Vehicle positioning management method and device, equipment and medium

Abstract

The invention relates to the technical field of vehicle positioning, and discloses a vehicle positioning management method and device, equipment and a medium, and the method comprises the steps: obtaining the battery information of positioning equipment in a target vehicle and the number of positioning monitoring days of the target vehicle; analyzing the remaining battery electric quantity, the battery health state and the battery temperature through a deep learning model to obtain supportable positioning reporting times of the positioning equipment, and obtaining supportable positioning reporting days of the positioning equipment according to the supportable positioning reporting times and a preset positioning reporting frequency; judging whether the number of supportable positioning report days of the positioning equipment is equal to the number of positioning monitoring days of the target vehicle; and if the number of supportable positioning reporting days of the positioning equipment is not equal to the number of positioning monitoring days of the target vehicle, adjusting the preset positioning reporting frequency to obtain the adjusted positioning reporting frequency, so that the number of supportable positioning reporting days is equal to the number of positioning monitoring days of the target vehicle. The invention aims to improve the accuracy of vehicle positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning, and particularly to a management method, device, equipment and medium for vehicle positioning. Background Art

[0002] In the financial field, vehicles are often used as assets for mortgage loans or financial leasing. Leasing companies and financial institutions rely on wireless positioning devices to monitor the location and status of vehicles in real time. If the positioning device fails due to depleted battery power, it may lead to the loss of connection of the vehicle, increasing the risks of theft, default or asset loss. Therefore, ensuring the normal operation of the positioning device during the lease period is crucial for ensuring the security of financial transactions.

[0003] Currently, leasing companies usually rely on regularly replacing batteries or manually checking the device status. However, these methods are not only costly and inefficient but also unable to respond to battery power changes in real time. Once the battery runs out, the device will not be able to provide real-time positioning data, resulting in the inability to accurately obtain the vehicle's location.

[0004] Therefore, there is an urgent need for a management method for vehicle positioning that can ensure the continuous operation of the positioning device during the lease period to improve the accuracy of vehicle positioning and reduce the risks faced by the financial field and leasing companies. Summary of the Invention

[0005] The present invention provides a management method, device, computer equipment and medium for vehicle positioning to solve the technical problem of low accuracy of vehicle positioning in related technologies.

[0006] In a first aspect, a management method for vehicle positioning is provided, including:

[0007] Obtaining battery information of a positioning device in a target vehicle and the number of days of positioning monitoring of the target vehicle; wherein, the battery information at least includes remaining battery power, battery health status and battery temperature;

[0008] Analyzing the remaining battery power, the battery health status and the battery temperature through a deep learning model to obtain the number of positioning report times supported by the positioning device, and obtaining the number of days of positioning report supported by the positioning device according to the number of positioning report times supported and a preset positioning report frequency;

[0009] Judging whether the number of days of positioning report supported by the positioning device is equal to the number of days of positioning monitoring of the target vehicle;

[0010] If the number of days of positioning report supported by the positioning device is not equal to the number of days of positioning monitoring of the target vehicle, adjusting the preset positioning report frequency to obtain an adjusted positioning report frequency so that the number of days of positioning report supported is equal to the number of days of positioning monitoring of the target vehicle.

[0011] In a second aspect, a management device for vehicle positioning is provided, including:

[0012] An acquisition module for acquiring the battery information of the positioning device in the target vehicle and the number of days of positioning monitoring of the target vehicle; wherein, the battery information at least includes the remaining battery power, the battery health status, and the battery temperature;

[0013] An analysis module for analyzing the remaining battery power, the battery health status, and the battery temperature through a deep learning model to obtain the supportable positioning reporting times of the positioning device, and obtaining the supportable positioning reporting days of the positioning device according to the supportable positioning reporting times and a preset positioning reporting frequency;

[0014] A judgment module for judging whether the supportable positioning reporting days of the positioning device are equal to the number of days of positioning monitoring of the target vehicle;

[0015] An adjustment module for, if the supportable positioning reporting days of the positioning device are not equal to the number of days of positioning monitoring of the target vehicle, adjusting the preset positioning reporting frequency to obtain an adjusted positioning reporting frequency, so that the supportable positioning reporting days are equal to the number of days of positioning monitoring of the target vehicle.

[0016] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned vehicle positioning management method are implemented.

[0017] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned vehicle positioning management method are implemented.

[0018] In the solution implemented by the above vehicle positioning management method, device, computer device, and storage medium, the battery information of the positioning device in the target vehicle and the positioning monitoring days of the target vehicle can be obtained. Among them, the battery information at least includes the remaining battery power, battery health status, and battery temperature. Further, the remaining battery power, battery health status, and battery temperature can be analyzed through a deep learning model to obtain the supportable positioning reporting times of the positioning device, and the supportable positioning reporting days of the positioning device can be obtained according to the supportable positioning reporting times and the preset positioning reporting frequency. Thus, it can be determined whether the supportable positioning reporting days of the positioning device are equal to the positioning monitoring days of the target vehicle. If the supportable positioning reporting days of the positioning device are not equal to the positioning monitoring days of the target vehicle, the preset positioning reporting frequency is adjusted to obtain the adjusted positioning reporting frequency so that the supportable positioning reporting days are equal to the positioning monitoring days of the target vehicle. In the present invention, by obtaining the battery information of the target vehicle positioning device, analyzing the remaining battery power, battery health status, and battery temperature in combination with the deep learning model, the supportable positioning reporting days of the positioning device are accurately calculated and compared with the positioning monitoring days of the target vehicle. By adjusting the positioning reporting frequency, it is ensured that the device can meet the actual monitoring requirements, improving the battery usage efficiency and monitoring continuity of the positioning device, thereby enhancing the reliability and accuracy of the overall vehicle positioning management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram of an application environment of the vehicle positioning management method in an embodiment of the present invention;

[0021] Figure 2 It is a schematic flowchart of the vehicle positioning management method in an embodiment of the present invention;

[0022] Figure 3 is Figure 2 a schematic flowchart of a specific implementation manner of step S20 in

[0023] Figure 4 It is a schematic structural diagram of the vehicle positioning management device in an embodiment of the present invention;

[0024] Figure 5 It is a schematic structural diagram of a computer device in an embodiment of the present invention;

[0025] Figure 6It is another schematic structural diagram of a computer device in an embodiment of the present invention. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] The vehicle positioning management method provided by the embodiments of the present invention can be applied in an application environment such as Figure 1 . Among them, the client communicates with the server through the network. The server can obtain the battery information of the positioning device in the target vehicle and the positioning monitoring days of the target vehicle through the client; among them, the battery information at least includes the remaining battery power, battery health status, and battery temperature. Further, the remaining battery power, battery health status, and battery temperature can be analyzed through a deep learning model to obtain the number of positioning report times that the positioning device can support, and the number of positioning report days that the positioning device can support can be obtained according to the number of positioning report times that the positioning device can support and the preset positioning report frequency. Thus, it can be determined whether the number of positioning report days that the positioning device can support is equal to the positioning monitoring days of the target vehicle; if the number of positioning report days that the positioning device can support is not equal to the positioning monitoring days of the target vehicle, the preset positioning report frequency is adjusted to obtain the adjusted positioning report frequency, so that the number of positioning report days that the positioning device can support is equal to the positioning monitoring days of the target vehicle. In the present invention, by obtaining the battery information of the positioning device of the target vehicle, combining the deep learning model to analyze the remaining battery power, battery health status, and battery temperature, accurately calculating the number of positioning report days that the positioning device can support, and comparing it with the positioning monitoring days of the target vehicle. By adjusting the positioning report frequency, it is ensured that the device can meet the actual monitoring requirements, improving the battery usage efficiency and monitoring continuity of the positioning device, thereby enhancing the reliability and accuracy of the overall vehicle positioning management. Among them, the client can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers. The present invention will be described in detail below through specific embodiments.

[0028] Please refer to Figure 2 as shown in Figure 2 which is a flowchart of a vehicle positioning management method provided by an embodiment of the present invention, including the following steps:

[0029] S10: Obtain the battery information of the positioning device in the target vehicle and the positioning monitoring days of the target vehicle.

[0030] Among them, the battery information at least includes the remaining battery power, the battery health status, and the battery temperature.

[0031] It should be noted that the remaining battery power refers to the percentage of the remaining power in the current battery of the positioning device, which helps to estimate how long the positioning device can continue to work under the current battery power; the battery health status describes the overall health of the battery, including the degree of battery aging, the number of charge and discharge cycles, and whether there are any faults, etc. If the battery health status is poor, it means that the battery may not be able to maintain a high power level for a long time, affecting the stability and working continuity of the device. The battery temperature has a great impact on the performance of the battery. Too high or too low temperature will affect the service life and performance of the battery. Under extreme temperatures, the battery may run out of power prematurely or cannot be charged normally.

[0032] Furthermore, the number of days for which the target vehicle needs to be positioned and monitored within a certain time period can also be obtained. Among them, the number of days for positioning and monitoring is usually determined by a predetermined monitoring plan, the actual usage of the vehicle, and the time for which the vehicle needs to be tracked.

[0033] By obtaining the battery information of the positioning device in the target vehicle and the number of days for positioning and monitoring of the target vehicle, the battery condition of the positioning device can be analyzed to determine whether it can support the entire positioning and monitoring cycle, and then decide whether it is necessary to adjust the working strategy of the positioning device (such as adjusting the reporting frequency, etc.).

[0034] S20: Analyze the remaining battery power, the battery health status, and the battery temperature through a deep learning model to obtain the number of positioning reporting times that the positioning device can support, and obtain the number of days for positioning reporting that the positioning device can support according to the number of positioning reporting times that can be supported and a preset positioning reporting frequency.

[0035] In S20, through comprehensive analysis of the battery information (remaining battery power, battery health status, battery temperature) of the positioning device by a deep learning model, the deep learning model can, through learning historical data, determine the maximum number of positioning reporting times that the positioning device can support under different battery states. These battery parameters affect the power consumption mode of the device, and thus determine the working duration of the battery under different conditions. According to the calculated number of positioning reporting times that can be supported, combined with the preset positioning reporting frequency (i.e., the number of positioning reports per unit time of the device), the number of days that the device can continuously provide positioning services can be estimated. Thus, it can be known in advance whether the battery can support the monitoring cycle of the target vehicle, and if necessary, the reporting frequency of the positioning device can be adjusted to ensure that the battery can complete the positioning task within the predetermined time.

[0036] Among them, such as Figure 3As shown, in step S20, that is, by analyzing the remaining battery power, the battery health status, and the battery temperature through a deep learning model to obtain the supportable positioning reporting times of the positioning device, the following steps are included:

[0037] S21: Construct the deep learning model.

[0038] Among them, the deep learning model includes a convolutional neural network model or a long short-term memory model; the deep learning model uses the remaining battery power, the battery health status, and the battery temperature as input features.

[0039] S22: Use historical battery power consumption data, historical battery health status, historical battery temperature, and the corresponding positioning reporting times as a training data set to train the deep learning model to obtain a trained deep learning model.

[0040] S23: Input the remaining battery power, the battery health status, and the battery temperature into the trained deep learning model to obtain the supportable positioning reporting times of the positioning device.

[0041] For steps S21 - S23, by analyzing the remaining battery power, battery health status, and battery temperature through a deep learning model, the performance of the battery in different states can be evaluated more accurately. The deep learning model will identify the performance of the battery under different working conditions through the training of a large amount of historical data. For example: when the remaining battery power is low, the positioning device may need to reduce the reporting frequency or take other power-saving measures; when the battery health status is poor, the battery may not be able to support normal positioning work, and it is necessary to calculate the maximum number of positioning times that can be supported; abnormal battery temperature (too high or too low) may affect the battery performance, and thus affect the available time of the positioning device. Through the training of these data by the deep learning model, a prediction model, that is, the trained deep learning model, is obtained, which can output the supportable positioning reporting times of the battery in the current state.

[0042] Through in-depth analysis of the battery information, the trained deep learning model can estimate how many times the positioning device can report positioning information at most in the current battery state. This reporting times is related to the remaining battery power, battery health status, temperature, and the expected service life of the battery. Further, the calculated supportable positioning reporting times and the preset positioning reporting frequency can be used to estimate how many days of positioning monitoring the positioning device can support in the current battery state. For example, if the positioning device supports 100 reports and the preset frequency is to report once a day, then the supportable positioning reporting days of the positioning device are 100 days.

[0043] Through the above analysis, it is possible to predict in advance the usage of the device battery and determine whether the device can complete the required positioning and monitoring tasks, or whether it is necessary to adjust the reporting frequency of the device to extend the battery usage time.

[0044] Furthermore, using the historical battery power consumption data, historical battery health status, historical battery temperature, and the corresponding number of positioning reports as the training data set to train the deep learning model, the trained deep learning model is obtained, including: annotating the training data set to obtain an annotation result; iteratively training the deep learning model based on the training data set and the annotation result to extract data features, and calculating a loss function; using a preset method to iteratively train the loss function with the aim of reducing the value of the loss function until the expected threshold is met; obtaining the iterated deep learning model based on the iteratively trained loss function.

[0045] Based on the above embodiments, inputting the remaining battery power, the battery health status, and the battery temperature into the trained deep learning model to obtain the supportable number of positioning reports of the positioning device, including: inputting the remaining battery power, the battery health status, and the battery temperature into the iterated deep learning model to obtain the supportable number of positioning reports of the positioning device.

[0046] Specifically, the annotation result corresponding to the training data set can be used as the label of this group of input data, and then each group of training sets with labels is input into the deep learning model for supervised learning. When the training end condition is met, such as when the number of training times reaches the number threshold or the output accuracy of the model reaches the accuracy threshold, the training ends, and the trained deep learning model is obtained.

[0047] It can be understood that in order to train a deep learning model with higher accuracy, the deep learning model can be repeatedly iteratively trained to continuously reduce the loss function until the loss function meets the expected threshold requirement.

[0048] It should be noted that this application does not limit the above preset method and the expected threshold. For example, the preset method can be a gradient descent algorithm, a batch gradient descent algorithm, a stochastic gradient descent algorithm, etc. This application takes the gradient descent algorithm as an example for illustration.

[0049] The purpose of the gradient descent algorithm is to find the minimum value of the loss function through iteration or converge to the minimum value. Geometrically speaking, in the gradient descent algorithm, at the place where the function changes and increases the fastest, along the opposite direction of the vector, the gradient decreases the fastest, so it is easier to find the minimum value of the function. Based on this, in the embodiments of the present application, the gradient descent algorithm can be used to repeatedly iterate and train the deep learning model to continuously reduce the loss function, thereby reducing the error of the calculation result. Therefore, the remaining battery power, the battery health state, and the battery temperature can be further input into the iterated deep learning model to obtain the supportable positioning report times of the positioning device.

[0050] S30: Determine whether the supportable positioning report days of the positioning device are equal to the positioning monitoring days of the target vehicle.

[0051] S40: If the supportable positioning report days of the positioning device are not equal to the positioning monitoring days of the target vehicle, adjust the preset positioning report frequency to obtain the adjusted positioning report frequency so that the supportable positioning report days are equal to the positioning monitoring days of the target vehicle.

[0052] Exemplarily, it can be determined whether the supportable positioning report days of the positioning device are equal to the positioning monitoring days of the target vehicle. Among them, the supportable positioning report days of the positioning device represent the result obtained by analyzing through the previous deep learning model, that is, under the existing battery power, battery health state, and battery temperature and other conditions, the number of days that the positioning device can continuously support positioning reports. The positioning monitoring days of the target vehicle are used to characterize the predetermined number of days required for positioning monitoring, which is usually determined by the usage of the vehicle, monitoring requirements, or the plan set by the user. Thus, they can be compared to determine whether the positioning device can meet the positioning monitoring requirements of the target vehicle. If the two are equal, it means that the positioning device can complete all positioning tasks under the condition of allowing battery power; if they are not equal, it means that the battery of the positioning device may not be able to support the entire monitoring period and needs to be further adjusted.

[0053] Furthermore, if the judgment result shows that the supportable positioning report days of the device are not enough to cover the positioning monitoring days of the target vehicle, actions need to be taken to adjust the reporting frequency of the device, that is, the preset positioning report frequency. For example, if the original preset positioning report frequency is once per hour, but the battery of the positioning device cannot support such a high reporting frequency, the preset positioning report frequency can be reduced to once per day or other lower frequencies. Through this adjustment, it can be ensured that the positioning device can continue to operate under the battery conditions and complete all positioning monitoring tasks of the target vehicle before the battery runs out.

[0054] In some embodiments, the preset positioning reporting frequency includes the number of positioning reports per minute. Obtaining the supportable positioning reporting days of the positioning device according to the supportable positioning reporting times and the preset positioning reporting frequency includes: determining the number of positioning reports per day according to the number of positioning reports per minute; dividing the supportable positioning reporting times by the number of positioning reports per day to obtain the supportable positioning reporting days of the positioning device.

[0055] Exemplarily, the total number of reports in a day can be calculated based on the number of positioning reports per minute. Suppose one positioning report is sent per minute, then the number of positioning reports in a day is 24 hours × 60 minutes = 1440 times. Further, the total supportable positioning reporting times of the positioning device can be divided by the number of reports per day to obtain the number of days the positioning device can continuously work. For example, if the positioning device can support a total of 43200 positioning reports and 1440 reports are sent per day, then the number of days the device can support is 43200 ÷ 1440 = 30 days.

[0056] In other embodiments, if the supportable positioning reporting days of the positioning device are not equal to the positioning monitoring days of the target vehicle, adjusting the preset positioning reporting frequency to obtain the adjusted positioning reporting frequency includes: if the supportable positioning reporting days of the positioning device are not equal to the positioning monitoring days of the target vehicle, dividing the supportable positioning reporting times by the positioning monitoring days to obtain the adjusted number of positioning reports per day; obtaining the adjusted number of positioning reports per minute based on the adjusted number of reports per day, and determining the adjusted number of positioning reports per minute as the adjusted positioning reporting frequency.

[0057] To adjust the positioning device to meet the requirements of the positioning monitoring days of the target vehicle, the following calculations can be performed: The total number of positioning reports (supportable positioning reporting times) of the positioning device within the monitoring days needs to be evenly distributed over each day. Therefore, it can be calculated how many positioning reports need to be sent per day, that is, dividing the total supportable positioning reporting times of the device by the positioning monitoring days of the target vehicle. Next, the adjusted number of positioning reports per day can be converted into the number of reports per minute. Assuming there are 1440 minutes in each day (24 hours × 60 minutes), then the adjusted number of reports per day can be divided by 1440 to obtain the adjusted number of positioning reports per minute.

[0058] On the basis of the above embodiments, after adjusting the preset positioning reporting frequency to obtain the adjusted positioning reporting frequency so that the supportable positioning reporting days are equal to the positioning monitoring days of the target vehicle, it further includes: performing positioning reporting according to the adjusted positioning reporting frequency.

[0059] Exemplarily, the positioning device may need to ensure the strict implementation of the new reporting frequency through software configuration or hardware control. For example, the positioning device may perform timing control in the background or dynamically adjust the upload frequency according to the battery power and network status. The adjusted frequency not only takes into account the requirements of the monitoring days but also needs to balance the energy consumption of the positioning device and the accuracy of the monitoring data. If the positioning device still reports the location too frequently after adjusting the frequency, it may affect the battery life, and vice versa, it may not meet the requirements of monitoring accuracy.

[0060] Through this step, the positioning device can reasonably adjust the frequency within the range allowed by the device resources (such as battery, power, bandwidth, etc.) according to the monitoring days of the target vehicle and perform location reporting according to the new frequency. In this way, the positioning device can work stably and ensure that the location data of the target vehicle can be continuously and accurately reported within the predetermined monitoring period.

[0061] In other embodiments, after determining whether the supportable location reporting days of the positioning device are equal to the location monitoring days of the target vehicle, it further includes: if the supportable location reporting days of the positioning device are equal to the location monitoring days of the target vehicle, keep the preset location reporting frequency unchanged and perform location reporting according to the preset location reporting frequency.

[0062] Exemplarily, when the supportable location reporting days of the positioning device are equal to the location monitoring days of the target vehicle, the positioning device will continue to perform location reporting according to the preset location reporting frequency. For example, if the preset frequency is to report once a minute, the positioning device will collect and upload location data every minute. Without any other frequency adjustment, the location reporting process of the positioning device will remain unchanged until the end of the location monitoring period of the target vehicle.

[0063] In other embodiments, the method further includes: if the remaining battery power is lower than a preset threshold, send an alarm message to the display page, and the alarm message is used to instruct the user to replace the battery or charge the battery.

[0064] For example, when the positioning device detects that the remaining battery power is lower than the preset threshold, it will automatically trigger an alarm mechanism, send an alarm message to the display page, remind the user that the battery power is insufficient, and recommend that the user take measures such as replacing the battery or charging the battery. The main purpose of this alarm mechanism is to ensure that when the battery power of the positioning device is too low, the user can be informed and take actions in a timely manner, avoid the positioning device from stopping working due to battery exhaustion, and ensure the continuity of the positioning and monitoring tasks. Through timely battery power reminders, users can avoid the positioning device from losing power midway, thereby improving the stability and reliability of the positioning device. In addition, this alarm function can also enhance the user experience, reduce potential failures and data interruptions caused by battery problems, enable the positioning device to operate stably for a long time, especially in application scenarios that require continuous positioning and monitoring, and ensure the accuracy of data and the normal operation of the system.

[0065] It can be seen that in the above solution, by obtaining the battery information of the positioning device of the target vehicle, and combining the deep learning model to analyze the remaining battery power, battery health status and battery temperature, the number of days that the positioning device can support positioning reports is accurately calculated, and compared with the number of days of positioning and monitoring of the target vehicle. By adjusting the positioning report frequency, it is ensured that the positioning device can meet the actual monitoring requirements, improve the battery usage efficiency and monitoring continuity of the positioning device, thereby enhancing the reliability and accuracy of the overall vehicle positioning management.

[0066] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0067] In one embodiment, a management device for vehicle positioning is provided, and the management device for vehicle positioning corresponds one-to-one with the method for vehicle positioning management in the above embodiment. As Figure 4 shown, the management device for vehicle positioning includes an acquisition module 101, an analysis module 102, a judgment module 103, and an adjustment module 104. The detailed descriptions of each functional module are as follows:

[0068] The acquisition module 101 is used to acquire the battery information of the positioning device in the target vehicle and the number of days of positioning and monitoring of the target vehicle; wherein, the battery information at least includes the remaining battery power, battery health status, and battery temperature;

[0069] The analysis module 102 is used to analyze the remaining battery power, battery health status, and battery temperature through a deep learning model to obtain the number of times the positioning device can support positioning reports, and obtain the number of days the positioning device can support positioning reports according to the number of times the positioning device can support positioning reports and the preset positioning report frequency;

[0070] A judgment module 103, configured to judge whether the supportable positioning reporting days of the positioning device are equal to the positioning monitoring days of the target vehicle;

[0071] An adjustment module 104, configured to, if the supportable positioning reporting days of the positioning device are not equal to the positioning monitoring days of the target vehicle, adjust the preset positioning reporting frequency to obtain an adjusted positioning reporting frequency, so that the supportable positioning reporting days are equal to the positioning monitoring days of the target vehicle.

[0072] In one embodiment, the analysis module 102 is specifically configured to:

[0073] Construct the deep learning model, where the deep learning model includes a convolutional neural network model or a long short-term memory model; the deep learning model uses the remaining battery power, the battery health status, and the battery temperature as input features;

[0074] Use historical battery power consumption data, historical battery health status, historical battery temperature, and the corresponding positioning reporting times as a training data set to train the deep learning model to obtain a trained deep learning model;

[0075] Input the remaining battery power, the battery health status, and the battery temperature into the trained deep learning model to obtain the supportable positioning reporting times of the positioning device.

[0076] In one embodiment, the analysis module 102 is specifically configured to:

[0077] Annotate the training data set to obtain an annotation result;

[0078] Based on the training data set and the annotation result, perform iterative training on the deep learning model to extract data features and calculate a loss function;

[0079] Use a preset method to perform iterative training on the loss function for the purpose of reducing the value of the loss function until the expected threshold is met;

[0080] Based on the loss function after iterative training, obtain an iterated deep learning model;

[0081] The step of inputting the remaining battery power, the battery health status, and the battery temperature into the trained deep learning model to obtain the supportable positioning reporting times of the positioning device includes:

[0082] Input the remaining battery power, the battery health status, and the battery temperature into the iterated deep learning model to obtain the supportable positioning reporting times of the positioning device.

[0083] In one embodiment, the analysis module 102 is further configured to:

[0084] Determine the number of location reports per day according to the number of location reports per minute;

[0085] Divide the supportable number of location reports by the number of location reports per day to obtain the supportable number of days for location reports of the location device.

[0086] In one embodiment, the adjustment module 104 is specifically configured to:

[0087] If the supportable number of days for location reports of the location device is not equal to the location monitoring days of the target vehicle, divide the supportable number of location reports by the location monitoring days to obtain the adjusted number of location reports per day;

[0088] Based on the adjusted number of location reports per day, obtain the adjusted number of location reports per minute, and determine the adjusted location reporting frequency as the adjusted location reporting frequency;

[0089] After adjusting the preset location reporting frequency to obtain an adjusted location reporting frequency so that the supportable number of days for location reports is equal to the location monitoring days of the target vehicle, it further includes:

[0090] Perform location reporting according to the adjusted location reporting frequency.

[0091] In one embodiment, the adjustment module 104 is specifically configured to:

[0092] If the supportable number of days for location reports of the location device is equal to the location monitoring days of the target vehicle, keep the preset location reporting frequency unchanged and perform location reporting according to the preset location reporting frequency.

[0093] In one embodiment, the adjustment module 104 is further configured to:

[0094] If the remaining battery power is lower than a preset threshold, send an alarm message to the display page, and the alarm message is used to instruct the user to replace the battery or charge the battery.

[0095] The present invention provides a vehicle positioning management device. By obtaining the battery information of the target vehicle positioning device, analyzing the remaining battery power, battery health status and battery temperature in combination with a deep learning model, accurately calculating the supportable number of days for location reports of the positioning device, and comparing it with the location monitoring days of the target vehicle. By adjusting the location reporting frequency, it ensures that the device can meet the actual monitoring requirements, improves the battery usage efficiency and monitoring continuity of the positioning device, and thus improves the reliability and accuracy of the overall vehicle positioning management.

[0096] For the specific limitations of the vehicle positioning management device, reference can be made to the limitations of the intelligent Q&A processing method in the above text, which will not be elaborated here. Each module in the above vehicle positioning management device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0097] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage media. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the server side of a vehicle positioning management method.

[0098] In one embodiment, a computer device is provided. The computer device can be a client, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage media. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of a vehicle positioning management method

[0099] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0100] Obtain the battery information of the positioning device in the target vehicle and the number of days of positioning monitoring of the target vehicle; wherein, the battery information at least includes the remaining battery power, battery health status, and battery temperature;

[0101] Analyze the remaining battery power, the battery health status, and the battery temperature through a deep learning model to obtain the supportable positioning reporting times of the positioning device, and obtain the supportable positioning reporting days of the positioning device according to the supportable positioning reporting times and a preset positioning reporting frequency;

[0102] Determine whether the supportable positioning reporting days of the positioning device are equal to the positioning monitoring days of the target vehicle;

[0103] If the supportable positioning reporting days of the positioning device are not equal to the positioning monitoring days of the target vehicle, adjust the preset positioning reporting frequency to obtain an adjusted positioning reporting frequency so that the supportable positioning reporting days are equal to the positioning monitoring days of the target vehicle.

[0104] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0105] Obtain the battery information of the positioning device in the target vehicle and the positioning monitoring days of the target vehicle; wherein, the battery information at least includes the remaining battery power, the battery health status, and the battery temperature;

[0106] Analyze the remaining battery power, the battery health status, and the battery temperature through a deep learning model to obtain the supportable positioning reporting times of the positioning device, and obtain the supportable positioning reporting days of the positioning device according to the supportable positioning reporting times and a preset positioning reporting frequency;

[0107] Determine whether the supportable positioning reporting days of the positioning device are equal to the positioning monitoring days of the target vehicle;

[0108] If the supportable positioning reporting days of the positioning device are not equal to the positioning monitoring days of the target vehicle, adjust the preset positioning reporting frequency to obtain an adjusted positioning reporting frequency so that the supportable positioning reporting days are equal to the positioning monitoring days of the target vehicle.

[0109] It should be noted that for the functions or steps that the above computer-readable storage medium or computer device can achieve, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0110] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0111] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0112] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A vehicle positioning management method, characterized in that: include: Obtaining battery information of a positioning device in a target vehicle and the number of days for positioning monitoring of the target vehicle; wherein the battery information includes at least remaining battery power, battery health status, and battery temperature; The remaining battery power, the battery health status and the battery temperature are analyzed by a deep learning model to obtain the number of positioning reports that the positioning device can support, and the number of days for positioning reports that the positioning device can support is obtained according to the number of positioning reports that can be supported and a preset positioning reporting frequency; Determine whether the number of days that the positioning device can support for reporting positioning is equal to the number of days for monitoring the positioning of the target vehicle; If the number of days for positioning reporting supported by the positioning device is not equal to the number of days for positioning monitoring of the target vehicle, the preset positioning reporting frequency is adjusted to obtain an adjusted positioning reporting frequency so that the number of days for positioning reporting supported is equal to the number of days for positioning monitoring of the target vehicle.

2. The method according to claim 1, characterized in that The analyzing the remaining battery power, the battery health status, and the battery temperature by a deep learning model to obtain the number of positioning reports that can be supported by the positioning device includes: Constructing the deep learning model, wherein the deep learning model includes a convolutional neural network model or a long short-term memory model; the deep learning model uses the remaining battery power, the battery health status, and the battery temperature as input features; Using historical battery power consumption data, historical battery health status, and historical battery temperature, and corresponding positioning reporting times as a training data set, the deep learning model is trained to obtain a trained deep learning model; The remaining battery power, the battery health status, and the battery temperature are input into the trained deep learning model to obtain the number of positioning reports that can be supported by the positioning device.

3. The method according to claim 2, characterized in that The deep learning model is trained by using the historical battery power consumption data, the historical battery health status and the historical battery temperature, and the corresponding positioning reporting times as a training data set to obtain a trained deep learning model, including: Annotating the training data set to obtain an annotation result; Iteratively train the deep learning model based on the training data set and the annotation results to extract data features, and calculate the loss function; Iteratively training the loss function using a preset method for the purpose of reducing the value of the loss function until the expected threshold value is met; Based on the loss function after iterative training, obtaining an iterative deep learning model; The inputting the remaining battery power, the battery health status, and the battery temperature into the trained deep learning model to obtain the number of positioning reports that can be supported by the positioning device includes: The remaining battery power, the battery health status, and the battery temperature are input into the iterated deep learning model to obtain the number of positioning reports that can be supported by the positioning device.

4. The method according to claim 1, characterized in that: The preset positioning reporting frequency includes the number of positioning reports per minute, and obtaining the number of days that the positioning device can support positioning reporting according to the number of supportable positioning reports and the preset positioning reporting frequency includes: Determine the number of positioning reports per day according to the number of positioning reports per minute; The number of days for positioning reporting that can be supported by the positioning device is obtained by dividing the number of positioning reports that can be supported by the positioning device per day.

5. The method according to claim 1, characterized in that If the number of days for positioning reporting supported by the positioning device is not equal to the number of days for positioning monitoring of the target vehicle, adjusting the preset positioning reporting frequency to obtain the adjusted positioning reporting frequency includes: If the number of days that the positioning device can support for positioning reporting is not equal to the number of days for positioning monitoring of the target vehicle, then the number of days that the positioning device can support for positioning reporting is divided by the number of days for positioning monitoring to obtain the adjusted number of days for positioning reporting; Obtaining the adjusted number of positioning reports per minute based on the adjusted number of reports per day, and determining the adjusted number of positioning reports per minute as the adjusted positioning reporting frequency; After the preset positioning reporting frequency is adjusted to obtain the adjusted positioning reporting frequency so that the number of days for supporting positioning reporting is equal to the number of days for positioning monitoring of the target vehicle, the method further includes: Perform positioning reporting according to the adjusted positioning reporting frequency.

6. The method according to claim 1, characterized in that After determining whether the number of days for positioning reporting supported by the positioning device is equal to the number of days for positioning monitoring of the target vehicle, the method further includes: If the number of days for positioning reporting supported by the positioning device is equal to the number of days for positioning monitoring of the target vehicle, the preset positioning reporting frequency is kept unchanged, and positioning reporting is performed according to the preset positioning reporting frequency.

7. The method according to claim 1, characterized in that The method further comprises: If the remaining battery power is lower than a preset threshold, an alarm message is sent to the display page, and the alarm message is used to instruct the user to replace the battery or charge the battery.

8. A vehicle positioning management device, characterized in that: include: An acquisition module is used to acquire battery information of a positioning device in a target vehicle and the number of days for positioning monitoring of the target vehicle; wherein the battery information at least includes remaining battery power, battery health status and battery temperature; An analysis module is used to analyze the remaining battery power, the battery health status and the battery temperature through a deep learning model to obtain the number of positioning reports that the positioning device can support, and obtain the number of days that the positioning device can support positioning reports according to the number of positioning reports that can support and a preset positioning reporting frequency; A judgment module, used to judge whether the number of days that the positioning device can support positioning reporting is equal to the number of days for positioning monitoring of the target vehicle; The adjustment module is used to adjust the preset positioning reporting frequency if the number of days for positioning reporting supported by the positioning device is not equal to the number of days for positioning monitoring of the target vehicle, so as to obtain the adjusted positioning reporting frequency so that the number of days for positioning reporting supported is equal to the number of days for positioning monitoring of the target vehicle.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the vehicle positioning management method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of managing vehicle positioning according to any one of claims 1 to 7 are implemented.

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