into a log, and generate a warning report when there is abnormal data or overloaded data.

[0088] Among them, the multi-source data acquisition module 220 includes:

[0089] A monitoring period determination unit 221, configured to determine the monitoring period adopted for each cable by the current motion data according to the data interval corresponding to the motion data and the preset association relationship between the data interval and the monitoring period;

[0090] A data acquisition control unit 222, configured to perform multi-source data acquisition using pre-set sensors after the monitoring period arrives.

[0091] Among them, the multi-source data acquisition module 220 can be composed of two units, namely a monitoring period determination unit 221 and a data acquisition control unit 222.

[0092] A monitoring period determination unit 221 is configured to determine the monitoring period for each cable according to specific rules.

[0093] Among them, the data interval can be to divide the motion data into different ranges according to certain rules. For example, for vehicle speed data, it can be divided into a low-speed interval (0 - 30 km / h), a medium-speed interval (30 - 60 km / h), and a high-speed interval (above 60 km / h), etc. Different data intervals may correspond to different cable operating conditions, so different monitoring periods are required. In addition, different ranges can also be divided for multiple motion data such as steering angle and steering centrifugal force, and each range corresponds to a different monitoring period.

[0094] The preset association relationship is the corresponding relationship between the preset data interval and the monitoring period. For example, when the motion data is in the low-speed interval, the corresponding monitoring period may be set to a longer time, such as 30 minutes; when in the medium-speed interval, the monitoring period may be set to a shorter time, such as 15 minutes, and when in the high-speed interval, the monitoring period may be set to an even shorter time, such as 5 minutes, etc.

[0095] The monitoring period can be the time interval for data collection and monitoring of the cable, which is dynamically adjusted according to the data interval where the motion data is located to ensure that cable data can be obtained in a timely and accurate manner under different vehicle operating conditions.

[0096] This solution can calculate and obtain the monitoring period that should be adopted for each cable based on the data interval where the motion data is located and the preset association relationship. Here, an intelligent algorithm such as a fuzzy logic algorithm can be used, which can flexibly determine the monitoring period according to the uncertainty of the motion data. The fuzzy logic algorithm can perform fuzzy processing on different characteristics of the motion data, and then make inferences according to the preset fuzzy rules to finally determine the appropriate monitoring period.

[0097] A data collection control unit 222 can be used to perform data collection operations when the monitoring period is reached.

[0098] This solution can trigger subsequent data collection operations at the moment when the monitoring period determined by the monitoring period determination unit 221 is reached. Select and use the pre-set sensors for data collection work. Here, an intelligent sensor scheduling algorithm can be adopted to dynamically select the most suitable sensor for data collection according to factors such as the performance, collection frequency, and data quality of different sensors, improving the efficiency and quality of data collection.

[0099] In this technical solution, the monitoring period determination unit dynamically adjusts the monitoring period according to the motion data, and the data acquisition control unit accurately acquires multi-source data at an appropriate time. This intelligent acquisition method not only ensures the timeliness and accuracy of the data, but also reasonably utilizes system resources, reduces energy consumption and data processing pressure, provides strong support for the efficient operation of the entire vehicle cable monitoring system, helps to detect abnormal conditions of the cable in a timely manner, and ensures the safe operation of the vehicle.

[0100] In one embodiment, optionally, the data acquisition control unit 222 is specifically configured to:

[0101] After the monitoring period arrives, send an acquisition instruction to the pre-set sensors and receive the acquisition data fed back by the sensors;

[0102] Identify whether there are sensors that have not executed the acquisition instruction;

[0103] If not, construct the acquisition data fed back by the sensors into multi-source data;

[0104] If so, re-send the acquisition instruction to the sensors that have not executed the acquisition instruction, and generate a sensor failure prompt message if the acquisition data fed back by the sensors is not received within a preset time period.

[0105] Among them, the acquisition instruction can be an instruction message sent by the data acquisition control unit to the sensor, which is used to instruct the sensor to start the data acquisition operation. This instruction message can include relevant parameters of the acquisition, such as the type of physical quantity to be acquired, the acquisition frequency, and the acquisition duration, etc., to ensure that the sensor accurately acquires data according to the requirements of the system. After receiving this instruction message, the sensor can obtain the data after detecting various physical quantities of the cable. For example, the cable strain data collected by the micro optical fiber sensor, the cable current data collected by the wireless current transformer, and the capacitance change data collected by the high-frequency capacitance sensor, etc.

[0106] In this technical solution, the data acquisition control unit sends the acquisition instruction to each pre-set sensor through the communication link. It can adopt the broadcast or point-to-point distribution method, and utilize innovative low-power wide-area Internet of Things communication technologies, such as LoRa, NB-IoT, etc., to ensure that the instruction can be stably and efficiently transmitted to the sensor, while reducing communication energy consumption. The data acquisition control unit receives the acquisition data fed back from the sensor. This solution adopts high-speed data reception technology, which can quickly and accurately store the data collected by the sensor into the system for subsequent processing.

[0107] A sensor that has not executed the acquisition instruction is a sensor that, after the data acquisition control unit issues the acquisition instruction, fails to perform data acquisition and feedback the acquired data as required due to various reasons, such as hardware failures, communication failures, etc.

[0108] Specifically, the data acquisition control unit can monitor and analyze the feedback of each sensor to determine whether there is a sensor that has not executed the acquisition instruction. A heartbeat mechanism can be adopted. The sensor periodically sends status information to the data acquisition control unit. If the status information or acquired data of a certain sensor is not received within the specified time, it is determined that the sensor may not have executed the acquisition instruction.

[0109] In this solution, multi-source data refers to a collection of various data about the cable acquired by different types of sensors. These data come from the detection of different physical quantities, such as current, temperature, and strain, and reflect the operating status of the cable from multiple dimensions. Constructing multi-source data is to integrate and organize the acquired data fed back by each sensor to form a complete data set.

[0110] The data acquisition control unit sorts, classifies, and combines the acquired data fed back by all sensors that have normally executed the acquisition instruction to form multi-source data. Data structuring processing technology can be used to convert the acquired data in different formats and types into a unified format to facilitate subsequent data fusion and analysis.

[0111] Among them, the preset duration can be a pre-set time length used to determine whether the sensor can normally feedback the acquired data after reissuing the acquisition instruction. For example, the preset duration can be set to 3 seconds. If the acquired data fed back by the sensor is still not received within 3 seconds after reissuing the acquisition instruction, it is considered that the sensor may be faulty.

[0112] The sensor fault prompt information can be a prompt information generated when the data acquisition control unit determines that a certain sensor may be faulty. It contains the identification of the faulty sensor and the possible reasons for the fault, such as communication interruption, hardware damage, etc., and is used to notify relevant personnel to perform maintenance and processing in a timely manner.

[0113] In this solution, when the data acquisition control unit identifies that there are sensors that have not executed the acquisition instruction, it sends the acquisition instruction to these sensors again to try to restore normal data acquisition. An adaptive retransmission strategy can be adopted to dynamically adjust the number of retransmissions and the time interval according to the previous communication situation and the type of fault. In the case where the acquired data fed back by the sensor is not received within the preset duration, the data acquisition control unit generates sensor fault prompt information according to the fault situation. Specifically, an intelligent fault diagnosis algorithm can be used to more accurately judge the cause of the fault and generate detailed prompt information in combination with the historical data and current state of the sensor.

[0114] In this technical solution, the data acquisition control unit ensures the accurate acquisition of multi-source data of the cable through a series of operations such as issuing acquisition instructions, receiving acquisition data, identifying sensors with unexecuted instructions, constructing multi-source data, reissuing instructions, and generating fault prompt information. It can promptly detect and handle sensor failures, avoid introducing noise due to sensor problems, provide a reliable data basis for subsequent data fusion, intelligent identification, etc., and thus improve the performance of the entire vehicle cable monitoring system and the accuracy of the output results.

[0115] Embodiment III

[0116] Based on the above embodiments, this embodiment is further optimized. Specifically, the optimization is as follows: The data fusion module includes: A vectorization processing unit, which is used to perform vectorization processing on multi-source data according to a preset vectorization standard respectively to obtain a plurality of vectorized data; A splicing unit, which is used to perform splicing processing on the plurality of vectorized data to obtain a fusion result. Figure 3 It is a schematic structural diagram of the comprehensive monitoring device for vehicle cables provided in Embodiment III of this application. As Figure 3 shown, the device includes:

[0117] A motion data acquisition module 310, which is used to acquire the motion data of the vehicle;

[0118] A multi-source data acquisition module 320, which is used to determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, use pre-set sensors to acquire multi-source data; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor;

[0119] A data fusion module 330, which is used to perform fusion processing on multi-source data to obtain a fusion result;

[0120] An intelligent identification module 340, which is used to perform intelligent identification on the vehicle cable based on the fusion result, to identify whether there is abnormal data or overloaded data in the vehicle cable;

[0121] A recording and warning module 350, which is used to write the identification result into a log, and generate a warning report in the case of abnormal data or overloaded data.

[0122] Among them, the data fusion module 330 includes:

[0123] A vectorization processing unit 331, which is used to perform vectorization processing on multi-source data according to a preset vectorization standard respectively to obtain a plurality of vectorized data;

[0124] A splicing unit 332, which is used to perform splicing processing on the plurality of vectorized data to obtain a fusion result.

[0125] The data fusion module 330 consists of a vectorization processing unit 331 and a splicing unit 332.

[0126] The vectorization processing unit 331 is used to perform vectorization processing on multi-source data. Its function is to convert multi-source data of different types and formats into a unified vector form, facilitating subsequent splicing and analysis.

[0127] A preset vectorization standard can be a pre-established rule for converting multi-source data into vector data. Specific factors such as the type, features, and dimensions of the data can be considered. For example, for current data, it may be mapped to a vector space of a specific dimension according to its value range and change frequency; for temperature data, there will also be corresponding mapping rules.

[0128] Vectorized data is the data form obtained after being processed by the vectorization processing unit according to the preset vectorization standard. Each vectorized data is a vector, and each element of the vector represents a specific feature or attribute in the multi-source data. For example, a vectorized data representing the operating state of a cable may contain elements corresponding to features such as current, temperature, and strain.

[0129] Vectorization processing can be a process of converting multi-source data into vectors according to the preset vectorization standard. This may involve operations such as data normalization, encoding, and feature extraction. Using the autoencoder technology in deep learning for vectorization processing, the autoencoder can learn the intrinsic features of multi-source data, compress it into a low-dimensional vector, and at the same time retain the important information of the data. By training the autoencoder, an adaptive vectorization representation most suitable for multi-source data can be found, improving the data representation ability and the efficiency of subsequent processing.

[0130] Splicing processing can be an operation of combining multiple vectorized data together according to certain rules. Usually, the vectors of each vectorized data are connected end to end to form a longer vector.

[0131] The fusion result can be the final data result obtained after being processed by the splicing unit. It integrates the information contained in multiple vectorized data and reflects the operating state of the cable from multiple dimensions. The fusion result can be used as the input of the subsequent intelligent recognition module to judge whether there are abnormal data or overloaded data in the cable.

[0132] In this solution, it can be a process of combining multiple vectorized data into a whole. During the splicing process, it is necessary to ensure that the order and dimensions of each vectorized data are consistent to ensure the accuracy and effectiveness of the fusion result. Specifically, parallel computing technology can be used to perform splicing operations on multiple vectorized data simultaneously, improving the speed and efficiency of splicing processing.

[0133] For the technical solution provided in this embodiment, the multi-source data is first vectorized to eliminate data differences, and then the vectorized data is spliced to achieve deep fusion of the data. This processing method improves the expression ability and analyzability of the data, provides a more accurate and comprehensive fusion result for subsequent intelligent recognition, thereby enhancing the monitoring and diagnosis capabilities of the entire vehicle cable monitoring system for the operating status of the cable, helping to timely detect potential problems with the cable, and ensuring the safe operation of the vehicle.

[0134] In one embodiment, optionally, the intelligent recognition module is specifically configured to:

[0135] Based on the fusion result and the pre-set normal mapping interval between multi-source data, perform intelligent recognition on the vehicle cable to identify whether there is abnormal data or overloaded data in the vehicle cable.

[0136] Among them, the normal mapping interval can be a reasonable value range set for each feature of the multi-source data based on a large amount of experimental data, historical operation records, and industry standards. For example, for the current data of the cable, the normal mapping interval may be set to 0 - 100A; for the temperature data, it may be set to -20°C - 60°C. These intervals reflect the fluctuation range of each parameter of the cable under normal operating conditions.

[0137] Abnormal data can be that some data values in the fusion result deviate from the normal mapping interval, and this deviation may imply potential faults or problems with the cable. For example, when the actual current value of the cable exceeds the upper limit of the normal mapping interval, or the temperature rises abnormally, these data belong to abnormal data.

[0138] Overloaded data can be data in the fusion result that reflects that the operating parameters of the cable exceed its rated carrying capacity. For example, the rated current of the cable is 100A, and the actually monitored current continuously exceeds 120A. At this time, the current data is overloaded data. Long-term overloaded operation will accelerate the aging of the cable and even cause safety accidents.

[0139] In this solution, a comprehensive analysis and judgment can be performed on the fusion result. For example, machine learning algorithms such as decision trees and support vector machines, and deep learning models such as neural networks can be used. By comparing the fusion result with the pre-set normal mapping interval, it is identified whether there is abnormal data or overloaded data. An anomaly detection model based on deep learning is adopted. This model can automatically learn the characteristic patterns of normal data, and when data that does not conform to these patterns appears, it can quickly and accurately identify it as abnormal. By comparing each data in the fusion result with the corresponding normal mapping interval one by one, the recognition conclusion is obtained.

[0140] Through the effective utilization of the fusion result and the normal mapping interval, this technical solution can timely and accurately detect abnormal data and overload data during the operation of vehicle cables. This helps to early warn of possible cable failures, provide timely maintenance and handling information for maintenance personnel, avoid safety accidents and equipment damage caused by cable failures, and ensure the safe and stable operation of the vehicle. At the same time, the application of intelligent recognition technology improves the automation and intelligence level of the monitoring system, reducing the errors and workload of manual judgment.

[0141] In one embodiment, optionally, the intelligent recognition module is specifically configured to:

[0142] Based on the fusion results obtained in multiple monitoring cycles, perform data comparison between monitoring cycles on the vehicle cable to identify whether there is abnormal data or overload data in the vehicle cable.

[0143] Among them, the data comparison between monitoring cycles can be an operation of comparing and analyzing the obtained fusion results within multiple different monitoring cycles. Each monitoring cycle generates a fusion result, and these results contain multi-source data information on the cable operation status within that cycle. By comparing the fusion results of different monitoring cycles, the change of the cable operation status over time can be observed. Over a period of time, compare the results of multiple data acquisitions and fusion processes on the vehicle cable according to the pre-set monitoring cycle. Taking every 5 minutes as a monitoring cycle and comparing the data within 30 minutes, the longitudinal comparison result of the vehicle cable data within 30 minutes of the vehicle can be obtained.

[0144] This solution can use specific algorithms to implement the data comparison process. For example, the method based on time series analysis is adopted, and the fusion results of different monitoring cycles are regarded as a time series data, and the comparison is carried out by analyzing the characteristics such as the trend and fluctuation of the sequence. After completing the data comparison between monitoring cycles, judge whether there is abnormal data or overload data in the vehicle cable according to the comparison result. For example, when the comparison result exceeds these rules or thresholds, it is considered that there is an abnormal or overload situation. This solution can also use anomaly detection algorithms in machine learning, such as the Isolation Forest algorithm, which can automatically identify the abnormal points in the data, thereby judging whether there is an anomaly in the cable.

[0145] This technical solution identifies abnormal or overloaded data by comparing data between monitoring cycles through the fusion results of multiple monitoring cycles, and can more comprehensively and accurately grasp the changing trend of the operating state of vehicle cables. Compared with the analysis of a single monitoring cycle, this method can discover some problems that are not obvious in the short term but will have a serious impact in the long term, such as the slow decline of cable performance, potential fault hazards, etc. It can give early warnings of possible cable failures, take maintenance measures in a timely manner, reduce the risks and losses caused by cable failures, and improve the reliability and safety of vehicle cable operation. At the same time, technologies such as time series analysis and machine learning algorithms are used to improve the accuracy and intelligence of identification.

[0146] In one embodiment, optionally, the abnormal data includes: abnormal cable transmission capacity, abnormal change in the dielectric constant of the insulating layer, and abnormal change in the resistivity of the cable core.

[0147] In the vehicle cable monitoring system, abnormal data is data that deviates from the parameter characteristics under the normal operating state of the cable. Specifically, it includes abnormal cable transmission capacity, abnormal change in the dielectric constant of the insulating layer, and abnormal change in the resistivity of the cable core.

[0148] The transmission capacity of a cable mainly refers to its ability to stably transmit electric energy or signals per unit time. When the cable shows problems such as aging, damage, and poor contact, its transmission capacity may decrease. For example, phenomena such as reduced transmission power and increased signal attenuation may occur, resulting in a large deviation between the actual transmission capacity and the designed transmission capacity under normal conditions. This deviation is the abnormal cable transmission capacity.

[0149] The insulating layer is an important part of the cable, and its function is to prevent current leakage and ensure the electrical insulation performance of the cable. The dielectric constant is a physical quantity that describes the ability of an insulating material to store electrical energy in an electric field. Under normal conditions, the dielectric constant of the insulating layer is relatively stable. However, when the insulating layer is affected by factors such as moisture, aging, and chemical corrosion, its dielectric constant will change. If this change exceeds the normal fluctuation range, it is called abnormal change in the dielectric constant of the insulating layer.

[0150] The cable core is the main part for current conduction, and its resistivity reflects the magnitude of the resistance of the core material to the flow of current. Under normal operating conditions, the resistivity of the cable core is relatively stable. However, when the core is affected by factors such as high temperature, mechanical damage, and oxidation, its resistivity will change. If the change range of the resistivity exceeds the preset normal range, it belongs to the abnormal change in the resistivity of the cable core.

[0151] In this solution, various sensors installed on the cable, such as current sensors, temperature sensors, and capacitance sensors, can be used to collect relevant operation data of the cable in real time or regularly, including parameters such as transmission capacity, dielectric constant, and resistivity, so as to detect possible abnormal situations in a timely manner. Furthermore, based on the collected data and combined with the pre-set normal parameter range, the changes in the transmission capacity of the cable, the dielectric constant of the insulating layer, and the resistivity of the cable core are analyzed and judged to determine whether there are abnormal data. Machine learning algorithms, such as support vector machines and neural networks, can be used to classify and identify the data to improve the accuracy and efficiency of identification.

[0152] The specific types of abnormal data provided by this solution, such as abnormal transmission capacity of the cable, abnormal change in the dielectric constant of the insulating layer, and abnormal change range of the resistivity of the cable core, help the monitoring system to monitor and analyze different aspects of the cable more specifically. By accurately identifying these abnormal data, potential cable fault hazards can be detected in a timely manner, maintenance measures can be taken in advance to avoid the occurrence of cable faults, and the safe and stable operation of the vehicle cable can be ensured. At the same time, this solution can improve the detection sensitivity and accuracy of abnormal data, reduce misjudgment and missed judgment, and improve the accuracy of the entire monitoring system.

[0153] Embodiment 4

[0154] Figure 4 is a schematic flow chart of the comprehensive monitoring method for vehicle cables provided in Embodiment 4 of this application. As Figure 4 shown, it specifically includes the following steps:

[0155] S401. Obtain the motion data of the vehicle;

[0156] S402. Determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, use the pre-set sensors to collect multi-source data; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor;

[0157] S403. Perform fusion processing on the multi-source data to obtain a fusion result;

[0158] S404. Based on the fusion result, perform intelligent identification on the vehicle cable to identify whether there is abnormal data or overloaded data in the vehicle cable;

[0159] S405. Write the identification result into the log, and generate a warning report in the case of abnormal data or overloaded data.

[0160] Further, determine the monitoring period for each cable based on the motion data, and after the monitoring period arrives, use pre-set sensors to collect multi-source data, including:

[0161] Determine the monitoring period adopted for each cable by the current motion data according to the data interval corresponding to the motion data and the pre-set association relationship between the data interval and the monitoring period;

[0162] After the monitoring period arrives, use pre-set sensors to collect multi-source data.

[0163] In this embodiment, obtain the motion data of the vehicle; determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, use pre-set sensors to collect multi-source data; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor; perform fusion processing on the multi-source data to obtain a fusion result; based on the fusion result, perform intelligent identification on the vehicle cable to identify whether there is abnormal data or overloaded data in the vehicle cable; write the identification result into the log, and generate a warning report in the case of abnormal data or overloaded data. Through such settings, this solution can comprehensively and timely monitor the operating status of the vehicle cable, discover abnormalities and overload situations in advance, provide strong guarantee for the safe operation of the vehicle cable, and at the same time, the logged log information is also helpful for subsequent fault analysis and system optimization.

[0164] The comprehensive monitoring method for vehicle cables provided in the embodiments of the present application corresponds to the comprehensive monitoring device for vehicle cables provided in the above embodiments, has the same execution process and beneficial effects, and for the sake of avoiding repetition, will not be elaborated here.

[0165] Embodiment Five

[0166] As Figure 5 shown, the embodiments of the present application also provide an electronic device 500, including a processor 501, a memory 502, a program or instruction stored on the memory 502 and executable on the processor 501, and when the program or instruction is executed by the processor 501, it realizes each process of the above embodiment of the comprehensive monitoring device for vehicle cables, and can achieve the same technical effects. For the sake of avoiding repetition, it will not be elaborated here.

[0167] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0168] Embodiment Six

[0169] An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above-mentioned embodiment of the comprehensive monitoring device for vehicle cables and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0170] Wherein, the processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0171] Embodiment Seven

[0172] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement each process of the above-mentioned embodiment of the comprehensive monitoring device for vehicle cables and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0173] It should be understood that the chip mentioned in the embodiment of the present application may also be referred to as a system-level chip, a system chip, a chip system, or a system-on-chip, etc.

[0174] It should be noted that in this article, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described method may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0175] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0176] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

[0177] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it may also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.

Claims

1. A comprehensive monitoring device for vehicle cables, characterized in that The device includes: A motion data acquisition module, configured to acquire motion data of a vehicle; A multi-source data acquisition module, configured to determine a monitoring period for each cable according to the motion data, and after the monitoring period arrives, perform multi-source data acquisition using pre-set sensors; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor; A data fusion module, configured to perform fusion processing on the multi-source data to obtain a fusion result; An intelligent identification module, configured to perform intelligent identification on the vehicle cables based on the fusion result, to identify whether there is abnormal data or overloaded data in the vehicle cables; A recording and warning module, configured to write the identification result into a log, and generate a warning report when there is abnormal data or overloaded data.

2. The comprehensive monitoring device for vehicle cables according to claim 1, characterized in that, The multi-source data acquisition module includes: A monitoring period determination unit, configured to determine the monitoring period for each cable of the current motion data according to the data interval corresponding to the motion data and the preset association relationship between the data interval and the monitoring period; A data acquisition control unit, configured to perform multi-source data acquisition using pre-set sensors after the monitoring period arrives.

3. The comprehensive monitoring device for vehicle cables according to claim 2, characterized in that, The data acquisition control unit is specifically configured to: After the monitoring period arrives, send an acquisition instruction to the pre-set sensors, and receive the acquisition data fed back by the sensors; Identify whether there are sensors that have not executed the acquisition instruction; If not, construct the acquisition data fed back by the sensors into multi-source data; If so, re-send the acquisition instruction to the sensors that have not executed the acquisition instruction, and generate a sensor fault prompt message if the acquisition data fed back by the sensors is not received within a preset time period.

4. The comprehensive monitoring device for vehicle cables according to claim 1, characterized in that, The data fusion module includes: A vectorization processing unit, configured to perform vectorization processing on the multi-source data respectively according to a preset vectorization standard to obtain a plurality of vectorized data; A splicing unit, configured to perform splicing processing on the plurality of vectorized data to obtain a fusion result.

5. The comprehensive monitoring device for vehicle cables according to claim 4, characterized in that, The intelligent identification module is specifically configured to: Based on the fusion result and the normal mapping interval between the pre-set multi-source data, perform intelligent identification on the vehicle cables to identify whether there is abnormal data or overloaded data in the vehicle cables.

6. The comprehensive monitoring device for vehicle cables according to claim 1, characterized in that, The intelligent identification module is specifically configured to: Based on the fusion results obtained from multiple monitoring periods, perform data comparison between monitoring periods on the vehicle cables to identify whether there is abnormal data or overloaded data in the vehicle cables.

7. The comprehensive monitoring device for vehicle cables according to claim 6, characterized in that, The abnormal data includes: abnormal cable transmission capacity, abnormal change in the dielectric constant of the insulating layer, and abnormal change amplitude in the resistivity of the cable core.

8. A comprehensive monitoring method for vehicle cables, characterized in that, The method includes: Acquiring motion data of a vehicle; Determining a monitoring period for each cable according to the motion data, and after the monitoring period arrives, performing multi-source data acquisition using pre-set sensors; wherein, the sensors include one or more of a micro optical fiber sensor, a wireless current transformer, and a high-frequency capacitance sensor; Performing fusion processing on the multi-source data to obtain a fusion result; Based on the fusion result, perform intelligent identification on vehicle cables to identify whether there are abnormal data or overloaded data in the vehicle cables; Write the identification result into the log, and generate a warning report in the case of abnormal data or overloaded data.

9. The comprehensive monitoring method for a vehicle cable according to claim 8, characterized in that, Determine the monitoring period for each cable according to the motion data, and after the monitoring period arrives, use pre-set sensors to collect multi-source data, including: According to the data interval corresponding to the motion data and the preset association relationship between the data interval and the monitoring period, determine the monitoring period adopted by the current motion data for each cable; After the monitoring period arrives, use pre-set sensors to collect multi-source data.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it realizes the steps of the comprehensive monitoring method for vehicle cables as described in any one of claims 8-9.

Images