Shared bicycle fault vehicle marking method and system based on multi-dimensional data

Abstract

The application provides a shared bicycle fault vehicle marking method and system based on multidimensional data, relates to the technical field of data processing, and the server obtains decomposition information by decomposing multidimensional data of a bicycle end, a device end and a service end corresponding to a target bicycle, and obtains first classified information and second classified information by classifying the decomposition information; a management end generates a judgment specification tree of the classified information based on a standard specification tree and image interaction; if independent first classified information or combined second classified information reaches a judgment condition of the judgment specification tree, a fault label is added to the corresponding bicycle end and a first moment is extracted; multidimensional data of the target bicycle from a first moment to a second moment in a normal state is obtained, and the fault label and / or the standard specification tree are updated, so that the fault vehicle can be identified and marked in a timely and accurate manner.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for marking faulty shared bicycles based on multidimensional data. Background Technology

[0002] With the widespread application of shared bicycles in urban transportation systems, their usage frequency and coverage are constantly expanding. Whether on commuting routes during morning and evening rush hours or in densely populated areas such as scenic spots and commercial districts, shared bicycles have become an important means of transportation for citizens' short-distance travel. However, long-term and high-frequency use has led to various malfunctions of shared bicycles, such as lock failure, brake failure, and chain jamming. These malfunctions not only affect the user's riding experience but may also pose safety hazards and even lead to the idle waste of bicycle resources.

[0003] Currently, existing methods for detecting shared bicycle faults have many limitations. Some methods rely solely on users actively reporting fault information, which is greatly influenced by users' subjective will and level of understanding, resulting in issues such as missed or false reports. Furthermore, they cannot promptly detect potential fault hazards. In addition, traditional fault judgment rules often use fixed standards, which cannot adapt to differences in different bicycle models, usage environments, and operating areas, leading to insufficient accuracy and adaptability in fault judgment. For example, different cities and seasons have different tolerances for bicycle faults, and a uniform fault judgment standard may lead to over-repair of vehicles in some areas or failure to promptly address faulty vehicles.

[0004] Therefore, how to identify and mark faulty vehicles in a timely and accurate manner has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method and system for marking faulty shared bicycles based on multidimensional data, which can identify and mark faulty vehicles in a timely and accurate manner.

[0006] A first aspect of this invention provides a method for marking faulty shared bicycles based on multidimensional data, comprising:

[0007] The server decomposes the multi-dimensional data of the target bicycle from the bicycle end, device end and server end to obtain decomposition information, and classifies the decomposition information to obtain first classification information and second classification information.

[0008] The management system generates a classification information judgment specification tree based on the standard specification tree and image interaction.

[0009] If the independent first classification information or the combined second classification information meets the judgment conditions of the specification tree, then add a fault tag to the corresponding single vehicle and extract the first moment.

[0010] Acquire multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and update the fault labels and / or standard specification tree.

[0011] Optionally, in one possible implementation of the first aspect, the server decomposes the multi-dimensional data of the target bicycle (bicycle-side, device-side, and server-side) to obtain decomposed information, including:

[0012] Extract all data of the target bicycle within the first preset time period;

[0013] All data is decomposed to obtain automatic fault reporting information from the vehicle end, active fault reporting information from the device end, and analytical fault reporting information from the server end;

[0014] The information is decomposed by classifying automatic fault reporting information, proactive fault reporting information, and analytical fault reporting information according to dimensions.

[0015] Optionally, in one possible implementation of the first aspect, automatic fault reporting information is generated through the following steps:

[0016] If the target bicycle is determined not to be activated after being scanned a preset number of times within a second preset time period, an automatic fault report will be generated.

[0017] Optionally, in one possible implementation of the first aspect, the analysis of fault information is generated through the following steps:

[0018] If the number of times the riding distance is less than the first preset distance within the second preset time period is greater than the first preset number, then an analysis fault report is generated;

[0019] If the number of times the riding time within the third preset time is less than the fourth preset time is greater than the number of times the second preset time is determined, then an analysis fault report will be generated.

[0020] Optionally, in one possible implementation of the first aspect, the management terminal generates a classification information judgment specification tree based on a standard specification tree and image interaction, including:

[0021] The standard specification tree includes a first node at the first level, a first child node and a second child node at the second level, and multiple grandchild nodes at the third level. The first child node and the second child node correspond to the first threshold and the second threshold, respectively, and each grandchild node corresponds to at least one module of a shared bicycle.

[0022] The target specification tree is obtained by adjusting the standard specification tree based on the attributes of the target bicycle. The interactive image is obtained by extracting slots and creating new slots in the target specification tree.

[0023] The management system uses interactive images to adjust slot positions and generate a classification information specification tree.

[0024] The first child node corresponds to the first classification information, and the second child node corresponds to the second classification information.

[0025] Optionally, in one possible implementation of the first aspect, the step of adjusting the standard specification tree based on the attributes of the target bicycle to obtain a target specification tree, and extracting and creating slots in the target specification tree to obtain an interactive image, includes:

[0026] Remove modules that do not correspond to the target attributes in the standard specification tree; each bicycle with each target attribute has a preset module.

[0027] Extract the slot positions corresponding to the first child node, the second child node, and the grandchild node, which are in an selectable state, and create corresponding configuration slots for the slots corresponding to the first child node, the second child node, and the grandchild node;

[0028] The first interactive slot is obtained by counting the area formed by the grandchild nodes connected to the first child node, and the second interactive slot is obtained by counting the area formed by the grandchild nodes connected to the second child node. The interactive image is then obtained.

[0029] Optionally, in one possible implementation of the first aspect, the management terminal generates a classification information judgment specification tree based on the interactive image for slot adjustment, including:

[0030] If it is determined that the user selects a grandchild node and moves it to the first or second interaction slot, then the corresponding grandchild node is connected to the new first or second interaction slot.

[0031] Configure a new fault base value for the moved grandchild node;

[0032] If it is determined that the user has adjusted the fault base value and / or fault weight value of the newly created slot, then the corresponding slot adjustment will generate a judgment specification tree.

[0033] Optionally, in one possible implementation of the first aspect, the step of adding a fault tag to the corresponding vehicle and extracting the first moment if the independent first classification information or the combined second classification information meets the judgment condition of the specification tree includes:

[0034] If the grandchild node connected to the first child node triggers a fault, and the corresponding grandchild node's fault base value is greater than the first threshold of the first child node, then the judgment condition is met and a fault label is added.

[0035] If the grandchild node connected to the second child node triggers fault information, the fault base value and fault weight value of the grandchild node in the judgment specification tree are extracted and calculated to obtain the fault combination value. If the fault combination value is greater than the second threshold, the judgment condition is met and a fault label is added.

[0036] Optionally, in one possible implementation of the first aspect, the step of acquiring multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and updating the fault labels and / or standard specification tree, includes:

[0037] The multidimensional data includes at least maintenance information, and the grandchild nodes corresponding to the maintenance information are determined to obtain the first set;

[0038] The updated tree is obtained by selecting the grandchild nodes that trigger fault information in the specification tree, and the second set is obtained by counting the grandchild nodes in the updated tree.

[0039] Update fault labels and / or standard specification trees based on the first set and the second set.

[0040] Optionally, in one possible implementation of the first aspect, the updating of the fault labels and / or standard specification tree based on the first set and the second set includes:

[0041] If the first set is empty, then update the fault labels and / or standard specification tree based on the second set;

[0042] If the first set is not empty, then determine the intersection point of the first set and the second set. There exists a first difference point in the first set that does not exist in the second set, and there does not exist a second difference point in the first set that exists in the second set.

[0043] Update the fault labels and / or standard specification tree based on the intersection point, the first difference point, and the second difference point;

[0044] The update of fault labels and / or standard specification trees based on the second set includes:

[0045] Extract the fault base value of the grandchild node in the second set, subtract a preset value from the fault base value to obtain the updated fault base value, and add the fault tag of the error.

[0046] Optionally, in one possible implementation of the first aspect, the update of the fault label and / or standard specification tree based on the intersection point, the first difference point, and the second difference point includes:

[0047] Calculate the sum of the number of intersection points, the first difference point, and the second difference point; calculate the ratio of the first difference point to the sum of the numbers to obtain the first proportion; calculate the ratio of the second difference point to the sum of the numbers to obtain the second proportion.

[0048] Extract the fault weight value of the intersection point, multiply the first preset value by the sum of the quantities to obtain the positive adjustment coefficient, multiply the first adjustment value by the positive adjustment coefficient to obtain the first positive adjustment value, and add the fault weight value to the first positive adjustment value to obtain the updated fault weight value.

[0049] Extract the fault weight value of the first difference point, multiply the second adjustment value by the first ratio to obtain the second positive adjustment value, and add the fault weight value to the second positive adjustment value to obtain the updated fault weight value.

[0050] Extract the fault weight value of the second difference point, multiply the third adjustment value by the second ratio to obtain the third reverse adjustment value, and subtract the fault weight value from the second positive adjustment value to obtain the updated fault weight value.

[0051] A second aspect of the present invention provides a shared bicycle faulty vehicle marking system based on multi-dimensional data, comprising:

[0052] The decomposition module is used to enable the server to decompose the multi-dimensional data of the target bicycle from the bicycle end, device end and server end to obtain decomposition information, and classify the decomposition information to obtain first classification information and second classification information.

[0053] The classification module is used to enable the management end to generate a judgment specification tree based on the standard specification tree and image interaction to classify information;

[0054] The judgment module is used to add a fault tag to the corresponding single vehicle and extract the first moment if the independent first classification information or the combined second classification information meets the judgment conditions of the judgment specification tree.

[0055] The update module is used to acquire multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and update the fault labels and / or standard specification tree.

[0056] A second aspect of the present invention provides a storage medium storing a computer program, which, when executed by a processor, is used to implement the methods of the first aspect of the present invention and various possible designs of the first aspect.

[0057] The beneficial effects of this invention are as follows:

[0058] 1. This invention integrates multi-dimensional data to improve the comprehensiveness of fault detection, enabling timely and accurate identification and marking of faulty vehicles. By integrating multi-dimensional data from the vehicle, device, and service ends, this invention significantly improves the accuracy and comprehensiveness of fault detection. The server first extracts all data from the target vehicle within a preset time period and decomposes it into automatic fault reporting information, proactive fault reporting information, and analytical fault reporting information. These data from different sources complement each other, avoiding judgment bias caused by single data. At the same time, the fault reporting information is classified by dimension to form first and second classification information, providing comprehensive data support for subsequent accurate judgment of fault type and severity, effectively solving the problems of incomplete data and high missed detection rate in traditional methods.

[0059] 2. This invention improves the flexibility and accuracy of fault identification by adjusting and judging the specification tree. Specifically, this invention can flexibly adapt fault judgment rules to the characteristics and operational needs of different vehicles by using a standard specification tree and an image-generated judgment specification tree. The management end removes modules from the standard specification tree that do not correspond to the target vehicle's attributes, extracts and creates new slots, forming a visual interactive image. Based on this, slot adjustments are made to generate a personalized judgment specification tree. Furthermore, according to different regions, vehicle models, or usage scenarios, managers can adjust the fault baseline values ​​and weight values ​​of each module, such as increasing fault judgment sensitivity for new vehicles and appropriately relaxing standards for older vehicles. In addition, users can optimize fault judgment rules in real time by moving grandchild nodes and adjusting slot parameters. This dynamic rule generation mechanism breaks the limitations of traditional fixed standards, significantly improving the adaptability and accuracy of fault judgment, ensuring that faulty vehicles under different conditions can be marked promptly and accurately.

[0060] 3. This invention can provide information feedback on fault repair, thereby optimizing fault management and improving the accuracy of fault labeling. Specifically, this invention can extract multi-dimensional data between fault labels and normal operation, compare actual repair information with fault judgment results to form a first set and a second set. By analyzing intersection and difference points, the fault labels and standard specification tree are updated in a targeted manner. For modules that are actually repaired but not identified (i.e., the first difference point), their fault weight value is increased; for modules that are misjudged (i.e., the grandchild nodes in the second difference point), their weight value is decreased. This optimization mechanism based on actual data feedback not only corrects the deviation in fault judgment but also continuously improves the fault judgment rules, reducing misjudgments and omissions. With data accumulation and rule iteration, the system's labeling of faulty vehicles will become more accurate, effectively improving the overall operational quality and service level of shared bicycles. Attached Figure Description

[0061] Figure 1 A flowchart of a method for marking faulty shared bicycles based on multidimensional data provided by the present invention;

[0062] Figure 2 This invention provides a schematic diagram of an interactive image;

[0063] Figure 3 This invention provides a structural schematic diagram of a shared bicycle fault vehicle marking system based on multidimensional data. Detailed Implementation

[0064] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0065] like Figure 1 As shown, this invention provides a method for marking faulty shared bicycles based on multidimensional data, including:

[0066] S1, the server decomposes the multi-dimensional data of the target bicycle from the bicycle end, device end and server end to obtain decomposition information, and classifies the decomposition information to obtain first classification information and second classification information.

[0067] It should be noted that due to prolonged use, shared bicycles may develop malfunctions, which can affect users. Therefore, data can be collected through various devices and analyzed to determine the malfunction status of the bicycles. This allows faulty shared bicycles to be marked so that the supplier can repair them promptly.

[0068] Understandably, the target bicycle is the bicycle currently being unlocked and used by the user, the bicycle end is the information terminal on the bicycle, which can be a device module for receiving and sending data information, the device end is the user's corresponding information terminal, such as a mobile phone, and the server end is the server, that is, the service device corresponding to the bicycle's backend, used to analyze and process relevant data information so that faulty bicycles can be marked later.

[0069] Among them, multidimensional data is a combination of data from different device sources collected by the server, decomposed information is multiple individual pieces of information after the server splits the multidimensional data, first classification information is the first type of information after classifying the decomposed information, and second classification information is the second type of information after classifying the decomposed information.

[0070] It's easy to understand that the server is responsible for processing multi-dimensional data related to the target bicycle. The bicycle, device, and server sides generate a large amount of data related to the bicycle's operating status and usage. This data comes from a wide range of sources and is diverse in form, constituting multi-dimensional data. The server first decomposes this multi-dimensional data, much like breaking down a complex jigsaw puzzle into smaller pieces. The goal is to transform the raw, complex data into more easily analyzed and processed decomposed information. Next, the server categorizes the decomposed information, obtaining primary and secondary classification information. The purpose of classification is to make the data more organized, facilitating subsequent judgment and analysis. The primary and secondary classification information can be viewed as two different "data baskets." The server places the decomposed information into these two "baskets" according to certain rules, such as the nature of the data, its source, or its impact on fault diagnosis. This completes the initial processing of the multi-dimensional data, providing a clear data foundation for subsequent fault diagnosis.

[0071] In some embodiments, the specific implementation of step S1 (where the server decomposes the multi-dimensional data of the target bicycle, the device, and the server to obtain decomposed information) includes:

[0072] S11, extract all data of the target bicycle within the first preset time period.

[0073] Understandably, by extracting data from all devices of the target bicycle within the first preset time period, a comprehensive analysis can be conducted based on the extracted data, thereby improving the accuracy of determining whether the target bicycle has any faults.

[0074] The first preset time period is a pre-set time period, such as data information related to all devices and the target bicycle within the past week.

[0075] S12 decomposes all data to obtain automatic fault reporting information from the vehicle end, active fault reporting information from the device end, and analytical fault reporting information from the server end.

[0076] Understandably, when a bicycle cannot connect to the network, cannot be unlocked normally after multiple attempts, or experiences abnormal jamming or unusual noises during riding, the bicycle's terminal will automatically record and generate automatic fault reporting information. When a user cannot use the bicycle or sees an abnormal fault, they can report the corresponding fault through the device. Finally, the server can automatically analyze the collected data to obtain the fault reporting information.

[0077] Among them, automatic fault reporting information is fault alarm information automatically generated by the single vehicle terminal, active fault reporting information is fault information actively reported by the user through the device terminal, and analysis and protection information is fault alarm information generated by the server through data analysis and processing. That is, when the server finds that some indicators of the single vehicle deviate from the normal range or that some potential problem trends have appeared, it will generate analysis and fault reporting information.

[0078] Through the above decomposition method, fault reports from different sources and types are clearly separated, facilitating subsequent classification and analysis.

[0079] The automatic fault reporting information is generated through the following steps:

[0080] A1. If the target bicycle is determined not to be turned on after being scanned a preset number of times within the second preset time, an automatic fault report will be generated.

[0081] Understandably, in the usage scenario of shared bicycles, users usually unlock the bicycle by scanning a QR code or other means. In order to detect possible malfunctions of the bicycles in a timely manner, the server sets certain rules to determine whether to generate automatic fault reporting information.

[0082] The second preset time is a pre-set time range, such as 5 minutes. The preset number of scans is also pre-set, such as 3 times. If the user scans the target bicycle a preset number of times within the second preset time, but the bicycle still cannot be opened normally, it is likely that the bicycle has a malfunction, such as a malfunction in the unlocking system or a damaged electronic lock.

[0083] It's easy to understand that the server corresponding to the bicycle will monitor and count the time and number of scanning operations in real time. Once the condition of "not being activated after scanning a preset number of times within a second preset time" is met, the bicycle will immediately generate an automatic fault report. The automatic fault report will include the bicycle's relevant identification, such as the vehicle number, and may also record specific information such as the scanning time and number of times. This information will be sent to the server so that staff can understand the bicycle's fault status in a timely manner and arrange corresponding repair and handling work.

[0084] This method allows for the rapid location and handling of shared bikes that cannot be opened normally, improving the user experience and operational efficiency of the bikes.

[0085] The following steps are used to generate and analyze fault reporting information:

[0086] B1. If the number of times the riding distance is less than the first preset distance within the second preset time period is greater than the first preset number, then generate analysis and fault reporting information.

[0087] Understandably, in the operation and management of shared bicycles, riding distance is an important indicator reflecting the usage status of bicycles and potential malfunctions. The second preset time is a specific time period in advance, such as a week. The first preset distance is a pre-defined riding distance value, such as 2 kilometers. The first preset number of times is a pre-set number of times, such as 5 times.

[0088] The riding distance refers to the distance traveled by the target bicycle during use. The server continuously monitors the riding distance of the target bicycle within a second preset time period. If it is found that the number of times the riding distance of the target bicycle is less than the first preset distance within this time period exceeds the first preset number, it indicates that the usage of the bicycle is abnormal. Under normal circumstances, a certain percentage of riding distances of a shared bicycle should reach or exceed the first preset distance when used reasonably. However, if the riding distance is too short multiple times, it may indicate that the bicycle has a malfunction such as insufficient power or overly tight brakes, which affects the user's normal riding and causes the riding distance to be limited. Once the condition of "the number of times the riding distance is less than the first preset distance within the second preset time period exceeds the first preset number" is met, the server will generate an analysis and fault report.

[0089] It's easy to understand that analyzing fault reports will record detailed information about the bicycle, abnormal riding distance data, and the time period in which the abnormality occurred, providing a basis for subsequent troubleshooting and repair.

[0090] B2. If the number of times the riding time is less than the fourth preset time within the third preset time period is greater than the number of times the second preset time period is determined, then an analysis fault report will be generated.

[0091] Understandably, riding time is also an important dimension for judging whether a shared bicycle is faulty. The third and fourth preset times are two different time periods set separately. For example, the third preset time can be one month, the fourth preset time is set to 30 minutes, and the second preset number of times is assumed to be 8 times.

[0092] The riding time refers to the duration of a single use of the target bicycle by the user. The server will statistically analyze the riding time of the target bicycle within the third preset time period. If it is found that the number of times the riding time of the target bicycle is less than the fourth preset time period is greater than the second preset number within this time period, it indicates that the riding time of the bicycle is generally too short, which does not meet the normal usage conditions. The excessively short riding time may be due to the bicycle itself malfunction, such as vehicle jamming or damaged parts, which prevents the user from completing a longer riding time smoothly. When the condition of "the number of times the riding time is less than the fourth preset time period is greater than the second preset number within the third preset time period" is met, the server will generate analysis and fault information.

[0093] It's easy to understand that this information includes the bicycle's specific identification, abnormal riding time data, and related time statistics, which helps staff quickly locate potentially faulty bicycles and take appropriate repair measures to ensure the normal operation of shared bicycles and a good user experience.

[0094] S13, according to the dimensions, classify automatic fault reporting information, proactive fault reporting information, and analytical fault reporting information to obtain decomposed information.

[0095] It is understandable that after obtaining automatic fault reporting information, proactive fault reporting information, and analyzed fault reporting information, these fault reporting information can be classified according to different dimensions to obtain decomposed information. The dimensions can be various different classification criteria, such as the nature of the data, the importance of the fault judgment, etc.

[0096] For example, from the perspective of data nature, fault reports related to vehicle hardware failures can be categorized into one type, such as problems with tires, brakes, and other components, while fault reports related to vehicle software systems can be categorized into another type, such as unlocking system failures and inaccurate positioning. From the perspective of the importance of fault diagnosis, serious fault reports that directly render the bicycle unusable can be categorized into one type, while minor fault reports that may affect the riding experience but not basic use can be categorized into another type.

[0097] By classifying information according to dimensions, the server can further refine and organize the reported fault information, making the decomposed information more organized and providing clearer data support for accurate subsequent judgment of bicycle faults.

[0098] S2, the management end generates a classification information judgment specification tree based on the standard specification tree and image interaction.

[0099] Understandably, the standard specification tree is a pre-defined tree structure that includes information such as the various modules and components of the shared bicycle, as well as thresholds related to fault diagnosis. It forms the basic framework for the entire judgment process. Image interaction provides the management end with an intuitive and flexible way to adjust and generate the judgment specification tree.

[0100] The management terminal is the terminal for information processing personnel who manage bicycle fault diagnosis. Based on the standard specification tree, the management terminal will adjust and optimize the standard specification tree in combination with image interaction. The image interaction can be a visual interface that allows managers to intuitively see the structure of the specification tree and make modifications according to the actual situation.

[0101] For example, managers can directly operate on the interface to add, delete, or modify nodes and thresholds in the specification tree. Ultimately, the specification tree generated in this way is like a "fault diagnosis map," which clarifies under what circumstances each category of information can determine that a bicycle has a fault, providing detailed rules and basis for subsequent fault diagnosis.

[0102] In some embodiments, the specific implementation of step S2 (where the management terminal generates a classification information judgment specification tree based on the standard specification tree and image interaction) includes:

[0103] S21, the standard specification tree includes a first node at the first level, a first child node and a second child node at the second level, and multiple grandchild nodes at the third level. The first child node and the second child node correspond to the first threshold and the second threshold, respectively, and each grandchild node corresponds to at least one module of the shared bicycle.

[0104] Understandably, the standard specification tree is the basic framework for generating the judgment specification tree on the management side. It has a clear hierarchical structure. The first node of the first level is the root node of the entire specification tree, which can be the target bicycle. The second level contains the first child node and the second child node, which are connected to the first node of the first level. These two child nodes correspond to different judgment criteria, namely the first threshold and the second threshold. These thresholds are important reference values ​​for judging whether the bicycle has a fault. The first threshold is a pre-set first value for judging whether the target bicycle has a fault, which can be 0. The second threshold is a pre-set second value for judging whether the target bicycle has a fault, which can be 1.

[0105] The third-level sub-nodes are associated with specific modules of the shared bicycle. Each sub-node corresponds to at least one module. For example, one sub-node may correspond to the brake module, and another to the Bluetooth module of the lock. This hierarchical structure allows the standard specification tree to comprehensively and meticulously cover all aspects of the shared bicycle, providing a clear framework for subsequent fault diagnosis.

[0106] It's easy to understand that the connection between the third-level and second-level child nodes is determined by the degree of module damage. For example, if a bicycle suffers physical damage, such as a broken chain or no Bluetooth signal when unlocking, the target bicycle will be unusable, and it can be connected to the first child node. When the basket is tilted or the bell is stuck, meaning the corresponding module will not affect the user's use of the bicycle, the corresponding module's grandchild node can be connected to the corresponding second child node. This allows for quick determination of the target bicycle's fault condition based on the modules connected according to the threshold of the child node.

[0107] By associating different thresholds and modules with corresponding nodes, it is possible to accurately analyze and make decisions based on different situations in the subsequent judgment process.

[0108] The first child node corresponds to the first classification information.

[0109] Understandably, the first category of information refers to data about the direct damage to a module in the bicycle corresponding to the first child node, which renders the target bicycle unrideable.

[0110] The second child node corresponds to the second classification information.

[0111] It is understandable that the second classification information is data information that the damaged module connected to the second child node does not affect bicycle riding.

[0112] S22, adjust the standard specification tree based on the attributes of the target bicycle to obtain the target specification tree, and extract and create slots in the target specification tree to obtain the interactive image.

[0113] Understandably, after obtaining the standard specification tree, shared bicycles from different manufacturers may have different attributes, such as model and configuration. For example, some bicycles do not have baskets, while others do. When there is no basket, the corresponding node can be deleted so that a more intuitive interactive image corresponding to the target bicycle can be obtained later. Therefore, the standard specification tree needs to be adjusted to adapt to the specific bicycle situation. That is, the nodes and related information in the standard specification tree are modified or filtered according to the attributes of the target bicycle to obtain the target specification tree.

[0114] For example, if there is no basket, the corresponding node can be deleted. Then, slot extraction and slot creation operations are performed on the target specification tree to obtain an interactive image. Slot extraction determines the positions with specific meanings and functions in the target specification tree. These positions can be used to place information related to fault diagnosis. Slot creation creates new slots in the target specification tree according to actual needs to meet different judgment requirements. Through these two operations, the target specification tree is transformed into a visual interactive image, which facilitates intuitive operation and adjustment by management personnel.

[0115] The target specification tree is a specification structure tree adapted to the target bicycle, and the interactive image is a display image with new slots built into the target specification tree for information interaction.

[0116] It is easy to understand that each node has corresponding information, which has a slot for display. This slot can be extracted, and new slots can be built next to each node so that the corresponding decomposed information data can be filled in later, making it easier to determine the fault status of the target bicycle.

[0117] In some embodiments, the specific implementation of step S22 (adjusting the standard specification tree based on the attributes of the target bicycle to obtain the target specification tree, and extracting and creating slots in the target specification tree to obtain the interactive image) includes:

[0118] S221, Remove modules that do not correspond to the target attributes in the standard specification tree. Each type of bicycle has a preset module.

[0119] It is understandable that bicycles with different attributes will have different configuration modules. For example, high-end models may be equipped with intelligent anti-theft modules, electronic assist modules, etc., while basic models may not have these modules. The standard specification tree is a general framework that includes various possible bicycle modules. In order to make the specification tree more in line with the actual situation of the target bicycle, targeted adjustments are needed.

[0120] The target attribute refers to the module function attributes of the target bicycle. The server will determine the preset modules of the bicycle based on the target bicycle's attributes. Then, it will remove those modules in the standard specification tree that do not correspond to the target bicycle's attributes, so that the adjusted target specification tree only contains the modules that the target bicycle actually has, avoiding interference from irrelevant information and making subsequent fault diagnosis more accurate and efficient.

[0121] S222, extract the slot positions corresponding to the first child node, the second child node, and the grandchild node, which are selectable, and establish corresponding configuration slots for the slots corresponding to the first child node, the second child node, and the grandchild node.

[0122] Understandably, after obtaining the target specification tree, to facilitate subsequent interactive operations, it is necessary to process the slots corresponding to the relevant nodes, such as... Figure 2 As shown, you can select the corresponding node to create a new configuration slot for it, so that you can configure the corresponding values ​​or fault judgment data for each node in the configuration slot later. This allows for more flexible adjustment and management of the judgment conditions for each node, improving the accuracy and flexibility of fault judgment.

[0123] Among them, the selectable state is to trigger the corresponding node's slot to be set to the selected state, so that a new configuration slot can be built next to the node in the selectable state. The configuration slot can be used to store detailed configuration information related to the node, such as fault threshold, fault weight, etc.

[0124] S223, the first interactive slot is obtained by counting the area formed by the grandchild nodes connected to the first child node, and the second interactive slot is obtained by counting the area formed by the grandchild nodes connected to the second child node, and then the interactive image is obtained.

[0125] It is understandable that, such as Figure 2 As shown, the first and second child nodes are connected to multiple grandchild nodes, which correspond to different modules of the shared bicycle. To more intuitively display and manage the relationships between these nodes, the server performs regional statistics.

[0126] The server will count the area formed by the grandchild nodes connected to the first child node and define this area as the first interaction slot. Similarly, it will count the area formed by the grandchild nodes connected to the second child node and obtain the second interaction slot. These two interaction slots integrate nodes at different levels according to their association relationship and form two areas with specific meanings.

[0127] Through such statistics and division, the target specification tree is transformed into an image with a clear interactive area, namely an interactive image. In this interactive image, managers can intuitively see the relationship between nodes at different levels, as well as the interactive area corresponding to each module, thus making it easier to adjust slots and set fault judgment rules.

[0128] S23, the management end adjusts slots based on interactive images to generate a judgment specification tree for classification information.

[0129] Understandably, management personnel adjust slots based on the obtained interactive images. In the interactive images, operators can intuitively see each slot and the relationship between them. Operators can modify, move, or delete information in the slots according to the actual situation.

[0130] For example, if it is found that the weight of a grandchild node corresponding to a certain module is unreasonable during the judgment process, its judgment influence can be changed by adjusting the relevant parameters of the slot where the node is located.

[0131] By continuously adjusting the slots, a classification specification tree is eventually generated. This classification specification tree is customized based on the specific attributes of the target bicycle and actual management needs. It can more accurately judge the first and second classification information to determine whether the bicycle has a fault. It combines the basic framework of the standard specification tree with the personalized characteristics of the target bicycle, which facilitates the improvement of the accuracy of fault marking of shared bicycles in the future.

[0132] In some embodiments, the specific implementation of step S23 (where the management terminal adjusts slots based on interactive images to generate a classification information judgment specification tree) includes:

[0133] S231, if it is determined that the user selects a grandchild node and moves it to the first interaction slot or the second interaction slot, then connect the corresponding grandchild node to the new first interaction slot or the second interaction slot.

[0134] Understandably, when a user selects a grandchild node (representing a specific module of a shared bicycle) and moves it to the first or second interaction slot, the server will respond to this operation. The grandchild node originally had a fixed connection relationship in the target specification tree, but when it is moved to a new interaction slot, the server will re-establish the connection between the grandchild node and the new first or second interaction slot, changing the position and association of the module in the fault diagnosis system.

[0135] For example, a certain grandchild node originally corresponds to a module related to the bicycle bell. It is located in a specific position in the standard specification tree, such as in the second interaction slot. When the user moves it to the first interaction slot, it means that in the new fault judgment rules, the bicycle bell module has a closer connection with the judgment criteria represented by the first child node. Therefore, the fault judgment will be analyzed and evaluated according to the new connection relationship.

[0136] S232, Configure a new fault base value for the moved grandchild node.

[0137] It is understandable that the fault judgment weights of different grandchild nodes in the first and second interaction slots are different. When the position of a grandchild node is moved, the corresponding fault base value will be adjusted so that the adjusted fault judgment rule can better meet the user's needs. That is, when the bicycle bell is abnormal, the fault coefficient can be increased so that when the bicycle bell is abnormal, it is marked as a faulty bicycle and the corresponding target bicycle is not used.

[0138] The fault baseline value is a pre-set value used to determine the fault when a single-vehicle module is damaged.

[0139] In other embodiments, the specific implementation of step (the management terminal adjusts slots based on interactive images to generate a classification information specification tree) further includes:

[0140] If it is determined that the user has adjusted the fault base value and / or fault weight value of the newly created slot, then the corresponding slot adjustment will generate a judgment specification tree.

[0141] Understandably, since the fault baseline values ​​are set differently in different regions, the corresponding values ​​will also differ. For example, in region A, the bicycles are newly configured, so the fault baseline value and / or fault weight value can be increased. When any damage occurs to the target bicycle, it can be identified as a fault so that it can be repaired in a timely manner and maintain the functional integrity of the bicycle. When the bicycles in region B are older, the corresponding fault baseline value and / or fault weight value can be set to a smaller value, so that it can continue to be used when a minor fault occurs.

[0142] It is easy to understand that when the user adjusts the fault base value and / or fault weight value, the corresponding slot is adjusted to obtain the judgment specification tree.

[0143] S3, if the independent first classification information or the combined second classification information meets the judgment conditions of the judgment specification tree, then add a fault label to the corresponding single vehicle and extract the first moment.

[0144] Understandably, based on the generated judgment specification tree, the classification information of shared bicycles is judged to determine whether a fault tag should be added to the bicycle. In the above implementation steps, the server decomposes and classifies the multidimensional data to obtain the first classification information and the second classification information. The management end also generates a judgment specification tree. Then, when the independent first classification information or the combined second classification information meets the judgment conditions set in the judgment specification tree, it indicates that the bicycle may have a fault. At this time, a fault tag needs to be added to the corresponding bicycle for subsequent management and maintenance. At the same time, the first moment is extracted. This moment records the time when the bicycle is judged to be faulty in order to improve the accuracy of subsequent analysis of fault occurrence patterns.

[0145] Among them, the fault label is an information label that marks the bicycle as faulty, and the first moment is the time point when the bicycle is judged to be faulty.

[0146] In some embodiments, the specific implementation of step S3 (if the independent first classification information or the combined second classification information meets the judgment condition of the specification tree, then add a fault tag to the corresponding single vehicle and extract the first moment) includes:

[0147] S31. If the grandchild node connected to the first child node triggers fault information, and the corresponding grandchild node's fault base value is greater than the first threshold of the first child node, then the judgment condition is met and a fault label is added.

[0148] Understandably, in the specification tree, the first child node is connected to multiple grandchild nodes, and each grandchild node corresponds to one or more modules of the shared bicycle. When the grandchild node connected to the first child node triggers a fault message, it indicates that the module corresponding to the grandchild node may have malfunctioned. At the same time, the fault base value of the corresponding grandchild node must be greater than the first threshold of the first child node. When both of these conditions are met, the judgment condition is met, and a fault tag will be added to the corresponding bicycle to indicate that the bicycle has a fault.

[0149] Among them, the fault information is the damage information of the module failure. The fault base value is set when configuring the grandchild node. It reflects the basic impact of the module failure on the overall fault judgment. The first threshold is a pre-set standard value used to measure whether the severity of the grandchild node failure reaches the standard that requires adding a fault label.

[0150] S32, if the grandchild node connected to the second child node triggers fault information, the fault base value and fault weight value of the grandchild node in the judgment specification tree are extracted and calculated to obtain the fault combination value. If the fault combination value is greater than the second threshold, the judgment condition is met and a fault label is added.

[0151] Understandably, the judgment method is different for the grandchild nodes connected to the second child node. When the grandchild node connected to the second child node triggers the fault information, it means that the module corresponding to the grandchild node has a problem. At this time, it is necessary to extract the fault base value and fault weight value of the grandchild node in the judgment specification tree.

[0152] Among them, the fault weight value reflects the importance of the grandchild node in the overall fault judgment. It is combined with the fault base value for comprehensive calculation. For example, the fault base value and the fault weight value are multiplied to obtain the fault combination value. The fault combination value takes into account the degree of basic fault impact of the module and its weight in the overall judgment. Then, the fault combination value is compared with the second threshold, which is also a pre-set standard value. When the fault combination value is greater than the second threshold, the judgment condition is met, and a fault tag will be added to the corresponding single vehicle.

[0153] S4: Obtain multi-dimensional data of the target vehicle from the first moment to the second moment under normal conditions, and update the fault labels and / or standard specification tree.

[0154] Understandably, once a bicycle is identified as faulty and a fault tag is added, in order for the fault tag and standard specification tree to more accurately reflect the actual situation of the bicycle, it is necessary to obtain multi-dimensional data of the target bicycle from the time of fault identification (the first moment) to the time when the bicycle returns to normal (the second moment). This multi-dimensional data contains various information about the bicycle during the fault handling and recovery process. By analyzing this data, the fault tag and standard specification tree can be updated, thereby improving the accuracy and adaptability of subsequent fault judgments.

[0155] In some embodiments, the specific implementation of step S4 (acquiring the multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and updating the fault label and / or standard specification tree) includes:

[0156] S41, the multidimensional data includes at least maintenance information, and the grandchild nodes corresponding to the maintenance information are determined to obtain the first set.

[0157] Understandably, the maintenance information contained in the multidimensional data records the specific details of the repairs performed on the bicycles during the malfunction. The server will determine the corresponding grandchild nodes based on the maintenance information. Since each grandchild node in the specification tree corresponds to one or more modules of the shared bicycle, the maintenance information is often related to the malfunction of a specific module. By finding the grandchild nodes corresponding to the maintenance information, these grandchild nodes are collected to form the first set. The grandchild nodes in the first set represent the modules that were repaired during the malfunction handling process, providing an important basis for subsequent update operations.

[0158] For example, if the maintenance information shows that the bicycle's braking system has been repaired, the server will find the grandchild node corresponding to the braking system in the specification tree and include it in the first set.

[0159] Among them, the maintenance information is the information on maintenance of modules with fault tags, and the first set is the set of grandchild nodes corresponding to the modules that are actually maintained.

[0160] S42, select the grandchild nodes that trigger fault information in the specification tree to obtain the update tree, and count the grandchild nodes in the update tree to obtain the second set.

[0161] Understandably, the server will select and extract the grandchild nodes that trigger fault information in the specification tree. These grandchild nodes that trigger fault information were considered as possible causes of bicycle malfunctions in the previous fault judgment process. After extracting these grandchild nodes, an update tree is formed. Then, the grandchild nodes in the update tree are counted to obtain a second set. The grandchild nodes in this set represent the modules that were considered to have faults during fault judgment.

[0162] For example, if in the previous judgment it is found that the grandchild nodes corresponding to the bicycle's motor, lock and other modules have triggered fault information, then these grandchild nodes will be extracted into the update tree to form a second set.

[0163] Among them, the update tree is the tree structure corresponding to the grandchild node that triggers the fault information, and the second set is the set of grandchild nodes of the repair module, that is, the set of nodes corresponding to the module that is considered to have a fault during fault judgment.

[0164] S43, update the fault labels and / or standard specification tree based on the first set and the second set.

[0165] Understandably, based on the first set and the second set, the server will update the fault labels and / or standard specification tree. The first set reflects the modules that were actually repaired, and the second set reflects the modules that were considered to be problematic during fault diagnosis. By comparing these two sets, the difference between fault diagnosis and actual repair can be found.

[0166] If there are differences between the first set and the second set, it indicates that the previous fault diagnosis may have been inaccurate, and the fault labels need to be corrected. It may also be necessary to adjust the judgment conditions and parameters in the standard specification tree to improve the accuracy of subsequent fault diagnosis. If the two sets are basically consistent, the standard specification tree can also be further optimized according to the actual maintenance situation, such as adjusting the fault base value and fault weight value of certain modules. Through such update operations, the fault labels and standard specification tree can better adapt to the actual situation of the vehicle, improving the effectiveness and reliability of fault diagnosis.

[0167] In some embodiments, a specific implementation of step S43 (updating the fault labels and / or standard specification tree based on the first set and the second set) includes:

[0168] S431, if the first set is empty, then update the fault labels and / or standard specification tree based on the second set.

[0169] It is understandable that when the first set is empty, it means that the maintenance information obtained between the first and second moments does not have a corresponding grandchild node, that is, there is no actual maintenance module information. This indicates that the current target bicycle has not malfunctioned, meaning that there may be errors in the fault warranty. Therefore, the second set can be used as a basis for updating, and the system can be adjusted based on the previous fault judgment to improve the accuracy of subsequent fault judgment.

[0170] In some embodiments, a specific implementation of step S431 (the update of fault labels and / or standard specification trees based on the second set) includes:

[0171] S4311, extract the fault base value of the grandchild node in the second set, subtract a preset value from the fault base value to obtain the updated fault base value, and add the fault label of the error.

[0172] Understandably, the fault baseline value is a basic value used to measure the impact of a fault in the module represented by the grandchild node on the overall fault assessment. In order to make the system more reasonable in judging faults and avoid over-judgment, a preset value will be subtracted from the extracted fault baseline value.

[0173] The preset value is a fixed value that is set in advance and used to adjust the fault base value. Subtracting the preset value gives the updated fault base value. At the same time, in order to identify whether these modules have experienced faults, fault labels will be added to these sub-nodes. This helps to track and analyze the fault history and allows managers to better understand the fault status of the bicycle so that they can take corresponding measures, such as strengthening the inspection and maintenance of these modules.

[0174] S432, if the first set is not empty, then determine the intersection point of the first set and the second set, the first set contains a first difference point that does not exist in the second set, and the first set does not contain a second difference point that exists in the second set.

[0175] Understandably, if the first set is not empty, it means that there is actual maintenance information corresponding to some grandchild nodes. At this time, the server will compare and analyze the first set and the second set. The intersection point refers to the grandchild node that is in both the first set and the second set. These nodes represent modules that are considered to have problems in fault judgment and have actually been repaired.

[0176] The first difference point refers to a grandchild node that exists in the first set but not in the second set. This means that these modules were actually repaired but were not identified in the previous fault diagnosis, which may be due to omissions in the fault diagnosis. The second difference point refers to a grandchild node that exists in the second set but not in the first set. This means that these modules were considered to have problems in the previous fault diagnosis, but were not actually repaired, which may be due to misjudgment in the fault diagnosis.

[0177] By identifying these intersection points, the first difference point, and the second difference point, it is possible to clearly understand the difference between the fault diagnosis and the actual repair situation, thereby improving the accuracy of subsequent fault labeling.

[0178] S433 updates fault labels and / or standard specification trees based on intersection points, first difference points, and second difference points.

[0179] Understandably, for the module corresponding to the first difference point, it indicates that the previous fault judgment was insufficient, and it is necessary to increase the sensitivity of the judgment of these module faults in the standard specification tree, such as adjusting the relevant judgment threshold, increasing the fault base value or fault weight value, etc.

[0180] For the module corresponding to the second difference point, it indicates that the previous fault judgment was misjudged. It is necessary to reduce the sensitivity of the judgment of faults in these modules, such as reducing the fault base value or fault weight value. At the same time, the fault labels also need to be corrected accordingly to ensure that they accurately reflect the actual fault situation of the vehicle. Through such update operations, the fault labels and standard specification trees can be made more in line with the actual situation of the vehicle, thereby improving the reliability and effectiveness of the entire fault vehicle marking method.

[0181] In some embodiments, the specific implementation of step S433 (updating the fault label and / or standard specification tree based on the intersection point, the first difference point, and the second difference point) includes:

[0182] S4331, calculate the sum of the number of intersection points, the first difference point, and the second difference point, calculate the ratio of the first difference point to the sum of the numbers to obtain the first proportion, and calculate the ratio of the second difference point to the sum of the numbers to obtain the second proportion.

[0183] Understandably, we first calculate the sum of the number of intersection points, the first difference point, and the second difference point. The total number represents the number of all critical grandchild nodes involved in the fault diagnosis and actual maintenance comparison analysis. Then, we calculate the ratio of the number of the first difference point to the sum of the numbers to obtain the first proportion. The first proportion represents the relative number of the first difference point among all critical grandchild nodes. Similarly, we calculate the ratio of the number of the second difference point to the sum of the numbers to obtain the second proportion. The second proportion represents the relative percentage of the second difference point among all critical grandchild nodes.

[0184] Through the above implementation methods, the present invention obtains a first ratio and a second ratio, which can be used to subsequently determine the adjustment range of fault weight values ​​for different types of nodes.

[0185] S4332, extract the fault weight value of the intersection point, multiply the first preset value by the sum of the quantities to obtain the positive adjustment coefficient, multiply the first adjustment value by the positive adjustment coefficient to obtain the first positive adjustment value, and add the fault weight value to the first positive adjustment value to obtain the updated fault weight value.

[0186] Understandably, the server multiplies the first preset value (a pre-set fixed value used as a benchmark parameter for adjustment) by the sum of the previously calculated quantities to obtain a positive adjustment coefficient. The positive adjustment coefficient comprehensively considers the number of all key grandchild nodes and the preset adjustment benchmark, reflecting the overall trend of the adjustment of the intersection point fault weight value. Then, the first adjustment value is multiplied by the positive adjustment coefficient to obtain the first positive adjustment value, which is the specific value used to increase the intersection point fault weight value. Finally, the extracted fault weight value is added to the first positive adjustment value to obtain the updated fault weight value. Since the module corresponding to the intersection point is both identified as having a problem in the fault judgment and has actually been repaired, increasing its fault weight value can make the fault status of these modules more important in subsequent fault judgments, thereby improving the accuracy of fault judgment.

[0187] The first adjustment value is a pre-set value used for further refinement.

[0188] S4333, extract the fault weight value of the first difference point, multiply the second adjustment value by the first ratio to obtain the second positive adjustment value, and add the fault weight value to the second positive adjustment value to obtain the updated fault weight value.

[0189] Understandably, for the first difference point, the server extracts its fault weight value. Since the module represented by the first difference point is a case that has actually been repaired but was not previously identified by fault judgment, it is necessary to appropriately increase its fault weight value to improve the sensitivity to faults in these modules. The server multiplies the second adjustment value by the first ratio calculated earlier to obtain the second positive adjustment value. The second positive adjustment value is calculated based on the relative number of the first difference point among all critical grandchild nodes and the preset adjustment value, and is used to specifically increase the fault weight value of the first difference point.

[0190] The second adjustment value is a pre-set value used to adjust the fault weight value of the first difference point.

[0191] Finally, the extracted fault weight value is added to the second positive adjustment value to obtain the updated fault weight value. Through this adjustment, the server can pay more attention to the fault status of these previously overlooked modules in subsequent fault judgment, reduce the omission of fault judgment, and improve the comprehensiveness of fault judgment.

[0192] S4334, extract the fault weight value of the second difference point, multiply the third adjustment value by the second ratio to obtain the third reverse adjustment value, and subtract the fault weight value from the second positive adjustment value to obtain the updated fault weight value.

[0193] Understandably, since the module corresponding to the second difference point was considered to have a problem in the previous fault assessment but was not actually repaired, it indicates that the previous fault assessment of these modules may have been misjudged. Therefore, it is necessary to reduce its fault weight value. The server multiplies the third adjustment value by the previously calculated second ratio to obtain the third reverse adjustment value. The third reverse adjustment value is calculated based on the relative number of the second difference point among all critical grandchild nodes and the preset adjustment value, and is used to specifically reduce the fault weight value of the second difference point.

[0194] The third adjustment value is a pre-set value used to adjust the fault weight value of the second difference point.

[0195] Finally, the extracted fault weight value is subtracted from the third reverse adjustment value to obtain the updated fault weight value. This adjustment reduces the system's sensitivity to faults in modules that may be misjudged, reduces misjudgments, and improves the accuracy and reliability of fault diagnosis.

[0196] like Figure 3 As shown, this invention provides a schematic diagram of the results of a shared bicycle faulty vehicle marking system based on multidimensional data. This system includes:

[0197] The decomposition module enables the server to decompose the multi-dimensional data of the target bicycle from the bicycle end, device end, and server end to obtain decomposition information, and classify the decomposition information to obtain first classification information and second classification information.

[0198] The classification module is used to enable the management end to generate a judgment specification tree based on the standard specification tree and image interaction to classify information.

[0199] The judgment module is used to add a fault tag to the corresponding bicycle end and extract the first moment if the independent first classification information or the combined second classification information meets the judgment conditions of the judgment specification tree.

[0200] The update module is used to acquire multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and update the fault labels and / or standard specification tree.

[0201] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0202] The storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, the storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be a component of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). This ASIC can also be located within a user device. Alternatively, the processor and storage medium can exist as discrete components in a communication device. Storage media can be read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices, etc.

[0203] The present invention also provides a program product including execution instructions stored in a storage medium. At least one processor of the device can read the execution instructions from the storage medium, and the execution instructions by the at least one processor cause the device to implement the methods provided in the various embodiments described above.

[0204] In the above-described terminal or server embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for marking faulty shared bicycles based on multidimensional data, characterized in that, include: The server decomposes the multi-dimensional data of the target bicycle from the bicycle end, device end and server end to obtain decomposition information, and classifies the decomposition information to obtain first classification information and second classification information. The management system generates a classification information-based judgment specification tree based on the standard specification tree and image interaction, including: The standard specification tree includes a first node at the first level, a first child node and a second child node at the second level, and multiple grandchild nodes at the third level. The first child node and the second child node correspond to the first threshold and the second threshold, respectively, and each grandchild node corresponds to at least one module of a shared bicycle. Based on the attributes of the target bicycle, the standard specification tree is adjusted to obtain the target specification tree. Slot extraction and slot creation are then performed on the target specification tree to obtain the interactive image, including: Remove modules that do not correspond to the target attributes in the standard specification tree; each bicycle with each target attribute has a preset module. Extract the slot positions corresponding to the first child node, the second child node, and the grandchild node, which are in an selectable state, and create corresponding configuration slots for the slots corresponding to the first child node, the second child node, and the grandchild node; The first interactive slot is obtained by counting the area formed by the grandchild nodes connected to the first child node, and the second interactive slot is obtained by counting the area formed by the grandchild nodes connected to the second child node. The interactive image is then obtained. The management terminal generates a classification information judgment specification tree based on the interactive image to adjust the slots; wherein, the first child node corresponds to the first classification information, and the second child node corresponds to the second classification information; if the independent first classification information or the combined second classification information meets the judgment condition of the judgment specification tree, a fault tag is added to the corresponding bicycle and the first moment is extracted, including: If the grandchild node connected to the first child node triggers a fault, and the corresponding grandchild node's fault base value is greater than the first threshold of the first child node, then the judgment condition is met and a fault label is added. If the grandchild node connected to the second child node triggers fault information, the fault base value and fault weight value of the grandchild node in the judgment specification tree are extracted and comprehensively calculated to obtain the fault combination value. If the fault combination value is greater than the second threshold, the judgment condition is met and a fault label is added. Acquire multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and update the fault labels and / or standard specification tree, including: The multidimensional data includes at least maintenance information, and the grandchild nodes corresponding to the maintenance information are determined to obtain the first set; The updated tree is obtained by selecting the grandchild nodes that trigger fault information in the specification tree, and the second set is obtained by counting the grandchild nodes in the updated tree. Update the fault labels and / or standard specification tree based on the first set and the second set, including: If the first set is empty, then update the fault labels and / or standard specification tree based on the second set; If the first set is not empty, then determine the intersection point of the first set and the second set. There exists a first difference point in the first set that does not exist in the second set, and there does not exist a second difference point in the first set that exists in the second set. Update the fault labels and / or standard specification tree based on the intersection point, the first difference point, and the second difference point; The update of fault labels and / or standard specification trees based on the second set includes: Extract the fault base value of the grandchild node in the second set, subtract a preset value from the fault base value to obtain the updated fault base value, and add the fault tag of the error. The management terminal generates a classification information judgment specification tree based on the interactive image to adjust the slots, including: If it is determined that the user selects a grandchild node and moves it to the first or second interaction slot, then the corresponding grandchild node is connected to the new first or second interaction slot. Configure a new fault base value for the moved grandchild node; If it is determined that the user has adjusted the fault base value and / or fault weight value of the newly created slot, then the corresponding slot adjustment will generate a judgment specification tree.

2. The method according to claim 1, characterized in that, The server decomposes the multi-dimensional data from the bicycle end, device end, and server end corresponding to the target bicycle to obtain decomposition information, including: Extract all data of the target bicycle within the first preset time period; All data is decomposed to obtain automatic fault reporting information from the vehicle end, active fault reporting information from the device end, and analytical fault reporting information from the server end; The information is decomposed by classifying automatic fault reporting information, proactive fault reporting information, and analytical fault reporting information according to dimensions.

3. The method according to claim 2, characterized in that, The automatic fault reporting information is generated through the following steps: If the target bicycle is determined not to be activated after being scanned a preset number of times within a second preset time period, an automatic fault report will be generated.

4. The method according to claim 2, characterized in that, The following steps are used to generate and analyze fault reporting information: If the number of times the riding distance is less than the first preset distance within the second preset time period is greater than the first preset number, then an analysis fault report is generated; If the number of times the riding time within the third preset time is less than the fourth preset time is greater than the number of times the second preset time is determined, then an analysis fault report will be generated.

5. The method according to claim 1, characterized in that, The update of fault labels and / or standard specification trees based on intersection points, first difference points, and second difference points includes: Calculate the sum of the number of intersection points, the first difference point, and the second difference point; calculate the ratio of the first difference point to the sum of the numbers to obtain the first proportion; calculate the ratio of the second difference point to the sum of the numbers to obtain the second proportion. Extract the fault weight value of the intersection point, multiply the first preset value by the sum of the quantities to obtain the positive adjustment coefficient, multiply the first adjustment value by the positive adjustment coefficient to obtain the first positive adjustment value, and add the fault weight value to the first positive adjustment value to obtain the updated fault weight value. Extract the fault weight value of the first difference point, multiply the second adjustment value by the first ratio to obtain the second positive adjustment value, and add the fault weight value to the second positive adjustment value to obtain the updated fault weight value. Extract the fault weight value of the second difference point, multiply the third adjustment value by the second ratio to obtain the third reverse adjustment value, and subtract the fault weight value from the second positive adjustment value to obtain the updated fault weight value.

6. A shared bicycle faulty vehicle marking system based on multidimensional data, corresponding to the multidimensional data-based shared bicycle faulty vehicle marking method described in claim 1, characterized in that, include: The decomposition module is used to enable the server to decompose the multi-dimensional data of the target bicycle from the bicycle end, device end and server end to obtain decomposition information, and classify the decomposition information to obtain first classification information and second classification information. The classification module is used to enable the management end to generate a judgment specification tree based on the standard specification tree and image interaction to classify information; The judgment module is used to add a fault tag to the corresponding single vehicle and extract the first moment if the independent first classification information or the combined second classification information meets the judgment conditions of the judgment specification tree. The update module is used to acquire multidimensional data of the target vehicle from the first moment to the second moment under normal conditions, and update the fault labels and / or standard specification tree.

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