Detection method, system and device and storage medium

Abstract

The invention belongs to the technical field of automobile safety, and particularly relates to a detection method, system and device and a storage medium, and the detection method comprises the steps: collecting the static data of each connection fixed point, the static data comprising static pressure data and static position data; in the running process of the vehicle, dynamic data of each connection fixing point are continuously collected at a preset interval, and the dynamic data comprise dynamic pressure data and dynamic position data; a dynamic analysis algorithm is used, static data is used as a reference, pressure data variable quantity and position data variable quantity in continuous intervals are calculated, and change characteristics are extracted; if the pressure data variable quantity of any fixed point is within a preset pressure range or the position data variable quantity of any fixed point is within a preset offset range, judging that the fixed point is abnormal, and generating abnormal information; based on the abnormal information, a grading early warning mechanism is triggered, early warning information is generated and transmitted to a vehicle control system and a remote monitoring system through a communication module, and the early warning information is used for prompting a driver or performing remote diagnosis.

Description

Technical Field

[0001] This invention relates to the field of automotive safety technology, and in particular to a detection method, system, device, and storage medium. Background Technology

[0002] The development of new energy vehicles is rapid. As a way to replenish energy for new energy vehicles, battery swapping can quickly replenish the energy of new energy vehicles and avoid energy anxiety for car owners.

[0003] Frequent battery swapping operations can cause wear and tear on the locking and unlocking devices of battery-swapping vehicles, significantly reducing the reliability of the fasteners. As a result, during vehicle operation, physical vibrations such as road bumps and impacts can cause loose or even detached battery packs, leading to safety hazards. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, the present invention provides a detection method, system, device, and storage medium.

[0005] The first aspect of this invention provides a detection method for monitoring the fixation status of a battery pack to a vehicle chassis in a new energy vehicle, comprising: After the battery pack is installed and fixed, static data is collected at each connection and fixing point. The static data includes static pressure data and static position data. While the vehicle is in motion, dynamic data of each fixed connection point is continuously collected at preset intervals. The dynamic data includes dynamic pressure data and dynamic position data. Using dynamic analysis algorithms, based on static data, the changes in pressure and location data within continuous intervals are calculated, and the change characteristics are extracted. If the pressure data change at any fixed point is within the preset pressure range, or the position data change is within the preset offset range, it is determined to be abnormal and abnormal information is generated. Based on the abnormal information, a graded early warning mechanism is triggered to generate early warning information, which is then transmitted to the vehicle control system and remote monitoring system via the communication module for driver alerts or remote diagnostics.

[0006] In one embodiment, after collecting static pressure data and static position data but before starting the vehicle, a static installation test is also included, which includes: The static pressure data at each connection point is compared with a preset safety threshold. If the static pressure data at each connection point is not less than the preset safety threshold, the battery pack installation is deemed qualified and the vehicle can be started. If the static pressure data at any connection point is less than the preset safety threshold, the battery pack installation is deemed unqualified, and installation abnormality information is generated. The communication module transmits the installation error information to the vehicle control system and remote monitoring system to prompt the driver to reinstall the battery pack or perform remote diagnostics, and prevents the vehicle from starting through the vehicle control system.

[0007] In one embodiment, the tiered early warning mechanism includes: Level 1 warning: When the pressure change at any connection point falls within the first pressure change range of the preset pressure change range, or the position data change falls within the first position offset range of the preset position offset range, a fixation abnormality will be indicated on the vehicle display screen. Level 2 warning: When the pressure change at any fixed connection point falls within the second pressure change range of the preset pressure change range, or the position data change falls within the second position offset range of the preset position offset range, the vehicle speed is limited. Level 3 warning: When the pressure change at any fixed connection point falls within the third pressure change range of the preset pressure change range, or the position data change falls within the third position offset range of the preset position offset range, disconnect the vehicle's power output.

[0008] In one embodiment, static pressure data and dynamic pressure data are acquired by pressure sensors deployed at each fixed point; The static position data and the dynamic position data are collected by displacement sensors deployed at each fixed point.

[0009] In one embodiment, the pressure sensor includes a pressure magnetic sensor deployed at the locking screw or buckle base of the battery pack and the vehicle chassis, and the displacement sensor includes a miniature displacement sensor deployed at the root of the locking screw.

[0010] In one embodiment, the preset pressure variation range and preset offset range are adaptively adjusted based on a dynamic adjustment method; The dynamic adjustment method includes real-time acquisition of dynamic data from the connection fixed points, and preliminary establishment of a dynamic pressure model based on the data, thereby achieving real-time preliminary dynamic adjustment of the threshold.

[0011] In one embodiment, the dynamic adjustment method further includes an adaptive threshold algorithm based on previously collected static data and historical records of dynamic data. This algorithm is configured to integrate the real-time collected data and a preliminarily established dynamic stress model, continuously learn, optimize, and update the dynamic stress model, calibrating the threshold to a dynamic range based on the historical records. A second aspect of the invention provides a detection system for monitoring the fixed state of a battery pack and vehicle chassis in a new energy vehicle, comprising: The static data acquisition module is used to collect static pressure data and static position data at each connection and fixing point between the battery pack and the vehicle chassis after the battery pack is installed to the vehicle chassis and fixed. The dynamic acquisition module is used to continuously acquire dynamic pressure data and dynamic position data of each fixed connection point at preset time intervals during vehicle operation. The analysis module is used to calculate the change in pressure data and the change in position data of each connected fixed point within a continuous preset time interval based on the static pressure data and the static position data, using a dynamic analysis algorithm as a reference, and to determine its change characteristics. If the pressure data change at any fixed connection point falls within a preset pressure change range, or the position data change falls within a preset position offset range, the judgment module determines it to be an abnormal state and generates abnormal information. The early warning module generates early warning information based on the abnormal information through a tiered early warning mechanism, and transmits the abnormal information to the vehicle control system and remote monitoring system through the communication module for the purpose of alerting the driver or performing remote diagnostics.

[0012] A third aspect of the present invention provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, one of the processors of the electronic device, for the detection method described above.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the detection method described above.

[0014] The advantages of this invention over the prior art are as follows: The detection method provided in this application monitors the battery pack's fixation status in real time, promptly detecting the risk of loosening or detachment caused by road bumps, impacts, etc., and triggering corresponding early warning actions when abnormalities occur. This effectively prevents safety accidents caused by battery pack detachment and significantly improves the overall safety and reliability of battery swapping vehicles. By collecting static pressure and static position data at each connection point between the battery pack and the vehicle chassis as a baseline, and continuously collecting dynamic pressure and dynamic position data at preset time intervals during vehicle operation, the method combines dynamic analysis algorithms to calculate changes in pressure and position data, accurately identifying abnormal states of the fixation points. This method, based on dual-dimensional monitoring of pressure and position, can comprehensively assess the physical state of the fixation points, significantly improving monitoring accuracy and reducing false alarm rates caused by misjudgment of a single parameter. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 According to an embodiment of the present invention, a schematic flowchart of a detection method is shown.

[0017] Figure 2 According to an embodiment of the present invention, a schematic diagram of a static installation test process is shown.

[0018] Figure 3 According to an embodiment of the present invention, a schematic diagram of a detection system is shown.

[0019] Figure 4 According to an embodiment of the present invention, a schematic diagram of the structure of an electronic device is shown.

[0020] Figure 5 According to an embodiment of the present invention, a schematic diagram of the structure of a computer-readable storage medium is shown. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed herein. The present invention can also be implemented or applied through other different specific embodiments, and various details in the present invention can be modified or changed according to different viewpoints and application systems without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0022] The present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0023] In the representation of this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics represented in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. Furthermore, the specific features, structures, materials, or characteristics represented may be combined in any suitable manner in one or more embodiments or examples. Moreover, those skilled in the art can combine and integrate different embodiments or examples represented in this invention, as well as features of different embodiments or examples, without contradiction.

[0024] Furthermore, the terms "first" and "second" are used for illustrative purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the representation of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0025] To clearly illustrate the present invention, components unrelated to the description are omitted, and the same or similar constituent elements throughout the specification are given the same reference numerals.

[0026] Throughout this specification, when it is said that a device is "connected" to another device, this includes not only "direct connection" but also "indirect connection" by placing other components in between. Furthermore, when it is said that a device "comprises" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather implies that other constituent elements may be included.

[0027] When we say that a device is "above" another device, this can mean that it is directly above the other device, or it can mean that other devices are present in between. Conversely, when we say that a device is "directly" "above" another device, there are no other devices present in between.

[0028] Although the terms first, second, etc., are used in some instances herein to refer to various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, first interface and second interface, etc., are used. Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of features, steps, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0029] The technical terms used herein are for reference only to specific embodiments and are not intended to limit the invention. The singular form used herein includes the plural form unless the statement explicitly indicates otherwise. The word "comprising" as used in this specification means to specify a particular characteristic, region, integer, step, operation, element, and / or component, and does not exclude the presence or addition of other characteristics, regions, integers, steps, operations, elements, and / or components.

[0030] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with relevant technical literature and the content of this present instruction, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.

[0031] The detection method proposed in this invention achieves safe, automated, and efficient operation of battery swapping stations through real-time image acquisition, precise target detection, and intelligent linkage control. Its rapid gating response in cold weather not only ensures the stability of equipment operation but also significantly reduces energy consumption, achieving the dual goals of energy saving and high efficiency. It is suitable for practical application scenarios of modern intelligent battery swapping stations.

[0032] like Figure 1As shown, a detection method is used to monitor the fixed state of the battery pack and the vehicle chassis in a new energy vehicle, including: Step 110: After the battery pack is installed and fixed, static data of each connection and fixing point is collected. The static data includes static pressure data and static position data; Specifically, the static pressure data and static position data of each fixing point are collected by pressure sensors and displacement sensors deployed at each fixing point, respectively.

[0033] Step 130: While the vehicle is in motion, dynamic data of each fixed connection point is continuously collected at preset intervals. The dynamic data includes dynamic pressure data and dynamic position data. Specifically, the dynamic pressure data and dynamic position data of each fixed point are collected by pressure sensors and displacement sensors deployed at each fixed point, respectively.

[0034] Step 150: Using a dynamic analysis algorithm, based on static data, calculate the changes in pressure data and location data over continuous intervals, and extract the change characteristics; specifically, the dynamic analysis algorithm includes statistical methods based on moving averages or trend analysis to compare the change characteristics of pressure data changes and location data changes.

[0035] Step 170: If the pressure data change at any fixed point is within the preset pressure range, or the position data change is within the preset offset range, it is determined to be abnormal, and abnormal information is generated; specifically, the abnormal information includes the abnormal connection fixed point identifier, the pressure data change, the position data change and the time of the abnormal occurrence.

[0036] Step 190: Based on the abnormal information, a tiered early warning mechanism is triggered, generating early warning information, which is transmitted to the vehicle control system and remote monitoring system via the communication module for driver notification or remote diagnostics. It is understood that different levels of the early warning mechanism correspond to different abnormal information, transmitted to the vehicle control system and remote monitoring system via the communication module, so that the driver can quickly understand the stable relationship between the battery pack and the vehicle mounting base for timely handling of abnormalities. The following will further explain the specific implementation of steps 110 to 190 above: In the aforementioned embodiments 110 and steps 130, the pressure sensor includes a pressure magnetic sensor deployed at the locking screw or buckle base of the battery pack and the vehicle chassis, and the displacement sensor includes a miniature displacement sensor deployed at the root of the locking screw. Each locking screw or buckle base is equipped with a pair of pressure magnetic sensors and a miniature displacement sensor to ensure that pressure and position data at each fixing point are collected independently without interference.

[0037] In step 170 above, the fixed point is identified by factors such as the locking screw number (S1, S2, etc.) or the position of the latch base, indicating the location of the abnormality; the pressure data change is the specific percentage decrease (e.g., 35%); the position data change is the specific offset distance (e.g., 0.6mm); and the abnormality occurrence time is the timestamp of the first detection of the abnormality (e.g., 2025-08-07 11:54:32). By comparing the pressure and position changes with preset thresholds, the abnormal state of the fixed point (e.g., loosening, offset, or failure) is accurately identified, avoiding misjudgment or omission. The abnormality information includes the fixed point identification, change amount, and timestamp, providing detailed data for driver alerts and remote diagnostics, ensuring that the abnormality is traceable and located.

[0038] In step 190 above, the communication module supports multiple communication protocols, such as CAN bus, Bluetooth, 4G / 5G, etc., and can transmit the status data of the battery pack to the vehicle control system and remote monitoring center in real time.

[0039] The detection method provided in steps 110 to 190 above accurately identifies abnormalities in the battery pack's fixing status (such as loosening, displacement, or failure) by real-time monitoring of pressure and position changes at each fixing point. A tiered early warning mechanism (prompt, speed limit, power disconnection) provides a progressively lower level of response, from minor anomaly alerts to mandatory protection against severe anomalies, significantly reducing safety risks caused by battery pack fixing failures (such as battery pack detachment or short circuits). A dynamic analysis algorithm, combining the magnitude, persistence, and trend of changes, comprehensively characterizes the abnormal state of the fixing points. Statistical methods such as moving averages and trend analysis improve the accuracy of anomaly detection, avoiding false alarms (such as fluctuations caused by instantaneous vibrations) or missed alarms (such as gradually accumulating failures). Anomaly information (fixing point identifier, change amount, anomaly occurrence time) is transmitted to a remote monitoring system via multiple communication protocols (CAN bus, Bluetooth, 4G / 5G), supporting life prediction and maintenance reminder generation for the fixing mechanism. For example, analyzing the anomaly distribution of multiple fixing points can determine uneven chassis stress or screw fatigue, allowing for advance maintenance and extending battery pack lifespan. The pressure magnetic sensor and miniature displacement sensor are deployed on the locking screw or snap-fit ​​base, featuring a compact structure and accurate data acquisition. The communication module supports multiple protocols, ensuring real-time data transmission to the vehicle control system and remote monitoring center, meeting the needs of local control and remote diagnostics.

[0040] In some embodiments of this disclosure, step 120 is further included between steps 110 and 130: after collecting static pressure data and static position data, and before starting the vehicle, a static installation test is performed. Figure 2 A flowchart illustrating a static installation test is shown, such as... Figure 2 As shown, static installation testing includes: Step 121: Compare the static pressure data of each connection point with the preset safety threshold; in this embodiment, the preset safety threshold is 80 N / cm².

[0041] Step 122: If the static pressure data at each connection point is not less than the preset safety threshold, the battery pack installation is deemed qualified, and the vehicle is allowed to start. It can be understood that if the static pressure data at all connection points meets the requirements... If the battery pack strength is ≥80N / cm², the installation is deemed qualified, and an "installation qualified" signal is generated. This signal is transmitted to the vehicle control system (VCU) via the CAN bus, triggering the release of vehicle start-up permission.

[0042] Step 123: If the static pressure data at any connection point is less than the preset safety threshold, the battery pack installation is deemed unqualified, and installation error information is generated; it can be understood that if the static pressure data at any connection point is less than the preset safety threshold, the battery pack installation is deemed unqualified, and installation error information is generated; A pressure reading below 80 N / cm² indicates a faulty battery pack installation and abnormal sound field information. This information includes: fixed-point identifiers, pressure data values, and anomaly timestamps. The control module formats this information into a standard data packet (e.g., JSON), stores it locally, and prepares for transmission. The generation process is based on an embedded algorithm that automatically identifies fixed points below the threshold and marks them as abnormal. The fixed-point identifiers in the abnormal information allow for precise location of faulty fixed points, providing detailed data for subsequent anomaly handling. Furthermore, the information provided facilitates analysis of the problem's cause by the driver and the remote monitoring center.

[0043] Step 124: The installation anomaly information is transmitted to the vehicle control system and remote monitoring system via the communication module. This is used to prompt the driver to reinstall the battery pack or perform remote diagnostics, and to prevent the vehicle from starting via the vehicle control system. Understandably, the installation anomaly information is transmitted to the vehicle control system and remote monitoring center via the communication module (supporting CAN bus, Bluetooth, and 4G / 5G protocols). Specifically, this includes: sending the anomaly information (fixed point identifier, pressure value, timestamp) to the vehicle control system via the CAN bus, triggering a prompt on the vehicle display (e.g., "Fixed point S1 pressure is insufficient, please reinstall") and a voice warning, while the vehicle control system locks the starting function (e.g., disabling the motor start signal). The anomaly information is uploaded to the remote monitoring center via the 4G / 5G network, with data encryption (e.g., AES-128) to ensure security. The remote monitoring center analyzes the anomaly information, combining the fixed point location and pressure data to determine the cause of the installation problem (e.g., loose screws, faulty clips), and generates a remote diagnostic report or maintenance recommendations. After receiving the anomaly information, the vehicle control system sets a starting restriction flag, preventing ignition or motor start until the anomaly is resolved (e.g., passing the test after reinstallation). The following will further explain the specific implementation of steps 121 to 124 above: The static installation testing method provided in steps 121 to 124 outlines the process: data acquisition (step 110) → threshold comparison (step 121) → pass / fail judgment / startup permission (step 122) or fail / abnormal information generation (step 123) → information transmission and control (step 124). By comparing static pressure data with the 80 N / cm² threshold, the system ensures the battery pack installation meets design strength requirements, preventing loosening or detachment during driving due to insufficient initial fixation (e.g., loose screws or faulty clips). Abnormal information includes fixation point identification, pressure value, and timestamp, accurately locating unqualified fixation points (e.g., S1 screw pressure 75 N / cm²), facilitating quick inspection and repair by the driver and improving installation efficiency. If the test fails, step 124 disables startup via the vehicle control system, preventing the vehicle from operating in an unsafe state and protecting driver and vehicle safety. Abnormal information is transmitted to a remote monitoring center via 4G / 5G. The remote control center analyzes the cause of the installation problem (e.g., screw quality issues or installation errors) and generates targeted maintenance suggestions to reduce subsequent failures. It is worth mentioning that static installation testing provides reliable baseline data (static pressure data) for subsequent dynamic monitoring (steps 130-190), ensuring the accuracy of dynamic analysis (such as calculating the percentage of pressure drop). If the initial installation is not up to standard, invalid dynamic monitoring can be avoided, saving system resources.

[0044] In some embodiments of this disclosure, in step 150 above, the change characteristics of pressure data change and position data change are compared, wherein the change characteristics include: The magnitude of the change: Pressure data change: The percentage decrease in dynamic pressure data relative to static pressure data (the baseline value at installation time, such as 80 N / cm²), for example, [30%, 40%), [40%, 50%), [50%, ∞), which correspond to the first-level warning condition, the second-level warning condition, and the third-level warning condition, respectively.

[0045] Location data change: The offset distance of dynamic location data relative to static location data (the reference position at installation time), such as [0.5mm, 0.8mm), [0.8mm, 1.0mm), [1.0mm, ∞), corresponding to the first-level warning condition, the second-level warning condition, and the third-level warning condition, respectively. The magnitude of the location data change directly corresponds to the triggering conditions of the graded warning and is used to determine the severity of the fixed point anomaly, such as slight loosening, continuous displacement, or serious failure.

[0046] In some embodiments of this disclosure, the variation characteristics include whether the changes in pressure data and location data exceed a threshold multiple times within a consecutive preset time interval (e.g., 500ms), for example: Level 1 warning conditions: The change in pressure data in a single instance is greater than or equal to 30%, or the change in position data in a single instance is greater than or equal to 0.5 mm.

[0047] Level 2 warning conditions: Three consecutive pressure data changes greater than or equal to 30%, or three consecutive position data changes greater than or equal to 0.5 mm, or a single pressure data change falls within [40%, 50%), or a single position data change falls within [0.8 mm, 1.0 mm].

[0048] Level 3 warning conditions: Five consecutive pressure data changes greater than or equal to 30%, or five consecutive position data changes greater than or equal to 0.5 mm, or a single pressure data change falls within the range of [50%, +∞), or a single position data change falls within the range of [1.0 mm, +∞).

[0049] Level 1, Level 2, and Level 3 warning conditions are used to distinguish between instantaneous anomalies (possibly caused by vibration) and persistent anomalies (possibly caused by loosening or failure of anchor points), enhancing the accuracy and progression of tiered warnings. Combined with anchor point identification (such as locking screw numbers), the specific location and distribution pattern of the anomaly are analyzed, such as a single anchor point anomaly or multiple anchor points simultaneously exhibiting anomalies. Data support is provided for anomaly information generation (such as anchor point identification, changes, and anomaly levels), facilitating prompts on-board displays to prompt drivers to check specific anchor points, or enabling remote monitoring systems to perform diagnostics.

[0050] By analyzing the characteristics of changes, the abnormalities in the battery pack's fixing status (such as loosening, displacement, or failure) can be accurately identified, triggering graded warnings (prompts, speed limits, and power disconnection) to reduce safety risks caused by fixing abnormalities. The characteristics of changes are transmitted to the vehicle control system (local execution) and the remote monitoring system (remote diagnostics) through abnormal information (fixing point identification, amount of change, and abnormality level), supporting the driver to handle the situation in a timely manner and remotely predict the lifespan of the fixing mechanism.

[0051] In some embodiments of this disclosure, the graded early warning mechanism in step 190 above includes: Level 1 Warning: When the pressure change at any fixed connection point falls within the first pressure change range of a preset pressure change range, or the position data change falls within the first position offset range of a preset position offset range, a fixing anomaly is indicated on the vehicle display screen; wherein, the first pressure change range and the first position offset range constitute the Level 1 warning condition. In other embodiments, the Level 1 warning condition includes a single pressure data change greater than or equal to 30%, or a single position data change greater than or equal to 0.5 mm.

[0052] Level 2 warning: When the pressure change at any fixed connection point falls within the second pressure change range of the preset pressure change range, or the position data change falls within the second position offset range of the preset position offset range, the vehicle speed is restricted; wherein, the second pressure change range and the second position offset range constitute the level 2 warning condition. In other embodiments, three consecutive pressure data changes are greater than or equal to 30%, or three consecutive position data changes are greater than or equal to 0.5 mm, or a single pressure data change falls within [40%, 50%), or a single position data change falls within [0.8 mm, 1.0 mm].

[0053] Level 3 Warning: When the pressure change at any fixed connection point falls within the third pressure change range of the preset pressure change range, or the position data change falls within the third position offset range of the preset position offset range, the vehicle power output is disconnected. The third pressure change range and the third position offset range constitute the Level 3 warning condition. In other embodiments, this is defined as: 5 consecutive pressure data changes greater than or equal to 30%, 5 consecutive position data changes greater than or equal to 0.5 mm, a single pressure data change falling within [50%, +∞), or a single position data change falling within [1.0 mm, +∞).

[0054] Specifically, in step 190, the tiered early warning mechanism triggers the following actions based on the changing characteristics in step 150: Level 1 Warning: Triggering conditions: The pressure data change at any fixed connection point is within [30%, 40%), or the position data change is within [0.5mm, 0.8mm].

[0055] Action: Display prompts (such as "Fixed point S1 abnormal, pressure drop of 32%, please check battery pack") and voice warnings on the vehicle display screen to remind the driver to check specific fixed points.

[0056] Example: During vehicle operation, the pressure at fixed point S1 (locking screw) decreased from 80 N / cm² to 54 N / cm². The calculated pressure change was 80 N / cm². 5480 100% = 32.5%, falling within the range of [30%, 40%]. The control module generates an anomaly message (fixed point: S1, change: 32.5%, level: Level 1, timestamp: 2025-08-08 05:27:32), which is transmitted to the vehicle display screen via the CAN bus, showing "Fixed point S1 pressure drops by 32.5%, please check." The voice module simultaneously broadcasts a warning; the driver can continue driving but should remain attentive.

[0057] If the position data of fixed point S2 is offset by 0.6mm (belonging to [0.5mm, 0.8mm)), the first-level warning will also be triggered, displaying "Fixed point S2 offset by 0.6mm, please check".

[0058] Level 2 Warning: Triggering conditions: The pressure data change at any fixed connection point is within [40%, 50%), or the position data change is within [0.8mm, 1.0mm), or the pressure data change is greater than or equal to 30% within three consecutive preset time intervals (500ms, i.e., 1.5 seconds), or the position data change is greater than or equal to 0.5mm.

[0059] Action: Limit the vehicle speed via the vehicle control system (VCU) (e.g., limit the maximum speed to 60km / h) and display a warning on the vehicle display screen (e.g., "Fixed point S2 pressure drops by 42%, speed limit has been set, please stop and check").

[0060] Example: The pressure data at fixed point S3 decreased by 31%, 32%, and 30.5% respectively in three consecutive data collections (timestamps: 2025-08-08 05:27:32, 05:27:32.500, 05:27:33) (all ≥30%), triggering a Level 2 warning. The control module generated an anomaly message (fixed point: S3, change: 31% / 32% / 30.5%, level: Level 2, timestamp: 2025-08-08 05:27:33), which was transmitted to the VCU via the CAN bus, limiting the vehicle speed to 60km / h. The display showed "Fixed point S3 continues to be abnormal, speed limit imposed, please stop and check".

[0061] If the pressure at fixed point S4 drops by 46% in a single instance (belonging to [40%, 50%)), or the position shifts by 0.9mm (belonging to [0.8mm, 1.0mm)), a second-level warning will also be triggered, and the VCU will limit the speed and display a warning.

[0062] Level 3 Warning: Triggering conditions: The pressure data change at any fixed connection point is within [50%, ∞), or the position data change is within [1.0mm, ∞), or the pressure data change is greater than or equal to 30% within 5 consecutive preset time intervals (500ms, i.e. 2.5 seconds), or the position data change is greater than or equal to 0.5mm.

[0063] Action: Disconnect the vehicle's power output via the vehicle control system (e.g., cut off the motor power supply), and provide a notification via the vehicle's display screen and voice module (e.g., "Stop point S1 is seriously abnormal, power has been disconnected, please stop immediately"). The abnormal information is transmitted to the remote monitoring center via 4G / 5G.

[0064] Example: The pressure data at fixed point S1 decreased by 33%, 32%, 31%, 30.5%, and 32% respectively in five consecutive data collections (timestamp: 2025-08-08 05:27:32 to 05:27:34.500), all ≥30%, triggering a Level 3 warning. The control module generated an anomaly message (fixed point: S1, change: 33% / 32% / 31% / 30.5% / 32%, level: Level 3, timestamp: 2025-08-08 05:27:34.500), which was transmitted to the VCU via the CAN bus. The motor power supply was cut off, and the display showed "Fixed point S1 is seriously abnormal, power has been disconnected, please stop immediately." The anomaly message was also uploaded to the remote monitoring center via the 4G network for analysis of the cause of the fixed point failure (e.g., screw breakage).

[0065] If the pressure at fixed point S2 drops by 55% in a single instance (belonging to [50%, ∞)) or the position shifts by 1.2mm (belonging to [1.0mm, ∞)), the third-level warning will be triggered directly, the power will be disconnected and the abnormal information will be uploaded.

[0066] In some embodiments of this disclosure, in step 170 above, the preset pressure variation range and the preset offset range are adaptively adjusted based on a dynamic adjustment method. The dynamic adjustment method includes real-time acquisition of dynamic data from the connection fixed point and preliminary establishment of a dynamic pressure model based on the data, thereby achieving real-time preliminary dynamic adjustment of the threshold. The dynamic adjustment method also includes an adaptive threshold algorithm based on previously acquired static data and historical records of dynamic data. This algorithm is configured to integrate the real-time acquired data and the preliminary established dynamic pressure model, continuously learn, optimize, and update the dynamic pressure model, and calibrate the threshold to the dynamic range based on the historical records.

[0067] In practical implementation, the functions of real-time data acquisition and preliminary establishment of a dynamic pressure model through the dynamic adjustment method can be achieved through a dynamic sensing layer. The dynamic sensing layer utilizes a sensor network to acquire data in real time. Specifically: Data Acquisition: Sensors deployed at each fixed connection point collect dynamic data at preset intervals (every 0.1 seconds), including dynamic pressure data and dynamic position data. This data originates from the actual operating conditions of the vehicle during operation, and dynamic data from each fixed connection point is continuously collected at preset intervals.

[0068] Model Building: Based on the collected data, the dynamic perception layer initially establishes a dynamic pressure model through the data processing module. For example, statistical analysis algorithms (such as moving average or Fourier transform) are used to calculate pressure change characteristics (amplitude of change, frequency) and location offset characteristics (offset vector, peak value).

[0069] The dynamic pressure model can be represented as a function model:

[0070] in, This is the change in pressure. The change in position, For time.

[0071] The model enables preliminary dynamic adjustment of the threshold. For example, the preset pressure change range can be temporarily adjusted to [μ - 2σ, μ + 2σ], where μ and σ are the mean and standard deviation of the real-time data, respectively, thus forming a preliminary "pressure fingerprint" (recording the pressure fluctuation range of the vehicle during normal operation after each successful battery swap, including the mean and variance).

[0072] Learning phase: During this phase, instead of setting fixed thresholds for each battery pack or interface, a dynamic stress model is established for it. Specifically, by recording the stress fluctuation range (mean and variance) during normal vehicle operation after each successful battery swap, a "stress fingerprint" of that specific battery pack on that vehicle interface is formed.

[0073] Component Configuration: The dynamic sensing layer includes a sensor network (an array of multiple pressure magnetic sensors and miniature displacement sensors) and a data processing module (an embedded unit based on an ARM processor). The data processing module is used to extract change features, for example, by removing noise through filtering algorithms, and outputting feature vectors for subsequent use.

[0074] Furthermore, in practical implementation, the adaptive threshold algorithm of the dynamic adjustment method can be implemented through an adaptive calibration layer; the adaptive calibration layer relies on the output of the dynamic sensing layer to form a progressive optimization. The adaptive pressure threshold algorithm based on historical data is implemented by the dynamic sensing layer and the adaptive calibration layer: this algorithm does not set a fixed threshold for each battery pack or interface, but instead establishes and maintains a dynamic pressure model for it. The specific steps are as follows: Historical data sources and processing: Historical data is generated based on previously collected static data (initial pressure / location data after installation) and dynamic data (continuously collected data during driving). This data can be stored in the vehicle's local memory or uploaded to a remote monitoring system via a communication module, forming a time-series database (historical connection data for each vehicle includes pressure change logs from the past N drives). For example, historical data can include anomaly information and warning history to quantify long-term trends.

[0075] Adaptive adjustment: As the number of battery swaps increases, the system continuously learns and updates this "stress fingerprint" model. The threshold is no longer a fixed value, but a dynamic range (confidence interval) based on historical data.

[0076] Continuous Learning and Model Updates: The adaptive calibration layer employs an adaptive thresholding algorithm (such as a machine learning model, like LSTM-based time-series prediction or Bayesian optimization), with inputs including real-time data collected by the dynamic perception layer and a preliminarily established dynamic stress model. The algorithm continuously learns, optimizes, and updates the dynamic stress model: for example, training the model using historical data and updating parameters to minimize prediction errors. Specific formula example: Threshold range = [ - + ],in and Here, k is the statistical value from historical records, and k is the adaptive coefficient (dynamically adjusted based on learning results). This calibrates the threshold to a dynamic range based on historical records, for example, expanding it from a static fixed range to a confidence interval that considers the vehicle's age (e.g., a 95% confidence level), thereby effectively overcoming baseline drift caused by mechanical wear, improving alarm accuracy, and reducing false alarms and missed alarms.

[0077] Application in battery swapping scenarios: The adaptive calibration layer is also configured to read historical connection data of previously collected connection points (retrieved from a cloud database based on past installation / driving data) in battery swapping scenarios and perform risk prediction based on the dynamic stress model. For example, calculating a risk score: ,in and The weights are learned from the model. Based on these weights, it is determined whether use is permitted (battery swapping is allowed if the risk is less than a threshold) or maintenance is required first (indicating a need to check the screws). This enables full lifecycle management of the battery pack, including monitoring and optimization from installation to disposal, ensuring that battery packs with abnormal historical records ("black history") are not used blindly, thus improving overall operational safety. In some embodiments of this disclosure, Figure 3 A schematic diagram of the structure of a detection system is shown, such as... Figure 3 As shown, a method for monitoring the fixed state of a battery pack and vehicle chassis in a new energy vehicle includes: The static acquisition module 501 is used to collect static pressure data and static position data of each connection and fixing point between the battery pack and the vehicle chassis after the battery pack is installed to the vehicle chassis and fixed. The dynamic acquisition module 502 is used to continuously acquire dynamic pressure data and dynamic position data of each fixed connection point at preset time intervals during vehicle operation. Analysis module 503 is used to calculate the change in pressure data and change in position data of each connected fixed point within a continuous preset time interval based on dynamic analysis algorithm and static pressure data and static position data, and to determine its change characteristics. If the pressure data change at any fixed connection point falls within a preset pressure change range, or the position data change falls within a preset position offset range, the judgment module 504 determines it to be an abnormal state and generates abnormal information. The early warning module 505 generates early warning information based on a graded early warning mechanism, and transmits the abnormal information to the vehicle control system and remote monitoring system through the communication module for the purpose of alerting the driver or performing remote diagnostics.

[0078] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0079] Specifically, Figure 4 A schematic diagram of the structure of an electronic device is shown according to an embodiment of this disclosure. Referring below... Figure 4 To describe an electronic device 600 according to such an embodiment of the present disclosure. Figure 4 The electronic device 600 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0080] like Figure 4 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0081] The storage unit stores program code, which can be executed by the processing unit 610, causing the processing unit 610 to perform steps according to various exemplary embodiments of this disclosure. For example, the processing unit 610 can perform actions such as... Figure 1 The relevant steps of the detection method shown are illustrated.

[0082] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0083] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0084] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0085] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0086] This disclosure also provides a computer-readable storage medium for storing a program, which, when executed, implements the steps of a detection method. In some possible implementations, various aspects of this disclosure can also be implemented as a program product including program code that, when run on a terminal device, causes the terminal device to perform the steps described in the foregoing document generation method section of this specification according to various exemplary embodiments of this disclosure.

[0087] Specifically, Figure 5 According to an embodiment of this disclosure, a schematic diagram of the structure of a computer-readable storage medium is shown. For example... Figure 5As shown, a program product 800 for implementing the above-described detection method according to an embodiment of the present disclosure is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, system, or device.

[0088] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or a combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, system, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, or any suitable combination thereof.

[0090] Program code for implementing the detection methods provided in the foregoing embodiments of this disclosure can be written in any combination of one or more programming languages. These programming languages ​​include object-oriented programming languages—such as Java and C++—and conventional procedural programming languages—such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0091] In summary, the detection method proposed in this disclosure accurately identifies abnormalities in the battery pack's fixing status by real-time monitoring of pressure and position changes at each fixing point. A tiered early warning mechanism provides a progressively clearing response, from minor anomaly alerts to mandatory protection against severe anomalies, significantly reducing safety risks caused by battery pack fixing failure. A dynamic analysis algorithm, combining the magnitude, persistence, and trend of changes, comprehensively characterizes the abnormal state of the fixing points. Anomaly information (fixing point identifier, amount of change, and time of anomaly occurrence) is transmitted to a remote monitoring system via multiple communication protocols (CAN bus, Bluetooth, 4G / 5G), supporting life prediction of the fixing mechanism and generation of maintenance reminders.

[0092] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A detection method for monitoring the fixing state of a battery pack and a vehicle chassis in a new energy vehicle, characterized in that, The method comprises: After the battery pack is installed and fixed, static data of each connection fixing point is collected, including static pressure data and static position data; During vehicle driving, dynamic data of each connection fixing point is continuously collected at preset intervals, including dynamic pressure data and dynamic position data; Using dynamic analysis algorithm, the pressure data change and position data change in continuous intervals are calculated based on static data, and change characteristics are extracted; If the pressure data change of any fixing point is in the preset pressure range, or the position data change is in the preset offset range, it is determined as abnormal, and abnormal information is generated; Based on the abnormal information, a hierarchical early warning mechanism is triggered to generate warning information, which is transmitted to the vehicle control system and remote monitoring system through the communication module, for prompting the driver or remote diagnosis.

2. The detection method of claim 1, wherein: After collecting static pressure data and static position data, before the vehicle starts, static installation detection is further included, which comprises: The static pressure data of each connection fixing point is compared with the preset safety threshold; If the static pressure data of each connection fixing point is not less than the preset safety threshold, it is determined that the battery pack installation is qualified, and the vehicle is allowed to start; If the static pressure data of any connection fixing point is less than the preset safety threshold, it is determined that the battery pack installation is unqualified, and installation abnormal information is generated; The installation abnormal information is transmitted to the vehicle control system and remote monitoring system through the communication module, for prompting the driver to reinstall the battery pack or for remote diagnosis, and the vehicle control system is prohibited to start the vehicle.

3. The method of claim 1, wherein: The hierarchical early warning mechanism comprises: First level warning: when the pressure change of any connection fixing point belongs to the first pressure change range of the preset pressure change range, or the position data change belongs to the first position offset range of the preset position offset range, the fixing abnormality is prompted through the vehicle display screen; Second level warning: when the pressure change of any connection fixing point belongs to the second pressure change range of the preset pressure change range, or the position data change belongs to the second position offset range of the preset position offset range, the vehicle driving speed is limited; Third level warning: when the pressure change of any connection fixing point belongs to the third pressure change range of the preset pressure change range, or the position data change belongs to the third position offset range of the preset position offset range, the vehicle power output is disconnected.

4. The method of claim 1, wherein: The static pressure data and the dynamic pressure data are collected by the pressure sensor arranged at each fixing point; The static position data and the dynamic position data are collected by the displacement sensor arranged at each fixing point.

5. The method of claim 4, wherein: The pressure sensor comprises a pressure magnetic attraction sensor arranged at the locking screw or buckle base of the battery pack and the vehicle chassis, and the displacement sensor comprises a micro displacement sensor arranged at the root of the locking screw.

6. The method of claim 1, wherein: The preset pressure change range and the preset offset range are adaptively adjusted based on the dynamic adjustment method; The dynamic adjustment method comprises collecting dynamic data of the connection fixing point in real time, and preliminarily establishing a dynamic pressure model based on the data, so as to realize real-time preliminary dynamic adjustment of the threshold.

7. The method of claim 6, wherein: The dynamic adjustment method further comprises an adaptive threshold algorithm based on a history record of previously collected static data and dynamic data, which is configured to integrate the real-time collected data and the initially established dynamic pressure model, continuously learn, optimize and update the dynamic pressure model, and calibrate the threshold value to a dynamic range based on the history record.

8. A detection system for monitoring the fixing state of a battery pack and a vehicle chassis in a new energy vehicle, characterized in that, The method comprises: a static collection module configured to collect static pressure data and static position data of each connection fixing point between the battery pack and the vehicle chassis after the battery pack is installed to the vehicle chassis and the fixation is completed; a dynamic collection module configured to continuously collect dynamic pressure data and dynamic position data of each connection fixing point at a preset time interval during vehicle driving; an analysis module configured to calculate pressure data variation and position data variation of each connection fixing point within a continuous preset time interval based on a dynamic analysis algorithm and taking the static pressure data and the static position data as a reference, and determine variation characteristics thereof; a judgment module configured to determine an abnormal state and generate abnormal information if the pressure data variation of any connection fixing point belongs to a preset pressure variation range or the position data variation belongs to a preset position offset range; a pre-warning module configured to initiate a hierarchical pre-warning mechanism based on the abnormal information, generate pre-warning information, and transmit the abnormal information to a vehicle control system and a remote monitoring system through a communication module for prompting a driver or performing remote diagnosis.

9. An electronic device, comprising: The method comprises: a memory configured to store instructions executed by one or more processors of an electronic device, and a processor, which is one of the processors of the electronic device, configured to execute the detection method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the detection method of any one of claims 1 to 7.

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