A Battery Remote Monitoring System and Method Based on Data Analysis

Abstract

The present invention relates to the field of battery monitoring, and specifically to a battery remote monitoring system and method based on data analysis, including a terminal data acquisition module, a data discrete value analysis module, a critical power value determination module, a discrimination limit value analysis module, and a terminal response module; the terminal data acquisition module is used to acquire user usage data recorded by a connection terminal of a riding device powered by a battery; the data discrete value analysis module is used to extract all transfer events recorded at each monitoring site within a monitoring period and the corresponding user usage data, and analyze the data discrete value of the monitoring site; the critical power value determination module is used to analyze the mileage data corresponding to the battery usage at an effective monitoring site to determine the critical power value; the discrimination limit value analysis module is used to determine the discrimination limit value of the target riding device corresponding to the monitoring site based on the data discrete value; the terminal response module is used to differentially respond to the user terminal based on the size relationship.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and specifically to a remote battery monitoring system and method based on data analysis. Background Technique

[0002] At present, the remote battery monitoring system is relatively mature. By using intelligent battery parameter sensors to conduct online monitoring and management of batteries, functions such as online monitoring of battery packs, accurate prediction of battery capacity, assessment of battery degradation degree, early warning of abnormal batteries, and recording the performance change trend of batteries throughout the life cycle are realized; monitoring personnel often focus on the state problems of the batteries themselves, but ignore the related problems reflected by the relationship between the batteries and users after being applied to equipment.

[0003] For example, in a specific application scenario, when a user uses a riding device equipped with a battery, the corresponding terminal only displays the remaining battery power. The user has no way to accurately evaluate the power required to reach the destination. The riding device also cannot actively judge according to the terminal interaction the destination that the user needs to reach in the initial state to remind the user whether the battery power situation meets the user's needs. There is an information barrier between the user and the battery device, which may lead to a situation where the battery runs out during the ride, reducing the user's favorability; in addition, due to the randomness of equipment use, it also brings a lot of inconvenience to the operation and maintenance personnel for battery management. Summary of the Invention

[0004] The purpose of the present invention is to provide a remote battery monitoring system and method based on data analysis to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A remote battery monitoring method based on data analysis, including the following analysis steps:

[0006] Step S100: Obtain user usage data recorded by the connection terminal of a riding device powered by a battery. Mark the event that the riding device leaves the current monitoring site recorded by each monitoring site as a mobilization event. Extract all mobilization events and corresponding user usage data recorded by each monitoring site within the monitoring period, and analyze the data discrete values of the monitoring sites.

[0007] Step S200: Output the monitoring sites with data discrete values less than the data discrete value threshold as effective monitoring sites, and analyze the mileage data corresponding to the battery usage in the effective monitoring sites to determine the critical power value.

[0008] Step S300: When there is a riding device with the remaining battery power less than the critical power value among the effective monitoring stations, mark the riding device as the target riding device, and determine the discrimination limit value corresponding to the target riding device of the monitoring station based on the data discrete value;

[0009] Step S400: When the user uses the terminal to execute the operation to be performed on the riding device, trigger a response signal to determine the size relationship between the number of real-time target riding devices at the monitoring station and the discrimination limit value, and respond to the user terminal differently based on the size relationship.

[0010] Further, analyzing the data discrete value of the monitoring station includes the following analysis steps:

[0011] Step S110: The monitoring station refers to a monitoring point where at least n riding devices are placed at the same location; the user usage data includes the driving mileage data and the driving characteristic data; extract the riding device that leaves in the mobilization event as the locked device, take the locked device in one mobilization event as an analysis unit, and form an analysis set with all the analysis units within the monitoring period;

[0012] Step S120: Store and record the average driving mileage data of the locked device of each analysis unit in the analysis set. The driving mileage data is the distance data from the location where the locked device is parked and locked and left to the monitoring station recorded by the user terminal. Use the formula:

[0013]

[0014] Calculate the mileage discrete value U0 of the analysis set; x i represents the average driving mileage of the i-th analysis unit in the analysis set, m represents the number of analysis units recorded in the analysis set, and x0 represents the average value of the average driving mileage corresponding to all the analysis units recorded in the analysis set; the smaller the mileage discrete value, the closer each analysis unit in the analysis set is to the average level;

[0015] Step S130: Store and record the driving characteristic data of the locked device of each analysis unit. The driving characteristic data includes the driving time period T and the driving destination P. Construct the characteristic data pair A of each analysis unit, A = (T, P); mark the analysis units in the characteristic data pair of the analysis set with the same driving time period and the same driving destination P as the target analysis unit, a ≠ 0, a is an arbitrary integer; calculate the characteristic discrete value V0 of the analysis set,

[0016]

[0017] where N1 represents the number of target analysis units, and N0 represents the total number of analysis units included in the analysis set;

[0018] Step S140: Calculate the data dispersion value Z of the monitoring station using the formula: Z = β1*U0 + β2*V0, where β1 represents the reference coefficient corresponding to the mileage dispersion value, and β2 represents the reference coefficient corresponding to the feature dispersion value.

[0019] Further, step S200 includes the following analysis steps:

[0020] Step S210: The data dispersion value threshold refers to the data dispersion value threshold formed based on the set mileage standard deviation threshold and 0.5 as the feature dispersion value;

[0021] Obtain the battery power consumption value Q0 corresponding to all analysis units in the analysis set for the mobilization event, Q0 = Q2 - Q1, where Q1 represents the power value displayed when the user closes the terminal upon arriving at the driving destination, and Q2 represents the power value displayed when the user uses the terminal to turn on the riding device at the monitoring station; calculate the average power consumption value Q of all mobilization events in the analysis set 0 ,

[0022] Step S220: Set the driving destination recorded by the target analysis unit as the key destination; use the formula: Q 1 =(Q 0 / x0)*x P to calculate the critical power value Q corresponding to the effective monitoring station 1 ,x P represents the driving mileage data from the effective monitoring station to the key destination.

[0023] Further, determining the discrimination limit value of the target riding device corresponding to the monitoring station based on the data dispersion value includes the following analysis steps:

[0024] Step S310: Mark the effective monitoring station corresponding to the minimum data dispersion value as the first monitoring station, and obtain the occurrence times D of the abnormal event recorded by the first monitoring station during the same monitoring period. The abnormal event refers to the event that the user replaces the vehicle at least once after leaving the riding device at the monitoring station and before arriving at the driving destination;

[0025] Step S320: Classify the same monitoring period according to the proportional value of the target riding device. The proportional value represents the ratio of the number of target riding devices to the total number of riding devices in the effective monitoring station. Each proportional value corresponds to a class of monitoring periods. Sort the monitoring periods in ascending order according to the size of the proportional value to generate the first sequence, and calculate the difference D0 in the occurrence times recorded in adjacent monitoring periods in the first sequence; Selecting the difference to analyze the discrimination limit value is to quantify the specific situation of abnormal events occurring in monitoring stations with different degrees of low power, so as to help decision-makers analyze different warning methods based on data.

[0026] Step S330: Select the data group corresponding to the maximum value of the difference D0. The data group represents the proportional values corresponding to adjacent data calculated in the first sequence; mark the proportional value with a larger serial number in the first sequence in the data group as the discrimination limit value.

[0027] Further, step S400 includes the following analysis steps:

[0028] Obtain the number D1 of real-time target riding devices at the effective monitoring site and the total number D2 of riding devices at the site, calculate the real-time ratio D1 / D2. When the real-time ratio is greater than or equal to the discrimination limit value, transmit a warning signal to the operation and maintenance personnel of the effective monitoring site to remind them to charge or replace the battery in the riding device;

[0029] When the real-time ratio is less than the discrimination limit value, obtain the moment T when the real-time user turns on the riding device and the terminal characteristics. The terminal characteristics refer to the third-party data displayed by the user when the terminal is turned on and the same as the key destination;

[0030] If the remaining power W of the battery in the riding device to be turned on 1 is less than or equal to the critical power value Q 1 , and the moment T belongs to the driving period in the target analysis unit or there are terminal characteristics, transmit the first warning signal; if the remaining power of the battery in the riding device to be turned on is greater than the critical power value Q 1 Do not remind;

[0031] If the remaining power of the battery in the riding device to be turned on is less than or equal to the critical power value Q 1 , but does not meet the condition that the moment T belongs to the driving period in the target analysis unit and there are terminal characteristics, transmit the second warning signal;

[0032] The first warning signal is a warning about the power risk of reaching the key destination;

[0033] The second warning signal is to display all monitoring sites within k kilometers of the real-time monitoring site to the user, where k = W 1 / (Q 0 / x0).

[0034] When the ratio is greater than the discrimination limit value, it indicates that the frequency of abnormal events caused by the monitoring site is increasing rapidly. Therefore, by reminding to replace the battery or charge, the effective operation of the riding device can be ensured to the greatest extent. When it is less than the discrimination limit, each user is evaluated and analyzed. For users who meet the characteristic conditions, a reminder of insufficient battery power is given, and for users who do not meet the characteristic conditions, the location where the device can be replaced is provided. This improves the initiative of users regarding the remaining battery power when using the riding device, reduces the occurrence of abnormal situations of insufficient battery power during riding, not only brings convenience to users, but also provides the convenience of visual management and unified management of battery power for the operation and maintenance management personnel of the monitoring site.

[0035] The battery remote monitoring system includes a terminal data acquisition module, a data discrete value analysis module, a critical battery power value determination module, a discrimination limit value analysis module, and a terminal response module;

[0036] The terminal data acquisition module is used to acquire the user usage data recorded by the terminal connected to the riding device powered by the battery;

[0037] The data discrete value analysis module is used to extract all transfer events recorded by each monitoring site during the monitoring period and the corresponding user usage data, and analyze the data discrete value of the monitoring site;

[0038] The critical battery power value determination module is used to analyze the mileage data corresponding to the battery usage in the effective monitoring site to determine the critical battery power value;

[0039] The discrimination limit value analysis module is used to determine the discrimination limit value of the target riding device corresponding to the monitoring site based on the data discrete value;

[0040] The terminal response module is used to trigger a response signal to judge the size relationship between the number of real-time target riding devices at the monitoring site and the discrimination limit value when the user uses the terminal to perform the operation of the riding device to be run, and differentially respond to the user terminal based on the size relationship.

[0041] Further, the data discrete value analysis module includes a transfer event determination unit, an analysis set composition unit, a mileage discrete value calculation unit, a characteristic discrete value calculation unit, and a data discrete value calculation unit;

[0042] The transfer event determination unit is used to mark the event that the riding device leaves the current monitoring site recorded by each monitoring site as a transfer event;

[0043] The analysis set composition unit is used to extract the riding device that leaves in the transfer event as a locked device, take the locked device in one transfer event as an analysis unit, and form an analysis set with all the analysis units during the monitoring period;

[0044] The mileage discrete value calculation unit is used to calculate the mileage discrete value corresponding to the analysis set;

[0045] The feature discrete value calculation unit is used to calculate the feature discrete value corresponding to the analysis set;

[0046] The data discrete value calculation unit is used to calculate the data discrete value based on the mileage discrete value and the feature discrete value.

[0047] Furthermore, the critical power value determination module includes an effective monitoring site determination unit, a key destination determination unit, and a critical power value calculation unit;

[0048] The effective monitoring site determination unit is used to output the monitoring sites with data discrete values less than the data discrete value threshold as effective monitoring sites;

[0049] The key destination determination unit is used to set the driving destination recorded by the target analysis unit as the key destination;

[0050] The critical power value calculation unit is used to obtain the average power consumption value of the storage battery and the driving mileage data to calculate the critical power value.

[0051] Furthermore, the discrimination limit value analysis module includes a first monitoring site marking unit, a first sequence generation unit, a numerical value selection unit, and a discrimination limit value marking unit;

[0052] The first monitoring site marking unit is used to mark the effective monitoring site corresponding to the minimum data discrete value as the first monitoring site;

[0053] The first sequence generation unit is used to sort the monitoring periods in ascending order according to the magnitude of the proportional values to generate a first sequence;

[0054] The numerical value selection unit is used to obtain the occurrence times of abnormal events recorded at the first monitoring site during the same monitoring period, calculate the difference in the occurrence times recorded in adjacent monitoring periods in the first sequence, and select the data group corresponding to the maximum difference;

[0055] The discrimination limit value marking unit is used to mark the proportional value with a larger serial number in the first sequence existing in the data group as the discrimination limit value.

[0056] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By analyzing effective monitoring stations, the present invention determines the stations where the connection between users and devices can be established and real-time early warning operations can be carried out, and determines the user judgment characteristics of key destinations and relevant data on the remaining battery power based on the characteristic analysis of effective monitoring stations; enabling early warning analysis based on different situations during real-time monitoring, reducing the information barrier between users and battery devices, reducing the probability of abnormal events occurring during user use, giving users a quantitative reminder of the required power for the destination to enable users to independently judge whether it meets the current demand, and improving the intelligence of device use; in addition, the present invention analyzes based on an overall monitoring station, enabling overall replacement or maintenance when the power differentiation limit value requirement is not met, bringing operation and maintenance convenience to managers and improving operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0058] Figure 1 is a schematic structural diagram of a battery remote monitoring system and method based on data analysis of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of 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.

[0060] Please refer to Figure 1 , the present invention provides a technical solution: A battery remote monitoring method based on data analysis, including the following analysis steps:

[0061] Step S100: Obtain user usage data recorded by the connection terminal of the riding device powered by the battery, mark the event that the riding device leaves the current monitoring station recorded by each monitoring station as a mobilization event, extract all mobilization events and corresponding user usage data recorded by each monitoring station within the monitoring period, and analyze the data discrete values of the monitoring stations;

[0062] Step S200: Output the monitoring stations with data discrete values less than the data discrete value threshold as effective monitoring stations, and analyze the mileage data corresponding to the battery usage in the effective monitoring stations to determine the critical power value;

[0063] Step S300: When there is a riding device with the remaining battery power less than the critical power value among the effective monitoring stations, mark the riding device as the target riding device, and determine the discrimination limit value of the monitoring station corresponding to the target riding device based on the data discrete value;

[0064] Step S400: When the user uses the terminal to perform the operation of the riding device to be run, trigger a response signal to judge the size relationship between the number of real-time target riding devices at the monitoring station and the discrimination limit value, and differentially respond to the user terminal based on the size relationship.

[0065] Analyze the data discrete value of the monitoring station, including the following analysis steps:

[0066] Step S110: The monitoring station refers to a monitoring point where at least n riding devices are placed at the same location; the user usage data includes the driving mileage data and the driving characteristic data; extract the riding device that leaves in the transfer event as the locked device, and take the locked device in one transfer event as an analysis unit, and all analysis units within the monitoring period form an analysis set;

[0067] Step S120: Store and record the average driving mileage data of the locked device of each analysis unit in the analysis set. The driving mileage data is the distance data recorded by the user terminal from the place where the locked device is parked and locked and left to the monitoring station. Use the formula:

[0068]

[0069] Calculate the mileage discrete value U0 of the analysis set; x i represents the average driving mileage of the i-th analysis unit in the analysis set, m represents the number of analysis units recorded in the analysis set, and x0 represents the average value of the average driving mileage corresponding to all analysis units recorded in the analysis set; the smaller the mileage discrete value, the closer each analysis unit in the analysis set is to the average level;

[0070] Step S130: Store and record the driving characteristic data of the locked device of each analysis unit. The driving characteristic data includes the driving time period T and the driving destination P, and construct the characteristic data pair A of each analysis unit, A = (T, P); mark the analysis units in the characteristic data pair of the analysis set with the same driving time period and the same driving destination P as the target analysis unit, a≠0, a is an arbitrary integer; calculate the characteristic discrete value V0 of the analysis set,

[0071]

[0072] where N1 represents the number of target analysis units, and N0 represents the total number of analysis units included in the analysis set;

[0073] Step S140: Calculate the data dispersion value Z of the monitoring station using the formula: Z = β1*U0 + β2*V0, where β1 represents the reference coefficient corresponding to the mileage dispersion value, and β2 represents the reference coefficient corresponding to the feature dispersion value. Generally, β1 is set to 0.55 and β2 is set to 0.45.

[0074] Step S200 includes the following analysis steps:

[0075] The data dispersion value threshold refers to the data dispersion value threshold formed based on the set mileage standard deviation threshold and 0.5 as the feature dispersion value; for example, the standard deviation threshold is set to 0.2.

[0076] Obtain the battery power consumption value Q0 corresponding to all analysis units in the analysis set for the mobilization event, Q0 = Q2 - Q1, where Q1 represents the power value displayed when the user closes the terminal upon arriving at the driving destination, and Q2 represents the power value displayed when the user uses the terminal to turn on the riding device at the monitoring station; calculate the average power consumption value Q of all mobilization events in the analysis set 0 ,

[0077] Step S220: Set the driving destination recorded by the target analysis unit as the key destination; use the formula: Q 1 =(Q 0 / x0)*x P , calculate the critical power value Q corresponding to the effective monitoring station 1 , x P represents the driving mileage data from the effective monitoring station to the key destination.

[0078] Determine the discrimination limit value of the target riding device corresponding to the monitoring station based on the data dispersion value, including the following analysis steps:

[0079] Step S310: Mark the effective monitoring station corresponding to the minimum data dispersion value as the first monitoring station, and obtain the occurrence times D of the abnormal event recorded at the first monitoring station during the same monitoring period. The abnormal event refers to the event that the user replaces the vehicle at least once after leaving the riding device at the monitoring station and before arriving at the driving destination;

[0080] Step S320: Classify the same monitoring period according to the proportional value of the target riding device. The proportional value represents the ratio of the number of target riding devices to the total number of riding devices in the effective monitoring station. Each proportional value corresponds to a class of monitoring periods, and the monitoring periods are sorted from small to large according to the size of the proportional value to generate the first sequence. Calculate the difference D0 in the occurrence times recorded in adjacent monitoring periods in the first sequence; Selecting the difference to analyze the discrimination limit value is to quantify the specific situation of abnormal events occurring in monitoring stations with different degrees of low power, so as to help decision-makers based on data analysis different warning methods.

[0081] Step S330: Select the data group corresponding to the maximum value of the difference D0. The data group represents the proportional values corresponding to the adjacent data calculated in the first sequence; mark the proportional value with a larger serial number in the first sequence existing in the data group as the discrimination limit value.

[0082] As shown in the embodiment:

[0083] Set the monitoring period of the first monitoring site to 24h. If the monitoring cycle is 5 days, there are 3 types of proportional values, which are 3 / 20 on the first day, 11 / 20 on the third day, and 1 / 5 on the fifth day;

[0084] The selection of the proportional value is the proportional value corresponding to the longest continuous duration within the monitoring period;

[0085] If there are multiple proportional values within a day, if 3 / 20 corresponds to the longest recorded duration within a day, then 3 / 20 can be output as the proportional value for the first day;

[0086] Sort the monitoring periods based on the proportional values as: the first day, the fifth day, the third day; and extract the number of abnormal event occurrences recorded in the corresponding sequence as 1, 2, 7;

[0087] Then calculate the differences in the number of occurrences as 2 - 1 = 1, 7 - 2 = 5;

[0088] The data group is the proportional values corresponding to the fifth day and the third day, which are 1 / 5, 11 / 20. Select 11 / 20 as the discrimination limit value.

[0089] Step S400 includes the following analysis steps:

[0090] Obtain the real-time target number D1 of riding devices at the effective monitoring site and the total number D2 of riding devices within the site, calculate the real-time ratio D1 / D2. When the real-time ratio is greater than or equal to the discrimination limit value, transmit a warning signal to the operation and maintenance personnel of the effective monitoring site to remind them to charge or replace the battery in the riding device;

[0091] When the real-time ratio is less than the discrimination limit value, obtain the moment T when the real-time user turns on the riding device and the terminal characteristics. The terminal characteristics refer to the third-party data displayed by the user when turning on the terminal that is the same as the key destination; such as the navigation destination. If the remaining power W of the battery in the riding device to be turned on 1 is less than or equal to the critical power value Q 1 , and the moment T belongs to the driving period in the target analysis unit or there are terminal characteristics, transmit the first warning signal; if the remaining power of the battery in the riding device to be turned on is greater than the critical power value Q 1 Do not remind;

[0092] If the remaining power of the battery in the riding device to be activated is less than or equal to the critical power value Q 1 , but it does not meet the condition that the moment T belongs to the driving period in the target analysis unit and there is a terminal feature, then transmit a second warning signal;

[0093] The first warning signal is a power risk warning for reminding of arriving at a key destination; the content can be that the remaining power of the battery is insufficient or the risk is high when arriving at the destination;

[0094] The second warning signal is to display all monitoring stations within k kilometers of the real-time monitoring station to the user, where k = W 1 / (Q 0 / x0).

[0095] When the ratio is greater than the discrimination limit value, it indicates that the frequency of abnormal events occurring at this monitoring station is increasing rapidly. Therefore, by reminding to replace the battery or charge, the effective operation of the riding device can be guaranteed to the greatest extent; when it is less than the discrimination limit, an evaluation and analysis are carried out for each user. For users who can meet the characteristic conditions, a reminder of insufficient power is given, and for users who cannot meet the characteristic conditions, the location where the device can be replaced is provided, which improves the initiative of users regarding the remaining power of the battery when using the riding device, reduces the occurrence of abnormal situations of insufficient power during riding, not only brings convenience to users, but also provides convenience for the visual management and unified management of power for the operation and maintenance management personnel of the monitoring station.

[0096] The battery remote monitoring system includes a terminal data acquisition module, a data discrete value analysis module, a critical power value determination module, a discrimination limit value analysis module, and a terminal response module;

[0097] The terminal data acquisition module is used to acquire user usage data recorded by the terminal connected to the riding device powered by the battery;

[0098] The data discrete value analysis module is used to extract all transfer events recorded at each monitoring station during the monitoring period and the corresponding user usage data, and analyze the data discrete value of the monitoring station;

[0099] The critical power value determination module is used to analyze the mileage data corresponding to the battery usage in the effective monitoring stations to determine the critical power value;

[0100] The discrimination limit value analysis module is used to determine the discrimination limit value of the monitoring station corresponding to the target riding device based on the data discrete value;

[0101] The terminal response module is used to trigger a response signal to judge the size relationship between the number of real-time target riding devices at the monitoring station and the discrimination limit value when the user uses the terminal to execute the operation of the riding device to be run, and differentially respond to the user terminal based on the size relationship.

[0102] The data discrete value analysis module includes a transfer event determination unit, an analysis set composition unit, a mileage discrete value calculation unit, a feature discrete value calculation unit, and a data discrete value calculation unit;

[0103] The transfer event determination unit is used to mark the event that the recorded riding device leaves the current monitoring site at each monitoring site as a transfer event;

[0104] The analysis set composition unit is used to extract the locked devices among the riding devices that leave in the transfer event, take the locked devices in one transfer event as one analysis unit, and form an analysis set with all the analysis units within the monitoring period;

[0105] The mileage discrete value calculation unit is used to calculate the mileage discrete value corresponding to the analysis set;

[0106] The feature discrete value calculation unit is used to calculate the feature discrete value corresponding to the analysis set;

[0107] The data discrete value calculation unit is used to calculate the data discrete value based on the mileage discrete value and the feature discrete value.

[0108] The critical power value determination module includes an effective monitoring site determination unit, a key destination determination unit, and a critical power value calculation unit;

[0109] The effective monitoring site determination unit is used to output the monitoring sites with data discrete values less than the data discrete value threshold as effective monitoring sites;

[0110] The key destination determination unit is used to set the driving destination recorded by the target analysis unit as the key destination;

[0111] The critical power value calculation unit is used to obtain the average power consumption value of the storage battery and the driving mileage data to calculate the critical power value.

[0112] The discrimination limit value analysis module includes a first monitoring site marking unit, a first sequence generation unit, a numerical value selection unit, and a discrimination limit value marking unit;

[0113] The first monitoring site marking unit is used to mark the effective monitoring site corresponding to the minimum data discrete value as the first monitoring site;

[0114] The first sequence generation unit is used to sort the monitoring periods in ascending order according to the size of the proportional values to generate a first sequence;

[0115] The numerical value selection unit is used to obtain the occurrence times of abnormal events recorded at the first monitoring site during the same monitoring period, calculate the difference in the occurrence times recorded in adjacent monitoring periods in the first sequence, and select the data group corresponding to the maximum difference;

[0116] The discrimination limit value marking unit is used to mark that the proportion value with a larger serial number in the first sequence existing in the data group is the discrimination limit value.

[0117] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0118] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A remote monitoring method for storage batteries based on data analysis, characterized in that, It includes the following analysis steps: Step S100: Obtain the user usage data recorded by the riding device powered by the battery and connected to the terminal. Mark the event that the riding device leaves the current monitoring site recorded at each monitoring site as a mobilization event. Extract all mobilization events and the corresponding user usage data recorded at each monitoring site within the monitoring period, and analyze the data discrete value of the monitoring site; The analysis of the data discrete value of the monitoring site includes the following analysis steps: Step S110: The monitoring site refers to a monitoring point where at least n riding devices are uniformly placed at the same location; the user usage data includes driving mileage data and driving characteristic data; Extract the locked devices among the riding devices that leave in the mobilization event. Use the locked devices in one mobilization event as one analysis unit, and form an analysis set with all analysis units within the monitoring period; Step S120: Store and record the average driving mileage data of the locked devices of each analysis unit in the analysis set. The driving mileage data is the distance data from the location where the locked device is parked and locked and left to the monitoring site recorded by the user terminal. Use the formula: Calculate and analyze the mileage discrete value U0 corresponding to the set; x i represents the average driving mileage of the i-th analysis unit in the analysis set, m represents the number of analysis units recorded in the analysis set, and x0 represents the average value of the average driving mileage corresponding to all analysis units recorded in the analysis set; Step S130: Store and record the driving characteristic data of the locking device for each analysis unit. The driving characteristic data includes the driving time period T and the driving destination P, and construct the characteristic data pair A for each analysis unit, A = (T, P); Mark the analysis units in the characteristic data pair of the analysis set with the same driving time period and the same driving destination P as the target analysis unit, a ≠ 0, a is any integer; Calculate the characteristic discrete value V0 corresponding to the analysis set, where N1 represents the number of target analysis units, and N0 represents the total number of analysis units included in the analysis set; Step S140: Use the formula: Z = β1*U0 + β2*V0 to calculate the data discrete value Z of the monitoring site, where β1 represents the reference coefficient corresponding to the mileage discrete value, and β2 represents the reference coefficient corresponding to the characteristic discrete value; Step S200: Output the monitoring sites with data discrete values less than the data discrete value threshold as valid monitoring sites, and analyze the mileage data corresponding to the battery usage in the valid monitoring sites to determine the critical power value; Step S200 includes the following analysis steps: Step S210: The data discrete value threshold refers to the data discrete value threshold formed based on the set mileage standard deviation threshold and 0.5 as the characteristic discrete value; Obtain the battery power consumption value Q0 recorded in all analysis units corresponding to the mobilization events in the analysis set. Q0 = Q2 - Q1, where Q1 represents the power value displayed when the user closes the terminal at the driving destination, and Q2 represents the power value displayed when the user uses the terminal to turn on the riding device at the monitoring site; Calculate and analyze the average power consumption value Q of all transfer events in the set 0 , Step S220: Set the driving destination recorded by the target analysis unit as the key destination; Use the formula: Q 1 =(Q 0 / x0)*x P , calculate the critical power value Q 1 corresponding to the effective monitoring site, where x p represents the driving mileage data from the effective monitoring site to the key destination; Step S300: When there is a riding device with a remaining battery power less than the critical power value in the valid monitoring site, mark the riding device as the target riding device, and determine the discrimination limit value of the monitoring site corresponding to the target riding device based on the data discrete value; The determination of the discrimination limit value of the monitoring site corresponding to the target riding device based on the data discrete value includes the following analysis steps: Step S310: Mark the valid monitoring site corresponding to the minimum data discrete value as the first monitoring site, and obtain the occurrence times D of the abnormal event recorded at the first monitoring site during the same monitoring period. The abnormal event refers to the event that the user replaces the vehicle at least once after leaving the riding device at the monitoring site and before reaching the driving destination; Step S320: Classify the same monitoring periods according to the proportional values of the target riding devices. The proportional value represents the ratio of the number of target riding devices to the total number of riding devices within the effective monitoring stations. Each proportional value corresponds to a category of monitoring periods. Sort the monitoring periods in ascending order according to the magnitudes of the proportional values to generate a first sequence, and calculate the difference D0 between the recorded occurrence times within adjacent monitoring periods in the first sequence. Step S330: Select the data group corresponding to the maximum value of the difference D0. The data group represents the proportional values corresponding to the adjacent data calculated in the first sequence. Mark the proportional value with a larger serial number in the first sequence that exists in the data group as the discrimination limit value. Step S400: When the user executes the operation of waiting for the riding device to run on the terminal, trigger a response signal to determine the relationship between the number of real-time target riding devices at the monitoring station and the discrimination limit value, and differentially respond to the user terminal based on the relationship. The step S400 includes the following analysis steps: Obtain the number D1 of real-time target riding devices at the effective monitoring station and the total number D2 of riding devices within the station, and calculate the real-time ratio D1 / D2. When the real-time ratio is greater than or equal to the discrimination limit value, transmit a warning signal to the operation and maintenance personnel of the effective monitoring station to remind them to charge or replace the battery in the riding device. When the real-time ratio is less than the discrimination limit value, obtain the moment when the real-time user turns on the riding device of the terminal and the terminal characteristics. The terminal characteristics refer to the third-party data displayed on the terminal when the user turns on the terminal, which is the same as the key destination. If the remaining power W of the battery in the riding device to be turned on 1 is less than or equal to the critical power value Q 1 , and the moment of turning on the riding device belongs to the driving period in the target analysis unit or there are terminal characteristics, transmit the first warning signal; If the remaining power of the storage battery in the riding device to be turned on is greater than the critical power value Q 1 Do not remind; If the remaining power of the storage battery in the riding device to be turned on is less than or equal to the critical power value Q 1 , but it does not meet the condition that the time to turn on the riding device belongs to the driving period and there is a terminal feature in the target analysis unit, then transmit a second warning signal; The first warning signal is a warning of the power risk when arriving at the key destination. The second warning signal is to display all monitoring stations within k kilometers of the real-time monitoring station to the user, where k = W 1 / (Q 0 / x0).

2. A battery remote monitoring system applying the battery remote monitoring method based on data analysis described in claim 1, characterized in that, It includes a terminal data acquisition module, a data discrete value analysis module, a critical power value determination module, a discrimination limit value analysis module, and a terminal response module. The terminal data acquisition module is used to acquire the user usage data recorded by the riding device powered by the battery and connected to the terminal. The data discrete value analysis module is used to extract all transfer events recorded at each monitoring station during the monitoring period and the corresponding user usage data, and analyze the data discrete values of the monitoring stations. The critical power value determination module is used to analyze the mileage data corresponding to the battery usage in the effective monitoring stations to determine the critical power value. The discrimination limit value analysis module is used to determine the discrimination limit value of the target riding devices corresponding to the monitoring stations based on the data discrete values. The terminal response module is used to trigger a response signal to determine the relationship between the number of real-time target riding devices at the monitoring station and the discrimination limit value when the user executes the operation of waiting for the riding device to run on the terminal, and differentially respond to the user terminal based on the relationship.

3. The battery remote monitoring system according to claim 2, wherein: The data discrete value analysis module includes a transfer event determination unit, an analysis set composition unit, a mileage discrete value calculation unit, a feature discrete value calculation unit, and a data discrete value calculation unit. The transfer event determination unit is used to mark the event that the riding device recorded at each monitoring station drives away from the current monitoring station as a transfer event. The analysis set composition unit is used to extract the locked devices among the riding devices that drive away in the transfer event. Take the locked devices in one transfer event as one analysis unit, and form an analysis set with all the analysis units during the monitoring period. The mileage discrete value calculation unit is used to calculate the mileage discrete value corresponding to the analysis set. The feature discrete value calculation unit is used to calculate the feature discrete value corresponding to the analysis set. The data discrete value calculation unit is used to calculate the data discrete value based on the mileage discrete value and the feature discrete value.

4. The battery remote monitoring system according to claim 3, wherein: The critical power value determination module includes an effective monitoring site determination unit, a key destination determination unit, and a critical power value calculation unit; The effective monitoring site determination unit is used to output the monitoring sites with data discrete values less than the data discrete value threshold as effective monitoring sites; The key destination determination unit is used to set the driving destination recorded by the target analysis unit as the key destination; The critical power value calculation unit is used to obtain the average power consumption value of the battery and the driving mileage data to calculate the critical power value.

5. The battery remote monitoring system according to claim 4, characterized in that: The discrimination limit value analysis module includes a first monitoring site marking unit, a first sequence generation unit, a numerical value selection unit, and a discrimination limit value marking unit; The first monitoring site marking unit is used to mark the effective monitoring site corresponding to the minimum data discrete value as the first monitoring site; The first sequence generation unit is used to sort the monitoring periods in ascending order according to the magnitude of the proportional values to generate a first sequence; The numerical value selection unit is used to obtain the occurrence times of the abnormal events recorded by the first monitoring site in the same monitoring period, calculate the difference between the occurrence times recorded in adjacent monitoring periods in the first sequence, and select the data group corresponding to the maximum difference; The discrimination limit value marking unit is used to mark the proportional value with a larger serial number in the first sequence existing in the data group as the discrimination limit value.

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