Remote positioning and short-distance sound source retrieving system for battery assets

Abstract

The invention discloses a remote positioning and short-distance sound source retrieving system for battery assets, which relates to the technical field of batteries, and is characterized by comprising the following steps: marking the battery assets according to energy consumption signal drift fluctuation data or vibration frequency fluctuation data of anti-theft data of the battery assets to obtain a target calibration position; determining whether the anti-theft response data of the battery asset and the matched positioning terminal are in a synchronous response state; if the anti-theft response data of the battery asset and the matched positioning terminal are in the synchronous response state, determining a vibration coverage ratio of the target calibration position; the anti-theft response section is divided into an edge response section and a centralized response section, a comprehensive energy consumption weight ratio is obtained, and a first correlation influence coefficient is obtained according to the vibration coverage ratio, the comprehensive energy consumption weight ratio and a comprehensive energy consumption value; the method has the effect of providing powerful support for safety management of battery assets.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically, to a remote positioning and near-field sound source retrieval system for battery assets. Background Technology

[0002] In the field of battery asset management and anti-theft, traditional positioning and retrieval systems, which locate batteries by detecting energy consumption signal drift, are easily affected by normal energy consumption fluctuations and cannot accurately distinguish between energy consumption changes caused by theft or normal operation. Relying solely on vibration frequency monitoring makes it difficult to eliminate the influence of irrelevant factors such as environmental vibration, which can easily lead to significant deviations in the calibration location of battery assets. Traditional methods lack multi-dimensional comprehensive verification of response trigger time, duration, and signal strength change trends, resulting in inaccurate synchronization status judgment. In other words, they cannot perform targeted processing based on the actual synchronization status, affecting the accuracy of the correlation coefficient. Consequently, the adjustment of remote positioning parameters and near-field sound source trigger thresholds lacks a basis, thus reducing the effectiveness of anti-theft tracking. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a remote positioning and near-field sound source retrieval system for battery assets.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A remote positioning and near-field sound source retrieval system for battery assets, comprising: Marking module: Marks battery assets to obtain target calibration positions based on energy consumption signal drift fluctuation data or vibration frequency fluctuation data of battery asset anti-theft data; Judgment Module: Determines whether the anti-theft response data of the battery asset and the associated positioning terminal are in a synchronized response state. First processing module: If the anti-theft response data of the battery asset and the supporting positioning terminal are in a synchronous response state, determine the vibration coverage ratio of the target calibration location, divide the anti-theft response segment into edge response segment and concentrated response segment and obtain the comprehensive energy consumption weight ratio, and obtain the first correlation influence coefficient based on the vibration coverage ratio, comprehensive energy consumption weight ratio and comprehensive energy consumption value. The second processing module: When the anti-theft response data of the battery asset and the matching positioning terminal are in an asynchronous response state, the signal distance between the battery asset and the matching positioning terminal is obtained, the signal amplitude difference value is obtained based on the signal fluctuation amplitude between the battery asset and the matching positioning terminal, and the second correlation influence coefficient is obtained based on the comprehensive energy consumption value, the signal amplitude difference value and the signal distance. Output module: Collects anti-theft response data of battery assets, and adjusts the original remote positioning parameters and near-field sound source trigger threshold based on the anti-theft response data, the first correlation influence coefficient and the second correlation influence coefficient to obtain the optimized anti-theft tracking result of battery assets.

[0005] Preferably, the target location is obtained by marking the battery asset based on the energy consumption signal drift fluctuation data or vibration frequency fluctuation data of the battery asset anti-theft data, specifically including the following steps: Under stable output conditions, the reference signal curve of the energy consumption signal is acquired, the drift segment in the reference signal curve that deviates from the reference range is identified, and the signal duration, signal amplitude change gradient and interval period of the drift segment are extracted to obtain the energy consumption signal drift fluctuation data. The reference vibration spectrum of the battery asset is collected under static conditions. The fluctuation components that exceed the bandwidth of the reference vibration spectrum are identified. The frequency peaks, peak durations and the order of occurrence of different frequency peaks of the fluctuation components are extracted to obtain vibration frequency fluctuation data. The energy consumption signal drift fluctuation data is compared with the preset energy consumption anomaly feature library to obtain the energy consumption anomaly matching degree; the vibration frequency fluctuation data is compared with the preset vibration anomaly feature library to obtain the vibration anomaly matching degree. If the energy consumption anomaly matching degree is greater than the vibration anomaly matching degree, the real-time position of the battery asset corresponding to the energy consumption signal drift is used as the initial calibration position; if the vibration anomaly matching degree is less than the vibration anomaly matching degree, the real-time position of the battery asset corresponding to the vibration frequency fluctuation is used as the initial calibration position; if the energy consumption anomaly matching degree is equal to the vibration anomaly matching degree, the real-time position of the battery asset corresponding to the energy consumption signal drift or vibration frequency fluctuation is used as the initial calibration position. The target calibration location is obtained by revising the initial calibration location based on the historical movement trajectory of the battery assets.

[0006] Preferably, the target calibration location is obtained by correcting the initial calibration location based on the historical movement trajectory of the battery asset, specifically including the following steps: If the initial calibration location is within the extended range of the historical movement trajectory, then it is determined as the target calibration location; If the initial calibration position deviates from the reasonable extension range of the historical movement trajectory, the offset of the initial calibration position is adjusted based on the intensity of environmental interference signals at the deviation position to obtain the target calibration position.

[0007] Preferably, determining whether the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronized response state specifically includes the following steps: The anti-theft response sequence of battery assets is obtained by collecting anti-theft response sequences of battery assets within the same monitoring period, and the anti-theft response sequence of the supporting positioning terminal is obtained by collecting anti-theft response sequences of the supporting positioning terminal within the same monitoring period. Extract the response trigger time, response duration, and response signal strength change trend of the anti-theft response sequence of battery assets to form an asset response feature group; Extract the response trigger time, response duration, and response signal strength change trend of the anti-theft response sequence of the matching positioning terminal to form a terminal response feature group; The anti-theft response data of the battery asset and the matching positioning terminal are determined based on the asset response feature group and the terminal response feature group.

[0008] Preferably, determining whether the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronized response state based on the asset response feature group and the terminal response feature group specifically includes the following steps: Calculate the deviation value of the response trigger time in the asset response feature group and the terminal response feature group. If the deviation value is within the preset timing fault tolerance range, compare the difference rate of the response duration in the asset response feature group and the terminal response feature group. When the difference rate does not exceed the preset duration fault tolerance threshold, determine the consistency of the response signal strength change trend in the asset response feature group and the terminal response feature group. If the consistency reaches the set trend matching threshold, determine that the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronous response state. If the deviation value at the response trigger time exceeds the preset timing fault tolerance range, or the difference rate of the response duration exceeds the preset duration fault tolerance threshold, or the consistency of the response signal strength change trend does not reach the preset trend matching threshold, then it is determined that the anti-theft response data of the battery asset and the matching positioning terminal are in an asynchronous response state.

[0009] Preferably, determining the vibration coverage ratio of the target calibration location specifically includes the following steps: Collect the signal fluctuation amplitude of the battery assets at the target calibration location; The vibration coverage length of the battery asset at the target calibration location is calculated based on the signal fluctuation amplitude. The vibration coverage ratio is obtained by comparing the battery vibration coverage length with the overall monitoring length of the battery assets.

[0010] Preferably, the anti-theft response zone is divided into an edge response zone and a centralized response zone, and a comprehensive energy consumption weighting ratio is obtained. This specifically includes the following steps: Based on the theft behavior of battery assets, the anti-theft response zone is divided into an edge response zone and a centralized response zone; Extract the comprehensive energy consumption value of battery assets affected by theft; The centralized energy consumption weight ratio is obtained by comparing the energy consumption value of the centralized response segment with the comprehensive energy consumption value. The edge energy consumption weight ratio is obtained by comparing the energy consumption value of the edge response segment with the overall energy consumption value. The combination of the centralized energy consumption weight ratio and the edge energy consumption weight ratio is the comprehensive energy consumption weight ratio.

[0011] Preferably, the first correlation influence coefficient is obtained based on the vibration coverage ratio, the comprehensive energy consumption weight ratio, and the comprehensive energy consumption value, specifically including the following steps: The feature processing data is obtained by summing the vibration coverage ratio and the comprehensive energy consumption weight ratio. The first correlation influence coefficient is obtained based on the correlation between the comprehensive energy consumption value, the first signal amplitude, and the feature processing data.

[0012] Preferably, the second correlation influence coefficient is obtained based on the comprehensive energy consumption value, the signal amplitude difference value, and the signal spacing, specifically including the following steps: The comprehensive energy consumption value is normalized to obtain the energy consumption processing value; The energy consumption processing value is multiplied by the energy consumption influence weight to obtain the energy consumption influence component; the signal amplitude difference value is multiplied by the amplitude influence weight to obtain the amplitude influence value; and the signal spacing is multiplied by the spacing influence weight to obtain the spacing influence value. The initial correlation value is obtained by adding the energy consumption impact component, amplitude impact value, and spacing impact value; The second correlation influence coefficient is obtained by correcting the initial correlation value.

[0013] Preferably, the initial correlation value is corrected to obtain the second correlation influence coefficient, specifically including the following steps: When the anti-theft response data of the battery asset and the supporting positioning terminal are in a asynchronous response state, obtain the duration of the asynchronous response state; The correction coefficient for the initial correlation value is obtained based on the duration. The second correlation influence coefficient is obtained by multiplying the correction coefficient and the initial correlation value.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention combines energy consumption signal drift fluctuation data and vibration frequency fluctuation data to mark battery assets, corrects the initial calibration position based on historical movement trajectories, and thus determines the target calibration position, providing an accurate location basis for subsequent anti-theft tracking and effectively avoiding tracking errors caused by inaccurate positioning. By comparing multiple features of the anti-theft response sequences of the battery assets and the matching positioning terminal, it accurately determines whether the anti-theft response data of the two are in a synchronized response state. This ensures that the coefficient calculations under different response states are more consistent with the actual situation. A first correlation influence coefficient is calculated by comprehensively considering the vibration coverage ratio, comprehensive energy consumption weight ratio, and comprehensive energy consumption value; a second correlation influence coefficient is calculated by combining the comprehensive energy consumption value, signal amplitude difference value, signal spacing, and duration of asynchronous operation. This comprehensively and scientifically reflects the correlation influence of various factors on battery assets under different anti-theft scenarios. By combining the first and second correlation influence coefficients, the original remote positioning parameters and near-field sound source triggering threshold are adjusted to obtain optimized anti-theft tracking results for battery assets, improving the accuracy of remote positioning and the timeliness of near-field sound source retrieval, effectively ensuring the safety of battery assets. This provides strong support for the security management of battery assets. Attached Figure Description

[0015] Figure 1 This invention provides a schematic diagram of a remote positioning and near-field sound source retrieval system for battery assets. Figure 2 This invention provides a schematic diagram illustrating the steps involved in obtaining the vibration coverage ratio in a remote positioning and near-field sound source retrieval system for battery assets. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0019] Reference Figures 1-2 As shown.

[0020] The embodiments further illustrate the remote positioning and near-field sound source retrieval system for battery assets proposed in this invention.

[0021] A remote positioning and near-field sound source retrieval system for battery assets, comprising: Marking module: Marks battery assets to obtain target calibration positions based on energy consumption signal drift fluctuation data or vibration frequency fluctuation data of battery asset anti-theft data; Judgment Module: Determines whether the anti-theft response data of the battery asset and the associated positioning terminal are in a synchronized response state. First processing module: If the anti-theft response data of the battery asset and the supporting positioning terminal are in a synchronous response state, determine the vibration coverage ratio of the target calibration location, divide the anti-theft response segment into edge response segment and concentrated response segment and obtain the comprehensive energy consumption weight ratio, and obtain the first correlation influence coefficient based on the vibration coverage ratio, comprehensive energy consumption weight ratio and comprehensive energy consumption value. The second processing module: When the anti-theft response data of the battery asset and the matching positioning terminal are in an asynchronous response state, the signal distance between the battery asset and the matching positioning terminal is obtained, the signal amplitude difference value is obtained based on the signal fluctuation amplitude between the battery asset and the matching positioning terminal, and the second correlation influence coefficient is obtained based on the comprehensive energy consumption value, the signal amplitude difference value and the signal distance. Output module: Collects anti-theft response data of battery assets, and adjusts the original remote positioning parameters and near-field sound source trigger threshold based on the anti-theft response data, the first correlation influence coefficient and the second correlation influence coefficient to obtain the optimized anti-theft tracking result of battery assets.

[0022] First, anti-theft response data of battery assets is collected. The anti-theft response data includes energy consumption, vibration and signal response information of the battery in different states. For example, when the battery asset is illegally moved, its energy consumption signal will drift abnormally and the vibration frequency will also fluctuate.

[0023] Based on the anti-theft response data, the first correlation influence coefficient, and the second correlation influence coefficient, the original remote positioning parameters (such as the transmission power of the positioning signal) and the near-field sound source trigger threshold (the signal strength threshold that triggers the sound source retrieval function) are adjusted. If the anti-theft response data shows a high degree of battery interference, and both the first and second correlation influence coefficients indicate the need for more accurate positioning and more sensitive sound source triggering, then the accuracy of the remote positioning parameters is increased, and the near-field sound source trigger threshold is decreased. This results in optimized anti-theft tracking of battery assets, improving the accuracy of remote positioning and the timeliness of near-field sound source retrieval in subsequent anti-theft tracking of battery assets, thereby effectively ensuring the safety of battery assets.

[0024] The target location of the battery asset is determined by marking the battery asset based on the energy consumption signal drift fluctuation data or vibration frequency fluctuation data of the battery asset anti-theft data. The specific steps include: Under stable output conditions, the reference signal curve of the energy consumption signal is acquired, the drift segment in the reference signal curve that deviates from the reference range is identified, and the signal duration, signal amplitude change gradient and interval period of the drift segment are extracted to obtain the energy consumption signal drift fluctuation data. The reference vibration spectrum of the battery asset is collected under static conditions. The fluctuation components that exceed the bandwidth of the reference vibration spectrum are identified. The frequency peaks, peak durations and the order of occurrence of different frequency peaks of the fluctuation components are extracted to obtain vibration frequency fluctuation data. The energy consumption signal drift fluctuation data is compared with the preset energy consumption anomaly feature library to obtain the energy consumption anomaly matching degree; the vibration frequency fluctuation data is compared with the preset vibration anomaly feature library to obtain the vibration anomaly matching degree. If the energy consumption anomaly matching degree is greater than the vibration anomaly matching degree, the real-time position of the battery asset corresponding to the energy consumption signal drift is used as the initial calibration position; if the vibration anomaly matching degree is less than the vibration anomaly matching degree, the real-time position of the battery asset corresponding to the vibration frequency fluctuation is used as the initial calibration position; if the energy consumption anomaly matching degree is equal to the vibration anomaly matching degree, the real-time position of the battery asset corresponding to the energy consumption signal drift or vibration frequency fluctuation is used as the initial calibration position. The target calibration location is obtained by revising the initial calibration location based on the historical movement trajectory of the battery assets.

[0025] If the initial calibration location is within the extended range of the historical movement trajectory, then it is determined as the target calibration location; If the initial calibration position deviates from the reasonable extension range of the historical movement trajectory, the offset of the initial calibration position is adjusted based on the intensity of environmental interference signals at the deviation position to obtain the target calibration position.

[0026] A baseline signal curve for energy consumption is collected when the battery asset is in a stable output state. For example, when a certain power battery is supplying power normally and stably, its voltage and current signals exhibit a regular baseline curve. When the battery encounters theft interference, the energy consumption signal deviates from the baseline range, thus forming a drift segment. At this time, it is necessary to extract the duration of the drift segment signal, that is, the duration of the drift state; this is calculated using the first formula. The gradient of the signal amplitude change was calculated. ,in, The magnitude change The time-varying quantity is the gradient of the signal amplitude change, which is the degree of change of the signal amplitude per unit time. The interval period between adjacent drift segments is the time interval between two adjacent drifts when multiple drifts occur. By extracting this information, energy consumption signal drift fluctuation data can be obtained.

[0027] When battery assets are in a static state, their reference vibration spectrum is collected. Taking a certain energy storage battery pack as an example, its reference vibration spectrum bandwidth is relatively fixed when normally stationary. If the battery is forcibly moved, causing the vibration spectrum to exceed this reference spectrum bandwidth, fluctuation components are generated. Then, the frequency peak value of the fluctuation component is extracted, that is, the highest frequency value in the fluctuation; the peak duration is the time that the frequency peak is held; and the order of occurrence of different frequency peaks is obtained. Vibration frequency fluctuation data is obtained from this information.

[0028] The energy consumption signal drift fluctuation data is compared with a preset energy consumption anomaly feature library to obtain the energy consumption anomaly matching degree. The energy consumption anomaly feature library stores the energy consumption anomaly pattern features corresponding to various theft behaviors such as electricity theft and short circuits. The vibration frequency fluctuation data is compared with a preset vibration anomaly feature library to obtain the vibration anomaly matching degree. The vibration anomaly feature library stores the vibration frequency features of theft behaviors such as handling and impact.

[0029] Compare the matching degree of energy consumption anomalies and the matching degree of vibration anomalies. If the matching degree of energy consumption anomalies is greater than that of vibration anomalies, the real-time location of the battery asset corresponding to the energy consumption signal drift is used as the initial calibration location. For example, if the battery experiences energy consumption drift due to power theft, the real-time location found in this case is the initial calibration location. If the matching degree of vibration anomalies is greater than that of energy consumption anomalies, the real-time location of the battery asset corresponding to the vibration frequency fluctuation is used as the initial calibration location. For example, if the battery experiences vibration fluctuation due to being moved, the corresponding real-time location is the initial calibration location. If the matching degrees of the two are equal, the real-time location of the battery asset corresponding to either the energy consumption signal drift or the vibration frequency fluctuation is used as the initial calibration location.

[0030] The initial calibration location is corrected based on the historical movement trajectory of the battery assets to obtain the target calibration location. If the initial calibration location is within the extension range of the historical movement trajectory, for example, if the battery has historically moved frequently between warehouse areas A and B, and the initial calibration location is on the extension path of area A, then it is directly determined as the target calibration location. If the initial calibration location deviates from the reasonable extension range of the historical movement trajectory, for example, if the initial calibration is in area C outside the warehouse, but the battery's historical trajectory has never exceeded the warehouse, then the offset of the initial calibration location is adjusted based on the strength of environmental interference signals at the deviation location, and the final target calibration location is obtained.

[0031] Determining whether the anti-theft response data of the battery asset and its associated positioning terminal are in a synchronized response state includes the following steps: The anti-theft response sequence of battery assets is obtained by collecting anti-theft response sequences of battery assets within the same monitoring period, and the anti-theft response sequence of the supporting positioning terminal is obtained by collecting anti-theft response sequences of the supporting positioning terminal within the same monitoring period. Extract the response trigger time, response duration, and response signal strength change trend of the anti-theft response sequence of battery assets to form an asset response feature group; Extract the response trigger time, response duration, and response signal strength change trend of the anti-theft response sequence of the matching positioning terminal to form a terminal response feature group; The anti-theft response data of the battery asset and the matching positioning terminal are determined based on the asset response feature group and the terminal response feature group.

[0032] The anti-theft response sequences of battery assets and their associated positioning terminals are collected within the same monitoring period. For example, anti-theft signals of battery assets are continuously collected during a specific time period to form an anti-theft response sequence for battery assets; simultaneously, anti-theft response signals of the associated positioning terminals are collected to obtain an anti-theft response sequence for the associated positioning terminals.

[0033] Extract the response trigger time, response duration, and response signal strength trend from the anti-theft response sequence of battery assets to form an asset response feature group. Taking the abnormal energy consumption response of battery assets caused by theft as an example, the response trigger time is the time when the energy consumption signal begins to deviate from the baseline; the response duration is the duration of the abnormal energy consumption state; and the response signal strength trend is the trend of whether the energy consumption signal amplitude is continuously increasing, decreasing, or fluctuating.

[0034] Extract the response trigger time, response duration, and response signal strength change trend from the anti-theft response sequence of the supporting positioning terminal to form a terminal response feature group. For example, if the supporting positioning terminal triggers positioning signal transmission due to theft of battery assets, the response trigger time is the time when the positioning signal begins to be transmitted; the response duration is the time of positioning signal transmission; and the response signal strength change trend is whether the positioning signal strength increases, decreases, or remains stable.

[0035] The system uses asset response characteristic groups and terminal response characteristic groups to determine whether the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronized response state. For example, if the trigger time of the anti-theft response of the battery asset and the anti-theft response of the matching positioning terminal are almost the same, the difference in response duration is very small, and the trend of response signal strength change is basically consistent, then they are determined to be in a synchronized response state; conversely, if the trigger time differs greatly, or the duration differs significantly, or the trend of signal strength change is different, then it is determined to be an asynchronous response state.

[0036] The determination of whether the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronized response state based on the asset response feature group and the terminal response feature group includes the following steps: Calculate the deviation value of the response trigger time in the asset response feature group and the terminal response feature group. If the deviation value is within the preset timing fault tolerance range, compare the difference rate of the response duration in the asset response feature group and the terminal response feature group. When the difference rate does not exceed the preset duration fault tolerance threshold, determine the consistency of the response signal strength change trend in the asset response feature group and the terminal response feature group. If the consistency reaches the set trend matching threshold, determine that the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronous response state. If the deviation value at the response trigger time exceeds the preset timing fault tolerance range, or the difference rate of the response duration exceeds the preset duration fault tolerance threshold, or the consistency of the response signal strength change trend does not reach the preset trend matching threshold, then it is determined that the anti-theft response data of the battery asset and the matching positioning terminal are in an asynchronous response state.

[0037] First, calculate the deviation between the trigger times of the responses in the asset response feature group and the terminal response feature group. For example, if a battery asset triggers an anti-theft response at 8:00:10 and the accompanying positioning terminal triggers a response at 8:00:12, the deviation is 2 seconds. If this deviation is within a preset timing tolerance range, such as within 5 seconds, then continue comparing the difference rate of response duration between the asset response feature group and the terminal response feature group. Assuming the battery asset's anti-theft response lasts 60 seconds and the accompanying positioning terminal's response lasts 65 seconds, the difference rate is (65-60) / 60≈8.33%. If this difference rate does not exceed a preset duration tolerance threshold, such as 10%, then further determine the consistency of the response signal strength change trends between the asset response feature group and the terminal response feature group. For example, if the signal strength of the battery asset first increases and then stabilizes, and the signal strength of the matching positioning terminal also shows the same trend, and the degree of matching reaches the set trend matching threshold, such as 90%, then it is determined that the anti-theft response data of the battery asset and the matching positioning terminal are in a synchronous response state.

[0038] If the deviation at the trigger time exceeds the preset timing tolerance range (e.g., the battery asset triggers at 8:00:10 and the matching terminal triggers at 8:00:20, the deviation of 10 seconds exceeds the 5-second tolerance range); or if the difference rate of the response duration exceeds the preset duration tolerance threshold (e.g., the battery asset's response lasts 60 seconds and the terminal's lasts 75 seconds, the difference rate of 25% exceeds the 10% threshold); or if the consistency of the response signal strength change trend does not reach the preset trend matching threshold (e.g., the battery asset's signal strength trend is increasing while the matching terminal's is decreasing, resulting in extremely low consistency), then the anti-theft response data of the battery asset and the matching terminal are determined to be in an asynchronous response state.

[0039] Determining the vibration coverage ratio at the target calibration location includes the following steps: Collect the signal fluctuation amplitude of the battery assets at the target calibration location; The vibration coverage length of the battery asset at the target calibration location is calculated based on the signal fluctuation amplitude. The vibration coverage ratio is obtained by comparing the battery vibration coverage length with the overall monitoring length of the battery assets.

[0040] Through the second calculation formula The vibration coverage ratio was calculated. , Vibration coverage ratio, This refers to the overall monitoring length of battery assets.

[0041] First, the signal fluctuation amplitude of the battery asset at the target calibration location is collected. For example, if a battery asset is calibrated in a specific area of ​​a warehouse, the vibration signal of the battery in that area is collected to obtain specific data on the signal fluctuation amplitude. Based on the signal fluctuation amplitude, the vibration coverage length of the battery asset at the target calibration location is calculated. For example, by judging the signal fluctuation amplitude, the length of the effective vibration range of the battery at that location is determined; assuming the calculated vibration coverage length is 5 meters.

[0042] The vibration coverage ratio is obtained by comparing the battery vibration coverage length with the overall monitoring length of the battery asset. Assuming the overall monitoring length of the battery asset is 10 meters, the vibration coverage ratio is 0.5. This calculation quantifies the vibration coverage of the battery at the target calibration location, providing crucial data support for subsequent calculations of correlation influence coefficients and other related steps.

[0043] The anti-theft response zone is divided into edge response zone and centralized response zone, and the comprehensive energy consumption weight ratio is obtained. The specific steps include: Based on the theft behavior of battery assets, the anti-theft response zone is divided into an edge response zone and a centralized response zone; Extract the comprehensive energy consumption value of battery assets affected by theft; The centralized energy consumption weight ratio is obtained by comparing the energy consumption value of the centralized response segment with the comprehensive energy consumption value. The edge energy consumption weight ratio is obtained by comparing the energy consumption value of the edge response segment with the overall energy consumption value. Among them, the combination of the centralized energy consumption weight ratio and the edge energy consumption weight ratio is the comprehensive energy consumption weight ratio.

[0044] Through the third calculation formula Calculate the overall energy consumption weight ratio ,in, Centralized energy consumption weighting ratio Edge energy consumption weighting ratio First weighting coefficient, Second weighting coefficient.

[0045] First, the anti-theft response zone is divided into an edge response zone and a concentrated response zone based on the theft behavior affecting the battery asset. For example, if a battery asset in a warehouse is initially probed from the edge area and then the main theft occurs in the middle area, the portion corresponding to the edge probe is designated as the edge response zone, and the portion corresponding to the main theft occurs in the middle is designated as the concentrated response zone. The overall energy consumption value of the battery asset's theft behavior is extracted. For example, the total energy consumption of the battery in response to the theft during the entire process is 100 units. The energy consumption value of the concentrated response zone is then compared with the overall energy consumption value to obtain the concentrated energy consumption weight ratio. Assuming the energy consumption value of the concentrated response zone is 70 units, the concentrated energy consumption weight ratio is 0.7.

[0046] The edge energy consumption weight ratio is obtained by comparing the energy consumption value of the edge response segment with the overall energy consumption value. If the energy consumption value of the edge response segment is 30 units, then the edge energy consumption weight ratio is 0.3.

[0047] Among them, the combination of centralized energy consumption weight ratio and edge energy consumption weight ratio is the comprehensive energy consumption weight ratio. Through such calculation, the energy consumption ratio of different response segments in the theft behavior can be clearly quantified.

[0048] The first correlation influence coefficient is obtained based on the vibration coverage ratio, the comprehensive energy consumption weight ratio, and the comprehensive energy consumption value, specifically including the following steps: The feature processing data is obtained by summing the vibration coverage ratio and the comprehensive energy consumption weight ratio. The first correlation influence coefficient is obtained based on the correlation between the comprehensive energy consumption value, the first signal amplitude, and the feature processing data.

[0049] First, the vibration coverage ratio and the overall energy consumption weight ratio are summed to obtain the feature processing data. Assuming a battery asset has a vibration coverage ratio of 0.5, and the overall energy consumption weight ratio includes a concentrated energy consumption weight ratio of 0.7 and an edge energy consumption weight ratio of 0.3, the sum of the overall energy consumption weight ratios is 1. Since the concentrated and edge energy consumption weight ratios are a division of the overall energy consumption value, and their sum is 1, the feature processing data is 1.5.

[0050] Next, the first correlation influence coefficient is obtained based on the correlation between the comprehensive energy consumption value, the first signal amplitude, and the feature processing data. For example, if the comprehensive energy consumption value is 100 units, the first signal amplitude is 5 units, and the feature processing data is 1.5, the first correlation influence coefficient is obtained by weighted summation. The first correlation influence coefficient can comprehensively reflect the degree of correlation between vibration coverage, energy consumption distribution, and signal amplitude factors on the anti-theft response of battery assets.

[0051] The second correlation coefficient is obtained based on the comprehensive energy consumption value, signal amplitude difference value, and signal spacing, specifically including the following steps: The comprehensive energy consumption value is normalized to obtain the energy consumption processing value; The energy consumption processing value is multiplied by the energy consumption influence weight to obtain the energy consumption influence component; the signal amplitude difference value is multiplied by the amplitude influence weight to obtain the amplitude influence value; and the signal spacing is multiplied by the spacing influence weight to obtain the spacing influence value. The initial correlation value is obtained by adding the energy consumption impact component, amplitude impact value, and spacing impact value; Through the fourth calculation formula The initial correlation value is calculated. The component affected by energy consumption, This is the magnitude effect value. This represents the influence value of the spacing.

[0052] The second correlation influence coefficient is obtained by correcting the initial correlation values, specifically including the following steps: When the anti-theft response data of the battery asset and the supporting positioning terminal are in a asynchronous response state, obtain the duration of the asynchronous response state; The correction coefficient for the initial correlation value is obtained based on the duration. The second correlation influence coefficient is obtained by multiplying the correction coefficient and the initial correlation value.

[0053] The comprehensive energy consumption value is normalized to obtain the normalized energy consumption value. For example, if the comprehensive energy consumption value of a battery asset is 100 units, and its energy consumption range is 0 to 200 units, then the normalized energy consumption value is 100 ÷ 200 = 0.5.

[0054] The energy consumption impact component is obtained by multiplying the processed energy consumption value by its weight. The amplitude impact value is obtained by multiplying the signal amplitude difference value by its weight. The distance between signals is obtained by multiplying the distance between signals by its weight. For example, if the energy consumption impact weight is 0.4, the energy consumption impact component is 0.5 × 0.4 = 0.2; if the signal amplitude difference is 8 units and the amplitude impact weight is 0.3, the amplitude impact value is 8 × 0.3 = 2.4; if the signal distance is 10 meters and the distance impact weight is 0.3, the distance impact value is 10 × 0.3 = 3. Adding the energy consumption impact component, amplitude impact value, and distance impact value together yields an initial correlation value of 5.6.

[0055] The initial correlation value is corrected to obtain the second correlation influence coefficient. Specifically, when the anti-theft response data of the battery asset and the matching positioning terminal are in an asynchronous response state, the duration of the asynchronous response state is obtained. For example, if the asynchronous response lasts for 20 minutes, the correction coefficient of the initial correlation value is obtained based on the duration. Assuming the correction coefficient for 20 minutes is 0.8, the correction coefficient is multiplied by the initial correlation value to obtain the second correlation influence coefficient of 4.48.

[0056] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0057] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0058] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote location and close-proximity acoustic source homing system for battery assets, characterized by, The method comprises the following steps: The marking module: according to the energy consumption signal drift fluctuation data or the vibration frequency fluctuation data of the battery asset anti-theft data, the battery asset is marked to obtain the target calibration position; The judgment module: whether the anti-theft response data of the battery asset and the matching positioning terminal is in a synchronous response state is judged: The first processing module: if the anti-theft response data of the battery asset and the matching positioning terminal is in a synchronous response state, the vibration coverage ratio of the target calibration position is determined, the anti-theft response section is divided into an edge response section and a concentrated response section, and the comprehensive energy consumption weight ratio is obtained, the first correlation influence coefficient is obtained according to the vibration coverage ratio, the comprehensive energy consumption weight ratio and the comprehensive energy consumption value; The second processing module: when the anti-theft response data of the battery asset and the matching positioning terminal is in a non-synchronous response state, the signal spacing between the battery asset and the matching positioning terminal is obtained, the signal amplitude difference value is obtained according to the signal fluctuation amplitude between the battery asset and the matching positioning terminal, and the second correlation influence coefficient is obtained according to the comprehensive energy consumption value, the signal amplitude difference value and the signal spacing; The output module: the anti-theft response data of the battery asset is collected, and the original remote positioning parameter and the near-distance sound source trigger threshold are adjusted to obtain the battery asset anti-theft tracking optimization result according to the anti-theft response data, the first correlation influence coefficient and the second correlation influence coefficient.

2. A battery asset remote location and close-proximity sound source homing system according to claim 1, wherein, According to the energy consumption signal drift fluctuation data or the vibration frequency fluctuation data of the battery asset anti-theft data, the battery asset is marked to obtain the target calibration position, which comprises the following steps: In the stable output state, the baseline signal curve of the energy consumption signal is collected, the drift section deviating from the baseline range in the baseline signal curve is identified, the signal duration, signal amplitude change gradient and interval period of adjacent drift sections are extracted to obtain the energy consumption signal drift fluctuation data; In the static state, the baseline vibration frequency spectrum of the battery asset is collected, the fluctuation component exceeding the baseline frequency spectrum bandwidth in the baseline vibration frequency spectrum is identified, the frequency peak value, peak duration and appearance order of different frequency peaks are extracted to obtain the vibration frequency fluctuation data; The energy consumption signal drift fluctuation data is compared with the preset energy consumption abnormal feature library to obtain the energy consumption abnormal matching degree; the vibration frequency fluctuation data is compared with the preset vibration abnormal feature library to obtain the vibration abnormal matching degree; If the energy consumption abnormal matching degree is greater than the vibration abnormal matching degree, the real-time position of the battery asset corresponding to the energy consumption signal drift is taken as the preliminary calibration position; if the vibration abnormal matching degree is less than the vibration abnormal matching degree, the real-time position of the battery asset corresponding to the vibration frequency fluctuation is taken as the preliminary calibration position; if the energy consumption abnormal matching degree is equal to the vibration abnormal matching degree, the real-time position of the battery asset corresponding to the energy consumption signal drift or the vibration frequency fluctuation is taken as the preliminary calibration position; The preliminary calibration position is corrected according to the historical moving track of the battery asset to obtain the target calibration position.

3. A battery asset remote location and close-proximity sound source homing system according to claim 2, wherein, The preliminary calibration position is corrected according to the historical moving track of the battery asset to obtain the target calibration position, which comprises the following steps: If the preliminary calibration position is in the extension range of the historical moving track, it is determined as the target calibration position; If the preliminary calibration position deviates from the reasonable extension range of the historical moving track, the offset of the preliminary calibration position is adjusted in combination with the environmental interference signal strength of the deviated position to obtain the target calibration position.

4. A battery asset remote location and close-proximity acoustic source homing system according to claim 1, wherein, The method comprises the following steps: The battery asset theft prevention response sequence of the battery asset is collected in the same monitoring period to obtain the battery asset theft prevention response sequence, and the battery asset theft prevention response sequence of the matching positioning terminal is collected in the same monitoring period to obtain the matching positioning terminal theft prevention response sequence. The response trigger time, response duration and response signal strength change trend of the battery asset theft prevention response sequence are extracted to form the asset response feature group. The response trigger time, response duration and response signal strength change trend of the matching positioning terminal theft prevention response sequence are extracted to form the terminal response feature group. The asset response feature group and the terminal response feature group are used to determine whether the battery asset and the matching positioning terminal theft prevention response data are in a synchronous response state.

5. A remote location and close-proximity acoustic source homing system for a battery asset as defined in claim 4, wherein, The asset response feature group and the terminal response feature group are used to determine whether the battery asset and the matching positioning terminal theft prevention response data are in a synchronous response state. The deviation value of the response trigger time in the asset response feature group and the terminal response feature group is calculated, if the deviation value is within a preset time sequence fault tolerance range, the difference rate of the response duration in the asset response feature group and the terminal response feature group is compared, when the difference rate does not exceed a preset time length fault tolerance threshold, the degree of fit of the response signal strength change trend in the asset response feature group and the terminal response feature group is determined, if the degree of fit reaches a set trend matching threshold, it is determined that the battery asset and the matching positioning terminal theft prevention response data are in a synchronous response state. If the deviation value of the response trigger time exceeds the preset time sequence fault tolerance range, or the difference rate of the response duration exceeds the preset time length fault tolerance threshold, or the degree of fit of the response signal strength change trend does not reach the preset trend matching threshold, it is determined that the battery asset and the matching positioning terminal theft prevention response data are in a non-synchronous response state.

6. A battery asset remote location and close-proximity sound source homing system according to claim 5, wherein, The vibration coverage ratio of the target calibration position is determined, which comprises the following steps: The signal fluctuation amplitude of the battery asset at the target calibration position is collected; The battery vibration coverage length of the battery asset at the target calibration position is counted according to the signal fluctuation amplitude; The vibration coverage ratio is obtained by ratio processing of the battery vibration coverage length and the overall monitoring length of the battery asset.

7. A battery asset remote location and close-proximity sound source homing system according to claim 6, wherein, The theft prevention response section is divided into an edge response section and a concentrated response section, and a comprehensive energy consumption weight ratio is obtained, which comprises the following steps: The theft prevention response section is divided into an edge response section and a concentrated response section according to the battery asset theft disturbance behavior; The comprehensive energy consumption value of the battery asset theft disturbance behavior is extracted; The energy consumption value of the concentrated response section and the comprehensive energy consumption value are ratio processed to obtain a concentrated energy consumption weight ratio; The energy consumption value of the edge response section and the comprehensive energy consumption value are ratio processed to obtain an edge energy consumption weight ratio; The concentrated energy consumption weight ratio and the edge energy consumption weight ratio are combined to obtain a comprehensive energy consumption weight ratio.

8. A battery asset remote location and close-proximity sound source homing system according to claim 7, wherein, The first correlation influence coefficient is obtained according to the vibration coverage ratio, the comprehensive energy consumption weight ratio and the comprehensive energy consumption value, which comprises the following steps: Summing the vibration coverage ratio and the comprehensive energy consumption weight ratio to obtain feature processing data; According to the correlation influence between the comprehensive energy consumption value, the first signal amplitude and the feature processing data, a first correlation influence coefficient is obtained.

9. A battery asset remote location and close-proximity sound source homing system according to claim 8, wherein, According to the comprehensive energy consumption value, the signal amplitude difference value and the signal interval, a second correlation influence coefficient is obtained, which specifically includes the following steps: Normalizing the comprehensive energy consumption value to obtain an energy consumption processing value; Multiplying the energy consumption processing value by the energy consumption influence weight to obtain an energy consumption influence component, multiplying the signal amplitude difference value by the amplitude influence weight to obtain an amplitude influence value, and multiplying the signal interval by the interval influence weight to obtain an interval influence value; Adding the energy consumption influence component, the amplitude influence value and the interval influence value to obtain an initial correlation value; Correcting the initial correlation value to obtain the second correlation influence coefficient.

10. A battery asset remote location and close-proximity sound source homing system according to claim 9, wherein, Correcting the initial correlation value to obtain the second correlation influence coefficient, which specifically includes the following steps: When the battery asset and the matching positioning terminal are in a non-synchronous response state, the duration of the non-synchronous response state is obtained; According to the duration, a correction coefficient of the initial correlation value is obtained; Multiplying the correction coefficient and the initial correlation value to obtain the second correlation influence coefficient.

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